Identifying candidate antimalarial compounds by searching for molecular mimetics of endogenous parasite metabolites

Reis Fitzsimmons

Bioinformatics Internship

May 16, 2016 to July 20, 2016

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Table of Contents

1. Summary………………………………………………………………………………..32. Introduction………………………………………………………………………..……43. The Basics of Identification of Antimalarial Compounds……………………..…...…..54. Importance of the ECFP4 Fingerprint and the Tanimoto Coefficient…………..……....75. Determining a Similarity Score………………………………………………..…….….86. Presenting an Objective…………………………………………………………...……107. Basic Review of Procedure……………………………………………………...……...118. Beginning the Process of Collecting Metabolite Data……………………………….....129. Database Searching…………………………………………………………………......1510. More Issues Dealing with Database Searching……………………………………...….1611. Learning KNIME…………………………………………………………………..…...1712. A Change in Strategy……………………………………………………………..…….1813. The Temporary Solution to the Metabolite Problem…………………………………. .1914. Progress Check and Preparation for Workflow…………………………………..……..2015. Explaining the Layout of the Workflow………………………………………..……….2216. The Resolution to the Chemical Identifier Resolver Node……………………..……….2317. Trial-and-Error Using the Workflow…………………………………………..………..2518. The First Results………………………………………………………………..……….2919. Results of the General MetaCyc Metabolites……………………………………..…….3020. Results of the Malaria Metabolites (Excluding Plasmodium falciparum)……….……..3221. Results of the Plasmodium falciparum 3D7 strain…………………………….………..3822. Conclusion About MetaCyc Metabolites…………………………………….…………4023. Statistics of the Molecular Weights of the Compounds…………………….…………..4124. Statistical Inference………………………………………………………….………….4425. Chi-Squared Test of Independence………………………………………….………….4526. The Optimal Malaria Metabolites…………………………………………….………...4727. Statistics of Malaria Metabolites………………………………………………………..4928. Statistical Inference of the Malaria Metabolites………………………………………...5129. Another Chi Squared Analysis………………………………………………………….5230. Three Z-Tests of Sample Means………………………………………………………...5331. Z-Test for the Compounds from Adams et al. and Justin’s List of Antimalarials……....5532. Z-Test for the Compounds from Adams et al. and the Malaria Metabolites…………....5633. Z-Test for Justin’s List of Antimalarials and the Malaria Metabolites…………………5634. Final Conclusions……………………………………………………………………….5735. Discussion………………………………………………………………………………5836. Acknowledgments………………………………………………………………………6037. Works Cited.…………………………………………………………………………….60

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Summary

The objective is to use KNIME, a cheminformatics workflow platform, to determine

which compounds are promising candidates as antimalarial drug targets from a given list of

antimalarial compounds. Justin Gibbons, one of Dr. Jiang’s doctoral students, created a list of

roughly 284 antimalarial compounds prioritized for a chemogenomics screening project. I used

bioinformatics resources and metabolite databases to determine which compounds would be

most suitable against malaria at a molecular level, based on their similarity with endogenous

compounds generated from parasite metabolic pathways. Compound-metabolite similarity was

measured using the ECFP4 fingerprint and the Tanimoto coefficient. Some databases which I

used to find the metabolite data were MPMP, KEGG Ligand, MetaCyc, BioCyc, PlasmoDB, and

others. It took me a while to come across the right metabolites, but I had to keep redefining my

criteria. The samples had to be chemically diverse and large. I tried a three-tiered approach in

which the first sample would consist of a general list of metabolites from many species, another

consisting of only malaria metabolites, and a final sample consisting of those only derived from

Plasmodium falciparum. Eventually, I found a general list of 4,998 compounds from over 900

species from a scientific paper which discussed small molecule metabolism used for drug

mapping (Adams et al). KNIME showed that none of the metabolites had a great degree of

chemical similarity with any of the antimalarials. I also found many metabolites from the

MetaCyc database. However, they could not be downloaded in the right format due to technical

issues with the database. Therefore, this resulted in poor results when they were processed in the

KNIME workflow. So, I downloaded 250,642 hits from ChEMBL Malaria Data in tab-delimited

format, including SMILES data. KNIME could process this information much more efficiently

compared to the MetaCyc compounds. Surprisingly, no metabolites had a great degree of

chemical similarity compared with the antimalarials. Finally, I ran statistical tests on the

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molecular weight distributions of all three datasets and found that the compounds from Adams et

al. were significantly different from the antimalarials and the malaria metabolites. However, the

malaria metabolites and Justin’s list of antimalarials showed a significant degree of similarity in

regards to the means of their molecular weight distributions. This was a surprising discovery

considering the low level of chemical similarity between them. In order to find specific drug

targets against Plasmodium, at least millions of metabolites might have to be analyzed through

the workflow. Unfortunately, we do not have enough sufficient information on the annotation of

the genome of Plasmodium falciparum and metabolic pathways of the malaria parasite.

Introduction

A major issue regarding the search for molecular mimetics of antimalarial compounds is

that the number of compounds remains limited and that drug resistance has risen recently.

Malaria has become an ever-growing threat, especially in underdeveloped nations around the

world. Therefore, researchers have had to act quickly and effectively in determining more

antimalarial compounds from endogenous parasite metabolites to replace the current drugs used

in antimalarial therapeutics and medicine. To put this problem into perspective, the US Army

Antimalarial Drug Developmental Program has screened over 200,000 chemical compounds for

antimalarial activity in the last decade (Canfield and Rozman). Of these 200,000 compounds,

only two of them demonstrated greater antimalarial activity than any other known drug against

drug-resistant Plasmodium falciparum. There have been a few other compounds presently being

tested on human subjects that have shown to be more potent. However, Canfield and Rozman

showed that the need for antimalarial compounds is desperately needed because of the low rate

of actual success in antimalarial activity in the drug resistant parasite. In order to prepare for a

better search towards finding the appropriate compounds, biologists must examine molecular

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mimetics from endogenous metabolic pathways found within the parasite. Molecular mimetics

would be more useful and harder to resist because they would be evolutionarily tied to the

metabolism of the parasite and require a more drastic change in its evolution to create resistance

to the compound. Thus, the modern approach to designing new compounds against complex

diseases involves the use of molecular targets (Basso et al). Basso mentions that the advantages

of using molecular targets are that the approach would permit the identification of lead

compounds against a defined target at the molecular level, analysis of a huge number of

compounds with an excellent benefit-to-cost ratio, development even for compounds that have

selective toxicity, and the evaluation of pure, natural compounds. Therefore, molecular targets

provide a solid backbone in determining appropriate antimalarial compounds that could

effectively eradicate drug-resistant Plasmodium falciparum.

The Basics of Identification of Antimalarial Compounds

In order to begin the process, I must collect metabolite data from certain databases, such

as MetaCyc, BioCyc, MPMP, and others, that contain metabolites found within the metabolic

pathways of Plasmodium falciparum. The guidelines for collecting the metabolite data involve

identifying endogenous parasite ligands that specifically target malaria. The true purpose of

collecting metabolites is to identify appropriate ones that can be used to identify compounds,

which are chemically similar to those metabolites. The process involves the chemogenomic

profiling of the parasite because it is a well-defined tool that can classify specific drug targets by

comparing drug fitness profiles in a collection of mutants (Pradhan et al). The associations

between genetic changes in the mutants and shifts in drug fitness are helpful in the identification

of novel antimalarial drugs and their mechanisms. Once metabolites have been successfully

identified as potential chemogenomic indicators for antimalarial compounds, then they must be

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integrated into a workflow run by KNIME. Cheminformaticians usually process and analyze

large libraries of data of small molecules (Beisken et al). Molecules become standardized,

downstream analysis is performed, various descriptors are calculated, and molecular structures

become visualized through the simulation of the workflow. Hence, a workflow-based

cheminformatics resource provides ease-of-use and interpretation between complementary

cheminformatics packages that share similar features. Therefore, this project requires the

assistance of KNIME-CDK, which has functions that allow for molecular conversion between

common formats, fingerprints, generation of signatures, and molecular properties (Beisken et al).

KNIME is based on the Chemistry Development Toolkit and can support a wide range of

chemical classes, which could induce better functionality to the framework of the data.

Workflow environments are necessary for calculating chemical similarity between the

metabolites and compounds because they can interpret data in various formats using different

tools and can override the understanding of a scripting language to concatenate input and output

file formats. KMIME-CDK is unique in that it takes elements of the library’s core functionality

and allows the user to manipulate the data very effectively. Thus, its core library is open and

community-driven (Beisken et al). The KNIME-CDK plug-in has a node repository, workflow

used for calculation of descriptors, and an example row from the out-port view of the Atom

Signatures node (Figure 1). In the workflow, the library is read and filtered for structures

containing phenol groups before counting hydrogen donors and acceptors. At the same time,

MACCS fingerprints and atom signatures become calculated for the atom-filtered molecules.

The plug-in can accept molecules in CML, SDFile, MDL Mol, InChI, and SMILES data formats.

It is also capable of converting the CDK molecule back to its SDFile cell, which could be used

by other plug-ins. KNIME-CDK represents a user-friendly cheminformatics plug-in that

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produces more efficiency and functionality to the platform through a workflow-based,

community-driven molecule library.

Figure 1. Overview of the KNIME-CDK plug-in (Beisken et al)

Importance of the ECFP4 Fingerprint and the Tanimoto Coefficient

Chemical similarity is determined between the metabolites and the compounds with the

assistance of the ECFP4 fingerprint and the Tanimoto coefficient. The evaluation of chemical

similarity between datasets of molecules is being further studied in recent years due to the

advances in computational combinatorial chemistry (Godden et al). The most popular forms of

expressing molecular structure and properties to calculate chemical similarity between

compounds are binary string representations, called fingerprints (FPs). Fingerprints are popular

for chemists to use because they can identify molecular features in a binary format. They can be

hashed, folded, or keyed, such that each bit is associated with a particular fragment or descriptor

value. The ECFP4 fingerprint is especially effective in determining structural diversity of

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compounds because it is an extended connectivity fingerprint that encodes a circular substructure

that has a diameter of four bonds (Gardiner et al). Due to its desired diameter, it generally

provides the greatest enrichment when comparing molecular structures. Molecular similarity can

also be assessed in other ways, including a wide array of algorithms and descriptions of

molecular structure and properties. When chemical similarity is calculated, it usually involves

comparisons between molecular fingerprints. The Tanimoto coefficient is a type of metric that

runs pairwise comparisons between different molecules and is the most common estimator for

molecular similarity (Godden et al). The equation for the coefficient is Tc = Nab / (Na + Nb – Nab).

Na means the number of bits set in Molecule 1, Nb is the number of bits set in Molecule 2, and

finally, Nab is the number of bits shared between the two molecules. The ECFP4 fingerprint and

the Tanimoto coefficient are crucial parameters used in calculating the chemical similarity

between the compound and endogenous metabolites to prove which compounds would be best

suited as antimalarial drug targets.

Determining a Similarity Score

Similarity score is important to determine which antimalarial compounds would be most

chemically similar to the endogenous ligands because the score sets an appropriate threshold at

which certain compounds are chosen based on how well they resemble ligands at the molecular

level. The Similarity Ensemble Approach is a useful method to determine similarity scores

because it compares groups of ligands based on bond topology (Adams et al). Bond topology is

measured by the use of molecular fingerprints. Raw scores are determined between compound

sets by calculating Tanimoto coefficients between the fingerprints for all molecular pairs. At last,

raw scores are compared to a background distribution in which an expectation value is generated

to represent the chemical similarity between metabolite and compound datasets. Figure 2 below

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represents how the Similarity Ensemble Approach is used based on individual steps to determine

the E-value, which evaluates chemical similarity between datasets.

Figure 2. Similarity Ensemble Approach methodology (Adams et al)

According to Adams, an appropriate cutoff value was E = 1.0*10-10 in which 54% of the

drug sets were linked to 0.9% of the metabolic reactions. This E value was inspired by the

BLAST search statistics. The results showed that an appropriate cutoff value must be made in

order to determine a level of significance in which a fair proportion of drug sets were specific for

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certain targeted metabolic pathways. Adams also hypothesized that since he had to recover

known drug-target interactions, he argued that chemical similarity between MetaCyc reaction

sets and MDDR drug sets could recover these known interactions. Figure 3 highlights Adams’

hypothesis that showed the importance of chemical similarity in hypothesizing that it could be

used to represent known drug-target interactions.

Figure 3. Best hits between the reaction and drug sets (Adams et al)

Presenting an Objective

There are many key steps to developing a successful cheminformatics workflow

approach and determining chemical similarity of antimalarial compounds. The first major step is

designing a clear objective to make a structured outline of the entire project. In order to present a

clear objective, one must explain their objective, claim why it is important, and show how they

will do it. Before conducting research, a scientist must be able to write their outline and have

their peers help them go over it in detail. The list of 284 compounds is unpredictable because

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some compounds lack annotated information. Therefore, I must compare the compounds to the

endogenous ligands carefully to find the best hits. In my outline, my objective is to compare

these compounds to the reference metabolites and design a workflow in KNIME to create the

most effective specific drug targets. The reason why this is important is because antimalarials are

needed to treat the disease and specific drug targets could help act against certain metabolic

pathways and enzymes in the parasite. As before-mentioned, the ECFP4 fingerprint is necessary

for determining chemical similarity between the compounds and metabolites. I will be able to

achieve my goal by retrieving metabolite data and then creating the workflow based on the

information from the data. I am supposed to read the data, calculate the fingerprint and calculate

the similarity between the two sets. A decent similarity score is crucial for the identification of

antimalarials because it can set an ideal number of these potential candidates. Once the best hits

have been discovered, further workflows might be used for more analysis of the specific drug

targets.

Basic Review of Procedure

The next step for the process of identifying potential candidates as specific drug targets

for malaria is briefly going over the actual procedure to determine them. As recalled, the ECFP4

fingerprint is used for determining chemical similarity by giving a referenced structure to

compare molecules. The Tanimoto coefficient is the central parameter behind these calculations

because it takes into account the atomic coordinates and molecular similarity between sets of

molecules. The measurements will allow for identification of the candidate drug targets based on

chemical similarity between the compound list and reference metabolites retrieved from the

metabolic pathways of malaria. KNIME is the cheminformatics workflow platform that gives

functionality to determining the chemical similarity of the specific drug targets. To collect

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metabolite data, I must find the appropriate bioinformatics resources and metabolite databases in

order to effectively compare compounds to ideal endogenous ligands found within the parasite’s

metabolic pathways. Some useful databases include PlasmoDB, Uniprot STRING, MPMP, and

many others. As the search continues, the correct database that provides the most relevant data

will be used for the project. If the best hits are eventually discovered, then more analysis could

be performed to annotate them, such as structural analysis, proteomics, organic chemistry

reactions, and ligand interaction networks. It would also be beneficial to research the disease

process and mechanism behind malaria to determine how molecular mimetics will be effective at

preventing the invasion and rupture of red blood cells. Once the project has been complete, then

a new list of the best hits is produced and can show how compounds are selected based on their

molecular mimicry to the metabolites found within Plasmodium. Specifically, Plasmodium

falciparum is being targeted due its hardly known metabolic pathways.

Beginning the Process of Collecting Metabolite Data

When I began my search for collecting the appropriate metabolites, I was first suggested

to get data from MPMP. MPMP stands for Malaria Parasite Metabolic Pathways, which is a

curated database for the metabolic pathways of the Plasmodium genus. Many of the pathways

found in MPMP are relevant to the erythrocytic (red blood cell) phase of the parasite cycle

(Ginsburg). Therefore, the database could prove to be an adequate source of information for

malaria metabolites. Justin mentioned that the key to adequate metabolite information is to

collect a large enough sample to represent the chemical diversity of endogenous ligands found

within the parasite. He mentioned to me that I might have to filter out ATP and other types of

generic metabolites which are not specific to malaria.

An important reference set of drug targets was used in understanding the process of

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obtaining metabolite data, which was a subset of 246 targets from the MDL Drug Data Report

collection in which ligands were annotated to their respective targets (Adams et al). The sets had

65,241 unique ligands. Adams et al. showed the criteria for which I had to use to select the

appropriate metabolites. According to the paper, they used small molecule drugs which targeted

metabolic enzymes in humans and various pathogens. They usually mimic endogenous ligands in

which their effects could be therapeutic or toxic. Generally, their effects are frequently

unexpected. Perhaps the most important part of Adams et al. was that their project required a

large-scale mapping of the drug space in order to create a guide for novel drug discovery. A main

component of their strategy was grouping drugs and metabolites by their associated targets and

enzymes with ligand-based set signatures which were used to quantify the degree of chemical

similarity. The paper showed an effective manner by which I could use associated drug targets to

easily determine the chemical similarity between the given list of 284 compounds and a diverse

sample of small molecules. The results showed that chemical space had been exploited for drug

targets where successful drug discovery is possible. They created an online resource of

interactive maps linking the drugs to metabolic pathways i.e. MPMP. The 385 species-specific

maps were used to predict the “effect space” of over 900 species and 6000 reactions from the

BioCyc database (Adams et al). The chemical similarity linked between the drug sets and

metabolites which is used for predicting potential toxicity, suggesting routes of metabolism, and

observing drug polypharmacology. Metabolic maps gave interactive navigation of the biological

data on potential drug targets and drug chemistry, currently available for prosecuting the specific

targets. This information has provided me great details on how to obtain the correct metabolite

data and use it to map the appropriate drug targets.

Although Adams et al. prepared me for guidelines to seek the correct metabolites, the

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paper did not specifically mention malarial metabolites. So, Dr. Jiang sent me another paper

which specifically mentioned the metabolic pathways of Plasmodium falciparum. The abstract

showed that in order to improve existing drugs, one must identify new drug targets and

understand the basis of drug resistance (Cobbold et al). Time-resolved MS-based metabolite

profiling was used as an approach to map molecular perturbations caused by a panel of clinical

antimalarial drugs on Plasmodium falciparum during asexual blood stages. Dihydroartemisinin

was used to disrupt the hemoglobin catabolism within 1 hour of exposure, which caused a

transient decline in hemoglobin-derived peptides. This also disrupted pyrimidine biosynthesis,

leading to susceptibility of Plasmodium falciparum to DNA during the early blood stages. To

effectively control the disease, one should identify novel antimalarial compounds. However, the

information on the modes of action is still limited. Therefore, scientists need to understand the

mode of action and develop new strategies to prevent continued drug resistance. Some of these

methods may include resistance screening, whole-genome sequencing, analysis of changes in

transcriptome expression, and proteomics analysis. Most antimalarials stop the disease by

targeting metabolic enzymes. Metabolic fluxes are sensitive to changes in other biological

processes. Therefore, metabolic approaches are the most effective at identifying specific drug

targets and provide more diverse drug actions on protozoan diseases.

Plasmodium falciparum is the causative agent of the most severe form of malaria.

Targeted metabolic profiling was used to investigate polyamine inhibitors (Cobbold et al). An

untargeted, dual MS approach was used to map drug-dependent perturbations in metabolic

networks of the parasite-infected erythrocytes. The approach provided important information on

the speed of action of the antimalarials, including hierarchy of the metabolic dysregulation

induced by compounds with pleiotropic modes of action. This allowed for dissection of early

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specific effects of the inhibition of malaria. It is necessary to characterize the metabolic

signatures of existing antimalarial compounds to confirm validity of the approach. Secondly,

metabolic signatures were identified which could help target identification of novel compounds.

Treatments that use the metabolic signatures induce distinct metabolic perturbations, providing a

more accurate approach to identifying the appropriate drug targets. There are potential modes of

action and downstream consequences when these pathways are disrupted.

The conclusion showed that there has been considerable progress in fighting the global

impact of malaria, except that there still remains a need to develop new antimalarial drugs to

avoid the overreliance of existing ones and counter the threat of achievable drug resistance

(Cobbold et al). There has been success of large-scale phenotypic screens for new antimalarial

drugs and new methods developed, so the modes of action of certain inhibitors can be broadly

identified. They became prioritized to find which ones are most needed for optimization. A

metabolomics pipeline is suitable for investigating the modes of action of these compounds with

pleiotropic effects, highly needed for clinical development. The approach has to be combined

with genomics and proteomics approaches to guarantee the identification of specific drug targets

and acceleration of the hit prioritization.

Database Searching

After analyzing the research from Adams et al. and Cobbold et al., I began searching for

the appropriate metabolites through an extensive database search. The first one was MPMP in

which I obtained a list of 1010 compounds. However, Justin felt that the sample was not

chemically diverse enough. Therefore, I needed more information and sharper criteria in finding

the right metabolites. I needed a website with a more accurate built-in search engine. Other

databases, which were suggested to me, were KEGG Ligand and MetaCyc. Adams et al.

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retrieved their data from MetaCyc, so I decided to use that database instead. If I was unable to

find Plasmodium falciparum-specific endogenous ligands, then I would be required to find

metabolites from other species and map them back to malaria if possible. I eventually found a list

of roughly 5600 polypeptides unique to Plasmodium falciparum, but I knew that I had to

continue finding more metabolites to obtain a chemically diverse dataset. After discussing

options with Justin, he mentioned that polypeptides could be used in the similarity search

although they would not be as significant as classical metabolites. A difficulty encountered was

lack of knowledge in changing the polypeptide file into a CSV format. I could have set up a

pipeline to query the websites directly, but Justin believed that it would not have been desirable

to do so. This method would have been too slow and I would have had less control over the

analysis of the metabolites. I also had to look for metabolites which bind to and are products of

malarial enzymes. Another factor that helped with establishing the right criteria was that I

needed chemical similarity of known antimalarials to predict specific drug targets. The most

frustrating issues involved in my journey to extract the correct metabolites were determining

sharp criteria for the search and file conversions.

More Issues Dealing with Database Searching

There were more issues as I continued my search for these metabolites. For example, the

vast majority of websites which I encountered were not user-friendly since I had to keep

downloading data in their original formats. It got to the point in which Justin instructed me on

how to email the curators of MetaCyc and other databases on what we specifically needed and

see how they could convert the files to CSV if feasible. I was recommended to reference from

Adams et al. as an example of how the data was to be extracted. However, emailing was

confusing and left more questions unanswered than before. The only file formats available from

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MetaCyc were spreadsheets and SDF file formats. Another idea I developed was to use ligands

from MDDR which was demonstrated in Adams et al. However, Justin informed me that those

researchers had to pay for their ligands. As I kept database searching, I also realized that the

MetaCyc database was part of the larger BioCyc database. The difference between them was that

MetaCyc represented metabolic pathways and the BioCyc database had a collection of general

pathways and genomes. I found another sample of small compounds from different species in

MetaCyc, which contained 4023 of them. However, I did not use it because it was still not large

enough to be chemically diverse to come from a general list of different species. I kept reading

documentation of MetaCyc and KEGG Ligand, but they remained confusing. I emailed more

curators, but continued to get very little results. Justin wanted me to obtain accession numbers,

so I could use them to map the ligands back to malarial enzymes and pathways. However, I did

not know exactly how to get them and eventually had to integrate my information into one zip

folder. I could find the chemical structures and common names, but could not get the accession

numbers due to the stubborn setup of the MetaCyc database.

Learning KNIME

At one point, I downloaded KNIME and started my amateur experience as a

cheminformatician. I knew that my objective was to create a workflow to compare the chemical

similarities between compounds and reference metabolites to obtain best hits. I learned that

nodes had specific functions and that most of them were used to read and write in different file

formats. I noticed that KNIME had a database reader that could be used to transfer data from

MetaCyc if conversion to a CSV file might be too difficult to do or simply the fact that the

database is not user-friendly. There were still many questions about using KNIME and I knew

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that playing with the nodes was my only way of ensuring how to complete an effective

workflow.

A Change in Strategy

After unsuccessful searching for the metabolite data, I decided to change my strategy.

Instead of targeting specific databases, it was wiser to find metabolites from any source since

many of the databases had metabolites shared by many species in common. I also decided that

building an effective workflow would require the files to be in SDF or text file instead of CSV

because they could provide the structural characteristics and annotation of each compound per

row. Another method I tried using was the Special Smart Tables found in MetaCyc. Since

accession numbers were unavailable, I used common names and SDF files due to the constraints

of file conversion from the database. Justin recommended me to add more samples from humans

and species of yeast to increase chemical diversity. Another limitation to the metabolite data

search was that MetaCyc would soon go private and lose its government funding. Therefore, my

project had to be completed within a short amount of time although the date for MetaCyc to go

private was never clarified. Another change in strategy was that the metabolites would be from

different species in different files and to figure out why there were so few malaria metabolites in

MetaCyc. Separate files proved to be effective since SDF files were considered non-human-

friendly. Eventually, I discovered 1843 compounds in Homo sapiens, 1198 compounds in

Saccharomyces cerevisiae 5288c, 529 compounds in Plasmodium berghei ANKA, and 577

compounds in Plasmodium vivax Sal-1. I also had the general list of 4023 compounds from

earlier, 5603 polypeptides and 660 compounds in the Plasmodium falciparum 3D7 strain, and the

4,998 unique metabolites detected in Adams et al. After combining all results, I realized that I

had roughly 15284 total possible ligands if there was no overlap. Justin also brought up the idea

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that files should be kept separate at first when being run through the workflow in KNIME and

have them combined together after the data source is annotated. Although this change in strategy

improved finding metabolite data tremendously, it was still very unorganized and the files were

in many different, confusingly convertible formats. Therefore, a final redo had to be made to

obtain organized, diverse metabolite data.

The Temporary Solution to the Metabolite Problem

Although my strategy helped improve the metabolite search, it did not facilitate the

organization of the data. Justin thought of the final solution in which we would use a three-tiered

approach. He suggested that the first tier would consist of a general Smart Table of metabolite

data from every species in the MetaCyc database, the second tier would be malaria specific (all

species of Plasmodium), and finally the last tier would be made of Plasmodium falciparum

metabolites. I returned to the database and retrieved 13,191 compounds from all species. I used a

Special Smart Table in which I generated a spreadsheet file and a separate SDF file for each

category of metabolites. There were also 12,997 polypeptides found from the database and

another 8,038 additional pathway compounds. This produced a total of a possible 34226 possible

ligands if no overlap exists. The next tier involved the Plasmodium genus. MetaCyc only has

metabolite data from the following species: P. berghei ANKA, P. chabaudi, P. falciparum 3D7,

P. vivax Sal1, and P. yoelii yoelii 17XNL. Plasmodium berghei contained 12,238 polypeptides,

476 regular compounds, and an additional 580 pathway compounds. P. chabaudi had 527 regular

compounds, 579 additional ones from pathways, and 15011 polypeptides. Due to errors in

downloading, I could not obtain the polypeptides. Then P. falciparum 3D7 had 5603

polypeptides, 660 normal compounds, and 736 pathway compounds. P. vivax Sal-1 had 577

compounds, 631 ones retrieved from pathways, and 5344 polypeptides. P. yoelii yoelii 17XNL

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contained 557 regular compounds, 607 pathway-derived ligands, 7865 polypeptides. Excluding

the polypeptides that could not be downloaded from P. chabaudi, there were a total of 36980

metabolites specific to malaria. The reason that it seemed that I retrieved more metabolites

specific to malaria than general compounds is because MetaCyc does not allow one to retrieve

compounds from all species from the database. Instead, the curators only allow a general list of

universal metabolites found amongst all species. Therefore, many of these numbers are only

exaggerated since overlap is not factored. If overlap of compounds between the species of

malaria was factored, the actual quantity of metabolites would be much lower. However, the

database does not offer the number of shared compounds between malarial species. Once again,

the curators did not make the website user-friendly enough for cheminformaticians to obtain the

most accurate numbers. As a review, I designed a table that contains the number of compounds

and polypeptides obtained for each tier. Table 1 below shows the total amount of compounds and

polypeptides that were used in the KNIME workflow.

Table 1. Number of polypeptides and compounds for general sample, malaria sample, and Plasmodium falciparum sample obtained from MetaCyc

General Malaria Plasmodium falciparum

Overall total

Polypeptides 12997 31050 5603 49650Compounds 21284 5930 1396 28610

Total metabolites 34281 36980 6999 78260

Progress Check and Preparation for Workflow

After successfully determining the most suitable metabolite data that I could retrieve, I

organized everything into 12 files, six SDF and six common name files. They were divided into

all compounds and polypeptides and again divided into those from general species, malaria-

specific, and Plasmodium falciparum-specific. Then I reported my notes and progress report to

Dr. Jiang to get feedback. I discussed my main objectives and how I retrieved the metabolite

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data. We also discussed the future of the pipeline in KNIME. Justin also mentioned he would

help me determine an appropriate cutoff score for identifying ideal candidates to act as molecular

mimetics. As recalled from Figure 1, we decided that it would be the template for the workflow

in KNIME. However, Justin suggested that I would need to add an extra filter node for redundant

compounds. I might not even use the element filter or the atom signatures if I only require the

ECFP4 fingerprint. Another idea was that the template used MACCS instead of the ECFP4

fingerprint, so I had to substitute it. I also researched the element filter and the atom signatures to

find their purpose and determined if they would help identify appropriate drug targets. I realized

that the template from Figure 1 was designed to read a molecule library and filter for structures

containing phenol groups before counting hydrogen acceptors and donors (Beisken et al). Due to

the simplicity of the workflow that I designed, I wouldn’t require the element filter or the atom

structures because none of the molecules had a defined element and I was not concerned with

how the hydrogen and carbon atoms were neighbored with each other within a given molecule.

Instead, I developed a simplistic layout of what the workflow would look like. Figure 4 below

shows the rough layout of the workflow.

Figure 4. Basic Layout of Cheminformatics Workflow in KNIME

22

Explaining the Layout of the Workflow

The layout of the workflow shown in Figure 4 highlights the steps needed to identify the

best hits. First, compounds and metabolites must be read based on their file format. Since

Justin’s compounds were not in SDF, I decided to use a general file reader for his data. For my

metabolites, I decided to use the SDF file reader node to examine the structural annotation of

each metabolite. Before the compounds can be compared to the metabolites in the ECFP4

fingerprint, the metabolites must be processed by a chemical identifier resolver. In KNIME, a

chemical identifier resolver does the job of converting chemical structures to different file

formats. In the case of filtering redundant metabolites, Justin felt that it would be best to convert

my SMILES data to InChI format. A useful tool is UniChem, an extension of InChI-based

compound mapping (Chambers et al). UniChem is a low-maintenance compound identifier

mapping service found online which has ‘Connectivity Search.’ This allows for molecules to be

matched based on their structural identity between the connectivity layer of their Standard

InChIs. The remaining layers become compared to show stereochemical and isotopic differences.

Unlike SMILES, InChI was designed to compare molecules on different types of structural

specification. Even the chemical name of the compound is enough to identify it from other

compounds using InChI file formats. As a bonus, the features of the Standard InChI had been

exploited to provide more functionality for UniChem and allow for mappings between molecules

that have the same atom connectivity. This allows for the user to define their own criteria for

molecular equivalence since criteria can vary between users and areas of expertise. After

metabolites have been changed to InChI file format and filtered for redundancy, then the

compounds and metabolites can be compared using the ECFP4 fingerprint to determine

23

Tanimoto coefficients between the datasets. The Fingerprint Similarity node functions by

calculating the Tanimoto coefficients to represent the chemical similarity between the

compounds and the metabolites. Then the Statistics node could be used to analyze the Tanimoto

coefficients and determine measures of central tendency. From there, the final node could

produce a histogram to graphically represent the data of the Tanimoto coefficients. This would

be used to assess which compounds are potential candidates to be identified as best hits. Despite

several kinks and the lack of practical experience in cheminformatics, the layout showed an

appropriate depiction of the steps required to identify specific drug targets for malaria.

The Resolution to the Chemical Identifier Resolver Node

The previous issue mentioned before was that I was having difficulty finding the right

node to convert SMILES data to Standard InChI. After extensive searching, I came across the

CIR (Chemical Identifier Resolver) KNIME integration node, created by the CADD group of

NCI and NIH. Being installed from the trusted extension, the CIR node became added to the

SDF file reader node for interpreting the structural annotation of the SDF files of the metabolites.

Its basic function is to allow conversion between different chemical structure identifiers.

Although this node is useful for the conversion of the structural annotation, it cannot simply filter

the redundant compounds by itself. With further investigation, I found the GroupBy node which

has the function of grouping rows of a table by the unique values in the selected group columns

of a file. An output table is generated based on the fact that each row is made for each unique

value combination of the selected group columns. This technique would be critical for filtering

the data to detect which compounds might be unique, or in this sense, redundant. Another

dilemma I faced was that I could not connect the file reader node for the antimalarial compounds

and the GroupBy node for the metabolites to the same fingerprint node. Therefore, the workflow

24

must accommodate the problem by having two fingerprints, one for each dataset instead. It

would be a better approach because then each fingerprint can accurately model the molecular

representation of each dataset of compounds. The Fingerprint Similarity node can be used to

integrate the overall data and calculate the final Tanimoto coefficients to identify the best hits. A

question that developed over time was if the fingerprint nodes were specifically ECFP4. I found

that by configuring their internal details, I could manipulate both fingerprints to follow an

extended connectivity of 4. If it was not possible, then I was suggested to use the Morgan

Fingerprint and set the radius to 2. The difference is that ECFPs are based off of extended

connectivity while Morgan fingerprints follow the older Morgan algorithm. In the Morgan

algorithm, an iterative process assigns numeric identifiers to each atom in a given molecule

(Rogers and Hahn). Identifiers are independent of the original number of atoms. The process of

the algorithm becomes continued until every identifier is considered unique. The ECFP

fingerprints on the other hand follow certain changes to the original Morgan algorithm. ECFP

generation stops after a predetermined number of iterations instead of achieving complete

identifier uniqueness. The ECFP algorithm also does not discard the intermediate atom

identifiers, which means the iteration process does not have to be fully complete (Rogers and

Hahn). Another key difference is that the identifiers in the Morgan process must be carefully

recoded after each iteration to prevent mathematical overflow and “collisions.” The ECFP

algorithm is able to withstand the extra computational expense by using a fast-hashing scheme

that generates identifiers across comparable molecules. Therefore, it would be more desired to

rely on the accuracy of the ECFP4 node to identify the ideal candidates for antimalarial drug

targets. Finally, Figure 5 provides the new layout of the workflow after suggestions and tweaking

the nodes.

25

Figure 5. New Improved Layout of KNIME Workflow

Trial-and-Error Using the Workflow

Although the workflow appeared to be functional, trial-and-error showed that there still

had to be more improvements. The majority of files I tried to use were Excel or text which were

processed by the CIR and Fingerprint nodes as SDF input. However, I had trouble with their

conversion. Then I tried using a different node to interpret the data into proper SDF input.

Instead of directly attaching either file reader to a Fingerprint or CIR node, I found that the SDF

input required the Molecule to CDK node because it can convert the elements in one of the input

table’s columns to usable molecules, such as CDKCell. The changed format allows for the

dataset to be read as molecules in further computations in the KNIME workflow. Once the data

has been read, then the analysis can be complete. The Fingerprint and CIR nodes can therefore

accept the text file when it has been converted through the Molecule to CDK node. Figure 6

shows an improved workflow below.

26

Figure 6. Workflow After Adding Molecule to CDK Nodes

I encountered more issues when I realized that the metabolite data could not be read in

the right format or perhaps it might have not been organized enough for the workflow to process.

Therefore, I tried to use more compatible data using the structural annotations of 4,998

compounds from Adams et al. The workflow was then able to give the different measurements of

the Tanimoto coefficients for each compound. However, I believed that another change in

approach had to be necessary. Perhaps I would need a dataset from a scientific paper instead of

the regular MetaCyc data which I had used. Figures 7 and 8 show the success of the workflow

after its first run using Adams et al. and a portion of a table showing all the compounds that had

their Tanimoto coefficients measured.

27

Figure 7. Success of Workflow Using Adams et al. Compounds

Figure 8. Table of Compounds and Their Tanimoto Coefficients (Adams et al)

Unexpectedly, I found that the metabolite data should be read by the file reader. It

seemed that the main issues were rather the filtering of the redundant compounds and that the

28

Statistics node had no apparent use in producing the statistics for the histogram. I tried reading

the nodes more carefully and produced a modified workflow (Figure 9).

Figure 9. Modified Workflow with No CIR and Statistics Nodes

The modified workflow from Figure 9 is a rather more simplified version of the previous

workflow layouts because the Statistics and the CIR nodes were eventually deemed useless for

the actual objective of the workflow. The reason why the CIR node was useless was because the

Smiles data did not have to be changed to Standard InChI format. The GroupBy node can

automatically filter the redundant compounds because it can group rows by the unique values

indicated in the selected group column. This allowed for easier filtering, including the fact that

the interactive Histogram node could simply create a histogram of the calculated Tanimoto

coefficients from the Fingerprint Similarity node. Another feature was that the Fingerprint

Similarity node was set on minimum similarity to generate the most results possible. The Adams

et al. compounds became ran again to be able to produce the first histogram of this project.

29

Figure 10 shows the first accurate histogram performed by the cheminformatics platform

workflow in KNIME.

Figure 10. Histogram Results of Adams et al. Compounds

The First Results

Although Adams et al. was not actually the most ideal sample for determining chemical

similarity for antimalarial drug targets, the diverse chemical sample still provided a useful

depiction of the accuracy of the workflow results. Of the 4,998 compounds which became

filtered from the original dataset extracted from over 900 species, GroupBy lowered the number

to 3593. Based on the histogram from Fig. 10, over half of the compounds had less than 5.1%

chemical similarity with the antimalarial compounds. This was not a surprising result due to the

fact that Adams et al. went after several different species-specific pathogens instead of only

malaria. Only one compound was found to be in the most chemically similar category between

30

4.59% and 5.1%. Since there were no compounds that were found to have over 5.1% chemical

similarity, the metabolites extracted from Adams et al. showed that even a very chemically

diverse sample designed for various human pathogens is still unable to determine candidate

antimalarial drug targets. Based on the observations of the histogram, it would be best to

conclude that we would require metabolites that share at least 25 to 30% chemical similarity

before they can be considered as ideal candidates as molecular mimetics to the malaria parasite.

Results of the General MetaCyc Metabolites

KNIME processed 21,284 compounds and 12,997 polypeptides taken from the MetaCyc

database. The files were separated into the 12,997 polypeptides, a general list of 13191

compounds, and 8093 additional ligands retrieved from pathways. However, there might have

been overlap between the 8093 additional ligands retrieved directly from pathways and the

general list of 13191 compounds. The first sample to be processed was the 12,997 polypeptides.

The GroupBy node filtered the redundant polypeptides down to 42 and allowed for the buildup

of the molecular fingerprint. Figure 11 provides the histogram for the list of polypeptides

retrieved from the MetaCyc database. All 42 polypeptides had less than 1% chemical similarity

with each of the antimalarial compounds. Next, the list of 13191 compounds were ran through

the workflow. This time the redundant compounds became filtered down to 43. Figure 12 shows

the results of the histogram representing the general list of MetaCyc compounds. Once again, all

compounds had less than 1% chemical similarity. Finally, the sample of 8093 additional

compounds was ran for histogram results. 8093 compounds were filtered down to 34. Figure 13

provides the histogram results of the additional compounds retrieved from pathways. All

compounds had less than 1% chemical similarity. Based on all of the histogram results, the data

31

provides the fact that there might have been an error in processing the data and that an even more

chemically diverse sample does not guarantee best hits for specific malaria drug targets.

Figure 11. Histogram Results of Polypeptides Retrieved from MetaCyc

Figure 12. Histogram Results of General List of MetaCyc Compounds

32

Figure 13. Histogram Results for Additional Pathway Compounds

Results of the Malaria Metabolites (Excluding Plasmodium falciparum)

The malaria metabolites were run through KNIME divided into the 5930 compounds and

31050 polypeptides. Since Plasmodium falciparum was excluded, the files were separated based

upon species. They were further split into the groupings of polypeptides, general compounds,

and additional compounds retrieved from pathways. The four species involved were P. berghei

ANKA strain, P. chabaudi, P. vivax Sal1, and P. yoelii yoelii 17XNL. P. berghei ANKA has

12,238 polypeptides, 476 general compounds, and 580 pathways compounds. P. chabaudi has

527 general compounds and 579 compounds retrieved from pathways. As mentioned earlier, the

15011 polypeptides could not be retrieved based on technical issues with MetaCyc. P. vivax Sal-

1 has 577 normal compounds, 631 pathway-derived compounds, and 5344 polypeptides. P.

yoelii yoelii 17XNL has 557 regular compounds, 607 pathway-derived compounds, and 7865

polypeptides.

P. berghei ANKA was processed through KNIME. Its 12238 polypeptides were filtered

down to 4. Figure 14 shows the histogram outcome of the polypeptide data. Next, the regular

33

compounds of this species were processed. 476 compounds were filtered to 18. The histogram

data is provided in Figure 15. The pathway compounds were finally processed. Of the 580

pathway-derived compounds, the filtered number became 19. The histogram data is shown below

in Figure 16. All histograms showed a chemical similarity of 1% or below for all filtered

metabolites.

Figure 14. Histogram Results for P. berghei ANKA polypeptides

34

Figure 15. Histogram Results for P. berghei ANKA regular compounds

Figure 16. Histogram Results for P. berghei ANKA pathway-derived compounds

The next species to be processed through KNIME was P. chabaudi. There were no

polypeptides to be measured due to technical errors involved in the MetaCyc database. Regular

compounds were filtered from 527 to 18. Histogram results are shown below in Fig. 17.

Pathway-derived compounds were filtered from 579 to 19. Histogram results can be found in

Fig. 18 shown below. Once again, all of the compounds found in this species have 1% chemical

similarity or below.

35

Figure 17. Histogram Results for P. chabaudi regular compounds

Figure 18. Histogram Results of P. chabaudi pathway-derived compounds

P. vivax Sal1 was also processed through the workflow. The polypeptides were filtered

from 5344 to 6. Figure 19 shows the histogram results of the polypeptides for this species. The

regular compounds were filtered from 577 to 19. Figure 20 provides the histogram results of the

compounds. The pathway-derived compounds were filtered from 631 to 4. Figure 21 provides

36

the histogram results of the pathway-derived compounds. All filtered metabolites showed

chemical similarity of 1% or below.

Figure 19. Histogram Results of P. vivax Sal1 polypeptides

Figure 20. Histogram results for P. vivax Sal1 regular compounds

37

Figure 21. Histogram Results for P. vivax Sal1 pathway-derived compounds

Lastly, P. yoelii yoelii 17XNL was processed through KNIME. The polypeptides were

filtered from 7865 to 8. Fig. 22 provides the histogram outcome for the polypeptides. The regular

compounds were filtered from 557 to 19. The histogram for these results can be found in Fig. 23.

The pathway-derived compounds were filtered from 607 to 20. The histogram for these results

can be found in Fig. 24. All filtered metabolites showed chemical similarity between 0% and 1%.

Figure 22. Histogram Results for P. yeolii yoelii 17XNL polypeptides

38

Figure 23. Histogram Results for P. yoelii yoelii 17XNL regular compounds

Figure 24. Histogram Results for P. yoelii yoelii 17XNL pathway-derived compounds

Results of the Plasmodium falciparum 3D7 strain

Plasmodium falciparum is the specific target for the antimalarial drug targets because it is

the species which generates the deadliest type of malaria. The parasite has a total of 5603

polypeptides and 1396 compounds retrieved from MetaCyc. 5603 polypeptides, 660 regular

39

compounds, and 736 pathway-derived compounds were run through the workflow to collect

information on the chemical similarity of the parasite’s metabolites. The number of polypeptides

reduced to 4 after being filtered by GroupBy. Histogram results are provided below in Fig. 25.

The number of regular compounds decreased to 20 after being filtered. Histogram results are

shown in Fig. 26. Pathway compounds dropped to 19 after GroupBy filtered the data. Histogram

results are shown in Fig. 27. All of the filtered metabolites showed chemical similarity less than

1%.

Figure 25. Histogram Results for P. falciparum 3D7 polypeptides

40

Figure 26. Histogram Results for P. falciparum 3D7 regular compounds

Figure 27. Histogram Results for P. falciparum 3D7 pathway-derived compounds

Conclusion About MetaCyc Metabolites

Based on all histogram results, every single compound retrieved from the MetaCyc

database showed no more chemical similarity than 1%. Since the range for chemical similarity

for the metabolites extracted from Adams et al. was from 0% to 5.1%, there must have been an

41

error in how the data was set up. This observation showed that MetaCyc is not a user-friendly

database and does not offer efficient means in extracting its data. Other indicators of this

technical issue were extremely low filtered numbers of ligands and single bar histograms.

Logically, a sample of malaria metabolites should render higher chemical similarity on average

than the compounds from Adams et al. Therefore, malaria metabolites have to be derived from a

scientific paper which has the data organized the same way as Adams et al. It is also

recommended to determine how the Adams et al. compounds were filtered based on the range of

their molecular weight. The range for their molecular weights could be measured using the

Molecular Properties node to determine the appropriate range of molecular weights for the

malaria metabolites. Further searching for the appropriate malaria metabolites was advised to get

better results.

Statistics of the Molecular Weights of the Compounds

After discussing the results with Justin, he suggested that if I can find better results, I

should find malaria metabolites which are similar in molecular weight to the 4,998 compounds

which I ran through the workflow (Adams et al). Molecular weight is probably the most

significant factor in determining chemical similarity because molecular weight can influence the

degree of similarity based on common physical features. Therefore, I was advised to determine

the statistics of the 4,998 compounds and the list of antimalarial compounds to characterize the

range of molecular weights of both molecular datasets. Then I can use the statistics of the

datasets to create a more accurate depiction of the next malaria metabolite data.

I designed two workflows in KNIME, one for the list of antimalarial compounds and the

other for the 4,998 compounds found in Adams et al. The only difference was that Adams et al.

compounds had to be filtered by the GroupBy node. These workflows were similar to the past

42

ones, except that Fingerprint and Fingerprint Similarity nodes were replaced by the Molecular

Properties node which can be used to determine the molecular weights of the compounds.

Histogram nodes were added, but the Statistics node was also added to calculate the various

statistical values of each dataset. Figure 27 shows the two workflows in KNIME. The one at the

top ran the statistics for Adams et al. while the one at the bottom ran the statistics for Justin’s list

of antimalarials. Figure 28 shows the histogram results of the molecular weights of the

compounds in Adams et al. It is also important to recall that the number of compounds after

being filtered dropped from 4,998 to 3593. Figure 29 features the histogram results of the 284

compounds from Justin’s list of antimalarial compounds. Table 2 shows all statistical values of

the molecular weights of the compounds from Adams et al. and Justin’s list.

Figure 27. Two Workflows Used for Calculating Statistics of Molecular Weights of

Compounds

43

Figure 28. Histogram Results for the Molecular Weights of the Filtered Compounds from

Adams et al.

Figure 29. Histogram Results for the Molecular Weights of the Antimalarial Compounds

44

Table 2. Statistics of the Molecular Weights of the Compounds (g/mol)

Mean Median Standard

Deviation

Minimum Maximu

m

Range Highest

Frequenc

y

Range of

Highest

Frequency

Adams et

al.

Compounds

(n = 3592)

319.3 288.1 169.3 14.0 797.6 783.6 853 80-160

Justin’s list

of

antimalarial

compounds

(n = 284)

372.7 364.7 117.8 76.0 1300.7 1224.7 178 280-420

Statistical Inference

Based on the statistics conducted on the molecular weight of the two datasets, it seems

that the compounds taken from Adams et al. tend to have a lower mean and median molecular

weight, are more heterogeneous (expected if sample size is larger), have less range, and are more

evenly distributed in molecular weight than the antimalarial compounds. However, it is still

difficult to determine if the compounds from Adams et al. are truly similar to the antimalarial

compounds based on molecular weight. In this case, a statistical test must be performed to

further evaluate the statistical properties of the molecular weights of the two datasets. Although a

z-test could determine if they are similar based on their means, I would want a test which can

determine the accuracy of their similarity based on all values in the table, except for the highest

45

frequency and range of highest frequency. The highest frequency and the range of highest

frequency refer to the histogram data, which are irrelevant to the statistical test. The highest

frequency is based on the sample size, which will not be factored, and the range of highest

frequency refers to an inner minimum and maximum values within the range that set the inner

range for the highest bar in the histogram. Logically, the best test would be a chi-squared test of

independence to determine if there is a statistical similarity between the two datasets.

Chi-Squared Test of Independence

First, the null hypothesis states that the two datasets are independent of each other, or in

this case, different. The alternative hypothesis states that they are similar in some way depending

on the values which characterize their distribution of molecular weights. Second, the right test

statistic to be used is the chi-squared test for independence. The only way for this test statistic to

properly work based on the sample size is to take into consideration that the expected frequency

of each cell must be at least 5. This can be seen in the expected frequencies table (Table 3).

Third, the decision rule must be set up. Based on the information, the degrees of freedom equal

the number of columns minus one times the numbers of rows minus one. Df = (r-1)(c-1). Since I

mentioned that I would not take into account the highest frequency or the range for the highest

frequency, then there are 2 rows and 6 columns. Df = (r-1)(c-1) = (2-1)(6-1) = 1*5 = 5. The

degrees of freedom are equal to 5. I would use a 5% level of significance because this is the most

commonly used level of significance in biostatistics. The table highlights that I must reject the

null hypothesis if chi squared is equal to or greater than 11.070. Then we calculate the expected

frequencies in each cell. The table below shows how the expected frequencies are calculated

based on the sums added up from all columns and rows (Table 3). Note that the expected

frequencies can be found within the parentheses. They are calculated by multiplying the totals

46

corresponding to that frequency’s row and column and then dividing by the complete total

located in the most bottom-right box.

Table 3. Expected Frequencies of Statistics of the Molecular Weights of the Compounds

(g/mol)

Mean Median Standard

Deviation

Minimum Maximum Range Totals

Adams et al.

Compounds

(n=3592)

319.3

(281.6)

288.1

(265.7)

169.3

(116.8)

14.0

(36.6)

797.6

(853.9)

783.6

(817.3)

2371.9

Justin’s list

of

antimalarial

compounds

(n=284)

372.7

(410.4)

364.7

(387.1)

117.8

(170.3)

76.0

(53.4)

1300.7

(1244.4)

1224.7

(1191.0)

3456.6

Totals 692 652.8 287.1 90 2098.3 2008.3 5828.5

Calculating chi squared is basically squaring the differences between each expected and

observed frequency, dividing each square by the expected frequency, and finally taking the sum

of every number. The formula is shown below in Figure 30.

Figure 30. The Chi Squared Formula

47

After running the calculation, chi squared equals 83.6. Because this value is higher than

11.07, it means that we reject the null hypothesis. Therefore, the statistical conclusion is that the

two datasets have an association, indicating that they are statistically similar at a molecular level.

Although the chi-squared test showed that the compounds from Adams et al. are ideal enough to

be compared to the antimalarial compounds, the malarial metabolites must be more chemically

similar to the antimalarial compounds. The molecular weights of the 4,998 compounds from

Adams et al. can be used as a guide when comparing the next dataset for malaria metabolites.

The Optimal Malaria Metabolites

In order to determine the correct data for malaria metabolites, it must follow stricter

criteria. The MetaCyc metabolites were not formatted properly for KNIME to process

effectively, so I decided to use a new database, ChEMBL Malaria Data. ChEMBL is far more

user-friendly than MetaCyc because it allows for easier data input and better search options. It

also provides more file format options and is able to provide more organized data. Since Adams

et al. provided 4,998 metabolites which lead to no ideal drug targets and was taken from over

900 species and various pathogens, this time I had to collect metabolites which were specific to

malaria and have a larger sample size. Optimally, I used the substructure search tool in ChEMBL

Malaria Data. In order to create a very chemically diverse sample of malaria metabolites, I told it

to find all metabolites which had a carbon atom in it. Obviously, this led to a very diverse sample

of 250,642 hits. This time I downloaded the data as tab-delimited and to include the SMILES

format for easier file organization and processing through the workflow in KNIME. MetaCyc did

not have this option which shows the lack of convenience for cheminformaticians to do their

research. The malaria data was quite organized and could be read in columns similar to those

from Adams et al. Before I ran the data, I predicted that the malaria data would result in more

48

specific drug targets than the previous data because this time it was specific for the malaria

parasite and had a larger sample size. When the metabolites were filtered by the GroupBy node,

the number of compounds went down to 223,196. This meant that the data was mostly original

and that there were not a significant proportion of redundant ligands. Surprisingly, the histogram

results revealed that all metabolites were between 0% and 5.49% chemical similarity. They can

be found in Figure 31. Compared to the metabolites taken from Adams et al., the luck in finding

specific drug targets against malaria did not significantly change. The data was so terribly

dissimilar that even 104,264 of the 223,196 filtered metabolites were in the range of 0 to 0.61%

chemical similarity. Roughly 47% of the metabolites from ChEMBL Malaria Data had less than

approximately 0.6% chemical similarity amongst the antimalarial compounds. Another

surprising statistic was that only 56 metabolites had between 4.88% and 5.49% chemical

similarity. Even a very chemically diverse sample of over a quarter million malarial metabolites

is not enough to find one specific drug target against the malaria parasite. An optimal search

would involve millions of malarial metabolites, but I am not even sure that any database might

have that many. Due to the unavailability of a larger sample size of malarial metabolites and the

lack of annotation of the genome of the Plasmodium genus, it will take a long time and much

effort for cheminformaticians to encounter coming across new specific drug targets against the

malaria parasite, particularly Plasmodium falciparum.

49

Figure 31. Histogram Results for Chemical Similarity of Malaria Metabolites from

ChEMBL

Statistics of Malaria Metabolites

Although the malaria metabolites were not able to help us determine specific drug targets,

their molecular weights must still be analyzed to ensure that they were an appropriate example

for testing chemical similarity with the antimalarial compounds. Therefore, I processed the

malaria data through the workflow from Fig. 27 and got new results shown in Table 4. The

histogram in Figure 31 shows the distribution of molecular weights of the malaria metabolites.

50

Table 4. Statistics of Molecular Weights of All Tested Compounds (g/mol)

Mean Median Standard

Deviatio

n

Minimum Maximu

m

Range Highest

Frequency

Range of

Highest

Frequency

Adams et

al.

Compounds

(n = 3593)

319.3 288.1 169.3 14.0 797.6 783.6 853 80-160

Justin’s list

of

antimalarial

compounds

(n = 284)

372.7 364.7 117.8 76.0 1300.7 1224.7 178 280-420

Malaria

metabolites

(n=223,196)

375.6 370.1 95.1 30.0 3964.1 3934.1 142466 0-400

51

Figure 31. Histogram Results of the Molecular Weights of the Malaria Metabolites from

ChEMBL

Statistical Inference of the Malaria Metabolites

In comparison to the compounds from Adams et al. and Justin’s list of antimalarials, the

malaria metabolites appear more similar to the antimalarials. However, the malaria metabolites

have a smaller standard deviation and a much broader range of molecular weights than the other

two datasets due to its sheer size. Even the largest metabolite is much larger than the biggest

antimalarial, indicating that perhaps the list of antimalarials needs to incorporate larger

molecules to mimic the metabolic products of the malaria parasite. Although the malaria

metabolites seem to be similar in molecular weight to the other two datasets despite the broader

range, a chi-squared test of independence can confirm the true degree of similarity between all

three datasets.

52

Another Chi Squared Analysis

This is the same procedure as the first chi squared analysis, except that the malarial

metabolites are factored this time. First, the null hypothesis is that all datasets are independent of

each other. The alternative hypothesis states that there is an association between all of them, or

they share some degree of statistical similarity. The level of significance is 5% again. I am not

using highest frequency or range of the highest frequency because they can be biased by the

sample size. Next, a test statistic must be chosen which is clearly the chi squared test of

independence. We must check that each expected frequency is at least five, which is shown in

the calculations. Next a decision rule has to be made. The degrees of freedom are the number of

rows minus one times the number of columns minus 1. So, df = (r-1)(c-1) = (3-1)(6-1) = 2*5 =

10. Based on the level of significance and degrees of freedom, the null hypothesis would be

rejected if chi squared exceeded 18.307. Then calculations are carried out. The expected

frequencies are shown in Table 5 below in parentheses. The chi squared was equal to 1087.942.

Since it was greater than 18.307, then the null hypothesis must be rejected. Therefore, there is an

association between all three datasets. Although the chi squared test of independence showed

that the malarial metabolites are somewhat similar to the antimalarials and the compounds from

Adams et al., the malarial metabolites still greatly differ due to larger sample size and broader

range, especially on the heavier side of their molecular weights. Therefore, a different statistical

test must be performed to validate the similarity between the three datasets.

53

Table 5. The Expected Frequencies of the Molecular Weights of All Tested Compounds

(g/mol)

Mean Median Standard

Deviation

Minimum Maximum Range Totals

Adams et al.

Compounds

(n=3593)

319.3

(239.3)

288.1

(166.2)

169.3

(62.1)

14.0

(19.5)

797.6

(985.1)

783.6

(965.6)

2371.9

Justin’s list

of

antimalarial

compounds

(n=284)

372.7

(252.8)

364.7

(242.2)

117.8

(90.5)

76.0

(28.4)

1300.7

(1435.5)

1224.7

(1407.1)

3456.6

Malaria

Metabolites

(n=223,196)

375.6

(641.3)

370.1

(614.5)

95.1

(229.6)

30.0

(72.1)

3964.1

(3641.8)

3934.1

(3569.7)

8769

Totals 1067.6 1022.9 382.2 120 6062.4 5942.4 14597.5

Three Z-Tests of Sample Means

To ensure the best chance that all three datasets are similar to each other, I figured that

the simplest way would be to conduct three Z-tests to determine any significant differences

between the sample means. The means are the most representative measures of central tendency

of the samples. A Z-test is even more simple to conduct than a chi-squared analysis. First, the

null hypothesis states that there is no statistical difference between means. Second, the

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alternative hypothesis states that there is a statistical difference between two of the means. The

level of significance is 5%, which is the most commonly used level in statistics. The Z test is

chosen because all sample sizes exceed 30. The decision rule is based on the degrees of freedom

in which two of the sample sizes are added together and subtracted by 2. However, the Z test

does not use the degrees of freedom because it involves very large sample sizes. The formula for

the Z statistic is shown below in Fig. 32. The Z statistic basically consists of the difference

between the means in the numerator. Then the denominator has Sp which is the pooled estimate

for common standard deviation. It has its own formula which can be shown in Fig. 33. The

pooled estimate for the common standard deviation is then multiplied by the square root of the

sum of the multiplicative reciprocals of the two sample sizes. When calculating the pooled

estimate for the common standard deviation, it is equal to the square root of a numerator divided

by a denominator. The numerator is the sum of the products of each sample size subtracted by

one and the variance of the sample (standard deviation squared). The denominator is the same as

the degrees of freedom mentioned earlier. Another important aspect when calculating the pooled

estimate is to make sure that the proportion of the sample variances is between 0.5 and 2 to

ensure that the samples are not too different from each other. Finally, the test statistic can be

calculated and be determined. If the test result is outside of the range of the two-tailed test, then

it means that the null hypothesis must be rejected and there is a statistical difference between the

means of the two samples. Since I want to determine if three samples are statistically different

from each other by their means, then I must run three individual Z tests.

Z = (X1 -X2)/(Sp * sqrt(1/n1 + 1/n2)

Figure 32. Formula for Two Sample Z Test

Sp = sqrt(((n1-1)(s1)2 + (n2 – 1)(s2)2)/(n1 + n2 – 2))

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Figure 33. Formula for Pooled Estimate for Common Standard Deviation

Z-Test for the Compounds from Adams et al. and Justin’s List of Antimalarials

First, the null hypothesis states that the means are not different. Secondly, the alternative

hypothesis states that there is a difference between the means. Then the Z statistic is used

because both sample sizes easily exceed 30. We must also calculate the proportion of sample

variances. In this case, (s1)2/(s2)2 represents the proportion of sample variances. The standard

deviation of the compounds from Adams et al. is 169.3 and the standard deviation of Justin’s list

of antimalarials is 117.8. 169.32/117.82 = 2.065. This means that the proportion of sample

variances might not be reasonable, but the test could still give reliable results. The decision rule

states that if we are using a Z statistic, this is a two-tailed test, and that the level of significance is

5%, then the null hypothesis will be rejected if Z is greater than or equal to 1.96 or if Z is less

than or equal than to -1.96. We then calculate the Z statistic. First, we calculate the pooled

estimate for the common standard deviation, in which Sp = sqrt(((n1-1)(s1)2 + (n2 – 1)(s2)2)/(n1 + n2

– 2)). Sp = sqrt((3593-1)(169.3)2 + (284-1)(117.8)2)/(3593+284-2)) = 166.08. Next the Z test

becomes calculated in which Z = (X1 -X2)/(Sp * sqrt(1/n1 + 1/n2). Z =

(319.3-372.7)/(166.08*sqrt(1/3593+1/284)) = -5.216. Since -5.216 is less than -1.96, then the

null hypothesis has to be rejected. The means of the compounds from Adams et al. and Justin’s

list of antimalarials are different. This is not surprising because the compounds from Adams et

al. are not even specifically malarial and come from a huge variety of different pathogens and

species. Also, the proportion of sample variances was quite different.

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Z-Test for the Compounds from Adams et al. and the Malaria Metabolites

The null hypothesis states that there is no difference between means and the alternative

hypothesis states that there is a difference between the means of these two samples. The Z test

statistic is appropriate once again due to the large sample sizes. The proportion of sample

variances must be between 0.5 and 2.0 to ensure that the assumption is reasonable. (s1)2/(s2)2 =

(169.3)2/(95.1)2 = 3.17. This means that the proportion of sample variances might be

unreasonable for the test statistic to perform on these samples. The decision rule is the same as

the last example, so the null hypothesis becomes rejected if Z is not between -1.96 and 1.96. We

calculate the pooled estimate for the common standard deviation. Sp = sqrt(((n1-1)(s1)2 + (n2 – 1)

(s2)2)/(n1 + n2 – 2)) = sqrt((3593-1)(169.3)2 + (223,196-1)(95.1)2)/(3593+223,196-2)) = 96.7. Then

we calculate the Z score. Z = (X1 -X2)/(Sp * sqrt(1/n1 + 1/n2). Z =

(319.3-375.6)/(95.7*sqrt(1/3593+1/223,196)) = -34.98. Since -34.98 is less than -1.96, it means

that the null hypothesis becomes rejected. The means of the compounds from Adams et al. and

the malaria metabolites are statistically different. Thus the proportion of the sample variances is

unreasonable too. Other factors are that the sample sizes are very different and that the malaria

metabolites are not as heterogeneous as the compounds from Adams et al.

Z-Test for Justin’s List of Antimalarials and the Malaria Metabolites

So far, this is probably the most important Z-test because it will determine if the malaria

metabolites were a suitable match for Justin’s antimalarials and if their molecular weights were

roughly similar. Once again, the null hypothesis states that there is no difference between the

means and the alternative hypothesis states otherwise. The Z statistic is used again due to the

sample sizes. We then measure the proportion of sample variances. (s1)2/(s2)2 = 117.82/95.12 =

1.534. This time the proportion of sample variances appears to be reasonable. Next the decision

57

rule is the same, in which the null hypothesis becomes rejected if Z exceeds 1.96 or goes below -

1.96. We calculate the pooled estimate for the common standard deviation. Sp = sqrt(((n1-1)(s1)2 +

(n2 – 1)(s2)2)/(n1 + n2 – 2)) = sqrt((284-1)(117.8)2 + (223,196-1)(95.1)2)/(284+223,196-2)) = 95.13.

Next the Z statistic becomes calculated. Z = (X1 -X2)/(Sp * sqrt(1/n1 + 1/n2) =

(372.7-375.6)/(95.13*sqrt(1/284+1/223,196)) = -0.51. Since -0.51 is between -1.96 and 1.96, it

means that we fail to reject the null hypothesis. Although the compounds from Adams et al. were

statistically different in molecular weight, the antimalarials and the malaria metabolites showed

similarity in molecular weight. The proportion of sample variances was also reasonable. The Z-

test confirmed that the malaria metabolites were suitable in molecular weight as a sample to

compare chemical similarity against the antimalarials. However, a confusing observation is that

despite the similarity in molecular weight, the samples still did not share at least 5.5% chemical

similarity.

Final Conclusions

My main conclusions are that it is extremely difficult to identify best hits for specific

drug targets to the malaria parasite because the metabolic pathways are still not well-annotated

and the information about the genome of malaria still remains quite unsolved. At the beginning, I

brought up the statistics that only two of 200,000 compounds screened by the US Army

Antimalarial Drug Developmental Program in the last decade were successful in demonstrating

greater antimalarial activity than any other known drug against drug-resistant P. falciparum

(Canfield and Rozman). Another important note is that the MetaCyc database is not user-friendly

and makes it harder for cheminformaticians to complete this type of work due to the constraints

of file conversion. ChEMBL was much more user-friendly because it provided access to the

metabolite data by downloading it as tab-delimited. As a bonus, the structural annotation could

58

be provided in SMILES format unlike the MetaCyc database. However, it was still astonishing to

encounter 250,642 hits through KNIME and not find one specific drug target with chemical

similarity exceeding 5.49%. This led to my other conclusion that even a very chemically diverse

sample of malarial metabolites is still not enough to determine specific drug targets against the

malaria parasite. Therefore, millions of metabolites have to be processed through KNIME to find

the appropriate drug targets. Although it was not surprising to see that the results of the

compounds from Adams et al. did not have any compounds exceeding 5.1% chemical similarity,

it was still shocking that the malaria metabolites only had slightly more chemical similarity than

the general compounds. We could have gotten better chemical similarity if the SEA Approach

was used, but time was limited (Figure 2). The statistical tests provided an even stranger

conclusion that although the general compounds were statistically different in molecular weight,

the antimalarials and the malaria metabolites were statistically similar in their means of

molecular weight distribution. It is not strange that this result occurred, but it is strange due to

the circumstances of similar chemical similarity results between the general and malarial

metabolites. However, other factors that could explain this phenomenon could be that the malaria

metabolites had a very large sample size and extremely broad range and that the maximum

molecular weight of the malarial metabolites greatly exceeded even that of the antimalarials.

Perhaps a more diverse list of antimalarials might be needed to obtain more chemically similar

results.

Discussion

In order to better prepare for finding antimalarial drug targets, we must find larger sample

sizes of malaria metabolites and find a sample specific to Plasmodium falciparum. I did not try

to find a sample specific to this species because I figured that after the failure of the malaria

59

metabolites from ChEMBL, it would have taken much more time to find a larger sample for

malaria metabolites and that future samples for only one species would result in further failures.

Another error was that I used the chi squared test of independence to determine similarity in the

statistics of the distribution of molecular weight between the general compounds, malaria

metabolites, and antimalarials. The Z tests provided much more accurate results than the chi

squared test of independence because the chi squared test of independence does not determine

similarity between independent samples. Analysis would have also been more successful if

MetaCyc had established more downloading options for collecting metabolite data. Another

major error that I also discovered was that the compounds I used from ChEMBL were mostly

antimalarial compounds. Since chemical similarity between Justin’s compounds and those from

ChEMBL were so significantly low, this raised many new questions. Perhaps Justin’s list has

much newer compounds that were not shown in the ChEMBL database or maybe the pipeline did

not work. However, the compounds from ChEMBL could be used as a much chemically diverse

sample of antimalarials to detect chemical similarity among future metabolites. In the future, a

sample of millions of metabolites specific to malaria and a larger sample of antimalarial drugs

might provide the key to determining specific drug targets to the malaria parasite.

60

Acknowledgments

This project would have not been made possible without the suggestions and careful

guidance proposed by my peer, Justin Gibbons. Acknowledgments also go out to Dr. Rays Jiang

for judging my performance as a bioinformatician and professional in presenting the data of this

project. Finally, acknowledgments also go to Dr. Vladimir Uversky for assisting my performance

in determining a key site for my bioinformatics internship and advising me on how to carry out

my bioinformatics analysis.

Works Cited

Adams, James Corey, et al. "A mapping of drug space from the viewpoint of small molecule

metabolism." PLoS Comput Biol 5.8 (2009): e1000474.

Basso, Luiz Augusto, et al. "The use of biodiversity as source of new chemical entities against

defined molecular targets for treatment of malaria, tuberculosis, and T-cell mediated

diseases: a review." Memórias do Instituto Oswaldo Cruz 100.6 (2005): 475-506.

Beisken, Stephan, et al. "KNIME-CDK: Workflow-driven cheminformatics." BMC

bioinformatics 14.1 (2013): 1.

Canfield, C. J., and R. S. Rozman. "Clinical testing of new antimalarial compounds." Bulletin of the World Health Organization 50.3-4 (1974): 203.

Chambers, Jon et al. “UniChem: Extension of InChI-Based Compound Mapping to Salt,

Connectivity and Stereochemistry Layers.” Journal of Cheminformatics 6.1 (2014): 43.

PMC. Web. 22 June 2016.

"Chi-Square Statistic: How to Calculate It." Statistics How To. N.p., 2016. Web. 1 July 2016.

Cobbold, Simon A., et al. "Metabolic Dysregulation Induced in Plasmodium falciparum by

61

Dihydroartemisinin and Other Front-Line Antimalarial Drugs." Journal of Infectious

Diseases 213.2 (2016): 276-286.

Gardiner, Eleanor J., et al. "Effectiveness of 2D fingerprints for scaffold hopping." Future

medicinal chemistry 3.4 (2011): 405-414.

Ginsburg, Hagai. "Progress in in silico functional genomics: the malaria Metabolic Pathways

database." Trends in parasitology 22.6 (2006): 238-240.

Godden, Jeffrey W., Ling Xue, and Jürgen Bajorath. "Combinatorial preferences affect

molecular similarity/diversity calculations using binary fingerprints and Tanimoto

coefficients." Journal of Chemical Information and Computer Sciences 40.1 (2000): 163

166.

Pradhan, Anupam, et al. "Chemogenomic profiling of Plasmodium falciparum as a tool to aid

antimalarial drug discovery." Scientific reports 5 (2015).

Rogers, David, and Mathew Hahn. "Extended-connectivity fingerprints." Journal of chemical

information and modeling 50.5 (2010): 742-754.