Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion Predicting virus mutations through relational learning AIMM 2012 E Cilia 1 , S Teso 2 , S Ammendola 3 , T Lenaerts 1 , and A Passerini 2 September 9 th , 2012 1 - D´ epartement d’Informatique, FS, Universit´ e Libre de Bruxelles 2 - Department of Computer Science and Information Engineering, University of Trento 3 - Ambiotec sas E Cilia 1 , S Teso 2 , S Ammendola 3 , T Lenaerts 1 , and A Passerini 2 — Predicting virus mutations through relational learning 1/24
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Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Predicting virus mutations through
relational learningAIMM 2012
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and APasserini2
September 9th, 2012
1 - Departement d’Informatique, FS, Universite Libre de Bruxelles2 - Department of Computer Science and Information Engineering, University of Trento
3 - Ambiotec sas
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 1/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Motivations
Mining relevant features from protein mutation data
understanding the properties of functional sites
developing novel proteins with useful/relevant function
Rational Design
engineering technique modifying existing proteins by sitedirected mutagenesis
assumes knowledge (or intuition) about the e↵ects ofspecific mutations
involves extensive trial-and-error experiments
also serves to improve understanding protein function
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 2/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
IntroductionAn artificial system mimicking rational design
Goal
To build an artificial system mimicking the rational designprocess
A relational learning approach to:
1 mine rules from mutation data describing mutationsrelevant to a certain behavior
2 use the rules to infer novel mutations that may induce asimilar behavior
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 3/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
A Relational Learning Approach
backgroundknowledge
dataset of mutations / mutants
rank of novel
relevant mutations
hypothesis
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 4/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Step 1: Relational Learning PhaseLearning in First Order Logic
data D, background knowledge B and features inducedduring learning are represented in first order logicres against(M,nnrti) mut(M,P) AND close to site(P)
head body
searching for a set of clauses (hypothesis) covering all ormost positive examples, and none or few negative ones.
Advantages
expressivity and interpretability of the learned model
possibility to make use of specific background knowledge
ability to learn rules from description of complex,structured entities
the learnt rules constrain the rational design space
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 5/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Algorithm Mutation generation1: input: background knowledge B, learned model H, k2: output: rank of the most relevant mutations R3: procedure GenerateMutations(B,H, k)4: Initialize DM ;5: A find all mutations m that satisfy at least one clause c
i
2 H6: for m 2 M do
7: score SM
(m) . number of clauses ci
satisfied by m8: DM DM [ {(m, score)}9: end for
10: R RankMuts(DM,B,H, k) . rank relevant mutations11: return R12: end procedure
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 6/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
HIV-1 RT Drug Resistance
mining rules from HIV mutation data
understand the virus adaptation mechanism
design drugs that e↵ectively counter potentiallyresistant mutants
Datasets1 Reverse Transcriptase (RT) mutations from the Los Alamos
National Laboratories HIV resistance database
NRTI ! 95 mutationsNNRTI ! 56 mutations
2 RT mutants from the Stanford HIV drug resistance
database
NRTI ! 639 mutantsNNRTI ! 747 mutants
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 7/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Learning settingsLearning from mutations
Mutation -based learning
Input examples: single amino-acid mutations conferringresistance to a class of drugs
aa(Pos,AA)
mut(MutationID,AA,Pos,AA1)
Target concept: a model (i.e. set of rules) describing amutation conferring resistance to a certain classof drugs
res against(MutationID,Drug)
Learning setting: learn from positive examples only(annotation on mutations NOT conferringresistance is scarce)
Output: generated resistance mutations
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 8/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Background Knowledge
Background Knowledge Predicates(excerpt)
typeaa(T,AA)
same type aa(R1,R2,T)
same type mut t(MutID,Pos,T)
close to site(Pos)
location(L,Pos)
catalytic propensity(AA,CP)
(Betts and Russell, 2003)
Background Knowledge Rules (example)
same type aa(R1,R2,T) typeaa(T,R1) AND typeaa(T,R2)
different type mut t(MutID,Pos) mut(MutID,R1,Pos,R2)AND NOT same type aa(R1,R2,T)
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 9/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Learned HypothesisModel for the resistance to NNRTI
Other identified mutations (from the literature):219H
Other key positions from the rules are: 33, 194,218
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 16/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Summary
Relational learning approach mimicking the rational designprocess:
HIV RT mutations/mutants
we built a relational knowledge base
we mined relevant relational features for modelingresistance mutations/mutants
we generated candidate mutations satisfying the learnedrules
promising results, both in the mutation-based and in themutant-based learning settings, suggest a potential inguiding mutant engineering or predicting virus evolution
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 17/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Future WorkWork in progress
extend the background knowledge
single nucleotide change(a,d).
aamutations single(R1,R2) mut(M,R1,P,R2) AND
(single nucleotide change(R1,R2) OR
single nucleotide change(R2,R1))
post-processing involving mutant evaluation by statisticallearning approaches and stability predictors or searchagainst HIV genome databases
and generalize the approach to jointly generate sets ofrelated mutations (mutants with multiple mutations)
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 18/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Future WorkFrom single to multiple amino acid mutations
Observations
multiple mutations are often required in order to a↵ectprotein function
neutral network theory claims that neutral mutations arerequired as intermediate steps to e↵ective ones (debated)
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Motivation
Multiple point mutations (MPM)
Multiple mutations are often required in order to a↵ectprotein function
Neutral network theory claims that neutral mutations arerequired as intermediate steps to e↵ective ones (debated)
Andrea Passerini — Frankenstein Junior 4/14
Motivation
Multiple point mutations (MPM)
Multiple mutations are often required in order to a↵ectprotein function
Neutral network theory claims that neutral mutations arerequired as intermediate steps to e↵ective ones (debated)
Andrea Passerini — Frankenstein Junior 4/14
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 19/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Future WorkFrom single to multiple amino acid mutations
Predicting multiple mutations
predicting single mutations does not consider the jointe↵ect of multiple mutations
trying all possible combinations is computationallyinfeasible (and not enough data)
...EYIQAKVQM...LDNLLNIEVAY...
...EYIQAKVQM...LDNLLDIEVAY...
...EYIQAKVQM...LENLLDIEVAY...
...EYIQAKVQM...LENLLNIEVAY...
Motivation
Multiple point mutations (MPM)
Multiple mutations are often required in order to a↵ectprotein function
Neutral network theory claims that neutral mutations arerequired as intermediate steps to e↵ective ones (debated)
Andrea Passerini — Frankenstein Junior 4/14
Motivation
Multiple point mutations (MPM)
Multiple mutations are often required in order to a↵ectprotein function
Neutral network theory claims that neutral mutations arerequired as intermediate steps to e↵ective ones (debated)
Andrea Passerini — Frankenstein Junior 4/14
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 20/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
Predicting multiple mutations
E Cilia1, S Teso2, S Ammendola3, T Lenaerts1, and A Passerini2 — Predicting virus mutations through relational learning 21/24
Intro A Relational Learning Approach HIV RT Drug Resistance Learning from mutations Learning from mutants Conclusion
