Allosteric Modulator Discovery: From Serendipity to Structure-Based DesignShaoyong Lu,†,‡,§ Xinheng He,†,§ Duan Ni,†,§ and Jian Zhang*,†,‡
†Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental ResearchCenter, Renji Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China‡Medicinal Bioinformatics Center, Shanghai Jiao-Tong University School of Medicine, Shanghai 200025, China
ABSTRACT: Allosteric modulators bound to structurally diverse allosteric sites can achieve better pharmacological advantagesthan orthosteric ligands. The discovery of allosteric modulators, however, has been traditionally serendipitous, owing to thecomplex nature of allosteric modulation. Recent advances in the understanding of allosteric regulatory mechanisms andremarkable availability of structural data of allosteric proteins and modulators, as well as their complexes, have greatlycontributed to the development of various computational approaches to guide the structure-based discovery of allostericmodulators. This Perspective first outlines the evolution of the allosteric concept and describes the advantages and hurdlesfacing allosteric modulator discovery. The current available computational approaches, together with experimental approachesaiming to highlight allosteric studies, are then highlighted, emphasizing successful examples with their combined applications.We aimed to increase the awareness of the feasibility of the structure-based discovery of allosteric modulators using anintegrated computational and experimental paradigm.
1. INTRODUCTION
Allostery is an inherent property of biomacromolecules inwhich topographically distinct binding sites within a proteinare functionally coupled and can communicate over adistance.1−3 Typically, the allosteric signal propagation fromallosteric to functional, orthosteric sites is elicited byperturbations at the allosteric site such as effector (ions,small molecules, proteins, DNA, and RNA) binding, pointmutations, and posttranslational modifications.4,5 The exis-tence of allosteric modulation in the control of biomacromo-lecule function allows for exquisite control of innumerablebiological processes, ranging from enzyme catalysis, geneexpression, and cellular differentiation to metabolism andhomeostasis.6−10 Because of the omnipresence of allostericcontrol over normal cellular processes, allosteric modulationhas thus been regarded as the “second secret of life”.11 Thisnotion has been well established as a fundamental biologicalphenomenon for insights into cellular functions and dis-eases.5,12
From a pharmaceutical standpoint, protein allostery providesa promising, novel opportunity for the development ofinnovative therapeutics. Owing to the nonoverlap of allostericand orthosteric sites, allosteric modulators, by attaching toallosteric sites, did not compete with endogenous or exogenous
ligands bound to orthosteric sites. This feature guarantees thecooperative regulation of protein function by both allostericmodulators and orthosteric ligands in individual proteins.13−15
Moreover, the much more diversified allosteric sites relative tothe highly conserved orthosteric sites endow allostericmodulators with potentially enhanced selectivity and reducedtoxicity than orthosteric ligands.16−18
Despite the potential advantages of allosteric therapeutics,allosteric modulator discovery has encountered a keychallenge. Over the years, most allosteric modulators havebeen discovered serendipitously by high-throughput screeningexperiments.19 Such unexpected findings manifest a lack of acomprehensive understanding of allosteric interactions andtheir effects on the molecular mechanism of proteins.Fortunately, structural data pertaining to allosteric proteinsand their allosteric sites and modulators have become moreavailable in the past few years.20 Such data are beneficial todevelop computational approaches to predict allostericinteractions and mechanisms and subsequently screenallosteric modulators for the intended allosteric sites.Mounting evidence suggests that allosteric modulators can be
Received: November 11, 2018Published: February 28, 2019
Perspective
pubs.acs.org/jmcCite This: J. Med. Chem. XXXX, XXX, XXX−XXX
discovered in conjunction with computational and exper-imental methods, indicative of the advent of structure-basedallosteric modulator discovery.21−27
In this Perspective, we briefly overview the evolution of theallosteric concept and describe the advantages and hurdlesfacing allosteric modulator discovery. Importantly, we elucidatethe currently available computational methods that comple-ment experimental methods and provide powerful tools tofacilitate allosteric modulator discovery. We mainly focus onthe identification of allosteric modulators guided by computa-tional methods, followed by experimental validations. Weoutline some guidelines to increase the awareness of thestructure-based discovery of allosteric modulators using anintegrated computational and experimental paradigm. Finally,we discuss the future directions that can contribute to allostericmodulator discovery.
2. PAST AND PRESENT OF THE ALLOSTERICCONCEPT
The pioneering work by Bohr in 1904 uncovered an allostericphenomenon where the binding of carbon dioxide influencesthe binding affinity of oxygen to hemoglobin. This well-recognized phenomenon describing the cooperative binding ofmolecules to two distinct sites is known as the “Bohr effect”,which is currently named the “allosteric effect”.5
In 1961, Monod and Jacob first proposed the concept ofallostery to account for the mechanism of feedback inhibitionof enzyme activity in which “the inhibitor is not a stericanalogue of the substrate”.28 Later, in the 1960s, two classicmodels of allosteric interactions were created to explain thecooperative binding of regulatory molecules; one is the“concerted” or MWC (Monod−Wyman−Changeux)(1965)29 model, and the other is the “sequential” or KNF(Koshland−Nemethy−Filmer) (1966)30 model. Both modelsunderscore the role of conformational changes in the allostericregulation of two end-state structures.In 1984, Cooper and Dryden proposed a theoretical model
named “dynamics-driven allostery” by which allostery occurswithout significant conformational changes in the averagestructure, revealing the importance of the entropic contribu-tion to allostery.31 Advances in solution nuclear magneticresonance (NMR) spectroscopy32 have substantially contrib-uted to probe “dynamics/entropy-driven allostery” through thecharacterization of protein internal motions such as in thecatabolite activator protein (CAP),33,34 PDZ domains,35
calmodulin,36−38 and protein kinase A.39 Dramatically,Frederick et al. found a linear relationship between the proteinconformational entropy and change in the overall bindingentropy,37 revealing the important role of conformationalentropy in the recognition of proteins.40 They furtherdeveloped an empirical, quantitative method to describe therelationship between changes in conformational dynamicscharacterized by the motion of side chains and changes inprotein conformational entropy.41,42
In 1999, Nussinov and co-workers proposed a “conforma-tional selection and population shift” model that explains howallostery works from the viewpoint of free energy landscapetheory.43−45 This model broadens the concept of allosteryfrom two structural states to conformational ensembles ofmultiple states. In the same year, Ranganathan and co-workersproposed an “allosteric networks” model by which signaltransmission between distinct sites of proteins is achieved by anetwork of physically interconnected residues.46 This struc-
tural view of allostery has been demonstrated by numerousstudies using high-resolution structures.47−50
Recently, Hilser and co-workers have proposed an“ensemble allosteric” model to explain the origin ofallostery.51−53 The Hilser’s model is similar to the Nussinov’smodel in the explanation of the allosteric origin. Both modelsassume that all possible conformations of a protein ensembleare populated in terms of their respective energies, and thebinding of allosteric effectors to the protein reshapes the freeenergy landscape. However, Hilser’s model further extendsNussinov’s model to account for allostery in intrinsicallydisordered proteins; the former provides a framework thatunifies the illustrations of allostery in structured, dynamic, anddisordered systems.As mentioned above, allosteric concepts and models have
advanced over 50 years since the word “allostery” was coinedby Monod and Jacob.28 It is obvious that the concept ofallostery has evolved from the two-state model, the staticstructural model to the dynamic ensemble model.54−56
Importantly, some models have been applied to developcomputational methods to predict allosteric sites andmutations, discover allosteric modulators, assess allostericinteractions, and investigate allosteric mechanisms, all ofwhich will be elaborated in the following sections.
3. ADVANTAGES AND CHALLENGES FACINGALLOSTERIC MODULATOR DISCOVERY
3.1. Advantages. Allosteric modulators harbor severaldistinct advantages over orthosteric ligands that occupy aprotein’s active site. The advantageous characteristics ofallosteric modulators can be witnessed by their high specificityand low adverse effects.57−59 G-protein-coupled receptors(GPCRs) and protein kinases are the two most important drugtargets in therapeutic pharmacology. The highly evolutionarilyconserved endogenous orthosteric binding sites represent a keychallenge to develop selective orthosteric drugs for GPCRs andprotein kinases. It is recognized that orthosteric ligands of anintended GPCR or protein kinase frequently suffer from cross-reactivities with its homologous proteins, leading to unwantedside effects and off-target toxicity.16,60 As an alternative,targeting the structural diversity and topological difference ofallosteric sites enables one to surmount two major obstaclesencountered by orthosteric ligands.61,62 For instance, pyr-azolopyridone-based compound 10 (1) bound to the allostericsite at the C-lobe of the kinase exhibits excellent selectivity forp38α (IC50 = 1.2 μM) against three closely related structures,p38β, p38γ, and p38δ (IC50 > 40 μM) (Figure 1), due to theresidue differences within this site.63
Allosteric modulators have a spatiotemporal basis inspecificity. They, together with endogenous ligands, exerttheir cooperative actions by the fine-tuned modulation of theprotein function rather than by shutting off or turning onendogenous physiological signaling.13 This feature can result inincreased safety in the event of an overdose of allostericdrugs.16
Importantly, allosteric modulators have the potential tocombat drug-resistant mutations situated at orthosteric sites inpatients. For example, the “gatekeeper mutation” T315I ofBCR-ABL1 causes resistance to a set of clinically approvedorthosteric drugs such as imatinib, bosutinib, nilotinib, anddasatinib.64 Treatment of the T315I mutant with an allostericinhibitor ABL001 (asciminib) bound to the myristoyl pocket
at the C-lobe of the kinase can overcome drug resistance(Figure 2).65,66
A protein exists in an ensemble consisting of numerous,distinct conformational states that may be involved in multiplesignaling pathways.9,67 An allosteric modulator binding to aunique conformation of the protein can achieve biasedsignaling.13 For example, G-protein- or β-arrestin-mediateddownstream signaling cascades of GPCRs can be activated bydifferent conformational states of receptor−ligand com-plexes.68
Remarkably, allosteric regulation also provides a new avenuefor drugging “undruggable” targets that are difficult to targetpharmacologically.69,70 This category of proteins is intractableto drugs because of strong substrate binding, the lack of deepbinding sites, or large and flat protein−protein interaction(PPI) interfaces.71,72 Under these circumstances, allostericmodulators can bypass direct competition with orthosteric sitesor PPI interfaces and bind alternative sites to fine-tune proteinactivities. Furthermore, cryptic (or hidden) allosteric sites canbe captured from the minor or intermediate conformations of
the protein and exploited for drug design, if the evidentbinding sites are invisible in the solved crystal structures.73−75
Currently, many previously considered “undruggable” targetshave proven to be treatable through allosteric regulatorymodules, including K-Ras,76,77 transcription factors (MYC78
and nuclear factor-κB79), the SH2-kinase domain PPI interfaceof BCR-ABL,80 phosphatases (SHP2,81 PTP1B,82 andPP2A83), hypoxia inducible factor-2,84 and STAT3.85
The explosive growth in the number of allosteric modulatorsin recent years has demonstrated advantages to the explorationof allostery in drug discovery.86 However, allosteric modulatordiscovery also poses significant challenges, which may berelieved by the deeper understanding of the molecular detailsof allosteric mechanisms and structural and biochemicalproperties of allosteric proteins and modulators.
3.2. Challenges. A key step for allosteric modulatordiscovery is the identification of bona fide allosteric sites in aprotein. Unlike orthosteric ligands that occupy a conserved,known orthosteric site, allosteric sites are less evolutionallyconserved than orthosteric sites (vide infra).87,88 Theoretically,any binding pocket in a protein spatially distinct from theorthosteric site can be regarded as a latent allosteric site.Moreover, the regulatory effect of the latent allosteric site onthe orthosteric site is difficult to determine. These disadvan-tages have severely impeded the allosteric modulator discoveryprocess over a long period. Fortunately, the ample availabilityof structural complexes of allosteric protein−modulatorinteraction provided by X-ray crystallography and NMRspectroscopy in recent years has partially addressed thisquandary.20 Many computational methods (see below) havebeen developed to predict allosteric sites based on thecharacteristics of known allosteric sites and the underlyingmechanisms of allosteric regulation,89,90 facilitating thestructure-based discovery of allosteric modulators.Challenges can also stem from the emergence of drug-
resistant mutations at allosteric sites, the same situation facedby orthosteric drugs. For example, in the case of Ba/F3 celllines expressing BCR-ABL1 variants, single-point mutations(A337V, P465S, V468F, and I502L) at the allosteric myristoylsite in the C-lobe of the kinase confer resistance to theallosteric inhibitor ABL001 (Figure 3).65 Furthermore, acombination of ABL001 and an orthosteric drug nilotinibcan inhibit the mutant more effectively than each agentalone.66 This additive effect suggests the possibility of asynergistic effect of allosteric and orthosteric drugs in thetreatment of human diseases. A large body of evidence suggeststhat allosteric sites have evolved under lower evolutionarypressure compared with orthosteric sites, implying thatallosteric mutations occur more frequently than orthostericmutations.87,88 Indeed, more than 20 disease-associatedmutations at the allosteric regulator binding domain ofpyruvate kinase have been observed owing to the evolutionarilyless conserved allosteric site.91 Additionally, mutations canoccur at the allosteric communication pathways beyondallosteric sites, altering the protein dynamics and causingindirect resistance to allosteric modulators.47 Such mutationsare very difficult to identify experimentally because of thecomplexity of allosteric networks in proteins.Additionally, allosteric modulators show significant species
differences owing to the low evolutionary conservation ofallosteric sites.5 For instance, the actions of allostericcandidates are effective in recombinant human receptors invitro. However, when using rodent receptors or models to
Figure 1. Surface representation of the X-ray crystal structure of p38αcomplexed to an allosteric compound 10 (1) (PDB code 3NEW).The orthosteric ATP binding site and allosteric site at the C-lobe ofthe kinase are highlighted in blue and red, respectively. Multiplesequence alignments of allosteric site residues in p38α, p38β, p38γ,and p38δ are shown, with identical and similarity residues marked byblack and gray, respectively.
Figure 2. Surface representation of the X-ray crystal structure of BCR-ABL1 complexed to both orthosteric drug imatinib and the allostericinhibitor ABL001 (asciminib) (PDB code 5MO4), with orthostericand allosteric sites highlighted in blue and red, respectively.
examine drug effects, it is possible to obtain the oppositeresult.92
In general, allosteric modulators have lower binding affinitythan orthosteric ligands93 and often encounter “flat,” non-tractable structure−activity relationships.16 Furthermore, allos-teric modulators often have lower aqueous solubility thanorthosteric ligands.16 These properties can lead not only to thedifficulty of advancing allosteric modulators as drug candidatesfor clinical testing but also to the crystallization of allostericprotein−modulator complexes.
4. METHODS TO INVESTIGATE ALLOSTERY4.1. Experimental Approaches. X-ray crystallography,
the most frequently used experimental approach in structuralbiology, has provided insights into structural and mechanisticinvestigations of biologically important macromolecules.94
Advances in X-ray crystallographic technology in the pastdecade have substantially contributed to determine thestructures of challenging macromolecules such as GPCRs,95
the proteasome,96 the maltose transporter,97 and eukaryoticexosomes.98 Remarkably, recent breakthroughs in the field ofsingle-particle cryoelectron microscopy (cryo-EM) haveenabled the characterization of the conformation of largemacromolecular assemblies such as viruses,99 cytoskeletalproteins,100 and spliceosomes101 at near-atomic resolution,providing potential applications in drug discovery.102 Both X-ray crystallography and cryo-EM can provide detailed essentialinformation about the three-dimensional (3D) structures ofligand-bound (holo) and unbound (apo) forms. Thesemethods have enabled a structural view of allostery. In anallosterically regulated protein, the conformational change atthe orthosteric site that occurs upon modulator binding can begenerally uncovered by structural comparison of the holo andapo forms of the protein. By contrast, in the case of dynamics-driven allosteric proteins such as CAP33,34 and PDZdomains,35 the orthosteric site and whole protein exhibit nostructural change between the holo and apo forms. In fact, X-ray and cryo-EM crystal structures are static average snapshotsof macromolecules. However, these molecules can interconvertalong many distinct conformations under physiological
conditions to mediate different biological functions. The lackof conformational change based on the static crystal structureoccasionally limits the usefulness of X-ray crystallography andcryo-EM in studies of allostery at the molecular level.In nature, allostery is a dynamic process. NMR spectroscopy,
on the other hand, can probe the dynamic processes ofbiomolecules on a wide range of ps−ms time scales andobserve conformational states in solution that are sparselypopulated, yielding detailed dynamical information that is verysuitable for the characterization of their allosteric mecha-nisms.103−106 It can be used to study allosterically regulatedproteins with significant conformational changes when boundto allosteric modulators. Most notably, it is particularly suitedto explore protein allostery when conformational change of theprotein is rarely detected in response to allosteric modulatorbinding, thus providing direct experimental evidence toestablish the dynamics or ensemble view of allostery. As anexample, using chemical shift and relaxation dispersion NMRanalyses, Tzeng and Kalodimos revealed that one cAMPmolecule binding to the first homodimeric CAP has a minoreffect on the fast dynamics of CAP, while the binding ofanother cAMP molecule to the second homodimeric CAPquenches such dynamics, further highlighting the role ofconformational entropy in the allosteric regulation of CAP,which was validated by ITC measurements.34 Traditionally,solution NMR spectroscopy has focused on relatively smallproteins. Recent advances in the amide-/methyl-TROSYapproaches, isotope labeling, and pulse sequence techniques107
have made it possible to characterize large-molecular-weightproteins such as the 20S proteasome core particle108 andmolecular chaperones such as HSP90109 and GroEL.110
X-ray crystallography, cryo-EM, and NMR spectroscopy canprovide direct experimental proof for allosteric modulation ofbiological macromolecule function. In addition to these directapproaches, a few indirect experimental approaches have beendeveloped to explore allostery in a particular protein. Theseinclude site-directed mutagenesis such as disulfide trapping (ortethering)111 and alanine scanning,112 fluorescent113,114 andphotoaffinity115 labeling, and hydrogen/deuterium exchange(HDX) mass spectrometry.116
4.2. Computational Approaches. It is typically time-consuming and frequently unsatisfactory to determineallosteric sites in proteins and the mechanism of allosteryusing experimental approaches such as X-ray crystallographyand NMR because of the complex nature of allostery inbiological processes. As a viable alternative to experimentalmethods, computational approaches for the study of allosteryprovide robust tools to aid in drug design, which has been anarea of intensive research.21,89,90,117 For example, AlloStericDatabase (ASD)20,118,119 and ASBench120 benchmark are twomajor developments that have facilitated the development ofcomputational methods to predict allosteric sites. A largeamount of the state-of-the-art computational methods haveemerged, facilitated by ASD and ASBench data in the past fewyears.21,89,90,117 Most critically, many of the theoreticalpredictions have been confirmed by experimental observations,which will be described in the following section, suggesting thepotential usefulness of computational methods in structure-based drug discovery. As such, a combined computational andexperimental approach should be become a new strategy inallosteric modulator discovery.Table 1 summarizes the current availability of representative
computational allosteric prediction methods. Such methods
Figure 3. Cartoon representation of the X-ray crystal structure ofBCR-ABL1. The drug-resistant mutation residues at the orthostericsite (Thr315) and allosteric site (Ala337, Pro465, Val468, and Ile502)are depicted by sphere representations.
include the prediction of allosteric sites, allosteric mutations,allosteric communication in proteins, assessment of allostericprotein−modulator interactions, and virtual screening ofallosteric modulators. Remarkably, most computationalmethods focus on the identification of allosteric sites becausethe determination of the location of allosteric sites is the firststep for structure-based allosteric modulator discovery. Awealth of predictive models for allosteric sites have beencreated based on different methodologies, including structure-based methods (e.g., Allosite,121 AllositePro,122 ALLO,123 anda random forest model124), evolution-based methods (e.g.,statistical coupling analysis (SCA)125), normal model analysis-based methods (e.g., AlloPred126 and PARS127), dynamics-based methods (a two-state Go model,128 ExProSE,129
molecular dynamics (MD) simulations with enhanced
conformational sampling,130 and AllosMod131), energy-basedperturbation methods (e.g., reverse perturbation),132 andcorrelation-based methods (e.g., CorrSite).133 Moreover,some of these programs, such as Allosite,121 PARS,127
AllosMod,131 and CorrSite,133 have been deployed as Webservers, facilitating medicinal chemists to find allostericmodulators. In addition to allosteric site prediction, Webservers or tools such as MCPath,134 SPACER,135 DynOmicsENM,136 AlloSigMA,137 AlloMAPS,138 and AlloDriver139 forallosteric communication and mutation analysis, STRESS140
for allosteric hotspot residues prediction, Alloscore141 forallosteric interaction evaluation, and CovCys142 for covalentallosteric modulator design have also been developed.
Table 1. Databases of Structures Containing Allosteric Sites and Software To Detect and Predict Them
name ref Web server available content, methods, and applications
databaseASD 20, 118, 119 http://mdl.shsmu.edu.cn/
ASDcollection of experimentally determined allosteric proteins, modulators, andpathways
benchmarkASBench 120 http://mdl.shsmu.edu.cn/
asbenchtwo benchmark sets of allosteric sites: “core set” and “core-diversity set”
allosteric site identificationAllosite 121 http://mdl.shsmu.edu.cn/
ASTdetection of allosteric sites based on a structure-based machine learning method
AllositePro 122 no detection of allosteric sites based on a combined structure- and normal-modeanalysis-based method
ALLO 123 no detection of allosteric sites based on a structure-based machine learning methodrandom forest model 124 no detection of allosteric sites based on a structure-based machine learning methodSCA 125 no detection of allosteric sites based on an evolutionary methodAlloPred 126 no detection of allosteric sites based on the normal-mode analysis methodPARS 127 http://bioinf.uab.cat/pars detection of allosteric sites based on the normal-mode analysis methodtwo-state Go model 128 no detection of allosteric sites based on an ensemble generated by coarse-grained
simulationsExProSE 129 no detection of allosteric sites based on an ensemble generated by a distance
ucsf.edu/allosmoddetection of allosteric sites and allosteric mechanism research from MDsimulations based on a modeled energy landscape
reverse perturbation approach 132 no detection of allosteric sites based on analysis of energetics of reverse allostericcommunication from orthosteric to allosteric sites
CorrSite 133, 142 http://www.pkumdl.cn/cavityplus
detection of allosteric sites based on motion correlation analysis of allosteric andorthosteric sites
allosteric hotspot residues predictionSTRESS 140 http://stress.molmovdb.org Prediction of allosteric hotspot residues based on models of conformational
change generated by Monte Carlo simulationsallosteric communication and mutation analysisMCPath 134 http://safir.prc.boun.edu.
tr/clbet_serverprediction of allosteric communication and allosteric residues based on anensemble generated by Monte Carlo path simulation
SPACER 135 http://allostery.bii.a-star.edu.sg
prediction of allosteric communication and allosteric sites based on the normal-mode analysis method
DynOmics ENM 136 http://dyn.life.nthu.edu.tw/oENM
prediction of allosteric communication and functional residues based on elasticnetwork model
The latest release of ASD (Nov 2018) contains ∼1800allosteric proteins regardless of species (Figure 4), which aremainly distributed among eight protein families: kinases,GPCRs, transcription factors, ion channels, oxidoreductases,lyases, hydrolases, and transferases (Figure 5). At the nascent
stage of allosteric research, the allosteric regulatory phenomenawere observed in multimeric proteins with hemoglobinrepresenting the prototype of this class. Owing to the progressin chemical and structural biology techniques, allostericbinding has been observed in monomeric proteins such asGPCRs, kinases, and enzymes, greatly expanding the repertoireof available allosteric drug targets.The number of allosteric modulators has been rapidly
increasing in recent years due to the growing investment of thescientific community in allosteric modulator discovery (Figure6). ASD now contains ∼80 000 allosteric modulators thattarget more than 1300 proteins. Among the allostericmodulators, five chemical entities have been approved by theU.S. FDA as marketed drugs, including three GPCR(Cinacalcet, Maraviroc, and Plerixafor) and two kinase(Gleevec and Mekinist) allosteric drugs. Additionally, manypromising candidates are now entering clinical trials.5.1. Characteristics of Allosteric Sites. The molecular
evolution of allosteric and orthosteric sites is different. Kim
and co-workers analyzed the sequence conservation ofallosteric and orthosteric sites in 56 allosterically regulatedenzymes.88 The results showed that allosteric site residues(average conservation score = 0.58) are significantly lessconserved than orthosteric site residues (average conservationscore = 0.94; P = 1.3 × 10−67). Similarly, comparing thesequence conservation of ATP binding sites in the allostericand orthosteric data sets, we found that residues in theorthosteric ATP binding site (average conservation score =0.63) are less conserved than residues in the orthosteric ATPbinding site (average conservation score = 0.83; P = 1.2 ×10−3).87 Furthermore, the shapes calculated by the pocketsimilarity score (PS-score) between the allosteric andorthosteric ATP binding sites are distinct. The global structuralsimilarity of the allosteric ATP binding site is lower (averagePS-score = 0.3) than that of the orthosteric ATP binding site(average PS-score = 0.6).87 Collectively, these data areconsistent with the higher target selectivity observed forallosteric modulators relative to orthosteric ligands.The physicochemical properties of allosteric and orthosteric
sites in an individual protein were further explored bycomparison of the amino acid compositions of the two setsof binding sites curated from ASD version 3.0.20 The resultsshowed that hydrophobic residues such as isoleucine andtyrosine are highly enriched in allosteric sites (Figure 7). Bycontrast, polar residues such as aspartic acid, glutamine,histidine, and glycine are highly enriched in orthosteric sites.These data suggest that allosteric sites are more hydrophobicthan orthosteric sites.
5.2. Characteristics of Allosteric Modulators. On thebasis of the allosteric modulators from ASD version 3.0 andorthosteric ligands from ChEMBL, Carlson and co-workerscompared the physicochemical properties of allostericmodulators and orthosteric ligands.143 The results showedthat allosteric modulators are more rigid and aromatic thanorthosteric ligands, in accordance with previous studies byOverington and co-workers.93 This principle can be applied tohelp select potential allosteric modulators for a protein aftervirtual screening; that is, in cases where it is known there aredifferences in polarity between orthosteric and allosteric sites,
Figure 4. Total number of X-ray or NMR structures of allosteric proteins and yearly growth of total structures in the AlloSteric Database (ASD).Representative structures of allosteric proteins are shown.
Figure 5. Major class distribution of the structures of allostericproteins in ASD.
choosing compounds that are rigid and more aromatic canincrease the likelihood of occupying an allosteric site.
6. EXAMPLES OF THE STRUCTURE-BASED DESIGNOF ALLOSTERIC MODULATORS
Due to the recent progress in the molecular understanding ofthe allosteric process in recent years, many computationalapproaches have been developed for various aspects ofallosteric applications. Such approaches proved successful in
finding putative allosteric modulators, confirmed by subse-quent crystallographic/NMR or biological experiments. In thissection, we will present some representative examples of usingcombined computational and experimental tactics to identifyallosteric modulators.
6.1. SIRT6 Allosteric Activators. Sirtuin 6 (SIRT6), amember of the human sirtuin family (SIRT1−7), is a histonedeacetylase that transfers the acetyl group from the lysine sidechain of a protein or peptide substrate to nicotinamide adeninedinucleotide (NAD+).144 The regulation of SIRT6 has beenclosely associated with multiple biological processes, encom-passing DNA damage repair, organ metabolism, aging, andtumorigenesis.145 Accumulating evidence strongly indicatesthat SIRT6 knockout mice are accompanied by a severepremature aging syndrome, while mice containing SIRT6overexpression display a dramatically longer lifespan. They alsoshow massive apoptosis in various cancer cells but not innormal cells.146−148 Pharmacological activation of SIRT6 bysmall-molecule compounds may, therefore, provide a potentialnew avenue in age- and cancer-related treatment.149,150
To increase SIRT6 catalytic activity, the design ofcompounds to directly compete with endogenous NAD+ is
Figure 6. Yearly growth of allosteric modulators in ASD. The chemical structures of the marketed allosteric drugs are indicated in the chart.
Figure 7. Residue compositions in the orthosteric (light blue) andallosteric (blue) sites calculated from ASD version 3.0.
Figure 8. Workflow of the rational discovery of SIRT6 allosteric activators. The potential allosteric site of SIRT6 was predicted using Allosite(http://mdl.shsmu.edu.cn/AST). SIRT6 is shown as surface colored in gray, with the orthosteric and predicted allosteric sites highlighted in blueand red, respectively.
infeasible because NAD+ is the SIRT6 cofactor involved in thecatalytic reaction. As such, it is advisable and effective to targetallosteric sites outside the NAD+ orthosteric site. However, theallosteric sites of SIRT6 were unavailable at the onset of ourstudies on SIRT6 allosteric activators in 2013.To address this issue, we employed our previously
developed structure-based Allosite121 approach to computa-tionally predict potential allosteric sites (Figure 8). Theprediction informed us of a potential allosteric site formed byresidues Phe82 and Phe86 adjacent to the N-terminus ofSIRT6. On the basis of this predicted allosteric site, virtualscreening of seven commercial chemical libraries (SPECS,ChemBridge, ChemDiv, Enamine, InterBioScreen, Life Chem-icals, and Maybridge) consisting of more than 5 000 000compounds was performed. According to the principle thatallosteric modulators are rigid and more aromatic thanorthosteric ligands, we chose and purchased 20 rigid andaromatic compounds from the top-ranked compoundscalculated by our Alloscore141 approach to assess allostericinteractions. After experimental testing, two initial hits fromthe SPECS chemical library, AN-988/40889624 (2) and AH-487/41802661 (3) (Figure 7), were found to dose-depend-ently activate SIRT6 deacetylation, with half-maximal effectiveconcentration values of 173.8 ± 1.3 μM and 217.6 ± 1.1 μM,respectively.25 To improve the activation potency of the hits,medicinal chemistry optimizations of 3 led to two activatorsMDL-800 (4) and MDL-801 (5) (Figure 8), both of whichsignificantly enhanced the deacetylation activity of SIRT6, withEC50 values of 10.3 ± 0.3 μM and 5.7 ± 0.3 μM, respectively.Crystallographic studies further confirmed that 5 binds to theallosteric site located at the surface exit of the long SIRT6channel pocket but not to the substrate site of SIRT6. Thisallosteric site is formed by the N-terminal residues Val3-Leu9,Val70, Glu74, Phe82, Phe86, Val153, and Met157 (Figure9),25 in agreement with the computationally predictedallosteric site by Allosite121 and supporting the feasibility ofAllosite in the identification of as-yet-unknown allosteric sites.Cellular and animal model studies on the growth inhibitory
effects of human hepatocellular carcinoma (HCC) furtherdemonstrated that allosteric SIRT6 activators can suppress theproliferation of HCC.25 These findings should provide astarting point to facilitate the exploration of allosteric SIRT6activators as therapeutic agents or as probe compounds tounderstand SIRT6 biology.
6.2. STAT3 Allosteric Inhibitors. Signal transducer andactivator of transcription 3 (STAT3) play an important role inmany biological processes, including differentiation, survival,proliferation, and angiogenesis.151 However, constitutive oraberrant activation of STAT3 has been linked to malignanttransformation and tumorigenesis.152 As such, inhibition ofSTAT3 activity represents an attractive strategy for anticancerdrug discovery.153
STAT3 is a multidomain protein consisting of six distinctfunctional domains (Figure 10). Many STAT3 inhibitors werepreviously developed that targeted to the Src homology 2(SH2) domain and, to a lesser extent, the DNA-bindingdomain (DBD).154−157 Such inhibitors often encounter poorpharmacokinetic properties and cytotoxicity during clinicaltrials, making them ineffective drug candidates.158 Unlike theSH2 domain and DBD, inhibitors targeted to other domainssuch as the coiled-coil domain (CCD) are scarce. The CCDcan be regarded as an allosteric domain to regulate STAT3functional activity.85
On the basis of the STAT3 CCD, we used our developedAlloFinder26 approach to screen in silico for potential allostericinhibitors. This approach can automatically predict allostericsites by Allosite,121 perform molecular docking by AutoDockVina,159 and rank the docked poses by Alloscore.141 We choseand purchased the top 15 hits for bioassays; these hits arebound to the predicted allosteric site formed by residuesAsp171, Asn175, Gln202, and Met213 (Figure 10). Afterbiological testing, we observed that the small-moleculecompound K116 (AH-034/11936955) (6) from the SPECSchemical library can bind to STAT3 CCD, with a Kd value of3.22 ± 0.85 μM.26 Site-directed mutagenesis experimentsshowed that both double mutants (N175G/K177G andQ212A/M213G) decreased the binding affinity of 6 to themutants by ∼10-fold compared with wild-type STAT3.26
These data support the possibility of the discovery of STAT3allosteric inhibitors by the AlloFinder approach. However, afurther crystallographic structural complex is required toconfirm the allosteric inhibition of STAT3 by 6 directly.Currently, medicinal chemistry optimization of 6 is in progress,and structural determination of STAT3 in complex with 6derivatives will be performed soon.
6.3. GPX4 Allosteric Activators. By analysis of thecorrelations between allosteric sites and correspondingorthosteric sites in monomer or oligomeric allosteric proteinsusing a Gaussian network model, Lai and co-workers foundthat the motions of allosteric and orthosteric sites in proteinsare highly correlated.133 According to this principle, theydeveloped a correlation analysis method named CorrSite133 topredict potential allosteric sites in proteins.Glutathione peroxidase 4 (GPX4) controls many biological
processes, including cell membrane repair, inflammationsuppression, and ferroptosis inhibition.160 UpregulatingGPX4 enzyme activity has a clinical benefit in the treatmentof inflammation- and ferroptosis-associated diseases.161,162 Assuch, the development of GPX4 activators can provide apharmacological benefit.
Figure 9. Detailed interactions between MDL-801 and SIRT6 at theallosteric site determined by the cocrystal structure (PDB code5Y2F). SIRT6 is shown in gray, and MDL-801 is depicted by stickswith carbon atoms colored in yellow. The key residues within theallosteric site of MDL-801 are represented by sticks with carbonatoms colored in cyan.
To discover GPX4 activators, Lai and co-workers recentlyhave combined the computational methods CAVITY163 andCorrSite133 to predict a potential allosteric site in GPX4.27 Thepredicted allosteric site, with a CorreSite score of 1.49 (cavitieswith CorreSite scores of >0.5 are considered potentialallosteric sites by the CorrSite method), is located at theopposite side of the protein from the substrate binding site(Figure 11). This allosteric site is formed by three acidic
residues (Asp21, Asp23, and Asp101), two basic residues(Lys31 and Lys90), and seven hydrophobic residues (Ile22,Ala93, Ala94, Val98, Phe100, Met102, and Phe103). Virtualscreening of the SPECS chemical library was then performedbased on the predicted allosteric site, and 251 compoundswere selected for biological testing with ≥1 protein−ligandhydrogen bond and good hydrophobic interactions. Amongthese compounds, compound 1 (7) (Figure 11) was observedto dose-dependently increase GPX4 activity to 260% at anactivator concentration of 500 μM in the cell-free assay, with aKd value of 63 ± 5 μM. On the basis of the modeling of CPX4-7 protein−ligand interactions, SAR studies of 7 were furtherperformed to generate the strongest compound, 1d4 (8)(Figure 11), which significantly increased GPX4 activity to150% at 20 μM in the cell-free assay and 61 μM in cell extracts.
A double mutant (D21A/D23A) and two single mutants(D21A and D23A) showed reduced activation of GPX4 inresponse to both 7 and 8, confirming the predicted allostericsite by the CorrSite133 approach.
6.4. Cathepsin K Allosteric Inhibitors. Evolution-basedprediction of allosteric sites exploit sequence alignments andother genetics analysis methods to identify conserved residuesthroughout evolution.164 SCA is a prototype of an evolution-based prediction method, which utilizes multiple sequencealignment (MSA) to uncover evolutionarily conserved net-works of residues within a protein family.46 Such networks,referred to as protein sectors, are proposed to mediateallosteric communications between functional orthostericsites and regulatory allosteric sites.46,165 Nevertheless, thepracticality of SCA has limitations.166−170 Major concernsinclude that this method is only applicable to residues in closephysical contacts that are immediately subject to evolutionselection167,169,170 and whether its prediction of the evolutio-narily constrained positions can completely reflect thethermodynamic or allosteric couplings within protein struc-tures.166
Despite its advantages and disadvantages, the potentialapplication of SCA has been exemplified in the discovery of thefirst low-molecular-weight allosteric inhibitor of cathepsin K,NSC13345 (9) (Figure 12).171 Novinec and co-workers firstconstructed an MSA of 1239 catalytic domains from thepapain-like cysteine peptidase family.171 The pairwisecorrelations between all position pairs within the alignmentwere revealed through the positional correlation matrixcalculations, which were then clustered using hierarchicalclustering. A continuous network surrounding the orthostericsite and stretching around cathepsin K (i.e., the protein sector)was identified through automated sector identification. Thissector comprises the coevolving residues in the protein familywith functional importance. A few potential allosteric siteswere subsequently detected using the AutoLigand172 cavityprediction method. 9 from the NCI Diversity Set IIIcompound library was identified as a potential hit. A crystalstructure of cathepsin K in complex with 9, solved todetermine the allosteric regulatory mechanism for its binding,showed that it was bound to a remote surface-exposedallosteric site (Figure 12), site 6. The latter mainly compriseshelix α10 and adjacent β sheets and loops. Within this site, 9makes hydrogen bonds with Gly113, Lys236, Arg237, and
Figure 10. Workflow of the rational discovery of a STAT3 allosteric inhibitor. An X-ray structure of STAT3 (residues 130−722) (PDB code1BG1) is colored by the domain. The predicted allosteric site at the coiled-coil domain (CCD), large-scale molecular docking of compoundlibraries at the CCD allosteric site, and allosteric interaction evaluation after docking were executed automatically by AlloFinder (http://mdl.shsmu.edu.cn/ALF). Enlarged portion shows the potential interactions between the allosteric inhibitor K116 (6) and STAT3, consistent withmutagenesis experiments.
Figure 11. Workflow of the rational discovery of GPX4 allostericactivators. The allosteric site of GPX4 was predicted using CAVITYand CorrSite (http://www.pkumdl.cn/cavityplus) based on thecrystal structure of GPX4 (PDB code 2OBI). The predicted allostericsite (red) is on the opposite side of the orthosteric site (blue). Thepredicted binding mode of 1d4 (8) represented by blue sticks toGPX4 is shown, with key residues within the binding site highlightedin orange by stick representations.
Gln313. Further biochemical assays revealed that 9 abolishedthe collagenolytic activity of cathepsin K, with an IC50 value of80 ± 30 μM. Together, these results suggest the possibility ofapplying an evolution-based allosteric method in the discoveryof allosteric modulators. In fact, this method has been appliedin other cases, such as the PDZ domain,173 G proteins,174 andTonB-dependent transporters.175
6.5. c-Src Allosteric Inhibitors. Protein allostery is rootedin protein dynamics.176 The proper understanding of allostericeffects and accurate prediction of allostery cannot be achievedunless the conformational dynamics and energetic landscape ofprotein structures are thoroughly dissected. MD simulationscan characterize both large-scale conformational changes inoverall structures and subtle alterations in residue orientations,as well as provide energetic insights into conformationaldynamics.177−180 Thus, MD-based approaches, coupled withsubsequent correlated motions and community analyses, canuncover allosteric sites coupled to orthosteric sites with a highaccuracy.181−184 Furthermore, MD simulations enable theproduction of complete dynamic biological processes, facilitat-ing the detection of the highly flexible or even transient, so-called “hidden” allosteric sites.73,74
Pande and co-workers performed 550 μs of massivelydistributed MD simulations and Markov state model (MSM)analyses to identify potential allosteric sites on the anticancerdrug target c-Src.185 Snapshot structures from simulatedtrajectories were clustered using MSM analysis, whichseparated the dynamic process into a series of discrete statesand calculated the population of each state and transitionsbetween them. Two intermediate states (I1 and I2) werethereby identified along the activation pathway of c-Src. A highsimilarity was observed between the I2 conformational stateand previously reported structure of an allosteric inhibitor(ANS) (10)-bound cyclin-dependent kinase 2 (CDK2).186 Inthese two complexes, the activation loop (A-loop) adopted anunfolded conformation, forming a potential allosteric site withthe C-helix and adjacent β-sheets (Figure 13A), which cannot
be found in both the active (Figure 13B and Figure 13C) andinactive (Figure 13D) states of c-Src. The binding of 10 to thec-Src was further confirmed by simulations of a 10-bound I2structure. 10 was bound to the pocket formed by the A-loop,C-helix, and β3, 5, 6 of c-Src, similar to that in the crystalstructure of the CDK2-10 complex (Figure 13A). Throughblocking the displacement of the C-helix, 10 allostericallyinhibited c-Src by trapping it in a partially active intermediatestate. Collectively, this study highlighted that large-scale MDsimulations can detect a novel, cryptic allosteric site in theintermediate state structure of c-Src. Due to their ability tocharacterize the kinetic, thermodynamic, and structuralfeatures of the protein ensembles, MD simulations, coupledwith subsequent MSM analysis, have become a popular, robusttool for allosteric site and modulator discovery.187−190
7. CONCLUSIONS AND PERSPECTIVESAllostery underlies a myriad of biological processes thatregulate protein functions, where binding of an effector at theallosteric site affects catalytic processing at an orthosteric site.From a medicinal chemistry perspective, allostery represents aneffective tool for the medicinal chemist to drug “undruggable”proteins or alternatively to expand the repertoire of theavailable drug targets. Allosteric drugs differ from orthostericdrugs in the mode of drug action, with the possibility ofachieving higher selectivity, fewer adverse effects, and lowertoxicity than orthosteric drugs. Allosterism in drug discoveryhas, therefore, emerged as a promising strategy to developefficient and safe therapeutic agents. Because the nature of
Figure 12. Structural overview of cathepsin K in complex with itsallosteric inhibitor NSC13345 (9) and substrate TCO (PDB code5J94). The backbone structure of cathepsin K is shown in gray. Theallosteric site (site 6) and orthosteric site are highlighted in red andblue, respectively. The upper panel shows the chemical structure of 9,and the lower panel shows the detailed interactions between 9 andcathepsin K. Hydrogen bonds are displayed in red dashed lines, andresidues within the allosteric site are represented by sticks. SubstrateTCO is manually docked into the orthosteric site based on thesuperposition of PDB code 5J94 with PDB code 1Q6K. Figure 13. (A) Chemical structure of ANS (10) and structural
overview of CDK2 in complex with 10 (PDB code 3PXF). (B) Thebinding site of 10 is in a closed conformation in the active state of c-Src (PDB code 1Y57). (C) Structural comparison between the 10-bound CDK2 (yellow) and active state of c-Src (blue) shows that theallosteric site is in a closed conformation mainly due to thedisplacements of the C-helix and A-loop. (D) The binding site of10 is also in a closed conformation in the inactive state of c-Src (PDBcode 2SRC). The C-helix, A-loop, and β-sheets involved in thebinding site of 10 are colored in orange, pink, and cyan, respectively.
allosteric modulation is complicated, allosteric modulatorswere traditionally discovered serendipitously by high-through-put screening.Over the past decade, understanding of the basic
mechanisms of allosteric modulation, technology advances inthe experimental approaches to study allostery, and remarkableavailability of structural data have contributed to thedevelopment of various computational approaches to predictallosteric phenomena.191 Such predictive models were createdbased on the structure-, evolution-, or dynamics-basedmethods to detect and discover allosteric sites andmodulators.89,90 Some of these computational predictionshave been confirmed by experimental observations, high-lighting the possibility of computational approaches in thediscovery of allosteric modulators.Despite decades of allosteric research and the development
of many models for allosteric applications, allosteric modulatordiscovery continues to face challenges. One of the biggestchallenges originates from identifying putative allosteric sites.Particularly, in the case of cryptic allosteric sites that areinvisible in apo crystal structures but can emerge in theconformational transition of proteins such as in holo crystalstructures, current structure- and evolution-based allostericcomputational methods cannot detect such sites in the apostructures.73 Large-scale MD simulations, coupled with MSManalyses, can provide a potential opportunity to identify crypticallosteric sites, but such simulations with an effectiveconformational sampling are extremely time-consuming.192,193
Alternatively, using normal-mode analysis to rapidly capturethe conformational ensemble of a protein and then employingcomputational approaches to identify cryptic allosteric sites canoffer guidelines to help to circumvent this dilemma.Despite the current understanding of the general character-
istics of known allosteric sites and modulators, the guidelinesfor the structural modifications of initial allosteric hits toincrease affinities (hit-to-lead optimization) are lacking.Because the properties of allosteric sites and modulators differfrom the corresponding orthosteric sites and ligands, softwaretraditionally used for orthosteric drug design faces challengesin guiding allosteric modulator design. There is a strongdemand for insights into the unique protein−modulatorinteractions underlying allosteric binding. Our previouslycreated ASBench120 benchmark of high-quality data ofallosteric protein−modulator complexes can assist in uncover-ing interactions useful for designing allosteric-active com-pounds.Many reports of analyses of allostery on the single-molecule
level have emerged, leaving the role of allostery at the cellularlevel unexplored.194 Allostery is a fundamental cellularphenomenon. Thus, it is required to develop multiscaleapproaches that unite computational and experimentalmethods to explore allosteric effects on cellular networks.The system-centric method will identify new targets tobroaden the allosteric modulator discovery scope.195−197 Forexample, the allo-network drug concept has recently beenproposed to design allosteric modulators in system-based drugdesign.198
Overall, recently developed computational allosteric pre-diction approaches have shown some success in designingallosteric modulators for known protein targets. Thesetechniques, coupled with advances in experimental approachesto understand allosteric activation or inhibition, indicate that
allosteric modulator discovery has emerged as a new strategyfor drug design.
■ AUTHOR INFORMATIONCorresponding Author*Phone: +86-21-63846590-776922. Fax: +86-21-64154900. E-mail: [email protected] Zhang: 0000-0002-6558-791XAuthor Contributions§S.L., X.H., and D.N. contributed equally to this work.NotesThe authors declare no competing financial interest.BiographiesShaoyong Lu received his undergraduate Applied Chemistry degreefrom Hangzhou Normal University in 2007. Thereafter, he obtained aPh.D. degree in Chemistry from Zhejiang University, China, in 2012.He then continued with postdoctoral research at Shanghai Jiao TongUniversity, School of Medicine, in Professor Jian Zhang’s laboratoryand is now an Associate Professor. During 2014−2015, he studied as aVisiting Scholar in Professor Ruth Nussinov’s group at the NationalCancer InstituteFrederick. His main interests include the study ofallosteric protein−ligand interactions and the development ofallosteric methods and their applications.
Xinheng He is an undergraduate student from the Discipline ofBiomedical Science, Shanghai Jiao Tong University, School ofMedicine, in Professor Jian Zhang’s group. He mainly focuses onallosteric mechanism research and allosteric drug design.
Duan Ni is an undergraduate student from the Discipline ofBiomedical Science, Shanghai Jiao Tong University, School ofMedicine, in Professor Jian Zhang’s group. He mainly focuses onusing computational methods to investigate the allosteric mechanismsand predict allosteric sites.
Jian Zhang received a B.M. degree in Pharmacology in 2002 fromPeking University and a Ph.D. in 2007 from Shanghai Institute ofMateria Medica, Chinese Academy of Sciences. After receiving hisPh.D., he moved to the University of Michigan to conductpostdoctoral research in Professor Shaomeng Wang’s laboratory. In2009, he joined Shanghai Jiao Tong University, School of Medicine,where he is a full-time researcher and doctoral supervisor. Now, he isalso the director of the Medicinal Bioinformatics Center. His fields ofresearch include first-in-class drug discovery and chemical biology thatmainly pertains to the repertoire of allostery. His Web site providesfurther details: http://mdl.shsmu.edu.cn/.
■ ACKNOWLEDGMENTSThis work was supported by the National Natural ScienceFoundation of China (Grants 21778037, 81603023, 81322046,91753117, 81473137), the National Basic Research Programof China (973 Program) (Grant 2015CB910403), and theInnovation Team “Diagnosis and Treatment of MajorCardiovascular Diseases” of High-Level Universities inShanghai.
receptor; HCC, human hepatocellular carcinoma; HDX,hydrogen/deuterium exchange; KNF, Koshland−Nemethy−Filmer; MSM, Markov state model; MD, molecular dynamics;MWC, Monod−Wyman−Changeux; MSA, multiple sequencealignment; NAD+, nicotinamide adenine dinucleotide; NMR,nuclear magnetic resonance; PPI, protein−protein interaction;STAT3, signal transducer and activator of transcription 3;SH2, Src homology 2; SCA, statistical coupling analysis;SIRT6, sirtuin 6
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