Allosteric effects in the marginally stable von Hippel-Lindau tumor suppressor protein and allostery-based rescue mutant design

Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function

In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.

Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge

Charting future innovations is challenging. Yet, allosteric and orthosteric anticancer drugs are undergoing a revolution and taxing unresolved dilemmas await. Among the imaginative innovations, here we discuss cereblon and thalidomide derivatives as a means of recruiting neosubstrates and their degradation, allosteric heterogeneous bifunctional drugs like PROTACs, drugging phosphatases, inducers of targeted posttranslational protein modifications, antibody-drug conjugates, exploiting membrane interactions to increase local concentration, stabilizing the folded state, and more. These couple with harnessing allosteric cryptic pockets whose discovery offers more options to modulate the affinity of orthosteric, active site inhibitors. Added to these are strategies to counter drug resistance through drug combinations co-targeting pathways to bypass signaling blockades. Here, we discuss on the molecular and cellular levels, such inspiring advances, provide examples of their applications, their mechanisms and rational. We start with an overview on difficult to target proteins and their properties-rarely, if ever-conceptualized before, discuss emerging innovative drugs, and proceed to the increasingly popular allosteric cryptic pockets-their advantages-and critically, issues to be aware of. We follow with drug resistance and in-depth discussion of tumor heterogeneity. Heterogeneity is a hallmark of highly aggressive cancers, the core of drug resistance unresolved challenge. We discuss potential ways to target heterogeneity by predicting it. The increase in experimental and clinical data, computed (cell-type specific) interactomes, capturing transient cryptic pockets, learned drug resistance, workings of regulatory mechanisms, heterogeneity, and resistance-based cell signaling drug combinations, assisted by AI-driven reasoning and recognition, couple with creative allosteric drug discovery, charting future innovations.

Allostery

Allostery describes the ability of biological macromolecules to transmit signals spatially through the molecule from an allosteric site – a site that is distinct from orthosteric binding sites of primary, endogenous ligands – to the functional or active site. This review starts with a historical overview and a description of the classical example of allostery – hemoglobin – and other well-known examples (aspartate transcarbamoylase, Lac repressor, kinases, G-protein-coupled receptors, adenosine triphosphate synthase, and chaperonin). We then discuss fringe examples of allostery, including intrinsically disordered proteins and inter-enzyme allostery, and the influence of dynamics, entropy, and conformational ensembles and landscapes on allosteric mechanisms, to capture the essence of the field. Thereafter, we give an overview over central methods for investigating molecular mechanisms, covering experimental techniques as well as simulations and artificial intelligence (AI)-based methods. We conclude with a review of allostery-based drug discovery, with its challenges and opportunities: with the recent advent of AI-based methods, allosteric compounds are set to revolutionize drug discovery and medical treatments.

Toward the Design of Allosteric Effectors: Gaining Comprehensive Control of Drug Properties and Actions

While the therapeutic potential of allosteric drugs is increasingly realized, the discovery of effectors is largely incidental. The rational design of allosteric effectors requires new state-of-the-art approaches to account for the distinct characteristics of allosteric ligands and their modes of action. We present a broadly applicable computational framework for obtaining allosteric site-effector pairs, providing targeted, highly specific, and tunable regulation to any functional site. We validated the framework using the main protease from SARS-CoV-2 and the K-RasG12D oncoprotein. High-throughput per-residue quantification of the energetics of allosteric signaling and effector binding revealed known drugs capable of inducing the required modulation upon binding. Starting from fragments of known well-characterized drugs, allosteric effectors and binding sites were designed and optimized simultaneously to achieve targeted and specific signaling to distinct functional sites, such as, for example, the switch regions of K-RasG12D. The generic framework proposed in this work will be instrumental in developing allosteric therapies aligned with a precision medicine approach.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Allosteric regulation of autoinhibition and activation of c-Abl

c-Abl, a non-receptor tyrosine kinase, regulates cell growth and survival in healthy cells and causes chronic myeloid leukemia (CML) when fused by Bcr. Its activity is blocked in the assembled inactive state, where the SH3 and SH2 domains dock into the kinase domain, reducing its conformational flexibility, resulting in the autoinhibited state. It is active in an extended 'open' conformation. Allostery governs the transitions between the autoinhibited and active states. Even though experiments revealed the structural hallmarks of the two states, a detailed grasp of the determinants of c-Abl autoinhibition and activation at the atomic level, which may help innovative drug discovery, is still lacking. Here, using extensive molecular dynamics simulations, we decipher exactly how these determinants regulate it. Our simulations confirm and extend experimental data that the myristoyl group serves as the switch for c-Abl inhibition/activation. Its dissociation from the kinase domain promotes the SH2-SH3 release, initiating c-Abl activation. We show that the precise SH2/N-lobe interaction is required for full activation of c-Abl. It stabilizes a catalysis-favored conformation, priming it for catalytic action. Bcr-Abl allosteric drugs elegantly mimic the endogenous myristoyl-mediated autoinhibition state of c-Abl 1b. Allosteric activating mutations shift the ensemble to the active state, blocking ATP-competitive drugs. Allosteric drugs alter the active-site conformation, shifting the ensemble to re-favor ATP-competitive drugs. Our work provides a complete mechanism of c-Abl activation and insights into critical parameters controlling at the atomic level c-Abl inactivation, leading us to propose possible strategies to counter reemergence of drug resistance.

Learning about allosteric drugs and ways to design them

While the accelerating quest for precision medicine requires new individually targeting and selective drugs, and the ability to work with so-called undruggable targets, the realm of allosteric drugs meeting this need remains largely uncharted. Generalizing the observations on two major drug targets with widely observed inherent allostery, GPCRs and kinases, we describe and discuss basic allosteric modes of action that are universally applicable in all types of structures and functions. Using examples of Class A GPCRs and CMGC protein kinases, we show how Allosteric Signalling and Probing Fingerprints can be used to identify potential allosteric sites and reveal effector-leads that may serve as a starting point for the development of allosteric drugs targeting these regulatory sites. A set of distinct characteristics of allosteric ligands was established, which highlights the versatility of their design and make them advantageous before their orthosteric counterparts in personalized medicine. We argue that rational design of allosteric drugs should begin with the search for latent sites or design of non-natural binding sites followed by fragment-based design of allosteric ligands and by the mutual adjustment of the site-ligand pair in order to achieve required effects. On the basis of the perturbative nature and reversibility of allosteric communication, we propose a generic protocol for computational design of allosteric effectors, enabling also the allosteric tuning of biologics, in obtaining allosteric control over protein functions.

Allosteric perspective on the mutability and druggability of the SARS-CoV-2 Spike protein

Recent developments in the SARS-CoV-2 pandemic point to its inevitable transformation into an endemic disease, urging both refinement of diagnostics for emerging variants of concern (VOCs) and design of variant-specific drugs in addition to vaccine adjustments. Exploring the structure and dynamics of the SARS-CoV-2 Spike protein, we argue that the high-mutability characteristic of RNA viruses coupled with the remarkable flexibility and dynamics of viral proteins result in a substantial involvement of allosteric mechanisms. While allosteric effects of mutations should be considered in predictions and diagnostics of new VOCs, allosteric drugs advantageously avoid escape mutations via non-competitive inhibition originating from alternative distal locations. The exhaustive allosteric signaling and probing maps presented herein provide a comprehensive picture of allostery in the spike protein, making it possible to locate potential mutations that could work as new VOC "drivers" and to determine binding patches that may be targeted by newly developed allosteric drugs.

Asian Upper Blepharoplasty with the Hinge Technique

Asian upper blepharoplasty is one of the most commonly requested procedures in Asian patients. Many incisional and suture methods have been described in the literature. While the suture method is advantageous for its simplicity and quick recovery, the incision method is more versatile and able to deliver predictable and reproducible results for Asian patients presenting with a diverse range of anatomy and requests. Accordingly, the incision method remains the preferred approach for many surgeons performing Asian upper blepharoplasty. In this paper, we detail our open incision hinge upper blepharoplasty technique to create dynamic upper eyelid creases in Asian patients. The surgical videos associated with this paper present our surgical technique in detail, highlighting technical refinements and surgical nuances to perform the surgery precisely and predictably. The conceptual core of our approach is the use of a vascularized orbital septum as a flap to create a fibrous extension from the levator aponeurosis to the dermis at the location of eyelid crease. This vascularized flap securely connects the posterior lamella with the anterior lamella to securely form the eyelid crease with eye opening. This most accurately recreates the anatomy that is present in attractive Asian patients with naturally occurring double eyelid and predictably creates a dynamic and crisp upper eyelid crease. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Mechanism of activation and the rewired network: New drug design concepts

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

The mechanism of full activation of tumor suppressor PTEN at the phosphoinositide-enriched membrane

Tumor suppressor PTEN, the second most highly mutated protein in cancer, dephosphorylates signaling lipid PIP₃ produced by PI3Ks. Excess PIP₃ promotes cell proliferation. The mechanism at the membrane of this pivotal phosphatase is unknown hindering drug discovery. Exploiting explicit solvent simulations, we tracked full-length PTEN trafficking from the cytosol to the membrane. We observed its interaction with membranes composed of zwitterionic phosphatidylcholine, anionic phosphatidylserine, and phosphoinositides, including signaling lipids PIP₂ and PIP₃. We tracked its moving away from the zwitterionic and getting absorbed onto anionic membrane that harbors PIP₃. We followed it localizing on microdomains enriched in signaling lipids, as PI3K does, and observed PIP₃ allosterically unfolding the N-terminal PIP₂ binding domain, positioning it favorably for the polybasic motif interaction with PIP₂. Finally, we determined PTEN catalytic action at the membrane, all in line with experimental observations, deciphering the mechanisms of how PTEN anchors to the membrane and restrains cancer.

•

PTEN localizes on membrane microdomains enriched in phosphoinositides, as PI3K does

•

Full PTEN activation requires both signaling lipids, PIP₂ and PIP₃

•

Strong salt bridge interactions sustain stable PTEN membrane localization

•

Substrate-induced P loop conformational change implicates PTEN catalytic activity

Conformational Strain Indicated by Ramachandran Angles for the Protein Backbone Is Only Weakly Related to the Flexibility

Studies on energy associated with free dipeptides have shown that conformers with unfavorable (ϕ,ψ) torsion angles have higher energy compared to conformers with favorable (ϕ,ψ) angles. It is expected that higher energy confers higher dynamics and flexibility to that part of the protein. Here, we explore a potential relationship between conformational strain in a residue due to unfavorable (ϕ,ψ) angles and its flexibility and dynamics in the context of protein structures. We compared flexibility of strained and relaxed residues, which are recognized based on outlier/allowed and favorable (ϕ,ψ) angles respectively, using normal-mode analysis (NMA). We also performed in-depth analysis on flexibility and dynamics at catalytic residues in protein kinases, which exhibit different strain status in different kinase structures using NMA and molecular dynamics simulations. We underline that strain of a residue, as defined by backbone torsion angles, is almost unrelated to the flexibility and dynamics associated with it. Even the overall trend observed among all high-resolution structures in which relaxed residues tend to have slightly higher flexibility than strained residues is counterintuitive. Consequently, we propose that identifying strained residues based on (ϕ,ψ) values is not an effective way to recognize energetic strain in protein structures.

PI3K inhibitors: review and new strategies

The search is on for effective specific inhibitors for PI3Kα mutants. PI3Kα, a critical lipid kinase, has two subunits, catalytic and inhibitory. PIK3CA, the gene that encodes the p110α catalytic subunit is a highly mutated protein in cancer. Dysregulation of PI3Kα signalling is commonly associated with tumorigenesis and drug resistance. Despite its vast importance, only recently the FDA approved the first drug (alpelisib by Novartis) for breast cancer. A second (GDC0077), classified as PI3Kα isoform-specific, is undergoing clinical trials. Not surprisingly, these ATP-competitive drugs commonly elicit severe concentration-dependent side effects. Here we briefly review PI3Kα mutations, focus on PI3K drug repertoire and propose new, to-date unexplored PI3Kα therapeutic strategies. These include (1) an allosteric and orthosteric inhibitor combination and (2) taking advantage of allosteric rescue mutations to guide drug discovery.

Integrated Computational Approaches and Tools forAllosteric Drug Discovery

Understanding molecular mechanisms underlying the complexity of allosteric regulationin proteins has attracted considerable attention in drug discovery due to the benefits and versatilityof allosteric modulators in providing desirable selectivity against protein targets while minimizingtoxicity and other side effects. The proliferation of novel computational approaches for predictingligand-protein interactions and binding using dynamic and network-centric perspectives has ledto new insights into allosteric mechanisms and facilitated computer-based discovery of allostericdrugs. Although no absolute method of experimental and in silico allosteric drug/site discoveryexists, current methods are still being improved. As such, the critical analysis and integration ofestablished approaches into robust, reproducible, and customizable computational pipelines withexperimental feedback could make allosteric drug discovery more efficient and reliable. In this article,we review computational approaches for allosteric drug discovery and discuss how these tools can beutilized to develop consensus workflows for in silico identification of allosteric sites and modulatorswith some applications to pathogen resistance and precision medicine. The emerging realization thatallosteric modulators can exploit distinct regulatory mechanisms and can provide access to targetedmodulation of protein activities could open opportunities for probing biological processes and insilico design of drug combinations with improved therapeutic indices and a broad range of activities.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses—all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Precision medicine review: rare driver mutations and their biophysical classification

How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.

A comprehensive ensemble model for comparing the allosteric effect of ordered and disordered proteins

Intrinsically disordered proteins/regions (IDPs/IDRs) are prevalent in allosteric regulation. It was previously thought that intrinsic disorder is favorable for maximizing the allosteric coupling. Here, we propose a comprehensive ensemble model to compare the roles of both order-order transition and disorder-order transition in allosteric effect. It is revealed that the MWC pathway (order-order transition) has a higher probability than the EAM pathway (disorder-order transition) in allostery, suggesting a complicated role of IDPs/IDRs in regulatory proteins. In addition, an analytic formula for the maximal allosteric coupling response is obtained, which shows that too stable or too unstable state is unfavorable to endow allostery, and is thus helpful for rational design of allosteric drugs.

Allosteric effect is an important regulation mechanism in biological processes, where the binding of a ligand at one site of a protein influences the function of a distant site. Conventionally, allostery was thought to originate from structural transition. However, in recent years, intrinsically disordered proteins (IDPs) were found to be widely involved in allosteric regulation in despite of their lack of ordered structure under physiological condition. It is still a mystery why IDPs are prevalent in allosteric proteins and how they differ from ordered proteins in allostery. Here, we propose a comprehensive ensemble model which includes both ordered and disordered states of a two-domain protein, and investigate the role of various state combinations in allosteric effect. By sampling the parameter space, we conclude that disordered proteins are less competitive than ordered proteins in performing allostery from a thermodynamic point of view. The prevalence of IDPs in allosteric regulation is likely determined by all their advantage, but not only by their capacity in endowing allostery.

"Infostery" analysis of short molecular dynamics simulations identifies highly sensitive residues and predicts deleterious mutations

Characterizing a protein mutational landscape is a very challenging problem in Biology. Many disease-associated mutations do not seem to produce any effect on the global shape nor motions of the protein. Here, we use relatively short all-atom biomolecular simulations to predict mutational outcomes and we quantitatively assess the predictions on several hundreds of mutants. We perform simulations of the wild type and 175 mutants of PSD95’s third PDZ domain in complex with its cognate ligand. By recording residue displacements correlations and interactions, we identify “communication pathways” and quantify them to predict the severity of the mutations. Moreover, we show that by exploiting simulations of the wild type, one can detect 80% of the positions highly sensitive to mutations with a precision of 89%. Importantly, our analysis describes the role of these positions in the inter-residue communication and dynamical architecture of the complex. We assess our approach on three different systems using data from deep mutational scanning experiments and high-throughput exome sequencing. We refer to our analysis as “infostery”, from “info” - information - and “steric” - arrangement of residues in space. We provide a fully automated tool, COMMA2 (www.lcqb.upmc.fr/COMMA2), that can be used to guide medicinal research by selecting important positions/mutations.

Crystal Structure of the Cul2-Rbx1-EloBC-VHL Ubiquitin Ligase Complex

Cullin RING E3 ubiquitin ligases (CRLs) function in the ubiquitin proteasome system to catalyze the transfer of ubiquitin from E2 conjugating enzymes to specific substrate proteins. CRLs are large dynamic complexes and attractive drug targets for the development of small-molecule inhibitors and chemical inducers of protein degradation. The atomic details of whole CRL assembly and interactions that dictate subunit specificity remain elusive. Here we present the crystal structure of a pentameric CRL2^VHL complex, composed of Cul2, Rbx1, Elongin B, Elongin C, and pVHL. The structure traps a closed state of full-length Cul2 and a new pose of Rbx1 in a trajectory from closed to open conformation. We characterize hotspots and binding thermodynamics at the interface between Cul2 and pVHL-EloBC and identify mutations that contribute toward a selectivity switch for Cul2 versus Cul5 recognition. Our findings provide structural and biophysical insights into the whole Cul2 complex that could aid future drug targeting.

In silico identification of rescue sites by double force scanning

Motivation

A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant.

Results

We present the first general computational method to detect rescue sites. By mimicking the effect of mutations through the application of forces, the double force scanning (DFS) method identifies the second-site residues that make the protein structure most resilient to the effect of pathogenic mutations. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites. A remarkably good agreement was found between predictions and experimental data. Indeed, almost half of the rescue sites in p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset.

Availability and implementation

The DFS code is available under GPL at https://fornililab.github.io/dfs/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Proline hydroxylation at different sites in hypoxia-inducible factor 1α modulates its interactions with the von Hippel–Lindau tumor suppressor protein

Proline hydroxylation of HIF-1α affects the interaction affinity between pVHL and HIF-1α and allosterically induces the conformational change of pVHL.

Study of Functional and Allosteric Sites in Protein Superfamilies

The interaction of proteins (enzymes) with a variety of low-molecular-weight compounds, as well as protein-protein interactions, is the most important factor in the regulation of their functional properties. To date, research effort has routinely focused on studying ligand binding to the functional sites of proteins (active sites of enzymes), whereas the molecular mechanisms of allosteric regulation, as well as binding to other pockets and cavities in protein structures, remained poorly understood. Recent studies have shown that allostery may be an intrinsic property of virtually all proteins. Novel approaches are needed to systematically analyze the architecture and role of various binding sites and establish the relationship between structure, function, and regulation. Computational biology, bioinformatics, and molecular modeling can be used to search for new regulatory centers, characterize their structural peculiarities, as well as compare different pockets in homologous proteins, study the molecular mechanisms of allostery, and understand the communication between topologically independent binding sites in protein structures. The establishment of an evolutionary relationship between different binding centers within protein superfamilies and the discovery of new functional and allosteric (regulatory) sites using computational approaches can improve our understanding of the structure-function relationship in proteins and provide new opportunities for drug design and enzyme engineering.

Structural Basis for Modulation of Quality Control Fate in a Marginally Stable Protein

The human von Hippel-Lindau (VHL) tumor suppressor is a marginally stable protein previously used as a model substrate of eukaryotic refolding and degradation pathways. When expressed in the absence of its cofactors, VHL cannot fold and is quickly degraded by the quality control machinery of the cell. We combined computational methods with in vivo experiments to examine the basis of the misfolding propensity of VHL. By expressing a set of randomly mutated VHL sequences in yeast, we discovered a more stable mutant form. Subsequent modeling suggested the mutation had caused a conformational change affecting cofactor and chaperone interaction, and this hypothesis was then confirmed by additional knockout and overexpression experiments targeting a yeast cofactor homolog. These findings offer a detailed structural basis for the modulation of quality control fate in a model misfolded protein and highlight burial mode modeling as a rapid means to detect functionally important conformational changes in marginally stable globular domains.

Principles of allosteric interactions in cell signaling

Linking cell signaling events to the fundamental physicochemical basis of the conformational behavior of single molecules and ultimately to cellular function is a key challenge facing the life sciences. Here we outline the emerging principles of allosteric interactions in cell signaling, with emphasis on the following points. (1) Allosteric efficacy is not a function of the chemical composition of the allosteric pocket but reflects the extent of the population shift between the inactive and active states. That is, the allosteric effect is determined by the extent of preferred binding, not by the overall binding affinity. (2) Coupling between the allosteric and active sites does not decide the allosteric effect; however, it does define the propagation pathways, the allosteric binding sites, and key on-path residues. (3) Atoms of allosteric effectors can act as "driver" or "anchor" and create attractive "pulling" or repulsive "pushing" interactions. Deciphering, quantifying, and integrating the multiple co-occurring events present daunting challenges to our scientific community.

Identifying key residues for protein allostery through rigid residue scan

Allostery is a ubiquitous process for protein regulatory activity in which a binding event can change a protein's function carried out at a distal site. Despite intensive theoretical and experimental investigation of protein allostery in the past five decades, effective methods have yet to be developed that can systematically identify key residues involved in allosteric mechanisms. In this study, we propose the rigid residue scan as a systematic approach to identify important allosteric residues. The third PDZ domain (PDZ3) in the postsynaptic density 95 protein (PSD-95) is used as a model system, and each amino acid residue is treated as a single rigid body during independent molecular dynamics simulations. Various indices based on cross-correlation matrices are used, which allow for two groups of residues with different functions to be identified. The first group is proposed as "switches" that are needed to "turn on" the binding effect of protein allostery. The second group is proposed as "wire residues" that are needed to propagate energy or information from the binding site to distal locations within the same protein. Among the nine residues suggested as important for PDZ3 intramolecular communication in this study, eight have been reported as critical for allostery in PDZ3. Therefore, the rigid residue scan approach is demonstrated to be an effective method for systemically identifying key residues in protein intramolecular communication and allosteric mechanisms.

The structural basis for cancer treatment decisions

Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.

The design of covalent allosteric drugs

A key issue in drug discovery is how to reduce drug dosage and increase specificity while retaining or increasing efficacy, as high dosage is often linked to toxicity. There are two types of drugs on the market: orthosteric and allosteric. Orthosteric drugs can be noncovalent or covalent. The latter are advantageous because they may be prescribed in lower doses, but their potential off-target toxicity is a primary concern. The chief advantages of allosteric drugs are their higher specificity and their consequently lower chance of toxic side effects. Covalent allosteric drugs combine the pharmacological merits of covalent drugs with the additional benefit of the higher specificity of allosteric drugs. In a recent promising step in therapeutic drug development, allosteric, disulfide-tethered fragments successfully modulated the activity of a protein kinase and K-Ras.

Protein-protein interaction networks studies and importance of 3D structure knowledge

Protein-protein interaction networks (PPINs) are a powerful tool to study biological processes in living cells. In this review, we present the progress of PPIN studies from abstract to more detailed representations. We will focus on 3D interactome networks, which offer detailed information at the atomic level. This information can be exploited in understanding not only the underlying cellular mechanisms, but also how human variants and disease-causing mutations affect protein functions and complexes' stability. Recent studies have used structural information on PPINs to also understand the molecular mechanisms of binding partner selection. We will address the challenges in generating 3D PPINs due to the restricted number of solved protein structures. Finally, some of the current use of 3D PPINs will be discussed, highlighting their contribution to the studies in genotype-phenotype relationships and in the optimization of targeted studies to design novel chemical compounds for medical treatments.

Harnessing allostery: a novel approach to drug discovery

Allostery is the most direct and efficient way for regulation of biological macromolecule function, ranging from the control of metabolic mechanisms to signal transduction pathways. Allosteric modulators target to allosteric sites, offering distinct advantages compared to orthosteric ligands that target to active sites, such as greater specificity, reduced side effects, and lower toxicity. Allosteric modulators have therefore drawn increasing attention as potential therapeutic drugs in the design and development of new drugs. In recent years, advancements in our understanding of the fundamental principles underlying allostery, coupled with the exploitation of powerful techniques and methods in the field of allostery, provide unprecedented opportunities to discover allosteric proteins, detect and characterize allosteric sites, design and develop novel efficient allosteric drugs, and recapitulate the universal features of allosteric proteins and allosteric modulators. In the present review, we summarize the recent advances in the repertoire of allostery, with a particular focus on the aforementioned allosteric compounds.

An automated approach to network features of protein structure ensembles

Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

A unified view of "how allostery works"

The question of how allostery works was posed almost 50 years ago. Since then it has been the focus of much effort. This is for two reasons: first, the intellectual curiosity of basic science and the desire to understand fundamental phenomena, and second, its vast practical importance. Allostery is at play in all processes in the living cell, and increasingly in drug discovery. Many models have been successfully formulated, and are able to describe allostery even in the absence of a detailed structural mechanism. However, conceptual schemes designed to qualitatively explain allosteric mechanisms usually lack a quantitative mathematical model, and are unable to link its thermodynamic and structural foundations. This hampers insight into oncogenic mutations in cancer progression and biased agonists' actions. Here, we describe how allostery works from three different standpoints: thermodynamics, free energy landscape of population shift, and structure; all with exactly the same allosteric descriptors. This results in a unified view which not only clarifies the elusive allosteric mechanism but also provides structural grasp of agonist-mediated signaling pathways, and guides allosteric drug discovery. Of note, the unified view reasons that allosteric coupling (or communication) does not determine the allosteric efficacy; however, a communication channel is what makes potential binding sites allosteric.

An allosteric signaling pathway of human 3-phosphoglycerate kinase from force distribution analysis

3-Phosphogycerate kinase (PGK) is a two domain enzyme, which transfers a phosphate group between its two substrates, 1,3-bisphosphoglycerate bound to the N-domain and ADP bound to the C-domain. Indispensable for the phosphoryl transfer reaction is a large conformational change from an inactive open to an active closed conformation via a hinge motion that should bring substrates into close proximity. The allosteric pathway resulting in the active closed conformation has only been partially uncovered. Using Molecular Dynamics simulations combined with Force Distribution Analysis (FDA), we describe an allosteric pathway, which connects the substrate binding sites to the interdomain hinge region. Glu192 of alpha-helix 7 and Gly394 of loop L14 act as hinge points, at which these two secondary structure elements straighten, thereby moving the substrate-binding domains towards each other. The long-range allosteric pathway regulating hPGK catalytic activity, which is partially validated and can be further tested by mutagenesis, highlights the virtue of monitoring internal forces to reveal signal propagation, even if only minor conformational distortions, such as helix bending, initiate the large functional rearrangement of the macromolecule.

3-Phosphoglycerate kinase (PGK) is an essential enzyme for living organisms. It catalyzes the phospho-transfer reaction between two catabolites during carbohydrate metabolism. In addition to this physiological role, human PGK has been shown to phosphorylate L-nucleoside analogues, potential drugs against viral infection and cancer. PGK is a two domain enzyme, with the two substrates bound to the two separate domains. In order to perform its function the enzyme has to undergo a large conformational change involving a hinge bending to bring the substrates into close proximity. The allosteric pathway from the open non-reactive state of PGK to the closed reactive state as triggered by substrate binding has only been partially uncovered by experimental studies. Here we describe a complete allosteric pathway, which connects the substrate binding sites to the interdomain hinge region using Molecular Dynamics simulations combined with Force Distribution Analysis (FDA). While previously identified key residues involved in PGK domain closure are part of this pathway, we here fill the numerous gaps in the pathway by identifying newly uncovered residues and interesting candidates for future mutational studies.

Identifying cytochrome p450 functional networks and their allosteric regulatory elements

Cytochrome P450 (CYP) enzymes play key roles in drug metabolism and adverse drug-drug interactions. Despite tremendous efforts in the past decades, essential questions regarding the function and activity of CYPs remain unanswered. Here, we used a combination of sequence-based co-evolutionary analysis and structure-based anisotropic thermal diffusion (ATD) molecular dynamics simulations to detect allosteric networks of amino acid residues and characterize their biological and molecular functions. We investigated four CYP subfamilies (CYP1A, CYP2D, CYP2C, and CYP3A) that are involved in 90% of all metabolic drug transformations and identified four amino acid interaction networks associated with specific CYP functionalities, i.e., membrane binding, heme binding, catalytic activity, and dimerization. Interestingly, we did not detect any co-evolved substrate-binding network, suggesting that substrate recognition is specific for each subfamily. Analysis of the membrane binding networks revealed that different CYP proteins adopt different membrane-bound orientations, consistent with the differing substrate preference for each isoform. The catalytic networks were associated with conservation of catalytic function among CYP isoforms, whereas the dimerization network was specific to different CYP isoforms. We further applied low-temperature ATD simulations to verify proposed allosteric sites associated with the heme-binding network and their role in regulating metabolic fate. Our approach allowed for a broad characterization of CYP properties, such as membrane interactions, catalytic mechanisms, dimerization, and linking these to groups of residues that can serve as allosteric regulators. The presented combined co-evolutionary analysis and ATD simulation approach is also generally applicable to other biological systems where allostery plays a role.

A novel missense mutation (N78D) in a family with von Hippel-Lindau disease with central nervous system haemangioblastomas, pancreatic and renal cysts

von Hippel-Lindau (VHL) disease is a hereditary tumor syndrome caused by mutations in the VHL tumor suppressor gene. In a family with VHL, we identified a novel missense mutation (N78D), which affects a fully conserved residue in the VHL protein. Interestingly, several other missense mutations reported at same codon in the VHL protein that might be associated with a low risk of renal cell carcinoma (RCC) but not pheochromocytoma appear to be associated with a VHL type 1 phenotype. At the moment, RCC is present in none of the affected mutation carriers in the family described here. In contrast to other missense changes at codon 78, the change in our VHL family is predicted to have a mild effect on VHL function, which apparently is insufficient to cause predisposition to RCC. Our findings suggest that the risk of RCC in VHL is attributable to the severity of the amino acid substitution at this particular codon in the VHL protein.

Protein mechanics: how force regulates molecular function

BACKGROUND

Regulation of proteins is ubiquitous and vital for any organism. Protein activity can be altered chemically, by covalent modifications or non-covalent binding of co-factors. Mechanical forces are emerging as an additional way of regulating proteins, by inducing a conformational change or by partial unfolding.

SCOPE

We review some advances in experimental and theoretical techniques to study protein allostery driven by mechanical forces, as opposed to the more conventional ligand driven allostery. In this respect, we discuss recent single molecule pulling experiments as they have substantially augmented our view on the protein allostery by mechanical signals in recent years. Finally, we present a computational analysis technique, Force Distribution Analysis, that we developed to reveal allosteric pathways in proteins.

MAJOR CONCLUSIONS

Any kind of external perturbation, being it ligand binding or mechanical stretching, can be viewed as an external force acting on the macromolecule, rendering force-based experimental or computational techniques, a very general approach to the mechanics involved in protein allostery.

GENERAL SIGNIFICANCE

This unifying view might aid to decipher how complex allosteric protein machineries are regulated on the single molecular level.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

The role of allostery in the ubiquitin-proteasome system

The ubiquitin-proteasome system (UPS) is involved in many cellular processes including protein degradation. Degradation of a protein via this system involves two successive steps: ubiquitination and degradation. Ubiquitination tags the target protein with ubiquitin-like proteins (UBLs), such as ubiquitin, small ubiquitin-like modifier (SUMO) and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2 and E3 ubiquitin ligases. The proteasomes recognize the UBL-tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degradation. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the UPS action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degradation. Finally, we propose that allostery may similarly play key roles in the regulation of molecular machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.

Impact of mutations on the allosteric conformational equilibrium

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and effector-unbound protein structures. These simulations can be performed using our web server (http://salilab.org/allosmod/). We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a data set of 10 proteins and 179 mutations, we predict both the magnitude and the sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1k(B)T. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction.

Force distribution reveals signal transduction in E. coli Hsp90

Heat-shock protein 90 (Hsp90) is an ubiquitous chaperone that is essential for cell function in that it promotes client-protein folding and stabilization. Its function is tightly controlled by an ATP-dependent large conformational transition between the open and closed states of the Hsp90 dimer. The underlying allosteric pathway has remained largely unknown, but it is revealed here in atomistic detail for the Escherichia coli homolog HtpG. Using force-distribution analysis based on molecular-dynamics simulations (>1 μs in total), we identify an internal signaling pathway that spans from the nucleotide-binding site to an ~2.3-nm-distant region in the HtpG middle domain, that serves as a dynamic hinge region, and to a putative client-protein-binding site in the middle domain. The force transmission is triggered by ATP capturing a magnesium ion and thereby rotating and bending a proximal long α-helix, which represents the major force channel into the middle domain. This allosteric mechanism is, with statistical significance, distinct from the dynamics in the ADP and apo states. Tracking the distribution of forces is likely to be a promising tool for understanding and guiding experiments of complex allosteric proteins in general.

Simulation of the mutation F76del on the von Hippel-Lindau tumor suppressor protein: mechanism of the disease and implications for drug development

The von Hippel-Lindau tumor suppressor protein (pVHL) plays a central role in the oxygen-sensing pathway by regulating the degradation of the hypoxia-inducible factor (HIF-1α). The capture of HIF-1α by pVHL is regulated by an oxygen-dependent hydroxylation of a specific conserved prolyl residue. The VHL gene is mutated in the von Hippel-Lindau cancer predisposition syndrome, which is characterized by the development of highly vascularized tumors and is associated with constitutively high levels of HIF-1α. The disturbance of the dynamic coupling between HIF-1α and pVHL bearing the commonly found mutation F76del was experimentally confirmed but the mechanism of such complex disruption is still not clear. Performing unbiased molecular dynamics simulations, we show that the F76del mutation may enlarge the HIF binding pocket in pVHL and induce the formation of an internal cavity in the hydrophobic core of the β-domain, which can lead to a partial destabilization of the β-sheets S1, S4, and S7 and a consequent loss of hydrogen bonds with a conserved recognition motif in HIF. The newly formed cavity has a significant druggability score and may be a suitable target for stabilizing ligands. Studies of this nature may help to fill the information gap between genotype-phenotype correlations with details obtained at atomic level and provide basis for future development of drug candidates, such as pharmacological chaperones, with the specific aim of reverting the dysfunction of such pathological protein complexes found in patients with VHL.

Exploiting protein flexibility to predict the location of allosteric sites

BACKGROUND

Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure.

RESULTS

By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature.

CONCLUSIONS

We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.