SPACER: Server for predicting allosteric communication and effects of regulation

Tunnel-like region observed as a potential allosteric site in Staphylococcus aureus Glyceraldehyde-3-phosphate dehydrogenase

Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) catalyzing the sixth step of glycolysis has been investigated for allosteric features that might be used as potential target for specific inhibition of Staphylococcus aureus (S.aureus). X-ray structure of bacterial enzyme for which a tunnel-like opening passing through the center previously proposed as an allosteric site has been subjected to six independent 500 ns long Molecular Dynamics simulations. Harmonic bond restraints were employed at key residues to underline the allosteric feature of this region. A noticeable reduction was observed in the mobility of NAD+ binding domains when restrictions were applied. Also, a substantial decrease in cross-correlations between distant Cα fluctuations was detected throughout the structure. Mutual information (MI) analysis revealed a similar decrease in the degree of correspondence in positional fluctuations in all directions everywhere in the receptor. MI between backbone and side-chain torsional variations changed its distribution profile and decreased considerably around the catalytic sites when restraints were employed. Principal component analysis clearly showed that the restrained state sampled a narrower range of conformations than apo state, especially in the first principal mode due to restriction in the conformational flexibility of NAD+ binding domain. Clustering the trajectory based on catalytic site residues displayed a smaller repertoire of conformations for restrained state compared to apo. Representative snapshots subjected to k-shortest pathway analysis revealed the impact of bond restraints on the allosteric communication which displayed distinct optimal and suboptimal pathways for two states, where observed frequencies of critical residues Gln51 and Val283 at the proposed site changed considerably.

PASSerRank: Prediction of allosteric sites with learning to rank

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.

PASSerRank: Prediction of Allosteric Sites with Learning to Rank

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, i.e., how well a pocket meets the characteristics of known allosteric sites. The model outperforms other common machine learning models with higher F1 score and Matthews correlation coefficient. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top 3 positions for 83.6% and 80.5% of test proteins, respectively. The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.

Allosteric Modulators of Dopamine D2 Receptors for Fine-Tuning of Dopaminergic Neurotransmission in CNS Diseases: Overview, Pharmacology, Structural Aspects and Synthesis

Allosteric modulation of G protein-coupled receptors (GPCRs) is nowadays a hot topic in medicinal chemistry. Allosteric modulators, i.e., compounds which bind in a receptor site topologically distinct from orthosteric sites, exhibit a number of advantages. They are more selective, safer and display a ceiling effect which prevents overdosing. Allosteric modulators of dopamine D₂ receptor are potential drugs against a number of psychiatric and neurological diseases, such as schizophrenia and Parkinson's disease. In this review, an insightful summary of current research on D₂ receptor modulators is presented, ranging from their pharmacology and structural aspects of ligand-receptor interactions to their synthesis.

Prediction of allosteric druggable pockets of cyclin-dependent kinases

Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.

PASSer2.0: Accurate Prediction of Protein Allosteric Sites Through Automated Machine Learning

Allostery is a fundamental process in regulating protein activities. The discovery, design, and development of allosteric drugs demand better identification of allosteric sites. Several computational methods have been developed previously to predict allosteric sites using static pocket features and protein dynamics. Here, we define a baseline model for allosteric site prediction and present a computational model using automated machine learning. Our model, PASSer2.0, advanced the previous results and performed well across multiple indicators with 82.7% of allosteric pockets appearing among the top three positions. The trained machine learning model has been integrated with the Protein Allosteric Sites Server (PASSer) to facilitate allosteric drug discovery.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

A Framework for Investigating Rules of Life by Establishing Zones of Influence

The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (Smocovitis, 1992) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (Leroi, 2014) island biogeography, and (Gropp, 2016) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.

ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules

The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

PASSer: Prediction of Allosteric Sites Server

Allostery is considered important in regulating protein's activity. Drug development depends on the understanding of allosteric mechanisms, especially the identification of allosteric sites, which is a prerequisite in drug discovery and design. Many computational methods have been developed for allosteric site prediction using pocket features and protein dynamics. Here, we present an ensemble learning method, consisting of eXtreme gradient boosting (XGBoost) and graph convolutional neural network (GCNN), to predict allosteric sites. Our model can learn physical properties and topology without any prior information, and shows good performance under multiple indicators. Prediction results showed that 84.9% of allosteric pockets in the test set appeared in the top 3 positions. The PASSer: Protein Allosteric Sites Server (https://passer.smu.edu), along with a command line interface (CLI, https://github.com/smutaogroup/passerCLI) provide insights for further analysis in drug discovery.

Exploring the Allosteric Territory of Protein Function

While the pervasiveness of allostery in proteins is commonly accepted, we further show the generic nature of allosteric mechanisms by analyzing here transmembrane ion-channel viroporin 3a and RNA-dependent RNA polymerase (RdRp) from SARS-CoV-2 along with metabolic enzymes isocitrate dehydrogenase 1 (IDH1) and fumarate hydratase (FH) implicated in cancers. Using the previously developed structure-based statistical mechanical model of allostery (SBSMMA), we share our experience in analyzing the allosteric signaling, predicting latent allosteric sites, inducing and tuning targeted allosteric response, and exploring the allosteric effects of mutations. This, yet incomplete list of phenomenology, forms a complex and unique allosteric territory of protein function, which should be thoroughly explored. We propose a generic computational framework, which not only allows one to obtain a comprehensive allosteric control over proteins but also provides an opportunity to approach the fragment-based design of allosteric effectors and drug candidates. The advantages of allosteric drugs over traditional orthosteric compounds, complemented by the emerging role of the allosteric effects of mutations in the expansion of the cancer mutational landscape and in the increased mutability of viral proteins, leave no choice besides further extensive studies of allosteric mechanisms and their biomedical implications.

Engineering an Allosteric Control of Protein Function

Allosteric regulation in proteins is fundamental to many important biological processes. Allostery has been employed to control protein functions by regulating protein activity. Engineered allosteric regulation allows controlling protein activity in subsecond time scale and has a broad range of applications, from dissecting spatiotemporal dynamics in biochemical cascades to applications in biotechnology and medicine. Here, we review the concept of allostery in proteins and various approaches to identify allosteric sites and pathways. We then provide an overview of strategies and tools used in allosteric protein regulation and their utility in biological applications. We highlight various classes of proteins, where regulation is achieved through allostery. Finally, we analyze the current problems, critical challenges, and future prospective in achieving allosteric regulation in proteins.

Identification of Allosteric Effects in Proteins by Elastic Network Models

Allostery is a fundamental regulatory mechanism in the majority of biological processes of molecular machines. Allostery is well-known as a dynamic-driven process, and thus, the molecular mechanism of allosteric signal transmission needs to be established. Elastic network models (ENMs) provide efficient methods for investigating the intrinsic dynamics and allosteric communication pathways in proteins. In this chapter, two ENM methods including Gaussian network model (GNM) coupled with Markovian stochastic model, as well as the anisotropic network model (ANM), were introduced to identify allosteric effects in hemoglobins. Techniques on model parameters, scripting and calculation, analysis, and visualization are shown step by step.

Dynamical Correlations Reveal Allosteric Sites in G Protein-Coupled Receptors

G protein-coupled Receptors (GPCRs) play a central role in many physiological processes and, consequently, constitute important drug targets. In particular, the search for allosteric drugs has recently drawn attention, since they could be more selective and lead to fewer side effects. Accordingly, computational tools have been used to estimate the druggability of allosteric sites in these receptors. In spite of many successful results, the problem is still challenging, particularly the prediction of hydrophobic sites in the interface between the protein and the membrane. In this work, we propose a complementary approach, based on dynamical correlations. Our basic hypothesis was that allosteric sites are strongly coupled to regions of the receptor that undergo important conformational changes upon activation. Therefore, using ensembles of experimental structures, normal mode analysis and molecular dynamics simulations we calculated correlations between internal fluctuations of different sites and a collective variable describing the activation state of the receptor. Then, we ranked the sites based on the strength of their coupling to the collective dynamics. In the β2 adrenergic (β2AR), glucagon (GCGR) and M2 muscarinic receptors, this procedure allowed us to correctly identify known allosteric sites, suggesting it has predictive value. Our results indicate that this dynamics-based approach can be a complementary tool to the existing toolbox to characterize allosteric sites in GPCRs.

AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations

The AlloSigMA 2 server provides an interactive platform for exploring the allosteric signaling caused by ligand binding and/or mutations, for analyzing the allosteric effects of mutations and for detecting potential cancer drivers and pathogenic nsSNPs. It can also be used for searching latent allosteric sites and for computationally designing allosteric effectors for these sites with required agonist/antagonist activity. The server is based on the implementation of the Structure-Based Statistical Mechanical Model of Allostery (SBSMMA), which allows one to evaluate the allosteric free energy as a result of the perturbation at per-residue resolution. The Allosteric Signaling Map (ASM) providing a comprehensive residue-by-residue allosteric control over the protein activity can be obtained for any structure of interest. The Allosteric Probing Map (APM), in turn, allows one to perform the fragment-based-like computational design experiment aimed at finding leads for potential allosteric effectors. The server can be instrumental in elucidating of allosteric mechanisms and actions of allosteric mutations, and in the efforts on design of new elements of allosteric control. The server is freely available at: http://allosigma.bii.a-star.edu.sg.

Mapping allosteric communications within individual proteins

Allostery in proteins influences various biological processes such as regulation of gene transcription and activities of enzymes and cell signaling. Computational approaches for analysis of allosteric coupling provide inexpensive opportunities to predict mutations and to design small-molecule agents to control protein function and cellular activity. We develop a computationally efficient network-based method, Ohm, to identify and characterize allosteric communication networks within proteins. Unlike previously developed simulation-based approaches, Ohm relies solely on the structure of the protein of interest. We use Ohm to map allosteric networks in a dataset composed of 20 proteins experimentally identified to be allosterically regulated. Further, the Ohm allostery prediction for the protein CheY correlates well with NMR CHESCA studies. Our webserver, Ohm.dokhlab.org, automatically determines allosteric network architecture and identifies critical coupled residues within this network.

Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning

Allosteric regulation is a common mechanism employed by complex biomolecular systems for regulation of activity and adaptability in the cellular environment, serving as an effective molecular tool for cellular communication. As an intrinsic but elusive property, allostery is a ubiquitous phenomenon where binding or disturbing of a distal site in a protein can functionally control its activity and is considered as the "second secret of life." The fundamental biological importance and complexity of these processes require a multi-faceted platform of synergistically integrated approaches for prediction and characterization of allosteric functional states, atomistic reconstruction of allosteric regulatory mechanisms and discovery of allosteric modulators. The unifying theme and overarching goal of allosteric regulation studies in recent years have been integration between emerging experiment and computational approaches and technologies to advance quantitative characterization of allosteric mechanisms in proteins. Despite significant advances, the quantitative characterization and reliable prediction of functional allosteric states, interactions, and mechanisms continue to present highly challenging problems in the field. In this review, we discuss simulation-based multiscale approaches, experiment-informed Markovian models, and network modeling of allostery and information-theoretical approaches that can describe the thermodynamics and hierarchy allosteric states and the molecular basis of allosteric mechanisms. The wealth of structural and functional information along with diversity and complexity of allosteric mechanisms in therapeutically important protein families have provided a well-suited platform for development of data-driven research strategies. Data-centric integration of chemistry, biology and computer science using artificial intelligence technologies has gained a significant momentum and at the forefront of many cross-disciplinary efforts. We discuss new developments in the machine learning field and the emergence of deep learning and deep reinforcement learning applications in modeling of molecular mechanisms and allosteric proteins. The experiment-guided integrated approaches empowered by recent advances in multiscale modeling, network science, and machine learning can lead to more reliable prediction of allosteric regulatory mechanisms and discovery of allosteric modulators for therapeutically important protein targets.

AlloMAPS: allosteric mutation analysis and polymorphism of signaling database

AlloMAPS database provides data on the causality and energetics of allosteric communication obtained with the structure-based statistical mechanical model of allostery (SBSMMA). The database contains data on allosteric signaling in three sets of proteins and protein chains: (i) 46 proteins with comprehensively annotated functional and allosteric sites; (ii) 1908 protein chains from PDBselect set of chains with low (<25%) sequence identity; (iii) 33 proteins with more than 50 known pathological SNPs in each molecule. In addition to energetics of allosteric signaling between known functional and regulatory sites, allosteric modulation caused by the binding to these sites, by SNPs, and by mutations designated by the user can be explored. Allosteric Signaling Maps (ASMs), which are produced via the exhaustive computational scanning for stabilizing and destabilizing mutations and for the modulation range caused by the sequence position are available for each protein/protein chain in the database. We propose to use this database for evaluating the effects of allosteric signaling in the search for latent regulatory sites and in the design of allosteric sites and effectors. The database is freely available at: http://allomaps.bii.a-star.edu.sg.

Big data and artificial intelligence discover novel drugs targeting proteins without 3D structure and overcome the undruggable targets

The discovery of targeted drugs heavily relies on three-dimensional (3D) structures of target proteins. When the 3D structure of a protein target is unknown, it is very difficult to design its corresponding targeted drugs. Although the 3D structures of some proteins (the so-called undruggable targets) are known, their targeted drugs are still absent. As increasing crystal/cryogenic electron microscopy structures are deposited in Protein Data Bank, it is much more possible to discover the targeted drugs. Moreover, it is also highly probable to turn previous undruggable targets into druggable ones when we identify their hidden allosteric sites. In this review, we focus on the currently available advanced methods for the discovery of novel compounds targeting proteins without 3D structure and how to turn undruggable targets into druggable ones.

Computational Analysis of Crystallization Additives for the Identification of New Allosteric Sites

Allosteric effect can modulate the biological activity of a protein. Thus, the discovery of new allosteric sites is very attractive for designing new modulators or inhibitors. Here, we propose an innovative way to identify allosteric sites, based on crystallization additives (CA), used to stabilize proteins during the crystallization process. Density and clustering analyses of CA, applied on protein kinase and nuclear receptor families, revealed that CA are not randomly distributed around protein structures, but they tend to aggregate near common sites. All orthosteric and allosteric cavities described in the literature are retrieved from the analysis of CA distribution. In addition, new sites were identified, which could be associated to putative allosteric sites. We proposed an efficient and easy way to use the structural information of CA to identify allosteric sites. This method could assist medicinal chemists for the design of new allosteric compounds targeting cavities of new drug targets.

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.

AlloFinder: a strategy for allosteric modulator discovery and allosterome analyses

Allostery tweaks innumerable biological processes and plays a fundamental role in human disease and drug discovery. Exploration of allostery has thus been regarded as a crucial requirement for research on biological mechanisms and the development of novel therapeutics. Here, based on our previously developed allosteric data and methods, we present an interactive platform called AlloFinder that identifies potential endogenous or exogenous allosteric modulators and their involvement in human allosterome. AlloFinder automatically amalgamates allosteric site identification, allosteric screening and allosteric scoring evaluation of modulator-protein complexes to identify allosteric modulators, followed by allosterome mapping analyses of predicted allosteric sites and modulators in human proteome. This web server exhibits prominent performance in the reemergence of allosteric metabolites and exogenous allosteric modulators in known allosteric proteins. Specifically, AlloFinder enables identification of allosteric metabolites for metabolic enzymes and screening of potential allosteric compounds for disease-related targets. Significantly, the feasibility of AlloFinder to discover allosteric modulators was tested in a real case of signal transduction and activation of transcription 3 (STAT3) and validated by mutagenesis and functional experiments. Collectively, AlloFinder is expected to contribute to exploration of the mechanisms of allosteric regulation between metabolites and metabolic enzymes, and to accelerate allosteric drug discovery. The AlloFinder web server is freely available to all users at http://mdl.shsmu.edu.cn/ALF/.

AlloDriver: a method for the identification and analysis of cancer driver targets

Identifying the variants that alter protein function is a promising strategy for deciphering the biological consequences of somatic mutations during tumorigenesis, which could provide novel targets for the development of cancer therapies. Here, based on our previously developed method, we present a strategy called AlloDriver that identifies cancer driver genes/proteins as possible targets from mutations. AlloDriver utilizes structural and dynamic features to prioritize potentially functional genes/proteins in individual cancers via mapping mutations generated from clinical cancer samples to allosteric/orthosteric sites derived from three-dimensional protein structures. This strategy exhibits desirable performance in the reemergence of known cancer driver mutations and genes/proteins from clinical samples. Significantly, the practicability of AlloDriver to discover novel cancer driver proteins in head and neck squamous cell carcinoma (HNSC) was tested in a real case of human protein tyrosine phosphatase, receptor type K (PTPRK) through a L1143F driver mutation located at the allosteric site of PTPRK, which was experimentally validated by cell proliferation assay. AlloDriver is expected to help to uncover innovative molecular mechanisms of tumorigenesis by perturbing proteins and to discover novel targets based on cancer driver mutations. The AlloDriver is freely available to all users at http://mdl.shsmu.edu.cn/ALD.

Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR-Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well-defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR-Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source.

Perspective: The promises of a holistic view of proteins-impact on antibody engineering and drug discovery

The reductionist approach is prevalent in biomedical science. However, increasing evidence now shows that biological systems cannot be simply considered as the sum of its parts. With experimental, technological, and computational advances, we can now do more than view parts in isolation, thus we propose that an increasing holistic view (where a protein is investigated as much as a whole as possible) is now timely. To further advocate this, we review and discuss several studies and applications involving allostery, where distant protein regions can cross-talk to influence functionality. Therefore, we believe that an increasing big picture approach holds great promise, particularly in the areas of antibody engineering and drug discovery in rational drug design.

Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins

With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.

Are the Apo Proteins Suitable for the Rational Discovery of Allosteric Drugs?

Allosteric modulators, by targeting the less-conserved allosteric sites, represent an innovative strategy in drug discovery. These modulators have a distinctive advantage over orthosteric ligands that attach to the conserved, functional orthosteric sites. However, in structure-based drug design, it remains unclear whether allosteric protein structures determined without orthosteric ligand binding are suitable for allosteric drug screening. In this study, we performed large-scale conformational samplings of six representative allosteric proteins uncomplexed ( apo) and complexed ( holo) with orthosteric ligands to explore the effect of orthosteric site binding on the conformational dynamics of allosteric sites. The results, coupled with the redocking evaluation of allosteric modulators to their apo and holo proteins using their MD trajectories, indicated that orthosteric site binding had an effect on the dynamics of the allosteric sites and allosteric modulators preferentially bound to their holo proteins. According to the analysis data, we constructed a new correlation model for quantifying the allosteric site change driven by substrate binding to the orthosteric site. These results highlight the strong demand to select holo allosteric proteins as initial inputs in structure-based allosteric drug screening when the distance between orthosteric and allosteric sites in the protein is below 5 Å, which is expected to contribute to allosteric drug discovery.

Singular value decomposition for the correlation of atomic fluctuations with arbitrary angle

Many proteins exhibit a critical property called allostery, which enables intra-molecular transmission of information between distal sites. Microscopically, allosteric response is closely related to correlated atomic fluctuations. Conventional correlation analysis correlates the atomic fluctuations at two sites by taking the dot product (DP) between the fluctuations, which accounts only for the parallel and antiparallel components. Here, we present a singular value decomposition (SVD) method that analyzes the correlation coefficient of fluctuation dynamics with an arbitrary angle between the correlated directions. In a model allosteric system, the second PDZ domain (PDZ2) in the human PTP1E protein, approximately one third of the strong correlations have near-perpendicular directions, which are underestimated in the conventional method. The discrimination becomes more prominent for residue pairs with larger separation. The results of the proposed SVD method are more consistent with the experimentally determined PDZ2 dynamics than those of conventional method. In addition, the SVD method improved the prediction accuracy of the allosteric sites in a dataset of 23 known allosteric monomer proteins. The proposed method may inspire extended investigation not only into allostery, but also into protein dynamics and drug design.

A computational study for rational HIV-1 non-nucleoside reverse transcriptase inhibitor selection and the discovery of novel allosteric pockets for inhibitor design

HIV drug resistant mutations that render the current Highly Active Anti-Retroviral Therapy (HAART) cocktail drugs ineffective are increasingly reported. To study the mechanisms of these mutations in conferring drug resistance, we computationally analyzed 14 reverse transcriptase (RT) structures of HIV-1 on the following parameters: drug-binding pocket volume, allosteric effects caused by the mutations, and structural thermal stability. We constructed structural correlation-based networks of the mutant RT-drug complexes and the analyses support the use of efavirenz (EFZ) as the first-line drug, given that cross-resistance is least likely to develop from EFZ-resistant mutations. On the other hand, rilpivirine (RPV)-resistant mutations showed the highest cross-resistance to the other non-nucleoside RT inhibitors. With significant drug cross-resistance associated with the known allosteric drug-binding site, there is a need to identify new allosteric druggable sites in the structure of RT. Through computational analyses, we found such a novel druggable pocket on the HIV-1 RT structure that is comparable with the original allosteric drug site, opening the possibility to the design of new inhibitors.

Single Binding Pockets Versus Allosteric Binding

An orthosteric site is commonly viewed as the primary, functionally binding pocket on a receptor. Signal molecules, endogenous agonists, and substrates are recognized by and bind to the orthosteric site of a specific target, resulting in a biological effect. A malfunctioning active site on a crucial receptor has been confirmed as the culprit that causes many metabolic disturbances, neurologic disorders, and genetic diseases. A competitive inhibitor that has a stronger binding affinity can outcompete an orthosteric ligand. An allosteric site, which is nonoverlapping and topographically distinct from the active pocket, can emerge as a potential regulatory site on the protein surface. An allosteric modulator interacts with a specific binding site, affecting the atoms of nearby residues, thus eliciting a series of conformational changes in the residues at the active site through propagation pathways. Allosteric regulation can potentiate or inhibit function instead of blocking it, and this is a promising strategy for drug design. In this chapter, we describe the tools and protocols for allosteric site analysis and allosteric ligand design.

Modeling the full length HIV-1 Gag polyprotein reveals the role of its p6 subunit in viral maturation and the effect of non-cleavage site mutations in protease drug resistance

HIV polyprotein Gag is increasingly found to contribute to protease inhibitor resistance. Despite its role in viral maturation and in developing drug resistance, there remain gaps in the knowledge of the role of certain Gag subunits (e.g. p6), and that of non-cleavage mutations in drug resistance. As p6 is flexible, it poses a problem for structural experiments, and is hence often omitted in experimental Gag structural studies. Nonetheless, as p6 is an indispensable component for viral assembly and maturation, we have modeled the full length Gag structure based on several experimentally determined constraints and studied its structural dynamics. Our findings suggest that p6 can mechanistically modulate Gag conformations. In addition, the full length Gag model reveals that allosteric communication between the non-cleavage site mutations and the first Gag cleavage site could possibly result in protease drug resistance, particularly in the absence of mutations in Gag cleavage sites. Our study provides a mechanistic understanding to the structural dynamics of HIV-1 Gag, and also proposes p6 as a possible drug target in anti-HIV therapy.

Insulin-Degrading Enzyme in the Fight against Alzheimer's Disease

After decades of research and clinical trials there is still no cure for Alzheimer's disease (AD). While impaired clearance of amyloid beta (Aβ) peptides is considered as one of the major causes of AD, it was recently complemented by a potential role of other toxic amyloidogenic species. Insulin-degrading enzyme (IDE) is the proteolytic culprit of various β-forming peptides, both extracellular and intracellular. On the basis of demonstrated allosteric activation of IDE against Aβ, it is possible to propose a new strategy for the targeted IDE-based cleansing of different toxic aggregation-prone peptides. Consequently, specific allosteric activation of IDE coupled with state-of-the-art compound delivery and CRISP-Cas9 technique of transgene insertion can be instrumental in the fight against AD and related neurodegenerative maladies.

Identification of potential allosteric communication pathways between functional sites of the bacterial ribosome by graph and elastic network models

BACKGROUND

Accumulated evidence indicates that bacterial ribosome employs allostery throughout its structure for protein synthesis. The nature of the allosteric communication between remote functional sites remains unclear, but the contact topology and dynamics of residues may play role in transmission of a perturbation to distant sites.

METHODS/RESULTS

We employ two computationally efficient approaches - graph and elastic network modeling to gain insights about the allosteric communication in ribosome. Using graph representation of the structure, we perform k-shortest pathways analysis between peptidyl transferase center-ribosomal tunnel, decoding center-peptidyl transferase center - previously reported functional sites having allosteric communication. Detailed analysis on intact structures points to common and alternative shortest pathways preferred by different states of translation. All shortest pathways capture drug target sites and allosterically important regions. Elastic network model further reveals that residues along all pathways have the ability of quickly establishing pair-wise communication and to help the propagation of a perturbation in long-ranges during functional motions of the complex.

CONCLUSIONS

Contact topology and inherent dynamics of ribosome configure potential communication pathways between functional sites in different translation states. Inter-subunit bridges B2a, B3 and P-tRNA come forward for their high potential in assisting allostery during translation. Especially B3 emerges as a potential druggable site.

GENERAL SIGNIFICANCE

This study indicates that the ribosome topology forms a basis for allosteric communication, which can be disrupted by novel drugs to kill drug-resistant bacteria. Our computationally efficient approach not only overlaps with experimental evidence on allosteric regulation in ribosome but also proposes new druggable sites.