ASD v3.0: unraveling allosteric regulation with structural mechanisms and biological networks

Assessing structural uncertainty of biochemical regulatory networks in metabolic pathways under varying data quality

Ordinary differential equation (ODE) models are powerful tools for studying the dynamics of metabolic pathways. However, key challenges lie in constructing ODE models for metabolic pathways, specifically in our limited knowledge about which metabolite levels control which reaction rates. Identification of these regulatory networks is further complicated by the limited availability of relevant data. Here, we assess the conditions under which it is feasible to accurately identify regulatory networks in metabolic pathways by computationally fitting candidate network models with biochemical systems theory (BST) kinetics to data of varying quality. We use network motifs commonly found in metabolic pathways as a simplified testbed. Key features correlated with the level of difficulty in identifying the correct regulatory network were identified, highlighting the impact of sampling rate, data noise, and data incompleteness on structural uncertainty. We found that for a simple branched network motif with an equal number of metabolites and fluxes, identification of the correct regulatory network can be largely achieved and is robust to missing one of the metabolite profiles. However, with a bi-substrate bi-product reaction or more fluxes than metabolites in the network motif, the identification becomes more challenging. Stronger regulatory interactions and higher metabolite concentrations were found to be correlated with less structural uncertainty. These results could aid efforts to predict whether the true metabolic regulatory network can be computationally identified for a given stoichiometric network topology and dataset quality, thus helping to identify optimal measures to mitigate such identifiability issues in kinetic model development.

Machine learning approaches in predicting allosteric sites

Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of machine learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of artificial intelligence such as protein language models.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

ASD2023: towards the integrating landscapes of allosteric knowledgebase

Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible online at http://mdl.shsmu.edu.cn/ASD) has been systematically developed since 2009 to provide comprehensive information on allosteric regulation. In recent years, allostery has seen sustained growth and wide-ranging applications in life sciences, from basic research to new therapeutics development, while also elucidating emerging obstacles across allosteric research stages. To overcome these challenges and maintain high-quality data center services, novel features were curated in the ASD2023 update: (i) 66 589 potential allosteric sites, covering > 80% of the human proteome and constituting the human allosteric pocketome; (ii) 748 allosteric protein-protein interaction (PPI) modulators with clear mechanisms, aiding protein machine studies and PPI-targeted drug discovery; (iii) 'Allosteric Hit-to-Lead,' a pioneering dataset providing panoramic views from 87 well-defined allosteric hits to 6565 leads and (iv) 456 dualsteric modulators for exploring the simultaneous regulation of allosteric and orthosteric sites. Meanwhile, ASD2023 maintains a significant growth of foundational allosteric data. Based on these efforts, the allosteric knowledgebase is progressively evolving towards an integrated landscape, facilitating advancements in allosteric target identification, mechanistic exploration and drug discovery.

A Structure-Based Allosteric Modulator Design Paradigm

Importance: Allosteric drugs bound to topologically distal allosteric sites hold a substantial promise in modulating therapeutic targets deemed undruggable at their orthosteric sites. Traditionally, allosteric modulator discovery has predominantly relied on serendipitous high-throughput screening. Nevertheless, the landscape has undergone a transformative shift due to recent advancements in our understanding of allosteric modulation mechanisms, coupled with a significant increase in the accessibility of allosteric structural data. These factors have extensively promoted the development of various computational methodologies, especially for machine-learning approaches, to guide the rational design of structure-based allosteric modulators. Highlights: We here presented a comprehensive structure-based allosteric modulator design paradigm encompassing 3 critical stages: drug target acquisition, allosteric binding site, and modulator discovery. The recent advances in computational methods in each stage are encapsulated. Furthermore, we delve into analyzing the successes and obstacles encountered in the rational design of allosteric modulators. Conclusion: The structure-based allosteric modulator design paradigm holds immense potential for the rational design of allosteric modulators. We hope that this review would heighten awareness of the use of structure-based computational methodologies in advancing the field of allosteric drug discovery.

Designing drugs and chemical probes with the dualsteric approach

Traditionally, drugs are monovalent, targeting only one site on the protein surface. This includes orthosteric and allosteric drugs, which bind the protein at orthosteric and allosteric sites, respectively. Orthosteric drugs are good in potency, whereas allosteric drugs have better selectivity and are solutions to classically undruggable targets. However, it would be difficult to simultaneously reach high potency and selectivity when targeting only one site. Also, both kinds of monovalent drugs suffer from mutation-caused drug resistance. To overcome these obstacles, dualsteric modulators have been proposed in the past twenty years. Compared to orthosteric or allosteric drugs, dualsteric modulators are bivalent (or bitopic) with two pharmacophores. Each of the two pharmacophores bind the protein at the orthosteric and an allosteric site, which could bring the modulator with special properties beyond monovalent drugs. In this study, we comprehensively review the current development of dualsteric modulators. Our main effort reason and illustrate the aims to apply the dualsteric approach, including a "double win" of potency and selectivity, overcoming mutation-caused drug resistance, developments of function-biased modulators, and design of partial agonists. Moreover, the strengths of the dualsteric technique also led to its application outside pharmacy, including the design of highly sensitive fluorescent tracers and usage as molecular rulers. Besides, we also introduced drug targets, designing strategies, and validation methods of dualsteric modulators. Finally, we detail the conclusions and perspectives.

An Exploration of the Inhibitory Mechanism of Rationally Screened Benzofuran-1,3,4-Oxadiazoles and-1,2,4-Triazoles as Inhibitors of NS5B RdRp Hepatitis C Virus through Pharmacoinformatic Approaches

Benzofuran, 1,3,4-oxadiazole, and 1,2,4-triazole are privileged heterocyclic moieties that display the most promising and wide spectrum of biological activities against a wide variety of diseases. In the current study, benzofuran-1,3,4-oxadiazole BF1-BF7 and benzofuran-1,2,4-triazole compounds BF8-BF15 were tested against HCV NS5B RNA-dependent RNA polymerase (RdRp) utilizing structure-based screening via a computer-aided drug design (CADD) approach. A molecular docking approach was applied to evaluate the binding potential of benzofuran-appended 1,3,4-oxadiazole and 1,2,4-triazole BF1-BF15 molecules. Benzofuran-1,3,4-oxadiazole scaffolds BF1-BF7 showed lesser binding affinities (-12.63 to -14.04 Kcal/mol) than benzofuran-1,2,4-triazole scaffolds BF8-BF15 (-14.11 to -16.09 Kcal/mol) against the HCV NS5B enzyme. Molecular docking studies revealed the excellent binding affinity scores exhibited by benzofuran-1,2,4-triazole structural motifs BF-9 (-16.09 Kcal/mol), BF-12 (-15.75 Kcal/mol), and BF-13 (-15.82 Kcal/mol), respectively, which were comparatively better than benzofuran-based HCV NS5B inhibitors' standard reference drug Nesbuvir (-15.42 Kcal/mol). A molecular dynamics simulation assay was also conducted to obtain valuable insights about the enzyme-compounds interaction profile and structural stability, which indicated the strong intermolecular energies of the BF-9+NS5B complex and the BF-12+NS5B complex as per the MM-PBSA method, while the BF-12+NS5B complex was the most stable system as per the MM-GBSA calculation. The drug-likeness and ADMET studies of all the benzofuran-1,2,4-triazole derivatives BF8-BF15 revealed that these compounds possessed good medicinal chemistry profiles in agreement with all the evaluated parameters for being drugs. The molecular docking affinity scores, MM-PBSA/MM-GBSA and MD-simulation stability analysis, drug-likeness profiling, and ADMET study assessment indicated that N-4-fluorophenyl-S-linked benzofuran-1,2,4-triazole BF-12 could be a future promising anti-HCV NS5B RdRp inhibitor therapeutic drug candidate that has a structural agreement with the Nesbuvir standard reference drug.

Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials

Undruggable proteins are a class of proteins that are often characterized by large, complex structures or functions that are difficult to interfere with using conventional drug design strategies. Targeting such undruggable targets has been considered also a great opportunity for treatment of human diseases and has attracted substantial efforts in the field of medicine. Therefore, in this review, we focus on the recent development of drug discovery targeting "undruggable" proteins and their application in clinic. To make this review well organized, we discuss the design strategies targeting the undruggable proteins, including covalent regulation, allosteric inhibition, protein-protein/DNA interaction inhibition, targeted proteins regulation, nucleic acid-based approach, immunotherapy and others.

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.

Conservation of Allosteric Ligand Binding Sites in G-Protein Coupled Receptors

Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.

19F NMR: A promising tool for dynamic conformational studies of G protein-coupled receptors

Advances in X-ray crystallography and cryoelectron microscopy enabled unprecedented insights into the activation processes of G protein-coupled receptors (GPCRs). However, these static receptor structures provide limited information about dynamics and conformational transitions that play pivotal roles in mediating signaling diversity through the multifaceted interactions between ligands, receptors, and transducers. Developing NMR approaches to probe the dynamics of conformational transitions will push the frontier of receptor science toward a more comprehensive understanding of these signaling processes. Although much progress has been made during the last decades, it remains challenging to delineate receptor conformational states and interrogate the functions of the individual states at a quantitative level. Here we cover the progress of ¹⁹F NMR applications in GPCR conformational and dynamic studies during the past 20 years. Current challenges and limitations of ¹⁹F NMR for studying GPCR dynamics are also discussed, along with experimental strategies that will drive this field forward.

Identification and Inhibition of the Druggable Allosteric Site of SARS-CoV-2 NSP10/NSP16 Methyltransferase through Computational Approaches

Since its emergence in early 2019, the respiratory infectious virus, SARS-CoV-2, has ravaged the health of millions of people globally and has affected almost every sphere of life. Many efforts are being made to combat the COVID-19 pandemic’s emerging and recurrent waves caused by its evolving and more infectious variants. As a result, novel and unexpected targets for SARS-CoV-2 have been considered for drug discovery. 2′-O-Methyltransferase (nsp10/nsp16) is a significant and appealing target in the SARS-CoV-2 life cycle because it protects viral RNA from the host degradative enzymes via a cap formation process. In this work, we propose prospective allosteric inhibitors that target the allosteric site, SARS-CoV-2 MTase. Four drug libraries containing ~119,483 compounds were screened against the allosteric site of SARS-CoV-2 MTase identified in our research. The identified best compounds exhibited robust molecular interactions and alloscore-score rankings with the allosteric site of SARS-CoV-2 MTase. Moreover, to further assess the dynamic stability of these compounds (CHEMBL2229121, ZINC000009464451, SPECS AK-91811684151, NCI-ID = 715319), a 100 ns molecular dynamics simulation, along with its holo-form, was performed to provide insights on the dynamic nature of these allosteric inhibitors at the allosteric site of the SARS-CoV-2 MTase. Additionally, investigations of MM-GBSA binding free energies revealed a good perspective for these allosteric inhibitor–enzyme complexes, indicating their robust antagonistic action on SARS-CoV-2 (nsp10/nsp16) methyltransferase. We conclude that these allosteric repressive agents should be further evaluated through investigational assessments in order to combat the proliferation of SARS-CoV-2.

The Role of Conformational Dynamics and Allostery in the Control of Distinct Efficacies of Agonists to the Glucocorticoid Receptor

Glucocorticoid receptor (GR) regulates various cellular functions. Given its broad influence on metabolic activities, it has been the target of drug discovery for decades. However, how drugs induce conformational changes in GR has remained elusive. Herein, we used five GR agonists (dex, AZ938, pred, cor, and dibC) with different efficacies to investigate which aspect of the ligand induced the differences in efficacy. We performed molecular dynamics simulations on the five systems (dex-, AZ938-, pred-, cor-, and dibC-bound systems) and observed a distinct discrepancy in the conformation of the cofactor TIF2. Moreover, we discovered ligand-induced differences regarding the level of conformational changes posed by the binding of cofactor TIF2 and identified a pair of essential residues D590 and T39. We further found a positive correlation between the efficacies of ligands and the interaction of the two binding pockets' domains, where D590 and T739 were involved, implying their significance in the participation of allosteric communication. Using community network analysis, two essential communities containing D590 and T739 were identified with their connectivity correlating to the efficacy of ligands. The potential communication pathways between these two residues were revealed. These results revealed the underlying mechanism of allosteric communication between the ligand-binding and cofactor-binding pockets and identified a pair of important residues in the allosteric communication pathway, which can serve as a guide for future drug discovery.

FTMove: A Web Server for Detection and Analysis of Cryptic and Allosteric Binding Sites by Mapping Multiple Protein Structures

Protein mapping distributes many copies of different molecular probes on the surface of a target protein in order to determine binding hot spots, regions that are highly preferable for ligand binding. While mapping of X-ray structures by the FTMap server is inherently static, this limitation can be overcome by the simultaneous analysis of multiple structures of the protein. FTMove is an automated web server that implements this approach. From the input of a target protein, by PDB code, the server identifies all structures of the protein available in the PDB, runs mapping on them, and combines the results to form binding hot spots and binding sites. The user may also upload their own protein structures, bypassing the PDB search for similar structures. Output of the server consists of the consensus binding sites and the individual mapping results for each structure - including the number of probes located in each binding site, for each structure. This level of detail allows the users to investigate how the strength of a binding site relates to the protein conformation, other binding sites, and the presence of ligands or mutations. In addition, the structures are clustered on the basis of their binding properties. The use of FTMove is demonstrated by application to 22 proteins with known allosteric binding sites; the orthosteric and allosteric binding sites were identified in all but one case, and the sites were typically ranked among the top five. The FTMove server is publicly available at https://ftmove.bu.edu.

Prediction of allosteric sites and signaling: Insights from benchmarking datasets

Allostery is a pervasive mechanism that regulates protein activity through ligand binding at a site different from the orthosteric site. The universality of allosteric regulation complemented by the benefits of highly specific and potentially non-toxic allosteric drugs makes uncovering allosteric sites invaluable. However, there are few computational methods to effectively predict them. Bond-to-bond propensity analysis has successfully predicted allosteric sites in 19 of 20 cases using an energy-weighted atomistic graph. We here extended the analysis onto 432 structures of 146 proteins from two benchmarking datasets for allosteric proteins: ASBench and CASBench. We further introduced two statistical measures to account for the cumulative effect of high-propensity residues and the crucial residues in a given site. The allosteric site is recovered for 127 of 146 proteins (407 of 432 structures) knowing only the orthosteric sites or ligands. The quantitative analysis using a range of statistical measures enables better characterization of potential allosteric sites and mechanisms involved.

Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery

Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.

An update on allosteric modulators as a promising strategy targeting histone methyltransferase

Histone methylation is a vital post-translational modification process in epigenetic regulation. The perturbation of histone methylation accounts for many diseases, including malignant cancers. Although achieving significant advances over past decades, orthosteric inhibitors targeting histone methyltransferases still suffer from challenges on subtype selectivity and acquired drug-resistant mutations. As an alternative, new compounds targeting the evolutionarily less conserved allosteric sites, exemplified by HKMTs and PRMTs inhibitors, offer a promising strategy to address this quandary. Herein, we highlight the allosteric sites and mechanisms in histone methyltransferases along with representative allosteric modulators, expecting to facilitate the discovery of allosteric modulators in favor of epigenetic therapy.

Flux-augmented bifurcation analysis in chemical reaction network systems

The dynamics of biochemical reaction networks are considered to be responsible for biological functions in living systems. Since real networks are immense and complicated, it is difficult to determine which reactions can cause a significant change of dynamical behaviors, namely, bifurcations. Also to what extent numerical results of network systems depend on the chosen kinetic rate parameters is not known. In this paper, an analytical setting that splits the information of the dynamics into the network structure and reaction kinetics is introduced. This setting possesses a factorization structure for some class of network systems which allows one to determine which subnetworks are responsible for the occurrence of a bifurcation. Subsequently, the bifurcation criteria are reformulated in a manner that allows the efficient determination of relevant reactions for bifurcations.

Integrated Multi-Class Classification and Prediction of GPCR Allosteric Modulators by Machine Learning Intelligence

G-protein-coupled receptors (GPCRs) are the largest and most diverse group of cell surface receptors that respond to various extracellular signals. The allosteric modulation of GPCRs has emerged in recent years as a promising approach for developing target-selective therapies. Moreover, the discovery of new GPCR allosteric modulators can greatly benefit the further understanding of GPCR cell signaling mechanisms. It is critical but also challenging to make an accurate distinction of modulators for different GPCR groups in an efficient and effective manner. In this study, we focus on an 11-class classification task with 10 GPCR subtype classes and a random compounds class. We used a dataset containing 34,434 compounds with allosteric modulators collected from classical GPCR families A, B, and C, as well as random drug-like compounds. Six types of machine learning models, including support vector machine, naïve Bayes, decision tree, random forest, logistic regression, and multilayer perceptron, were trained using different combinations of features including molecular descriptors, Atom-pair fingerprints, MACCS fingerprints, and ECFP6 fingerprints. The performances of trained machine learning models with different feature combinations were closely investigated and discussed. To the best of our knowledge, this is the first work on the multi-class classification of GPCR allosteric modulators. We believe that the classification models developed in this study can be used as simple and accurate tools for the discovery and development of GPCR allosteric modulators.

Current Trends in GPCR Allostery

GPCRs remain the most important drug target comprising ~ 34% of the Food and Drug Administration (FDA)-approved drugs. In modern pharmacology of GPCRs, modulating receptor signaling based on requirement of a specific disorder is of immense interest. Classical drugs targeting orthosteric sites in GPCRs completely block the binding of endogenous ligand and consequently inhibit all important signals from a GPCR. Some of many signals elicited by the endogenous ligands may play vital role and inhibiting these may also cause severe side effects in the long run. However, allosteric drugs can modulate GPCR signaling without blocking the endogenous ligand binding. Therefore, allosteric drugs can maintain beneficial signaling of the receptor and prevent unwanted side effects. In this chapter, we will discuss GPCR crystal structures solved with allosteric ligands, advantages of allosteric drugs, and allosteric drugs which are in clinical use or trials.

D3DistalMutation: a Database to Explore the Effect of Distal Mutations on Enzyme Activity

Enzyme activity is affected by amino acid mutations, particularly mutations near the active site. Increasing evidence has shown that distal mutations more than 10 Å away from the active site may significantly affect enzyme activity. However, it is difficult to study the enzyme regulation mechanism of distal mutations due to the lack of a systematic collection of three-dimensional (3D) structures, highlighting distal mutation site and the corresponding enzyme activity change. Therefore, we constructed a distal mutation database, namely, D3DistalMutation, which relates the distal mutation to enzyme activity. As a result, we observed that approximately 80% of distal mutations could affect enzyme activity and 72.7% of distal mutations would decrease or abolish enzyme activity in D3DistalMutation. Only 6.6% of distal mutations in D3DistalMutation could increase enzyme activity, which have great potential to the industrial field. Among these mutations, the Y to F, S to D, and T to D mutations are most likely to increase enzyme activity, which sheds some light on industrial catalysis. Distal mutations decreasing enzyme activity in the allosteric pocket play an indispensable role in allosteric drug design. In addition, the pockets in the enzyme structures are provided to explore the enzyme regulation mechanism of distal mutations. D3DistalMutation is accessible free of charge at https://www.d3pharma.com/D3DistalMutation/index.php.

Novel STAT3 small-molecule inhibitors identified by structure-based virtual ligand screening incorporating SH2 domain flexibility

Efforts to develop STAT3 inhibitors have focused on its SH2 domain starting with short phosphotyrosylated peptides based on STAT3 binding motifs, e.g. pY₉₀₅LPQTV within gp130. Despite binding to STAT3 with high affinity, issues regarding stability, bioavailability, and membrane permeability of these peptides, as well as peptidomimetics such as CJ-887, have limited their further clinical development and led to interest in small-molecule inhibitors. Some small molecule STAT3 inhibitors, identified using structure-based virtual ligand screening (SB-VLS); while having favorable drug-like properties, suffer from weak binding affinities, possibly due to the high flexibility of the target domain. We conducted molecular dynamic (MD) simulations of the SH2 domain in complex with CJ-887, and used an averaged structure from this MD trajectory as an "induced-active site" receptor model for SB-VLS of 110,000 compounds within the SPEC database. Screening was followed by re-docking and re-scoring of the top 30% of hits, selection for hit compounds that directly interact with pY + 0 binding pocket residues R609 and S613, and testing for STAT3 targeting in vitro, which identified two lead hits with good activity and favorable drug-like properties. Unlike most small-molecule STAT3 inhibitors previously identified, which contain negatively-charged moieties that mediate binding to the pY + 0 binding pocket, these compounds are uncharged and likely will serve as better candidates for anti-STAT3 drug development. IMPLICATIONS: SB-VLS, using an averaged structure from molecular dynamics (MD) simulations of STAT3 SH2 domain in a complex with CJ-887, a known peptidomimetic binder, identify two highly potent, neutral, low-molecular weight STAT3-inhibitors with favorable drug-like properties.

Quantifying evolutionary importance of protein sites: A Tale of two measures

A key challenge in evolutionary biology is the accurate quantification of selective pressure on proteins and other biological macromolecules at single-site resolution. The evolutionary importance of a protein site under purifying selection is typically measured by the degree of conservation of the protein site itself. A possible alternative measure is the strength of the site-induced conservation gradient in the rest of the protein structure. However, the quantitative relationship between these two measures remains unknown. Here, we show that despite major differences, there is a strong linear relationship between the two measures such that more conserved protein sites also induce stronger conservation gradient in the rest of the protein. This linear relationship is universal as it holds for different types of proteins and functional sites in proteins. Our results show that the strong selective pressure acting on the functional site in general percolates through the rest of the protein via residue-residue contacts. Surprisingly however, catalytic sites in enzymes are the principal exception to this rule. Catalytic sites induce significantly stronger conservation gradients in the rest of the protein than expected from the degree of conservation of the site alone. The unique requirement for the active site to selectively stabilize the transition state of the catalyzed chemical reaction imposes additional selective constraints on the rest of the enzyme.

Sites within proteins which are important for stability or function are under stronger selective pressure and evolve more slowly than other sites. Catalytic sites in enzymes are such highly conserved sites with relatively low evolutionary rates. Recently, catalytic sites were shown to induce a strong gradient of conservation such that the closer a residue is to the catalytic site, the more conserved it is. Here we show that there is a universal linear relationship between the degree of evolutionary conservation of a protein site and the conservation gradient it induces in the protein tertiary structure, applicable to all types of sites. Our findings suggest that selective pressure acting on a protein site generally percolates through the rest of the protein via residue-residue contacts. Remarkably however, catalytic sites induce significantly stronger conservation gradients than expected from their degree of conservation alone. Our results indicate that the strong conservation gradient induced by catalytic sites is driven by the unique function of enzyme catalysis, which requires the participation of many residues beyond the few key catalytic residues. Our results provide insights into evolutionary conservation patterns of and surrounding proteins functional sites, with implications for functional site prediction and protein design.

Core Fermentation (CoFe) granules focus coordinated glycolytic mRNA localization and translation to fuel glucose fermentation

Glycolysis is a fundamental metabolic pathway for glucose catabolism across biology, and glycolytic enzymes are among the most abundant proteins in cells. Their expression at such levels provides a particular challenge. Here we demonstrate that the glycolytic mRNAs are localized to granules in yeast and human cells. Detailed live cell and smFISH studies in yeast show that the mRNAs are actively translated in granules, and this translation appears critical for the localization. Furthermore, this arrangement is likely to facilitate the higher level organization and control of the glycolytic pathway. Indeed, the degree of fermentation required by cells is intrinsically connected to the extent of mRNA localization to granules. On this basis, we term these granules, core fermentation (CoFe) granules; they appear to represent translation factories, allowing high-level coordinated enzyme synthesis for a critical metabolic pathway.

Allosteric binding sites at the receptor-lipid bilayer interface: novel targets for GPCR drug discovery

As a superfamily of membrane receptors, G-protein-coupled receptors (GPCRs) have significant roles in human physiological processes, including cell proliferation, metabolism, and neuromodulation. GPCRs are vital targets of therapeutic drugs, and their allosteric regulation represents a novel direction for drug discovery. Given the numerous breakthroughs in structural biology, diverse allosteric sites on GPCRs have been identified within the extracellular and intracellular loops, and the seven core transmembrane helices. However, a unique type of allosteric site has also been discovered at the interface of the receptor-lipid bilayer, similar to the β₂-adrenergic receptor. Here, we review recent identifications of these allosteric sites and the detailed modulator-target interactions within the interface for each modulator to highlight the role of lipids in GPCR allosteric drug discovery.

Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs

Proteins often have both orthosteric and allosteric binding sites. Endogenous ligands, such as hormones and neurotransmitters, bind to the orthosteric site, while synthetic ligands may bind to orthosteric or allosteric sites, which has become a focal point in drug discovery. Usually, such allosteric modulators bind to a protein noncompetitively with its endogenous ligand or substrate. The growing interest in allosteric modulators has resulted in a substantial increase of these entities and their features such as binding data in chemical libraries and databases. Although this data surge fuels research focused on allosteric modulators, binding data is unfortunately not always clearly indicated as being allosteric or orthosteric. Therefore, allosteric binding data is difficult to retrieve from databases that contain a mixture of allosteric and orthosteric compounds. This decreases model performance when statistical methods, such as machine learning models, are applied. In previous work we generated an allosteric data subset of ChEMBL release 14. In the current study an improved text mining approach is used to retrieve the allosteric and orthosteric binding types from the literature in ChEMBL release 22. Moreover, convolutional deep neural networks were constructed to predict the binding types of compounds for class A G protein-coupled receptors (GPCRs). Temporal split validation showed the model predictiveness with Matthews correlation coefficient (MCC) = 0.54, sensitivity allosteric = 0.54, and sensitivity orthosteric = 0.94. Finally, this study shows that the inclusion of accurate binding types increases binding predictions by including them as descriptor (MCC = 0.27 improved to MCC = 0.34; validated for class A GPCRs, trained on all GPCRs). Although the focus of this study is mainly on class A GPCRs, binding types for all protein classes in ChEMBL were obtained and explored. The data set is included as a supplement to this study, allowing the reader to select the compounds and binding types of interest.

Druggable Lipid GPCRs: Past, Present, and Prospects

G protein-coupled receptors (GPCRs) have seven transmembrane spanning domains and comprise the largest superfamily with ~800 receptors in humans. GPCRs are attractive targets for drug discovery because they transduce intracellular signaling in response to endogenous ligands via heterotrimeric G proteins or arrestins, resulting in a wide variety of physiological and pathophysiological responses. The endogenous ligands for GPCRs are highly chemically diverse and include ions, biogenic amines, nucleotides, peptides, and lipids. In this review, we follow the KonMari method to better understand druggable lipid GPCRs. First, we have a comprehensive tidying up of lipid GPCRs including receptors for prostanoids, leukotrienes, specialized pro-resolving mediators (SPMs), lysophospholipids, sphingosine 1-phosphate (S1P), cannabinoids, platelet-activating factor (PAF), free fatty acids (FFAs), and sterols. This tidying up consolidates 46 lipid GPCRs and declutters several perplexing lipid GPCRs. Then, we further tidy up the lipid GPCR-directed drugs from the literature and databases, which identified 24 clinical drugs targeting 16 unique lipid GPCRs available in the market and 44 drugs under evaluation in more than 100 clinical trials as of 2019. Finally, we introduce drug designs for GPCRs that spark joy, such as positive or negative allosteric modulators (PAM or NAM), biased agonism, functional antagonism like fingolimod, and monoclonal antibodies (MAbs). These strategic drug designs may increase the efficacy and specificity of drugs and reduce side effects. Technological advances will help to discover more endogenous lipid ligands from the vast number of remaining orphan GPCRs and will also lead to the development novel lipid GPCR drugs to treat various diseases.

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition

In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.

Unraveling allosteric landscapes of allosterome with ASD

Allosteric regulation is one of the most direct and efficient ways to fine-tune protein function; it is induced by the binding of a ligand at an allosteric site that is topographically distinct from an orthosteric site. The Allosteric Database (ASD, available online at http://mdl.shsmu.edu.cn/ASD) was developed ten years ago to provide comprehensive information related to allosteric regulation. In recent years, allosteric regulation has received great attention in biological research, bioengineering, and drug discovery, leading to the emergence of entire allosteric landscapes as allosteromes. To facilitate research from the perspective of the allosterome, in ASD 2019, novel features were curated as follows: (i) >10 000 potential allosteric sites of human proteins were deposited for allosteric drug discovery; (ii) 7 human allosterome maps, including protease and ion channel maps, were built to reveal allosteric evolution within families; (iii) 1312 somatic missense mutations at allosteric sites were collected from patient samples from 33 cancer types and (iv) 1493 pharmacophores extracted from allosteric sites were provided for modulator screening. Over the past ten years, the ASD has become a central resource for studying allosteric regulation and will play more important roles in both target identification and allosteric drug discovery in the future.

Revisiting allostery in CREB-binding protein (CBP) using residue-based interaction energy

CREB-binding protein (CBP) is a multi-subunit scaffold protein complex in transcription regulation process, binding and interacting with ligands such as mixed-lineage leukemia (MLL) and c-Myb allosterically. Here in this study, we have revisited the concept of allostery in CBP via residue-based interaction energy calculation based on molecular dynamics (MD) simulations. To this end, we conducted MD simulations of KIX:MLL:c-Myb ternary complex, its binary components and kinase-inducible domain (KID) interacting domain (KIX) backbone. Interaction energy profiles and cross correlation analysis were performed and the results indicated that KIX:MLL and KIX:c-Myb:MLL complexes demonstrate significant similarities according to both analysis methods. Two regions in the KIX backbone were apparent from the interaction energy and cross correlation maps that hold a key to allostery phenomena observed in CBP. While one of these regions are related to the ligand binding residues, the other comprises of L₁₂-G₂ loop and α₃ helix regions that have been found to have a significant role in allosteric signal propagation. All in all, residue-based interaction energy calculation method is demonstrated to be a valuable calculation technique for the detection of allosteric signal propagation and ligand interaction regions.

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.

Recent advances suggest increased influence of selective pressure in allostery

Allosteric regulation of protein functions is ubiquitous in organismal biology, but the principles governing its evolution are not well understood. Here we discuss recent studies supporting the large-scale existence of latent allostery in ancestor proteins of superfamilies. As suggested, the evolution of allostery could be driven by the need for specificity in paralogs of slow evolving protein complexes with conserved active sites. The same slow evolution is displayed by purifying selection exhibited in allosteric proteins with somatic mutations involved in cancer, where disease-associated mutations are enriched in both orthosteric and allosteric sites. Consequently, disease-associated variants can be used to identify druggable allosteric sites that are specific for paralogs in protein superfamilies with otherwise similar functions.

Combining Allosteric and Orthosteric Drugs to Overcome Drug Resistance

Historically, most drugs target protein orthosteric sites. The gradual emergence of resistance hampers their therapeutic effectiveness, posing a challenge to drug development. Coadministration of allosteric and orthosteric drugs provides a revolutionary strategy to circumvent drug resistance, as drugs targeting the topologically distinct allosteric sites can restore or even enhance the efficacy of orthosteric drugs. Here, we comprehensively review the latest successful examples of such combination treatments against drug resistance, with a focus on their modes of action and the underlying structural mechanisms. Our work supplies an innovative insight into such promising methodology against the recalcitrant drug resistance conundrum and will be instructive for future clinical therapeutics.

Natural allosteric modulators and their biological targets: molecular signatures and mechanisms

Covering: 2008 to 2018Over the last decade more than two hundred single natural products were confirmed as natural allosteric modulators (alloNPs) of proteins. The compounds are presented and discussed with the support of a chemical space, constructed using a principal component analysis (PCA) of molecular descriptors from chemical compounds of distinct databases. This analysis showed that alloNPs are dispersed throughout the majority of the chemical space defined by natural products in general. Moreover, a cluster of alloNPs was shown to occupy a region almost devoid of allosteric modulators retrieved from a dataset composed mainly of synthetic compounds, further highlighting the importance to explore the entire natural chemical space for probing allosteric mechanisms. The protein targets which alloNPs bind to comprised 81 different proteins, which were classified into 5 major groups, with enzymes, in particular hydrolases, being the main representative group. The review also brings a critical interpretation on the mechanisms by which alloNPs display their molecular action on proteins. In the latter analysis, alloNPs were classified according to their final effect on the target protein, resulting in 3 major categories: (i) local alteration of the orthosteric site; (ii) global alteration in protein dynamics that change function; and (iii) oligomer stabilisation or protein complex destabilisation via protein-protein interaction in sites distant from the orthosteric site. G-protein coupled receptors (GPCRs), which use a combination of the three types of allosteric regulation found, were also probed by natural products. In summary, the natural allosteric modulators reviewed herein emphasise their importance for exploring alternative chemotherapeutic strategies, potentially pushing the boundaries of the druggable space of pharmacologically relevant 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.

Emergence of allosteric drug-resistance mutations: new challenges for allosteric drug discovery

Allosteric drugs have several significant advantages over traditional orthosteric drugs, encompassing higher selectivity and lower toxicity. Although allosteric drugs have potential advantages as therapeutic agents to treat human diseases, allosteric drug-resistance mutations still occur, rendering these drugs ineffective. Here, we review the emergence of allosteric drug-resistance mutations with an emphasis on examples covering clinically important therapeutic targets, including Breakpoint cluster region-Abelson tyrosine kinase (Bcr-Abl), Akt kinase [also called Protein Kinase B (PKB)], isocitrate dehydrogenase (IDH), MAPK/ERK kinase (MEK), and SRC homology 2 domain-containing phosphatase 2 (SHP2). We also discuss challenges associated with tackling allosteric drug resistance and the possible strategies to overcome this issue.

Systematic identification of metabolites controlling gene expression in E. coli

Metabolism controls gene expression through allosteric interactions between metabolites and transcription factors. These interactions are usually measured with in vitro assays, but there are no methods to identify them at a genome-scale in vivo. Here we show that dynamic transcriptome and metabolome data identify metabolites that control transcription factors in E. coli. By switching an E. coli culture between starvation and growth, we induce strong metabolite concentration changes and gene expression changes. Using Network Component Analysis we calculate the activities of 209 transcriptional regulators and correlate them with metabolites. This approach captures, for instance, the in vivo kinetics of CRP regulation by cyclic-AMP. By testing correlations between all pairs of transcription factors and metabolites, we predict putative effectors of 71 transcription factors, and validate five interactions in vitro. These results show that combining transcriptomics and metabolomics generates hypotheses about metabolism-transcription interactions that drive transitions between physiological states.

Interactions between metabolites and transcription factors are known to control gene expression but analyzing these events at genome-scale is challenging. Here, the authors integrate dynamic metabolome and transcriptome data from E.coli to predict regulatory metabolite-transcription factor interactions.

CavityPlus: a web server for protein cavity detection with pharmacophore modelling, allosteric site identification and covalent ligand binding ability prediction

CavityPlus is a web server that offers protein cavity detection and various functional analyses. Using protein three-dimensional structural information as the input, CavityPlus applies CAVITY to detect potential binding sites on the surface of a given protein structure and rank them based on ligandability and druggability scores. These potential binding sites can be further analysed using three submodules, CavPharmer, CorrSite, and CovCys. CavPharmer uses a receptor-based pharmacophore modelling program, Pocket, to automatically extract pharmacophore features within cavities. CorrSite identifies potential allosteric ligand-binding sites based on motion correlation analyses between cavities. CovCys automatically detects druggable cysteine residues, which is especially useful to identify novel binding sites for designing covalent allosteric ligands. Overall, CavityPlus provides an integrated platform for analysing comprehensive properties of protein binding cavities. Such analyses are useful for many aspects of drug design and discovery, including target selection and identification, virtual screening, de novo drug design, and allosteric and covalent-binding drug design. The CavityPlus web server is freely available at http://repharma.pku.edu.cn/cavityplus or http://www.pkumdl.cn/cavityplus.

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/.

Ligand-Binding-Site Structure Shapes Allosteric Signal Transduction and the Evolution of Allostery in Protein Complexes

The structure of ligand-binding sites has been shown to profoundly influence the evolution of function in homomeric protein complexes. Complexes with multichain binding sites (MBSs) have more conserved quaternary structure, more similar binding sites and ligands between homologs, and evolve new functions slower than homomers with single-chain binding sites (SBSs). Here, using in silico analyses of protein dynamics, we investigate whether ligand-binding-site structure shapes allosteric signal transduction pathways, and whether the structural similarity of binding sites influences the evolution of allostery. Our analyses show that: 1) allostery is more frequent among MBS complexes than in SBS complexes, particularly in homomers; 2) in MBS homomers, semirigid communities and critical residues frequently connect interfaces and thus they are characterized by signal transduction pathways that cross protein-protein interfaces, whereas SBS homomers usually not; 3) ligand binding alters community structure differently in MBS and SBS homomers; and 4) except MBS homomers, allosteric proteins are more likely to have homologs with similar binding site than nonallosteric proteins, suggesting that binding site similarity is an important factor driving the evolution of allostery.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks

BACKGROUND

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks.

METHODS

In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems.

RESULTS

The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators.

CONCLUSIONS

We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases.

GENERAL SIGNIFICANCE

This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Functional cross-talk between allosteric effects of activating and inhibiting ligands underlies PKM2 regulation

Several enzymes can simultaneously interact with multiple intracellular metabolites, however, how the allosteric effects of distinct ligands are integrated to coordinately control enzymatic activity remains poorly understood. We addressed this question using, as a model system, the glycolytic enzyme pyruvate kinase M2 (PKM2). We show that the PKM2 activator fructose 1,6-bisphosphate (FBP) alone promotes tetramerisation and increases PKM2 activity, but addition of the inhibitor L-phenylalanine (Phe) prevents maximal activation of FBP-bound PKM2 tetramers. We developed a method, AlloHubMat, that uses eigenvalue decomposition of mutual information derived from molecular dynamics trajectories to identify residues that mediate FBP-induced allostery. Experimental mutagenesis of these residues identified PKM2 variants in which activation by FBP remains intact but cannot be attenuated by Phe. Our findings reveal residues involved in FBP-induced allostery that enable the integration of allosteric input from Phe and provide a paradigm for the coordinate regulation of enzymatic activity by simultaneous allosteric inputs.

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.