Impact of mutations on the allosteric conformational equilibrium

Thermostability Enhancement of GH 62 α-l-Arabinofuranosidase by Directed Evolution and Rational Design

GH 62 arabinofuranosidases are known for their excellent specificity for arabinoxylan of agroindustrial residues and their synergism with endoxylanases and other hemicellulases. However, the low thermostability of some GH enzymes hampers potential industrial applications. Protein engineering research highly desires mutations that can enhance thermostability. Therefore, we employed directed evolution using one round of error-prone PCR and site-saturation mutagenesis for thermostability enhancement of GH 62 arabinofuranosidase from Aspergillus fumigatus. Single mutants with enhanced thermostability showed significant ΔΔG changes (<-2.5 kcal/mol) and improvements in perplexity scores from evolutionary scale modeling inverse folding. The best mutant, G205K, increased the melting temperature by 5 °C and the energy of denaturation by 41.3%. We discussed the functional mechanisms for improved stability. Analyzing the adjustments in α-helices, β-sheets, and loops resulting from point mutations, we have obtained significant knowledge regarding the potential impacts on protein stability, folding, and overall structural integrity.

Machine learning and protein allostery

The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.

A proteome-scale analysis of vertebrate protein amino acid occurrence: Thermoadaptation shows a correlation with protein solvation but less so with dynamics

Despite differences in behaviors and living conditions, vertebrate organisms share the great majority of proteins, often with subtle differences in amino acid sequence. Here, we present a simple way to analyze the difference in amino acid occurrence by comparing highly homologous proteins on a subproteome level between several vertebrate model organisms. Specifically, we use this method to identify a pattern of amino acid conservation as well as a shift in amino acid occurrence between homeotherms (warm-blooded species) and poikilotherms (cold-blooded species). Importantly, this general analysis and a specific example further establish a broad correlation, if not likely connection between the thermal adaptation of protein sequences and two of their physical features: on average a change in their protein dynamics and, even more strongly, in their solvation. For poikilotherms, such as frog and fish, the lower body temperature is expected to increase the protein-protein interaction due to a decrease in protein internal dynamics. In order to counteract the tendency for enhanced binding caused by low temperatures, poikilotherms enhance the solvation of their proteins by favoring polar amino acids. This feature appears to dominate over possible changes in dynamics for some proteins. The results suggest that a general trend for amino acid choice is part of the mechanism for thermoadaptation of vertebrate organisms at the molecular level.

Learning about allosteric drugs and ways to design them

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

Evolution of tunnels in α/β-hydrolase fold proteins—What can we learn from studying epoxide hydrolases?

The evolutionary variability of a protein’s residues is highly dependent on protein region and function. Solvent-exposed residues, excluding those at interaction interfaces, are more variable than buried residues whereas active site residues are considered to be conserved. The abovementioned rules apply also to α/β-hydrolase fold proteins—one of the oldest and the biggest superfamily of enzymes with buried active sites equipped with tunnels linking the reaction site with the exterior. We selected soluble epoxide hydrolases as representative of this family to conduct the first systematic study on the evolution of tunnels. We hypothesised that tunnels are lined by mostly conserved residues, and are equipped with a number of specific variable residues that are able to respond to evolutionary pressure. The hypothesis was confirmed, and we suggested a general and detailed way of the tunnels’ evolution analysis based on entropy values calculated for tunnels’ residues. We also found three different cases of entropy distribution among tunnel-lining residues. These observations can be applied for protein reengineering mimicking the natural evolution process. We propose a ‘perforation’ mechanism for new tunnels design via the merging of internal cavities or protein surface perforation. Based on the literature data, such a strategy of new tunnel design could significantly improve the enzyme’s performance and can be applied widely for enzymes with buried active sites.

So far very little is known about proteins tunnels evolution. The goal of this study is to evaluate the evolution of tunnels in the family of soluble epoxide hydrolases—representatives of numerous α/β-hydrolase fold enzymes. As a result two types of tunnels evolution analysis were proposed (a general and a detailed approach), as well as a ‘perforation’ mechanism which can mimic native evolution in proteins and can be used as an additional strategy for enzymes redesign.

Human variation in the protein receptor ACE2 affects its binding affinity to SARS-CoV-2 in a variant-dependent manner

SARS-CoV-2 infection depend on the binding of the viral Spike glycoprotein (S) to the human receptor Angiotensin Converting Enzyme 2 (ACE2) to induce virus-cell membrane fusion. S protein evolved diverse amino acid changes that are possibly linked to more efficient binding to human ACE2, which might explain part of the increase in frequency of SARS-CoV-2 Variants Of Concern (VOCs). In this work, we investigated the role of ACE2 protein variations that are naturally found in human populations and its binding affinity with S protein from SARS-CoV-2 representative genotypes, based on a series of in silico approaches involving molecular modelling, docking and molecular dynamics simulations. Our results indicate that SARS-CoV-2 VOCs bind more efficiently to the human receptor ACE2 than the ancestral Wuhan genotype. Additionally, variations in the ACE2 protein can affect SARS-CoV-2 binding and protein-protein stability, mostly making the interaction weaker and unstable in some cases. We show that some VOCs, such as B.1.1.7 and P.1 are much less sensitive to ACE2 variants, while others like B.1.351 appear to be specifically optimized to bind to the widespread wild-type ACE2 protein.Communicated by Ramaswamy H. Sarma.

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.

Influence of Disease-Causing Mutations on Protein Structural Networks

The interactions between residues in a protein tertiary structure can be studied effectively using the approach of protein structure network (PSN). A PSN is a node-edge representation of the structure with nodes representing residues and interactions between residues represented by edges. In this study, we have employed weighted PSNs to understand the influence of disease-causing mutations on proteins of known 3D structures. We have used manually curated information on disease mutations from UniProtKB/Swiss-Prot and their corresponding protein structures of wildtype and disease variant from the protein data bank. The PSNs of the wildtype and disease-causing mutant are compared to analyse variation of global and local dissimilarity in the overall network and at specific sites. We study how a mutation at a given site can affect the structural network at a distant site which may be involved in the function of the protein. We have discussed specific examples of the disease cases where the protein structure undergoes limited structural divergence in their backbone but have large dissimilarity in their all atom networks and vice versa, wherein large conformational alterations are observed while retaining overall network. We analyse the effect of variation of network parameters that characterize alteration of function or stability.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

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.

Congenital myasthenic syndrome due to mutations in MUSK suggests that the level of MuSK phosphorylation is crucial for governing synaptic structure

MUSK encodes the muscle‐specific receptor tyrosine kinase (MuSK), a key component of the agrin‐LRP4‐MuSK‐DOK7 signaling pathway, which is essential for the formation and maintenance of highly specialized synapses between motor neurons and muscle fibers. We report a patient with severe early‐onset congenital myasthenic syndrome and two novel missense mutations in MUSK (p.C317R and p.A617V). Functional studies show that MUSK p.C317R, located at the frizzled‐like cysteine‐rich domain of MuSK, disrupts an integral part of MuSK architecture resulting in ablated MuSK phosphorylation and acetylcholine receptor (AChR) cluster formation. MUSK p.A617V, located at the kinase domain of MuSK, enhances MuSK phosphorylation resulting in anomalous AChR cluster formation. The identification and evidence for pathogenicity of MUSK mutations supported the initiation of treatment with β2‐adrenergic agonists with a dramatic improvement of muscle strength in the patient. This work suggests uncharacterized mechanisms in which control of the precise level of MuSK phosphorylation is crucial in governing synaptic structure.

The importance of the quaternary structure to represent conformational ensembles of the major Mycobacterium tuberculosis drug target

Flexibility is a feature intimately related to protein function, since conformational changes can be used to describe environmental changes, chemical modifications, protein-protein and protein-ligand interactions. In this study, we have investigated the influence of the quaternary structure of 2-trans-enoyl-ACP (CoA) reductase or InhA, from Mycobacterium tuberculosis, to its flexibility. We carried out classical molecular dynamics simulations using monomeric and tetrameric forms to elucidate the enzyme's flexibility. Overall, we observed statistically significant differences between conformational ensembles of tertiary and quaternary structures. In addition, the enzyme's binding site is the most affected region, reinforcing the importance of the quaternary structure to evaluate the binding affinity of small molecules, as well as the effect of single point mutations to InhA protein dynamics.

Molecular insight into conformational transformation of human glucokinase: conventional and targeted molecular dynamics

Glucokinase (GK) plays a key role in the regulation of hepatic glucose metabolism. An unusual mechanism of positive cooperativity of monomeric GK containing only a single binding site for glucose is very interesting and still unclear. The activation process of GK is associated with a large-scale conformational change from the inactive to the active state. Here, conventional and targeted molecular dynamics simulations were used to study the conformational dynamics of GK in the stable configurations and in the transition from active to inactive state. Three phases of the structural reorganization of GK were detected. The first step is a transformation of GK from the active state to the intermediate structure, where the cleft between the domains is open, but alpha helix 13 is still inside the small domain. From this point, there are two alternative paths. One path leads to the inactive state through the release of helix 13 from the inside of small domain to the outside. Other path goes back to the active state. Simulation results reveal the critical role of helix 13 in the transformation of GK from the open state to inactive one and the influence of the loop 2 on the protein transformation between the open and the closed active states. Principal component analysis and covariance matrix analysis were carried out to analyze the dynamics of protein. Importance of hydrogen bonds in the stability of the closed conformation is shown. Overall, our simulations provide new information about the dynamics of GK and its structural transformation.Communicated by Ramaswamy H. Sarma.

Association of BUD13-ZNF259-APOA5-APOA1-SIK3 cluster polymorphism in 11q23.3 and structure of APOA5 with increased plasma triglyceride levels in a Korean population

In this association study on chromosome 11, the data from 12,537 Korean individuals within the Health Examinee (HEXA) and the Korea Association Resource (KARE) projects were analysed to identify genetic loci correlating with increased and decreased plasma triglyceride (TG) levels. We identified a locus in chromosomal region 11q23.3 that harbours genes BUD13, ZNF259, APOA5, APOA1, and SIK3, which may be associated with plasma TG levels. In this locus, 13 relevant single-nucleotide polymorphisms (SNPs) were found: rs184616707, rs118175510, rs60954647, rs79408961, and rs180373 (near BUD13); rs11604424 (in ZNF259); rs2075291, rs651821, and rs7123666 (in or near APOA5); rs525028 (near APOA1), and rs645258, rs10160754, and rs142395187 (in or near SIK3). All 13 SNPs satisfied the genome-wide significance level (P < 5.0 × 10^-8) in both meta-analysis and conditional analysis. Haplotype analysis of six SNPs (rs79408961, rs180373, rs2075291, rs651821, rs525028, and rs10160754) that were selected based on the β coefficient and conditional P values, revealed nine common haplotypes (with frequency 0.02-0.34) associated with both increased and reduced TG levels. Furthermore, to shed light on possible structural implications, we modelled and simulated the G185C variant of APOA5 (corresponding to rs2075291), which showed the strongest association. Molecular dynamics simulation results showed that this polymorphic variant of APOA5 has a different hydrogen bond network, increased average distance between chains, and an ability to form distinct clusters. Owing to the orientation of cysteine, the possibility of disulphide bond formation with other proteins is evident. In summary, our association and modelling analyses provided evidence that genetic variations in chromosomal region 11q23.3 are associated with elevated TG levels.

Quantifying correlations between mutational sites in the catalytic subunit of γ-secretase

Presenilin 1 (PS1) is the catalytic subunit of the γ-secretase complex which is involved in the generation of amyloid-β peptides (Aβ). Single point mutations in PS1 alter the cleavage pattern of the amyloid precursor protein (APP) and lead to the formation of aberrant Aβ peptides. To date, more than two hundred mutations distributed among almost a third of PS1's amino acids have been associated to the development of Alzheimer's disease (AD). Nevertheless, the mechanism by which mutations far from the catalytic site alter the γ-secretase's cleavage pattern remains unclear. In this work we analyzed correlated motions between amino acids in the wild type (WT) enzyme and 13 γ-secretase mutant models employing a multi-scale molecular dynamics approach. The effect of the protonation state of key catalytic residue Asp385 on the correlation networks was also evaluated. We observed that the strength and number of correlations is highly influenced in all mutant models in both protonation state models. The biggest changes were observed in mutants I83T, W165G, H214Y and L435F; the latest has been proved to drastically reduce γ-secretase activity. Finally, we made a classification of the studied mutations according to their correlation networks with amino acids at: (1) the interfaces with the other γ-secretase components, (2) the catalytic site, (3) the substrate entry site and (4) the substrate recognition site. Overall, this work provides insight into the allosteric communication networks of PS1.

Dissecting a novel allosteric mechanism of cruzain: A computer-aided approach

Trypanosoma cruzi is the causative agent of Chagas disease, a neglected infection affecting millions of people in tropical regions. There are several chemotherapeutic agents for the treatment of this disease, but most of them are highly toxic and generate resistance. Currently, the development of allosteric inhibitors constitutes a promising research field, since it can improve the accessibility to more selective and less toxic medicines. To date, the allosteric drugs prediction is a state-of-the-art topic in rational structure-based computational design. In this work, a simulation strategy was developed for computational discovery of allosteric inhibitors, and it was applied to cruzain, a promising target and the major cysteine protease of T. cruzi. Molecular dynamics simulations, binding free energy calculations and network-based modelling of residue interactions were combined to characterize and compare molecular distinctive features of the apo form and the cruzain-allosteric inhibitor complexes. By using geometry-based criteria on trajectory snapshots, we predicted two main allosteric sites suitable for drug targeting. The results suggest dissimilar mechanisms exerted by the same allosteric site when binding different potential allosteric inhibitors. Finally, we identified the residues involved in suboptimal paths linking the identified site and the orthosteric site. The present study constitutes the first approximation to the design of cruzain allosteric inhibitors and may serve for future pharmacological intervention. Here, no major effects on active site structure were observed due to compound binding (modification of distance and angles between catalytic residues), which indicates that allosteric regulation in cruzain might be mediated via alterations of its dynamical properties similarly to allosteric inhibition of human cathepsin K (HCatK). The current findings are particularly relevant for the design of allosteric modulators of papain-like cysteine proteases.

C1q and Mannose-Binding Lectin Interact with CR1 in the Same Region on CCP24-25 Modules

Complement receptor type 1 (CR1) is a multi modular membrane receptor composed of 30 homologous complement control protein modules (CCP) organized in four different functional regions called long homologous repeats (LHR A, B, C, and D). CR1 is a receptor for complement-opsonins C3b and C4b and specifically interacts through pairs of CCP modules located in LHR A, B, and C. Defense collagens such as mannose-binding lectin (MBL), ficolin-2, and C1q also act as opsonins and are involved in immune clearance through binding to the LHR-D region of CR1. Our previous results using deletion variants of CR1 mapped the interaction site for MBL and ficolin-2 on CCP24-25. The present work aimed at deciphering the interaction of C1q with CR1 using new CR1 variants concentrated around CCP24-25. CR1 bimodular fragment CCP24-25 and CR1 CCP22-30 deleted from CCP24-25 produced in eukaryotic cells enabled to highlight that the interaction site for both MBL and C1q is located on the same pair of modules CCP24-25. C1q binding to CR1 shares with MBL a main common interaction site on the collagen stalks but also subsidiary sites most probably located on C1q globular heads, contrarily to MBL.

Quantifying Allosteric Communication via Both Concerted Structural Changes and Conformational Disorder with CARDS

Allosteric (i.e., long-range) communication within proteins is crucial for many biological processes, such as the activation of signaling cascades in response to specific stimuli. However, the physical basis for this communication remains unclear. Existing computational methods for identifying allostery focus on the role of concerted structural changes, but recent experimental work demonstrates that disorder is also an important factor. Here, we introduce the Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a protein. To quantify disorder, we draw inspiration from methods for quantifying "dynamic heterogeneity" from chemical physics to classify segments of a dihedral's time evolution as being in either ordered or disordered regimes. To demonstrate the utility of this approach, we apply CARDS to the Catabolite Activator Protein (CAP), a transcriptional activator that is regulated by Cyclic Adenosine MonoPhosphate (cAMP) binding. We find that CARDS captures allosteric communication between the two cAMP-Binding Domains (CBDs). Importantly, CARDS reveals that this coupling is dominated by disorder-mediated correlations, consistent with NMR experiments that establish allosteric coupling between the CBDs occurs without a concerted structural change. CARDS also recapitulates an enhanced role for disorder in the communication between the DNA-Binding Domains (DBDs) and CBDs in the S62F variant of CAP. Finally, we demonstrate that using CARDS to find communication hotspots identifies regions of CAP that are in allosteric communication without foreknowledge of their identities. Therefore, we expect CARDS to be of great utility for both understanding and predicting allostery.

Classifying kinase conformations using a machine learning approach

Background

Signaling proteins such as protein kinases adopt a diverse array of conformations to respond to regulatory signals in signaling pathways. Perhaps the most fundamental conformational change of a kinase is the transition between active and inactive states, and defining the conformational features associated with kinase activation is critical for selectively targeting abnormally regulated kinases in diseases. While manual examination of crystal structures have led to the identification of key structural features associated with kinase activation, the large number of kinase crystal structures (~3,500) and extensive conformational diversity displayed by the protein kinase superfamily poses unique challenges in fully defining the conformational features associated with kinase activation. Although some computational approaches have been proposed, they are typically based on a small subset of crystal structures using measurements biased towards the active site geometry.

Results

We utilize an unbiased informatics based machine learning approach to classify all eukaryotic protein kinase conformations deposited in the PDB. We show that the orientation of the activation segment, measured by φ, ψ, χ1, and pseudo-dihedral angles more accurately classify kinase crystal conformations than existing methods. We show that the formation of the K-E salt bridge is statistically dependent upon the activation segment orientation and identify evolutionary differences between the activation segment conformation of tyrosine and serine/threonine kinases. We provide evidence that our method can identify conformational changes associated with the binding of allosteric regulatory proteins, and show that the greatest variation in inactive structures comes from kinase group and family specific side chain orientations.

Conclusion

We have provided the first comprehensive machine learning based classification of protein kinase active/inactive conformations, taking into account more structures and measurements than any previous classification effort. Further, our unbiased classification of inactive structures reveals residues associated with kinase functional specificity. To enable classification of new crystal structures, we have made our classifier publicly accessible through a stand-alone program housed at https://github.com/esbg/kinconform [DOI:10.5281/zenodo.249090].

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1506-2) contains supplementary material, which is available to authorized users.

Nucleoside Analogue Triphosphates Allosterically Regulate Human Ribonucleotide Reductase and Identify Chemical Determinants That Drive Substrate Specificity

Class I ribonucleotide reductase (RR) maintains balanced pools of deoxyribonucleotide substrates for DNA replication by converting ribonucleoside diphosphates (NDPs) to 2'-deoxyribonucleoside diphosphates (dNDPs). Binding of deoxynucleoside triphosphate (dNTP) effectors (ATP/dATP, dGTP, and dTTP) modulates the specificity of class I RR for CDP, UDP, ADP, and GDP substrates. Crystal structures of bacterial and eukaryotic RRs show that dNTP effectors and NDP substrates bind on either side of a flexible nine-amino acid loop (loop 2). Interactions with the effector nucleobase alter loop 2 geometry, resulting in changes in specificity among the four NDP substrates of RR. However, the functional groups proposed to drive specificity remain untested. Here, we use deoxynucleoside analogue triphosphates to determine the nucleobase functional groups that drive human RR (hRR) specificity. The results demonstrate that the 5-methyl, O4, and N3 groups of dTTP contribute to specificity for GDP. The O6 and protonated N1 of dGTP direct specificity for ADP. In contrast, the unprotonated N1 of adenosine is the primary determinant of ATP/dATP-directed specificity for CDP. Structural models from X-ray crystallography of eukaryotic RR suggest that the side chain of D287 in loop 2 is involved in binding of dGTP and dTTP, but not dATP/ATP. This feature is consistent with experimental results showing that a D287A mutant of hRR is deficient in allosteric regulation by dGTP and dTTP, but not ATP/dATP. Together, these data define the effector functional groups that are the drivers of human RR specificity and provide constraints for evaluating models of allosteric regulation.

The impact of heterogeneity in phosphoinositide 3-kinase pathway in human cancer and possible therapeutic treatments

Phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) plays a crucial role in the initiation and progress of cancerous tumors through the overexpression of the PI3K pathway promoting uncontrollable levels of cell proliferation. In addition only Class I PI3K has been discovered to be involved in human cancer due to its unique ability to produce phosphoinositide 3,4,5 trisphosphate (PIP3), which has been discovered to play a crucial role in human oncogenesis. The role of PIK3CA is lucubrated in breast cancer and gastric cancer, but is not well characterized in lung diseases. In this review, we summarized the common biology and mutations in PIK3CA with its related signaling pathways. Furthermore, we elucidated the PIK3CA heterogeneity in different domains, between various cancers and in different lung cancers. We also take a look at current inhibitors such as KP-372-1 (KP-1), KP-372-2 (KP-2), GSK690693, etc. in order to highlight potential treatment of PIK3CA mutations in human cancer and what directions future research should focus on.

Allosteric sites can be identified based on the residue-residue interaction energy difference

Allosteric drugs act at a distance to regulate protein functions. They have several advantages over conventional orthosteric drugs, including diverse regulation types and fewer side effects. However, the rational design of allosteric ligands remains a challenge, especially when it comes to the identification allosteric binding sites. As the binding of allosteric ligands may induce changes in the pattern of residue-residue interactions, we calculated the residue-residue interaction energies within the allosteric site based on the molecular mechanics generalized Born surface area energy decomposition scheme. Using a dataset of 17 allosteric proteins with structural data for both the apo and the ligand-bound state available, we used conformational ensembles generated by molecular dynamics simulations to compute the differences in the residue-residue interaction energies in known allosteric sites from both states. For all the known sites, distinct interaction energy differences (>25%) were observed. We then used CAVITY, a binding site detection program to identify novel putative allosteric sites in the same proteins. This yielded a total of 31 "druggable binding sites," of which 21 exhibited >25% difference in residue interaction energies, and were hence predicted as novel allosteric sites. Three of the predicted allosteric sites were supported by recent experimental studies. All the predicted sites may serve as novel allosteric sites for allosteric ligand design. Our study provides a computational method for identifying novel allosteric sites for allosteric drug design.

Rapid Characterization of Allosteric Networks with Ensemble Normal Mode Analysis

Allosteric regulation is a primary means of controlling protein function. By definition, allostery involves the propagation of structural dynamic changes between distal protein sites that yields a functional change. Gaining improved knowledge of these fundamental mechanisms is important for understanding many biomolecular processes and for guiding protein engineering and drug design efforts. In this work we compare and contrast a range of normal mode analysis (NMA) approaches together with network analysis for the prediction of structural dynamics and allosteric sites. Application to heterotrimeric G proteins, hemoglobin, and caspase 7 indicates that atomistic elastic network models provide improved predictions of experimental allosteric mutation sites. Results for G proteins also display an improved consistency with those derived from more computationally demanding MD simulations. Application of this approach across available experimental structures for a given protein family in a unified manner, that we refer to as ensemble NMA, yields the best overall predictive performance. We propose that this atomistic ensemble NMA approach represents an efficient and powerful tool for guiding the exploration of coupled motions and allosteric mechanisms in cases where multiple structures are available and where MD may prove prohibitively expensive.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.

Allosteric Dynamic Control of Binding

Proteins have a highly dynamic nature and there is a complex interrelation between their structural dynamics and binding behavior. By assuming various conformational ensembles, they perform both local and global fluctuations to interact with other proteins in a dynamic infrastructure adapted to functional motion. Here, we show that there is a significant association between allosteric mutations, which lead to high-binding-affinity changes, and the hinge positions of global modes, as revealed by a large-scale statistical analysis of data in the Structural Kinetic and Energetic Database of Mutant Protein Interactions (SKEMPI). We further examined the mechanism of allosteric dynamics by conducting studies on human growth hormone (hGH) and pyrin domain (PYD), and the results show how mutations at the hinge regions could allosterically affect the binding-site dynamics or induce alternative binding modes by modifying the ensemble of accessible conformations. The long-range dissemination of perturbations in local chemistry or physical interactions through an impact on global dynamics can restore the allosteric dynamics. Our findings suggest a mechanism for the coupling of structural dynamics to the modulation of protein interactions, which remains a critical phenomenon in understanding the effect of mutations that lead to functional changes in proteins.

Oncogenic mutations weaken the interactions that stabilize the p110α-p85α heterodimer in phosphatidylinositol 3-kinase α

Phosphatidylinositol 3-kinase (PI3K) α is a heterodimeric lipid kinase that catalyzes the conversion of phosphoinositol-4,5-bisphosphate to phosphoinositol-3,4,5-trisphosphate. The PI3Kα signaling pathway plays an important role in cell growth, proliferation, and survival. This pathway is activated in numerous cancers, where the PI3KCA gene, which encodes for the p110α PI3Kα subunit, is mutated. Its mutation often results in gain of enzymatic activity; however, the mechanism of activation by oncogenic mutations remains unknown. Here, using computational methods, we show that oncogenic mutations that are far from the catalytic site and increase the enzymatic affinity destabilize the p110α-p85α dimer. By affecting the dynamics of the protein, these mutations favor the conformations that reduce the autoinhibitory effect of the p85α nSH2 domain. For example, we determined that, in all of the mutants, the nSH2 domain shows increased positional heterogeneity as compared with the wild-type, as demonstrated by changes in the fluctuation profiles computed by normal mode analysis of coarse-grained elastic network models. Analysis of the interdomain interactions of the wild-type and mutants at the p110α-p85α interface obtained with molecular dynamics simulations suggest that all of the tumor-associated mutations effectively weaken the interactions between p110α and p85α by disrupting key stabilizing interactions. These findings have important implications for understanding how oncogenic mutations change the conformational multiplicity of PI3Kα and lead to increased enzymatic activity. This mechanism may apply to other enzymes and/or macromolecular complexes that play a key role in cell signaling.

Biophysics of protein evolution and evolutionary protein biophysics

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

Allosteric activation of Bordetella pertussis adenylyl cyclase by calmodulin: molecular dynamics and mutagenesis studies

Adenylyl cyclase (AC) toxin is an essential toxin that allows Bordetella pertussis to invade eukaryotic cells, where it is activated after binding to calmodulin (CaM). Based on the crystal structure of the AC catalytic domain in complex with the C-terminal half of CaM (C-CaM), our previous molecular dynamics simulations (Selwa, E., Laine, E., and Malliavin, T. (2012) Differential role of calmodulin and calcium ions in the stabilization of the catalytic domain of adenyl cyclase CyaA from Bordetella pertussis. Proteins 80, 1028–1040) suggested that three residues (i.e. Arg(338), Asn(347), and Asp(360)) might be important for stabilizing the AC/CaM interaction. These residues belong to a loop-helix-loop motif at the C-terminal end of AC, which is located at the interface between CaM and the AC catalytic loop. In the present study, we conducted the in silico and in vitro characterization of three AC variants, where one (Asn(347); ACm1A), two (Arg(338) and Asp(360); ACm2A), or three residues (Arg(338), Asn(347), and Asp(360); ACm3A) were substituted with Ala. Biochemical studies showed that the affinities of ACm1A and ACm2A for CaM were not affected significantly, whereas that of ACm3A was reduced dramatically. To understand the effects of these modifications, molecular dynamics simulations were performed based on the modified proteins. The molecular dynamics trajectories recorded for the ACm3AC-CaM complex showed that the calcium-binding loops of C-CaM exhibited large fluctuations, which could be related to the weakened interaction between ACm3A and its activator. Overall, our results suggest that the loop-helix-loop motif at the C-terminal end of AC is crucial during CaM binding for stabilizing the AC catalytic loop in an active configuration.

Allostery without a conformational change? Revisiting the paradigm

Classically, allostery induces a functional switch through a conformational change. However, lately an increasing number of studies concluded that the allostery they observe takes place through sheer dynamics. Here we explain that even if a structural comparison between the active and inactive states does not detect a conformational change, it does not mean that there is no conformational change. We list likely reasons for this lack of observation, including crystallization conditions and crystal effects; one of the states is disordered; the structural comparisons disregard the quaternary protein structure; overlooking synergy effects among allosteric effectors and graded incremental switches and too short molecular dynamics simulations. Specific functions are performed by distinct conformations; they emerge through specific interactions between conformationally selected states.

Novel RANK antagonists for the treatment of bone-resorptive disease: theoretical predictions and experimental validation

Receptor activator of nuclear factor-κB (RANK) and RANK ligand (RANKL) play a pivotal role in bone metabolism, and selective targeting of RANK signaling has become a promising therapeutic strategy in the management of resorptive bone diseases. Existing antibody-based therapies and novel inhibitors currently in development were designed to target the ligand, rather than the membrane receptor expressed on osteoclast precursors. We describe here an alternative approach to designing small peptides able to specifically bind to the hinge region of membrane RANK responsible for the conformational change upon RANKL association. A nonapeptide generated by this method was validated for its biological activity in vitro and in vivo and served as a lead compound for the generation of a series of peptide RANK antagonists derived from the original sequence. Our study presents a structure- and knowledge-based strategy for the design of novel effective and affordable small peptide inhibitors specifically targeting the receptor RANK and opens a new therapeutic opportunity for the treatment of resorptive bone disease.

The ensemble nature of allostery

Allostery is the process by which biological macromolecules (mostly proteins) transmit the effect of binding at one site to another, often distal, functional site, allowing for regulation of activity. Recent experimental observations demonstrating that allostery can be facilitated by dynamic and intrinsically disordered proteins have resulted in a new paradigm for understanding allosteric mechanisms, which focuses on the conformational ensemble and the statistical nature of the interactions responsible for the transmission of information. Analysis of allosteric ensembles reveals a rich spectrum of regulatory strategies, as well as a framework to unify the description of allosteric mechanisms from different systems.

Investigating models of protein function and allostery with a widespread mutational analysis of a light-activated protein

To investigate the relationship between a protein's sequence and its biophysical properties, we studied the effects of more than 100 mutations in Avena sativa light-oxygen-voltage domain 2, a model protein of the Per-Arnt-Sim family. The A. sativa light-oxygen-voltage domain 2 undergoes a photocycle with a conformational change involving the unfolding of the terminal helices. Whereas selection studies typically search for winners in a large population and fail to characterize many sites, we characterized the biophysical consequences of mutations throughout the protein using NMR, circular dichroism, and ultraviolet/visible spectroscopy. Despite our intention to introduce highly disruptive substitutions, most had modest or no effect on function, and many could even be considered to be more photoactive. Substitutions at evolutionarily conserved sites can have minimal effect, whereas those at nonconserved positions can have large effects, contrary to the view that the effects of mutations, especially at conserved positions, are predictable. Using predictive models, we found that the effects of mutations on biophysical function and allostery reflect a complex mixture of multiple characteristics including location, character, electrostatics, and chemistry.

Computational approaches to mapping allosteric pathways

Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.

Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity

The crucial prerequisite for proper biological function is the protein's ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein-protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein-protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol^-1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.

A unified view of "how allostery works"

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

ModBase, a database of annotated comparative protein structure models and associated resources

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains almost 30 million reliable models for domains in 4.7 million unique protein sequences. ModBase allows users to compute or update comparative models on demand, through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the AllosMod server for modeling ligand-induced protein dynamics (http://salilab.org/allosmod), the AllosMod-FoXS server for predicting a structural ensemble that fits an SAXS profile (http://salilab.org/allosmod-foxs), the FoXSDock server for protein–protein docking filtered by an SAXS profile (http://salilab.org/foxsdock), the SAXS Merge server for automatic merging of SAXS profiles (http://salilab.org/saxsmerge) and the Pose & Rank server for scoring protein–ligand complexes (http://salilab.org/poseandrank). In this update, we also highlight two applications of ModBase: a PSI:Biology initiative to maximize the structural coverage of the human alpha-helical transmembrane proteome and a determination of structural determinants of human immunodeficiency virus-1 protease specificity.

Mapping polymerization and allostery of hemoglobin S using point mutations

Hemoglobin is a complex system that undergoes conformational changes in response to oxygen, allosteric effectors, mutations, and environmental changes. Here, we study allostery and polymerization of hemoglobin and its variants by application of two previously described methods: (i) AllosMod for simulating allostery dynamics given two allosterically related input structures and (ii) a machine-learning method for dynamics- and structure-based prediction of the mutation impact on allostery (Weinkam et al. J. Mol. Biol. 2013, 425, 647-661), now applicable to systems with multiple coupled binding sites, such as hemoglobin. First, we predict the relative stabilities of substates and microstates of hemoglobin, which are determined primarily by entropy within our model. Next, we predict the impact of 866 annotated mutations on hemoglobin's oxygen binding equilibrium. We then discuss a subset of 30 mutations that occur in the presence of the sickle cell mutation and whose effects on polymerization have been measured. Seven of these HbS mutations occur in three predicted druggable binding pockets that might be exploited to directly inhibit polymerization; one of these binding pockets is not apparent in the crystal structure, but only in structures generated by AllosMod. For the 30 mutations, we predict that mutation-induced conformational changes within a single tetramer tend not to significantly impact polymerization; instead, these mutations more likely impact polymerization by directly perturbing a polymerization interface. Finally, our analysis of allostery allows us to hypothesize why hemoglobin evolved to have multiple subunits and a persistent low frequency sickle cell mutation.

Thermodynamics of allostery paves a way to allosteric drugs

We overview here our recent work on the thermodynamic view of allosteric regulation and communication. Starting from the geometry-based prediction of regulatory binding sites in a static structure, we move on to exploring a connection between ligand binding and the intrinsic dynamics of the protein molecule. We describe here two recently introduced measures, binding leverage and leverage coupling, which allow one to analyze the molecular basis of allosteric regulation. We discuss the advantages of these measures and show that they work universally in proteins of different sizes, oligomeric states, and functions. We also point the problems that have to be solved before completing an atomic level description of allostery, and briefly discuss ideas for computational design of allosteric drugs. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.