Computational approaches to mapping allosteric pathways

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data. The software integrates well-known techniques for understanding allosteric properties simplifying the process of accessing, rationalising, and representing protein allostery and communication routes. It overcomes the inefficiency of having multiple methods with heterogeneous implementations and showcases the advantages of using MD simulations and multiple replicas to obtain statistically sound information on protein dynamics; it also enables the calculation of "consensus-like" scores aggregating methods that consider multiple structural aspects of allosteric networks. We demonstrate the features of AlloViz on two proteins: β-arrestin 1, a key player for regulating G protein-coupled receptor (GPCR) signalling, and the protein tyrosine phosphatase 1B, an important pharmaceutical target for allosteric inhibitors. The software includes comprehensive documentation and examples, tutorials, and a user-friendly graphical interface.

G Protein Activation Occurs via a Largely Universal Mechanism

Understanding how signaling proteins like G proteins are allosterically activated is a long-standing challenge with significant biological and medical implications. Because it is difficult to directly observe such dynamic processes, much of our understanding is based on inferences from a limited number of static snapshots of relevant protein structures, mutagenesis data, and patterns of sequence conservation. Here, we use computer simulations to directly interrogate allosteric coupling in six G protein α-subunit isoforms covering all four G protein families. To analyze this data, we introduce automated methods for inferring allosteric networks from simulation data and assessing how allostery is conserved or diverged among related protein isoforms. We find that the allosteric networks in these six G protein α subunits are largely conserved and consist of two pathways, which we call pathway-I and pathway-II. This analysis predicts that pathway-I is generally dominant over pathway-II, which we experimentally corroborate by showing that mutations to pathway-I perturb nucleotide exchange more than mutations to pathway-II. In the future, insights into unique elements of each G protein family could inform the design of isoform-specific drugs. More broadly, our tools should also be useful for studying allostery in other proteins and assessing the extent to which this allostery is conserved in related proteins.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.

Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR

Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.

Disease mutations and phosphorylation alter the allosteric pathways involved in autoinhibition of protein phosphatase 2A

Mutations in protein phosphatase 2A (PP2A) are connected to intellectual disability and cancer. It has been hypothesized that these mutations might disrupt the autoinhibition and phosphorylation-induced activation of PP2A. Since they are located far from both the active and substrate binding sites, it is unclear how they exert their effect. We performed allosteric pathway analysis based on molecular dynamics simulations and combined it with biochemical experiments to investigate the autoinhibition of PP2A. In the wild type (WT), the C-arm of the regulatory subunit B56δ obstructs the active and substrate binding sites exerting a dual autoinhibition effect. We find that the disease mutant, E198K, severely weakens the allosteric pathways that stabilize the C-arm in the WT. Instead, the strongest allosteric pathways in E198K take a different route that promotes exposure of the substrate binding site. To facilitate the allosteric pathway analysis, we introduce a path clustering algorithm for lumping pathways into channels. We reveal remarkable similarities between the allosteric channels of E198K and those in phosphorylation-activated WT, suggesting that the autoinhibition can be alleviated through a conserved mechanism. In contrast, we find that another disease mutant, E200K, which is in spatial proximity of E198, does not repartition the allosteric pathways leading to the substrate binding site; however, it may still induce exposure of the active site. This finding agrees with our biochemical data, allowing us to predict the activity of PP2A with the phosphorylated B56δ and provide insight into how disease mutations in spatial proximity alter the enzymatic activity in surprisingly different mechanisms.

The influence of N-alkyl chains in benzoyl-thiourea derivatives on urease inhibition: Soil studies and biophysical and theoretical investigations on the mechanism of interaction

Ureases are enzymes produced by fungi, plants, and bacteria associated with agricultural and clinical problems. The urea hydrolysis in NH₃ and CO₂ leads to the loss of N-urea fertilizers in soils and changes the human stomach microenvironment, favoring the colonization of H. pylori. In this sense, it is necessary to evaluate potential enzyme inhibitors to mitigate the effects of their activities and respond to scientific and market demands to produce fertilizers with enhanced efficiency. Thus, biophysical and theoretical studies were carried out to evaluate the influence of the N-alkyl chain in benzoyl-thiourea derivatives on urease enzyme inhibition. A screening based on IC₅₀, binding constants, and theoretical studies demonstrated that BTU1 without the N-alkyl chain (R = H) was more active than other compounds, so the magnitude of the interaction was determined as BTU1 > BTU2 > BTU3 > BTU4 > BTU5, corresponding to progressively increased chain length. Thus, BTU1 was selected for interaction and soil application essays. The binding constants (K_b) for the supramolecular urease-BTU1 complex ranged from 7.95 to 5.71 × 10³ M^-1 at different temperatures (22, 30, and 38 °C), indicating that the preferential forces responsible for the stabilization of the complex are hydrogen bonds and van der Waals forces (ΔH = -15.84 kJ mol^-1 and ΔS = -36.61 J mol^-1 K^-1). Theoretical and experimental results (thermodynamics, synchronous fluorescence, and competition assay) agree and indicate that BTU1 is a mixed inhibitor. Finally, urease inhibition was evaluated in the four soil samples, where BTU1 was as efficient as NBPT (based on ANOVA two-way and Tukey test with 95% confidence), with an average inhibition of 20% of urease activity. Thus, the biophysics and theoretical studies are strategies for evaluating potential inhibitors and showed that increasing the N-alkyl chain in benzoyl-thiourea derivatives did not favor urease inhibition.

Turning up the heat mimics allosteric signaling in imidazole-glycerol phosphate synthase

Allosteric drugs have the potential to revolutionize biomedicine due to their enhanced selectivity and protection against overdosage. However, we need to better understand allosteric mechanisms in order to fully harness their potential in drug discovery. In this study, molecular dynamics simulations and nuclear magnetic resonance spectroscopy are used to investigate how increases in temperature affect allostery in imidazole glycerol phosphate synthase. Results demonstrate that temperature increase triggers a cascade of local amino acid-to-amino acid dynamics that remarkably resembles the allosteric activation that takes place upon effector binding. The differences in the allosteric response elicited by temperature increase as opposed to effector binding are conditional to the alterations of collective motions induced by either mode of activation. This work provides an atomistic picture of temperature-dependent allostery, which could be harnessed to more precisely control enzyme function.

Identification of an Evolutionarily Conserved Allosteric Network in Steroid Receptors

Allosteric pathways in proteins describe networks comprising amino acid residues which may facilitate the propagation of signals between distant sites. Through inter-residue interactions, dynamic and conformational changes can be transmitted from the site of perturbation to an allosteric site. While sophisticated computational methods have been developed to characterize such allosteric pathways linking specific sites on proteins, few attempts have been made to apply these approaches toward identifying new allosteric sites. Here, we use molecular dynamics simulations and suboptimal path analysis to discover new allosteric networks in steroid receptors with a focus on evolutionarily conserved pathways. Using modern receptors and a reconstructed ancestral receptor, we identify networks connecting several sites to the activation function surface 2 (AF-2), the site of coregulator recruitment. One of these networks is conserved across the entire family, connecting a predicted allosteric site located between helices 9 and 10 of the ligand-binding domain. We investigate the basis of this conserved network as well as the importance of this site, discovering that the site lies in a region of the ligand-binding domain characterized by conserved inter-residue contacts. This study suggests an evolutionarily importance of the helix 9-helix 10 site in steroid receptors and identifies an approach that may be applied to discover previously unknown allosteric sites in proteins.

Dynamics and Allosteric Information Pathways of Unphosphorylated c-Cbl

Human c-Cbl is a RING-type ligase and plays a central role in the protein degradation cascade. To elucidate its conformational changes related to substrate binding, we performed molecular dynamics simulations of different variants/states of c-Cbl for a cumulative time of 68 μs. Our simulations demonstrate that before the substrate binds, the RING domain samples a broad set of conformational states at a biologically relevant salt concentration, including the closed, partially open, and fully open states, whereas substrate binding leads to a restricted conformational sampling. Phe378 and the C-terminal region play an essential role in stabilizing the partially open state. To visualize the allosteric signal transmission pathways from the substrate-binding site to the 40 Å apart RING domain and identify the critical residues for allostery, we have created a subgraph from the optimal and suboptimal paths. Redundant paths are seen in the SH2 domain where the substrate binds, while the major bottlenecks are found at the junction between the SH2 domain and the linker helix region as well as that between the SH2 domain and the 4H bundle. These bottlenecks separate the paths into two overall routes. The nodes/residues at the bottlenecks on the subgraph are considered allosteric hot spots. This subgraph approach provides a general tool for network visualization and determination of critical residues for allostery. The structurally and allosterically critical residues identified in our work are testable and would provide valuable insights into the emerging strategies for drug discovery, such as targeted protein degradation.

Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs

Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.

Allosteric regulation of substrate channeling: Salmonella typhimurium tryptophan synthase

The regulation of the synthesis of L-tryptophan (L-Trp) in enteric bacteria begins at the level of gene expression where the cellular concentration of L-Trp tightly controls expression of the five enzymes of the Trp operon responsible for the synthesis of L-Trp. Two of these enzymes, trpA and trpB, form an αββα bienzyme complex, designated as tryptophan synthase (TS). TS carries out the last two enzymatic processes comprising the synthesis of L-Trp. The TS α-subunits catalyze the cleavage of 3-indole D-glyceraldehyde 3′-phosphate to indole and D-glyceraldehyde 3-phosphate; the pyridoxal phosphate-requiring β-subunits catalyze a nine-step reaction sequence to replace the L-Ser hydroxyl by indole giving L-Trp and a water molecule. Within αβ dimeric units of the αββα bienzyme complex, the common intermediate indole is channeled from the α site to the β site via an interconnecting 25 Å-long tunnel. The TS system provides an unusual example of allosteric control wherein the structures of the nine different covalent intermediates along the β-reaction catalytic path and substrate binding to the α-site provide the allosteric triggers for switching the αββα system between the open (T) and closed (R) allosteric states. This triggering provides a linkage that couples the allosteric conformational coordinate to the covalent chemical reaction coordinates at the α- and β-sites. This coupling drives the α- and β-sites between T and R conformations to achieve regulation of substrate binding and/or product release, modulation of the α- and β-site catalytic activities, prevention of indole escape from the confines of the active sites and the interconnecting tunnel, and synchronization of the α- and β-site catalytic activities. Here we review recent advances in the understanding of the relationships between structure, function, and allosteric regulation of the complex found in Salmonella typhimurium.

Sparse group selection and analysis of function-related residue for protein-state recognition

Machine learning methods have helped to advance wide range of scientific and technological field in recent years, including computational chemistry. As the chemical systems could become complex with high dimension, feature selection could be critical but challenging to develop reliable machine learning based prediction models, especially for proteins as bio-macromolecules. In this study, we applied sparse group lasso (SGL) method as a general feature selection method to develop classification model for an allosteric protein in different functional states. This results into a much improved model with comparable accuracy (Acc) and only 28 selected features comparing to 289 selected features from a previous study. The Acc achieves 91.50% with 1936 selected feature, which is far higher than that of baseline methods. In addition, grouping protein amino acids into secondary structures provides additional interpretability of the selected features. The selected features are verified as associated with key allosteric residues through comparison with both experimental and computational works about the model protein, and demonstrate the effectiveness and necessity of applying rigorous feature selection and evaluation methods on complex chemical systems.

Information Flow and Allosteric Communication in Proteins

Based on Schreiber's work on transfer entropy, a molecular theory of nonlinear information transfer in proteins is developed. The joint distribution function for residue fluctuations is expressed in terms of tensor Hermite polynomials which conveniently separate harmonic and nonlinear contributions to information transfer. The harmonic part of information transfer is expressed as the difference between time dependent and independent mutual information. Third order nonlinearities are discussed in detail. Amount and speed of information transfer between residues, important for understanding allosteric activity in proteins, are discussed. While mutual information shows the maximum amount of information that may be transferred between two residues, it does not explain the actual amount of transfer nor the transfer rate of information. For this, dynamic equations of the system are needed. The solution of the Langevin equation and molecular dynamics trajectories are used in the present work for this purpose. Allosteric communication in Human NAD-dependent isocitrate dehydrogenase is studied as an example. Calculations show that several paths contribute collectively to information transfer. Important residues on these paths are identified. Time resolved information transfer between these residues, their amplitudes and transfer rates, which are in agreement with time resolved ultraviolet resonance Raman measurements in general, are estimated. Estimated transfer rates are in the order of 1-20 megabits per second. Information transfer from third order contributions are one to two orders of magnitude smaller than the harmonic terms, showing that harmonic analysis is a good approximation to information transfer.

SenseNet, a tool for analysis of protein structure networks obtained from molecular dynamics simulations

Computational methods play a key role for investigating allosteric mechanisms in proteins, with the potential of generating valuable insights for innovative drug design. Here we present the SenseNet (“Structure ENSEmble NETworks”) framework for analysis of protein structure networks, which differs from established network models by focusing on interaction timelines obtained by molecular dynamics simulations. This approach is evaluated by predicting allosteric residues reported by NMR experiments in the PDZ2 domain of hPTP1e, a reference system for which previous computational predictions have shown considerable variance. We applied two models based on the mutual information between interaction timelines to estimate the conformational influence of each residue on its local environment. In terms of accuracy our prediction model is comparable to the top performing model published for this system, but by contrast benefits from its independence from NMR structures. Our results are complementary to experimental data and the consensus of previous predictions, demonstrating the potential of our new analysis tool SenseNet. Biochemical interpretation of our model suggests that allosteric residues in the PDZ2 domain form two distinct clusters of contiguous sidechain surfaces. SenseNet is provided as a plugin for the network analysis software Cytoscape, allowing for ease of future application and contributing to a system of compatible tools bridging the fields of system and structural biology.

Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells

SIRT6 belongs to the conserved NAD⁺-dependent deacetylase superfamily and mediates multiple biological and pathological processes. Targeting SIRT6 by allosteric modulators represents a novel direction for therapeutics, which can overcome the selectivity problem caused by the structural similarity of orthosteric sites among deacetylases. Here, developing a reversed allosteric strategy AlloReverse, we identified a cryptic allosteric site, Pocket Z, which was only induced by the bi-directional allosteric signal triggered upon orthosteric binding of NAD⁺. Based on Pocket Z, we discovered an SIRT6 allosteric inhibitor named JYQ-42. JYQ-42 selectively targets SIRT6 among other histone deacetylases and effectively inhibits SIRT6 deacetylation, with an IC₅₀ of 2.33 μmol/L. JYQ-42 significantly suppresses SIRT6-mediated cancer cell migration and pro-inflammatory cytokine production. JYQ-42, to our knowledge, is the most potent and selective allosteric SIRT6 inhibitor. This study provides a novel strategy for allosteric drug design and will help in the challenging development of therapeutic agents that can selectively bind SIRT6.

Pore formation by pore forming membrane proteins towards infections

Over the last 25 years, the biology of membrane proteins, including the PFPs-membranes interactions is seeking attention for the development of successful drug molecules against a number of infectious diseases. Pore forming toxins (PFTs), the largest family of PFPs are considered as a group of virulence factors produced in a large number of pathogenic systems which include streptococcus, pneumonia, Staphylococcus aureus, E. coli, Mycobacterium tuberculosis, group A and B streptococci, Corynebacterium diphtheria and many more. PFTs are generally utilized by the disease causing pathogens to disrupt the host first line of defense i.e. host cell membranes through pore formation strategy. Although, pore formation is the principal mode of action of the PFTs but they can have additional adverse effects on the hosts including immune evasion. Recently, structural investigation of different PFTs have imparted the molecular mechanistic insights into how PFTs get transformed from its inactive state to active toxic state. On the basis of their structural entity, PFTs have been classified in different types and their mode of actions alters in terms of pore formation and corresponding cellular toxicity. Although pathogen genome analysis can identify the probable PFTs depending upon their structural diversity, there are so many PFTs which utilize the local environmental conditions to generate their pore forming ability using a novel strategy which is known as "conformational switch" of a protein. This conformational switch is considered as characteristics of the phase shifting proteins which were often utilized by many pathogenic systems to protect them from the invaders through allosteric communication between distant regions of the protein. In this chapter, we discuss the structure function relationships of PFTs and how activity of PFTs varies with the change in the environmental conditions has been explored. Finally, we demonstrate these structural insights to develop therapeutic potential to treat the infections caused by multidrug resistant pathogens.

Impacts of mutations on dynamic allostery of adenylate kinase

Escherichia coli adenylate kinase (AK) is composed of CORE domain and two branch domains: LID and AMP-binding domain (AMPbd). AK exhibits considerable allostery in a reversible phosphoryl transfer reaction, which is largely attributed to the relative motion of LID and AMPbd with respect to CORE. Such an allosteric conformational change is also evident in the absence of ligands. Recent studies showed that the mutations in branch domains can adjust dynamic allostery and alter the substrate affinity and enzyme activity. In this work, we use all-atom molecular dynamics simulation to study the impacts of mutations in branch domains on AK's dynamic allostery by comparing two double mutants, i.e., LID mutant (Val135Gly, Val142Gly) and AMPbd mutant (Ala37Gly, Ala55Gly), with wild-type. Two mutants undergo considerable conformational fluctuation and exhibit deviation from the initially extended apo state to more compact structures. The LID domain in the LID mutant adjusts its relative position and firmly adheres to CORE. More strikingly, AMPbd mutations affect the relative positions of both the AMPbd domain and remote LID domain. Both domains undergo considerable movement, especially the inherent hinge swing motion of the flexible LID domain. In both mutants, the mutations can enhance the inter-domain interaction. The results about the conformation change of AK in both mutants are in line with the experiment of AK's affinity and activity. As revealed by our findings, the flexibility of branch domains and their inherent motions, especially LID domain, is highly relevant to dynamic allostery in the AK system.

Activation-Induced Reorganization of Energy Transport Networks in the β2 Adrenergic Receptor

We compute energy exchange networks (EENs) through the β₂ adrenergic receptor (β₂AR), a G-protein coupled receptor (GPCR), in inactive and active states, based on the results of molecular dynamics simulations of this membrane bound protein. We introduce a new definition for the reorganization of EENs upon activation that depends on the relative change in rates of energy transfer across noncovalent contacts throughout the protein. On the basis of the reorganized network that we obtain for β₂AR upon activation, we identify a branched pathway between the agonist binding site and the cytoplasmic region, where a G-protein binds to the receptor when activated. The pathway includes all of the motifs containing molecular switches previously identified as contributing to the allosteric transition of β₂AR upon agonist binding. EENs and their reorganization upon activation are compared with structure-based contact networks computed for the inactive and active states of β₂AR.

Substitution of a Surface-Exposed Residue Involved in an Allosteric Network Enhances Tryptophan Synthase Function in Cells

Networks of noncovalent amino acid interactions propagate allosteric signals throughout proteins. Tryptophan synthase (TS) is an allosterically controlled bienzyme in which the indole product of the alpha subunit (αTS) is transferred through a 25 Å hydrophobic tunnel to the active site of the beta subunit (βTS). Previous nuclear magnetic resonance and molecular dynamics simulations identified allosteric networks in αTS important for its function. We show here that substitution of a distant, surface-exposed network residue in αTS enhances tryptophan production, not by activating αTS function, but through dynamically controlling the opening of the indole channel and stimulating βTS activity. While stimulation is modest, the substitution also enhances cell growth in a tryptophan-auxotrophic strain of Escherichia coli compared to complementation with wild-type αTS, emphasizing the biological importance of the network. Surface-exposed networks provide new opportunities in allosteric drug design and protein engineering, and hint at potential information conduits through which the functions of a metabolon or even larger proteome might be coordinated and regulated.

Bcl-xL Dynamics under the Lens of Protein Structure Networks

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-x_L) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-x_L prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-x_L upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-x_L in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-x_L found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.

All-Atom MD Simulations of the HBV Capsid Complexed with AT130 Reveal Secondary and Tertiary Structural Changes and Mechanisms of Allostery

The hepatitis B virus (HBV) capsid is an attractive drug target, relevant to combating viral hepatitis as a major public health concern. Among small molecules known to interfere with capsid assembly, the phenylpropenamides, including AT130, represent an important antiviral paradigm based on disrupting the timing of genome packaging. Here, all-atom molecular dynamics simulations of an intact AT130-bound HBV capsid reveal that the compound increases spike flexibility and improves recovery of helical secondary structure in the spike tips. Regions of the capsid-incorporated dimer that undergo correlated motion correspond to established sub-domains that pivot around the central chassis. AT130 alters patterns of correlated motion and other essential dynamics. A new conformational state of the dimer is identified, which can lead to dramatic opening of the intradimer interface and disruption of communication within the spike tip. A novel salt bridge is also discovered, which can mediate contact between the spike tip and fulcrum even in closed conformations, revealing a mechanism of direct communication across these sub-domains. Altogether, results describe a dynamical connection between the intra- and interdimer interfaces and enable mapping of allostery traversing the entire core protein dimer.

Exploring the putative mechanism of allosteric modulations by mixed-action kappa/mu opioid receptor bitopic modulators

The modulation and selectivity mechanisms of seven mixed-action kappa opioid receptor (KOR)/mu opioid receptor (MOR) bitopic modulators were explored. Molecular modeling results indicated that the 'message' moiety of seven bitopic modulators shared the same binding mode with the orthosteric site of the KOR and MOR, whereas the 'address' moiety bound with different subdomains of the allosteric site of the KOR and MOR. The 'address' moiety of seven bitopic modulators bound to different subdomains of the allosteric site of the KOR and MOR may exhibit distinguishable allosteric modulations to the binding affinity and/or efficacy of the 'message' moiety. Moreover, the 3-hydroxy group on the phenolic moiety of the seven bitopic modulators induced selectivity to the KOR over the MOR.

Identification of Allosteric Effects in Proteins by Elastic Network Models

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

Community Network Analysis of Allosteric Proteins

Community network analysis (CNA) of correlated protein motions allows modeling of signals propagation in allosteric proteic systems. From standard classical molecular dynamics (MD) simulations, protein motions can be analysed by means of mutual information between pairs of amino acid residues, providing dynamical weighted networks that contains fundamental information of the communication among amino acids. The CNA method has been successfully applied to a variety of allosteric systems including an enzyme, a nuclear receptor and a bacterial adaptive immune system, providing characterization of the allosteric pathways. This method is complementary to network analyses based on different metrics and it is particularly powerful for studying large proteic systems, as it provides a coarse-grained view of the communication flows within large and complex networks.

Investigating Conformational Dynamics and Allostery in the p53 DNA-Binding Domain Using Molecular Simulations

The p53 tumor suppressor is a multifaceted context-dependent protein, which is involved in multiple cellular pathways, with the ability to either keep the cells alive or to kill them through mechanisms such as apoptosis. To complicate this picture, cancer cells that express mutant p53 becomes addicted to the mutant activity, so that the mutant variant features a myriad of gain-of-function activities, opening different venues for therapy. This makes essential to think outside the box and apply new approaches to the study of p53 structure-(mis)function relationship to find new critical components of its pathway or to understand how known parts are interconnected, compete, or cooperate. In this context, I will here illustrate how to integrate different computational methods to the identification of possible allosteric effects transmitted from the DNA binding interface of p53 to regions for cofactor recruitment. The protocol can be extended to any other cases of study. Indeed, it does not necessarily apply only to the study of DNA-induced effects, but more broadly to the investigation of long-range effects induced by a biological partner that binds to a biomolecule of interest.

webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules

A mixed Protein Structure Network (PSN) and Elastic Network Model-Normal Mode Analysis (ENM-NMA)-based strategy (i.e. PSN-ENM) was developed to investigate structural communication in bio-macromolecules. Protein Structure Graphs (PSGs) are computed on a single structure, whereas information on system dynamics is supplied by ENM-NMA. The approach was implemented in a webserver (webPSN), which was significantly updated herein. The webserver now handles both proteins and nucleic acids and relies on an internal upgradable database of network parameters for ions and small molecules in all PDB structures. Apart from the radical restyle of the server and some changes in the calculation setup, other major novelties concern the possibility to: a) compute the differences in nodes, links, and communication pathways between two structures (i.e. network difference) and b) infer links, hubs, communities, and metapaths from consensus networks computed on a number of structures. These new features are useful to identify commonalties and differences between two different functional states of the same system or structural-communication signatures in homologous or analogous systems. The output analysis relies on 3D-representations, interactive tables and graphs, also available for download. Speed and accuracy make this server suitable to comparatively investigate structural communication in large sets of bio-macromolecular systems. URL: http://webpsn.hpc.unimore.it.

Energy flow and intersubunit signalling in GSAM: A non-equilibrium molecular dynamics study

Non-equilibrium molecular dynamics simulations of vibrational energy flow induced by the imposition of a thermal gradient have been performed on the μ2-dimeric enzyme glutamate-1-semialdehyde aminomutase (GSAM), the key enzyme in the biosynthesis of chlorophyll, in order to identify energy transport pathways and to elucidate their role as potential allosteric communication networks for coordinating functional dynamics, specifically the negative cooperativity observed in the motion of the two active site gating loops. Fully atomistic MD simulations of thermal diffusion were executed with a GROMACS simulation package on a fully solvated GSAM enzyme by heating various active site target ligands (initially, catalytic intermediates and cofactors) to 300K while holding the remainder of the protein and the solvent bath at 10K and monitoring the temperature T(t) of all the enzyme residues as a function of time over a 1ns observation window. Energy is observed to be deposited in a relatively small number of discrete chains of residues most of which contribute to specific structural or biochemical functionality. Thermal linkages between all thermally active chains were established by isolating a specific pair of chains and performing a thermal diffusion simulation on the pair, one held at 300K and the other at 10K, with the rest of the protein frozen in its initial atomic configuration and thus thermally unresponsive. Proceeding in this way, it was possible to map out multiple pathways of vibrational energy flow leading from one of the active sites through a network of contiguous residues, many of which were evolutionarily conserved and linked by hydrogen bonds, into the other active site and ultimately to the other gating loop.

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.

Decrypting the Information Exchange Pathways across the Spliceosome Machinery

Intron splicing of a nascent mRNA transcript by spliceosome (SPL) is a hallmark of gene regulation in eukaryotes. SPL is a majestic molecular machine composed of an entangled network of proteins and RNAs that meticulously promotes intron splicing through the formation of eight intermediate complexes. Cross-communication among the critical distal proteins of the SPL assembly is pivotal for fast and accurate directing of the compositional and conformational readjustments necessary to achieve high splicing fidelity. Here, molecular dynamics (MD) simulations of an 800 000 atom model of SPL C complex from yeast Saccharomyces cerevisiae and community network analysis enabled us to decrypt the complexity of this huge molecular machine, by identifying the key channels of information transfer across long distances separating key protein components. The reported study represents an unprecedented attempt in dissecting cross-communication pathways within one of the most complex machines of eukaryotic cells, supporting the critical role of Clf1 and Cwc2 splicing cofactors and specific domains of the Prp8 protein as signal conveyors for pre-mRNA maturation. Our findings provide fundamental advances into mechanistic aspects of SPL, providing a conceptual basis for controlling the SPL via small-molecule modulators able to tackle splicing-associated diseases by altering/obstructing information-exchange paths.

Long-range Regulation of Partially Folded Amyloidogenic Peptides

Neurodegeneration involves abnormal aggregation of intrinsically disordered amyloidogenic peptides (IDPs), usually mediated by hydrophobic protein-protein interactions. There is mounting evidence that formation of α-helical intermediates is an early event during self-assembly of amyloid-β42 (Aβ42) and α-synuclein (αS) IDPs in Alzheimer’s and Parkinson’s disease pathogenesis, respectively. However, the driving force behind on-pathway molecular assembly of partially folded helical monomers into helical oligomers assembly remains unknown. Here, we employ extensive molecular dynamics simulations to sample the helical conformational sub-spaces of monomeric peptides of both Aβ42 and αS. Our computed free energies, population shifts, and dynamic cross-correlation network analyses reveal a common feature of long-range intra-peptide modulation of partial helical folds of the amyloidogenic central hydrophobic domains via concerted coupling with their charged terminal tails (N-terminus of Aβ42 and C-terminus of αS). The absence of such inter-domain fluctuations in both fully helical and completely unfolded (disordered) states suggests that long-range coupling regulates the dynamicity of partially folded helices, in both Aβ42 and αS peptides. The inter-domain coupling suggests a form of intra-molecular allosteric regulation of the aggregation trigger in partially folded helical monomers. This approach could be applied to study the broad range of amyloidogenic peptides, which could provide a new path to curbing pathogenic aggregation of partially folded conformers into oligomers, by inhibition of sites far from the hydrophobic core.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

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.

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.

ProSNEx: a web-based application for exploration and analysis of protein structures using network formalism

ProSNEx (Protein Structure Network Explorer) is a web service for construction and analysis of Protein Structure Networks (PSNs) alongside amino acid flexibility, sequence conservation and annotation features. ProSNEx constructs a PSN by adding nodes to represent residues and edges between these nodes using user-specified interaction distance cutoffs for either carbon-alpha, carbon-beta or atom-pair contact networks. Different types of weighted networks can also be constructed by using either (i) the residue-residue interaction energies in the format returned by gRINN, resulting in a Protein Energy Network (PEN); (ii) the dynamical cross correlations from a coarse-grained Normal Mode Analysis (NMA) of the protein structure; (iii) interaction strength. Upon construction of the network, common network metrics (such as node centralities) as well as shortest paths between nodes and k-cliques are calculated. Moreover, additional features of each residue in the form of conservation scores and mutation/natural variant information are included in the analysis. By this way, tool offers an enhanced and direct comparison of network-based residue metrics with other types of biological information. ProSNEx is free and open to all users without login requirement at http://prosnex-tool.com.

Coordinated Network Changes across the Catalytic Cycle of Alpha Tryptophan Synthase

Networks of noncovalent interactions are important for protein structural dynamics. We used nuclear magnetic resonance chemical shift covariance analyses on an inactive variant of the alpha subunit of tryptophan synthase to map amino acid interaction networks across its catalytic cycle. Although some network connections were common to every enzyme state, many of the network connections strengthened or weakened over the catalytic cycle; these changes were highly coordinated. These results suggest a higher level of network organization. Our analyses identified periodic, second-order networks that show highly coordinated interaction changes across the catalytic cycle. These periodic networks may help synchronize the sequence of structural transitions necessary for enzyme function. Molecular dynamics simulations identified interaction changes across the catalytic cycle, including those involving the catalytic residue Glu49, which may help drive other interaction changes throughout the enzyme structure. Similar periodic networks may direct structural transitions and allosteric interactions in other proteins.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

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

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

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Identifying coupled clusters of allostery participants through chemical shift perturbations

Allosteric couplings underlie many cellular signaling processes and provide an exciting avenue for development of new diagnostics and therapeutics. A general method for identifying important residues in allosteric mechanisms would be very useful, but remains elusive due to the complexity of long-range phenomena. Here, we introduce an NMR method to identify residues involved in allosteric coupling between two ligand-binding sites in a protein, which we call chemical shift detection of allostery participants (CAP). Networks of functional groups responding to each ligand are defined through correlated NMR perturbations. In this process, we also identify allostery participants, groups that respond to both binding events and likely play a role in the coupling between the binding sites. Such residues exhibit multiple functional states with distinct NMR chemical shifts, depending on binding status at both binding sites. Such a strategy was applied to the prototypical ion channel KcsA. We had previously shown that the potassium affinity at the extracellular selectivity filter is strongly dependent on proton binding at the intracellular pH sensor. Here, we analyzed proton and potassium binding networks and identified groups that depend on both proton and potassium binding (allostery participants). These groups are viewed as candidates for transmitting information between functional units. The vital role of one such identified amino acid was validated through site-specific mutagenesis, electrophysiology functional studies, and NMR-detected thermodynamic analysis of allosteric coupling. This strategy for identifying allostery participants is likely to have applications for many other systems.

Precision medicine review: rare driver mutations and their biophysical classification

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