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Data availability statement: The trajectories of Mpro simulations and models of the metastable states can be downloaded from 10.5281/zenodo.4782284. β-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand–protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across β-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor. There was no funding for this work

Glycosylation of secondary metabolites involves plant UDP-dependent glycosyltransferases (UGTs). UGTs have shown promise as catalysts in the synthesis of glycosides for medical treatment. However, limited understanding at the molecular level due to insufficient biochemical and structural information has hindered potential applications of most of these UGTs. In the absence of experimental crystal structures, we employed advanced molecular modelling and simulations in conjunction with biochemical characterisation to design a workflow to study five Group H Arabidopsis thaliana (76E1, 76E2, 76E4, 76E5, 76D1) UGTs. Based on our rational structural manipulation and analysis, we identified key amino acids (P129 in 76D1; D374 in 76E2; K275 in 76E4), which when mutated improved donor-substrate recognition than wildtype UGTs. Molecular dynamics simulations and deep learning analysis identified structural differences, which drive substrate preferences. The design of these UGTs with broader substrate specificity may play important role in biotechnological and industrial applications. These findings can also serve as basis to study other plant UGTs and thereby advancing UGT enzyme engineering. Federal Scholarship Board/Presidential Special Scholarship Scheme for Innovation and Development (PRESSID), Nigeria; Sichuan Science and Technology Program

Motivation A deleterious amino acid change in a protein can be compensated by a second-site rescue mutation. These compensatory mechanisms can be mimicked by drugs. In particular, the location of rescue mutations can be used to identify protein regions that can be targeted by small molecules to reactivate a damaged mutant. Results We present the first general computational method to detect rescue sites. By mimicking the effect of mutations through the application of forces, the double force scanning (DFS) method identifies the second-site residues that make the protein structure most resilient to the effect of pathogenic mutations. We tested DFS predictions against two datasets containing experimentally validated and putative evolutionary-related rescue sites. A remarkably good agreement was found between predictions and experimental data. Indeed, almost half of the rescue sites in p53 was correctly predicted by DFS, with 65% of remaining sites in contact with DFS predictions. Similar results were found for other proteins in the evolutionary dataset. Availability and implementation The DFS code is available under GPL at https://fornililab.github.io/dfs/ Supplementary information Supplementary data are available at Bioinformatics online.

Hypoxia inducible factors (HIFs) are transcription factors belonging to the basic helix−loop−helix PER-ARNT-SIM (bHLH-PAS) protein family with a role in sensing oxygen levels in the cell. Under hypoxia, the HIF-α degradation pathway is blocked and dimerization with the aryl hydrocarbon receptor nuclear translocator (ARNT) makes HIF-α transcriptionally active. Due to the common hypoxic environment of tumors, inhibition of this mechanism by destabilization of HIF-α:ARNT dimerization has been proposed as a promising therapeutic strategy. Following the discovery of a druggable cavity within the PAS-B domain of HIF-2α, research efforts have been directed to identify artificial ligands that can impair heterodimerization. Although the crystallographic structures of the HIF-2α:ARNT complex have elucidated the dimer architecture and the 0X3-inhibitor placement within the HIF-2α PAS-B, unveiling the inhibition mechanism requires investigation of how ligand-induced perturbations could dynamically propagate through the structure and affect dimerization. To this end, we compared evolutionary features, intrinsic dynamics and energetic properties of the dimerization interfaces of HIF-2α:ARNT in both the apo and holo forms. Residue conservation analysis highlighted inter-domain connecting elements that have a role in dimerization. Analysis of domain contributions to the dimerization energy demonstrated the importance of bHLH and PAS-A of both partners and of HIF-2α PAS-B domain in dimer stabilization. Among quaternary structure oscillations revealed by Molecular Dynamics simulations, the hinge-bending motion of the ARNT PAS-B domain around the flexible PAS-A/PAS-B linker supports a general model for ARNT dimerization in different heterodimers. Comparison of the HIF-2α:ARNT dynamics in the apo and 0X3-bound forms indicated a model of inhibition where the HIF-2α-PAS-B interfaces are destabilised as a result of water-bridged ligand-protein interactions and these local effects allosterically propagate to perturb the correlated motions of the domains and inter-domain communication. These findings will guide the design of improved inhibitors to contrast cell survival in tumor masses., Author summary A low oxygen condition, called hypoxia, often occurs in tumor masses and generally correlates with worse prognosis. Cells in a tumor react to low oxygen levels with a metabolism modification induced by the activation of hypoxia inducible factors (HIFs) through dimerization with a partner protein and binding to a DNA target. Disrupting this protein-protein interaction could be a potential therapeutic strategy, but directly interfering with dimer formation can be troublesome because of the difficulty to design drugs that bind to protein interfaces. However, ligands that bind internal protein cavities can indirectly perturb the interfaces reducing dimers stability. Albeit protein crystallography had offered a detailed static picture of a HIF dimer bound to candidate inhibitors, it is not able to describe either the perturbation caused by binding or the molecular mechanism of dimer destabilization. Here we exploit molecular dynamics to identify the crucial interfaces in the HIF dimer stabilization and, by comparing the results obtained in the bound and unbound forms, we reveal the mechanism of ligand inhibition at atomic detail. All these findings will guide toward the design of improved dimerization inhibitors, to contrast cell survival in tumor masses.

Switching of bacterial flagellar rotation is caused by large domain movements of the FliG protein triggered by binding of the signal protein CheY to FliM. FliG and FliM form adjacent multi-subunit arrays within the basal body C-ring. The movements alter the interaction of the FliG C-terminal (FliGC) "torque" helix with the stator complexes. Atomic models based on the Salmonella entrovar C-ring electron microscopy reconstruction have implications for switching, but lack consensus on the relative locations of the FliG armadillo (ARM) domains (amino-terminal (FliGN), middle (FliGM) and FliGC) as well as changes during chemotaxis. The generality of the Salmonella model is challenged by the variation in motor morphology and response between species. We studied coevolved residue mutations to determine the unifying elements of switch architecture. Residue interactions, measured by their coevolution, were formalized as a network, guided by structural data. Our measurements reveal a common design with dedicated switch and motor modules. The FliM middle domain (FliMM) has extensive connectivity most simply explained by conserved intra and inter-subunit contacts. In contrast, FliG has patchy, complex architecture. Conserved structural motifs form interacting nodes in the coevolution network that wire FliMM to the FliGC C-terminal, four-helix motor module (C3-6). FliG C3-6 coevolution is organized around the torque helix, differently from other ARM domains. The nodes form separated, surface-proximal patches that are targeted by deleterious mutations as in other allosteric systems. The dominant node is formed by the EHPQ motif at the FliMMFliGM contact interface and adjacent helix residues at a central location within FliGM. The node interacts with nodes in the N-terminal FliGc α-helix triad (ARM-C) and FliGN. ARM-C, separated from C3-6 by the MFVF motif, has poor intra-network connectivity consistent with its variable orientation revealed by structural data. ARM-C could be the convertor element that provides mechanistic and species diversity. JK was supported by Medical Research Council grant U117581331. SK was supported by seed funds from Lahore University of Managment Sciences (LUMS) and the Molecular Biology Consortium.

M10 is the most C-terminal immunoglobulin (Ig) domain of the giant protein titin and a frequent target of disease-linked mutations. Currently, it is the only known muscle Ig domain able to interact with two alternative ligands-obscurin and obscurin-like-1 (Obsl1)-in different sarcomeric subregions. Obscurin and Obsl1 use their homologous N-terminal Ig domain (O1 in obscurin and OL1 in Obsl1) to bind M10 in a mutually exclusive manner. We present here the X-ray structure of the human titin:obscurin M10:O1 complex extending our previous work on the M10:OL1 interaction. Similar to M10:OL1, the M10:O1 complex displays a chevron-shaped antiparallel Ig-Ig architecture held together by a conserved molecular interface, which we validated by isothermal titration calorimetry and sorting experiments in neonatal rat cardiomyocytes. O1, although structurally related to OL1 and M10, both members of the intermediate set (I-set) Ig family, presents an intriguing switch of its βA' strand. This leads to structural differences between the complexes, particularly for the “open side“ of the chevron-shaped assembly. A bioinformatics analysis reveals that the βA'-switch observed for O1 is rare and that it is involved in mediating protein-protein interactions. Molecular dynamics simulations also suggest that this topological alteration substantially increases local flexibility compared to the conventional I-set Ig domains. The O1/OL1 Ig domains are candidate discriminatory structural modules potentially directing the binding of specific additional partners at the M-band. Cellular sorting experiments in neonatal rat cardiomyocytes are consistent with the view that the titin:obscurin/Obsl1 complexes might be a platform for higher-order interactions. This work was supported by a British Heart Foundation grant PG/10/67/28527 awarded to R.A.S. and M.G.

Patients with chronic myeloid leukemia in whom tyrosine kinase inhibitors (TKIs) fail often present mutations in the BCR-ABL catalytic domain. We noticed a lack of substitutions involving 4 amino acids (E286, M318, I360, and D381) that form hydrogen bonds with ponatinib. We therefore introduced mutations in each of these residues, either preserving or altering their physicochemical properties. We found that E286, M318, I360, and D381 are dispensable for ABL and BCR-ABL protein stability but are critical for preserving catalytic activity. Indeed, only a "conservative" I360T substitution retained kinase proficiency and transforming potential. Molecular dynamics simulations of BCR-ABLI360T revealed differences in both helix αC dynamics and protein-correlated motions, consistent with a modified ATP-binding pocket. Nevertheless, this mutant remained sensitive to ponatinib, imatinib, and dasatinib. These results suggest that changes in the 4 BCR-ABL residues described here would be selected against by a lack of kinase activity or by maintained responsiveness to TKIs. Notably, amino acids equivalent to those identified in BCR-ABL are conserved in 51% of human tyrosine kinases. Hence, these residues may represent an appealing target for the design of pharmacological compounds that would inhibit additional oncogenic tyrosine kinases while avoiding the emergence of resistance due to point mutations. This work was supported by an investigator grant to P.V. from Associazione Italiana per la Ricerca sul Cancro (AIRC) and by funding from the Biotechnology and Biological Sciences Research Council (BB/I023291/1 and BB/H018409/1 to AP and FF). P.B. is the recipient of an AIRC - Marie Curie fellowship.

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates the biological and toxic effects of a wide variety of structurally diverse chemicals, including the toxic environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). While significant interspecies differences in AHR ligand binding specificity, selectivity, and response have been observed, the structural determinants responsible for those differences have not been determined, and homology models of the AHR ligand-binding domain (LBD) are available for only a few species. Here we describe the development and comparative analysis of homology models of the LBD of 16 AHRs from 12 mammalian and nonmammalian species and identify the specific residues contained within their ligand binding cavities. The ligand-binding cavity of the fish AHR exhibits differences from those of mammalian and avian AHRs, suggesting a slightly different TCDD binding mode. Comparison of the internal cavity in the LBD model of zebrafish (zf) AHR2, which binds TCDD with high affinity, to that of zfAHR1a, which does not bind TCDD, revealed that the latter has a dramatically shortened binding cavity due to the side chains of three residues (Tyr296, Thr386, and His388) that reduce the amount of internal space available to TCDD. Mutagenesis of two of these residues in zfAHR1a to those present in zfAHR2 (Y296H and T386A) restored the ability of zfAHR1a to bind TCDD and to exhibit TCDD-dependent binding to DNA. These results demonstrate the importance of these two amino acids and highlight the predictive potential of comparative analysis of homology models from diverse species. The availability of these AHR LBD homology models will facilitate in-depth comparative studies of AHR ligand binding and ligand-dependent AHR activation and provide a novel avenue for examining species-specific differences in AHR responsiveness. © 2013 American Chemical Society.

Multiple comparison of the Molecular Dynamics (MD) trajectories of mutants in a cold-adapted α-amylase (AHA) could be used to elucidate functional features required to restore mesophilic-like activity. Unfortunately it is challenging to identify the different dynamic behaviors and correctly relate them to functional activity by routine analysis. We here employed a previously developed and robust two-stage approach that combines Self-Organising Maps (SOMs) and hierarchical clustering to compare conformational ensembles of proteins. Moreover, we designed a novel strategy to identify the specific mutations that more efficiently convert the dynamic signature of the psychrophilic enzyme (AHA) to that of the mesophilic counterpart (PPA). The SOM trained on AHA and its variants was used to classify a PPA MD ensemble and successfully highlighted the relationships between the flexibilities of the target enzyme and of the different mutants. Moreover the local features of the mutants that mostly influence their global flexibility in a mesophilic-like direction were detected. It turns out that mutations of the cold-adapted enzyme to hydrophobic and aromatic residues are the most effective in restoring the PPA dynamic features and could guide the design of more mesophilic-like mutants. In conclusion, our strategy can efficiently extract specific dynamic signatures related to function from multiple comparisons of MD conformational ensembles. Therefore, it can be a promising tool for protein engineering.

Background The hierarchical and partially redundant nature of protein structures justifies the definition of frequently occurring conformations of short fragments as 'states'. Collections of selected representatives for these states define Structural Alphabets, describing the most typical local conformations within protein structures. These alphabets form a bridge between the string-oriented methods of sequence analysis and the coordinate-oriented methods of protein structure analysis. Results A Structural Alphabet has been derived by clustering all four-residue fragments of a high-resolution subset of the protein data bank and extracting the high-density states as representative conformational states. Each fragment is uniquely defined by a set of three independent angles corresponding to its degrees of freedom, capturing in simple and intuitive terms the properties of the conformational space. The fragments of the Structural Alphabet are equivalent to the conformational attractors and therefore yield a most informative encoding of proteins. Proteins can be reconstructed within the experimental uncertainty in structure determination and ensembles of structures can be encoded with accuracy and robustness. Conclusions The density-based Structural Alphabet provides a novel tool to describe local conformations and it is specifically suitable for application in studies of protein dynamics.

Ligand-protein docking is a computational approach that is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of experimental structures of target proteins have been overcome by theoretical modeling with bioinformatics methods as homology modelling.1 While this greatly extended the use of docking simulations, it also introduced the need for general criteria to estimate the reliability of docking results given the model quality.2 To this end, we performed a large-scale experiment on a diverse set including experimental structures and homology models for a group of representative ligandprotein complexes.3 A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target-template alignment and modelling. The obtained models were evaluated with a selection of model quality indices.4 The binding geometries were generated using AutoDock,5 a widely used docking program. The results demonstrated that general relationships do exist between the accuracy of the binding geometries obtained by docking and the quality of the homology models. In particular, it was highlighted that successful docking requires an accurate modelling of the binding site and that, conversely, the development of docking approaches that allow flexibility in the binding site may have a great impact on the effective use of docking into homology models. More importantly, state-of-the-art indices for model quality assessment are an effective tool for a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics. On these bases, we presented a strategy to exploit information on homologous proteins to predict the accuracy of docking results on theoretical models.

Motivation: The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset.Results: The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1–4.Availability: Contact: jkleinj@nimr.mrc.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Dynamical properties of proteins may have a significant role in regulating protein-protein interactions. In particular, intrinsic disorder and disorder-order transitions have been claimed to be especially involved in the binding of proteins known as “hubs” [1], which are characterized by a high level of connectivity in protein-protein interaction (PPI) networks. We therefore started to investigate the dynamical properties of hubs through molecular simulations, to assess the role of conformational flexibility in promoting “promiscuity” of interactions.For this study, a dataset of proteins with known structure and interaction partners was first prepared. To cope with the incompleteness of the interaction data contained in the Protein Data Bank (PDB), we mapped the PPI database IntAct [2], which collects interactions from a wide range of experimental techniques, onto the structure-based database PiSite [3]. The proteins were then partitioned into ‘classes’ with increasing number of interactions.A preliminary survey of the dynamical properties of each class was done using two independent approaches, namely tCONCOORD [4] and the Gaussian Network Model [5]. For each complex, the interfaces were extracted using the POPSComp method [6,7]. The availability of information on both the dynamics and the interaction properties will allow to determine possible correlations between flexibility and binding diversity.[1] Dosztnyi et al., J. Proteome Res., 5, 2985-2995 (2006).[2] Kerrien et al., Nucleic Acids Res., 35, D561-D565 (2006).[3] Higurashi et al., Nucleic Acids Res., 37, D360-D364 (2009).[4] Seeliger et al., Structure, 15, 1482-1492 (2007).[5] Bahar et al., Folding & Design, 2, 173-181 (1997).[6] Fraternali et al., Nucleic Acids Res., 30, 2950-2960 (2002).[7] Kleinjung et al., Nucleic Acids Res., 33, W342-W346 (2005).

IntroductionAllosteric inhibitors were shown to be effective in targeting multiple conformational states and more selective than competitive inhibitors [1]. However, allosteric signal transmission is difficult to model because often mediated by small local changes propagating along multiple pathways in the protein structure [2]. Therefore, we developed a method to identify these pathways by an information-theoretic analysis of Molecular Dynamics (MD) simulations.MethodsFirst, MD ensembles are mapped onto a canonical set of representative fragments, i.e. a set of discrete 4-residue states [3]. By this mapping, the contribution of rigid fragment roto-translations is removed and relevant changes are detected, even if subtle. Second, local motions are modelled as transitions between the fragment states. This removes background noise from harmonic high-frequency fluctuations. Third, the communication between parts of the molecule is modelled by the coupling of fragment transitions. These are resolved spatially and temporally. The time-averaged spatial couplings are analyzed with a network model of fragment correlations. Then communication pathways are characterised by time-resolved analysis of fragment couplings.ResultsThe method was applied to the NtrC, CheY, and FixJ receiver domains. The results are consistent with the emerging picture of allosteric regulation [2]: a pre-organized network of fragment couplings connects the allosteric and functional sites already in the inactive state, and fragments with high network centrality (hubs) are part of functionally relevant regions. Critical nodes on the signalling pathways correspond to residues experimentally known to affect the kinetics and thermodynamics of the allosteric mechanism.References1. Kar, G., Keskin, O., Gursoy, A. & Nussinov, R. Curr Opin Pharmacol 10, 715–22 (2010).2. del Sol, A., Tsai, C.J., Ma, B. & Nussinov, R. Structure 17, 1042–1050 (2009).3. Pandini, A., Fornili, A., Kleinjung, J. BMC Bioinformatics 11, 97 (2010).

BackgroundBiomolecular motions play a key role in several biological functions: enzymatic activity, protein-protein interactions, ligand binding and allosteric regulation. Computational approaches, such as Molecular Dynamics (MD), are now routinely used to reproduce the intrinsic dynamics of proteins, but effective tools are still required to gain functional insight from the simulated data.Methods and ResultsWe previously suggested a method aimed at recovering the role of local conformational changes in functional motions. To this purpose we developed a Structural Alphabet (SA): a set of 25 canonical states of 4-residue protein fragments (Cα atoms only) describing the most probable local conformations in high-resolution protein structures [1]. The SA provides a mean for the coarse-grained annotation and processing of local conformations in a string format, which lends itself to a range of efficient sequence analysis algorithms. The SA has been successfully used in analysing local changes and allosteric signal transmission [2].Here we present GSATools [3], a set of SA-related tools interfacing with GROMACS for the analysis of conformational ensembles. GSATools is designed for the investigation of the conformational dynamics of local structures, the functional correlations between local and global motions, and the mechanisms of allosteric communication.ConclusionsGSATools is a free, easy-to-use and fully documented software for the analysis of conformational ensembles of proteins. The GSATools complement the GROMACS toolkit with a powerful set of analyses to detect, annotate and interpret local motions of functional relevance.1. Pandini A., Fornili A. & Kleinjung J. BMC Bioinformatics 11, 97 (2010).2. Pandini A., Fornili A., Fraternali F. & Kleinjung J., FASEB J., 26, 868 (2012).3. Pandini A., Fornili A., Fraternali F. & Kleinjung J., Bioinformatics, 29, 2053 (2013).