Amyloid beta (Aβ of Alzheimer’s disease) and α-synuclein (α-Syn of Parkinson’s disease) form large fibrils. Evidence is increasing however that much smaller oligomers are more toxic and that these oligomers can form transmembrane ion channels. We have proposed previously that Aβ42 oligomers, annular protofibrils, and ion channels adopt concentric β-barrel molecular structures. Here we extend that hypothesis to the superfamily of α, β, and γ-synucleins. Our models of numerous Synuclein oligomers, annular protofibrils, tubular protofibrils, lipoproteins, and ion channels were developed to be consistent with sizes, shapes, molecular weights, and secondary structures of assemblies as determined by EM and other studies. The models have the following features: 1) all subunits have identical structures and interactions; 2) they are consistent with conventional β-barrel theory; 3) the distance between walls of adjacent β-barrels is between 0.6 and 1.2 nm; 4) hydrogen bonds, salt bridges, interactions among aromatic side-chains, burial and tight packing of hydrophobic side-chains, and aqueous solvent exposure of hydrophilic side-chains are relatively optimal; and 5) residues that are identical among distantly related homologous proteins cluster in the interior of most oligomers whereas residues that are hypervariable are exposed on protein surfaces. Atomic scale models of some assemblies were developed.

We report here an assessment of the model refinement category of the 14th round of Critical Assessment of Structure Prediction (CASP14). As before, predictors submitted up to five ranked refinements, along with associated residue-level error estimates, for targets that had a wide range of starting quality. The ability of groups to accurately rank their submissions and to predict coordinate error varied widely. Overall only four groups out-performed a “naïve predictor” corresponding to resubmission of the starting model. Among the top groups there are interesting differences of approach and in the spread of improvements seen: some methods are more conservative, others more adventurous. Some targets were “double-barrelled” for which predictors were offered a high-quality AlphaFold 2 (AF2)-derived prediction alongside another of lower quality. The AF2-derived models were largely unimprovable, their apparent errors being found to reside very largely at domain and, especially, crystal lattice contacts. Refinement is shown to have a mixed impact overall on structure-based function annotation methods to predict nucleic acid binding, spot catalytic sites and dock protein structures.

Glycoside hydrolase family 57 glycogen branching enzymes (GH57GBE) catalyze the formation of an α-1,6 glycosidic bond between α-1,4 linked glucooliogosaccharides. As an atypical family, a limited number of GH57GBEs have been biochemically characterized so far. This study aimed at acquiring a better understanding of the GH57GBE family by a systematic sequence-based bioinformatics analysis of almost 2,500 gene sequences and determining the branching activity of several native and mutant GH57GBEs. A correlation was found between a very low or even no branching activity with the absence of a flexible loop, a tyrosine at the loop tip, and two β-sheets.

The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template-based modeling and free docking. Structural templates were identified using sensitive sequence-based searches. If sequence-based searches failed, we performed structure-based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody-antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best predicted interfaces according to several protein-protein interface accuracy measures. Since we did not use co-evolution-based prediction of inter-chain contacts, we attribute our relative success in predicting interfaces primarily to the emphasis on the interaction interface when modeling and scoring.

Ryanodine receptor 1 (RyR1) is an intracellular calcium ion (Ca2+) release channel required for skeletal muscle contraction. Although cryo-electron microscopy identified binding sites of three coactivators Ca2+, ATP and caffeine (CFF), the mechanism of co-regulation and synergy of these activators is unknown. Here, we report allosteric connections among the three ligand binding sites and pore region in (i) Ca2+ bound-closed, (ii) ATP/CFF bound- closed, (iii) Ca2+/ATP/CFF bound-closed, and (iv) Ca2+/ATP/CFF bound-open RyR1 states. We identified two dominant interactions that mediate interactions between the Ca2+ binding site and pore region in Ca2+ bound-closed state, which partially overlapped with the pore communications in ATP/CFF bound-closed RyR1 state. In Ca2+/ATP/CFF bound-closed and -open RyR1 states, co-regulatory interactions were analogous to communications in the Ca2+ bound-closed and ATP/CFF bound- closed states. Both ATP- and CFF- binding sites mediate communication between the Ca2+ binding site and the pore region in Ca2+/ATP/CFF bound - open RyR1 structure. We conclude that Ca2+, ATP, and CFF propagate their effects to the pore region through a network of overlapping interactions that mediate allosteric control and molecular synergy in channel regulation.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has serious negative effects on health, social life, and economics. Recently, vaccines from various companies have been urgently approved to control SARS-CoV-2 infections. However, any specific antiviral drug has not been confirmed so far for regular treatment. An important target is the main protease (Mpro), which plays a major role in replication of the virus. In this study, Gaussian and residue network models are employed to reveal two distinct potential allosteric sites on Mpro that can be evaluated as drug targets besides the active site. Then, FDA-approved drugs are docked to three distinct sites with flexible docking using AutoDock Vina to identify potential drug candidates. 14 best molecule hits for the active site of Mpro are determined. 6 of these also exhibit high docking scores for the potential allosteric regions. Full-atom molecular dynamics simulations with MM-GBSA method indicate that compounds docked to active and potential allosteric sites form stable interactions with high binding free energy (∆Gbind) values. ∆Gbind values reach -52.06 kcal/mol for the active site, -51.08 kcal/mol for the potential allosteric site 1, and -42.93 kcal/mol for the potential allosteric site 2. Energy decomposition calculations per residue elucidate key binding residues stabilizing the ligands that can further serve to design pharmacophores. This systematic and efficient computational analysis successfully determines ivermectine, diosmin and selinexor currently subjected to clinical trials, and further proposes bromocriptine, elbasvir as Mpro inhibitor candidates to be evaluated against SARS-CoV-2 infection

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system in three main aspects: (1) a new deep learning based protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) tertiary structure prediction, (2) an enhanced template-based tertiary structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in protein tertiary structure prediction and ranked 3rd out of 136 predictors in inter-domain structure predic-tion. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free tertiary structure prediction largely depends on the accuracy of distance pre-dictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.

Elucidation of signalling events in a pathogen is potentially important to tackle the infection caused by it. Such events mediated by protein phosphorylation play important roles in infection and therefore to predict the phosphosites and substrates of the serine/threonine protein kinases, we have developed a Machine learning based approach and predicted the phosphosites for Mycobacterium tuberculosis serine/threonine protein kinases using kinase-peptide structure-sequence data. This approach utilizes features derived from kinase 3D-structure environment and known phosphosite sequences to generate Support Vector Machine based kinase specific predictions of phosphosites making it suitable for prediction of phosphosites of STPKs with no or scarce data of their phosphosites. Support vector machine outperformed the four machine learning algorithms we tried (random forest, logistic regression, support vector machine and k-nearest neighbours) with aucROC value of 0.88 on the independent testing dataset and a ten-fold cross validation accuracy of ~81.6% for the final model. Our predicted phosphosites of M. tuberculosis STPKs form an useful resource for experimental biologists enabling elucidation of STPK mediated post-translational regulation of important cellular processes. The training features file and model files, together with usage instructions file, are available at: https://github.com/vipulbiocoder/Mtb-KSPP

Lignin is one of the world’s most abundant organic polymers, and 2-pyrone-4,6-dicarboxylate lactonase (LigI) catalyzes the hydrolysis of 2-pyrone-4,6-dicarboxylate (PDC) in the degradation of lignin. The pH has profound effects on enzyme catalysis and therefore we studied this in the context of LigI. We found that changes of the pH mostly affects surface residues, while the residues at the active site are more subject to changes of the surrounding microenvironment. In accordance with this, a high pH facilitates the deprotonation of the substrate. Detailed free energy calculations by the empirical valence bond (EVB) approach revealed that the overall hydrolysis reaction is more likely when the three active site histidines (His31, His33 and His180) are protonated at the ɛ site, however, protonation at the δ site may be favored during specific steps of reaction. Our studies have uncovered the determinant role of the protonation state of the active site residues His31, His33 and His180 in the hydrolysis of PDC.

Histone is a scaffold protein that constitutes nucleosomes with DNA in the cell nucleus. When forming histone, hetero octamer is assisted by histone chaperone proteins. As a histone chaperone protein, the crystal structure of yeast nucleosome assembly protein (yNap1) has been determined. For yNap1, a nuclear export signal/sequence (NES) has been identified as a part of the long -helix. Experimental evidence via mutagenesis on budding yeast suggests the NES is necessary for transport out from the cell nucleus. However, the NES is masked by a region defined as an accessory domain (AD). In addition, the role of the AD in nuclear transport has not been elucidated yet. To address the role of the AD, we focused on phosphorylation in the AD because proteome experiments have identified multiple phosphorylation sites of yNap1. To computationally treat phosphorylation, we performed all-atom molecular dynamics (MD) simulations for a set of non-phosphorylated and phosphorylated yNap1 (Nap1-nonP and Nap1-P). As an analysis, we addressed how the NES is exposed to the protein surface by measuring its solvent-access surface area (SASA). As a result, there was a difference in the SASA distributions between both systems. Quantitatively, the median of the SASA distribution of Nap1-P was greater than that of Nap1-nonP, meaning that phosphorylation in the AD exposed to the NES, resulting in increasing its accessibility. In conclusion, yNap1 might modulate the accessibility of the NES by dislocating the AD through phosphorylation.

Recently, a bacterium strain of Ideonella sakaiensis was identified with the uncommon ability to degrade the poly(ethylene terephthalate) (PET). The PETase from I. sakaiensis strain 201-F6 catalyzes the hydrolysis of PET converting it to mono(2-hydroxyethyl) terephthalic acid (MHET), bis(2-hydroxyethyl)-TPA (BHET), and terephthalic acid (TPA). Despite the potential of this enzyme for mitigation or elimination of environmental contaminants, one of the limitations of the use of PETase for PET degradation is the fact that it acts only at moderate temperature due to its low thermal stability. Besides, molecular details of the main interaction of PET in the active site of PETase remains unclear. Herein, molecular docking and molecular dynamics (MD) simulations were applied to analyze structural changes of PETase induced by PET binding. Results from the essential dynamics revealed that β1-β2 connecting loop is very flexible. This Loop is located far from the active site of PETase and we suggest that it can be considered for mutagenesis in order to increase the thermal stability of PETase. The free energy landscape (FEL) demonstrates that the main change in the transition between the unbounded to the bounded state is associated with β7-α5 connecting loop, where the catalytic residue Asp206 is located. Overall, the present study provides insights into the molecular binding mechanism of PET into the PETase structure and a computational strategy for mapping flexible regions of this enzyme, which can be useful for the engineering of more efficient enzymes for recycling the plastic polymers using biological systems.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused substantially more infections, deaths, and economic disruptions than the 2002-2003 SARS-CoV. The key to understanding SARS-CoV-2’s higher infectivity lies partly in its host receptor recognition mechanism. Experiments show that the human ACE2 protein, which serves as the primary receptor for both CoVs, binds to the receptor binding domain (RBD) of CoV-2’s spike protein stronger than SARS-CoV’s spike RBD. The molecular basis for this difference in binding affinity, however, remains unexplained from X-ray structures. To go beyond insights gained from X-ray structures and investigate the role of thermal fluctuations in structure, we employ all-atom molecular dynamics simulations. Microseconds-long simulations reveal that while CoV and CoV-2 spike-ACE2 interfaces have similar conformational binding modes, CoV-2 spike interacts with ACE2 via a larger combinatorics of polar contacts, and on average, makes 45\% more polar contacts. Correlation analysis and thermodynamic calculations indicate that these differences in the density and dynamics of polar contacts arise from differences in spatial arrangements of interfacial residues, and dynamical coupling between interfacial and non-interfacial residues. These results recommend that ongoing efforts to design spike-ACE2 peptide blockers will benefit from incorporating dynamical information as well as allosteric coupling effects.

Structure-based computational protein design (CPD) refers to the problem of finding a sequence of amino acids which folds into a specific desired protein structure, and possibly fulfills some targeted biochemical properties. Recent studies point out the particularly rugged CPD energy landscape, suggesting that local search optimization methods should be designed and tuned to easily escape local minima attraction basins. In this paper, we analyze the performance and search dynamics of an iterated local search (ILS) algorithm enhanced with partition crossover. Our algorithm, PILS, quickly finds local minima and escapes their basins of attraction by solution perturbation. Additionally, the partition crossover operator exploits the structure of the residue interaction graph in order to efficiently mix solutions and find new unexplored basins. Our results on a benchmark of 30 proteins of various topology and size show that PILS consistently finds lower energy solutions compared to Rosetta fixbb and a classic ILS, and that the corresponding sequences are mostly closer to the native.

Protein structure networks (PSNs) have long been used to provide a coarse yet meaningful representation of protein structure, dynamics, and internal communication pathways. An important question is what criteria should be applied to construct the network so that to include relevant interresidue contacts while avoiding unnecessary connections. To address this issue we systematically considered varying residue distance cutoff length and the probability threshold for contact formation to construct PSNs based on atomistic molecular dynamics in order to assess the amount of mutual information within the resulting representations. We found that the minimum in mutual information is universally achieved at the cutoff length of 5 Å, irrespective of the applied contact formation probability threshold in all considered, distinct proteins. Assuming that the optimal PSNs should be characterised by the least amount of redundancy, which corresponds to the minimum in mutual information, this finding suggests an objective criterion for cutoff distance and supports the existing preference towards its customary selection around 5 Å length, typically based to date on heuristic criteria.

Several plastic degrading enzymes have been described in the literature, most notably PETases that are capable of hydrolyzing polyethylene terephthalate (PET) plastic. One of them, the PETase from Ideonella sakaiensis, a bacterium isolated from environmental samples within a PET bottle recycling site, was the subject of extensive studies. To test how widespread PETase functionality is in other bacterial communities, we used a cascade of BLAST searches in the JGI metagenomic datasets and showed that PETases can also be found in other metagenomic environmental samples from both human affected and relatively pristine sites. To confirm their classification as PETases, we verified that the newly identified proteins have the PETase sequence signatures common to all PETases and that phylogenetic analyses group them with the experimentally characterized PETases. Additionally, docking analysis was performed in order to further confirm the functional assignment of the putative environmental PETases.

The multi-domain bacterial S1 protein is the largest and most functionally important ribosomal protein of the 30S subunit, which interacts with both mRNA and proteins. The family of ribosomal S1 proteins differs in the classical sense from a protein with tandem repeats and has a “bead-on-string” organization, where each repeat is folded into a globular domain. Based on our recent data, the study of evolutionary relationships for the bacterial phyla will provide evidence for one of the proposed theories of the evolutionary development of proteins with structural repeats: from multiple repeats of assembles to single repeats, or vice versa. In this comparative analysis of 1333 S1 sequences that were identified in 24 different phyla; we demonstrate how such phyla can independently/dependently form during evolution. To our knowledge, this work is the first study of the evolutionary history of bacterial ribosomal S1 proteins. The collected and structured data can be useful to computer biologists as a resource for determining percent identity, amino acid composition and logo motifs, as well as dN/dS ratio in bacterial S1 protein. The obtained research data suggested that the evolutionary development of bacterial ribosomal proteins S1 evolved from multiple assemblies to single repeat. The presented data are integrated into the server, which can be accessed at http://oka.protres.ru:4200.

SARS-CoV-2 is neutralized by proteins that block receptor-binding sites on spikes that project from the viral envelope. In particular, substantial research investment has advanced monoclonal antibody therapies to the clinic where there are signs of partial efficacy in reducing viral burden and hospitalization. An alternative is to use the host entry receptor, ACE2, as a soluble decoy that broadly blocks SARS-associated coronaviruses with limited potential for viral escape. Here, we summarize efforts to engineer higher affinity variants of soluble ACE2 that rival the potency of affinity-matured antibodies. Strategies have also been used to increase the valency of ACE2 decoys for avid spike interactions and to improve pharmacokinetics via IgG fusions. Finally, the intrinsic catalytic activity of ACE2 for the turnover of the vasoconstrictor angiotensin II may directly address COVID-19 symptoms and protect against lung and cardiovascular injury, conferring dual mechanisms of action unachievable by monoclonal antibodies. Soluble ACE2 derivatives therefore have the potential to be next generation therapeutics for addressing the immediate needs of the current pandemic and possible future outbreaks.

Multi-domain proteins are not only formed through natural evolution but can also be generated by recombinant DNA technology. Because many fusion proteins can enhance the selectivity of cell targeting, these artificially produced molecules, called multi-specific biologics, are promising drug candidates, especially for immunotherapy. Moreover, the rational design of domain linkers in fusion proteins is becoming an essential step toward a quantitative understanding of the dynamics in these biopharmaceutics. We developed a computational framework to characterize the impacts of peptide linkers on the dynamics of multi-specific biologics. We constructed a benchmark containing six types of linkers that represent various lengths and degrees of flexibility and used them to connect two natural proteins as a test system. The microsecond dynamics of these proteins generated from Anton were projected onto a coarse-grained conformational space. The similarity of dynamics among different proteins in this low-dimensional space was further analyzed by a neural network model. Finally, hierarchical clustering was applied to place linkers into different subgroups based on the neural network classification results. The clustering results suggest that the length of linkers used to spatially separate different functional modules plays the most important role in regulating the dynamics of this fusion protein. Given the same number of amino acids, linker flexibility functions as a regulator of protein dynamics. In summary, we illustrated that a new computational strategy can be used to study the dynamics of multi-domain fusion proteins by a combination of long timescale molecular dynamics simulation, coarse-grained modeling, and artificial intelligence.