A document by Nitin Kumar Singh. Click on the document to view its contents.

Protein-protein interactions (PPIs) are ubiquitous and functionally of great importance in biological systems. Hence, the ac-curate prediction of PPIs by protein-protein docking and scoring tools is highly desirable in order to characterize their structure and biological function. Ab initio docking protocols are divided into the sampling of docking poses to produce at least one near-native structure, then to evaluate the vast candidate structures by scoring. Concurrent development in both sampling and scoring is crucial for the deployment of protein-protein docking software. In the present work, we apply a machine learning model on pairwise potentials to refine the task of protein quaternary structure native structure detection among decoys. A decoy set was featurized using the Knowledge and Empirical Combined Scoring Algorithm 2 (KECSA2) pairwise potential. The highly unbalanced decoy set was then balanced using a comparison concept between native and decoy structures. The resultant comparison descriptors were used to train a logistic regression (LR) classifier. The LR model yielded the optimal performance for native detection among decoys compared to conventional scoring functions, while exhibiting lesser performance for the detection of low root mean square deviation (RMSD) decoy structures. Its deployment on an independent benchmark set confirms that the scoring function performs competitively relative to other scoring functions. All data and scripts used are available at: https://github.com/TanemuraKiyoto/PPI-native-detection-via-LR .

The FastDesign protocol in the molecular modeling program Rosetta iterates between sequence optimization and structure refinement to stabilize de novo designed protein structures and complexes. FastDesign has been used previously to design novel protein folds and assemblies with important applications in research and medicine. To promote sampling of alternative conformations and sequences, FastDesign includes stages where the energy landscape is smoothened by reducing repulsive forces. Here, we discover that this process disfavors larger amino acids in the protein core because the protein compresses in the early stages of refinement. By testing alternative ramping strategies for the repulsive weight, we arrive at a scheme that produces lower energy designs with more native-like sequence composition in the protein core. We further validate the protocol by designing and experimentally characterizing over 4000 proteins and show that the new protocol produces higher stability proteins.

The c subunits, which constitutes the c-ring apparatus of the F 1F O-ATPase, could be the main components of the mitochondrial permeability transition pore (mPTP). The well-known modulator of the mPTP formation and opening is the cyclophilin D (CyPD), a peptidyl-prolyl cis- trans isomerase. On the loop, which connects the two hairpin α-helix of c subunit, is present the unique proline residue (Pro 40) that could be a biological target of CyPD. Indeed, the proline cis- trans isomerization might provide the switch that interconverts the open/closed states of the pore by pulling out the c-ring lipid plug.

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognising the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single chain predictions were assessed together, irrespective of whether a template was available. At CASP15 there was no single stand-out group, with most of the best-scoring groups - led by PEZYFoldings, UM-TBM and Yang Server - employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues: small size, high α-helical content and monomeric structure were other likely aggravating factors. Local divergence between prediction and target correlated with localisation at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, but should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups, including those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas, produced high quality predictions for most targets which are valuable for experimental structure determination, functional analysis and many other tasks across biology.

Ncb5or (NADH cytochrome b5 oxidoreductase) is a cytosolic ferric reductase implicated in diabetes and neurological conditions. Ncb5or comprises cytochrome b5 (b5) and cytochrome b5 reductase (b5R) domains separated by a CHORD-Sgt1 (CS) linker domain. Ncb5or redox activity depends on proper interdomain interactions to mediate electron transfer from NADH or NADPH via FAD to heme. While full-length human Ncb5or has proven resistant to crystallization, we have succeeded in obtaining high-resolution atomic structures of the b5 domain and a construct containing the CS and b5R domains (CS/b5R). Ncb5or also contains an N-terminal intrinsically disordered region of 50 residues with a distinctive, conserved L 34MDWIRL 40 motif that has no homologs in animals but is present in root lateral formation protein (RLF) in rice and Increased Recombination Center 21 (IRC21) in baker’s yeast, and in these proteins, it is likewise attached to a b5 domain. After unsuccessful attempts at crystallizing a human Ncb5or construct comprising the N-terminal region naturally fused to the b5 domain, we were able to obtain a high-resolution atomic structure of a recombinant rice RLF construct corresponding to residues 25-129 of human Ncb5or (52% sequence identity; 74% similarity). The structure reveals Trp120 (corresponding to invariant Trp37 in Ncb5or) to be part of an 11-residue α-helix (S 116QMDWLKLTRT 126) packing against two of the four helices in the b5 domain that surround heme (α2 and α5). The Trp120 side chain forms a network of interactions with the side chains of four highly conserved residues corresponding to Tyr85 and Tyr88 (α2), Cys124 (α5), and Leu47 in Ncb5or. Circular dichroism (CD) measurements of human Ncb5or fragments further support a key role of Trp37 in nucleating the formation of the N-terminal helix, whose location in the N/b5 module suggests a role in regulating the function of this multidomain redox enzyme. This study revealed for the first time an ancient origin of a helical motif in the N/b5 module as reflected by its existence in a class of cytochrome b5 proteins from three kingdoms among eukaryotes.

Experimenters face challenges and limitations while analyzing glycoproteins due to their high flexibility, stereochemistry, anisotropic effects, and hydration phenomena. Computational studies complement experiments and have been used in characterization of the structural properties of glycoproteins. However, recent investigations revealed that computational studies face significant challenges as well. Here, we introduce and discuss some of these challenges and weaknesses in the investigations of glycoproteins. We also present requirements of future developments in computational biochemistry and computational biology areas that could be necessary for providing more accurate structural property analyses of glycopro-teins using computational tools. Further theoretical strategies that need to be and can be developed are discussed herein.

The Continuous Automated Model EvaluatiOn (CAMEO) platform complements the biennial CASP experiment by conducting fully automated blind evaluations of 3D protein prediction servers based on the weekly pre‐release of sequences of those structures, which are going to be published in the upcoming release of the Protein Data Bank (PDB). While in CASP14 significant success was observed in predicting the structures of individual protein chains with high accuracy, significant challenges remain in correctly predicting the structures of complexes. By implementing fully automated evaluation of predictions for protein-protein complexes, as well as for proteins in complex with ligands, peptides, nucleic acids, or proteins containing non-canonical amino acid residues, CAMEO will assist new developments in those challenging areas of active research.

The canonical function of glutamyl-tRNA synthetase (GluRS) is to glutamylate tRNA Glu. Yet, not all bacterial GluRSs glutamylate tRNA Glu; many glutamylate both tRNA Glu and tRNA Gln, while some glutamylate only tRNA Gln and not the cognate substrate tRNA Glu. Understanding the basis of this unique tRNA Glx-specificity is important. Mutational studies have hinted at hotspot residues, both on tRNA Glx and GluRS, that play crucial roles in tRNA Glx-specificity. But the underlying structural basis remains unexplored. Majority of biochemical studies related to tRNA Glx-specificity have been performed on GluRS from Escherichia coli and other proteobacterial species. However, since the early crystal structures of GluRS and tRNA Glu-bound GluRS were from non-proteobacterial species ( Thermus thermophilus), the proteobacterial biochemical data have often been interpreted in the context of non-proteobacterial GluRS structures. Marked differences between proteo- and non-proteobacterial GluRSs have been demonstrated and therefore it is important that tRNA Glx-specificity be understood vis-a-vis proteobacterial GluRS structures. Towards this goal we have solved the crystal structure of GluRS from E. coli. Using the solved structure and several other currently available proteo- and non-proteobacterial GluRS crystal structures, we have probed the structural basis of tRNA Glx-specificity of bacterial GluRSs. Specifically, our analysis suggests a unique role played by a tRNA Glx D-helix contacting loop of GluRS in modulation of tRNA Gln-specificity. While earlier studies had identified functional hotspots on tRNA Glx that controlled tRNA Glx-specificity of GluRS, this is the first report of complementary signatures of tRNA Glx-specificity in GluRS.

Protein allergens is a health risk for consumption of soybeans. To understand allerginicity mechanism, T cell epitopes of 7 soybean allergens were predicted and screened by abilities to induce cytokine interleukin 4. The relationships among amino acid composition, properties, allergenicity and pepsin hydrolysis sites were analyzed. Among the 138 T cell epitopes identified, YIKDVFRVIPSEVLS, KDVFRVIPSEVLSNS, DVFRVIPSEVLSNSY of Gly m 6.0501 (P04347), and AKADALFKAIEAYLL, ADALFKAIEAYLLAH of Gly m 4.0101 (P26987) were the most possible epitope candidates. In T cell epitopes pattern, the frequencies of amino acids Q, D, E, P and G decreased, while F, I, N, V, K and H increased. Hydrophobic residues at positions p1 and p2 and positively charged residues in positions p13 might contribute to allergenicity. Most of epitopes could be hydrolyzed by pepsin into small polypeptides within 12 residues length, and the anti-digestive epitope regions contained I, V, S, N, and Q residues. T cell epitopes EEQRQQEGVIVELSK from Gly m 5.03 (P25974) showed resistantence to pepsin hydrolysis and would cause a higher Th2 cell response. This research provides basis for the development of hypoallergenic soybean products in the soybean industry as well as for the immunotherapy design for protein allergy.

The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without the need for time-consuming multiple sequence alignment (MSA) calculations. LSTM-based SPOT-1D-LM and NetSurfP-3.0 are the latest examples of such predictors. We present the ProteinUnetLM model using a convolutional Attention U-Net architecture that provides prediction quality and inference times at least as good as the best LSTM-based models for 8-class SS prediction (SS8). Additionally, we address the issue of the heavily imbalanced nature of the SS8 problem by extending the loss function with the Matthews correlation coefficient (MCC), and by proper assessment using previously introduced adjusted geometric mean metric (AGM). ProteinUnetLM achieved better AGM and sequence overlap score (SOV) than LSTM-based predictors, especially for the rare structures 310-helix (G), beta-bridge (B), and high curvature loop (S). It is also competitive on challenging datasets without homologs, free-modeling targets, and chameleon sequences. Moreover, ProteinUnetLM outperformed its previous MSA-based version ProteinUnet2, and provided better AGM than AlphaFold2 for 1/3 of proteins from the CASP14 dataset, proving its potential for making a significant step forward in the domain. To facilitate the usage of our solution by protein scientists, we provide an easy-to-use web interface under [https://biolib.com/SUT/ProteinUnetLM/](https://biolib.com/SUT/ProteinUnetLM/).

Many proteins must interact with molecular chaperones to achieve their native folded state in the cell. Yet, how chaperone binding and binding-site characteristics affect the folding process is poorly understood. The ubiquitous Hsp70 chaperone system prevents client-protein aggregation by holding unfolded conformations or by unfolding misfolded states. Hsp70 binding sites of client proteins comprise a nonpolar core surrounded by positively charged residues. However, a detailed analysis of Hsp70 binding sites on a proteome-wide scale is still lacking. Further, it is not known whether proteins undergo some degree of folding while chaperone bound. Here, we begin to address the above questions by identifying Hsp70 binding sites in 2,258 E. coli proteins. We find that most proteins bear at least one Hsp70 binding site and that the number of Hsp70 binding sites is directly proportional to protein size. Aggregation propensity upon release from the ribosome correlates with number of Hsp70 binding sites only in the case of large proteins. Interestingly, Hsp70 binding sites are more solvent-exposed than other nonpolar sites, in protein native states. Our findings show that the majority of E. coli proteins are systematically enabled to interact with Hsp70 even if this interaction only takes place during a fraction of the protein lifetime. In addition, our data suggest that some conformational sampling may take place within Hsp70-bound states, due to the solvent exposure of some chaperone binding sites in native proteins. In all, we propose that Hsp70-chaperone-binding traits have evolved to favor Hsp70-assisted protein folding devoid of aggregation.

Crystallographic B-factors provide direct dynamical information on the internal mobility of proteins that is closely linked to function, and are also widely used as a benchmark in assessing elastic network models. A significant question in the field is: what is the exact amount of thermal vibrations in protein crystallographic B-factors? This work sets out to answer this question. First, we carry out a thorough, statistically sound analysis of crystallographic B-factors of over 10,000 structures. Second, by employing a highly accurate all-atom model with the well-known CHARMM force field, we obtain computationally the magnitudes of thermal vibrations of nearly 1,000 structures. Our key findings are: (i) the magnitude of thermal vibrations, surprisingly, is nearly protein-independent, as a corollary to the universality in vibrational spectra of globular proteins established earlier; (ii) the magnitude of thermal vibrations is small, less than 0.1 Å2 at 100 K; (iii) the percentage of thermal vibrations in B-factors is the lowest at low resolution and low temperature (<10%) but increases to as high as 60% for structures determined at high resolution and at room temperature. The significance of this work is that it provides for the first time, using an extremely large dataset, a thorough analysis of B-factors and their thermal and static disorder components. The results clearly demonstrate that structures determined at high resolution and at room temperature have the richest dynamics information. Since such structures are relatively rare in the PDB database, the work naturally calls for more such structures to be determined experimentally.

The M42 aminopeptidases are a family of dinuclear aminopeptidases widely distributed in Prokaryotes. They are potentially associated to the proteasome, achieving complete peptide destruction. Their most peculiar characteristic is their quaternary structure, a tetrahedron-shaped particle made of twelve subunits. The catalytic site of M42 aminopeptidases is defined by seven conserved residues. Five of them are involved in metal ion binding which is important to maintain both the activity and the oligomeric state. The sixth conserved residue, a glutamate, is the catalytic base deprotonating the water molecule during peptide bond hydrolysis. The seventh residue is an aspartate whose function remains poorly understood. This aspartate residue, however, must have a critical role as it is strictly conserved in all MH clan enzymes. It forms some kind of catalytic triad with the histidine residue and the metal ion of the M2 binding site. We assess its role in TmPep1050, an M42 aminopeptidase of Thermotoga maritima, through a mutational approach. Asp-62 was substituted with alanine, asparagine, or glutamate residue. The three Asp-62 substitutions completely abolished TmPep1050 activity and impeded dodecamer formation. They also interfered with metal ion binding as only one cobalt ion is bound per subunit instead of two. The structural data showed that the Asp62Ala substitution has an impact on the active site folds becoming similar to TmPep1050 dimer. We propose a structural role for Asp-62, helping to stabilize a crucial loop in the active site and to position correctly the catalytic base and a metal ion ligand of the M1 site.

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.

As a key cellular sensor, the TRPV1 channel undergoes a gating transition from a closed state to an open state in response to many physical and chemical stimuli. This transition is regulated by small-molecule ligands including lipids and various agonists/antagonists, but the underlying molecular mechanisms remain obscure. Thanks to recent revolution in cryo-electron microscopy, a growing list of new structures of TRPV1 and other TRPV channels have been solved in complex with various ligands including lipids. Toward elucidating how ligand binding correlates with TRPV1 gating, we have performed extensive molecular dynamics simulations (with cumulative time of 20 μs), starting from high-resolution structures of TRPV1 in both the closed and open states. By comparing between the open and closed state ensembles, we have identified state-dependent binding sites for small-molecule ligands in general and lipids in particular. We further use machine learning to predict top ligand-binding sites as important features to classify the closed vs open states. The predicted binding sites are thoroughly validated by matching homologous sites in all structures of TRPV channels bound to lipids and other ligands, and with previous functional/mutational studies of ligand binding in TRPV1. Taken together, this study has integrated rich structural, dynamic, and functional data to inform future design of small-molecular drugs targeting TRPV1.

In Azospirillum brasilense, an extra-cytoplasmic function sigma factor (RpoE10) shows the characteristic 119 amino acid long C-terminal extension found in ECF41-type sigma factors, which possesses three conserved motifs (WLPEP, DGGGR, and NPDKV), one in the linker region between the sigma 2 and sigma 4, and the other two in the SnoaL_2 domain of the C-terminal extension. Here, we have described the role of the two conserved motifs in the SnoaL_2 domain of RpoE10 in the inhibition and activation of its activity, respectively. Truncation of the distal part of the C-terminal sequence of the RpoE10 (including NPDKV but excluding the DGGGR motif) results in its promoter’s activation suggesting autoregulation. Further truncation of the C-terminal sequence up to its proximal part, including NPDKV and DGGGR motif, abolished promoter activation. Replacement of NPDKV motif with NAAAV in RpoE10 increased its ability to activate its promoter, whereas replacement of DGGGR motif led to reduced promoter activation. We have explored the dynamic modulation of sigma2 – sigma4 domains and the relevant molecular interactions mediated by the two conserved motifs of the SnoaL2 domain using molecular dynamics simulation. The analysis enabled us to explain that the NPDKV motif located distally in the C-terminus negatively impacts transcriptional activation. In contrast, the DGGGR motif found proximally of the C-terminal extension is required to activate RpoE1

G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and represent the primary targets of about one third of currently marketed drugs. Despite the critical importance, experimental structures have been determined for only a limited portion of GPCRs and functional mechanisms of GPCRs remain poorly understood. Here, we have constructed novel sequence coevolutionary models of the A and B classes of GPCRs and compared them with residue contact frequency maps generated with available experimental structures. Significant portions of structural residue contacts were successfully detected in the sequence-based covariational models. “Exception” residue contacts predicted from sequence coevolutionary models but not available structures added missing links that were important for GPCR activation and allosteric modulation. Moreover, we identified distinct residue contacts involving different sets of functional motifs for GPCR activation, such as the Na+ pocket, CWxP, DRY, PIF and NPxxY motifs in the class A and the HETx and PxxG motifs in the class B. Finally, we systematically uncovered critical residue contacts tuned by allosteric modulation in the two classes of GPCRs, including those from the activation motifs and particularly the extracellular and intracellular loops in class A GPCRs. These findings provide a promising framework for rational design of ligands to regulate GPCR activation and allosteric modulation.