Proteins: Structure, Function, and Bioinformatics

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ISSN / EISSN : 0887-3585 / 1097-0134
Published by: Wiley (10.1002)
Total articles ≅ 9,201
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Ruojing Zhang, Michael C. Stahr,
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26190

Protein β-turn classification remains an area of ongoing development in structural biology research. While the commonly used nomenclature defining type I, type II and type IV β-turns was introduced in the 1970s and 1980s, refinements of β-turn type definitions have been introduced as recently as 2019 by Dunbrack, Jr and co-workers who expanded the number of β-turn types to 18 [Shapovalov et al, PLOS Computat. Biol., 15 , e1006844, 2019]. Based on their analysis of 13 030 turns from 1074 ultrahigh resolution (≤ 1.2Å) protein structures, they used a new clustering algorithm to expand the definitions used to classify protein β-turns and introduced a new nomenclature system. We recently encountered a specific problem when classifying β-turns in crystal structures of pentapeptide repeat proteins (PRPs) determined in our lab that are largely composed of β-turns that often lie close to, but just outside of, canonical β-turn regions. To address this problem, we devised a new scheme that merges the Klyne-Prelog stereochemistry nomenclature and definitions with the Ramachandran plot. The resulting Klyne-Prelog-modified Ramachandran plot scheme defines 1296 distinct potential β-turn classifications that cover all possible protein β-turn space with a nomenclature that indicates the stereochemistry of i + 1 and i + 2 backbone dihedral angles. The utility of the new classification scheme was illustrated by re-classification of the β-turns in all known protein structures in the PRP superfamily and further assessed using a database of 16 657 high-resolution protein structures (≤ 1.5 Å) from which 522 776 β-turns were identified and classified.
H. I. Rösner, M. Caldarini, G. Potel, D. Malmodin, M. A. Vanoni, A. Aliverti, R. A. Broglia, ,
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26189

The denatured state of several proteins has been shown to display transient structures that are relevant for folding, stability and aggregation. To detect them by nuclear magnetic resonance (NMR) spectroscopy, the denatured state must be stabilized by chemical agents or changes in temperature. This makes the environment different from that experienced in biologically relevant processes. Using high-resolution heteronuclear NMR spectroscopy, we have characterized several denatured states of a monomeric variant of HIV-1 protease, which is natively structured in water, induced by different concentrations of urea, guanidinium chloride and acetic acid. We have extrapolated the chemical shifts and the relaxation parameters to the denaturant-free denatured state at native conditions, showing that they converge to the same values. Subsequently, we characterized the conformational properties of this biologically relevant denatured state under native conditions by advanced molecular dynamics simulations and validated the results by comparison to experimental data. We show that the denatured state of HIV-1 protease under native conditions displays rich patterns of transient native and non-native structures, which could be of relevance to its guidance through a complex folding process.
Elias Primetis, Spyridon Chavlis,
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26178

Intra-protein residual vicinities depend on the involved amino acids. Energetically favorable vicinities (or interactions) have been preserved during evolution, while unfavorable vicinities have been eliminated. We describe, statistically, the interactions between amino acids using resolved protein structures. Based on the frequency of amino acid interactions, we have devised an amino acid substitution model that implements the following idea: amino acids that have similar neighbors in the protein tertiary structure can replace each other, while substitution is more difficult between amino acids that prefer different spatial neighbors. Using known tertiary structures for α-helical membrane (HM) proteins, we build evolutionary substitution matrices. We constructed maximum likelihood phylogenies using our amino acid substitution matrices and compared them to widely-used methods. Our results suggest that amino acid substitutions are associated with the spatial neighborhoods of amino acid residuals, providing, therefore, insights into the amino acid substitution process.
Ma Contreras, L Macaya, V Manrique, F Camacho, A González, R Montesinos, Jr Toledo,
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26177

The neutralization of TNFα with biopharmaceuticals is a successful therapy for inflammatory diseases. Currently, one of the main TNFα-antagonists is Etanercept, a dimeric TNF-R2 ectodomain. Considering that TNFα and its receptors are homotrimers, we proposed that a trimeric TNF-R2 ectodomain could be an innovative TNFα-antagonist. Here, the 3cTNFR2 protein was designed by the fusion of the TNF-R2 ectodomain with the collagen XV trimerization domain. 3cTNFR2 was produced in HEK293 cells and purified by IMAC. Monomers, dimers, and trimers of 3cTNFR2 were detected. The interaction 3cTNFR2-TNFα was assessed. By microscale thermophoresis, the KD value for the interaction was 4.17 ± 0.88 nM, and complexes with different molecular weights were detected by SEC-HPLC. Moreover, 3cTNFR2 neutralized the TNFα-induced cytotoxicity totally in vitro. Although more studies are required to evaluate the anti-inflammatory effect, the results suggest that 3cTNFR2 could be a TNFα-antagonist agent.
Shiliang Li, Chaoqian Cai, Jiayu Gong, Xiaofeng Liu,
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26176

The expansion of three-dimensional protein structures and enhanced computing power have significantly facilitated our understanding of protein sequence/structure/function relationships. A challenge in structural genomics is to predict the function of uncharacterized proteins. Protein function deconvolution based on global sequence or structural homology is impracticable when a protein relates to no other proteins with known function, and in such cases, functional relationships can be established by detecting their local ligand binding site similarity. Here, we introduce a sequence order-independent comparison algorithm, PocketShape, for structural proteome-wide exploration of protein functional site by fully considering the geometry of the backbones, orientation of the sidechains and physiochemical properties of the pocket-lining residues. PocketShape is efficient in distinguishing similar from dissimilar ligand binding site pairs by retrieving 99.3% of the similar pairs while rejecting 100% of the dissimilar pairs on a dataset containing 1538 binding site pairs. This method successfully classifies 83 enzyme structures with diverse functions into 12 clusters, which is highly in accordance with the actual SCOP classification. PocketShape also achieves superior performances than other methods in protein profiling based on experimental data. Potential new applications for representative SARS-CoV-2 drugs Remdesivir and 11a are predicted. The high accuracy and time-efficient characteristics of PocketShape will undoubtedly make it a promising complementary tool for proteome-wide protein function inference and drug repurposing study.
, Jimin Pei, , , Nick V Grishin
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26172

This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.
Sameer Hassan, Mats Töpel,
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26175

Interaction between protein and ligands are ubiquitous in a biological cell, and understanding these interactions at the atom level in protein-ligand complexes is crucial for structural bioinformatics and drug discovery. Here, we present a web-based protein-ligand interaction application named Ligand Binding Site Comparison (LiBiSCo) for comparing the amino acid residues interacting with atoms of a ligand molecule between different protein-ligand complexes available in the Protein Data Bank (PDB) database. The comparison is performed at the ligand atom level irrespectively of having binding site similarity or not between the protein structures of interest. The input used in LiBiSCo is one or several PDB IDs of protein-ligand complex(es) and the tool returns a list of identified interactions at ligand atom level including both bonded and non-bonded interactions. A sequence profile for the interaction for each ligand atoms is provided as a WebLogo. The LiBiSco is useful in understanding ligand binding specificity and structural promiscuity among families that are structurally unrelated. The LiBiSCo tool can be accessed through
François Beuvin, Simon de Givry, Thomas Schiex, , David Simoncini
Proteins: Structure, Function, and Bioinformatics; doi:10.1002/prot.26174

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.
Proteins: Structure, Function, and Bioinformatics, Volume 89, pp 1061-1061; doi:10.1002/prot.25931

Proteins: Structure, Function, and Bioinformatics, Volume 89; doi:10.1002/prot.25933

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