Investigating Protein Structure and Evolution with SCOP2

A Conserved Ribosomal Protein Has Entirely Dissimilar Structures in Different Organisms

Ribosomes from different species can markedly differ in their composition by including dozens of ribosomal proteins that are unique to specific lineages but absent in others. However, it remains unknown how ribosomes acquire new proteins throughout evolution. Here, to help answer this question, we describe the evolution of the ribosomal protein msL1/msL2 that was recently found in ribosomes from the parasitic microorganism clade, microsporidia. We show that this protein has a conserved location in the ribosome but entirely dissimilar structures in different organisms: in each of the analyzed species, msL1/msL2 exhibits an altered secondary structure, an inverted orientation of the N-termini and C-termini on the ribosomal binding surface, and a completely transformed 3D fold. We then show that this fold switching is likely caused by changes in the ribosomal msL1/msL2-binding site, specifically, by variations in rRNA. These observations allow us to infer an evolutionary scenario in which a small, positively charged, de novo-born unfolded protein was first captured by rRNA to become part of the ribosome and subsequently underwent complete fold switching to optimize its binding to its evolving ribosomal binding site. Overall, our work provides a striking example of how a protein can switch its fold in the context of a complex biological assembly, while retaining its specificity for its molecular partner. This finding will help us better understand the origin and evolution of new protein components of complex molecular assemblies-thereby enhancing our ability to engineer biological molecules, identify protein homologs, and peer into the history of life on Earth.

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.

What Have We Learned from Design of Function in Large Proteins?

The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.

SIFTS: updated Structure Integration with Function, Taxonomy and Sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins

The Structure Integration with Function, Taxonomy and Sequences resource (SIFTS; http://pdbe.org/sifts/) was established in 2002 and continues to operate as a collaboration between the Protein Data Bank in Europe (PDBe; http://pdbe.org) and the UniProt Knowledgebase (UniProtKB; http://uniprot.org). The resource is instrumental in the transfer of annotations between protein structure and protein sequence resources through provision of up-to-date residue-level mappings between entries from the PDB and from UniProtKB. SIFTS also incorporates residue-level annotations from other biological resources, currently comprising the NCBI taxonomy database, IntEnz, GO, Pfam, InterPro, SCOP, CATH, PubMed, Ensembl, Homologene and automatic Pfam domain assignments based on HMM profiles. The recently released implementation of SIFTS includes support for multiple cross-references for proteins in the PDB, allowing mappings to UniProtKB isoforms and UniRef90 cluster members. This development makes structure data in the PDB readily available to over 1.8 million UniProtKB accessions.

Structure of the BRK domain of the SWI/SNF chromatin remodeling complex subunit BRG1 reveals a potential role in protein-protein interactions

BRG1/SMARCA4 and its paralog BRM/SMARCA2 are the ATPase subunits of human SWI/SNF chromatin remodeling complexes. These multisubunit assemblies can act as either tumor suppressors or drivers of cancer, and inhibiting both BRG1 and BRM, is emerging as an effective therapeutic strategy in diverse cancers. BRG1 and BRM contain a BRK domain. The function of this domain is unknown, but it is often found in proteins involved in transcription and developmental signaling in higher eukaryotes, in particular in proteins that remodel chromatin. We report the NMR structure of the BRG1 BRK domain. It shows similarity to the glycine-tyrosine-phenylalanine (GYF) domain, an established protein-protein interaction module. Computational peptide-binding-site analysis of the BRK domain identifies a binding site that coincides with a highly conserved groove on the surface of the protein. This sets the scene for experiments to elucidate the role of this domain, and evaluate the potential of targeting it for cancer therapy.

The Genome3D Consortium for Structural Annotations of Selected Model Organisms

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

An In-Silico Sequence-Structure-Function Analysis of the N-Terminal Lobe in CT Group Bacterial ADP-Ribosyltransferase Toxins

The C3-like toxins are single-domain proteins that represent a minimal mono-ADP-ribosyl transferase (mART) enzyme with a simple model scaffold for the entire cholera toxin (CT)-group. These proteins possess a single (A-domain) that modifies Rho proteins. In contrast, C2-like toxins require a binding/translocation partner (B-component) for intoxication. These are A-only toxins that contain the E-x-E motif, modify G-actin, but are two-domains with a C-domain possessing enzymatic activity. The N-domain of the C2-like toxins is unstructured, and its function is currently unknown. A sequence-structure-function comparison was performed on the N-terminal region of the mART domain of the enzymatic component of the CT toxin group in the CATCH fold (3.90.210.10). Special consideration was given to the N-domain distal segment, the α-lobe (α₁–α₄), and its different roles in these toxin sub-groups. These results show that the role of the N-terminal α-lobe is to provide a suitable configuration (i) of the α₂–α₃ helices to feature the α3-motif that has a role in NAD⁺ substrate binding and possibly in the interaction with the protein target; (ii) the α₃–α₄ helices to provide the α_3/4-loop with protein-protein interaction capability; and (iii) the α₁-N_tail that features specialized motif(s) according to the toxin type (A-only or A-B toxins) exhibiting an effect on the catalytic activity via the ARTT-loop, with a role in the inter-domain stability, and with a function in the binding and/or translocation steps during the internalization process.

HITS-PR-HHblits: protein remote homology detection by combining PageRank and Hyperlink-Induced Topic Search

As one of the most important fundamental problems in protein sequence analysis, protein remote homology detection is critical for both theoretical research (protein structure and function studies) and real world applications (drug design). Although several computational predictors have been proposed, their detection performance is still limited. In this study, we treat protein remote homology detection as a document retrieval task, where the proteins are considered as documents and its aim is to find the highly related documents with the query documents in a database. A protein similarity network was constructed based on the true labels of proteins in the database, and the query proteins were then connected into the network based on the similarity scores calculated by three ranking methods, including PSI-BLAST, Hmmer and HHblits. The PageRank algorithm and Hyperlink-Induced Topic Search (HITS) algorithm were respectively performed on this network to move the homologous proteins of query proteins to the neighbors of the query proteins in the network. Finally, PageRank and HITS algorithms were combined, and a predictor called HITS-PR-HHblits was proposed to further improve the predictive performance. Tested on the SCOP and SCOPe benchmark datasets, the experimental results showed that the proposed protocols outperformed other state-of-the-art methods. For the convenience of the most experimental scientists, a web server for HITS-PR-HHblits was established at http://bioinformatics.hitsz.edu.cn/HITS-PR-HHblits, by which the users can easily get the results without the need to go through the mathematical details. The HITS-PR-HHblits predictor is a protocol for protein remote homology detection using different sets of programs, which will become a very useful computational tool for proteome analysis.

Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER

With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITE^CSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc.

Evolution-Inspired Computational Design of Symmetric Proteins

Monomeric proteins with a number of identical repeats creating symmetrical structures are potentially very valuable building blocks with a variety of bionanotechnological applications. As such proteins do not occur naturally, the emerging field of computational protein design serves as an excellent tool to create them from nonsymmetrical templates. Existing pseudo-symmetrical proteins are believed to have evolved from oligomeric precursors by duplication and fusion of identical repeats. Here we describe a computational workflow to reverse-engineer this evolutionary process in order to create stable proteins consisting of identical sequence repeats.