On the difference of the enhanced power graph and the power graph of a finite group

Funding: The first author is supported by the PhD fellowship of CSIR (File no. 08/155 (0086)/2020 − EMR − I), Govt. of India. The second author acknowledges the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme Groups, representations and applications: new perspectives (supported by EPSRC grant no. EP/R014604/1), where he held a Simons Fellowship. The third author acknowledges the funding of DST grant SR/F ST/MS − I/2019/41 and MT R/2022/000020, Govt. of India. The fourth author acknowledges SERB-National Post-Doctoral Fellowship (File No. PDF/2021/001899) during the preparation of this work.The difference graph of a finite group D (G) is the difference of the enhanced power graph of G and the power graph of G, where all isolated vertices are removed. In this paper we study the connectedness and perfectness of D (G) with respect to various properties of the underlying group G. We also find several connections between the difference graph of G and the Gruenberg-Kegel graph of G. We also examine the operation of twin reduction on graphs, a technique which produces smaller graphs which may be easier to analyze. Applying this technique to simple groups can have a number of outcomes, not fully understood, but including some graphs with large girth.Peer reviewe

PDB-wide identification of biological assemblies from conserved quaternary structure geometry

International audienceProtein structures are key to understanding bio-molecular mechanisms and diseases, yet their interpretation is hampered by limited knowledge of their biologically relevant quaternary structures (QSs). A critical challenge in obtaining QSs from crystallographic data is to distinguish biological interfaces from crystal packing contacts. We tackled this challenge with two strategies for aligning and comparing QS states, both across homologs (QSalign), and across data repositories (QSbio). QS conservation across homologs was a remarkably strong predictor of biological relevance and allowed annotating of >80,000 biological QS states. QS conservation across methods enabled us to create a meta-predictor, QSbio, from which we inferred confidence estimates for >110,000 assemblies in the Protein Data Bank, which approach the accuracy of manual curation. Based on the dataset obtained, we analyzed interaction interfaces among pairs of structurally conserved QSs. This revealed a striking plasticity of interfaces, which can maintain a similar interaction geometry through widely different chemical properties

The Role of Intrinsically Unstructured Proteins in Neurodegenerative Diseases

The number and importance of intrinsically disordered proteins (IUP), known to be involved in various human disorders, are growing rapidly. To test for the generalized implications of intrinsic disorders in proteins involved in Neurodegenerative diseases, disorder prediction tools have been applied to three datasets comprising of proteins involved in Huntington Disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD). Results show, in general, proteins in disease datasets possess significantly enhanced intrinsic unstructuredness. Most of these disordered proteins in the disease datasets are found to be involved in neuronal activities, signal transduction, apoptosis, intracellular traffic, cell differentiation etc. Also these proteins are found to have more number of interactors and hence as the proportion of disorderedness (i.e., the length of the unfolded stretch) increased, the size of the interaction network simultaneously increased. All these observations reflect that, “Moonlighting” i.e. the contextual acquisition of different structural conformations (transient), eventually may allow these disordered proteins to act as network “hubs” and thus they may have crucial influences in the pathogenecity of neurodegenerative diseases

Interaction of Virstatin with Human Serum Albumin: Spectroscopic Analysis and Molecular Modeling

Virstatin is a small molecule that inhibits Vibrio cholerae virulence regulation, the causative agent for cholera. Here we report the interaction of virstatin with human serum albumin (HSA) using various biophysical methods. The drug binding was monitored using different isomeric forms of HSA (N form ∼pH 7.2, B form ∼pH 9.0 and F form ∼pH 3.5) by absorption and fluorescence spectroscopy. There is a considerable quenching of the intrinsic fluorescence of HSA on binding the drug. The distance (r) between donor (Trp214 in HSA) and acceptor (virstatin), obtained from Forster-type fluorescence resonance energy transfer (FRET), was found to be 3.05 nm. The ITC data revealed that the binding was an enthalpy-driven process and the binding constants Ka for N and B isomers were found to be 6.09×105 M−1 and 4.47×105 M−1, respectively. The conformational changes of HSA due to the interaction with the drug were investigated from circular dichroism (CD) and Fourier Transform Infrared (FTIR) spectroscopy. For 1∶1 molar ratio of the protein and the drug the far-UV CD spectra showed an increase in α- helicity for all the conformers of HSA, and the protein is stabilized against urea and thermal unfolding. Molecular docking studies revealed possible residues involved in the protein-drug interaction and indicated that virstatin binds to Site I (subdomain IIA), also known as the warfarin binding site

PDBe-KB: a community-driven resource for structural and functional annotations.

The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages-the PDBe-KB aggregated views of structure data-which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy

Inferring and Using Protein Quaternary Structure Information from Crystallographic Data

A precise knowledge of the quaternary structure of proteins is essential to illuminate both their function and their evolution. The major part of our knowledge on quaternary structure is inferred from X-ray crystallography data, but this inference process is hard and error-prone. The difficulty lies in discriminating fortuitous protein contacts, which make up the lattice of protein crystals, from biological protein contacts that exist in the native cellular environment. Here, we review methods devised to discriminate between both types of contacts and describe resources for downloading protein quaternary structure information and identifying high-confidence quaternary structures. The use of high-confidence datasets of quaternary structures will be critical for the analysis of structural, functional, and evolutionary properties of proteins.</p

PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

An accurate understanding of biomolecular mechanisms and diseases requires information on protein quaternary structure (QS). A critical challenge in inferring QS information from crystallography data is distinguishing biological interfaces from fortuitous crystal-packing contacts. Here, we employ QS conservation across homologs to infer the biological relevance of hetero-oligomers. We compare the structures and compositions of hetero-oligomers, which allow us to annotate 7,810 complexes as physiologically relevant, 1,060 as likely errors, and 1,432 with comparative information on subunit stoichiometry and composition. Excluding immunoglobulins, these annotations encompass over 51% of hetero-oligomers in the PDB. We curate a dataset of 577 hetero-oligomeric complexes to benchmark these annotations, which reveals an accuracy &gt;94%. When homology information is not available, we compare QS across repositories (PDB, PISA, and EPPIC) to derive confidence estimates. This work provides high-quality annotations along with a large benchmark dataset of hetero-assemblies.</p

<b>On the abundance and importance of AXXXA sequence motifs in globular proteins and their involvement in </b>C_β<b>—</b>C_β<b>interaction</b>

This file includes dataset and figures.</p

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

The identification of physiologically relevant quaternary structures (QSs) in crystal lattices is challenging. To predict the physiological relevance of a particular QS, QSalign searches for homologous structures in which subunits interact in the same geometry. This approach proved accurate but was limited to structures already present in the Protein Data Bank (PDB). Here, we introduce a webserver ( www.QSalign.org ) allowing users to submit homo-oligomeric structures of their choice to the QSalign pipeline. Given a user-uploaded structure, the sequence is extracted and used to search homologs based on sequence similarity and PFAM domain architecture. If structural conservation is detected between a homolog and the user-uploaded QS, physiological relevance is inferred. The web server also generates alternative QSs with PISA and processes them the same way as the query submitted to widen the predictions. The result page also shows representative QSs in the protein family of the query, which is informative if no QS conservation was detected or if the protein appears monomeric. These representative QSs can also serve as a starting point for homology modeling. </p