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138 results for “protein mutation”
Overcoming Limitation of AlphaFold2 by Deep-mutational Scanning and Stability-Selection of Protein Sequences
<p>This repository contains the processed datasets and corresponding code used in our study. While AlphaFold2 revolutionizes protein structure prediction, its accuracy critically depends on evolutionary information from natural homologs—limiting applications for proteins with sparse sequence families. Here, we bypass this bottleneck by employing deep mutational scanning and stability-guided selection to generate artificial homologs. Fed into AlphaFold2, these synthetic sequences match the accuracy achieved on well-predicted proteins with rich natural homology, while providing highly accurate predictions for difficult targets—including orphan proteins previously deemed "unpredictable." Our approach achieves high accuracy (<3 Å RMSD for 5/8 and <2 Å RMSD for 7/8 targets after excluding intrinsically flexible regions). Thus, integrating simple, scalable molecular biology (mutagenesis/selection) with high-throughput sequencing can deliver the accuracy similar to but at a fraction of the cost and time of traditional experimental structure-determination methods. This hybrid framework could democratize high-resolution structural biology, opening avenues to determine structures of protein complexes, modified proteins, and condition-dependent conformations. </p>
Characterization of a loss-offunction NSF attachment protein beta mutation in monozygotic triplets affected with epilepsy and autism using cortical neurons from proband-derived and CRISPR-corrected induced pluripotent stem cell lines
<p>RNA-seq data of matured cortical neurons (8-weeks old) derived from the induced pluripoent stem cells (iPSC) of control parents (CtrlF and CtrlM) and corrected proband. There are three replicates (Rep1, Rep2, Rep3) for each sample with Forwad read (R1_001.fastq.gz)</p> <p>CtrlF: Control Father sample</p> <p>CtrlM: Control mother sample</p> <p>NDD_01_Corr_Het: Heterozygous correction of NAPB mutation (c.354+2T>G) in NDD_01 proband</p> <p>NDD_05_Corr_Hom: Homozygous correction of NAPB mutation (c.354+2T>G) in NDD_05 proband</p>
Dynamics of SARS-CoV-2 spike protein in open and closed states and identification of key structural perturbations upon mutations
<p>The SARS-Cov-2 spike protein resides on the exterior surface of the coronavirus, and therefore, acts as the first point of contact that mediates cell attachment and fusion. During this process, it undergoes dramatic conformational changes upon host receptor binding. We are leveraging high-performance computing to identify these structural perturbations in wildtype and mutant spike protein models. The files contain structures from molecular dynamics simulations of closed SARS-Cov-2 spike protein embedded in POPC membrane.</p>
Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues
<p>AlphaFold and RoseTTAFold models of domains of disease associated human proteins without structures/known homologues.</p> <p>Tables containing the model quality, model region, sequence alignment statistics, matched FunFam, associated GO terms for the FunFam, ddG of mutation, pathogenicity of mutation, if mutation is near a predicted functional site (conserved residue/ligand binding site/protein-protein interface)</p>
Secretin-like class B G protein-coupled receptor (GPCR) mutation data set
<p>Curated set of 2463 quantitative mutation data points covering 13 secretin-like class B G protein-coupled receptors.</p> <p>Annotated according to GPCRdb standards (http://gpcrdb.org/), complemented by GPCRdb Ballesteros-Weinstein numbers assigned using GPCRdb KNIME nodes (https://github.com/3D-e-Chem/knime-gpcrdb).</p> <p>The data set is build from data sets published in:</p> <p>- Siu et al. Nature 2013, 499: 444-449. doi:10.1038/nature12393</p> <p>- Hollenstein, de Graaf et al. Tr Pharmacol Sci 2014, 35: 12-22. doi: 10.1016/j.tips.2013.11.001</p> <p>- Yang, de Graaf et al. J Biol Chem 2016, 291: 12991-3004. doi: 10.1074/jbc.M116.721977</p>
Data from: The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function
<p>An underexplored question in evolutionary genetics concerns the extent to which mutational bias in the production of genetic variation influences outcomes and pathways of adaptive molecular evolution. In the genomes of at least some vertebrate taxa, an important form of mutation bias involves changes at CpG dinucleotides: If the DNA nucleotide cytosine (C) is immediately 5' to guanine (G) on the same coding strand, and if the C is methylated, then C→T and G→A mutations occur at an elevated rate relative to mutations at non-CpG sites. Here we examine experimental data from case studies in which it has been possible to identify the causative substitutions that are responsible for adaptive changes in the functional properties of vertebrate hemoglobin (Hb). Specifically, we examine the molecular basis of convergent increases in Hb-O<sub>2</sub> affinity in high-altitude birds. Using a data set of experimentally verified, affinity-enhancing mutations in the Hbs of highland avian taxa, we tested whether causative changes are enriched for mutations at CpG dinucleotides relative to the frequency of CpG mutations among all possible missense mutations. The tests revealed that a disproportionate number of causative amino acid replacements were attributable to CpG mutations, demonstrating that mutation bias can influence outcomes of molecular adaptation.</p>
All-atom Molecular Dynamics Simulations of Meiosis 1-associated protein (M1AP) to Investagate the Impact of Known Missense Mutations Associated with Male Infertility through Non-obstructive Azoospermia
<p>Protein structure of meiosis 1-associated protein (M1AP) was modelled by using GalaxyWeb (from Seok Lab). We used this model to investigate the impact of variants (i.e., S50P, R266Q, P389L, G317R, and L430P) in M1AP which were recently associated with non-obstructive azoospermia (NOA). NOA is a male infertility-related condition causing absence of sperm in the seminal fluid due to meiosis failure. We aimed to elucidate the pathogenicity mechanisms of these five missense NOA-related mutations on M1AP by performing molecular modeling and molecular dynamics (MD) simulations. This dataset includes the results of 1000 ns MD simulations (two repeats, each 500 ns) for each of the mutant and wild-type systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system charge with 0.15 M KCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. NPT ensemble was applied for whole simulations. Two step minimization & equilibration procedure was performed: (1) 5,000-step minimization and 1 ns equilibrium with constraints on the protein; (2) 5,000-step minimization and 1 ns equilibrium without the constraints on the protein. All related configuration files for wild-type system were also included to the dataset. Production simulations were run twice along 500 ns by using different random seeds to assign the velocities from Boltzmann distribution (total simulation time for each system was 1000 ns, which are given as 500 ns repeat 1, and 500 ns repeat 2). The production simulations were supplied in the dataset. "out" and "log" files were used for energy analysis.</p> <p>For all analysis scripts, see https://github.com/ugerlevik/M1AP_analysis.</p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</p>
Data from: The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function
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Fast and exact single and double mutation-response scanning of proteins: data and results
<p>Data, data analysis code, and results accompanying the manuscript: <strong>Fast and exact single and double mutation-response scanning of proteins: </strong>https://doi.org/10.1101/2020.10.23.352955</p> <p>In addition, the code needed to generate the data must be downloaded from <a href="https://doi.org/10.5281/zenodo.4123149">https://doi.org/10.5281/zenodo.4123149</a> and put in a subfolder named mutenm/</p>
Aminergic G protein-coupled receptor (GPCR) mutation data set
<p>Curated set of 6553 quantitative mutation data points covering 34 aminergic G protein-coupled receptors, annotated according to GPCRdb standards (http://gpcrdb.org/) building from the data set published by Kooistra et al. in BJP 2013, 170, 101.</p>
Chemokine G protein-coupled receptor (GPCR) mutation data set
<p>Curated set of 2004 quantitative mutation data points covering 10 chemokine G protein-coupled receptors, annotated according to GPCRdb standards (http://gpcrdb.org/) building from the data set published by Scholten et al. in BJP 2012, 165, 1617.</p>
Chemokine G protein-coupled receptor (GPCR) mutation data set - revision
<p>Curated set of 2004 quantitative mutation data points covering 10 chemokine G protein-coupled receptors, annotated according to GPCRdb standards (http://gpcrdb.org/) building from the data set published by Scholten et al. in BJP 2012, 165, 1617.</p> <p>Errors in the previous version were corrected and new data points for CCR2 and CCR9 were added.</p>
Data for: Viral receptor-binding protein evolves new function through mutations that cause trimer instability and functional heterogeneity
<p>When proteins evolve new activity, a concomitant decrease in stability is often observed because the mutations that confer new activity can destabilize the native fold. In the conventional model of protein evolution, reduced stability is considered a purely deleterious cost of molecular innovation because unstable proteins are prone to aggregation and are sensitive to environmental stressors. However, recent work has revealed that non-native, often unstable protein conformations play an important role in mediating evolutionary transitions, raising the question of whether instability can itself potentiate the evolution of new activity. We explored this question in a bacteriophage receptor binding protein (RBP) during host-range evolution. We studied the properties of the RBP of bacteriophage before and after host-range evolution and demonstrated that the evolved protein is relatively unstable and may exist in multiple conformations with unique receptor preferences. Through a combination of structural modeling and in vitro oligomeric state analysis, we found that the instability arises from mutations that interfere with trimer formation. This study raises the intriguing possibility that protein instability might play a previously unrecognized role in mediating host-range expansions in viruses.</p>
PRESCOTT/ESCOTT/iGEMME mutational effect predictions of all single point mutations for ~3000 proteins
<p>This dataset contains all necessary data to reproduce our analyses on ~3000 proteins:</p> <p>It is made up of 4 compressed datasets. </p> <ol> <li>All colabfold MSAs and structures for ~3000 proteins: colabfold-sequences-structures-3000-proteins.tar.bz2</li> <li>All escott prediction: escott-v-1-6-0-max-two-components-colabfold-msas-entire-single-point-mutations-cvRC7.tgz</li> <li>All igemme predictions: escott-v-1-6-0-tjet-only-colabfold-msas-entire-single-point-mutations.tgz</li> <li>All gnomad v4.0.0 csv files used for prescott predictions: gnomadv4-0-0-csv-files.tgz</li> </ol> <p>The dataset contains list of 500 proteins used in determining PRESCOTT coefficients in TRAINING-all_gene_names_v4_set1_no_acmg.txt. </p> <p>Furthermore, the list of 1883 proteins used to measure for testing purpose is given in TESTING-all_gene_names_v4_set1_with_acmg.txt. </p>
Data from: Protein Conformational Space at the Edge of Allostery: Turning a Non-allosteric Malate Dehydrogenase into an "Allosterized" Enzyme using Evolution Guided Punctual Mutations
<p>This data accompanies the paper entitled <em>Protein Conformational Space at the Edge of Allostery: Turning a Non-allosteric Malate Dehydrogenase into an “Allosterized” Enzyme using Evolution Guided Punctual Mutations</em></p> <p>The zip archive contains the results of molecular dynamics simulations of the 4 systems investigated in the paper: wt of A. ful MalDH and three mutants. Each system has been simulated at two temperatures, 300 K and 340 K. Starting configurations of the proteins after equilibration are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Raw NGS Data for "Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins"
<p>This directory contains relevant fastq files used for deep sequencing analysis in the publication “Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins”. </p> <p>Fastq files are provided for presorted, uninduced and induced populations from DMS experiments of four homologs (TtgR, TetR, RolR, and MphR). Three replicates were performed for each sample.</p> <p>Data analysis of this deep sequencing data was performed using custom scripts, which are described in the methods section of the publication.</p>
Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD
<p>Set of data and scripts for the analysis of RAMD dissociation trajectories generated for BN-NS and BT/BCT-BPTI mutants, reported in the manuscript:</p> <p><br>"Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD"</p> <p>by Giulia D'Arrigo, Daria B. Kokh, Ariane Nunes-Alves and Rebecca C. Wade</p> <p> </p> <ol> <li>RAMD movies of WT Bn-Bs and D35Abn mutant dissociation pathway (WT-diss.mp4 and D35Abn-diss.mp4)</li> <li>Protein-Protein.zip contains:</li> </ol> <ul> <li>README - set of instructions to go through files and for the use of the scripts</li> <li>Jupyter notebooks: tauRAMD_PP_Residence_time.ipynb and tauRAMD_PP_Unbinding_pathways.ipynb</li> <li>Scripts used for analysis and postprocessing RAMD simulations - for the correct usage of the scripts check the corresponding file header</li> <li>System input files in BN-BS/ and BT_BCT-BPTI/</li> <li>Experimental data used</li> <li>2 RAMD movies (.mp4) showing dissociation of WT Bn-Bs and D35Abn mutant</li> </ul>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Data accompanying "In silico analysis of the profilaggrin sequence indicates alterations in the stability, degradation route, and intracellular protein fate in filaggrin null mutation carriers" article.
<p>This research was supported by the National Science Centre, Poland, grant PRELUDIUM number 2021/41/N/NZ1/03473 to NS, National Science Centre, Poland, grant SONATA BIS number 2019/34/E/NZ6/00354 to DG-O, as well as POIR.04.04.00-00-21FA/16–00 grant, carried out within the First TEAM programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund (awarded to DG-O). WP was supported by the National Science Centre, Poland, grant SONATA-BIS number 2021/42/E/NZ1/00190. SB is supported by a Wellcome Trust Senior Research Fellowship (220875/Z/20/Z).</p>
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International Brain Laboratory public data
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OpenNeuro
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