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65 results for “protein-protein interactions”
Scored protein-protein interactions accompanying "A pan-plant protein complex map reveals deep conservation and novel assemblies"
<p><a href="http://plants.proteincomplexes.org/static/data/panplant_cfms_scores_annot.txt.gz">All scored pairwise protein-protein interactions with CF-MS scores (3,076,999 unique pairwise interactions)</a></p> <ul> <li>Description: Scores between Orthogroups with the corresponding CF-MS score and eggNOG generated orthogroup descriptions.</li> <li>Note: Only the highest scoring pairs are considered significant. A CF-MS score >= 0.509 corresponds to 10% FDR, >= 0.207 corresponds to 50% FDR</li> <li>Format: OrthogroupID1 [tab] OrthogroupID2 [tab] Score [tab] Annotation1 [tab] Annotation2</li> </ul>
Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions
<p>The metadata, plots and microscopy images for the manuscript "Amino Acids Modulate Liquid-Liquid Phase Separation in vitro and in vivo by Regulating Protein-Protein Interactions".</p>
Input features of E. coli proteome for predicting and modeling protein-protein interactions with AF2Complex
<p>Input features to be used with AF2Complex for predicting protein-protein interactions among ~4400 E. coli proteins. A pickled feature file was generated by the feature data pipeline of AF2Complex for each E. coli protein. To reduce storage size, we limited up to 10,000 MSA sequences and up to 10 structural templates from the Protein Data Bank. The cutoff date for sequence libraries and the Protein Data Bank releases used for feature generation is no later than 11-30-2021.</p> <ul> <li>ecoli_af2c_fea.txt -- A list of all E coli protein with pre-generated input features</li> <li>af2c_fea_ecoli_220331_msa10ktem10.tar -- Input features named after the UniProt ID of each proteins. Note that after untar the tarball, you may use the gzipped feature pickle files directly with AF2Complex w/o gunzip.</li> </ul>
Nuclear Magnetic resonance Dataset of 2D spectra of S100B and Tau to study their protein-protein interaction
<p>Nuclear Magnetic resonance dataset of 2D spectra corresponding to raw data of research published in Nature Communication in a communication entitled "Dynamic interactions and Ca2+ 1 -binding modulate the holdase-type chaperone activity of S100B preventing tau aggregation and seeding" by Moreira G. et al.</p> <p>Dataset corresponds to</p> <p>raw data files in Bruker format of NMR 2D spectra (ser), associated with files of acquisition parameters and processing parameters (pdata),</p> <p>files in .ucsf format that can be read with NMRFAM-Sparky (free download) of 2D spectra (in sub-directory pdata/1)</p> <p>files of chemical shift value lists that can be read as text files or in NMRFAM sparky together with the corresponding ucsf files.</p> <p>physico-chemical conditions are found in title in pdata\1</p> <p>Data were acquired on a Bruker 900-MHz spectrometer equipped with a triple-resonance cryogenic probe (Bruker, Karlsruhe, Germany)</p>
Towards a reproducible interactome: semantic-based detection of redundancies to unify protein-protein interaction databases
<p>Protein-protein interactions (PPIs) play an ubiquitous and fundamental role in all biological processes. Information on PPIs described in the literature is annotated and made available by several protein-interaction databases. Because most databases have their own curation rules and priorities, they often annotate overlapping sets of publications, which leads to redundancies. We developed a semantic-based approach which enables to accurately detect redundancies within PPI datasets from multiple databases. We applied this approach to assemble a "reproducible interactome", with PPIs supported by at least two methods or publications.</p>
Dataset for article: Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
<p>Proteins involved in interactions throughout the course of evolution tend to co-evolve and compensatory changes may occur in interacting proteins to maintain or refine such interactions. However, certain residue pair alterations may prove to be detrimental for functional interactions. Hence, determining co-evolutionary pairings that could be structurally or functionally relevant for maintaining the conservation of an inter-protein interaction is important. Inter-protein co-evolution analysis in several complexes utilizing multiple existing methodologies suggested that co-evolutionary pairings can occur in spatially proximal and distant regions in inter-protein interactions. Subsequently, the Co-Var (<b>Co</b>rrelated <b>Var</b>iation) method based on mutual information and Bhattacharyya coefficient was developed, validated, and found to perform relatively better than CAPS and EV-complex. Interestingly, while applying the Co-Var measure and EV-complex program on a set of protein-protein interaction complexes, co-evolutionary pairings were obtained in interface and non-interface regions in protein complexes. The Co-Var approach involves determining high degree co-evolutionary pairings that include multiple co-evolutionary connections between particular co-evolved residue positions in one protein with multiple residue positions in the binding partner. Detailed analyses of high degree co-evolutionary pairings in protein-protein complexes involved in cancer metastasis suggested that most of the residue positions forming such co-evolutionary connections mainly occurred within functional domains of constituent proteins and substitution mutations were also common among these positions. The physiological relevance of these predictions suggests that Co-Var can predict residues that could be crucial for preserving functional protein-protein interactions. Finally, <b>Co-Var </b>web server (<a href="http://www.hpppi.iicb.res.in/ishi/covar/index.html">http://www.hpppi.iicb.res.in/ishi/covar/index.html</a>) that implements this methodology identifies co-evolutionary pairings in intra and inter-protein interactions.</p>
A dataset for predicting protein-protein interactions in humans
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Dataset for article: Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
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HDAC6 protein-protein interaction network in CNS
<p>HDAC6 stands out as a distinctive member within the histone deacetylase family due to its predominant presence in the cytosol, facilitating its interaction with a wide array of non-histone proteins. Its dysregulation has been linked to various outcomes, encompassing diverse cancer types, immune-related disorders, and neurological conditions, including Alzheimer's, Parkinson's, ALS, Huntington's, Charcot-Marie-Tooth disease, and Rett syndrome.</p><p>The current network represents the known HDAC6 interactions in the central nervous system (CNS).</p><p>The latest version of the current network is available in WikiPathways under accession number WP5426.</p>
Serine-129 phosphorylation of a-synuclein is an activity-dependent trigger for physiologic protein-protein interactions and synaptic function
<p><strong>Phosphorylation of a-synuclein at the Serine-129 site (a-syn Ser129P) is an established pathologic hallmark of synucleinopathies and a therapeutic target. In physiologic states, only a small fraction of a-syn is phosphorylated at this site, and most studies have focused on the pathologic roles of this post-translational modification. We found that unlike wild-type (WT) a-syn which is widely expressed throughout the brain, the overall pattern of a-syn Ser129P is restricted, suggesting intrinsic regulation. Surprisingly, preventing Ser129P blocked activity-dependent synaptic attenuation by a-syn – thought to reflect its normal function. Exploring mechanisms, we found that neuronal activity augments Ser129P, which is a trigger for protein-protein interactions that are necessary for mediating a-syn function at the synapse. AlphaFold2-driven modeling and membrane-binding simulations suggest a scenario where Ser129P induces conformational changes that facilitate interactions with binding partners. Our experiments offer a new conceptual platform for investigating the role of Ser129 in synucleinopathies, with implications for drug-development. </strong></p>
A Comprehensive Dataset of protein-protein interactions and Ligand Binding Pockets for Advancing Drug Discovery
<p>This dataset presents a comprehensive collection of structural data related to protein-protein interactions (PPIs) and ligand binding pockets. The dataset includes high-quality structural information that can aid researchers in the fields of bioinformatics, structural biology, and drug discovery. It encompasses a diverse set of PPI complexes and associated ligands, enabling detailed investigations into molecular interactions at the atomic level. This article introduces an indispensable resource designed to unlock the full potential of PPIs while pioneering a novel metric for pocket similarity for repurposing protein partners.</p>
Data of Protein Adsorption Driven by a Combination of Protein-Protein and Protein-Surface Interactions
<p><span>It is a study of protein adsorption at solid-liquid interface by molecular dynamic simulations and experimental techniques. Data here reported regard results obtained.</span></p>
Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation
<p>Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7</p> <p><strong>Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics</strong> <strong>identify cancer-specific mechanisms of stress adaptation </strong></p> <p>Anna Rodina<sup>1,11</sup>, Chao Xu<sup>1,11</sup>, Chander S. Digwal<sup>1,11</sup>, Suhasini Joshi<sup>1,11</sup>, Anand R. Santhaseela<sup>1</sup>, Sadik Bay<sup>1</sup>, Swathi Merugu<sup>1</sup>, Aftab Alam<sup>1</sup>, Pengrong Yan<sup>1</sup>, Chenghua Yang<sup>1,12</sup>, Tanaya Roychowdhury<sup>1</sup>, Palak Panchal<sup>1</sup>, Liza Shrestha<sup>1</sup>, Yanlong Kang<sup>1</sup>, Sahil Sharma<sup>1</sup>, Yogita Patel<sup>2</sup>, Justina Almadovar<sup>1</sup>, Adriana Corben<sup>3,13</sup>, Mary Alpaugh<sup>1,14</sup>, Shanu Modi<sup>4</sup>, Monica L. Guzman<sup>5</sup>, Teng Fei<sup>6</sup>, Tony Taldone<sup>1</sup>, Stephen D. Ginsberg<sup>7,8</sup>, Hediye Erdjument-Bromage<sup>9</sup>, Thomas A. Neubert<sup>9</sup>, Katia Manova-Todorova<sup>10</sup>, Jason C. Young<sup>2</sup>,<strong> </strong>Meng-Fu Bryan Tsou<sup>10</sup><strong>, </strong>Tai Wang<sup>1,*</sup>, Gabriela Chiosis<sup>1,4,*</sup></p> <p><strong>Abstract </strong></p> <p>Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks. </p>
Predicting and modeling protein-protein interactions in E. coli envelopome
<p>Structural models and Supplementary data described in the reference:</p> <p>Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria.</p> <p>Mu Gao, Davi Nakajima An, and Jeffrey Skolnick<em>. eLife</em>, 2022. <strong>11</strong>: p. e82885.</p> <p>List of files:</p> <ul> <li>af2c_fea_220331.tar -- A tarball of input features of full E coli proteome to AF2Complex version 1.3.0. Feature files are pickled and gzipped, which AF2Complex v1.3 takes as input directly.</li> </ul> <p>Results of an application to E coli envelopome on four query proteins from the outer membrane biogenesis pathway.</p> <ul> <li>Supplementary Data.xlsx -- Virtual PPI screening results of PpiD, YfgM, SurA, and BamA.</li> <li>screening_top1_models.zip -- Compressed top 1 dimeric models of top hits from the PPI screening. Note that these models are unrelaxed.</li> <li>predicted structural models.zip -- Compressed structural models of supercomplexes formed in the OMP biogenesis pathway described in the reference.</li> </ul>
Molecular dynamics trajectories of MTDH-SND1 protein-protein interaction inhibitors
<p>This includes the prmtop and MD trajectories (production stage) and Amber outfiles of L1-L12, C26-A6/2, and MTDH peptide, GaMD trajectories of the protein, and the umbrella sampling trajectories of L5 and C26-A6/2. To reduce the file size, the trajectory files had been compressed using “xz” algorithm, and frames were saved by one frame per 1ns. However, for reference, one additional larger traj file with higher saving frequency for L5 was also uploaded. The input files and input coordinate files were uploaded as a separate attachment at http://pubs.acs.org.</p>
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
<p>Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a synthetic platform for protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes<span class="Apple-converted-space"> </span>drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pre-trained protein language models to expand, <em>in silico</em>, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of generating protein complexes with diverse molecular recognition properties as tools for biotechnology and synthetic biology.</p>
Structural ontogeny of protein-protein interactions
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NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
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Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2
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A SARS-CoV-2-Human Protein-Protein Interaction Map Reveals Drug Targets and Potential Drug-Repurposing
<p>The Krogan Laboratory used affinity-purification mass spectrometry to identify 332 high confidence SARS-CoV-2-human protein-protein interactions, including 67 druggable human proteins or host factors targeted by 69 known drugs. Results may be relevant to antiviral drug production.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.