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11 results for “protein-protein complex”
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>
A dynamical view of protein-protein complexes: studies by molecular dynamics simulations
<p>All-atom MD simulations generated for the manuscript "A dynamical view of protein-protein complexes: studies by molecular dynamics simulations". Eight binary protein-protein complexes from the Docking benchmark and the Affinity benchmark are studied in this work: 2OOB (an ubiquitin/ubiquitin ligase complex), 1AY7 (a ribonuclease Sa/barstar complex), 1BRS (a barnase/barstar complex), 3SGB (a proteinase B/inhibitor), 1EMV (a colicin/immunity protein complex), 1PVH (complex between Interleukine 6 receptor and leukemia inhibitory factor), 1GCQ (Vav/GRB2 SH3 domains complex) and 1AK4 (cyclophilin/HIV capsid complex)</p> <p>Each folder for a binary complex is organised as followed:</p> <p>- in <strong>A</strong> and <strong>B</strong> there are the dry MD simulations for the unbound proteins</p> <p>- in <strong>complex</strong> there are two folders (<strong>without_water</strong> and <strong>water</strong>) where the dry simulation and the simulation with water molecules are provided</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>
Dataset for article: Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
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A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions
<p>Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ~380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands, and binding partners for 'orphan' receptors. New PPIs include proteins expressed on multiple cell types, and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance, and may offer new therapeutic drug targets.</p>
Molecular dynamics simulations of 20 complexes from the Protein-Protein Docking Benchmark
<p>We selected 20 complexes from the Protein-Protein Docking Benchmark 5.0 dataset based on structure resolution and parameterization difficulty. For each complex, we conducted a standard 1 µs-long molecular dynamics (MD) simulation in the NPT ensemble (at 1 atm and 300 K, following a 2 ns NVT equilibration) for the bound receptor, unbound receptor, bound ligand and unbound ligand. We set up all systems using Amber ff14SB<sup> </sup>and its recommended TIP3P water model, running MD simulations with Amber 16. For the 80 (single chain structure) MD, we sampled 500 frames for each simulation and computed the average prediction confidence.</p>
A human IgSF cell-surface interactome reveals a complex network of protein-protein interactions
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Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex
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Data from: Determining the minimum number of protein-protein interactions required to support known protein complexes
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Data from: Encounter complexes and dimensionality reduction in protein-protein association
An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition.
Data from: Encounter complexes and dimensionality reduction in protein-protein association
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International Brain Laboratory public data
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OpenNeuro
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