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Datasets and Jupyter notebook for the structural analysis of protein-RNA interface evolution
<p>The present repository contains data and code related to our manuscript "Structural comparison of protein-RNA homologous interfaces reveals widespread overall conservation contrasted with versatility in polar contacts". In the manuscript, we analyze the evolution of protein-RNA interfaces by building a dataset of protein-RNA interologs (homologous interfaces) and exploring how interface contacts are conserved between homologous interfaces, as well as possible explanations for non-conserved contacts.</p> <p>This repository contains the following files:</p> <ul> <li>DataAnalysisNotebook.ipynb is a Jupyter notebook to reproduce contact conservation analysis and all figures from our manuscript, and to explore data</li> <li>env.yaml is an environment file in order to build a Conda/Mamba environment to run the Jupyter notebook </li> <li>2022-02-21-PDB.csv contains data from the PDB about 3D structures of complexes containing interacting protein and RNA chains (PDB structure identifier, chain identifiers, experimental technique and resolution)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.tsv contains more detailed information about interacting protein and RNA chains from these complexes (PDB and chain identifiers, protein and RNA size, interface size and number of contacts)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.txt.selectXE_2.50_p30_r10_pi5_ri5_rep_bc-100.out_RNAcl_0.99.tsv contains the same detailed information, restricted to the filtered dataset used as a starting point in our interolog search pipeline</li> <li>PDBinterfaceAlign.csv contains information about the structural alignment of pairs of protein-RNA interactions (structural alignment TM-scores, sequence identity and coverage)</li> <li>DataInterologsParam.tsv contains information about a pre-filtered set of 2587 potential interologs (including interface RMSD, sequence identity and coverage and interface size)</li> <li>DataInterologsContactsFixedSASA.tsv contains detailed information about conserved and non-conserved contacts in the final set of 2022 interologs (atomic contacts, apolar contacts, hydrogen bonds, salt bridges and stacking information for aminoacid-nucleotide pairs, as well as information about whether each belongs to the interface, secondary structures, and the aminoacid surface accessibility and evolutionary conservation metrics) - compared to version 1, the calculation of solvent accessibility was fixed for a number of interolog pairs</li> <li>DataCons.csv contains precomputed contact conservation metrics for each of the 2022 interolog pairs, for fast reproduction of manuscript figures</li> <li>DataInterologsContactsResampledMaintainStructSeqId.tsv, DataInterologsContactsShuffled.tsv and DataInterologsShuffled.tsv relate to baselines computed for contact conservation assessment</li> <li>clan.txt, clan_membership.txt, ecod.latest.domains.uniq.txt, rfam_interfaces_977.txt, DataGroupsECOD.tsv, DataGroupesRFAM.tsv, DataGroupsRFAMClan.tsv, DataInterfaceGroupsECOD.tsv and DataInterfaceGroupsRFAM.tsv relate to the ECOD (respectively Rfam) classification of protein domains (respectively RNA) in protein-RNA interfaces from our dataset</li> <li>ListeIntraHbonds.pkl and ListeIntraSaltBridges.pkl are pickle-format data files containing intra-molecular hydrogen bonds and salt bridges (respectively) that are used to analyse scenarii of compensation for non-conserved polar contacts.</li> </ul>
Replication Data for: Geometric Transformers for Protein Interface Contact Prediction
<p>This dataset contains replication data for the paper titled "Geometric Transformers for Protein Interface Contact Prediction". The dataset consists of pickled Python dictionaries containing pairs of DGLGraphs that can be used to train and validate protein interface contact prediction models. It also contains our best model checkpoints saved as PyTorch LightningModules. Our GitHub repository, DeepInteract, linked in the "Additional notes" metadata section below provides more details on how we use these files as examples for cross-validation.</p>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
Supplementary Data for NIPS Publication: Protein Interface Prediction using Graph Convolutional Networks.
<p>These data sets can be used to re-run the experiments from our paper, Protein Interface Prediction using Graph Convolutional Networks. The data are derived from protein complexes in the docking benchmark dataset v. 5.0. Each file is a python tuple that has been saved using cPickle and compressed using gzip.</p> <p>Links:</p> <p>Paper: https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks</p> <p>Poster: https://zenodo.org/record/1134154</p> <p>Code: https://github.com/fouticus/pipgcn</p> <p> </p> <p><strong>File Descriptions:</strong></p> <p>train.cpkl.gz and test.cpkl.gz have the data formatted for neighborhood based graph convolutions. The diffc_ files are the same data formatted for the diffusion convolutional neural networks that we compare against. </p> <p>train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of length 175 containing the PDB codes from the docking benchmark dataset</li> <li>element 1 is a list of length 175 containing features for each protein. Each element is a dictionary containing the following keys: <ul> <li>r_vertex: vertex (residue) features for the receptor. numpy array of shape (x, 70) where x is the number of residues in the receptor and 70 is the number of features.</li> <li>l_vertex: vertex (residue) features for the ligand. analogous to above, with shape (y, 70) where y is the number of residues in the ligand.</li> <li>complex_code: PDB code of the complex. matches the list of codes described above.</li> <li>l_edge: edge features for the neighborhood around each residue in the ligand. numpy array of shape (y, 20, 2) where y is defined as above. the second dimension is the edges to the 20 nearest neighboring residues, ordered by decreasing distance. The third dimension allows for two features per edge. </li> <li>r_edge: edge features for the neighborhood around each residue in the receptor. numpy array of shape (x, 20, 2) where x is as above. </li> <li>l_hood_indices: the index of the 20 closest residues to each residue, ordered by decreasing distance. numpy array of shape (y, 20, 1). "Index" means which row in l_vertex gives the vertex features for the closest neighbor, second closest neighbor, etc. </li> <li>r_hood_indices: analogous to above, shape (x, 20, 1).</li> <li>label: 1 or -1 label for each residue pair. numpy array of shape (x*y, 3). Each row looks like (i, j, k) where i is the index of the ligand residue, j is the index of the receptor residue, and k is either -1 (negative example) or 1 (positive example).</li> </ul> </li> </ul> <p>test.cpkl.gz matches the structure of train.cpkl.gz except it has the test set of 55 complexes. </p> <p>Descriptions of the vertex and edge features can be found in Appendix A of <a href="https://mountainscholar.org/handle/10217/185661">this.</a></p> <p>diffc_g2_p2_train.cpkl.gz is a tuple of length 2:</p> <ul> <li>element 0 is a list of the same 175 PDB codes as above. </li> <li>element 1 is a list of features for the 175 complexes. Each element is a dictionary of features with these keys: <ul> <li>r_vertex, l_vertex, complex_code, label: these are the same as described above. </li> <li>'r_power_series': Stacked diffusion matrices which are powers of the similarity matrix used in the DCNN method. numpy array of shape (x, 2, x) where x is the number of receptor residues. the middle dimension 2 indicates how many "hops" is used for that diffusion (1 vs. 2). In other words, element (i, 0, j) is the similarity after 1 hops between residues i and j. element (i, 1, j) is the similarity after 2 hops. See DCNN paper for details.</li> <li>'l_power_series': same as above but for the ligand. shape is (y, 2, y).</li> </ul> </li> </ul> <p>diffc_g2_p2_test.cpkl.gz is the same as diffc_g2_p2_train.cpkl.gz but for the 55 test complexes.</p> <p>diff_g2_p5_train.cpkl.gz and diff_g2_p5_test.cpkl.gz are the same as the p2 version above, except that the diffusion matrices have shape (x, 5, x) and (y, 5, y) because one of our comparisons against the DCNN model uses 5 hops instead of just 2. </p> <p> </p> <p>Note: these files were pickled with Python 2.7. If you're unpickling with Python 3.x you might have to specify encoding as 'latin1'. </p> <p> </p> <p>Please direct any questions to:</p> <ul> <li>Alex Fout (fout@colostate.edu)</li> <li>Jonathon Byrd (jonbyrd@colostate.edu)</li> <li>Basir Shariat (basir@cs.colostate.edu</li> <li>Asa Ben-Hur (asa@cs.colostate.edu)</li> </ul>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
<p>This dataset contains replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". The dataset consists of pickled Pandas DataFrame files, along with training, validation, and (for DB5-Plus) test filename lists for cross-validation, that can be used to develop and evaluate protein interface prediction models. This dataset also contains the externally generated residue-level PSAIA and HH-suite3 features for users' convenience (e.g. raw MSAs and profile HMMs for each protein complex). Our GitHub repository linked in the "Additional notes" metadata section below provides more details on how we parsed through these files to create our cross-validation datasets. The GitHub repository for DIPS-Plus also includes scripts that can be used to impute missing feature values and convert the final "raw" complexes into DGL-compatible graph objects. Since our final DGL graph representation for each complex uses PyTorch tensors in its construction of residue embeddings, the final representation of each complex can easily be adapted to fit the users' needs (e.g. feeding a complex's 2D residue feature tensors into a convolutional neural network).</p>
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction (Supplementary Data)
<p>This dataset contains supplementary replication data for the paper titled "DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction". In particular, it contains a new version of our `final_raw_dips.tar.gz` protein pair representations which now contain (1) residue-level annotations for intrinsic disorder regions (IDRs) as well as (2) a copy of each protein pair representation in the HDF5 file format for programming language-agnostic read capabilities. In addition, this record also contains (3) raw MSAs (in HDF5 file format) generated for each protein pair using Jackhmmer and AlphaFold's small version of the Big Fantastic Database (BFD). Lastly, this record contains (4) PDB metadata derived for each DIPS-Plus complex using Graphein's PDBManager API as well as (5) structure-based (i.e., FoldSeek-based) training and validation splits of the dataset's complexes in the form of respective text files containing the file paths of complexes assigned to each split.</p>
Dataset for "Molecular modeling of the interface of an egg yolk protein-based emulsion"
<p>Dataset for figures of upcoming article "Molecular modeling of the interface of an egg yolk protein-based emulsion" submitted to the journal "Physics of Fluids".</p> <p>dimerApovitellenin1_AA.pdb is the all-atom protein structure file used in MD simulations.</p> <p>DPDparameters.csv is the parametrization file used in DPD simulations.</p>
A structural database of chain-chain and domain-domain interfaces of proteins
<p>Library of protein-protein and domain-domain interfaces from the protein data bank. The data also contains the structural clusters of protein-protein and domain-domain interfaces.</p>
Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface
<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> <p>We ran simulations for four different systems:</p> <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> <p>For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis.</p> <p>This data repository consists of<br> (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units<br> (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units<br> (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs)<br>Units are also clarified in each file's header.</p> <p>The simulation units can be converted to SI units via:</p> <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul> <p> </p> <p>Details for (1) and (2):<br>Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files:</p> <p><strong>Ree:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>Rg:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>resDist:</strong></p> <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> <p>distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14] </p> <p> </p> <p>Details for (3):<br>The folder '<strong>droplet</strong>' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files:</p> <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>
DBv5 features and codified complexes used in BIPSPI: a method for the prediction of partner-specific protein–protein interfaces
<p>Features and protein complexes codified for DBv5 BIPSPI paper</p> <p>(BIPSPI: a method for the prediction of partner-specific protein–protein interfaces</p> <p><a>Ruben Sanchez-Garcia</a> <a>C O S Sorzano</a> <a>J M Carazo</a> <a>Joan Segura</a></p> <p><em>Bioinformatics</em>, bty647, <a href="https://doi.org/10.1093/bioinformatics/bty647">https://doi.org/10.1093/bioinformatics/bty647</a>)</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>
PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks (dataset)
<p>Computational protein-binding studies are widely used to investigate fundamental biological processes and facilitate the development of modern drugs, vaccines, and therapeutics. Scoring functions aim to assess and rank the binding strength of the predicted protein complex. Accurate scoring of protein binding interfaces remains a challenge. PIsToN (evaluating Protein binding Interfaces with Transformer Networks) represents a novel approach to distinguish native-like protein complexes from incorrect conformations. Protein interfaces are transformed into a collection of 2D images (interface maps), each corresponding to a geometric or biochemical property. Pixel intensities represent the feature values. A neural network was adapted from a popular vision transformer (ViT) with several enhancements: a hybrid component to accept empirical-based energy terms, a multi-attention module to highlight essential features and binding sites, and the use of contrastive learning for better ranking performance. The resulting PIsToN model significantly outperforms state-of-the-art scoring functions on well-known datasets.</p> <p>This repository contains proteins and pre-computed interface maps for the PIsToN work.</p>
Dataset for article: Co-evolutionary landscape at the interface and non-interface regions of protein-protein interaction complexes
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Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
<p>The dynamics and organization of the actin cytoskeleton are crucial to many cellular events such as motility, polarization, cell shaping, and cell division. The intracellular and extracellular signaling associated with this cytoskeletal network is communicated through cell membranes. Hence the organization of membrane macromolecules and actin filament assembly are highly interdependent. Although the actin-membrane linkage is known to happen through many routes, the major class of interactions is through the direct interaction of actin-binding proteins with the lipid class containing poly-phosphatidylinositols (PPIs). Among the PPIs, phosphatidylinositol bisphosphate (PI(4,5)P<sub>2</sub>) acts as a significant factor controlling actin polymerization in the proximity of the membrane by binding to actin-associated proteins. The molecular interactions between these actin-binding proteins and the membrane lipids remain elusive. Here, using molecular modeling, analytical theory, and experimental methods, we investigate the binding of three different actin-binding proteins, mDia2, NWASP, and gelsolin, to membranes containing PI(4,5)P<sub>2</sub> lipids. We perform molecular dynamics simulations on the protein-bilayer system and analyze the membrane binding in the form of hydrogen bonds and salt bridges at various PI(4,5)P<sub>2</sub> and cholesterol concentrations. Our experimental study with PI(4,5)P<sub>2</sub>-containing large unilamellar vesicles mimics the computational experiments. Using the multivalencies of the proteins obtained in molecular simulations and the cooperative binding mechanisms of the proteins, we also propose a multivalent binding model that predicts the actin filament distributions at various PI(4,5)P<sub>2 </sub>and protein concentrations.</p>
Data of "High-speed cryo-microscopy proves that ice-nucleating proteins of Pseudomonas syringae trigger freezing at hydrophobic interfaces"
<p>Raw data of a study titled <strong><em>"High-speed cryo-microscopy proves that ice-nucleating proteins of Pseudomonas syringae trigger freezing at hydrophobic interfaces"</em></strong>.</p> <p>The <strong>onset_locations.zip</strong> folder contains all analyzed images which are screenshots from the cryo-microscopic videos. Raw screenshots and evaluated images are included in two sub-folders per samples. The sample description is the name of the folders.</p> <p>The <strong>ice_propogation_velocity.zip</strong> folder contains all images that were used for the evaluation of the propagation velocity of ice. Every sample folder contains the original spot detection image, one image at a later time point, the subtracted image, and one image with the measured distance of the ice front indicated as white scale bar.</p> <p>The <strong>Results_(ice_propagation_velocity).xlsx</strong> contains the results from the velocity calculations, the <strong>Results_(surface tension).xlsx</strong> contains the evaluated surface tension values and the <strong>Results_(temperatures and locations).xlsx</strong> file contains all evaluated freezing locations (polar coordinates) and temperatures of all analyzed samples.</p>
Data from: Gα and regulator of G-protein signaling (RGS) protein pairs maintain functional compatibility and conserved interaction interfaces throughout evolution despite frequent loss of RGS proteins in plants
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Data from: A multiscale biophysical model for the recruitment of actin nucleating proteins at the membrane interface
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The dynamics of protein-RNA interfaces using all-atom molecular dynamics simulations
<p>We investigated to characterize the dynamics of protein-RNA complexes and their interfaces at molecular level by performing a more systematic analysis. To get insights on the dynamics of protein-RNA complexes, all-atom MD simulations were generated for the manuscript "The dynamics of protein-RNA interfaces using all-atom molecular dynamics simulations". Nine protein-RNA complexes are studied in this work: 1ASY (an aspartyl-tRNA synthase/tRNA), 1JBS (a ribotoxin restrictocin/SRD RNA), 1MMS (a ribosomal protein L11/23S), 1OOA (a nuclear factor NF-kappaB p105 subunit/RNA aptamer), 1RKJ (a nucleolin/pre-rRNA), 2R8S (a FAB/P4-P6 RNA ribozyme domain), 2VPL (a 50S ribosomal protein/mRNA), 2ZM5 (a tRNA delta(2)-isopentenylpyrophosphate transferase/tRNA), 3IEV (a GTP-binding protein era/3' end of 16S rRNA). </p><p>Each folder for a complex is organised as followed:</p><ul><li>in <strong>complex</strong> there are the dry MD simulations for the complex protein-RNA with the starting structure</li><li>in <strong>protein</strong> there are the dry MD simulations for the unbound protein with the starting structure</li><li>in <strong>rna</strong> there are the dry MD simulations for the unbound RNA with the starting structure</li></ul><p>In each folder, all the trajectory files are named : <strong>md_(times of simulations).xtc</strong> and the starting structure called : <strong>start.gro</strong>.</p>
Datasets used for Automated EffortLess MicroED Graphic User Interface (AutoLEI): Tyrosine (12), MOF SU-100 (16), protein MutT homolog 1 (38) and Lysozyme (71)
<p>The <strong>Auto</strong>mated<strong> </strong>Effort<strong>L</strong>ess<strong> </strong>Micro<strong>E</strong>D<strong> </strong>Graphic User<strong> I</strong>nterface<strong> (AutoLEI)</strong> is designed to automatically process and merge batches of rotation electron diffraction datasets using <strong>XDS[1]</strong>. This GUI aims to streamline data processing and minimize the need for manual data processing.</p> <p>The four datasets below are examples used in the Automated EffortLess MicroED Graphic User Interface (AutoLEI) paper.</p> <p>GUI available: https://zenodo.org/records/15206752 </p> <p> </p> <p><strong>A. Data information</strong></p> <p>Dataset 1: Tyrosine (Small molecule), 12 datasets in total</p> <p>Dataset 2: SU-100 (Small molecule), 16 datasets in total</p> <p>Dataset 3: MutT homolog 1 (Macro molecule), 38 datasets in total</p> <p>Dataset 4: Lysozyme (Macro molecule), 71 datasets in total</p> <p> </p> <p><strong>B. Data collection </strong></p> <p><strong>Tyrosine</strong> data was collected with an ASI Timepix hybrid detector installed on a JEOL JEM-2100 (200 kV) microscope equipped with a LaB6 filament. A Gatan 914 cryo-holder is employed to collect data at cryo temperature.</p> <p>Data collection software: Instamatic.</p> <p>Electron Microscopy Center, the Department of Materials and Environmental Chemistry, Stockholm University.</p> <table> <tbody> <tr> <th>Data</th> <th>#Frame</th> <th>Step (°)</th> <th>Start (°)</th> <th>End (°)</th> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>data1</td> <td>415</td> <td>0.233</td> <td>-50.82</td> <td>45.44</td> <td>129.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data2</td> <td>475</td> <td>0.233</td> <td>-57.19</td> <td>53.07</td> <td>129.4</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data3</td> <td>139</td> <td>0.232</td> <td>-51.23</td> <td>-19.17</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data4</td> <td>421</td> <td>0.233</td> <td>-50.67</td> <td>47.01</td> <td>128.9</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data5</td> <td>448</td> <td>0.233</td> <td>-54.51</td> <td>49.43</td> <td>130.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data6</td> <td>330</td> <td>0.233</td> <td>-54.97</td> <td>21.54</td> <td>129.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data7</td> <td>41</td> <td>0.232</td> <td>-57.39</td> <td>-48.12</td> <td>128.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data8</td> <td>459</td> <td>0.233</td> <td>-53.60</td> <td>53.03</td> <td>128.7</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data9</td> <td>430</td> <td>0.233</td> <td>-38.49</td> <td>61.25</td> <td>129.7</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data10</td> <td>509</td> <td>0.233</td> <td>-56.38</td> <td>61.91</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data11</td> <td>353</td> <td>0.223</td> <td>-61.69</td> <td>16.79</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data12</td> <td>514</td> <td>0.233</td> <td>-52.99</td> <td>66.36</td> <td>129.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> </tbody> </table> <p> </p> <p><strong>SU-100 </strong>data was collected with an ASI Timepix hybrid detector installed on a JEOL JEM-2100 (200 kV) microscope equipped with a LaB6 filament. A Gatan 914 cryo-holder is employed to collect data at cryo temperature.</p> <p>Data collection software: Instamatic.</p> <p>Electron Microscopy Center, the Department of Materials and Environmental Chemistry, Stockholm University.</p> <table> <tbody> <tr> <th>Data</th> <th>#Frame</th> <th>Step (°)</th> <th>Start (°)</th> <th>End (°)</th> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>data1</td> <td>248</td> <td>0.233</td> <td>-16.82</td> <td>40.64</td> <td>131.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data2</td> <td>486</td> <td>0.233</td> <td>-55.22</td> <td>57.62</td> <td>129.4</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data3</td> <td>472</td> <td>0.232</td> <td>-51.78</td> <td>57.67</td> <td>129.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data4</td> <td>399</td> <td>0.232</td> <td>-56.68</td> <td>35.79</td> <td>129.8</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data5</td> <td>319</td> <td>0.233</td> <td>-15.51</td> <td>58.42</td> <td>126.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data6</td> <td>302</td> <td>0.233</td> <td>-50.47</td> <td>19.52</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data7</td> <td>87</td> <td>0.233</td> <td>-50.01</td> <td>-29.93</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data8</td> <td>349</td> <td>0.232</td> <td>-57.04</td> <td>23.81</td> <td>130.5</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data9</td> <td>475</td> <td>0.233</td> <td>-48.75</td> <td>61.56</td> <td>128.8</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data10</td> <td>422</td> <td>0.233</td> <td>-48.35</td> <td>49.53</td> <td>128.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data11</td> <td>415</td> <td>0.233</td> <td>-57.64</td> <td>38.62</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data12</td> <td>351</td> <td>0.232</td> <td>-54.26</td> <td>26.95</td> <td>128.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data13</td> <td>466</td> <td>0.233</td> <td>-53.95</td> <td>54.18</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data14</td> <td>450</td> <td>0.233</td> <td>-51.83</td> <td>52.56</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data15</td> <td>443</td> <td>0.233</td> <td>-49.31</td> <td>53.57</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data16</td> <td>374</td> <td>0.233</td> <td>-39.30</td> <td>47.46</td> <td>129.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> </tbody> </table> <p> </p> <p><strong>MutT homolog 1</strong> data was collected with a CMOS CetaD detector installed on a Titan Krios G3i with an autoloader. </p> <p>Data collection software: EPUD</p> <p>Cryo-EM infrastructure unit, Scilifelab, Stockholm.</p> <table> <tbody> <tr> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>-6.0</td> <td>0.019687</td> <td>2487.83</td> <td>2048</td> <td>2048</td> <td>5.717</td> </tr> </tbody> </table> <p> </p> <p><strong>Lysozyme</strong> data was collected with a CMOS CetaD detector installed on a Titan Krios G2 with an autoloader. </p> <p>Data collection software: EPUD</p> <p>Cryo-EM infrastructure unit, Scilifelab, Stockholm.</p> <table> <tbody> <tr> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> <tr> <td>-174.4</td> <td>0.01968</td> <td>1155.0</td> <td>2048</td> <td>2048</td> <td>12.318</td> </tr> </tbody> </table> <p><strong>C. Reference</strong></p> <p>[1] Kabsch. W. “XDS”, <em>ACTA CRYSTALLOGRAPHICA SECTION D</em>, 2010</p>
Data from: Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information
Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood based method to covary across protein-protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex provided that the number of aligned sequences is greater than the average of the lengths of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.
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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.