scholarly journals SURFMAP: a software for mapping in two dimensions protein surface features

2021 ◽  
Author(s):  
Hugo Schweke ◽  
Marie-Helene Mucchielli ◽  
Nicolas Chevrollier ◽  
Simon Gosset ◽  
Anne Lopes
Keyword(s):  
Surface Relief ◽  
Visual Analysis ◽  
Two Dimensions ◽  
Quantitative Comparison ◽  
File Format ◽  
Text File ◽  
Protein Surface ◽  
Homologous Proteins ◽  
Protein Surfaces ◽  
Surface Features

Molecular cartography using two-dimensional (2D) representation of protein surfaces has been shown to be very promising for protein surface analysis. Here, we present SURFMAP, a free standalone and easy-to-use software that enables the fast and automated 2D projection of either predefined features of protein surface (i.e., electrostatic potential, Kyte-Doolittle hydrophobicity, stickiness, and surface relief) or any descriptor encoded in the temperature factor column of a PDB file. SURFMAP uses a pseudo-cylindrical sinusoidal "equal-area" projection that has the advantage of preserving the area measures. It provides the user with (i) 2D maps that enable the easy and visual analysis of protein surface features of interest and (ii) maps in a text file format allowing the fast and straightforward quantitative comparison of 2D maps of homologous proteins.

The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families

2018 ◽  
Vol 16 (02) ◽  
pp. 1840005 ◽  
Author(s):  
Dmitry Suplatov ◽  
Yana Sharapova ◽  
Daria Timonina ◽  
Kirill Kopylov ◽  
Vytas Švedas
Keyword(s):  
Rational Design ◽  
Visual Analysis ◽  
Structural Information ◽  
Protein Structures ◽  
Web Server ◽  
Physical Contact ◽  
Protein Families ◽  
Sequence Alignments ◽  
Homologous Proteins ◽  
Correlated Mutations

The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand’s binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.

scholarly journals Protein Surface Characterization Using an Invariant Descriptor

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Zainab Abu Deeb ◽  
Donald A. Adjeroh ◽  
Bing-Hua Jiang
Keyword(s):  
Surface Characterization ◽  
Large Scale ◽  
Area Under The Curve ◽  
Protein Families ◽  
Protein Surface ◽  
Local Descriptor ◽  
Protein Surfaces ◽  
Related Family ◽  
Distance Distributions ◽  
Comparative Results

Aim. To develop a new invariant descriptor for the characterization of protein surfaces, suitable for various analysis tasks, such as protein functional classification, and search and retrieval of protein surfaces over a large database.Methods. We start with a local descriptor of selected circular patches on the protein surface. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed by combining information from the local descriptors. Our method is novel in its focus on residue-specific distance distributions, and the use of residue-distance co-occurrences as the basis for the proposed protein surface descriptors.Results. Results are presented for protein classification and for retrieval for three protein families. For the three families, we obtained an area under the curve for precision and recall ranging from 0.6494 (without residue co-occurrences) to 0.6683 (with residue co-occurrences). Large-scale screening using two other protein families placed related family members at the top of the rank, with a number of uncharacterized proteins also retrieved. Comparative results with other proposed methods are included.

scholarly journals Pairs and Pairix: a file format and a tool for efficient storage and retrieval for Hi-C read pairs

2021 ◽  
Author(s):  
Soohyun Lee ◽  
Carl Vitzthum ◽  
Burak H. Alver ◽  
Peter J. Park
Keyword(s):  
Quality Control ◽  
Open Source ◽  
Source Code ◽  
Three Dimensional ◽  
File Format ◽  
Interaction Data ◽  
Text File ◽  
Storage And Retrieval ◽  
Link Type ◽  
Efficient Storage

AbstractSummaryAs the amount of three-dimensional chromosomal interaction data continues to increase, storing and accessing such data efficiently becomes paramount. We introduce Pairs, a block-compressed text file format for storing paired genomic coordinates from Hi-C data, and Pairix, an open-source C application to index and query Pairs files. Pairix (also available in Python and R) extends the functionalities of Tabix to paired coordinates data. We have also developed PairsQC, a collapsible HTML quality control report generator for Pairs files.AvailabilityThe format specification and source code are available at https://github.com/4dn-dcic/pairix, https://github.com/4dn-dcic/Rpairix and https://github.com/4dn-dcic/[email protected] or [email protected]

A QUANTITATIVE ANALYSIS OF INTERFACIAL AMINO ACID CONSERVATION IN PROTEIN-PROTEIN HETERO COMPLEXES

2005 ◽  
Vol 03 (05) ◽  
pp. 1137-1150 ◽  
Author(s):  
BOOJALA V. B. REDDY ◽  
YIANNIS N. KAZNESSIS
Keyword(s):  
Amino Acid ◽  
Protein Binding ◽  
Binding Sites ◽  
Protein Structures ◽  
Complex Structures ◽  
Protein Surface ◽  
Protein Surfaces ◽  
Protein Binding Sites ◽  
Interaction Sites ◽  
Conserved Residue

A long-standing question in molecular biology is whether interfaces of protein-protein complexes are more conserved than the rest of the protein surfaces. Although it has been reported that conservation can be used as an indicator for predicting interaction sites on proteins, there are recent reports stating that the interface regions are only slightly more conserved than the rest of the protein surfaces, with conservation signals not being statistically significant enough for predicting protein-protein binding sites. In order to properly address these controversial reports we have studied a set of 28 well resolved hetero complex structures of proteins that consists of transient and non-transient complexes. The surface positions were classified into four conservation classes and the conservation index of the surface positions was quantitatively analyzed. The results indicate that the surface density of highly conserved positions is significantly higher in the protein-protein interface regions compared with the other regions of the protein surface. However, the average conservation index of the patches in the interface region is not significantly higher compared with other surface regions of the protein structures. This finding demonstrates that the number of conserved residue positions is a more appropriate indicator for predicting protein-protein binding sites than the average conservation index in the interacting region. We have further validated our findings on a set of 59 benchmark complex structures. Furthermore, an analysis of 19 complexes of antigen-antibody interactions shows that there is no conservation of amino acid positions in the interacting regions of these complexes, as expected, with the variable region of the immunoglobulins interacting mostly with the antigens. Interestingly, antigen interacting regions also have a higher number of non-conserved residue positions in the interacting region than the rest of the protein surface.

scholarly journals Decrypting protein surfaces by combining evolution, geometry and molecular docking

2018 ◽  
Author(s):  
Chloé Dequeker ◽  
Elodie Laine ◽  
Alessandra Carbone
Keyword(s):  
Protein Interactions ◽  
Sources Of Information ◽  
Protein Surface ◽  
Protein Surfaces ◽  
Geometrical Properties ◽  
Cross Docking ◽  
Protein Interfaces ◽  
New Concepts ◽  
Physico Chemical ◽  
Computational Data

The growing body of experimental and computational data describing how proteins interact with each other has emphasized the multiplicity of protein interactions and the complexity underlying protein surface usage and deformability. In this work, we propose new concepts and methods toward deciphering such complexity. We introduce the notion of interacting region to account for the multiple usage of a protein's surface residues by several partners and for the variability of protein interfaces coming from molecular flexibility. We predict interacting patches by crossing evolutionary, physico-chemical and geometrical properties of the protein surface with information coming from complete cross-docking (CC-D) simulations. We show that our predictions match well interacting regions and that the dierent sources of information are complementary. We further propose an indicator of whether a protein has a few or many partners. Our prediction strategies are implemented in the dynJET2 algorithm and assessed on a new dataset of 262 protein on which we performed CC-D. The code and the data are available at: http://www.lcqb.upmc.fr/dynJET2/.

scholarly journals From Protein Features to Sensing Surfaces

2018 ◽  
Author(s):  
Greta Faccio
Keyword(s):  
Catalytic Activity ◽  
Protein Structure ◽  
Molecular Recognition ◽  
Protein Functionality ◽  
Protein Surface ◽  
Surface Features ◽  
Physico Chemical

Proteins play a major role in biosensors in which they provide catalytic activity and specificity in molecular recognition. The immobilization process is however far from straightforward as it often affects the protein functionality. An extensive interaction of the protein with the surface or a significant surface crowding can lead to changes in the mobility and conformation of the protein structure. This review will provide an insight of how the analysis of the physico-chemical features of the protein surface features before the immobilization process can help to identify the optimal immobilization approach to preserve the functionality of the protein when on the surface of the biosensor.

scholarly journals Deciphering interaction fingerprints from protein molecular surfaces

2019 ◽  
Author(s):  
P Gainza ◽  
F Sverrisson ◽  
F Monti ◽  
E Rodolà ◽  
MM Bronstein ◽  
...  
Keyword(s):  
Conceptual Framework ◽  
Protein Function ◽  
Large Scale ◽  
Visual Analysis ◽  
Protein Complexes ◽  
Molecular Surface ◽  
Protein Surfaces ◽  
Protein Protein Interaction ◽  
High Level ◽  
Interaction Site Prediction

AbstractPredicting interactions between proteins and other biomolecules purely based on structure is an unsolved problem in biology. A high-level description of protein structure, the molecular surface, displays patterns of chemical and geometric features thatfingerprinta protein’s modes of interactions with other biomolecules. We hypothesize that proteins performing similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We presentMaSIF, a conceptual framework based on a new geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction, and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

Adjustable general line coordinates for visual knowledge discovery in n-D data

2017 ◽  
Vol 18 (1) ◽  
pp. 3-32 ◽  
Author(s):  
Boris Kovalerchuk ◽  
Vladimir Grishin
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Real Data ◽  
Two Dimensions ◽  
Visual Pattern ◽  
Multidimensional Data ◽  
Support Vector ◽  
Parallel Coordinates ◽  
Visual Knowledge ◽  
General Line

Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning/Data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n = 160 dimensions.

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