Feature Extraction of Protein Contact Maps from Protein 3D-Coordinates

2017 ◽  
pp. 311-320 ◽  
Author(s):  
K. Suvarna Vani ◽  
K. Praveen Kumar
Keyword(s):  
Feature Extraction ◽  
Contact Maps ◽  
Protein Contact Maps

scholarly journals FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps

2008 ◽  
Vol 24 (10) ◽  
pp. 1313-1315 ◽  
Author(s):  
Marco Vassura ◽  
Luciano Margara ◽  
Pietro Di Lena ◽  
Filippo Medri ◽  
Piero Fariselli ◽  
...  
Keyword(s):  
Fault Tolerant ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Contact Maps ◽  
Structure Reconstruction ◽  
Protein Contact Maps

scholarly journals Protein contact map prediction using multiple sequence alignment dropout and consistency learning for sequences with less homologs

2021 ◽  
Author(s):  
Xuyang Liu ◽  
Lei Jin ◽  
Shenghua Gao ◽  
Suwen Zhao
Keyword(s):  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Prediction Accuracy ◽  
Contact Map ◽  
Multiple Sequence ◽  
Learning Network ◽  
Contact Maps ◽  
Protein Contact Maps

The prediction of protein contact map needs enough normalized number of effective sequence (Nf) in multiple sequence alignment (MSA). When Nf is small, the predicted contact maps are often not satisfactory. To solve this problem, we randomly selected a small part of sequence homologs for proteins with large Nf to generate MSAs with small Nf. From these MSAs, input features were generated and were passed through a consistency learning network, aiming to get the same results when using the features generated from the MSA with large Nf. The results showed that this method effectively improves the prediction accuracy of protein contact maps with small Nf.

scholarly journals Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

2021 ◽  
Vol 17 (3) ◽  
pp. e1008865
Author(s):  
Yang Li ◽  
Chengxin Zhang ◽  
Eric W. Bell ◽  
Wei Zheng ◽  
Xiaogen Zhou ◽  
...  
Keyword(s):  
Long Range ◽  
3D Structure ◽  
High Accuracy ◽  
Large Set ◽  
Contact Map ◽  
Homologous Proteins ◽  
Convolutional Networks ◽  
Contact Maps ◽  
Model Training ◽  
Protein Contact Maps

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

scholarly journals ConPlot: web-based application for the visualization of protein contact maps integrated with other data

2021 ◽  
Author(s):  
Filomeno Sánchez Rodríguez ◽  
Shahram Mesdaghi ◽  
Adam J Simpkin ◽  
J Javier Burgos-Mármol ◽  
David L Murphy ◽  
...  
Keyword(s):  
Empty Space ◽  
Structural Features ◽  
Supplementary Information ◽  
Contact Map ◽  
Web Based ◽  
Contact Maps ◽  
File Formats ◽  
Residue Contacts ◽  
Contact Data ◽  
Protein Contact Maps

Abstract Summary Covariance-based predictions of residue contacts and inter-residue distances are an increasingly popular data type in protein bioinformatics. Here we present ConPlot, a web-based application for convenient display and analysis of contact maps and distograms. Integration of predicted contact data with other predictions is often required to facilitate inference of structural features. ConPlot can therefore use the empty space near the contact map diagonal to display multiple coloured tracks representing other sequence-based predictions. Popular file formats are natively read and bespoke data can also be flexibly displayed. This novel visualization will enable easier interpretation of predicted contact maps. Availability and implementation available online at www.conplot.org, along with documentation and examples. Alternatively, ConPlot can be installed and used locally using the docker image from the project’s Docker Hub repository. ConPlot is licensed under the BSD 3-Clause. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Reconstruction of 3D Structures from Protein Contact Maps

2007 ◽  
pp. 578-589 ◽  
Author(s):  
Marco Vassura ◽  
Luciano Margara ◽  
Filippo Medri ◽  
Pietro di Lena ◽  
Piero Fariselli ◽  
...  
Keyword(s):  
3D Structures ◽  
Contact Maps ◽  
Protein Contact Maps

scholarly journals Reconstruction of 3D Structures From Protein Contact Maps

2008 ◽  
Vol 5 (3) ◽  
pp. 357-367 ◽  
Author(s):  
M. Vassura ◽  
L. Margara ◽  
P. Di Lena ◽  
F. Medri ◽  
P. Fariselli ◽  
...  
Keyword(s):  
3D Structures ◽  
Contact Maps ◽  
Protein Contact Maps

Mining of protein contact maps for protein fold prediction

2011 ◽  
Vol 1 (4) ◽  
pp. 362-368 ◽  
Author(s):  
Durga Bhavani S ◽  
Suvarnavani K ◽  
Somdatta Sinha
Keyword(s):  
Protein Fold ◽  
Contact Maps ◽  
Protein Contact Maps ◽  
Fold Prediction
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