NDLI: Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

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Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Content Provider	Semantic Scholar
Author	Liu, Yang Palmedo, Perry Ye, Qing Berger, Bonnie Peng, Jian
Copyright Year	2018
Abstract	While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction.
Starting Page	159
Ending Page	183
Page Count	25
File Format	PDF HTM / HTML
DOI	10.1016/j.cels.2017.11.014
Alternate Webpage(s)	https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/7c/nihms930472.PMC5808454.pdf
Alternate Webpage(s)	http://isiarticles.com/bundles/Article/pre/pdf/137892.pdf
PubMed reference number	29275173
Alternate Webpage(s)	https://doi.org/10.1016/j.cels.2017.11.014
Journal	Medline
Volume Number	6
Journal	Cell systems
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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