Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling
Author(s) -
Jaswinder Singh,
Kuldip K. Paliwal,
Thomas Litfin,
Jaspreet Singh,
Yaoqi Zhou
Publication year - 2022
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btac421
Subject(s) - rna , casp , computer science , nucleic acid secondary structure , nucleic acid structure , protein structure prediction , web server , computational biology , source code , protein secondary structure , code (set theory) , artificial intelligence , biology , data mining , bioinformatics , protein structure , genetics , the internet , gene , set (abstract data type) , biochemistry , world wide web , programming language , operating system
Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins.
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