Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile
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Author(s)
Faraggi, Eshel
Kouza, Maksim
Zhou, Yaoqi
Kloczkowski, Andrzej
Griffith University Author(s)
Year published
2017
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A fast accessible surface area (ASA) predictor is presented. In this new approach no residue mutation profiles generated by multiple sequence alignments are used as inputs. Instead, we use only single sequence information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is found to perform similarly well for so-called easy and hard cases ...
View more >A fast accessible surface area (ASA) predictor is presented. In this new approach no residue mutation profiles generated by multiple sequence alignments are used as inputs. Instead, we use only single sequence information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction.
View less >
View more >A fast accessible surface area (ASA) predictor is presented. In this new approach no residue mutation profiles generated by multiple sequence alignments are used as inputs. Instead, we use only single sequence information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction.
View less >
Book Title
Prediction of Protein Secondary Structure
Copyright Statement
© 2017 Springer. This is the author-manuscript version of this paper. It is reproduced here in accordance with the copyright policy of the publisher. Please refer to the publisher’s website for further information.
Subject
Other chemical sciences
Biochemistry and cell biology
Biochemistry and cell biology not elsewhere classified