Faraggi, E (reprint author), Indiana Univ Sch Med, Dept Biochem & Mol Biol, Indianapolis, IN 46202 USA.
[Faraggi, Eshel] Indiana Univ Sch Med, Dept Biochem & Mol Biol, Indianapolis, IN 46202 USA
; [Faraggi, Eshel
; Kloczkowski, Andrzej] Nationwide Childrens Hosp, Battelle Ctr Math Med, Columbus, OH 43215 USA
; [Faraggi, Eshel] Res & Informat Syst LLC, Div Phys, Carmel, IN 46032 USA
; [Zhou, Yaoqi] Indiana Univ Sch Med, Sch Informat, Indianapolis, IN 46202 USA
; [Zhou, Yaoqi] Indiana Univ Sch Med, Ctr Computat Biol & Bioinformat, Indianapolis, IN 46202 USA
; [Zhou, Yaoqi] Griffith Univ, Inst Glyc, Southport, Qld 4222, Australia
; [Zhou, Yaoqi] Griffith Univ, Sch Informat & Commun Technol, Southport, Qld 4222, Australia
; [Kloczkowski, Andrzej] Ohio State Univ, Dept Pediat, Columbus, OH 43215 USA
; [Kloczkowski, Andrzej] Chinese Acad Sci, Kavli Inst Theoret Phys China, Beijing 100190, Peoples R China
We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window 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 tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at , from the SPARKS Lab at , and from the Battelle Center for Mathematical Medicine at . Proteins 2014; 82:3170-3176. (c) 2014 Wiley Periodicals, Inc.
Faraggi, E,Zhou, YQ,Kloczkowski, A. Accurate single-sequence prediction of solvent accessible surface area using local and global features[J]. PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS,2014,82(11):3170-3176.