期刊論文

學年 108
學期 2
出版（發表）日期 2020-07-02
作品名稱 AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes
作品名稱（其他語言）
著者 Shikai Jin; Vinicius G Contessoto; Mingchen Chen; Nicholas P Schafer; Wei Lu; Xun Chen; Carlos Bueno; Arya Hajitaheri; Brian J Sirovetz; Aram Davtyan; Garegin A Papoian; Min-Yeh Tsai; Peter G Wolynes
單位
出版者
著錄名稱、卷期、頁數 Nucleic Acids Research 48(W1), p.W25-W30
摘要 The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.
關鍵字
語言 en_US
ISSN 0305-1048
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/119164 )