A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy.

ABSTRACT: The secondary structure prediction of proteins is a classic topic of computational structural biology with a variety of applications. During the past decade, the accuracy of prediction achieved by state-of-the-art algorithms has been >80%; meanwhile, the time cost of prediction increased rapidly because of the exponential growth of fundamental protein sequence data. Based on literature studies and preliminary observations on the relationships between the size/homology of the fundamental protein dataset and the speed/accuracy of predictions, we raised two hypotheses that might be helpful to determine the main influence factors of the efficiency of secondary structure prediction. Experimental results of size and homology reductions of the fundamental protein dataset supported those hypothese

SUBMITTER: Juan SH 

PROVIDER: S-EPMC7326220 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature