AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.

ABSTRACT: The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification of anti-inflammatory peptides (AIPs) through wet-lab experimentation is expensive and often time consuming. Therefore, the development of novel computational methods is needed to identify potential AIP candidates prior to in vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (AIP predictor in primary amino acid sequences), which was trained with 354 optimal features. First, we systematically studied the contribution of individual composition [amino acid-, dipeptide composition (DPC), amino acid index, chain-transition-distribution, and physicochemical properties] in AIP

SUBMITTER: Manavalan B 

PROVIDER: S-EPMC5881105 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature