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 prediction. Since the performance of the DPC-based model is significantly better than that of other composition-based models, we applied a feature selection protocol on this model and identified the optimal features. AIPpred achieved an area under the curve (AUC) value of 0.801 in a 5-fold cross-validation test, which was ∼2% higher than that of the control RF predictor trained with all DPC composition features, indicating the efficiency of the feature selection protocol. Furthermore, we evaluated the performance of AIPpred on an independent dataset, with results showing that our method outperformed an existing method, as well as 3 different machine learning methods developed in this study, with an AUC value of 0.814. These results indicated that AIPpred will be a useful tool for predicting AIPs and might efficiently assist the development of AIP therapeutics and biomedical research. AIPpred is freely accessible at www.thegleelab.org/AIPpred.
Highlights
Inflammatory responses are tightly controlled under normal conditions and are essential for the initiation of protective immunity (Medzhitov, 2008; Basith et al, 2011b, 2012)
We developed a random forest (RF)-based method to predict anti-inflammatory peptides (AIPs), called AIPpred (AIP predictor from primary amino acid sequences), in which optimal features were selected using a feature selection protocol, which has been implemented in addressing various biological problems (Manavalan and Lee, 2017; Manavalan et al, 2017b, 2018)
Since the dipeptide composition (DPC)-based model significantly outperformed other composition-based models, we applied a feature selection protocol on DPC and identified the optimal features
Summary
Inflammatory responses are tightly controlled under normal conditions and are essential for the initiation of protective immunity (Medzhitov, 2008; Basith et al, 2011b, 2012). When these responses occur in the absence of infection or persist after their routine function, these processes become pathological, resulting in chronic inflammation and autoimmune disorders, including neurodegenerative disease, rheumatoid arthritis, asthma, psoriasis, diabetes, and multiple sclerosis (Asadullah et al, 2002; Balague et al, 2009; Murdoch and Lloyd, 2010; Steinman et al, 2012; Patterson et al, 2014). AIPs act as potent candidates for cancer prevention and therapy because inflammation is closely linked to cancer (Rayburn et al, 2009)
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