Systematic analysis of global features and model building for recognition of antimicrobial peptides

With growing bacterial resistance to antibiotics, it is becoming paramount to seek out new antibacterials. Antimicrobial peptides (AMPs) provide interesting templates for antibacterial drug research. Our understanding of what it is that confers to these peptides their antimicrobial activity is currently poor. Yet, such understanding is the first step towards modification or design of novel AMPs for treatment. Research in machine learning is beginning to focus on recognition of AMPs from non-AMPs as a means of understanding what features confer to an AMP its activity. Methods either seek new features and test them in the context of classification or measure the classification power of features provided by biologists. In this paper, we provide a rigorous evaluation of features provided by a biologist or resulting from a combination of experimental and computational research. We present a statistics-based approach to carefully measure the significance of each feature and use this knowledge to construct predictive models. We present here logistic regression models, which are capable of associating probabilities on whether a peptide is antimicrobial or not with the feature values of the peptide. We provide access to the proposed methodology through a web server. The server allows users to replicate the findings in this paper or evaluate their own features.We believe research in this direction will allow the community to make further progress and elucidate features that capture antimicrobial activity. This is an important first step towards assisting modification and/or de novo design of AMPs in the wet laboratory.