Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Abstract

Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. Methods Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. Results We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. Conclusions Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences.

Discovering highly potent antimicrobial peptides with deep generative model HydrAMP

“…In each iteration, a subsequence of the peptide constructed so far is given at input, and the model is used to propose the consecutive amino acid in the sequence. Other approaches are based on the genetic algorithms [19,20,21,12]. These methods iteratively evolve a population of peptide sequences by adding random mutations, evaluating their fitness, and performing crossover and other evolutionary operations.…”

Identification of Bacterial Membrane Selectivity of Romo1-Derived Antimicrobial Peptide AMPR-22 via Molecular Dynamics

“…Although the cost of solid-phase peptide synthesis has been decreasing, it is still expensive to study all the possible synthetic AMP derivatives at the screening stage. Therefore, computational tools have been developed to screen AMPs for either de novo design or optimization [31][32][33]. One of these strategies is an MD simulation that is widely used as a powerful predictive tool to define the antimicrobial mechanism of AMPs at an atomic level [34][35][36].…”

Biocompatibility pathways and mechanisms for bioactive materials: The bioactivity zone

“…There have been several attempts to improve antimicrobial peptide performance in infection control in the last year or so, including hierarchical architecture of peptide-conjugated polymer brushes [ 129 ], molecular engineering and self-assembly [ 130 ], truncated antimicrobial peptide sequences that are combined with protein films [ 131 ] and a combination of genetic algorithms and machine learning for peptide design [ 132 ]. It remains to be seen whether these result in clinical utility.…”