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

Autor: Kyle V. Camarda, Kyle Boone, Cate Wisdom, Candan Tamerler, Paulette Spencer
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Pore Forming Cytotoxic Proteins
Computer science
QH301-705.5
Secondary infection
Computer applications to medicine. Medical informatics
R858-859.7
02 engineering and technology
Machine learning
computer.software_genre
Biochemistry
03 medical and health sciences
Structural Biology
Genetic algorithm
Amino Acid Sequence
Biology (General)
Molecular Biology
Selection (genetic algorithm)
030304 developmental biology
Flexibility (engineering)
0303 health sciences
Rough set theory
Artificial neural network
business.industry
Applied Mathematics
Deep learning
Supervised learning
Drug Resistance
Microbial
021001 nanoscience & nanotechnology
Computer Science Applications
Antibacterial
Rough set
Artificial intelligence
0210 nano-technology
business
Antimicrobial peptide
computer
Antimicrobial Cationic Peptides
Research Article
Zdroj: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-17 (2021)
BMC Bioinformatics
ISSN: 1471-2105
Popis: 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.
Databáze: OpenAIRE
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