Most available antimicrobial peptides (AMP) prediction methods use common approach for different classes of AMP. Contrary to available approaches, we suggest, that a strategy of prediction should be based on the fact, that there are several kinds of AMP which are vary in mechanisms of action, structure, mode of interaction with membrane etc. According to our suggestion for each kind of AMP a particular approach has to be developed in order to get high efficacy. Consequently in this paper a particular but the biggest class of AMP - linear cationic antimicrobial peptides (LCAP) - has been considered and a newly developed simple method of LCAP prediction described. The aim of this study is the development of a simple method of discrimination of AMP from non-AMP, the efficiency of which will be determined by efficiencies of selected descriptors only and comparison the results of the discrimination procedure with the results obtained by more complicated discriminative methods. As descriptors the physicochemical characteristics responsible for capability of the peptide to interact with an anionic membrane were considered. The following characteristics such as hydrophobicity, amphiphaticity, location of the peptide in relation to membrane, charge, propensity to disordered structure were studied. On the basis of these characteristics a new simple algorithm of prediction is developed and evaluation of efficacies of the characteristics as descriptors performed. The results show that three descriptors: hydrophobic moment, charge and location of the peptide along the membranes can be used as discriminators of LCAPs. For the training set our method gives the same level of accuracy as more complicated machine learning approaches offered as CAMP database service tools. For the test set sensitivity obtained by our method gives even higher value than the one obtained by CAMP prediction tools.
Short peptides with antimicrobial activity have therapeutic potential for treating bacterial infections. Mechanisms of actions for antimicrobial peptides require binding the biological membrane of their target, which often represents a key mechanistic step. A multitude of data-driven approaches have been developed to predict potential antimicrobial peptide sequences; however, these methods are usually agnostic to the physical interactions between the peptide and the membrane. Towards developing higher throughput screening methodologies, here we use Markov State Modeling and all-atom molecular …
Antimicrobial peptides (AMPs) are anti-infectives that have potential as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides imply interaction with cell envelope. Comprehensive understanding of peculiarities of interactions of antimicrobial peptides with cell envelope is necessary to perform the task-oriented design of new biotherapeutics, against which for microbes it is hard to work out resistance. In order to enable a de novo design with low costs and in high throughput, in silico predictive models have to be required. To develop the performant predictiv…
Pakistan Institute of Engineering and Applied Sciences（巴基斯坦工程与应用科学学院）；University of Warwick（华威大学）
The evolution of drug-resistant microbial species is one of the major challenges to global health. The development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel antimicrobial peptides is hampered by low-throughput biochemical assays. Computational techniques can be used for rapid screening of promising antimicrobial peptide candidates prior to testing in the wet lab. The vast majority of existing antimicrobial peptide predictors are non-targeted in nature, i.e., they can predict whether a given pept…
Accelerating Antimicrobial Peptide Discovery with Latent Structure
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Huazhong University of Science and Technology（华中科技大学）；ByteDance Research（字节跳动研究院）；Institute for AI Industry Research, Tsinghua University（清华大学人工智能产业研究院）
Antimicrobial resistance is one of the biggest health problem, especially in the current period of COVID-19 pandemic. Due to the unique membrane-destruction bactericidal mechanism, antimicrobial peptide-mimetic copolymers are paid more attention and it is urgent to find more potential candidates with broad-spectrum antibacterial efficacy and low toxicity. Artificial intelligence has shown significant performance on small molecule or biotech drugs, however, the higher-dimension of polymer space and the limited experimental data restrict the application of existing methods on copolymer design. H…
Mila Quebec AI Institute（米拉魁北克人工智能研究所）；Jacobs University Bremen（不来梅雅各布大学）；Institute of Immunology and Cancer（免疫学与癌症研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院（CIFAR））
As antibiotic-resistant bacterial strains are rapidly spreading worldwide, infections caused by these strains are emerging as a global crisis causing the death of millions of people every year. Antimicrobial Peptides (AMPs) are one of the candidates to tackle this problem because of their potential diversity, and ability to favorably modulate the host immune response. However, large-scale screening of new AMP candidates is expensive, time-consuming, and now affordable in developing countries, which need the treatments the most. In this work, we propose a novel active machine learning-based fra…
AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
Jilin University（吉林大学）；Columbia University（哥伦比亚大学）；Guizhou Medical University（贵州医科大学）；Changchun University of Engineering（长春工程学院）；Changchun Wenli High School（长春文理高级中学）
OmegAMP: Targeted AMP Discovery via Biologically Informed Generation
Institute of AI for Health, Helmholtz Munich（海德堡-慕尼黑人工智能与健康研究所）；School of Computation, Information and Technology, Technical University of Munich（慕尼黑技术大学计算、信息与技术学院）；Institute of Computational Biology, Helmholtz Munich（海德堡-慕尼黑计算生物学研究所）；Munich Data Science Institute, Technical University of Munich（慕尼黑技术大学慕尼黑数据科学研究所）；Faculty of Medicine, Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学医学院）；Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学马歇尔医学中心机器生物学组）；Departments of Bioengineering and Chemical and Biomolecular Engineering, University of Pennsylvania（宾夕法尼亚大学生物工程与化学与生物分子工程系）；Department of Chemistry, School of Arts and Sciences, University of Pennsylvania（宾夕法尼亚大学化学系）；Penn Institute for Computational Science, University of Pennsylvania（宾夕法尼亚大学宾夕法尼亚计算科学研究所）；TUM School of Life Sciences, Technical University of Munich（慕尼黑技术大学生命科学学院）；Faculty of Mathematics, Informatics and Mechanics, University of Warsaw（华沙大学数学、信息学与力学系）
