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 dynamics simulations to quantify the membrane binding and insertion kinetics of three prototypical and antimicrobial peptides (alpha-helical magainin 2 and PGLa and beta-hairpin tachyplesin 1). By leveraging a set of collective variables that capture the essential physics of the amphiphilic and cationic peptide-membrane interactions we reveal how the slowest kinetic process of membrane insertion is the dynamic rolling of the peptide from a prebound to fully inserted state. These results add critical details to how antimicrobial peptides insert into bacterial membranes.
Antimicrobial peptides are a class of small, usually positively charged amphiphilic peptides that are used by the innate immune system to combat bacterial infection in multicellular eukaryotes. Antimicrobial peptides are known for their broad-spectrum antimicrobial activity and thus can be used as a basis for a development of new antibiotics against multidrug-resistant bacteria. The most challengeous task on the way to a therapeutic use of antimicrobial peptides is a rational design of new peptides with enhanced activity and reduced toxicity. Here we report a molecular dynamics and circular di…
Development of the new antimicrobial agents against antibiotic resistance pathogens is the nowadays challenge. Antimicrobial peptides (AMP) occur as important defence agents in many organisms and offer a viable alternative to conventional antibiotics. Therefore they have become increasingly recognized in current research as templates for prospective antibiotic agents. The efficient designing of the new antimicrobials on the basis of antimicrobial peptides requires comprehensive knowledge on those general physical-chemical characteristics which allow to differ antimicrobial peptides from non-ac…
Bayesian network models are finding success in characterizing enzyme-catalyzed reactions, slow conformational changes, predicting enzyme inhibition, and genomics. In this work, we apply them to statistical modeling of peptides by simultaneously identifying amino acid sequence motifs and using a motif-based model to clarify the role motifs may play in antimicrobial activity. We construct models of increasing sophistication, demonstrating how chemical knowledge of a peptide system may be embedded without requiring new derivation of model fitting equations after changing model structure. These mo…
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…
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…
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）
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 consid…
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）
Pulsed-field-gradient nuclear magnetic resonance (PFG-NMR) is used to obtain the true hydrodynamic size of complexes of peptides with sodium dodecyl sulfate SDS micelles. The peptide used in this study is a 19-residue antimicrobial peptide, GAD-2. Two smaller dipeptides, alanine-glycine (Ala-Gly) and tyrosine-leucine (Tyr-Leu), are used for comparison. We use PFG-NMR to simultaneously measure diffusion coefficients of both peptide and surfactant. These two inputs, as a function of SDS concentration, are then fit to a simple two species model that neglects hydrodynamic interactions between comp…
An important step in a phospholipid membrane pore formation by melittin antimicrobial peptide is a reorientation of the peptide from a surface into a transmembrane conformation. In this work we perform umbrella sampling simulations to calculate the potential of mean force (PMF) for the reorientation of melittin from a surface-bound state to a transmembrane state and provide a molecular level insight into understanding peptide and lipid properties that influence the existence of the free energy barrier. The PMFs were calculated for a peptide to lipid (P/L) ratio of 1/128 and 4/128. We observe t…
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）
