Abstract：
The alarming rise of antibiotic-resistant bacterial infections is driving efforts to develop alternatives to conventional antibiotics. In this context, antimicrobial peptides (AMPs) have emerged as promising candidates for their ability to target a broad range of microorganisms. However, the development of AMPs with optimal potency, selectivity, and/or stability profiles remains a challenge. To address it, computational tools for predicting AMP properties and designing novel peptides have gained increasing attention. PyAMPA is a novel platform for AMP discovery. It consists of five modules, namely AMPScreen, AMPValidate, AMPSolve, AMPMutate, and AMPOptimize, that allow high-throughput proteome inspection, candidate screening, and optimization through point-mutation and genetic algorithms. The platform also offers additional tools for predicting and evaluating AMP properties, including antimicrobial and cytotoxic activity, and peptide half-life. By providing innovative and accessible inroads into AMP motifs in proteomes, PyAMPA will enable advances in AMP development and potential translation into clinically useful molecules. PyAMPA is available at: https://github.com/SysBioUAB/PyAMPA.
OBJECTIVE: This paper introduces PyAMPA, a new bioinformatics platform designed for the discovery and optimization of antimicrobial peptides (AMPs). It addresses the urgent need for new antimicrobials due to the rise of antibiotic-resistant infections. PyAMPA, with its five predictive modules -AMPScreen, AMPValidate, AMPSolve, AMPMutate and AMPOptimize, enables high-throughput screening of proteomes to identify potential AMP motifs and optimize them for clinical use. Its unique approach, combining prediction, design, and optimization tools, makes PyAMPA a robust solution for developing new AMP-based therapies, offering a significant advance in combatting antibiotic resistance.
OBJECTIVE: This paper introduces PyAMPA, a new bioinformatics platform designed for the discovery and optimization of antimicrobial peptides (AMPs). It addresses the urgent need for new antimicrobials due to the rise of antibiotic-resistant infections. PyAMPA, with its five predictive modules -AMPScreen, AMPValidate, AMPSolve, AMPMutate and AMPOptimize, enables high-throughput screening of proteomes to identify potential AMP motifs and optimize them for clinical use. Its unique approach, combining prediction, design, and optimization tools, makes PyAMPA a robust solution for developing new AMP-based therapies, offering a significant advance in combatting antibiotic resistance.
摘要：
抗生素耐药性细菌感染的惊人增长正在推动开发常规抗生素替代品的努力。在这种情况下，抗微生物肽（AMP）已成为有希望的候选物，因为它们具有靶向广泛微生物的能力。然而,开发具有最佳效力的AMP，选择性,和/或稳定性概况仍然是一个挑战。为了解决这个问题，用于预测AMP特性和设计新型肽的计算工具已获得越来越多的关注。PyAMPA是一个新的AMP发现平台。它由五个模块组成，即AMPScreen，AMPValidate,AMPSolve,AMPMutate,和AMPOptimize，允许高通量蛋白质组检查，候选人筛选,并通过点突变和遗传算法进行优化。该平台还提供了用于预测和评估AMP属性的其他工具，包括抗菌和细胞毒活性，和肽的半衰期。通过在蛋白质组中提供对AMP主题的创新和可访问的侵入，PyAMPA将使AMP发育和潜在的翻译成为临床上有用的分子。PyAMPA可在以下网址获得：https://github.com/SysBioUAB/PyAMPA。
目的：本文介绍了PyAMPA，一个新的生物信息学平台，旨在发现和优化抗菌肽（AMPs）。由于抗生素耐药性感染的增加，它解决了对新抗菌药物的迫切需求。PyAMPA,凭借其五个预测模块-AMPScreen，AMPValidate,AMPSolve,AMPMutate和AMPOptimize,能够对蛋白质组进行高通量筛选，以鉴定潜在的AMP基序并将其优化以供临床使用。其独特的方法，结合预测，设计,和优化工具，使PyAMPA成为开发新的基于AMP的疗法的强大解决方案，在对抗抗生素耐药性方面取得了重大进展。
目的：本文介绍了PyAMPA，一个新的生物信息学平台，旨在发现和优化抗菌肽（AMPs）。由于抗生素耐药性感染的增加，它解决了对新抗菌药物的迫切需求。PyAMPA,凭借其五个预测模块-AMPScreen，AMPValidate,AMPSolve,AMPMutate和AMPOptimize,能够对蛋白质组进行高通量筛选，以鉴定潜在的AMP基序并将其优化以供临床使用。其独特的方法，结合预测，设计,和优化工具，使PyAMPA成为开发新的基于AMP的疗法的强大解决方案，在对抗抗生素耐药性方面取得了重大进展。
