Anti-microbial Peptides against Methicillin-resistant Staphylococcus aureus: Promising Therapeutics.

IF 1.9 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Current protein & peptide science Pub Date : 2023-01-01 DOI:10.2174/1389203724666221216115850
Priyanka Sinoliya, Pooran Singh Solanki, Sakshi Piplani, Ravi Ranjan Kumar Niraj, Vinay Sharma
{"title":"Anti-microbial Peptides against Methicillin-resistant <i>Staphylococcus aureus</i>: Promising Therapeutics.","authors":"Priyanka Sinoliya,&nbsp;Pooran Singh Solanki,&nbsp;Sakshi Piplani,&nbsp;Ravi Ranjan Kumar Niraj,&nbsp;Vinay Sharma","doi":"10.2174/1389203724666221216115850","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Multidrug-resistant (MDR) methicillin-resistant Staphylococcus aureus (MRSA) has become a prime health concern globally. These bacteria are found in hospital areas where they are regularly dealing with antibiotics. This brings many possibilities for its mutation, so drug resistance occurs.</p><p><strong>Introduction: </strong>Nowadays, these nosocomial MRSA strains spread into the community and live stocks. Resistance in Staphylococcus aureus is due to mutations in their genetic elements.</p><p><strong>Methods: </strong>As the bacteria become resistant to antibiotics, new approaches like antimicrobial peptides (AMPs) play a vital role and are more efficacious, economical, time, and energy saviours.</p><p><strong>Results: </strong>Machine learning approaches of Artificial Intelligence are the in silico technique which has their importance in better prediction, analysis, and fetching of important details regarding AMPs.</p><p><strong>Conclusion: </strong>Anti-microbial peptides could be the next-generation solution to combat drug resistance among Superbugs. For better prediction and analysis, implementing the in silico technique is beneficial for fast and more accurate results.</p>","PeriodicalId":10859,"journal":{"name":"Current protein & peptide science","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current protein & peptide science","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.2174/1389203724666221216115850","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Multidrug-resistant (MDR) methicillin-resistant Staphylococcus aureus (MRSA) has become a prime health concern globally. These bacteria are found in hospital areas where they are regularly dealing with antibiotics. This brings many possibilities for its mutation, so drug resistance occurs.

Introduction: Nowadays, these nosocomial MRSA strains spread into the community and live stocks. Resistance in Staphylococcus aureus is due to mutations in their genetic elements.

Methods: As the bacteria become resistant to antibiotics, new approaches like antimicrobial peptides (AMPs) play a vital role and are more efficacious, economical, time, and energy saviours.

Results: Machine learning approaches of Artificial Intelligence are the in silico technique which has their importance in better prediction, analysis, and fetching of important details regarding AMPs.

Conclusion: Anti-microbial peptides could be the next-generation solution to combat drug resistance among Superbugs. For better prediction and analysis, implementing the in silico technique is beneficial for fast and more accurate results.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
抗甲氧西林金黄色葡萄球菌的抗菌肽:有希望的治疗方法。
背景:耐多药(MDR)耐甲氧西林金黄色葡萄球菌(MRSA)已成为全球主要的健康问题。这些细菌是在经常使用抗生素的医院区域发现的。这为其突变带来了许多可能性,因此产生了耐药性。简介:目前,这些院内MRSA菌株传播到社区和活禽。金黄色葡萄球菌的耐药性是由于其遗传成分的突变。方法:随着细菌对抗生素产生耐药性,抗菌肽(AMPs)等新方法发挥了至关重要的作用,更有效,更经济,节省时间和能源。结果:人工智能中的机器学习方法在更好地预测、分析和获取amp的重要细节方面具有重要意义。结论:抗菌肽有望成为超级细菌耐药的新一代解决方案。为了更好的预测和分析,采用硅技术有利于快速和更准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Current protein & peptide science
Current protein & peptide science 生物-生化与分子生物学
CiteScore
5.20
自引率
0.00%
发文量
73
审稿时长
6 months
期刊介绍: Current Protein & Peptide Science publishes full-length/mini review articles on specific aspects involving proteins, peptides, and interactions between the enzymes, the binding interactions of hormones and their receptors; the properties of transcription factors and other molecules that regulate gene expression; the reactions leading to the immune response; the process of signal transduction; the structure and function of proteins involved in the cytoskeleton and molecular motors; the properties of membrane channels and transporters; and the generation and storage of metabolic energy. In addition, reviews of experimental studies of protein folding and design are given special emphasis. Manuscripts submitted to Current Protein and Peptide Science should cover a field by discussing research from the leading laboratories in a field and should pose questions for future studies. Original papers, research articles and letter articles/short communications are not considered for publication in Current Protein & Peptide Science.
期刊最新文献
Comparative Study of Lactogenic Effect and Milk Mutritional Density of Oral Galactagogues in Female Rabbit. Diet-induced Obesity: Pathophysiology, Consequences and Target Specific Therapeutic Strategies. Ferritin Hinders Ferroptosis in Non-Tumorous Diseases: Regulatory Mechanisms and Potential Consequences. Unveiling the Emerging Role of Klotho: A Comprehensive Narrative Review of an Anti-aging Factor in Human Fertility. Utilizing AfDesign for Developing a Small Molecule Inhibitor of PICK 1-PDZ.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1