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

被引:3
|
作者
Sinoliya, Priyanka [1 ]
Solanki, Pooran Singh [2 ]
Piplani, Sakshi [3 ]
Niraj, Ravi Ranjan Kumar [1 ]
Sharma, Vinay [1 ]
机构
[1] Amity Univ Rajasthan, Amity Inst Biotechnol, Jaipur, India
[2] Birla Inst Sci Res, Jaipur, India
[3] Flinders Univ S Australia, Vaxine Pty Ltd, Coll Med & Publ Hlth, Adelaide, Australia
关键词
Multiple drug resistance; MRSA; antimicrobial peptide; peptide design; computational approach; artificial intelligence; ANTI-BIOFILM AGENTS; MRSA BACTEREMIA; DESIGN; MODEL; PREDICTION; DATABASE; INFECTIONS; INHIBITION; VANCOMYCIN; PROTECTION;
D O I
10.2174/1389203724666221216115850
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页码:156 / 177
页数:22
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