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虚拟筛选肽库:使用计算工具寻找基于肽的治疗方法。

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools.

机构信息

Institute of Biostructures and Bioimaging, Via Pietro Castellino 111, 80131 Naples, Italy.

出版信息

Int J Mol Sci. 2024 Feb 1;25(3):1798. doi: 10.3390/ijms25031798.

Abstract

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

摘要

在过去的几十年中,学术和工业实验室都对肽作为潜在治疗药物产生了越来越大的兴趣。生物活性肽具有特异性和生物安全性,非常有潜力治疗各种疾病。与小分子相比,肽作为关键蛋白-蛋白相互作用的抑制剂(或通用调节剂)具有更好的候选性。事实上,含有大而光滑表面的不可成药蛋白可以通过肽的构象灵活性更容易地被靶向。针对与疾病相关的蛋白靶标的生物活性肽的发现通常需要对大型文库进行高通量筛选,并且计算方法因其低成本和高效率而得到了高度的开发利用。本文综述了将肽作为治疗药物使用所面临的潜在挑战,并描述了计算方法,主要是基于结构的虚拟筛选 (SBVS),以支持新型肽治疗方法的鉴定。本文回顾了前沿的 SBVS 策略,并举例说明了针对不同类别的生物活性肽(即抗癌肽、抗微生物/抗病毒肽、阻止淀粉样纤维形成的肽)的应用。

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