Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil.
Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, RN, Brazil.
Int J Mol Sci. 2024 Sep 6;25(17):9646. doi: 10.3390/ijms25179646.
Bioinformatics has emerged as a valuable tool for screening drugs and understanding their effects. This systematic review aimed to evaluate whether in silico studies using anti-obesity peptides targeting therapeutic pathways for obesity, when subsequently evaluated in vitro and in vivo, demonstrated effects consistent with those predicted in the computational analysis. The review was framed by the question: "What peptides or proteins have been used to treat obesity in in silico studies?" and structured according to the acronym PECo. The systematic review protocol was developed and registered in PROSPERO (CRD42022355540) in accordance with the PRISMA-P, and all stages of the review adhered to these guidelines. Studies were sourced from the following databases: PubMed, ScienceDirect, Scopus, Web of Science, Virtual Heath Library, and EMBASE. The search strategies resulted in 1015 articles, of which, based on the exclusion and inclusion criteria, 7 were included in this systematic review. The anti-obesity peptides identified originated from various sources including bovine alpha-lactalbumin from cocoa seed ( L.), chia seed ( L.), rice bran (), sesame ( L.), sea buckthorn seed flour (), and adzuki beans (). All articles underwent in vitro and in vivo reassessment and used molecular docking methodology in their in silico studies. Among the studies included in the review, 46.15% were classified as having an "uncertain risk of bias" in six of the thirteen criteria evaluated. The primary target investigated was pancreatic lipase (n = 5), with all peptides targeting this enzyme demonstrating inhibition, a finding supported both in vitro and in vivo. Additionally, other peptides were identified as PPARγ and PPARα agonists (n = 2). Notably, all peptides exhibited different mechanisms of action in lipid metabolism and adipogenesis. The findings of this systematic review underscore the effectiveness of computational simulation as a screening tool, providing crucial insights and guiding in vitro and in vivo investigations for the discovery of novel anti-obesity peptides.
生物信息学已成为筛选药物和了解其作用的重要工具。本系统评价旨在评估针对肥胖治疗途径的抗肥胖肽的计算机研究,当随后在体外和体内进行评估时,是否显示出与计算分析预测一致的效果。该评价的问题框架是:“在计算机研究中使用了哪些肽或蛋白质来治疗肥胖症?”并根据缩写 PECo 进行了构建。系统评价方案是根据 PRISMA-P 制定并在 PROSPERO(CRD42022355540)中注册的,并且该评价的所有阶段都遵循这些指南。研究来源于以下数据库:PubMed、ScienceDirect、Scopus、Web of Science、Virtual Heath Library 和 EMBASE。搜索策略产生了 1015 篇文章,根据排除和纳入标准,其中 7 篇文章被纳入本系统评价。鉴定的抗肥胖肽源自多种来源,包括可可种子( L.)、奇亚籽( L.)、米糠()、芝麻( L.)、沙棘种子粉()和红豆()中的牛α-乳白蛋白。所有文章均经过体外和体内重新评估,并在计算机研究中使用分子对接方法。在本综述中包括的研究中,在评估的 13 个标准中的 6 个标准中,有 46.15%被归类为“不确定偏倚风险”。主要研究的靶标是胰腺脂肪酶(n = 5),所有针对该酶的肽均表现出抑制作用,这一发现得到了体外和体内的支持。此外,还鉴定出其他两种肽是 PPARγ 和 PPARα 激动剂(n = 2)。值得注意的是,所有肽在脂质代谢和脂肪生成中表现出不同的作用机制。本系统评价的结果强调了计算模拟作为筛选工具的有效性,为新型抗肥胖肽的发现提供了关键的见解,并指导了体外和体内的研究。