Peptides Evaluated In Silico, In Vitro, and In Vivo as Therapeutic Tools for Obesity: A Systematic Review.
Ana Júlia Felipe Camelo AguiarWendjilla Fortunato de MedeirosJuliana Kelly da Silva-MaiaIngrid Wilza Leal BezerraGrasiela PiuvezamAna Heloneida de Araújo MoraisPublished in: International journal of molecular sciences (2024)
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 ( Theobroma cacao L.), chia seed ( Salvia hispanica L.), rice bran ( Oryza sativa ), sesame ( Sesamum indicum L.), sea buckthorn seed flour ( Hippophae rhamnoides ), and adzuki beans ( Vigna angularis ). 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.
Keyphrases
- systematic review
- molecular docking
- insulin resistance
- metabolic syndrome
- meta analyses
- high fat diet induced
- weight loss
- type diabetes
- weight gain
- amino acid
- randomized controlled trial
- case control
- public health
- adipose tissue
- molecular dynamics simulations
- skeletal muscle
- machine learning
- body mass index
- physical activity
- cancer therapy
- high throughput
- big data
- clinical practice
- deep learning
- single cell
- high speed