Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil.
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil.
Nucleic Acids Res. 2022 Jul 5;50(W1):W392-W397. doi: 10.1093/nar/gkac323.
Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fundamental importance to determine protein function, being a fundamental step in processes such as drug design and discovery. However, identifying such binding regions is not trivial due to the drawbacks of experimental methods, which are costly and time-consuming. Here we propose GRaSP-web, a web server that uses GRaSP (Graph-based Residue neighborhood Strategy to Predict binding sites), a residue-centric method based on graphs that uses machine learning to predict putative ligand binding site residues. The method outperformed 6 state-of-the-art residue-centric methods (MCC of 0.61). Also, GRaSP-web is scalable as it takes 10-20 seconds to predict binding sites for a protein complex (the state-of-the-art residue-centric method takes 2-5h on the average). It proved to be consistent in predicting binding sites for bound/unbound structures (MCC 0.61 for both) and for a large dataset of multi-chain proteins (4500 entries, MCC 0.61). GRaSPWeb is freely available at https://grasp.ufv.br.
蛋白质是维持生命系统所必需的生物大分子。它们中的许多通过与其他分子在称为结合部位的区域相互作用来执行其功能。鉴定和描述这些区域对于确定蛋白质功能至关重要，是药物设计和发现等过程的基本步骤。然而，由于实验方法成本高且耗时，因此识别这些结合区域并非易事。在这里，我们提出了 GRaSP-web，这是一个使用 GRaSP（基于图的残基邻域策略来预测结合位点）的网络服务器，GRaSP 是一种基于图的残基中心方法，它使用机器学习来预测潜在的配体结合位点残基。该方法的表现优于 6 种最先进的残基中心方法（MCC 为 0.61）。此外，GRaSP-web 是可扩展的，因为它可以在 10-20 秒内预测蛋白质复合物的结合位点（最先进的残基中心方法平均需要 2-5 小时）。它在预测结合/未结合结构的结合位点时表现一致（MCC 分别为 0.61），并且在包含 4500 个条目多链蛋白质的大型数据集上也表现一致（MCC 为 0.61）。GRaSPWeb 可免费在 https://grasp.ufv.br 上获得。
