Li Shi-Wei, Ren Peng-Xuan, Wang Lin, Han Qi-Lei, Li Feng-Lei, Li Hong-Lin, Bai Fang
Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
Acta Pharmacol Sin. 2025 May;46(5):1462-1475. doi: 10.1038/s41401-024-01444-z. Epub 2025 Jan 27.
Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method. Our method aims to localize drug targets and detect potential off-target effects early in the drug discovery process, thereby improving the success rate of drug development. We have constructed a high-quality database of protein structures with annotated potential binding sites, covering 82% of the protein-coding genome. On the basis of this database, to enhance our search capabilities, we have integrated computational techniques, including both artificial intelligence-based and biophysical model-based methods. This integration led to the development of a target identification method called Multi-Algorithm Integrated Target Fisher (MAI-TargetFisher). MAI-TargetFisher leverages the complementary strengths of various methods while minimizing their weaknesses, enabling precise database navigation to generate a reliably ranked set of candidate targets for an active query molecule. Importantly, our work is the first comprehensive scan of protein surfaces across the entire human genome, aimed at evaluating potential small molecule binding sites on each protein. Through a series of evaluations on benchmark and a target identification task, the results demonstrate the high hit rates and good reliability of our method under the validation of wet experiments. We have also made available a freely accessible web server at https://bailab.siais.shanghaitech.edu.cn/mai-targetfisher for non-commercial use.
计算靶点识别在药物研发过程中起着关键作用。随着蛋白质结构预测深度学习方法的重大进展，人类蛋白质组的结构覆盖率大幅提高。这一进展推动了首个全基因组小分子靶点扫描方法的开发。我们的方法旨在在药物发现过程的早期定位药物靶点并检测潜在的脱靶效应，从而提高药物研发的成功率。我们构建了一个高质量的蛋白质结构数据库，其中标注了潜在的结合位点，覆盖了82%的蛋白质编码基因组。基于该数据库，为了增强我们的搜索能力，我们整合了计算技术，包括基于人工智能和基于生物物理模型的方法。这种整合促成了一种名为多算法集成靶点Fisher（MAI-TargetFisher）的靶点识别方法的开发。MAI-TargetFisher利用了各种方法的互补优势，同时尽量减少其弱点，能够精确地在数据库中导航，为活性查询分子生成一组可靠排序的候选靶点。重要的是，我们的工作是对整个人类基因组的蛋白质表面进行的首次全面扫描，旨在评估每个蛋白质上潜在的小分子结合位点。通过对基准数据集的一系列评估和一个靶点识别任务，结果表明我们的方法在湿实验验证下具有高命中率和良好的可靠性。我们还在https://bailab.siais.shanghaitech.edu.cn/mai-targetfisher提供了一个可免费访问的网络服务器，供非商业使用。
