De novo design of drug-binding proteins with predictable binding energy and specificity

• 成果类型: Article
• 署名作者: Lu, Lei; Gou, Xuxu; Tan, Sophia K.; Mann, Samuel I.; Yang, Hyunjun; Zhong, Xiaofang; Gazgalis, Dimitrios; Valdiviezo, Jesus; Jo, Hyunil; Wu, Yibing; Diolaiti, Morgan E.; Ashworth, Alan; Polizzi, Nicholas F.; DeGrado, William F.
• 署名单位: University of California System; University of California San Francisco; University of California System; University of California San Francisco; University of California System; University of California San Francisco; UCSF Medical Center; UCSF Helen Diller Family Comprehensive Cancer Center; University of California System; University of California Riverside; University of California System; University of California San Francisco; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Harvard Medical School
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-12246
• DOI: 10.1126/science.adl5364
• 发表日期: 2024-04-05
• 页码: 106-112
• 关键词: molecular-dynamics simulations; particle mesh ewald; ligand-binding; computational design; density functionals; atomic charges; inhibitor; affinity; library; potent

摘要

The de novo design of small molecule-binding proteins has seen exciting recent progress; however, high-affinity binding and tunable specificity typically require laborious screening and optimization after computational design. We developed a computational procedure to design a protein that recognizes a common pharmacophore in a series of poly(ADP-ribose) polymerase-1 inhibitors. One of three designed proteins bound different inhibitors with affinities ranging from <5 nM to low micromolar. X-ray crystal structures confirmed the accuracy of the designed protein-drug interactions. Molecular dynamics simulations informed the role of water in binding. Binding free energy calculations performed directly on the designed models were in excellent agreement with the experimentally measured affinities. We conclude that de novo design of high-affinity small molecule-binding proteins with tuned interaction energies is feasible entirely from computation.