Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, Udine, Italy.
Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy.
Comput Biol Chem. 2023 Apr;103:107819. doi: 10.1016/j.compbiolchem.2023.107819. Epub 2023 Jan 14.
In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.
在为生物传感应用设计能够与特定目标结合的大分子的合理框架内,我们在此进一步开发了一种进化协议,旨在优化蛋白质结合物的结合亲和力。特别是,我们专注于优化称为纳米体(或 VHH)的小抗体片段的结合部分,并选择鸡卵清溶菌酶(HEWL)作为我们的目标。通过为这种优化实现复制交换方案,我们表明不需要初始命中,并且可以通过优化已经已知的抗 HEWL VHH 或随机选择的结合物(这里是对另一种大分子具有选择性的 VHH)来找到类似的解决方案。虽然我们认为在仅需要优化少数关键残基时,对突变空间进行详尽搜索是最合适的,但如果没有先导结合物,则应选择所提出的进化算法。