Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein–ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein–ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.

中文翻译：

---

DynaBiS：一种分层采样算法，用于识别大型配体和肽的灵活结合位点

了解蛋白质中的配体或肽结合位点对于指导药物发现非常重要，但很难通过实验阐明结合位点。因此，已经开发了各种计算方法来识别蛋白质结构中的潜在结合位点。然而，尽管认识到蛋白质-配体相互作用会受到相互结构适应的强烈影响，但出于效率考虑，蛋白质和配体的灵活性在这些方法中经常被忽略。如果结合位点未知，则尤其如此，因为通常会根据未结合的蛋白质结构进行筛选。在这里，我们提出了 DynaBiS，一种分层采样算法，用于识别目标配体的灵活结合位点，并明确考虑蛋白质和配体的灵活性，灵感来自我们之前提出的灵活对接算法 DynaDock。DynaBiS 在配体和蛋白质之间应用软核电位，从而允许一定的蛋白质-配体重叠，从而有效地采样构象适应效应。我们针对由 26 种具有肽和小配体结合位点的蛋白质组成的多样化评估集评估了 DynaBiS 和其他常用的结合位点识别算法。我们表明 DynaBiS 优于其他评估的方法，用于识别大型和高度灵活的配体（如肽）的蛋白质结合位点，两者都具有用作输入的全息或 apo 结构。从而允许某种蛋白质-配体重叠，从而有效地采样构象适应效应。我们针对由 26 种具有肽和小配体结合位点的蛋白质组成的多样化评估集评估了 DynaBiS 和其他常用的结合位点识别算法。我们表明 DynaBiS 优于其他评估的方法，用于识别大型和高度灵活的配体（如肽）的蛋白质结合位点，两者都具有用作输入的全息或 apo 结构。从而允许某种蛋白质-配体重叠，从而有效地采样构象适应效应。我们针对由 26 种具有肽和小配体结合位点的蛋白质组成的多样化评估集评估了 DynaBiS 和其他常用的结合位点识别算法。我们表明 DynaBiS 优于其他评估的方法，用于识别大型和高度灵活的配体（如肽）的蛋白质结合位点，两者都具有用作输入的全息或 apo 结构。