Novel Kinase Inhibitors by Reshuffling Ligand Functionalities Across the Human Kinome

Protein kinases remain among the
most versatile and prospective
therapeutic drug targets with currently 15 distinct compounds approved
for use in humans and numerous clinical development programs. The
vast majority of kinase inhibitors bind at the ATP site. Here we present
an integrated workflow to amplify the rapidly increasing space of
structurally resolved small molecule kinase ligands to generate novel
inhibitors. Our approach considers both receptor-based similarity
constraints in cocomplexes and ligand-based filtering/refinement methods
to generate novel, drug-like matter. After building a comprehensive
database of the structural kinome and identifying ATP-competitive
ligands, we leverage local site similarities and site alignments to
shuffle ligand fragments across the kinome. After extensive curation
and standardization, our automated protocol starting from 936 cocrystal
ATP-competitive binding sites generated about 150 000 new ligand
structures among them over 26 000 lead-/drug-like compounds;
the majority of those are novel based on structural similarity and
scaffolds. In a retrospective analysis we demonstrate that our protocol
produced known potent kinase inhibitors and we show how docking can
be applied to prioritize the most likely efficacious compounds. Our
workflow emulates a common strategy in medicinal chemistry to identify
and swap corresponding moieties from known inhibitors to generate
novel and potent leads. Here, we systematize and automate this approach
leveraging available knowledge covering the entire human Kinome.

蛋白激酶（Protein kinases）仍是当前最具通用性与开发前景的治疗性药物靶点之一：目前已有15种不同化合物获批用于人体临床，另有大量项目处于临床开发进程中。绝大多数激酶抑制剂均结合于ATP结合位点（ATP site）。本研究提出一套整合型工作流程，用于拓展快速增长的已解析结构小分子激酶配体库，以开发新型激酶抑制剂。本方法同时兼顾共复合物（cocomplexes）中基于受体的相似性约束，以及基于配体的筛选与优化手段，以生成全新的类药化合物。在构建涵盖结构激酶组（kinome）的综合数据库并筛选出ATP竞争性配体后，我们利用局部位点相似性与位点比对技术，在整个激酶组范围内对配体片段进行重排组合。经过全面整理与标准化处理后，我们的自动化流程以936个共晶ATP竞争性结合位点为起点，生成了约15万个全新的配体结构，其中包含超过2.6万个先导/类药化合物；其中绝大多数化合物基于结构相似性与分子骨架来看均为全新分子。通过回顾性分析，我们证实本流程可生成已知的强效激酶抑制剂，并展示了如何利用分子对接（docking）技术对最具潜在药效的化合物进行优先级排序。本工作流程模拟了药物化学领域的经典策略：从已知抑制剂中识别并替换对应基团，以生成全新且强效的先导化合物。本研究借助覆盖整个人类激酶组的现有知识，将该策略系统化并实现自动化。