Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, USA.
Adv Exp Med Biol. 2019;1163:187-223. doi: 10.1007/978-981-13-8719-7_9.
Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks. In this chapter, we discuss recent developments in understanding of allosteric mechanisms and interactions of protein systems, particularly in the context of structural, functional, and computational studies of allosteric inhibitors and activators. Computational and experimental approaches and advances in understanding allosteric regulatory mechanisms are reviewed to provide a systematic and critical view of the current progress in the development of allosteric modulators and highlight most challenging questions in the field. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Structural and computational studies of protein kinases have generated in recent decade significant insights that allowed leveraging knowledge about conformational diversity and allosteric regulation of protein kinases in the design and discovery of novel kinase drugs. We discuss recent developments in understanding multilayered allosteric regulatory machinery of protein kinases and provide a systematic view of the current state in understanding molecular basis of allostery mediated by kinase inhibitors and activators. In conclusion, we highlight the current status and future prospects of computational biology approaches in bridging the basic science of protein kinases with the discovery of anticancer therapies.
计算研究的变构相互作用见证了最近的复兴，这得益于对复杂分子组装和生物网络建模的兴趣日益浓厚。蛋白质结构中的变构相互作用允许在信号转导网络中进行分子通讯。在本章中，我们讨论了对蛋白质系统变构机制和相互作用的理解的最新进展，特别是在变构抑制剂和激活剂的结构、功能和计算研究方面。我们回顾了计算和实验方法以及对变构调节机制的理解的进展，以提供对变构调节剂发展的当前进展的系统和批判性观点，并突出该领域最具挑战性的问题。靶向和个性化药物可以对抗蛋白激酶中突变谱的机制的复杂性，其基础是遗传、结构和生化数据的丰富性和多样性。近年来，对蛋白激酶的结构和计算研究产生了重大的见解，这些见解使我们能够利用关于蛋白激酶构象多样性和变构调节的知识，设计和发现新型激酶药物。我们讨论了理解蛋白激酶多层次变构调节机制的最新进展，并提供了对激酶抑制剂和激活剂介导的变构分子基础的理解的当前状态的系统观点。总之，我们强调了计算生物学方法在将蛋白激酶的基础科学与抗癌疗法的发现联系起来方面的现状和未来前景。
