TY - CHAP

T1 - Kinase-Centric Computational Drug Development

AU - Kooistra, Albert J.

AU - Volkamer, Andrea

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Kinases are among the most studied drug targets in industry and academia, due to their involvement in a majority of cellular processes and, upon dysregulation, in a variety of diseases including cancer, inflammation, and autoimmune disorders. The high interest in this druggable protein family triggered the generation of a large pool of data comprising sequence, structure, bioactivity, and mutation data. Together with this continuously growing amount of available data, comes the need as well as the opportunity to organize, analyze, and utilize this data in order to aid the design of novel, active, and potentially selective kinase inhibitors. In this chapter, we provide a comprehensive overview of kinase-centric data resources and tools that can be utilized for computationally driven kinase research. The contents of all resources are summarized, and all platforms focused on human kinases are discussed in more detail. Furthermore, practical applications from literature and illustrative examples showcasing the aforementioned sources and tools are presented. These applications utilize sequence, structure, and bioactivity data and range from single structure analysis, sequence comparisons, binding site predictions, druggability predictions, and protein–ligand interaction fingerprinting to activity predictions using machine learning methods. Finally, a perspective is given on the unmet needs, potential pitfalls, and current developments in kinase drug design.

AB - Kinases are among the most studied drug targets in industry and academia, due to their involvement in a majority of cellular processes and, upon dysregulation, in a variety of diseases including cancer, inflammation, and autoimmune disorders. The high interest in this druggable protein family triggered the generation of a large pool of data comprising sequence, structure, bioactivity, and mutation data. Together with this continuously growing amount of available data, comes the need as well as the opportunity to organize, analyze, and utilize this data in order to aid the design of novel, active, and potentially selective kinase inhibitors. In this chapter, we provide a comprehensive overview of kinase-centric data resources and tools that can be utilized for computationally driven kinase research. The contents of all resources are summarized, and all platforms focused on human kinases are discussed in more detail. Furthermore, practical applications from literature and illustrative examples showcasing the aforementioned sources and tools are presented. These applications utilize sequence, structure, and bioactivity data and range from single structure analysis, sequence comparisons, binding site predictions, druggability predictions, and protein–ligand interaction fingerprinting to activity predictions using machine learning methods. Finally, a perspective is given on the unmet needs, potential pitfalls, and current developments in kinase drug design.

KW - Activity

KW - Binding sites

KW - Bioactivity

KW - Computational tools

KW - Drug design

KW - Druggability

KW - In silico screening

KW - Kinase inhibitors

KW - Kinases

KW - Kinome

KW - Machine learning

KW - Polypharmacology

KW - Protein–ligand interactions

KW - Selectivity

KW - Sequence

KW - Structure

KW - Target assessment

UR - http://www.scopus.com/inward/record.url?scp=85031425675&partnerID=8YFLogxK

U2 - 10.1016/bs.armc.2017.08.001

DO - 10.1016/bs.armc.2017.08.001

M3 - Book chapter

AN - SCOPUS:85031425675

T3 - Annual Reports in Medicinal Chemistry

SP - 263

EP - 299

BT - Annual Reports in Medicinal Chemistry

PB - Academic Press

ER -