Assessing protein kinase target similarity: Comparing sequence, structure, and cheminformatics approaches

TY - JOUR

T1 - Assessing protein kinase target similarity

T2 - Comparing sequence, structure, and cheminformatics approaches

AU - Gani, Osman A

AU - Thakkar, Balmukund

AU - Narayanan, Dilip

AU - Alam, Kazi A

AU - Kyomuhendo, Peter

AU - Rothweiler, Ulli

AU - Tello-Franco, Veronica

AU - Engh, Richard A

N1 - Copyright © 2015 Elsevier B.V. All rights reserved.

PY - 2015/10/1

Y1 - 2015/10/1

N2 - In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

AB - In just over two decades, structure based protein kinase inhibitor discovery has grown from trial and error approaches, using individual target structures, to structure and data driven approaches that may aim to optimize inhibition properties across several targets. This is increasingly enabled by the growing availability of potent compounds and kinome-wide binding data. Assessing the prospects for adapting known compounds to new therapeutic uses is thus a key priority for current drug discovery efforts. Tools that can successfully link the diverse information regarding target sequence, structure, and ligand binding properties now accompany a transformation of protein kinase inhibitor research, away from single, block-buster drug models, and toward "personalized medicine" with niche applications and highly specialized research groups. Major hurdles for the transformation to data driven drug discovery include mismatches in data types, and disparities of methods and molecules used; at the core remains the problem that ligand binding energies cannot be predicted precisely from individual structures. However, there is a growing body of experimental data for increasingly successful focussing of efforts: focussed chemical libraries, drug repurposing, polypharmacological design, to name a few. Protein kinase target similarity is easily quantified by sequence, and its relevance to ligand design includes broad classification by key binding sites, evaluation of resistance mutations, and the use of surrogate proteins. Although structural evaluation offers more information, the flexibility of protein kinases, and differences between the crystal and physiological environments may make the use of crystal structures misleading when structures are considered individually. Cheminformatics may enable the "calibration" of sequence and crystal structure information, with statistical methods able to identify key correlates to activity but also here, "the devil is in the details." Examples from specific repurposing and polypharmacology applications illustrate these points. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases.

KW - Amino Acid Sequence

KW - Binding Sites

KW - Crystallography, X-Ray

KW - Drug Discovery

KW - Humans

KW - Protein Binding

KW - Protein Conformation

KW - Protein Kinase Inhibitors

KW - Protein Kinases

KW - Proto-Oncogene Proteins c-abl

KW - Small Molecule Libraries

KW - Structure-Activity Relationship

KW - Journal Article

KW - Research Support, Non-U.S. Gov't

U2 - 10.1016/j.bbapap.2015.05.004

DO - 10.1016/j.bbapap.2015.05.004

M3 - Journal article

C2 - 26001898

SN - 0304-4165

VL - 1854

SP - 1605

EP - 1616

JO - Biochimica et biophysica acta

JF - Biochimica et biophysica acta

IS - 10 Pt B

ER -