1 Introduction
One of the first molecular modeling studies dealing with protein kinases was
published by Fry, Kuby, and Mildvan [1] in the mid-1980s. The authors used
NMR NOE’s and molecular modeling to understand how MgATP interacts with
rabbit muscle adenylate kinase. This study can be understood as a starting point for a
great deal of and increasingly more active research on small molecule-kinase
interactions. Surprisingly, we are still facing partially the same obstacles as Fry
et al. some 35 years ago. In a highly simplified way, we are trying to understand
structural properties of kinases and how kinase inhibitors are modifying these
properties. The current main question is how kinase function and conformation
effect upon the inhibitor binding are related to each other. Although the current
paradigm in kinase drug design is to interfere the biological activity of kinase with
small molecules, we do now understand that this inhibition cannot be modeled only
by a simple docking experiment between a small drug-like molecule and the
ATP-binding site of the target kinase. Instead, it is mandatory to study the whole
kinase domain with solvent and, in many cases, with additional domains and
interacting proteins.
This chapter will deal with the molecular modeling of kinases. Although some
structural biology data is also presented, I would warmly recommend the reader to
study the excellent text by Röhm, Krämer, and Knapp in this book (Chap. XX) to
begin with. Modeling is, after all, based on our knowledge of structural biology, and
very little can be achieved without high-quality protein structures. In addition,
protein kinases share several unique structural features, like hydrophobic spines
[2], which one should know prior to looking at the details of molecular modeling
around kinases. This chapter is not to be taken as a guide on how to model kinases,
neither is it a complete review of the topic. The emphasis is more on indicating those
critical factors which one must consider when and if protein kinases are modeled. At
the same time, this chapter concentrates mainly on structure-based drug design
aspects, and detailed analysis of quantum mechanical studies or QSAR/machine
learning, for example, is not included. One reason why QSAR and related methods
are not analyzed is that high-quality QSAR studies of kinase inhibitors are rare and
most of the time only explanatory in nature. One can even argue that since the
invention of 3D-QSAR studies in the late 1980s [3], the development of QSAR
methods in drug discovery has been quite negligible, and structure-based methods
are now the mainstream in drug design.
So, what are the modeling issues we are currently struggling with, and what are
the main approaches computational medicinal chemists and molecular modelers are
utilizing? A simple answer to this question is “molecular motion” and “molecular
dynamics.” In other words, the aim is to go beyond simple virtual screening and
docking and look at how topics like solvent effects, local and global molecular
motions, and protein-protein interactions are modeled.
And yet, there is still one preliminary question to be answered: what is molecular
modeling? Maybe the best response is offered by Ander Leach: “Molecular
26
A. Poso
One of the first molecular modeling studies dealing with protein kinases was
published by Fry, Kuby, and Mildvan [1] in the mid-1980s. The authors used
NMR NOE’s and molecular modeling to understand how MgATP interacts with
rabbit muscle adenylate kinase. This study can be understood as a starting point for a
great deal of and increasingly more active research on small molecule-kinase
interactions. Surprisingly, we are still facing partially the same obstacles as Fry
et al. some 35 years ago. In a highly simplified way, we are trying to understand
structural properties of kinases and how kinase inhibitors are modifying these
properties. The current main question is how kinase function and conformation
effect upon the inhibitor binding are related to each other. Although the current
paradigm in kinase drug design is to interfere the biological activity of kinase with
small molecules, we do now understand that this inhibition cannot be modeled only
by a simple docking experiment between a small drug-like molecule and the
ATP-binding site of the target kinase. Instead, it is mandatory to study the whole
kinase domain with solvent and, in many cases, with additional domains and
interacting proteins.
This chapter will deal with the molecular modeling of kinases. Although some
structural biology data is also presented, I would warmly recommend the reader to
study the excellent text by Röhm, Krämer, and Knapp in this book (Chap. XX) to
begin with. Modeling is, after all, based on our knowledge of structural biology, and
very little can be achieved without high-quality protein structures. In addition,
protein kinases share several unique structural features, like hydrophobic spines
[2], which one should know prior to looking at the details of molecular modeling
around kinases. This chapter is not to be taken as a guide on how to model kinases,
neither is it a complete review of the topic. The emphasis is more on indicating those
critical factors which one must consider when and if protein kinases are modeled. At
the same time, this chapter concentrates mainly on structure-based drug design
aspects, and detailed analysis of quantum mechanical studies or QSAR/machine
learning, for example, is not included. One reason why QSAR and related methods
are not analyzed is that high-quality QSAR studies of kinase inhibitors are rare and
most of the time only explanatory in nature. One can even argue that since the
invention of 3D-QSAR studies in the late 1980s [3], the development of QSAR
methods in drug discovery has been quite negligible, and structure-based methods
are now the mainstream in drug design.
So, what are the modeling issues we are currently struggling with, and what are
the main approaches computational medicinal chemists and molecular modelers are
utilizing? A simple answer to this question is “molecular motion” and “molecular
dynamics.” In other words, the aim is to go beyond simple virtual screening and
docking and look at how topics like solvent effects, local and global molecular
motions, and protein-protein interactions are modeled.
And yet, there is still one preliminary question to be answered: what is molecular
modeling? Maybe the best response is offered by Ander Leach: “Molecular
26
A. Poso