Chemistry Reference
In-Depth Information
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 NMR Ligand Affinity Screens ............................................................... 4
2.1 Ligand-Based NMR Screens . ......................................................... 4
2.2 Target-Based NMR Screens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3 Molecular Docking ........................................................................... 7
3.1 Docking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.2 Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 Protein Flexibility . . . ................................................................... 13
3.4 Virtual Screening and Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4 Combining Molecular Docking with NMR Ligand Affinity Screens ...................... 17
4.1 Identification of New Therapeutic Targets ............................................ 18
4.2 Rapid Protein-Ligand Structure Determination ...................................... 20
4.3 Lead Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5 Concluding Remarks ......................................................................... 26
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1
Introduction
The completion of the human genome project [
1
] coupled with an increase in R&D
investments was widely anticipated to be the cornerstone of personalized medicine
with a corresponding explosion in new pharmaceutical drugs targeting a range of
diseases. Nearly a decade later, the rate at which new drugs enter clinical develop-
ment and reach the market has declined dramatically despite the influx of novel
therapeutic targets and R&D investments. In the past 5 years the number of new
molecular entities (NMEs) receiving FDA approval has decreased by 50% from the
previous 5 years [
2
]. There are several reasons for this decline, but most stem from
the fact that drug discovery is a complex and costly endeavor. Approximately
80-90% of drugs that reach the clinical testing phase fail to make it to market
[
3
,
4
]. Efforts to reduce costs often lead pharmaceutical companies to invest their
time and money in proven therapies, like “best-in-class” drugs, instead of “first-
in-class” drugs that target new mechanisms of action or diseases. As a result, many
diseases are “orphaned” and lack any therapeutic compounds in the discovery
pipeline. Addressing these issues will require fundamental changes to create
a more efficient drug discovery process.
The enormous costs and high failure rates inherent to the pharmaceutical indus-
try are clearly contributing factors to the declining number and diversity of new
therapeutics. Efforts that minimize costs without restricting research endeavors will
evidently benefit the development of drugs for various human diseases. The avail-
ability of hundreds of whole-genome sequences for numerous organisms provides
an invaluable data set for drug research [
1
,
5
,
6
]. Identifying a novel “druggable”
protein target is a critical first step for a successful and efficient drug discovery
effort. Unfortunately, bioinformatics analysis alone does not generally provide
enough information to justify embarking upon an expensive drug discovery pro-
gram [
7
,
8
]. Instead, knowing the three dimensional structure of a protein greatly
