Baumgartner, Matthew
(2016)
IMPROVING RATIONAL DRUG DESIGN BY INCORPORATING NOVEL BIOPHYSICAL INSIGHT.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
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Abstract
Computer-aided drug design is a valuable and effective complement to conventional experimental drug discovery methods. In this thesis, we will discuss our contributions to advancing a number of outstanding challenges in computational drug discovery: understanding protein flexibility and dynamics, the role of water in small molecule binding and using and understanding large amounts of data. First, we describe the molecular steps involved in the induced-fit binding mechanism of p53 and MDM2. We use molecular dynamics simulations to understand the key chemistry responsible for the dynamic transition between the apo and holo structures of MDM2. This chemistry involves not only the indole side chain of the anchor residue of p53, Trp23, but surprisingly, the beta-carbon as well. We demonstrate that this chemistry plays a key role in opening the binding site by coordinating the position and orientation of MDM2 residues, Val93 and His96, through a previously undescribed transition state. We confirm these findings by observing that this chemistry is preserved in all available inhibitor-bound MDM2 co-crystal structures. Second, we discuss our advances in understanding water molecules in ligand binding sites by data mining the structural information of water molecules found in X-ray crystal structures. We examine a large set of paired bound and unbound proteins and compare the water molecules found in the binding site of the unbound structure to the functional groups on the ligand that displace them upon binding. We identify a number of generalized functional groups that are associated with characteristic clusters of water molecules. This information has been utilized in several successful and ongoing virtual screens. Third, we discuss software that we have developed that allows for very efficient exploration and selection of virtual screening results. Implemented as a PyMOL plugin, ClusterMols clusters compounds based on a user-defined level of chemical similarity. The software also provides advanced visualization tools and a number of controls for quickly navigating and selecting compounds of interest, as well as the ability to check online for available vendors. Finally, we present several published examples of modeling protein-lipid and protein-small molecules interactions for a number of important targets including ABL, c-Src and 5-LOX.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
23 May 2016 |
| Date Type: |
Publication |
| Defense Date: |
12 February 2016 |
| Approval Date: |
23 May 2016 |
| Submission Date: |
19 May 2016 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
156 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Medicine > Computational Biology |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
drug design, computational drug discovery, molecular dynamics, biophysics, MDM2, p53, crystallographic water, virtual screening, PyMOL, software |
| Date Deposited: |
23 May 2016 14:12 |
| Last Modified: |
15 Nov 2016 14:33 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/28044 |
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