Tackling the bigger picture in computational drug design : theory, methods, and application to HIV-1 protease and erythropoietin systems

This thesis addresses challenging aspects of drug design that require explicit consideration of more than a single drug-target interaction in an unchanging environment. In the first half, the common challenge of designing a molecule that recognizes a desired subset of target molecules amidst a large set of potential binding partners is explored. Using theoretical approaches and simulation of lattice-model molecules, relationships between binding specificity and molecular properties such as hydrophobicity, size, and conformational flexibility were achieved. Methods were developed to design molecules and molecular cocktails capable of recognizing multiple target variants, and some were integrated with existing methods to design drug cocktails that were predicted to inhibit seven variants of HIV-1 protease. In the second half of the thesis, computational modeling and designs that were used to understand how cytokine binding and trafficking events affect potency are described. A general cellular-level model was systematically explored to analyze how signaling and trafficking properties can help dictate a cytokine-receptor binding affinity appropriate for long-term potency.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Chemistry.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2007

Author dc:creator

• Radhakrishnan, Mala Lakshmi

Advisor dc:contributor.advisor

• Bruce Tidor.

• Chemistry.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/39674

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/39674