Localized methods for protein interaction prediction

Identification of protein-protein interactions is important for drug design and the treatment of diseases. We propose a novel threading algorithm, LTHREADER, which generates accurate local sequence-structure alignments and integrates various statistical scores and experimental binding data to predict interactions. LTHREADER uses a profile of secondary structure and solvent accessibility predictions with residue contact maps to guide and constrain alignments. Using a decision tree classifier and low-throughput experimental data for training, it combines information inferred from statistical interaction potentials, energy functions, correlated mutations and conserved residue pairs to predict likely interactions. The significance of predicted interactions is evaluated using the scores for randomized binding surfaces within each family. We first apply our method to cytokines, which play a central role in the development of many diseases including cancer and inflammatory and autoimmune disorders. We tested our approach on two representative families from different structural classes (all-alpha and all-beta proteins) of cytokines. In comparison with the state-of-the-art threader RAPTOR, LTHREADER generates on average 20% more accurate alignments of interacting residues and shows dramatic improvement in prediction accuracy over existing methods. To further improve alignment accuracy for all PPI families, we also introduce the program CMAPi, a two dimensional dynamic programming algorithm that, given a pair of protein complexes, optimally aligns the contact maps of their interfaces. We demonstrate the efficacy of our algorithm on complexes from PPI families listed in the SCOPPI database and from highly divergent cytokine families. In comparison to existing techniques, CMAPi generates more accurate alignments of interacting residues within families of interacting proteins, especially for sequences with low similarity.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2008

Author dc:creator

• Pulim, Vinay, 1976-

Advisor dc:contributor.advisor

• Bonnie Berger.

• Electrical Engineering and Computer Science.

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/1721.1/44715

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/44715