Research Description

My Ph.D. projects focus on the integration of sequence analysis with protein structure and dynamics. In particular, exploiting the correlation derived from sequence variation profile and dynamical correlation derived from elastic network model (ENM).

On-Going Projects

• Heat Shock Protein Hsp70

  Hsp70 is a ubiquitous protein that exists in a wide variety of organisms. The Hsp70 molecule is composed of two domains, the ATPase domain and the substrate binding domain (SBD). The two domains regulate the activity of each other via allosteric communication: the hydrolysis of ATP to ADP at the ATPase domain increases the binding affinity of SBD, and the released substrate further stimulates ATP hydrolysis; on the other hand, the consequent ADP is replaced with ATP, which lowers the binding affinity of SBD. The ATPase cycle along with the regulated substrate binding form the basis of the chaperoning function of Hsp70, and notably, of other Hsp members as well. There are a number of co-chaperones and other binding partners involved in the process besides Hsp70 and the client protein. For example, the ATP hydrolysis is also catalyzed by the J-domain proteins, and the replacement of ADP with ATP is largely assisted by nucleotide exchange factors (NEFs) that bind the ATPase domain and significantly increase the ADP dissociation rate.

  We aim to investigate possible correlations between the two domains using correlated mutation analysis, combined with structural information and dynamical correlation derived from elastic network models.

  
  
• UvrA and UvrB Systems

  UvrA and UvrB proteins play an essential role in DNA repair. It has been observed that they bind to the targeted repair region in a sequential manner; UvrA scans along the DNA chain and recognizes the legion area, then it binds to UvrB, but gets released and hands over the job to UvrB. Recently the X-ray structure of their contact interface has been solved, but the mechanisms involved in the repair site recognition are still unclear. We hope that our computational approaches can help experimentalists gain more insight into the problem.

Past Projects

• HIV-1 protease

  Ying Liu, Eran Eyal, and Ivet Bahar. (2008)
  Analysis of correlated mutations in hiv-1 protease using spectral clustering. Bioinformatics, 24:1243-1250. (Abstract) (fulltext)

  Motivation: The ability of human immunodeficiency virus-1 (HIV-1) protease to develop mutations that confer multi-drug resistance (MDR) has been a major obstacle in designing rational therapies against HIV. Resistance is usually imparted by a cooperative mechanism that can be elucidated by a covariance analysis of sequence data. Identification of such correlated substitutions of amino acids may be obscured by evolutionary noise.
  Results: HIV-1 protease sequences from patients subjected to different specific treatments (set 1), and from untreated patients (set 2) were subjected to sequence covariance analysis by evaluating the mutual information (MI) between all residue pairs. Spectral clustering of the resulting covariance matrices disclosed two distinctive clusters of correlated residues: the first, observed in set 1 but absent in set 2, contained residues involved in MDR acquisition; and the second, included those residues differentiated in the various HIV-1 protease subtypes, shortly referred to as the phylogenetic cluster. The MDR cluster occupies sites close to the central symmetry axis of the enzyme, which overlap with the global hinge region identified from coarse-grained normal-mode analysis of the enzyme structure. The phylogenetic cluster, on the other hand, occupies solvent-exposed and highly mobile regions. This study demonstrates (i) the possibility of distinguishing between the correlated substitutions resulting from neutral mutations and those induced by MDR upon appropriate clustering analysis of sequence covariance data and (ii) a connection between global dynamics and functional substitution of amino acids.
  

• MI Suite Web Server (deployed internally)

Thesis Proposal

• Defense Slides