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Her work focuses on computational biology and bioinformatics on understanding the physical principles and dynamics of macromolecular systems, basically the principles of protein−protein interactions and prediction of interactions.

Ras proteins are classical members of small GTPases that function as molecular switches by alternating between inactive GDP-bound and active GTP-bound states. Ras activation is regulated by guanine nucleotide exchange factors that catalyze the exchange of GDP by GTP, and inactivation is terminated by GTPase-activating proteins that accelerate the intrinsic GTP hydrolysis rate by orders of magnitude. In this review, we focus on data that have accumulated over the past few years pertaining to the conformational ensembles and the allosteric regulation of Ras proteins and their interpretation from our conformational landscape standpoint. The Ras ensemble embodies all states, including the ligand-bound conformations, the activated (or inactivated) allosteric modulated states, post-translationally modified states, mutational states, transition states, and nonfunctional states serving as a reservoir for emerging functions. The ensemble is shifted by distinct mutational events, cofactors, post-translational modifications, and different membrane compositions. A better understanding of Ras biology can contribute to therapeutic strategies.

NAMD is a molecular dynamics program designed for high performance simulations of large biomolecular systems on parallel computers. An object-oriented design imple mented using C++ facilitates the incorporation of new algorithms into the program. NAMD uses spatial decom position coupled with a multithreaded, message-driven design, which is shown to scale efficiently to multiple processors. Also, NAMD incorporates the distributed par allel multipole tree algorithm for full electrostatic force evaluation in O( N) time. NAMD can be connected via a communication system to a molecular graphics program in order to provide an interactive modeling tool for viewing and modifying a running simulation. The application of NAMD to a protein-water system of 32,867 atoms illus trates the performance of NAMD.

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

SUMMARY Ras proteins recruit and activate effectors, including Raf, that transmit receptor-initiated signals. Monomeric Ras can bind Raf; however, activation of Raf requires its dimerization. It has been suspected that dimeric Ras may promote dimerization and activation of Raf. Here we show that the GTP-bound catalytic domain of K-Ras4B, a highly oncogenic splice variant of the K-Ras isoform, forms stable homodimers. We observe two major dimer interfaces. The first, highly populated β-sheet dimer interface is at the Switch I and effector binding regions, overlapping Raf’s, PI3K’s, RalGDS’ and additional effectors’ binding surfaces. This interface has to be inhibitory to such effectors. The second, helical interface also overlaps some effectors’ binding sites. This interface may promote Raf‘s activation. Our data reveal how Ras self-association can regulate effector binding and activity, and suggest that disruption of the helical dimer interface by drugs may abate Raf’s signaling in cancer.

Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction.

Supplementary data are available at Bioinformatics online.