Kavraki Lab

Physical and Biological Computing

Haspel, N, Geisbrecht B, Lambris JD, Kavraki LE.  2009.  Multi-scale Characterization of the Energy Landscape of Proteins with Application to the C3d/Efb-C Complex. Proteins: Structure, function and bioinformatics. In press

We have also looked at computational methods to characterize and systematize the definition of good motifs. Once such technique is Geometric Sieving. The goal of Geometric Sieving is to propose geometric criteria to optimize known motifs and make them more effective motifs. An effective motif retains geometric and chemical similarity to substructures in functional homologs, while maintaining geometric and chemical dissimilarity to with all substructures of functionally unrelated proteins.

Fofanov, VY, Chen BY, Bryant DH, Moll M, Lichtarge O, Kavraki LE, Kimmel M.  2008.  A Statistical Model to Correct Systematic Bias Introduced by Algorithmic Thresholds in Protein Structural Comparison Algorithms. IEEE Intl. Conf. on Bioinformatics and Biomedicine (BIBM).

Moll, M, Kavraki LE.  2008.  LabelHash: A Flexible and Extensible Method for Matching Structural Motifs. Automated Function Prediction / BioSapiens meeting (AFP-BioSapiens).

Chen, BY, Bryant DH, Cruess AE, Bylund JH, Fofanov VY, Kimmel M, Lichtarge O, Kavraki LE.  2007.  Composite Motifs Integrating Multiple Protein Structures Increase Sensitivity for Function Prediction. Computational Systems Bioinformatics Conference (CSB2007). :343-355.

Moll, M, Kavraki LE.  2008.  Matching of Structural Motifs Using Hashing on Residue Labels and Geometric Filtering for Protein Function Prediction. The Seventh Annual International Conference on Computational Systems Bioinformatics (CSB2008). :157-168.

Kristensen, DM, Ward MR, Lisewski AM, Edrin S, Chen BY, Fofanov VY, Kimmel M, Lichtarge O.  2008.  Prediction of enzyme function based on 3D templates of evolutionarily important amino acids. BMC Bioinformatics. 9(17)

Introduction

Our goal is to develop a set of computational methods for the analysis of molecular kinematics and molecular conformations that are relevant to receptor-ligand interactions. We are also interested in studying geometric and matching problems arising when the three-dimensional structure of molecules is considered in the analysis of receptor ligand interactions. Our target application is computer-assisted drug design, which is a significant component of rational drug design.

Introduction

Broad and extensive knowledge of the biological function of proteins would have immense practical impact on the identification of novel drug targets, the reduction of potential side effects, and on finding the molecular causes of disease. Unfortunately, the experimental determination of protein function is an expensive and time consuming process.

Chen, BY, Bryant DH, Fofanov VY, Kristensen DM, Cruess AE, Kimmel M, Lichtarge O, Kavraki LE.  2006.  Cavity-Aware Motifs Reduce False Positives in Protein Function Prediction. Systems Bioinformatics Conference (CSB). 4:311-323.