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Lydia Kavraki

Publications and source records attributed to Lydia Kavraki.

2 recordsLinked to original sources

Improving conformational searches by geometric screening.

MOTIVATION: Conformational searches in molecular docking are a time-consuming process with wide range of applications. Favorable conformations of the ligands that successfully bind with receptors are sought to form stable ligand-receptor complexes. Usually a large number of conformations are generated and their binding energies are examined. We propose adding a geometric screening phase before an energy minimization procedure so that only conformations that geometrically fit in the binding site will be prompted for energy calculation. RESULTS: Geometric screening can drastically reduce the number of conformations to be examined from millions (or higher) to thousands (or lower). The method can also handle cases when there are more variables than geometric constraints. An early-stage implementation is able to finish the geometric filtering of conformations for molecules with up to nine variables in 1 min. To the best of our knowledge, this is the first time such results are reported deterministically. CONTACT: mzhang@mdanderson.org.

Algorithms↗

An accurate, sensitive, and scalable method to identify functional sites in protein structures.

Functional sites determine the activity and interactions of proteins and as such constitute the targets of most drugs. However, the exponential growth of sequence and structure data far exceeds the ability of experimental techniques to identify their locations and key amino acids. To fill this gap we developed a computational Evolutionary Trace method that ranks the evolutionary importance of amino acids in protein sequences. Studies show that the best-ranked residues form fewer and larger structural clusters than expected by chance and overlap with functional sites, but until now the significance of this overlap has remained qualitative. Here, we use 86 diverse protein structures, including 20 determined by the structural genomics initiative, to show that this overlap is a recurrent and statistically significant feature. An automated ET correctly identifies seven of ten functional sites by the least favorable statistical measure, and nine of ten by the most favorable one. These results quantitatively demonstrate that a large fraction of functional sites in the proteome may be accurately identified from sequence and structure. This should help focus structure-function studies, rational drug design, protein engineering, and functional annotation to the relevant regions of a protein.

Amino Acid Motifs↗