Guide tree optimization with genetic algorithm to improve multiple protein 3D-structure alignment.

Motivation: With the increasing availability of 3D-data, the focus of comparative bioinformatic analysis is shifting from protein sequence alignments toward more content-rich 3D-alignments. This raises the need for new ways to improve the accuracy of 3D-superimposition.

Results: We proposed guide tree optimization with genetic algorithm (GA) as a universal tool to improve the alignment quality of multiple protein 3D-structures systematically.

parMATT: parallel multiple alignment of protein 3D-structures with translations and twists for distributed-memory systems.

Motivation: Accurate structural alignment of proteins is crucial at studying structure-function relationship in evolutionarily distant homologues. Various software tools were proposed to align multiple protein 3D-structures utilizing one CPU and thus are of limited productivity at large-scale analysis of protein families/superfamilies.

Results: The parMATT is a hybrid MPI/pthreads/OpenMP parallel re-implementation of the MATT algorithm to align multiple protein 3D-structures by allowing translations and twists.

Mustguseal: a server for multiple structure-guided sequence alignment of protein families.

Motivation: Comparative analysis of homologous proteins in a functionally diverse superfamily is a valuable tool at studying structure-function relationship, but represents a methodological challenge.

Results: The Mustguseal web-server can automatically build large structure-guided sequence alignments of functionally diverse protein families that include thousands of proteins basing on all available information about their structures and sequences in public databases. Superimposition of protein structures is implemented to compare evolutionarily distant relatives, whereas alignment of sequences is used to compare close homologues.

Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima.

The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB.