Statistical significance of protein structure prediction by threading.

Confidence Measures for Fold Recognition

It is a standard procedure to compare new amino acid sequences to databases of proteins that have been studied already in order to find similarities in structure and function. This comparison can be sequence–sequence or sequence– structure based. In order to compare, an alignment is performed of the target protein sequence (whose structure we are searching) with a template protein (whose struct...

Prediction of 3D protein Structure based on Mutation of AKAP3 and PLOD3 Gene in Case of Non-Obstructive Azoospermia

Background: The present study has been designed with the aim of evaluating A-kinase anchoring proteins 3 (AKAP3)and Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase 3 (PLOD3) gene mutations and prediction of 3D proteinstructure for ligand binding activity in the cases of non-obstructive azoospermic male.Materials and Methods: Clinically diagnosed cases of non-obstructive azoos...

Protein structure prediction by threading : force field philosophy ,

RaptorX: exploiting structure information for protein alignment by statistical inference.

This work presents RaptorX, a statistical method for template-based protein modeling that improves alignment accuracy by exploiting structural information in a single or multiple templates. RaptorX consists of three major components: single-template threading, alignment quality prediction, and multiple-template threading. This work summarizes the methods used by RaptorX and presents its CASP9 r...

LOMETS: A local meta-threading-server for protein structure prediction

We developed LOMETS, a local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. Nine state-of-the-art threading programs are installed and run in a local computer cluster, which ensure the quick generation of initial threading alignments compared with traditional remote-server-based meta-servers. Consensus models are generated from...