MUSTER: Improving protein sequence profile–profile alignments by using multiple sources of structure information

Abstract

We develop a new threading algorithm MUSTER by extending the previous sequence profile–profile alignment method, PPA. It combines various sequence and structure information into single-body terms which can be conveniently used in dynamic programming search: (1) sequence profiles; (2) secondary structures; (3) structure fragment profiles; (4) solvent accessibility; (5) dihedral torsion angles; (6) hydrophobic scoring matrix. The balance of the weighting parameters is optimized by a grading search based on the average TM-score of 111 training proteins which shows a better performance than using the conventional optimization methods based on the PROSUP data-base. The algorithm is tested on 500 nonhomologous proteins independent of the training sets. After removing the homologous templates with a sequence identity to the target >30%, in 224 cases, the first template alignment has the correct topology with a TM-score >0.5. Even with a more stringent cutoff by removing the templates with a sequence identity >20% or detectable by PSI-BLAST with an E-value <0.05, MUSTER is able to identify correct folds in 137 cases with the first model of TM-score >0.5. Dependent on the homology cutoffs, the average TM-score of the first threading alignments by MUSTER is 5.1–6.3% higher than that by PPA. This improvement is statistically significant by the Wilcoxon signed rank test with a P-value < 1.0 × 10−13, which demonstrates the effect of additional structural information on the protein fold recognition. The MUSTER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/MUSTER.

Consensus design of a NOD receptor leucine rich repeat domain with binding affinity for a muramyl dipeptide, a bacterial cell wall fragment

“…4(b)]. 50 The homology modeling was based on protein NLRX1 that shares 24.1% sequence identity with CLLR2. NLRX1, also known as NOD9, is a mitochondrial protein thought to be involved in an antiviral immune response against viral ribonucleic acid (RNA).…”

Progress and challenges in protein structure prediction

“…In CAFASP [22] and the recent CASP Server Section [23•], several sequence-profile-based methods were ranked at the top of single-threading servers. Wu and Zhang [24] recently showed that the accuracy of the sequence PPAs can be further improved by about 5-6% by incorporating a variety of additional structural information.…”

Multiple interests in structural models of DARC transmembrane protein

“…For this purpose, we tested LOMETS (LOcal MEtaThreading-Server [114]), a webserver that uses 8 different methods and ranks the results. Table I summarizes the different results obtained for MUSTER [115], SAM [116], PROSPECT2 [117], SP3 [118], PPA-I, HHsearch [119], SPARKS2 [120], and FUGUE [121]. The three first methods provide structural models with a medium confidence rate while the models constructed with the last methods are associated with a low confidence index.…”