LOMETS (Local Meta-Threading-Server)

Category Proteomics>Protein Structure/Modeling Systems/Tools

Abstract LOMETS (Local Meta-Threading-Server) is an on-line web service for protein structure prediction.

It generates 3D models by collecting high-scoring target-to-template alignments from Eight (8) locally-installed threading programs (FUGUE, HHsearch, MUSTER, PPA, PROSPECT2, SAM-T02, SPARKS, and SP3).

Spatial restraints are combined from the consensus of the top 20 threading alignments.

LOMETS Model ranking scheme --

For a given target, 160 models are generated by the eight (8) component servers where each server generates 20 models, as sorted by their Z-scores in each algorithm.

The best 10 models are selected from the 160 models based on a scoring function.

All the eight (8) servers are installed and run in the manufacturer’s local computer cluster with template libraries updated every week. The algorithms were selected to cover different threading methods.

LOMETS eight (8) locally-installed threading programs --

1) FUGUE - FUGUE was developed at the Blindell Lab. It aligns target sequence profiles against a template structural profile collected from HOMSTRAD - (HOMSTRAD is a database of protein structure alignments for homologous families).

A dynamic programming algorithm [A computer adaptable method for finding similarities in the amino acid sequences of two (2) proteins] is used to find the best sequence-structure match.

2) HHSEARCH - HHSEARCH was developed at the Soding Lab, and it aligns the profile Hidden Markov Model (HMM) of a target with the profile HMM of templates by maximizing the log-sum-of-odds score.

3) MUSTER - The manufacturer’s developed 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 a dynamic programming search:

The balance of the weighting parameters is optimized by a grading search based on the average TM-score (see below...) of 111 training proteins which shows a better performance than using the conventional optimization methods based on the PROSUP database.

TM-score - TM-score is an algorithm to calculate the similarity of topologies of two (2) protein structures. It can be exploited to quantitatively access the quality of protein structure predictions relative to native.

Because TM-score weights the close matches stronger than the distant matches, TM-score is more sensitive than root-mean-square deviation (RMSD).

4) PPA-I - PPA-I is a simple sequence Profile-Profile Alignment approach combined with secondary structure matches.

5) PROSPECT2 - PROSPECT2 was developed at the Xu Lab, which uses a scoring function including residue mutations, secondary structure propensity, solvent accessibility and pairwise contact potential.

A divide-and-conquer searching approach is exploited to generate the global optimization of alignments.

6) SAM-T02 - SAM-T02 was developed at the Karplus Lab, and it begins with a Position Specific Iterated (PSI)-Basic Local Alignment Search Tool (BLAST) sequence database search.

Based on the PSI-BLAST multiple sequence alignment, a hidden Markov model (HMM) is constructed in an iterative way, which is then exploited to search through the whole template library by the Viterbi algorithm.

Viterbi algorithm - The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states - called the Viterbi path - that results in a sequence of observed events, especially in the context of Markov information sources, and more generally, hidden Markov models. The algorithm makes a number of assumptions:

First, both the observed events and hidden events must be in a sequence. This sequence often corresponds to time.

Second, these two sequences need to be aligned, and an instance of an observed event needs to correspond to exactly one instance of a hidden event.

Third, computing the most likely hidden sequence up to a certain point t must depend only on the observed event at point t, and the most likely sequence at point t minus 1.

These assumptions are all satisfied in a first-order Hidden Markov model.

7) And 8) SPARKS2 and SP3 - Both methods have been developed at the Zhou Lab.

In SPARKS2, the authors exploit a sequence profile-profile alignment combined with a single-body knowledge-based statistical potential;

In SP3, they use a residue depth-dependent structure profile to replace the single-body potential in the SPARKS2. Both methods use dynamic programming for the sequence-structure alignment search.

The output of LOMETS includes --

1) The best ten (10) threading models selected from 160 models by the confidence score;

2) The top ten (10) target-template alignments of individual threading servers;

3) Prediction of spatial C-alpha and side-chain contact and distance map; and

4) Full-length models built by MODELLER guided by consensus restraints.

System Requirements

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Manufacturer Web Site LOMETS (Local Meta-Threading-Server)

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G6G Abstract Number 20719

G6G Manufacturer Number 104289