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Online Services
Introduction: I-TASSER server is an Internet service for protein structure and function predictions.
Models are built based on multiple-threading alignments by LOMETS and iterative
TASSER simulations. I-TASSER (as 'Zhang-Server') was ranked as the No 1 server
in recent CASP7 and CASP8 experiments. The server is in active development with
the goal to provide accurate structural and function predictions using
state-of-the-art algorithms.
References: Yang Zhang. I-TASSER server for protein 3D structure prediction.
BMC Bioinformatics, vol 9, 40 (2008).
(download the PDF file).
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Introduction: LOMETS (Local Meta-Threading-Server) is a locally installed
meta-server for protein structure prediction. It generates 3D models by collecting
consensus target-to-template alignments from 9 locally-installed threading programs
(FUGUE, HHsearch, PAINT, PPA-I, PPA-II, PROSPECT2, SAM-T02, SPARKS, SP3).
References: S. Wu, Y. Zhang.
LOMETS: A local meta-threading-server for protein structure prediction.
Nucleic Acids Research 2007; 35: 3375-3382
(download the PDF file).
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Introduction: MUSTER (MUlti-Sources ThreadER) is a new protein threading
algorithm to identify the template structures from the PDB library. It generate
sequence-template alignments by combining sequence profile-profile alignment
with multiple structural information.
References:
S. Wu, Y. Zhang.
MUSTER: Improving protein sequence profile-profile alignments by using multiple sources of structure information.
Proteins: Structure, Function, and Bioinformatics 2008; 72: 547-556.
(download the PDF file)
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Online Services with Downloadable Packages
Introduction:
TM-score is an algorithm to calculate the topological similarity of two
protein structures. It can be used to quantitatively access the quality of
protein structure predictions relative to the native. Because TM-score weights
the close matches stronger than the distant matches, TM-score is more sensitive
to the global topology of structures than the often-used
root-mean-square deviation (RMSD).
References:
Y. Zhang, J. Skolnick, Scoring function for automated assessment of
protein structure template quality. Proteins, 2004 57: 702-710
(download the PDF file and Correction).
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Introduction:
TM-align is a computer algorithm for quick and accurate protein structure
alignment using dynamic programming and TM-score rotation matrix. An optimal
alignment between two proteins, as well as the TM-score, will be reported for
each comparison.
References:
Y. Zhang, J. Skolnick, TM-align: A protein structure alignment algorithm
based on TM-score. Nucleic Acids Research, 2005 33: 2302-2309 (download the PDF file).
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Introduction:
MM-align is designed to structurally align multimeric protein complexes using
heuristic iteration of dynamic programming based on TM-score rotation matrix.
The multple chains in each complex are first joined, in every possible order,
and then simultaneously aligned with cross-chain alignment prevented. The
alignment on interface structures can be enhenced by MM-align by an
interface-specific weighting factor. A TM-score is reported for
assessing the structural similarity of two complexes.
References:
S. Mukherjee, Y. Zhang,
MM-align: a quick algorithm for aligning multiple-chain protein complex
structures using iterative dynamic programming.
Nucleic Acids Research 2009; 37: e83
(Download
PDF file and supporting materials).
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Introduction:
SVMSEQ is a new algorithm for protein residue-residue contact prediction
using Support Vector Machines.
References:
S. Wu, Y. Zhang.
A comprehensive assessment of sequence-based and template-based methods for protein contact prediction.
Bioinformatics, vol 24, 924-931 (2008).
(download the PDF file)
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Introduction:
ANGLOR is a machine-learning based algorithm for ab initio prediction
of protein backbone torsion angles. For a given amino acid sequence,
the real-value backbone torsion angles (phi and psi) for each residue
are predicted by the combination of the neural network training and
the support vector machine.
References:
S. Wu, Y. Zhang.
ANGLOR: A Composite Machine-Learning Algorithm for Protein Backbone Torsion Angle Prediction.
PLoS ONE 2008; 3: e3400.
(download the PDF file)
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Introduction:
REMO is a new algorithm for constructing protein atomic structures
from C-alpha traces by optimizing the backbone hydrogen-bonding networks.
References:
Yunqi Li and Yang Zhang.
REMO: A new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks.
Proteins, 2009, 76: 665-676.
(download the PDF file).
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Introduction:
HAAD is a computer algorithm for constructing hydrogen atoms
from protein heavy-atom structures. The hydrgen is added
by minimizing atomic overlap and encouraging hydrogen bonding.
References:
Yunqi Li, Roy Ambrish and Yang Zhang,
HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures,
PLoS One, 2009 4: e6701.
(download the PDF file)
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Downloadable Resources
Introduction:
The interaction parameters and the knowledge-based force field used by I-TASSER.
References:
1. Yang Zhang, Andrzej Kolinski, Jeffrey Skolnick.
Touchstone II: A new approach to ab initio protein Structure Prediction.
Biophysical Journal, vol 85, 1145 (2003).
[PDF]
2. Yang Zhang, Jeffrey Skolnick.
Automated structure prediction of weakly homologous proteins on a genomic scale.
Proceedings of the National Academy of Sciences of USA, vol 101, 7594 (2004).
[PDF]
3. Sitao Wu, Jeffrey Skolnick, Yang Zhang.
Ab initio modeling of small proteins by iterative TASSER simulations
BMC Biology, vol 5, 17 (2007).
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Introduction:
The atomic structure decoys of 56 non-homologous small proteins.
The backbone structures are generated by the I-TASSER ab initio
modeling; the side-chain and other atoms are added using Pulchra.
References:
Wu S, Skolnick J, Zhang Y: Ab initio modeling of small proteins by iterative TASSER simulations. BMC Biology 2007, 5(17).
(download PDF file)
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Introduction:
SPICKER is a clustering algorithm to identify the near-native models
from a pool of protein structure decoys. The cluster is defined by the
pair-wise RMSD metrics of the structural decoys.
References:
Y. Zhang, J. Skolnick, SPICKER: Approach to clustering protein structures for near-native model selection,
Journal of Computational Chemistry, 2004 25: 865-871.
(download the PDF file).
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Introduction:
An automated assessment of protein structure predictions generated by 189 human and server groups
in the CASP7 experiments. The assessment is based on TM-score, MaxSub and GDT-TS score
where 124 domains are split into HA (high accuracy), TBM (template-based modeling),
and FM (free-modeling) targets.
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Introduction:
An automated assessment of protein structure predictions generated by 81 server groups
in the CASP8 experiments. The assessment is based on TM-score, MaxSub and GDT-TS score
where 172 domains are split into Easy and Hard targets.
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