FINDSITE-LHM 1.0 – Homology Modeling Approach to Flexible Ligand Docking

FINDSITE-LHM 1.0

:: DESCRIPTION

FINDSITELHM is a homology modeling approach to flexible ligand docking. It uses a collection of common molecule substructures derived from evolutionarily related templates as the reference compounds in similarity-based ligand binding pose prediction. It also provides a simple scoring function to rank the docked compounds.

::DEVELOPER

Center for the Study of Systems Biology

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

FINDSITE-LHM

:: MORE INFORMATION

Citation

Brylinski M and Skolnick J (2009) FINDSITE(LHM): a threading-based approach to ligand homology modelingPLoS Comput Biol 5:e1000405

FINDSITE 1.0 – Ligand-binding Site Prediction & Functional Annotation

FINDSITE 1.0

:: DESCRIPTION

FINDSITE is a threading-based binding site prediction/protein functional inference/ligand screening algorithm that detects common ligand binding sites in a set of evolutionarily related proteins. Crystal structures as well as protein models can be used as the target structures.

::DEVELOPER

Center for the Study of Systems Biology

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

FINDSITE

:: MORE INFORMATION

Citation

Skolnick J and Brylinski M (2009)
FINDSITE: a combined evolution/structure-based approach to protein function prediction.
Brief Bioinform 10:378-91

TASSER-Lite 1.0 – Protein Structure Modeling tool

TASSER-Lite 1.0

:: DESCRIPTION

TASSER-Lite is a protein structure comparative modeling tool. It is limited to protein target-template pairs whose pairwise sequence identity is >25% to the best threading template. It is optimized to model single domain proteins whose lengths range from 41-200 residues.

::DEVELOPER

Center for the Study of Systems Biology

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

TASSER-Lite

:: MORE INFORMATION

Citation

Pandit, S B, Zhang Y, Skolnick J. 2006.
TASSER-Lite: an automated tool for protein comparative modeling.
Biophysical journal. 91(11):4180-90.

MetaTASSER – Protein Structure Prediction tool

MetaTASSER

:: DESCRIPTION

MetaTASSER is a protein tertiary prediction method that employs the 3D-Jury approach to select threading templates from SPARKS, SP3 and PROSPECTOR_3, which provides aligned fragments and tertiary restraints as an input to TASSER (Threading/ASSEmbly/Refinement) procedure to generate full-length models.

::DEVELOPER

Hongyi Zhou, Center for the Study of Systems Biology

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

MetaTASSER

:: MORE INFORMATION

Citation

H. Zhou, S. B. Pandit and J. Skolnick.
Performance of the Pro-sp3-TASSER Server in CASP8.
Proteins 2009:77(S9): 123-127.

 

GeneMark 2.5 – Gene Prediction Programs

GeneMark 2.5

:: DESCRIPTION

GeneMark developed in 1993 was the first gene finding method recognized as an efficient and accurate tool for genome projects. GeneMark was used for annotation of the first completely sequenced bacteria, Haemophilus influenzae, and the first completely sequenced archaea, Methanococcus jannaschii. The GeneMark algorithm uses species specific inhomogeneous Markov chain models of protein-coding DNA sequence as well as homogeneous Markov chain models of non- coding DNA. Parameters of the models are estimated from training sets of sequences of known type. The major step of the algorithm computes a posteriory probability of a sequence fragment to carry on a genetic code in one of six possible frames (including three frames in complementary DNA strand) or to be “non-coding”

GeneMark is documented as the most accurate prokaryotic gene finder.

GeneMark.hmm-P and GeneMark.hmm-E programs are predicting genes and intergenic regions in a sequence as a whole. They use the Hidden Markov models reflecting the “grammar” of gene organization.

The GeneMark.hmm (P and E) programs identify the maximum likely parse of the whole DNA sequence into protein coding genes (with possible introns) and intergenic regions.

To analyze ESTs and cDNAs you can use GeneMark-E.

::DEVELOPER

Mark Borodovsky , Georgia Institute of TechnologyAtlanta, Georgia, USA

:: REQUIREMENTS

  • Linux / Mac OsX

:: DOWNLOAD

GeneMark

:: MORE INFORMATION

Citation

Borodovsky M. and McIninch J.
GeneMark: parallel gene recognition for both DNA strands,
Computers & Chemistry, 1993, Vol. 17, No. 19, pp. 123-133.

Besemer J., Lomsadze A. and Borodovsky M.,
GeneMarkS: a self-training method for predicition of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions.
Nucleic Acids Research, 2001, Vol. 29, No. 12, 2607-2618

Desmond 2.4 – High-speed Molecular Dynamics Simulation

Desmond 2.4

:: DESCRIPTION

Desmond is a software package developed at D. E. Shaw Research to perform high-speed molecular dynamics simulations of biological systems on conventional commodity clusters. The code uses novel parallel algorithms and numerical techniques to achieve high performance and accuracy on platforms containing a large number of processors, but may also be executed on a single computer.

::DEVELOPER

D. E. Shaw Research

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Window / Linux / Mac OsX
  • Python

:: DOWNLOAD

Desmond

:: MORE INFORMATION

Citation

Kevin J. Bowers, Edmond Chow, Huafeng Xu, Ron O. Dror, Michael P. Eastwood, Brent A. Gregersen, John L. Klepeis, István Kolossváry, Mark A. Moraes, Federico D. Sacerdoti, John K. Salmon, Yibing Shan, and David E. Shaw,
Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters,”
Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, November 11–17, 2006

NMFF – Normal Mode Flexible Fitting

NMFF

:: DESCRIPTION

NMFF (Normal Mode Flexible Fitting) is an evolving package of programs and methods that enable the flexible multi-resolution fitting of large atomically detailed structures into electron density maps from cryoEM, tomography and related lower resolution methods.The theory and methods behind NMFF are based on searching along a few lowest frequency normal mode vectors, constructed from a multi-resolution elastic network representation of the atomic structure of interest, to maximize the correlation between the computed electron density for the flexible model and the experimental density.

::DEVELOPER

NIH Research Resource Center for the Development of Multiscale Modeling Tools for Structural Biology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

NMFF

:: MORE INFORMATION

Citation

F Tama, O Miyashita and CL Brooks, III. Flexible multi-scale fitting of atomic structures into low-resolution electron density maps with elastic network normal mode analysis.
Journal of Molecular Biology, 2004, 337 (4), 985-99.

GAP 1.2.14 – Geometric Analysis of Proteins

GAP 1.2.14

:: DESCRIPTION

GAP (Geometric Analysis Programs)   contains two separate program packages that were developed in parallel for the generation and analysis of structure ensembles.

::DEVELOPER

G.P. Gippert

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Window with Cygwin / Linux / Mac OsX

:: DOWNLOAD

GAP

:: MORE INFORMATION

Yammp Conversion Tools – Simulation of Yammp Models in CHARMM or Amber

Yammp Conversion Tools

:: DESCRIPTION

The Yammp Conversion Tools contain a set of utilities that allow the simulation of Yammp models in CHARMM or Amber.

::DEVELOPER

NIH Research Resource Center for the Development of Multiscale Modeling Tools for Structural Biology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / Mac OsX
  • Perl

:: DOWNLOAD

Yammp Conversion Tools

:: MORE INFORMATION

Citation:

Michael Feig, John Karanicolas, Charles L. Brooks, III: MMTSB Tool Set (2001), MMTSB NIH Research Resource, The Scripps Research Institute

YUP 1.080827 / Yammp 2 – Molecular Simulation

YUP 1.080827 / Yammp 2

:: DESCRIPTION

YUP (Yammp Under Python), also known as Yammp 2, is a molecular modeling program. Although a general purpose tool, development is currently concentrated on molecular simulations (mechanics) and on reduced representation and multiscale modeling. YUP is based on an earlier program Yammp.

::DEVELOPER

The Harvey Laboratory

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

YUP

:: MORE INFORMATION

Citation:

Tan, R. K. Z., Petrov, A. S., Harvey, S. C. YUP: A Molecular Simulation Program for Coarse-Grained and Multiscaled Models. J. Chem. Theory Comput., 2006, 2(3), 529-540