NASCA 20110516 – Side-chain Resonance Assignment & NOE Assignment

NASCA 20110516

:: DESCRIPTION

NASCA (NOE Assignment and Side-Chain Assignment) is an automated program for side-chain resonance assignment and nuclear Overhauser effect (NOE) assignment from NOESY data. It does not require data from TOCSY experiments.NASCA casts the assignment problem into a Markov Random Field (MRF), and extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity.

::DEVELOPER

Donald Lab at Duke University

:: SCREENSHOTS

:: REQUIREMENTS

Windows / Linux / MacOSX
Java

:: DOWNLOAD

NASCA

:: MORE INFORMATION

Citation

Jianyang Zeng, Pei Zhou, Bruce R. Donald.
Protein Side-Chain Resonance Assignment and NOE Assignment Using RDC-Defined Backbones without TOCSY Data.
J Biomol NMR. 2011 Aug;50(4):371-95. doi: 10.1007/s10858-011-9522-4.

ASSAM / SPRITE – Side Chain 3D-motif Searching in Protein Structures

ASSAM / SPRITE

:: DESCRIPTION

The ASSAM( Amino acid Pattern Search for Substructures And Motifs) program searches for 3D patterns of amino acid side chains in PDB formatted query structures.

The SPRITE (3D Search for protein sites) server searches for protein sites and motifs in a query protein structure. The output is a list of potential functional sites.

::DEVELOPER

The Molecular Function Regulation Lab (M. Firdaus Raih research group)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Nadzirin et al.:
SPRITE and ASSAM: web servers for side chain 3D-motif searching in protein structures.
Nucleic Acids Research. 2012 Jul;40(Web Server issue):W380-6.

SCWRL 4.0 – Prediction of Protein Side-chain Conformation

SCWRL 4.0

:: DESCRIPTION

SCWRL is the program for prediction of protein side-chain conformations. SCWRL is based on a new algorithm and new potential function that results in improved accuracy at reasonable speed. This has been achieved through: 1) a new backbone-dependent rotamer library based on kernel density estimates; 2) averaging over samples of conformations about the positions in the rotamer library; 3) a fast anisotropic hydrogen bonding function; 4) a short-range, soft van der Waals atom-atom interaction potential; 5) fast collision detection using k-discrete oriented polytopes; 6) a tree decomposition algorithm to solve the combinatorial problem; and 7) optimization of all parameters by determining the interaction graph within the crystal environment using symmetry operators of the crystallographic space group.

::DEVELOPER

Dunbrack Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Mac OsX / Linux

:: DOWNLOAD

SCWRL

:: MORE INFORMATION

Citation

G. G. Krivov, M. V. Shapovalov, and R. L. Dunbrack, Jr.
Improved prediction of protein side-chain conformations with SCWRL4.
Proteins (2009).

TreePack 1.2 – Side Chain Assignment via Tree Decomposition

TreePack 1.2

:: DESCRIPTION

The TreePack program uses a tree-decomposition based algorithm to solve the side-chain packing problem more efficiently. This algorithm is more efficient than SCWRL 3.0 while maintaining the same level of accuracy.

::DEVELOPER

Jinbo Xu, Bonnie Berger

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

TreePack

:: MORE INFORMATION

Citation:

Jinbo Xu and Bonnie Berger.
Fast and accurate algorithms for protein side-chain packing.
The Journal of the ACM, Volume 53, Issue 4 (July 2006), pp. 533-557.

OSCAR – Protein Side Chain Modeling

OSCAR

:: DESCRIPTION

OSCAR (Optimized Side Chain Atomic eneRgy) is a software of new force fields for protein side chain modeling

::DEVELOPER

Systems Immunology Laboratory , Osaka University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

OSCAR

:: MORE INFORMATION

Citation

J Comput Chem. 2011 Jun;32(8):1680-6. doi: 10.1002/jcc.21747.
Protein side chain modeling with orientation-dependent atomic force fields derived by series expansions.
Liang S, Zhou Y, Grishin N, Standley DM.

SARA 1.0 – Side-chain Angular Replacement Algorithm

SARA 1.0

:: DESCRIPTION

SARA is a very fast method for doing single side chain replacements in protein structures by using a coarse- grained method. It is over five times faster than the leading all-atom approach, and generates biologically realistic side-chain angles. The solutions found by SARA typically deviate less than 1 ? and 12 degrees from native structures or the best all-atom solution. Run-time for the algorithm is highly predictable and can easily be tuned by the user. These characteristics makes SARA an excellent choice for high-throughput applications like structural genomics, evolutionary simulations and structure-based phylogenetics.

::DEVELOPER

Liberles Research Group.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

SARA

:: MORE INFORMATION

Citation

J Mol Evol. 2011 Aug;73(1-2):23-33. Epub 2011 Jul 29.
Fast side chain replacement in proteins using a coarse-grained approach for evaluating the effects of mutation during evolution.
Grahnen JA, Kubelka J, Liberles DA.

SPRINT 3.0 – Side-chain PRediction INference Toolbox for Multistate Protein Design

SPRINT 3.0

:: DESCRIPTION

SPRINT is a software package that performs computational multistate protein design using state-of-the-art inference on probabilistic graphical models.

::DEVELOPER

Menachem Fromer and Chen Yanover

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
C++ Compiler

:: DOWNLOAD

SPRINT

:: MORE INFORMATION

Citation

Bioinformatics. 2010 Oct 1;26(19):2466-7. doi: 10.1093/bioinformatics/btq445. Epub 2010 Aug 4.
SPRINT: side-chain prediction inference toolbox for multistate protein design.
Fromer M1, Yanover C, Harel A, Shachar O, Weiss Y, Linial M.

RASP 1.90 – RApid Side-chain Predictor

RASP 1.90

:: DESCRIPTION

RASP is a RApid Side-chain Predictor. To achieve a much faster speed with a comparable accuracy to the best existing methods, the authors not only employ the clash elimination strategy of CIS-RR, but also carefully optimize energy terms and integrate different search algorithms. In comprehensive benchmark testings, RASP is over one order of magnitude faster (~ 40 times over CIS-RR) than the recently developed methods, while achieving comparable or even better accuracy.

::DEVELOPER

Zhichao (Chichau) MIAO

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

RASP

:: MORE INFORMATION

Citation:

Zhichao Miao,Yang Cao, Taijiao Jiang 2011
RASP:Rapid modeling of protein side-chain conformations
Bioinformatics (2011) 27 (22): 3117-3122.

Fitmunk – Improving Protein Structures by accurate, automatic Modeling of Side-chain Conformations

Fitmunk

:: DESCRIPTION

Fitmunk provides a framework for fitting conformations on a fixed backbone into electron density.

::DEVELOPER

Minor Lab at University of Virginia

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Fitmunk: improving protein structures by accurate, automatic modeling of side-chain conformations.
Porebski PJ, Cymborowski M, Pasenkiewicz-Gierula M, Minor W.
Acta Crystallogr D Struct Biol. 2016 Feb;72(Pt 2):266-80. doi: 10.1107/S2059798315024730.

BASILISK 0.1 – Probabilistic Model of Side Chains in Proteins

BASILISK 0.1

:: DESCRIPTION

BASILISK is a probabilistic model of the conformational space of amino acid side chains in proteins. Unlike rotamer libraries, BASILISK models the chi angles in continuous space, including the influence of the protein’s backbone.

::DEVELOPER

The Bioinformatics Centre , University of Copenhagen

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ MacOsX/ Windows
Python

:: DOWNLOAD

BASILISK

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2010 Jun 5;11:306. doi: 10.1186/1471-2105-11-306.
Beyond rotamers: a generative, probabilistic model of side chains in proteins.
Harder T, Boomsma W, Paluszewski M, Frellsen J, Johansson KE, Hamelryck T.