• the collective name for a suite of programs that allows users to carry out molecular dynamics simulations, particularly on biomolecules.

Amber was originally developed under the leadership of Peter Kollman. Current development stems from an active collaboration between David Case (Rutgers U), Tom Cheatham (U of Utah), Tom Darden (NIEHS, OpenEye, Ken Merz and Adrian Roitberg (Florida), Carlos Simmerling (SUNY-Stony Brook), Ray Luo (UC Irvine), Junmei Wang (UT Southwestern), and many others.

Developers

AMBER Admin

Nicole Flowers

Categories

Computational Chemistry

Versions

• Linux 32: various
• Linux 64: various
• OSX Intel: various

Citations

D.A. Case, T.A. Darden, T.E. Cheatham, III, C.L. Simmerling, J. Wang, R.E. Duke, R. Luo, R.C. Walker, W. Zhang, K.M. Merz, B. Roberts, B. Wang, S. Hayik, A. Roitberg, G. Seabra, I. Kolossvai, K.F. Wong, F. Paesani, J. Vanicek, J. Liu, X. Wu, S.R. Brozell, T. Steinbrecher, H. Gohlke, Q. Cai, X. Ye, J. Wang, M.-J. Hsieh, G. Cui, D.R. Roe, D.H. Mathews, M.G. Seetin, C. Sagui, V. Babin, T. Luchko, S. Gusarov, A. Kovalenko, and P.A. Kollman (2010), AMBER 11, University of California, San Francisco.

Technical Notes

Please note that the Amber licensing agreement allows us to distribute the software only to Harvard-affiliated members. Labs without Harvard affiliation must purchase a separate license for Amber. Once you have acquired an Amber license, please contact us and we will add it to your installation.

• a suite of automated docking tools. It is designed to predict how small molecules, such as substrates or drug candidates, bind to a receptor of a known 3D structure.

Developers

Art Olson

Richard Belew

William Hart

Scott Kurowski

David Goodsell

Garrett Morris

Categories

Computational Chemistry

Versions

• Linux 32: 4.2.3
• Linux 64: 4.2.3
• OSX Intel: 4.2.3
• OSX PPC: 4.2.3

Citations

Cosconati et al. Virtual Screening with AutoDock: Theory and Practice. Expert opinion on drug discovery (2010) vol. 5 (6) pp. 597-607

• an open-source program for drug discovery, molecular docking, and virtual screening, offering multi-core capability, high performance and enhanced accuracy and ease of use.

Developers

Oleg Trott

Categories

Computational Chemistry

Versions

• Linux 32: 1.1.2
• Linux 64: 1.1.2
• OSX Intel: 1.1.2
• OSX PPC: 1.1.1

Citations

Trott and Olson. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem (2010) vol. 31 (2) pp. 455-61

• a method to generate protein conformations around a known structure based on geometric restrictions.

Developers

Bert de Groot

Categories

Computational Chemistry

Versions

• Linux 32: 2.1.2
• Linux 64: 2.1.2
• OSX Intel: 2.1.2

Citations

de Groot et al. Prediction of protein conformational freedom from distance constraints. Proteins (1997) vol. 29 (2) pp. 240-51

• a molecular docking application that can be used to a) predict binding modes of small molecule-protein complexes; b)search databases of ligands for compounds that inhibit enzyme activity; c) search databases of ligands for compounds that bind a particular protein; d) search databases of ligands for compounds that bind nucleic acid targets; e) examine possible binding orientations of protein-protein and protein-DNA complexes; f) help guide synthetic efforts by examining small molecules that are computationally derivatized. A recent publication (see citations) describes how DOCK can be used to model RNA--small molecule complexes.

Developers

Scott Brozell

Dock licensor

Categories

Computational Chemistry

Versions

• Linux 32: 6.5
• Linux 64: 6.5
• OSX Intel: 6.5
• OSX PPC: 5.4.0
• SGI Irix: 4.0.1

Citations

Lang et al. DOCK 6: combining techniques to model RNA-small molecule complexes. RNA (2009) vol. 15 (6) pp. 1219-30

Moustakas et al. Development and validation of a modular, extensible docking program: DOCK 5. J Comput Aided Mol Des (2006) vol. 20 (10-11) pp. 601-19

• a versatile package that performs molecular dynamics of proteins, lipids and nucleic acids. GROMACS was first developed in Herman Berendsens group, department of Biophysical Chemistry of Groningen University. It is a team effort, which is currently lead by Erik Lindahl, David van der Spoel and Berk Hess. GROMACS offers a user friendly interface and provides high performance.

Developers

Matteo Frigo

Steven Johnson

Categories

Computational Chemistry

Versions

• Linux 32: 4.6
• Linux 64: 4.6
• OSX Intel: 4.6
• OSX PPC: 4.0.5

Citations

Van Der Spoel et al. GROMACS: fast, flexible, and free. Journal of computational chemistry (2005) vol. 26 (16) pp. 1701-18

• (High Ambiguity Driven biomolecular DOCKing) relies on an approach that makes use of biochemical and/or biophysical interaction data, such as chemical shift perturbation data resulting from NMR titration experiments, mutagenesis data or bioinformatic predictions. HADDOCK was written by Alexandre Bondvin is derived from ARIA scripts by Michael Nilges and Jens Linge.

Developers

Alexandre Bonvin

Categories

NMR Methods

Computational Chemistry

Versions

• Linux 32: 2.1
• Linux 64: 2.1
• OSX Intel: 2.1
• OSX PPC: 2.1

Citations

de Vries et al. HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets. Proteins (2007) vol. 69 (4) pp. 726-33

Dominguez et al. HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. Journal of the American Chemical Society (2003) vol. 125 (7) pp. 1731-7

• a subset of chemical compounds that are used for screening at the ICCB-Longwood Screening Facility. The dataset was compiled by David Wrobel (ICCB-Longwood) for in silico docking. The date-stamped datasets are available in /programs/share/iccb.

Developers

David Wrobel

Categories

Computational Chemistry

Versions

• is designed to allow mixing and matching of existing modeling components as well as the easy addition of new functionality.

Developers

Andrej Sali

Ben Webb

Categories

Computational Chemistry

Versions

• Linux 32: 1.0
• Linux 64: 1.0
• OSX Intel: 1.0

Citations

D. Russel, K. Lasker, B. Webb, D. Schneidman, J. Velázquez-Muriel, A. Sali, "Putting the pieces together: integrative structure determination of macromolecular assemblies", PLoS Biology, 2012.

Webb B, Lasker K, Schneidman-Duhovny D, Tjioe E, Phillips J, Kim SJ, Velázquez-Muriel J, Russel D, Sali A. Modeling of proteins and their assemblies with the integrative modeling platform. Methods Mol Biol. 2011;781:377-97.

• is used for homology or comparative modeling of protein three-dimensional structures. The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms. MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints, and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure with respect to a flexibly defined objective function, multiple alignment of protein sequences and/or structures, clustering, searching of sequence databases, comparison of protein structures, etc. A graphical interface to MODELLER is commercially available from Accelrys, as part of Discovery Studio Modeling 1.1.

Developers

Ben Webb

Categories

Computational Chemistry

Versions

• Linux 32: 9.11
• Linux 64: 9.11
• OSX Intel: 9.11
• OSX PPC: 9v9

Citations

Eswar et al. Protein structure modeling with MODELLER. Methods in molecular biology (Clifton, NJ) (2008) vol. 426 pp. 145-59

Eswar et al. Comparative protein structure modeling using MODELLER. Current protocols in protein science / editorial board, John E Coligan [et al] (2007) vol. Chapter 2 pp. Unit 2.9

Technical Notes

No graphical interface. To start the program type 'modeller'. There are some tutorials to get you started available at http://salilab.org/modeller/tutorial/ .

• a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems.

Developers

NAMD Developer Group

Categories

Computational Chemistry

Versions

• Linux 32: 2.9
• Linux 64: 2.9
• OSX Intel: 2.9
• OSX PPC: 2.7b3

Citations

Phillips et al. Scalable molecular dynamics with NAMD. Journal of computational chemistry (2005) vol. 26 (16) pp. 1781-802

• a threading-based protein structure prediction system.

Developers

Kyle Ellrott

Ying Xu

Dong Xu

Rober Cottingham

Categories

Computational Chemistry

Versions

• Linux 32: 1.0
• Linux 64: 1.0
• OSX PPC: 2.0

Citations

Ying Xu and Dong Xu. Protein threading using PROSPECT: Design and evaluation. Proteins: Structure, Function, and Genetics. 40:343-354. 2000.

• an interactive Python-based interface to the powerful Rosetta molecular modeling suite. It enables users to design their own custom molecular modeling algorithms using Rosetta sampling methods and energy functions.

Developers

PyRosetta Team

Sergey Lyskov,

Jeffrey Gray

Categories

Computational Chemistry

Versions

• Linux 32: 2.0b
• Linux 64: 2.012
• OSX Intel: 2.0b
• OSX PPC: 1.1

Citations

Chaudhury et al. PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta. Bioinformatics (2010) vol. 26 (5) pp. 689-91

• a software suite for predicting and designing protein structures, protein folding mechanisms, and protein-protein interactions. ROSETTA has been consistently successful in CASP and CAPRI competitions. Rosetta is also used for improving NMR and homology models before they are used in molecular replacement. Application developed and maintained by Baker Laboratory at the University of Washington.

Developers

Rosetta Developer Team

Alan Yen

Rosetta Licensing

Categories

Computational Chemistry

Versions

• Linux 32: 3.4
• Linux 64: 3.4
• OSX Intel: 3.4
• OSX PPC: 2.2.0

Citations

Fleishman et al. Computational design of proteins targeting the conserved stem region of influenza hemagglutinin. Science (New York, NY) (2011) vol. 332 (6031) pp. 816-21

Wang et al. Modeling disordered regions in proteins using rosetta. PloS one (2011) vol. 6 (7) pp. e22060

DiMaio et al. Refinement of protein structures into low-resolution density maps using rosetta. Journal of molecular biology (2009) vol. 392 (1) pp. 181-90

Ramelot et al. Improving NMR protein structure quality by Rosetta refinement: a molecular replacement study. Proteins (2009) vol. 75 (1) pp. 147-67

Rigden et al. Molecular replacement using ab initio polyalanine models generated with ROSETTA. Acta crystallographica Section D, Biological crystallography (2008) vol. 64 (Pt 12) pp. 1288-91

Technical Notes

Redistribution is permitted required that members are all academic labs.

• provides accurate, reliable, and high performance computational technology to solve real-world problems in life science research.

Categories

Computational Chemistry

Versions

• Linux 32: 2012-2
• Linux 64: 2012-2
• OSX Intel: 2012-2

Citations

CANVAS: Sastry et al. Large-Scale Systematic Analysis of 2D Fingerprint Methods and Parameters to Improve Virtual Screening Enrichments. J. Chem. Inf. Model. 2010, 50:771. Duan et al. Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods. J. Molec. Graph. Model., 2010. 29:157-170.

ConfGen: Watts et al. ConfGen: A Conformational Search Method for Efficient Generation of Bioactive Conformers. J.Chem. Inf. Model. 2010. 50:534-546. Chen and Foloppe. Drug-like Bioactive Structures and Conformational Coverage with the LigPrep/ConfGen Suite: Comparison to Programs MOE and Catalyst. J.Chem. Inf. Model. 2010. 50:822-839.

Desmond: Bowers et al. Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, November 11-17, 2006. Shivakumar et al. Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field. J. Chem. Theory Comput. 2010. 6:1509–1519. Guo et al. Probing the α-Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis. Chem Biol. Drug Des. 2010. 75:348-359.

Epik: Shelley et al. Epik: a software program for pKa prediction and protonation state generation for druglike molecules. J. Comput. Aided Mol. Des. 2007. 21:681–691. Greenwood et al. Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J. Comput. Aided Mol. Des. 2010. 24:591-604. Park et al. Estimating binding affinities by docking/scoring methods using variable protonation states. Proteins. 2010. 79(1):304-314.

Glide: Friesner et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy. J. Med. Chem. 2004. 47:1739–1749. Halgren et al. Glide: A New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening. J. Med. Chem. 2004. 47:1750–1759. Friesner et al. Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein-Ligand Complexes. J. Med. Chem. 2006. 49:6177–6196.

Induced Fit: Sherman et al. Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects. J. Med. Chem. 2006. 49:534-554. Sherman et al. Use of an Induced Fit Receptor Structure in Virtual Screening. Chem. Biol. Drug Des. 2006. 67:83-84.

Phase:
Dixon et al. PHASE: A New Engine for Pharmacophore Perception, 3D QSAR Model Development, and 3D Database Screening. 1. Methodology and Preliminary Results. J. Comput. Aided Mol. Des., 2006. 20:647-671. Dixon et al. PHASE: A Novel Approach to Pharmacophore Modeling and 3D Database Searching. Chem. Biol. Drug Des. 2006. 67:370-372.

Prime: Jacobson et al. A Hierarchical Approach to All-Atom Protein Loop Prediction. Proteins. 2004. 55:351-367. Jacobson et al. On the Role of Crystal Packing Forces in Determining Protein Sidechain Conformations. J. Mol. Biol. 2002. 320:597-608.

PrimeX:
Arnold et al. ed. International Tables for Crystallography, Volume F, Crystallography of Biological Macromolecules, 2nd edition, Chichester: John Wiley and Sons, in press.

QM-Polarized Ligand Docking: Cho et al. Importance of Accurate Charges in Molecular Docking: Quantum Mechanical/Molecular Mechanical (QM/MM) Approach. J. Comput. Chem. 2005. 26:915-931.

QSite: Murphy et al. A mixed quantum mechanics/molecular mechanics (QM/MM) method for large-scale modeling of chemistry in protein environments" J. Comp. Chem., 2000. 21:1442-1457. Philipp and Friesner. Mixed ab initio QM/MM modeling using frozen orbitals and tests with alanine dipeptide and tetrapeptide. J. Comp. Chem. 1999. 20:1468-1494.

SiteMap: Halgren. Identifying and Characterizing Binding Sites and Assessing Druggability. J. Chem. Inf. Model. 2009. 49:377–389. Halgren. "New Method for Fast and Accurate Binding-site Identification and Analysis. Chem. Biol. Drug Des. 2007. 69:146–148.

Technical Notes

The SBGrid Consortium has a license (US/Canada non-profit labs only) for a limited number of seats for the Schrodinger Molecular Modeling Suite. Licensed applications include Glide, Liaison, Qsite, Jaguar, pKa, MacroModel, ConfGen, QikProp, Prime, LigPrep, Phase, Strike, CombiGlide, Epik, SiteMap, PrimeX, XP Visualizer, Canvas, Core Hopping, as well as access to KNIME and Maestro.

Due to its large size, the software is not in the default installation. Please email bugs@sbgrid.org to request installation for your site.

• program for prediction of protein side-chain conformations.

Developers

Roland Dunbrack

Categories

Computational Chemistry

Versions

• Linux 32: 3.0
• Linux 64: 3.0
• OSX PPC: 3.0

Citations

Canutescu AA, Shelenkov AA, Dunbrack RL Jr.A graph-theory algorithm for rapid protein side-chain prediction.Protein Sci. 2003. 12(9):2001-14.

• program for prediction of protein side-chain conformations.

Developers

Roland Dunbrack

Categories

Computational Chemistry

Versions

• Linux 32: 4.0
• Linux 64: 4.0
• OSX Intel: 4.0

Citations

Canutescu AA, Shelenkov AA, Dunbrack RL Jr.A graph-theory algorithm for rapid protein side-chain prediction.Protein Sci. 2003. 12(9):2001-14.

Krivov et al. Improved prediction of protein side-chain conformations with SCWRL4. Proteins. 2009. 77(4):778-95.

• a program to construct an atomic solvent environment model for a given atomic macromolecule model (solute) for use in molecular dynamics simulations. Solvate generates irregularly-shaped solvent volumes, adapted to a given solute's structure; allows efficient computation of boundary forces as required in molecular dynamics simulations; guarantees a minimal solute-boundary-distance; allows to specify a minimum solvent surface curvature in order to avoid flat' surface regions; generatesdisordered' (= fluid) water, not a grid of water molecules (= ice); locally minimizes the positions of all water molecules; optionally places salt ions obeying a Debye-Hückel distribution; optionally places (and marks) buried water molecules; and is X-PLOR/CHARMm-compatible, i.e., input and output is in pdb-/psf-format.

Developers

Helmut Grubmuller

Categories

Computational Chemistry

Versions

• Linux 32: 1.0.1
• Linux 64: 1.0.1
• OSX Intel: 1.0.1

Citations

For a list of references, go to: http://www.mpibpc.mpg.de/home/grubmueller/downloads/solvate/References/index.html

• offers protein fold recognition by optimal protein sequence threading.

Developers

David Jones

Categories

Computational Chemistry

Versions

• Linux 32: 3.5
• Linux 64: 3.5
• SGI Irix: 3.5

Citations

Jones et al. A new approach to protein fold recognition. Nature. 1992. 358: 86-89.

Jones et al. Successful protein fold recognition by optimal sequence threading validated by rigorous blind testing. Proteins. 1995. 23: 387-397.

Jones. THREADER : Protein Sequence Threading by Double Dynamic Programming. (in) Computational Methods in Molecular Biology. Steven Salzberg, David Searls, and Simon Kasif, Eds. Elsevier Science. 1998. Chapter 13.

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

Developers

YUP Developer Group

Categories

Computational Chemistry

Versions

• Linux 32: 1.080827
• Linux 64: 1.080827
• OSX Intel: 1.080827
• OSX PPC: 1.080827

Citations

Tan et al. YUP: A Molecular Simulation Program for Coarse-Grained and Multiscaled Models. J. Chem. Theory Comput. 2006. 2(3): 529-540.

• two protein docking algorithms designed to operate in succession. ZDOCK is a rigid-body docking program, and RDOCK is a refinement program. ZDOCK uses a fast Fourier transform to search all possible binding modes for proteins, evaluating based on shape complementarity, desolvation energy, and electrostatics. The top 2000 predictions from ZDOCK are then given to RDOCK where they are minimized by CHARMM to improve the energies and eliminate clashes, and then the electrostatic and desolvation energies are recomputed. From the Zhiping Lab at Boston University. by RDOCK.

Developers

ZDOCK Developer Group

Categories

Computational Chemistry

Versions

• Linux 32: 3.0.1
• Linux 64: 3.0.2
• OSX Intel: 3.0.2

Citations

Chen et al. Docking Unbound Proteins Using Shape Complementarity, Desolvation, and Electrostatics. Proteins. 2002. 47: 281-294.

a free database of commercially-available compounds for virtual screening. ZINC contains over 21 million purchasable compounds in ready-to-dock, 3D formats. ZINC is provided by the Shoichet Laboratory in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF). To cite ZINC, please reference: Irwin, Sterling, Mysinger, Bolstad and Coleman, J. Chem. Inf. Model. 2012 DOI: 10.1021/ci3001277. The original publication is Irwin and Shoichet, J. Chem. Inf. Model. 2005;45(1):177-82 PDF, DOI. We thank NIGMS for financial support (GM71896).

Developers

Brian Shoichet

John Irwin

Categories

Computational Chemistry

Versions

Citations

Irwin, Sterling, Mysinger, Bolstad and Coleman, J. Chem. Inf. Model. 2012 DOI: 10.1021/ci3001277. The original publication is Irwin and Shoichet, J. Chem. Inf. Model. 2005;45(1):177-82 PDF, DOI. We thank NIGMS for financial support (GM71896).

Technical Notes

ZINC subsets are updated regularly, often quarterly or better. Thus if the timestamp on any subset you may have cached is older than 3 months, users are counseled to check the ZINC website for a more recent version.