ACPYPE - AnteChamber PYthon Parser interfacE

By A. W. Sousa da Silva, Wim F. Vranken. BMC Research Notes, 2012

2,867 citations recorded by the source corpus.

ACPYPE (or AnteChamber PYthon Parser interfacE) is a wrapper script around the ANTECHAMBER software that simplifies the generation of small molecule topologies and parameters for a variety of molecular dynamics programmes like GROMACS, CHARMM and CNS. It is written in the Python programming language and was developed as a tool for interfacing with other Python based applications such as the CCPN software suite (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http://www.ccpn.ac.uk/acpype and as a web portal application at http://webapps.ccpn.ac.uk/acpype. We verified the topologies generated by ACPYPE in three ways: by comparing with default AMBER topologies for standard amino acids; by generating and verifying topologies for a large set of ligands from the PDB; and by recalculating the structures for 5 protein–ligand complexes from the PDB. ACPYPE is a tool that simplifies the automatic generation of topology and parameters in different formats for different molecular mechanics programmes, including calculation of partial charges, while being object oriented for integration with other applications.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/d95afcfb097bdb472409a5ac9f8d15f57f3ce12b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Spectroscopy

Citations in the corpus

Performance of HADDOCK and a simple contact-based protein–ligand binding affinity predictor in the D3R Grand Challenge 2 (2017, methodology): “In a previous study on the prediction of the binding affinity of protein–protein interaction inhibitors [16], we have compared PRODRG and ACPYPE [47] for ligand parameter generation showing that the HADDOCK score calculated with the two parametrizations scheme are correlated (R 2 = 0.73).”
FreeSolv: A database of experimental and calculated hydration free energies, with input files (2014, methodology): “However, for the Nicholls set, we no longer had topology and coordinate files, so these were re-generated using Antechamber and ACPYPE[52].”
Lessons learned from comparing molecular dynamics engines on the SAMPL5 dataset (2016, methodology): “…from AMBER to GROMACS [12], CHAMBER [13] a converter from CHARMM to AMBER, TopoGro-macs, a converter from CHARMM to GROMACS [14], CHARMM-GUI [15] which can convert from CHARMM to GROMACS (and uses ParmEd to manipulate AMBER ﬁles), and amber2lmp , a script converting between LAMMPS and AMBER ﬁles.”
Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways (2013, methodology): “Ligands carazolol and BI-167107 were extracted from PDB entries 2RH1 and 3P0G, respectively, and parameterized for the General Amber force field (GAFF)(2)with acpype(3) and antechamber(4).”
Information-driven structural modelling of protein-protein interactions. (2015, methodology): “For those molecules not in the list, the user is left to generate their own parameterization scheme, using for example PRODRG [41], ACPYPE [42], or ATB [43], and provide the necessary files (topology and parameters in CNS format) in the toppar/directory: ligand.”