Skip to content

Theoretical and Computational Biochemistry Group

Software

MCMap

A Computational Tool for Mapping Transient Protein-Protein Complexes

J.M. Foerster, I. Poehner, and G.M. Ullmann, MCMap-computational tool for mapping energy landscapes of transient protein-protein interactions, ACS OMEGA, vol. 3, no. 6, pp. 6465–6475, 2018. doi:10.1021/acsomega.8b00572

MCMap is a tool particularly suited well for analyzing transient biomolecular complexes. The program applies a Monte Carlo strategy. The ligand is randomly moving in the electrostatic field of the receptor. By applying importance sampling, the major interaction sites are mapped.

PyCPR

An Python-based Implementation of the Conjugate Peak Refinement (CPR) Algorithm for Finding Transition State Structures

F.J. Gisdon, M. Culka, and G.M. Ullmann, PyCPR - a python-based implementation of the conjugate peak refinement (CPR) algorithm for finding transition state structures, JOURNAL OF MOLECULAR MODELING, vol. 22, no. 10, 2016. doi:10.1007/s00894-016-3116-8

Conjugate peak refinement (CPR) developed by S. Fischer and M. Karplus (Chem. Phys. Letters 194, 252-261, 1992) is a powerful and robust method to search transition states on a molecular potential energy surface. Nevertheless, the method was to the best of our knowledge so far only implemented in CHARMM. We provide with PyCPR, a new Python-based implementation of the CPR algorithm within the pDynamo framework which was developed by Martin Field. The integration of PyCPR into the framework pDynamo allows the combination of CPR with the large variety of methods implemented in pDynamo. PyCPR can be used in combination with quantum mechanical and molecular mechanical methods (and hybrid methods) implemented directly in pDynamo, but also in combination with external programs such as ORCA using pDynamo as interface. PyCPR is distributed as free, open source software (CeCILL license, very similar to GNU GPL license).

CoMoDo

Identifying Dynamic Protein Domains Based on Covariances of Motion

S.A. Wieninger and G.M. Ullmann, CoMoDo: Identifying dynamic protein domains based on covariances of motion, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, vol. 11, no. 6, pp. 2841–2854, 2015. doi:10.1021/acs.jctc.5b00150

CoMoDo clusters protein structures into dynamic domains based on covariances of motion.

GMCT

A Monte Carlo Simulation Package for Macromolecular Receptors

R.T. Ullmann and G.M. Ullmann, GMCT: A monte carlo simulation package for macromolecular receptors, JOURNAL OF COMPUTATIONAL CHEMISTRY, vol. 33, no. 8, pp. 887–900, 2012. doi:10.1002/jcc.22919

MEAD

A modified and extended Version of MEAD based on the version from Donald Bashford

Charmm - Force Fields

Force Field Parameter for Cholesterol and related Sterols

Z. Cournia, J. Smith, and G. Ullmann, A molecular mechanics force field for biologically important sterols, JOURNAL OF COMPUTATIONAL CHEMISTRY, vol. 26, no. 13, pp. 1383–1399, 2005. doi:10.1002/jcc.20277

McVol

Calculating the Volume of Proteins and finding internal cavities. (needs some reworking)

M.S. Till and G.M. Ullmann, McVol - a program for calculating protein volumes and identifying cavities by a monte carlo algorithm, JOURNAL OF MOLECULAR MODELING, vol. 16, no. 3, pp. 419–429, 2010. doi:10.1007/s00894-009-0541-y

HMM Type II

Hidden Markov Models for Aligning Type II Reaction Center Sequences

E. Krammer, P. Sebban, and G.M. Ullmann, Profile hidden markov models for analyzing similarities and dissimilarities in the bacterial reaction center and photosystem II, BIOCHEMISTRY, vol. 48, no. 6, pp. 1230–1243, 2009. doi:10.1021/bi802033kE. Krammer, P. Sebban, and G.M. Ullmann, Profile hidden markov models for analyzing similarities and dissimilarities in the bacterial reaction center and photosystem II, BIOCHEMISTRY, vol. 48, no. 6, pp. 1230–1243, 2009. doi:10.1021/bi802033k