First Release Announcement
We are excited to announce the first release version of ForceSolve, a python code to infer coarse Hamiltonians from forces observed at any fine-grained level of detail.
This software has been under development in the Dr. Beck Lab for the past two years and delivers robust state-of-the-art coarse-graining methods. It has been designed to be one of the easiest (possibly the only), most intuitive, coarse-graining programs currently available for small systems and validation of the force-matching method.
Since new file releases can take a while to be distributed to all the mirrors, you can download the complete project using subversion using the following command:svn co http://forcesolve.svn.sourceforge.net/svnroot/forcesolve
- Specification of Arbitrary Molecule Topologies
- Bond specification is done in easily structure-able topology files -- once for each residue type.
- Automatic listing of bond, angle, torsion, and pairwise distance interactions for any given PDB from this topology.
- Automatic assignment of force field atom types using a simple PDB atom:residue name lookup scheme.
- Included script for coarsening of atomic configurations with a site-specification file.
- Supported Calculations
- Maximum Likelihood Hamiltonian Inference
- Bayesian Minimum Mean-Squared Error (average) Hamiltonian Estimation
- Coarse-Grained MD using Langevin Dynamics
- Molecular Mechanics
- Each forcefield term can be specified by a B-spline of arbitrary range and degree using tabulated coefficients.
- Pre-coded terms exist for pairwise distance, bond, angle, and dihedral interactions.
- Adding novel terms (not in the above list) to the list of calculable interactions requires moderate effort and is well documented.
- Included APIs
- Complete implementation of arbitrary polynomial order B-splines and first derivatives.
- Included python library ucgrad handles array and pdb I/O, vector operations such as internal coordinate conversion and generation of rotation matrices, parameter file parsing, and multiple structural superposition.
- Unique Properties of our Method
- Generated Hamiltonians do not suffer from over-fitting at arbitrarily high number of spline knots, nor do they change with the energy or distance scales of the system under consideration.
- Runs on any system with Python 2.4 or greater and numpy (numerical python library) installed -- Linux or MS Windows.