FergLab GitHub
FERGLAB GITHUB
LAMMPS Tutorial
Molecular Dynamics with LAMMPS
Elements of Integrated Computational Materials Engineering Workshop, UIUC, 23-25 July 2014
[slides] [scripts] [YouTube]
MD Primer
Primer on the molecular dynamics method
[movie]
BayesWHAM
[v1] Open source MATLAB and Python 2.7 implementations of a Bayesian implementation of the weighted histogram analysis method (WHAM) to estimate free energy surfaces (FES) from biased sampling data.
[https://bitbucket.org/andrewlferguson/bayeswham_matlab]
[https://bitbucket.org/andrewlferguson/bayeswham_python]
A.L. Ferguson* “BayesWHAM: A Bayesian approach for free energy estimation, reweighting, and uncertainty quantification in the weighted histogram analysis method” J. Comput. Chem. 38 18 1583-1605 (2017) [ http://dx.doi.org/10.1002/jcc.24800 ]
[v2] Open source MATLAB generalization of BayesWHAM permitting incorporation of data at different T and P state points and extrapolative / interpolative estimation of unbiased FES at other T and P state points.
[https://bitbucket.org/andrewlferguson/bayeswham_matlab_v2]
Diffusion Maps
Memory efficient C++ implementation of sparse diffusion maps using Armadillo libraries
[https://github.com/awlong/DiffusionMap]
Potts Spin Glass Viral Fitness Landscapes
MATLAB code to translate viral protein multiple sequence alignment into an empirical Potts spin glass model of viral fitness landscape
[https://bitbucket.org/andrewlferguson/viralfitness_matlab]
Support Vector Machine Antimicrobial Peptide (SVMAMP) Predictor
Python web tool for structure-free prediction of antimicrobial peptides based on a support vector machine (SVM) classifier
[http://andrewlferguson.pythonanywhere.com/]
Takens’ Attractor Reconstruction
Matlab codes to conduct Takens’ delay embeddings, nonlinear dimensionality reduction, and diffeomorphism validation to reconstruct topologically equivalent attractors of dynamical systems from (univariate) time series measurements
[https://github.com/cwangjiang/TakensFES]
J. Wang and A.L. Ferguson* “Nonlinear reconstruction of single-molecule free-energy surfaces from univariate time series” Phys. Rev. E 93 032412 (2016) [ http://link.aps.org/doi/10.1103/PhysRevE.93.032412 ]
Self-Assembling Pi-Conjugated Oligopeptide Simulation and Inverse Design
Python scripts for the parameterization and deployment of coarse-grained models of pi-conjugated self-assembling peptides at the Martini and patchy particle levels of resolution
[https://github.com/ramansbach/cluster_analysis]
[https://github.com/ramansbach/simulation-examples]
[https://github.com/ramansbach/patchy-optimizer]
[https://github.com/ramansbach/mapping-martini-patchy]
R.A. Mansbach and A.L. Ferguson* “A patchy particle model of the hierarchical self-assembly of π-conjugated optoelectronic peptides” J. Phys. Chem. B 122 44 10219-10236 (2018) [ https://doi.org/10.1021/acs.jpcb.8b05781 ]
Python codes for the construction and deployment of quantitative structure activity relation (QSAR) models for the inverse design of self-assembling pi-conjugated peptides
[https://github.com/b-a-thurston/peptide-pi-peptide_QSAR]
B.A. Thurston and A.L. Ferguson* “Machine learning and molecular design of self-assembling π-conjugated oligopeptides” Mol. Sim. 44 11 930-945 (2018) [ https://doi.org/10.1080/08927022.2018.1469754 ]