EMMD is a computationally efficient algorithm based on statistical inference for fast estimation of key features in the two-dimensional FEL. This method avoids direct sampling of high free energy states. Rather, the transition states connecting metastable regions of comparable free energies are estimated using Bayesian likelihood maximization. [code, publication]
Here we present an autoencoder and graph attention neural network architecture to get the machine learned protein collective variable, and membrane surface prediction given a protein structure. [code, publication]
We have developed this algorithm to find the minimum free energy path (MFEP) existing on a free energy landscape (FEL). This is coupled with the EMMD-generated FEL. [code, publication]
The algorithm computes the Structural Persistence of a protein with respect to a reference structure of itself. The repository contains a TCL script for computing the structural persistence of the protein and of the selected region of the protein. [code, publication]
Fundings
Resources
Rosalind Cluster
Uni-datacenter (Kapsid)
Paramridra (SNBNCBS Super Computer )
Paramshakti (IIT-KGP Super Computer)
Amazon Web Services (AWS, India)