Sugar transporter mechanism

<p><strong>SP superfamily transport cycle</strong></p> <p>The Project consisted of three main parts:</p> <ol> <li> <p>Machine Learning using Python/Tensorflow</p> <p>a. ML_svm contains the machine learning script used for training and visualizing the linear SVM training.<br> b. neural_network contains the architecture and training regime of the network along with the trained weights.<br> c. inputs contains the inputs necessary for the ML_svm training, whereas the training set for the neural network would be all of the available sugar transporter PDBs as of January 2022.</p> </li> <li> <p>Structure Prediction using RosettaMP</p> <p>a. Inputs contains the HYBRIDIZE.zip file which in turn contains the RosettaMP inputs for all conformational states, each of which have a dedicated folder in this OSF project directory. For each point, relevant input files and crucial results are shared.</p> </li> <li> <p>Enhanced sampling on GLUT5</p> <p>a. awh_xvgs contains the necessary outputs to analyse convergence etc.<br> b. mdp files contain the input settings for GROMACS<br> c. topology and gro files contains the topology, parameters and structures for running the simulations.</p> </li> </ol>