Development of Enhanced Sampling Methods for Biomolecular Simulations applying Machine Learning methods.

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Biomolecular simulations of rare events are accessible for small proteins and simple enzymes with few microseconds of Molecular Dynamics (MD) simulations. Nevertheless, It is way harder to sample the interesting events in the free energy landscape of a protein when dealing with bigger and way more complex biomolecules. The Transition State (TS) and activation barrier of some of those events may be too high to climb by means of free MD, thus many different methods to overcome the barriers and sample the TS regions between them had been developed in the recent years. So far, the goal is to develop new methods that could improve on the sampling aspect with the aid of Machine Learning (ML) by treating the resulting data and updating the method or either applying it directly in the method. For instance, a Replica Exchange (Rex) method is being adapted for Mg charges to get an approximate of 30% probability of exchange, we will further study its behaviour and the amount of time required for a configuration to run from the first window to the end and get back, so we can improve on calculation time. This is done with for Crispr-Cas9 protein system with a DNA strand attached to it. The MG charge ranges from a meaningful charge (2.0) to a low one (0.4) by an exponential decay in 77 windows. With this we hope we will be able to use this method to find the correct metal coordination for this protein in the active site, and develop a pipeline to apply it easily to any other metal enzymes. Besides the Rex, we are also trying to come up with the optimal reaction coordinates to describe the FEL of a binding/unbinding event for the CDK2 system with a good inhibitor. We are testing a ML approach based on supervised learning by testing some Neural Network (NN) algorithms such as convolutional NN or Multi-layer Perceptron NN. The optimal reaction coordinates will be used then to adapt the sampling method to find the best path to the TS of the event.