Using Machine Learning To Predict Membrane Protein States Based On Their Lipid Environment

Data analysis, and particularly big data analysis and cluster determination is becoming an increasing challenge in the field of biomolecular simulation. We present our ongoing efforts on identification of orientational states of RAS proteins and associated lipids using Machine Learning-based models. With the advent of deep learning based approaches, it has become possible to use the raw coordinates of molecules to identify their states and their corresponding stability. We employ variational autoencoders using deep neural networks to encode the molecular coordinates of the RAS dimer and the interacting lipid density fields into a meaningful latent space. Our training data comes from a massive simulation campaign of 120,000 coarse-grained MD simulations each over 1 microsecond long, run using our MuMMI framework that on up to 4000 nodes of the Sierra supercomputer at LLNL. We train the neural network using NVIDIA Tesla V100 GPUs to reduce the spatial coordinates into a low-dimensional latent space. We perform spectral clustering in the latent space to determine, in an unsupervised manner, the number of distinct clusters—each corresponding to a distinct state of the RAS dimer. If distinct clusters are successfully identified then they are analyzed further for their geometrical information and lifetime to understand their stability. We believe that meaningful state identification in RAS dimers and associated lipids using ML-based unsupervised methods can provide key insights for experiments facilitating therapeutic strategies. This work has been supported in part by the Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program established by the U.S. Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC5207NA27344