GPU workstation for deep learning-based protein design and cryo-EM data processing

Univ Of North Carolina Chapel Hill, Chapel Hill NC

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Abstract

Abstract We are requesting funds to purchase a GPU workstation that will be used for (1) deep learning in the context of protein design and (2) for solving protein structures using cryo-EM data. Currently there is a revolution going on protein modeling as deep learning methods have shown impressive success in both protein structure prediction and protein design. We are leveraging these new approaches in several ways that require access to GPU processers. First, we have developed a new protein design pipeline that iterates between protein structure prediction with AlphaFold and sequence optimization with a graph neural network to evolve sequences for a specific function. Initial experimental validation of the pipeline is very encouraging, and we are now eager to test it on a variety of protein design problems including the design of competitive inhibitors, protein switches and biosensors. The pipeline requires large blocks of GPU time (several days with multiple processors) to identify promising designs for experimental validation. Second, we are training new neural networks for improved performance in protein design. Access to GPUs is required for rapidly testing alternative network architectures and hyperparameters. The last step of many protein design projects is validation of the design model with a high-resolution structure. Some of our projects involve systems large enough to be studied with cryo-EM and we are now in need of computational resources to structures from cryo-EM data. Almost all modern cryo EM packages rely on GPUs for processing data.