RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning

• URL: http://arxiv.org/abs/2310.13806v1
• Date: Fri, 20 Oct 2023 20:36:13 GMT
• Title: RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning
• Abstract summary: We make use of our data sets of exhaustive double InDel mutations for three proteins which we computationally generated using a robotics inspired inverse kinematics approach available in Rosetta. We develop and train a neural network, RoseNet, on several structural and energetic metrics output by Rosetta during the mutant generation process. We show that the model can be trained on minimal data (50%) and still retain a high level of accuracy.
• Score: 0.7776497736451751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An amino acid insertion or deletion, or InDel, can have profound and varying functional impacts on a protein's structure. InDel mutations in the transmembrane conductor regulator protein for example give rise to cystic fibrosis. Unfortunately performing InDel mutations on physical proteins and studying their effects is a time prohibitive process. Consequently, modeling InDels computationally can supplement and inform wet lab experiments. In this work, we make use of our data sets of exhaustive double InDel mutations for three proteins which we computationally generated using a robotics inspired inverse kinematics approach available in Rosetta. We develop and train a neural network, RoseNet, on several structural and energetic metrics output by Rosetta during the mutant generation process. We explore and present how RoseNet is able to emulate the exhaustive data set using deep learning methods, and show to what extent it can predict Rosetta metrics for unseen mutant sequences with two InDels. RoseNet achieves a Pearson correlation coefficient median accuracy of 0.775 over all Rosetta scores for the largest protein. Furthermore, a sensitivity analysis is performed to determine the necessary quantity of data required to accurately emulate the structural scores for computationally generated mutants. We show that the model can be trained on minimal data (<50%) and still retain a high level of accuracy.

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