ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics

• URL: http://arxiv.org/abs/2410.20317v1
• Date: Sun, 27 Oct 2024 02:59:48 GMT
• Title: ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
• Authors: Siddharth Viswanath, Dhananjay Bhaskar, David R. Johnson, Joao Felipe Rocha, Egbert Castro, Jackson D. Grady, Alex T. Grigas, Michael A. Perlmutter, Corey S. O'Hern, Smita Krishnaswamy,
• Abstract summary: We introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE) ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs. It integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories.
• Score: 3.6181745062052495
• License:
• Abstract: Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.

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