Learning the shape of protein micro-environments with a holographic convolutional neural network

• URL: http://arxiv.org/abs/2211.02936v1
• Date: Sat, 5 Nov 2022 16:29:15 GMT
• Title: Learning the shape of protein micro-environments with a holographic convolutional neural network
• Authors: Michael N. Pun, Andrew Ivanov, Quinn Bellamy, Zachary Montague, Colin LaMont, Philip Bradley, Jakub Otwinowski, Armita Nourmohammad
• Abstract summary: We introduce Holographic Convolutional Neural Network (H-CNN) for proteins. H-CNN is a physically motivated machine learning approach to model amino acid preferences in protein structures. It accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from structure remains a major challenge. Here, we introduce Holographic Convolutional Neural Network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein function, including stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

Related papers

• ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction [54.132290875513405]
  The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions. We propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time.
  arXiv  Detail & Related papers  (2024-03-30T05:32:42Z)
• NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks [60.48306899271866]
  We propose novel semantic data augmentation methods to incorporate backbone chemical and side-chain biophysical information into protein classification tasks. Specifically, we leverage molecular biophysical, secondary structure, chemical bonds, andionic features of proteins to facilitate classification tasks.
  arXiv  Detail & Related papers  (2024-03-21T13:27:57Z)
• Efficiently Predicting Mutational Effect on Homologous Proteins by Evolution Encoding [7.067145619709089]
  EvolMPNN is an efficient model to learn evolution-aware protein embeddings. Our model shows up to 6.4% better than state-of-the-art methods and attains 36X inference speedup.
  arXiv  Detail & Related papers  (2024-02-20T23:06:21Z)
• Structure-Informed Protein Language Model [38.019425619750265]
  We introduce the integration of remote homology detection to distill structural information into protein language models. We evaluate the impact of this structure-informed training on downstream protein function prediction tasks.
  arXiv  Detail & Related papers  (2024-02-07T09:32:35Z)
• Multi-level Protein Representation Learning for Blind Mutational Effect Prediction [5.207307163958806]
  This paper introduces a novel pre-training framework that cascades sequential and geometric analyzers for protein structures. It guides mutational directions toward desired traits by simulating natural selection on wild-type proteins. We assess the proposed approach using a public database and two new databases for a variety of variant effect prediction tasks.
  arXiv  Detail & Related papers  (2023-06-08T03:00:50Z)
• A Latent Diffusion Model for Protein Structure Generation [50.74232632854264]
  We propose a latent diffusion model that can reduce the complexity of protein modeling. We show that our method can effectively generate novel protein backbone structures with high designability and efficiency.
  arXiv  Detail & Related papers  (2023-05-06T19:10:19Z)
• Learning Geometrically Disentangled Representations of Protein Folding Simulations [72.03095377508856]
  This work focuses on learning a generative neural network on a structural ensemble of a drug-target protein. Model tasks involve characterizing the distinct structural fluctuations of the protein bound to various drug molecules. Results show that our geometric learning-based method enjoys both accuracy and efficiency for generating complex structural variations.
  arXiv  Detail & Related papers  (2022-05-20T19:38:00Z)
• Structure-aware Protein Self-supervised Learning [50.04673179816619]
  We propose a novel structure-aware protein self-supervised learning method to capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information. We identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme.
  arXiv  Detail & Related papers  (2022-04-06T02:18:41Z)
• PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction [0.07340017786387766]
  In this work, we isolate protein structure to make functional annotations for proteins in the Protein Data Bank. We present PersGNN - an end-to-end trainable deep learning model that combines graph representation learning with topological data analysis.
  arXiv  Detail & Related papers  (2020-10-30T02:24:35Z)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.