Related papers: Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification

Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification

URL: http://arxiv.org/abs/2503.19186v1
Date: Mon, 24 Mar 2025 22:22:51 GMT
Title: Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification
Authors: Parisa Mollaei, Amir Barati Farimani,
Abstract summary: We propose a Kernel-PCA model to capture structure-function relationships in a protein.<n>By leveraging machine learning techniques, our model uncovers meaningful patterns in high-dimensional protein data.
Score: 7.136205674624813
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In this study, we propose a Kernel-PCA model designed to capture structure-function relationships in a protein. This model also enables ranking of reaction coordinates according to their impact on protein properties. By leveraging machine learning techniques, including Kernel and principal component analysis (PCA), our model uncovers meaningful patterns in high-dimensional protein data obtained from molecular dynamics (MD) simulations. The effectiveness of our model in accurately identifying reaction coordinates has been demonstrated through its application to a G protein-coupled receptor. Furthermore, this model utilizes a network-based approach to uncover correlations in the dynamic behavior of residues associated with a specific protein property. These findings underscore the potential of our model as a powerful tool for protein structure-function analysis and visualization.

Related papers

SFM-Protein: Integrative Co-evolutionary Pre-training for Advanced Protein Sequence Representation [97.99658944212675]
We introduce a novel pre-training strategy for protein foundation models. It emphasizes the interactions among amino acid residues to enhance the extraction of both short-range and long-range co-evolutionary features. Trained on a large-scale protein sequence dataset, our model demonstrates superior generalization ability.
arXiv Detail & Related papers (2024-10-31T15:22:03Z)
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
arXiv Detail & Related papers (2022-09-30T01:46:38Z)
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)
Encoding protein dynamic information in graph representation for functional residue identification [0.0]
Recent advances in protein function prediction exploit graph-based deep learning approaches to correlate the structural and topological features of proteins with their molecular functions. Here we apply normal mode analysis to native protein conformations and augment protein graphs by connecting edges between dynamically correlated residue pairs. The proposed graph neural network, ProDAR, increases the interpretability and generalizability of residue-level annotations and robustly reflects structural nuance in proteins.
arXiv Detail & Related papers (2021-12-15T17:57:13Z)
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)
Energy-based models for atomic-resolution protein conformations [88.68597850243138]
We propose an energy-based model (EBM) of protein conformations that operates at atomic scale. The model is trained solely on crystallized protein data. An investigation of the model's outputs and hidden representations finds that it captures physicochemical properties relevant to protein energy.
arXiv Detail & Related papers (2020-04-27T20:45:12Z)

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