AIMC Topic: Proteins

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Sparse autoencoders uncover biologically interpretable features in protein language model representations.

Proceedings of the National Academy of Sciences of the United States of America
Foundation models in biology-particularly protein language models (PLMs)-have enabled ground-breaking predictions in protein structure, function, and beyond. However, the "black-box" nature of these representations limits transparency and explainabil...

Employing Artificial Neural Networks for Optimal Storage and Facile Sharing of Molecular Dynamics Simulation Trajectories.

Journal of chemical information and modeling
With the remarkable stride in computing power and advances in Molecular Dynamics (MD) simulation programs, the crucial challenge of storing and sharing large biomolecular simulation data sets has emerged. By leveraging AutoEncoders, a type of artific...

PepPCBench is a Comprehensive Benchmarking Framework for Protein-Peptide Complex Structure Prediction.

Journal of chemical information and modeling
Accurate modeling of protein-peptide interactions is essential for understanding fundamental biological processes and designing peptide-based drugs. However, predicting the complex structures of these interactions remains challenging, primarily due t...

MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.

BMC genomics
BACKGROUND: Drug development is a time-consuming and costly endeavor, and utilizing computer-aided methods to predict drug-target affinity (DTA) can significantly accelerate this process. The key to accurate DTA prediction lies in selecting appropria...

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference.

PloS one
Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is neede...

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context.

Nature communications
Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenici...

Augmenting MACCS Keys with Persistent Homology Fingerprints for Protein-Ligand Binding Classification.

Journal of chemical information and modeling
Machine learning has become an essential tool in computational drug design, enabling models to uncover patterns in molecular data and predict protein-ligand interactions. This study introduces a novel approach by integrating persistence images with M...

Benchmarking 3D Structure-Based Molecule Generators.

Journal of chemical information and modeling
To understand the benefits and drawbacks of 3D combinatorial and deep learning generators, a novel benchmark was created focusing on the recreation of important protein-ligand interactions and 3D ligand conformations. Using the BindingMOAD data set w...

Learning the sequence code of protein expression in human immune cells.

Science advances
Accurate protein expression in human immune cells is essential for appropriate cellular function. The mechanisms that define protein abundance are complex and are executed on transcriptional, posttranscriptional, and posttranslational levels. Here, w...

Applications of enhanced sampling methods to biomolecular self-assembly: a review.

Journal of physics. Condensed matter : an Institute of Physics journal
This review article discusses some common enhanced sampling methods in relation to the process of self-assembly of biomolecules. An introduction to self-assembly and its challenges is covered followed by a brief overview of the methods and analysis f...