AIMC Topic: Proteins

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Integrative modeling meets deep learning: Recent advances in modeling protein assemblies.

Current opinion in structural biology
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the succe...

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.

International journal of molecular sciences
The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determina...

Synergistic integration of deep learning with protein docking in cardiovascular disease treatment strategies.

IUBMB life
This research delves into the exploration of the potential of tocopherol-based nanoemulsion as a therapeutic agent for cardiovascular diseases (CVD) through an in-depth molecular docking analysis. The study focuses on elucidating the molecular intera...

Predicting hotspots for disease-causing single nucleotide variants using sequences-based coevolution, network analysis, and machine learning.

PloS one
To enable personalized medicine, it is important yet highly challenging to accurately predict disease-causing mutations in target proteins at high throughput. Previous computational methods have been developed using evolutionary information in combin...

Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence.

Current opinion in structural biology
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods c...

A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.

Biomolecules
The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them approp...

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Nature computational science
Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction data...

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Proteins
Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the...

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

BMC genomics
Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effec...