AIMC Topic: Proteins

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ProFlex as a linguistic bridge for decoding protein dynamics in normal mode analysis.

Nature communications
Artificial intelligence is revolutionizing structural bioinformatics, with AlphaFold arguably being the most impactful development to date. The structural atlases generated by these methods present significant opportunities for unraveling biological ...

Rising Stars: Bioinformatics of Post-translational Modifications.

Journal of molecular biology
Yu Xue is a professor in the College of Life Science and Technology at Huazhong University of Science and Technology, and hold a joint position in the Hubei Hongshan Laboratory at Huazhong Agricultural University. He received two B.E. degrees in poly...

SGcCA: Deciphering Drug-Target Interactions through an End-to-End Model with Spatial and Channel Reconstruction Convolution and Cross-Efficient-Additive Attention.

Journal of chemical information and modeling
Drug-Target Interaction (DTI) prediction is an indispensable process in drug repositioning. Wet-lab experiments for potential DTI identification are reliable but expensive, labor-intensive, and time-consuming. Deep learning demonstrates the superior ...

Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics.

The journal of physical chemistry. B
Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless γ metric from the Expon...

Predicting drug-target affinity through triple pre-activated random residual planet convolution coupled attention network and contact maps.

Journal of computer-aided molecular design
Drug discovery relies on the ability to predict drug-target affinity (DTA), which allows for the efficient identification of drug candidates for certain protein targets. Scalability, accuracy, and interpretability are issues that traditional methods ...

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design.

Proceedings of the National Academy of Sciences of the United States of America
Deep generative models have demonstrated success in learning the protein sequence to function relationship and designing synthetic sequences with engineered functionality. We introduce the Protein Transformer Variational AutoEncoder (ProT-VAE) as an ...

Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.

Nature communications
Co-folding models represent a major innovation in deep-learning-based protein-ligand structure prediction. The recent publications of RoseTTAFold All-Atom, AlphaFold3, and others have shown high-quality results on predicting the structures of protein...

AI-driven protein pocket detection through integrating deep Q-networks for structural analysis.

Journal of computer-aided molecular design
Protein pockets, or small cavities on the protein surface, are critical sites for enzymatic catalysis, molecular recognition, and drug binding. Accurately identifying these pockets is crucial for understanding protein function and designing therapeut...

ML-PLA: Enhancing Protein-Ligand Binding Affinity Prediction with Microenvironment and Long-Range Interaction-Aware Graph Neural Networks.

Journal of chemical information and modeling
Accurately predicting protein-ligand binding affinity (PLA) is essential in drug discovery for identifying lead compounds. The sequence and structural contexts of an amino acid residue (i.e., microenvironment) describe the surrounding chemical proper...

Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.

Journal of chemical information and modeling
Network methods and molecular dynamics (MD) simulations have become essential tools for studying protein dynamics. However, applying network methods to MD simulations of flexible proteins is a major challenge, since the high conformational heterogene...