AIMC Topic: Proteins

Clear Filters Showing 311 to 320 of 1967 articles

Prediction of order parameters based on protein NMR structure ensemble and machine learning.

Journal of biomolecular NMR Mar 26, 2024
The fast motions of proteins at the picosecond to nanosecond timescale, known as fast dynamics, are closely related to protein conformational entropy and rearrangement, which in turn affect catalysis, ligand binding and protein allosteric effects. Th...
Nuclear Magnetic Resonance, Biomolecular Proteins Protein Conformation Machine Learning
View on PubMed DOI

Security challenges by AI-assisted protein design : The ability to design proteins in silico could pose a new threat for biosecurity and biosafety.

EMBO reports Mar 26, 2024
Scientists and security experts are concerned that the increasing power of AI-assisted protein design and synthesis could be abused by various actors for terrorist or criminal purposes. [Image: see text]
Security Measures Proteins Protein Engineering Humans Computer Simulation Artificial Intelligence
View on PubMed DOI

Precise prediction of phase-separation key residues by machine learning.

Nature communications Mar 26, 2024
Understanding intracellular phase separation is crucial for deciphering transcriptional control, cell fate transitions, and disease mechanisms. However, the key residues, which impact phase separation the most for protein phase separation function ha...
Machine Learning Proteins Glycine
View on PubMed DOI

Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model.

Computers in biology and medicine Mar 21, 2024
N-terminal acetylation is one of the most common and important post-translational modifications (PTM) of eukaryotic proteins. PTM plays a crucial role in various cellular processes and disease pathogenesis. Thus, the accurate identification of N-term...
Humans Protein Processing, Post-Translational Neural Networks, Computer Proteins Algorithms Deep Learning Databases, Protein Acetylation
View on PubMed DOI

Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space.

PloS one Mar 21, 2024
Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainabi...
Proteins Machine Learning Drug Discovery Drug Development
View on PubMed DOI

A suite of designed protein cages using machine learning and protein fragment-based protocols.

Structure (London, England : 1993) Mar 20, 2024
Designed protein cages and related materials provide unique opportunities for applications in biotechnology and medicine, but their creation remains challenging. Here, we apply computational approaches to design a suite of tetrahedrally symmetric, se...
Cryoelectron Microscopy Protein Conformation Molecular Docking Simulation Proteins Models, Molecular Hydrophobic and Hydrophilic Interactions Machine Learning
View on PubMed DOI

PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures.

Analytical biochemistry Mar 19, 2024
Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made signi...
Phosphorylation Proteins Humans Neural Networks, Computer Deep Learning
View on PubMed DOI

Flattening the curve-How to get better results with small deep-mutational-scanning datasets.

Proteins Mar 19, 2024
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the ...
Humans Software Algorithms Neural Networks, Computer Machine Learning Databases, Protein Mutation Protein Conformation Deep Learning Computational Biology Proteins
View on PubMed DOI

Using protein language models for protein interaction hot spot prediction with limited data.

BMC bioinformatics Mar 16, 2024
BACKGROUND: Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained signifi...
Amino Acid Sequence Machine Learning Proteins Humans
View on PubMed DOI

Directional Δ Neural Network (DrΔ-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

Journal of chemical information and modeling Mar 12, 2024
The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of syste...
Neural Networks, Computer Ligands Entropy Protein Binding Proteins
View on PubMed DOI
Popular Topics
Recent Journals