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Proteins

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SurfPro-NN: A 3D point cloud neural network for the scoring of protein-protein docking models based on surfaces features and protein language models.

Computational biology and chemistry
Protein-protein interactions (PPI) play a crucial role in numerous key biological processes, and the structure of protein complexes provides valuable clues for in-depth exploration of molecular-level biological processes. Protein-protein docking tech...

DeepPI: Alignment-Free Analysis of Flexible Length Proteins Based on Deep Learning and Image Generator.

Interdisciplinary sciences, computational life sciences
With the rapid development of NGS technology, the number of protein sequences has increased exponentially. Computational methods have been introduced in protein functional studies because the analysis of large numbers of proteins through biological e...

Genomic language model predicts protein co-regulation and function.

Nature communications
Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function par...

HydraProt: A New Deep Learning Tool for Fast and Accurate Prediction of Water Molecule Positions for Protein Structures.

Journal of chemical information and modeling
Water molecules are integral to the structural stability of proteins and vital for facilitating molecular interactions. However, accurately predicting their precise position around protein structures remains a significant challenge, making it a vibra...

Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral re...

FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics.

International journal of biological macromolecules
Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework ...

A Review for Artificial Intelligence Based Protein Subcellular Localization.

Biomolecules
Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein re...

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

Journal of biomolecular NMR
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...

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
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]

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

Nature communications
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...