AIMC Topic: Proteins

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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...

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

Computers in biology and medicine
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...

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

PloS one
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...

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

Structure (London, England : 1993)
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...

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

Analytical biochemistry
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...