AIMC Topic: Proteins
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ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.
Journal of chemical information and modeling
Apr 8, 2024
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providi...
Advancing Drug-Target Interaction prediction with BERT and subsequence embedding.
Computational biology and chemistry
Apr 5, 2024
Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding prot...
Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.
Accounts of chemical research
Apr 5, 2024
Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search a...
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
Apr 4, 2024
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
Apr 3, 2024
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
Apr 3, 2024
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
Mar 29, 2024
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
Mar 28, 2024
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
Mar 27, 2024
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
Mar 27, 2024
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...
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