AIMC Topic: Proteins

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Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure.

Biophysical journal Jan 23, 2024
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achie...
Macromolecular Substances Cryoelectron Microscopy Protein Conformation Proteins Models, Molecular Machine Learning
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Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling.

Journal of the American Society for Mass Spectrometry Jan 23, 2024
Observed mass shifts associated with deuterium incorporation in hydrogen-deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these di...
Deep Learning Mass Spectrometry Retrospective Studies Deuterium Deuterium Exchange Measurement Proteins
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Analyzing domain features of small proteins using a machine-learning method.

Proteomics Jan 22, 2024
Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utiliz...
Databases, Protein Protein Domains Computational Biology Proteins Machine Learning Humans
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Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

Journal of the American Chemical Society Jan 19, 2024
The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciph...
Artificial Intelligence Spectrophotometry, Infrared Amides Hydrogen Bonding Proteins
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Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.

Journal of chemical theory and computation Jan 12, 2024
Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equili...
Artificial Intelligence Molecular Dynamics Simulation Proteins Thermodynamics Protein Conformation
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LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.

Interdisciplinary sciences, computational life sciences Jan 11, 2024
 Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA-protein interactions (LPIs) are expensive and time-con...
Humans Neural Networks, Computer Cluster Analysis RNA, Long Noncoding Computational Biology Algorithms Proteins
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PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.

Interdisciplinary sciences, computational life sciences Jan 11, 2024
The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological f...
Protein Domains Proteins Amino Acid Sequence Machine Learning Protein Processing, Post-Translational
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Computational Design of Peptide Assemblies.

Journal of chemical theory and computation Jan 11, 2024
With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expandi...
Amino Acid Sequence Peptides Molecular Dynamics Simulation Proteins Artificial Intelligence
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De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jan 10, 2024
One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to b...
Proteins Drug Discovery Machine Learning
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HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

Journal of chemical information and modeling Jan 7, 2024
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have b...
Drug Development Drug Discovery Proteins Neural Networks, Computer Machine Learning
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