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Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

Journal of the American Chemical Society
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...

Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.

Journal of chemical theory and computation
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...

LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.

Interdisciplinary sciences, computational life sciences
 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...

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.

Interdisciplinary sciences, computational life sciences
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...

Computational Design of Peptide Assemblies.

Journal of chemical theory and computation
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...

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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...

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

Journal of chemical information and modeling
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...

Prediction of protein structure and AI.

Journal of human genetics
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-functi...

DL-SPhos: Prediction of serine phosphorylation sites using transformer language model.

Computers in biology and medicine
Serine phosphorylation plays a pivotal role in the pathogenesis of various cellular processes and diseases. Roughly 81% of human diseases have links to phosphorylation, and an overwhelming 86.4% of protein phosphorylation takes place at serine residu...