AIMC Topic: Proteins

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One for All, All for One: A Unified Framework for Free-Energy Calculations.

Accounts of chemical research
ConspectusEnhanced-sampling techniques employed in free-energy calculations overcome the limitations of brute-force molecular dynamics (MD) and are widely used to interrogate complex biological and chemical systems at atomic resolution. Depending on ...

LGABAN: An Integrated Multi-Scale Approach Combining Graph and Sequence Features for Enhanced Prediction of Drug-Protein Interactions.

Journal of chemical information and modeling
The accurate identification of drug-target interactions is crucial for shortening the timeline and lowering the expenses of pharmaceutical research, as the discovery of novel drugs remains a highly complex, resource-intensive, and lengthy endeavor. D...

SSIF-Affinity: Multimodal Deep Learning of Sequence-Structure Features for Precise Protein-Protein Binding Affinity Prediction.

Journal of chemical information and modeling
Quantitative prediction of binding affinity in protein-protein interactions is critical for deciphering biological mechanisms and advancing therapeutic antibody development. While experimental methods for measuring binding affinity remain limited by ...

A Transformer-Based Deep Learning Approach to Predicting Air Organic Pollutant-Human Protein Interactions.

Environmental science & technology
Air pollution poses a critical global public health challenge. Molecular-level initiating events, such as pollutant-protein interactions, can trigger cascades of biological responses that may contribute to adverse health effects. However, current met...

BioFusionDTI: Assimilating Graph and Sequence Modalities for Generalizable Drug-Target Interaction Prediction.

Journal of chemical information and modeling
Accurate prediction of drug-target interactions (DTIs) is essential for drug discovery and repurposing. Despite recent advances, deep learning models often exhibit limited generalization under realistic cold-start scenarios and suffer from poor inter...

AcidProNet: Acidophilic Protein Classification via DCGAN-GP-Based Data Augmentation and Parameter-Shared Mixture-of-Experts Transformer.

Journal of chemical information and modeling
With the continued exploration of biological resources in extreme environments, functional proteins such as acidophilic proteins have attracted increasing attention. These proteins can maintain structural stability and biological functionality under ...

A Standardized Benchmark for Machine-Learned Molecular Dynamics Using Weighted Ensemble Sampling.

The journal of physical chemistry. B
The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comparison between simulation approaches is often hindered by inconsistent...

Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.

Scientific reports
Deep learning (DL) algorithms have increasingly been applied to predict protein-ligand binding affinity, a critical step in drug design. Yet, many models still struggle to generalize to unseen data, and when coupled with the absence of confidence est...

ProSECFPs: A Novel Fingerprint-Based Protein Representation Method for Missense Mutation Pathogenicity Prediction.

Journal of chemical information and modeling
Developing effective computational representations of protein sequences is crucial for advancing diverse areas of computational biology and bioinformatics. Ideal representations must be computationally efficient, scalable, informative, flexible acros...

Sensitive detection of structural dynamics using a statistical framework for comparative crystallography.

Science advances
Chemical and conformational changes are crucial to protein function and its pharmacological control. X-ray crystallography can reveal these changes in atomic detail, but standard analysis methods, which refine separate datasets, often overlook differ...