AIMC Topic: Molecular Dynamics Simulation

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Harnessing Allostery to Modulate Protein-Protein Interactions: From Function to Therapeutic Innovations.

Journal of molecular biology
Protein-protein interactions (PPIs) are ubiquitous mediators of cellular functions, and their dysregulation is central to numerous pathological conditions. Traditional drug discovery strategies targeting PPIs directly have faced considerable obstacle...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Food chemistry
Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In this study, machine learning (Gradient Boosting Decision Trees) and virtual screening were employed t...

Artificial Intelligence, Molecular Dynamics, and Beyond: Computational Insights In Cosmetics Research and Formulation Design.

ChemPlusChem
The vast field of cosmetics is mainly explored through experimental methods, while computational tools find broader application in structuralbiology. The world of formulations remains relatively untouched or nondisclosed due to commercial interests. ...

How good is generative diffusion model for enhanced sampling of protein conformations across scales and in all-atom resolution?

The Journal of chemical physics
Molecular dynamics (MD) simulations are fundamental for probing the structural dynamics of biomolecules, yet their efficiency is limited by the high computational cost of exploring long-timescale events. Generative machine learning (ML) models, parti...

Computational modeling and experimental validation of the interaction between tumor biomarker mesothelin and an engineered targeting protein with therapeutic activity.

Protein science : a publication of the Protein Society
Mesothelin (MSLN) is a cell surface glycoprotein overexpressed in many solid tumors, which is known to interact with cancer antigen CA125/MUC16, promoting cancer cell adhesion and metastasis. MSLN has been used as a target of multiple antibody-based ...

De novo design and bioactivity prediction of mitotic kinesin Eg5 inhibitors using MPNN and LSTM-based transfer learning.

Computers in biology and medicine
Breast cancer, the most commonly diagnosed disease worldwide, has been linked to the overexpression of the kinesin Eg5 protein, a spindle motor protein crucial for the assembly and maintenance of the bipolar spindle during mitosis. This makes Eg5 an ...

Study on the self-diffusion coefficients of binary mixtures of supercritical water and H, CO, CO, CH confined in carbon nanotubes.

Water research
Nano-confined binary mixtures are prevalent in the chemical industry, geology, and energy sectors. Investigating their mass transfer behavior can enhance process intensification. This study examines the confined self-diffusion coefficients of binary ...

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics.

Journal of chemical information and modeling
Molecular dynamics (MD) simulations are vital for exploring complex systems in computational physics and chemistry. While machine learning methods dramatically reduce computational costs relative to ab initio methods, their accuracy in long-lasting s...

Virtual Hydrolysis-Based Screening of Wheat-Derived DPP-IV Inhibitory Peptides: A Mechanistic Analysis Integrating Cell Experiments and Molecular Dynamics Simulations.

Journal of agricultural and food chemistry
Dipeptidyl peptidase-IV (DPP-IV) inhibitors play a critical role in the treatment of diabetes and metabolic diseases. This study combines computational simulations with experimental validation to identify peptides with potential DPP-IV inhibitory act...