AIMC Topic: Dipeptides

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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...

NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...

Combining Force Fields and Neural Networks for an Accurate Representation of Bonded Interactions.

The journal of physical chemistry. A
We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a cor...

PrUb-EL: A hybrid framework based on deep learning for identifying ubiquitination sites in Arabidopsis thaliana using ensemble learning strategy.

Analytical biochemistry
Identification of ubiquitination sites is central to many biological experiments. Ubiquitination is a kind of post-translational protein modification (PTM). It is a key mechanism for increasing protein diversity and plays a vital role in regulating c...

A New Clinical Utility for Tubular Markers to Identify Kidney Responders to Saxagliptin Treatment in Adults With Diabetic Nephropathy.

Canadian journal of diabetes
OBJECTIVES: In recent clinical studies, saxagliptin exhibited nephroprotective potential by lowering albuminuria. In this study, we aimed to determine whether these kidney effects of saxagliptin were mediated by changes in markers of kidney tubular d...

Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble.

BMC bioinformatics
BACKGROUND: Antifreeze proteins (AFPs) are a group of proteins that inhibit body fluids from growing to ice crystals and thus improve biological antifreeze ability. It is vital to the survival of living organisms in extremely cold environments. Howev...

Discovering Collective Variables of Molecular Transitions via Genetic Algorithms and Neural Networks.

Journal of chemical theory and computation
With the continual improvement of computing hardware and algorithms, simulations have become a powerful tool for understanding all sorts of (bio)molecular processes. To handle the large simulation data sets and to accelerate slow, activated transitio...

Classifying the superfamily of small heat shock proteins by using g-gap dipeptide compositions.

International journal of biological macromolecules
Small heat shock protein (sHSP) is a superfamily of molecular chaperone and is found from archaea to human. Recent researches have demonstrated that sHSPs participate in a series of biological processes and are even closely associated with serious di...

Learning molecular dynamics with simple language model built upon long short-term memory neural network.

Nature communications
Recurrent neural networks have led to breakthroughs in natural language processing and speech recognition. Here we show that recurrent networks, specifically long short-term memory networks can also capture the temporal evolution of chemical/biophysi...

Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.

Computational and mathematical methods in medicine
Heat shock proteins (HSPs) are ubiquitous in living organisms. HSPs are an essential component for cell growth and survival; the main function of HSPs is controlling the folding and unfolding process of proteins. According to molecular function and m...