AIMC Topic: Dipeptides

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RAFP-Pred: Robust Prediction of Antifreeze Proteins Using Localized Analysis of n-Peptide Compositions.

IEEE/ACM transactions on computational biology and bioinformatics Oct 13, 2016
In extreme cold weather, living organisms produce Antifreeze Proteins (AFPs) to counter the otherwise lethal intracellular formation of ice. Structures and sequences of various AFPs exhibit a high degree of heterogeneity, consequently the prediction ...
Machine Learning Dipeptides Sequence Analysis, Protein Antifreeze Proteins Algorithms Computational Biology ROC Curve Databases, Protein
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MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins.

Computational biology and chemistry Jul 29, 2015
The growth and spread of drug resistance in bacteria have been well established in both mankind and beasts and thus is a serious public health concern. Due to the increasing problem of drug resistance, control of infectious diseases like diarrhea, pn...
Position-Specific Scoring Matrices Dipeptides Support Vector Machine Hydrophobic and Hydrophilic Interactions Amino Acids Drug Resistance Organic Cation Transport Proteins Reproducibility of Results
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Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC.

Computer methods and programs in biomedicine Jul 22, 2015
Heat Shock Proteins (HSPs) are the substantial ingredients for cell growth and viability, which are found in all living organisms. HSPs manage the process of folding and unfolding of proteins, the quality of newly synthesized proteins and protecting ...
HSP70 Heat-Shock Proteins Sequence Alignment Sequence Analysis, Protein Machine Learning Pattern Recognition, Automated Algorithms Molecular Sequence Data Dipeptides Amino Acid Sequence Heat-Shock Proteins Reproducibility of Results Sensitivity and Specificity
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NepoIP/MM: Toward Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects.

Journal of chemical theory and computation Jun 10, 2025
Machine learning force fields offer the ability to simulate biomolecules with quantum mechanical accuracy while significantly reducing computational costs, attracting a growing amount of attention in biophysics. Meanwhile, by leveraging the efficienc...
Dipeptides Static Electricity Molecular Dynamics Simulation Quantum Theory Water Proteins Machine Learning
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AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.

Journal of chemical theory and computation May 13, 2025
Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) al...
Dipeptides Amyloid Precursor Protein Secretases Algorithms Molecular Dynamics Simulation
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Deep learning path-like collective variable for enhanced sampling molecular dynamics.

The Journal of chemical physics May 7, 2024
Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a numb...
Deep Learning Thermodynamics RNA Molecular Dynamics Simulation Dipeptides
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Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

The Journal of chemical physics Jul 14, 2023
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fi...
Neural Networks, Computer Machine Learning Dipeptides Water Molecular Dynamics Simulation
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Explaining reaction coordinates of alanine dipeptide isomerization obtained from deep neural networks using Explainable Artificial Intelligence (XAI).

The Journal of chemical physics Apr 21, 2022
A method for obtaining appropriate reaction coordinates is required to identify transition states distinguishing the product and reactant in complex molecular systems. Recently, abundant research has been devoted to obtaining reaction coordinates usi...
Alanine Neural Networks, Computer Artificial Intelligence Isomerism Dipeptides
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Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design.

The Journal of chemical physics Aug 21, 2018
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories. This technique furnishes explicit and differentiable ex...
Molecular Dynamics Simulation Dipeptides Peptides Neural Networks, Computer
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