AIMC Topic: Dipeptides

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Robosample: A rigid-body molecular simulation program based on robot mechanics.

Biochimica et biophysica acta. General subjects Apr 13, 2020
BACKGROUND: Compared with all-atom molecular dynamics (MD), constrained MD methods allow for larger time steps, potentially reducing computational cost. For this reason, there has been continued interest in improving constrained MD algorithms to incr...
Software Robotics Molecular Dynamics Simulation Dipeptides Monte Carlo Method Alanine
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Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation Jan 7, 2019
Accurate force fields are crucial for molecular dynamics investigation of complex biological systems. Building accurate protein force fields from quantum mechanical (QM) calculations is challenging due to the complexity of proteins and high computati...
Peptides Proteins Models, Chemical Amino Acids Neural Networks, Computer Thermodynamics Molecular Dynamics Simulation Quantum Theory Dipeptides Static Electricity
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Accurate prediction of Gram-negative bacterial secreted protein types by fusing multiple statistical features from PSI-BLAST profile.

SAR and QSAR in environmental research Apr 24, 2018
Gram-negative bacterial secreted proteins play different roles in invaded eukaryotic cells and cause various diseases. Prediction of Gram-negative bacterial secreted protein types is a meaningful and challenging task. In this paper, we develop a mult...
Dipeptides Gram-Negative Bacteria Support Vector Machine Models, Statistical Structure-Activity Relationship Bacterial Proteins
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Specific chemiluminescent protocol for dual-site recognition of Streptococcus mutans utilizing strong affinity between teicoplanin and Gram-positive bacteria.

Talanta Nov 6, 2017
A novel dual-site recognition protocol was developed for chemiluminescent (CL) detection of Streptococcus mutans (S. mutans) based on a designed antibiotic-affinity strategy. Teicoplanin, a broad-spectrum antibiotic against Gram-positive bacteria, wa...
Teicoplanin Animals Horseradish Peroxidase Dipeptides Fresh Water Luminescent Measurements Iodobenzenes Hydrogen Peroxide Limit of Detection Milk Protein Binding Luminol Humans Magnetite Nanoparticles Cell Wall Streptococcus mutans Rats Anti-Bacterial Agents Bacterial Proteins Saliva Immunoglobulin G
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In silico identification of milk antihypertensive di- and tripeptides involved in angiotensin I-converting enzyme inhibitory activity.

Nutrition research (New York, N.Y.) Jul 28, 2017
Identification of bioactive milk peptides could improve food technology through improved selection of food supplements with a focus on antihypertensive properties. We hypothesized that angiotensin I-converting enzyme (ACE) inhibitory activities of mi...
Peptidyl-Dipeptidase A Proteolysis Binding Sites Milk Proteins Angiotensin-Converting Enzyme Inhibitors Quantitative Structure-Activity Relationship Protein Conformation Hydrogen Bonding Expert Systems Catalytic Domain Oligopeptides Cattle Humans Computational Biology Models, Molecular Animals Dietary Supplements Molecular Docking Simulation Peptide Fragments Antihypertensive Agents Dipeptides Hydrophobic and Hydrophilic Interactions
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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 Algorithms ROC Curve Computational Biology Databases, Protein Antifreeze Proteins Dipeptides Sequence Analysis, 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...
Reproducibility of Results Drug Resistance Organic Cation Transport Proteins Position-Specific Scoring Matrices Support Vector Machine Amino Acids Dipeptides Hydrophobic and Hydrophilic Interactions
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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 ...
Molecular Sequence Data HSP70 Heat-Shock Proteins Sequence Alignment Sequence Analysis, Protein Amino Acid Sequence Heat-Shock Proteins Dipeptides Machine Learning Pattern Recognition, Automated Algorithms Reproducibility of Results Sensitivity and Specificity
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Machine Learning-based Dose Prediction in [Lu]Lu-PSMA-617 Therapy by Integrating Biomarkers and Radiomic Features from [Ga]Ga-PSMA-11 Positron Emission Tomography/Computed Tomography.

International journal of radiation oncology, biology, physics Nov 1, 2025
PURPOSE: The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for patients with metastatic castration-resistant prostate cancer (mCRPC) undergoing [Lu]Lu-PSMA-617 (Lu...
Prostate-Specific Antigen Radiopharmaceuticals Dipeptides Prostatic Neoplasms, Castration-Resistant Male Humans Middle Aged Gallium Radioisotopes Kidney Machine Learning Gallium Isotopes Radiotherapy Dosage Positron Emission Tomography Computed Tomography Lutetium Aged Monte Carlo Method Heterocyclic Compounds, 1-Ring Radiomics Radioisotopes Biomarkers, Tumor Organometallic Compounds
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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...
Quantum Theory Dipeptides Machine Learning Static Electricity Water Proteins Molecular Dynamics Simulation
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