AIMC Topic: Amino Acids

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Multipolar Electrostatic Energy Prediction for all 20 Natural Amino Acids Using Kriging Machine Learning.

Journal of chemical theory and computation
A machine learning method called kriging is applied to the set of all 20 naturally occurring amino acids. Kriging models are built that predict electrostatic multipole moments for all topological atoms in any amino acid based on molecular geometry on...

Application of self-organising maps towards segmentation of soybean samples by determination of amino acids concentration.

Plant physiology and biochemistry : PPB
Soybeans are widely used both for human nutrition and animal feed, since they are an important source of protein, and they also provide components such as phytosterols, isoflavones, and amino acids. In this study, were determined the concentrations o...

Machine learning approaches for discrimination of Extracellular Matrix proteins using hybrid feature space.

Journal of theoretical biology
Extracellular Matrix (ECM) proteins are the vital type of proteins that are secreted by resident cells. ECM proteins perform several significant functions including adhesion, differentiation, cell migration and proliferation. In addition, ECM protein...

Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC.

Journal of theoretical biology
As a new type of post-translational modification, lysine phosphoglycerylation plays a key role in regulating glycolytic process and metabolism in cells. Due to the traditional experimental methods are time-consuming and labor-intensive, it is importa...

A Prediction Model for Membrane Proteins Using Moments Based Features.

BioMed research international
The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are...

Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11.

Scientific reports
Quality assessment of a protein model is to predict the absolute or relative quality of a protein model using computational methods before the native structure is available. Single-model methods only need one model as input and can predict the absolu...

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

The New phytologist
Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pat...

PRIdictor: Protein-RNA Interaction predictor.

Bio Systems
Several computational methods have been developed to predict RNA-binding sites in protein, but its inverse problem (i.e., predicting protein-binding sites in RNA) has received much less attention. Furthermore, most methods that predict RNA-binding si...

MDD-SOH: exploiting maximal dependence decomposition to identify S-sulfenylation sites with substrate motifs.

Bioinformatics (Oxford, England)
UNLABELLED: S-sulfenylation (S-sulphenylation, or sulfenic acid), the covalent attachment of S-hydroxyl (-SOH) to cysteine thiol, plays a significant role in redox regulation of protein functions. Although sulfenic acid is transient and labile, most ...

MATEPRED-A-SVM-Based Prediction Method for Multidrug And Toxin Extrusion (MATE) Proteins.

Computational biology and chemistry
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...