AIMC Topic: Databases, Protein

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Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.

PloS one
In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolu...

Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...

SecProMTB: Support Vector Machine-Based Classifier for Secretory Proteins Using Imbalanced Data Sets Applied to Mycobacterium tuberculosis.

Proteomics
Secretory proteins of Mycobacterium tuberculosis have created more concern, given their dominant immunogenicity and role in pathogenesis. In view of expensive and time-consuming traditional biochemical experiments, an advanced support vector machine ...

Efficient utilization on PSSM combining with recurrent neural network for membrane protein types prediction.

Computational biology and chemistry
Position-Specific Scoring Matrix (PSSM) is an excellent feature extraction method that was proposed early in protein classifying prediction, but within the restriction of feature shape in PSSM, researchers make a lot attempts to process it so that PS...

Sequence assignment for low-resolution modelling of protein crystal structures.

Acta crystallographica. Section D, Structural biology
The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for...

Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...

Analysis of Protein-Protein Functional Associations by Using Gene Ontology and KEGG Pathway.

BioMed research international
Protein-protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used g...

An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model.

International journal of molecular sciences
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins....

Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network.

Journal of molecular graphics & modelling
Membrane proteins, the most important drug targets, account for around 30% of total proteins encoded by the genome of living organisms. An important role of these proteins is to bind adenosine triphosphate (ATP), facilitating crucial biological proce...

AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition...