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Amino Acids

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Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures.

Amino acids
Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be obtained from known protein structures. The pairwise potential between ato...

Effects of combining modern recovery techniques with neurotrophic medication and standard treatment in stroke patients.

The Science of the total environment
Stroke is the main cause of disability after age 65, leaving survivors with sequels that require care and recovery treatment lasting years. It is estimated that by the year 2030 this pathology will be leading cause of mortality. To determine the effi...

Prediction of aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier.

Mathematical biosciences
Aptamer-protein interacting pairs play important roles in physiological functions and structural characterization. Identifying aptamer-protein interacting pairs is challenging and limited, despite of the tremendous applications of aptamers. Therefore...

Intact metabolite spectrum mining by deep learning in proton magnetic resonance spectroscopy of the brain.

Magnetic resonance in medicine
PURPOSE: To develop a robust method for brain metabolite quantification in proton magnetic resonance spectroscopy ( H-MRS) using a convolutional neural network (CNN) that maps in vivo brain spectra that are typically degraded by low SNR, line broaden...

Prediction of apoptosis protein subcellular localization via heterogeneous features and hierarchical extreme learning machine.

SAR and QSAR in environmental research
Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting the subcellular location of apoptosis proteins is very helpful for understanding the mechanism of progra...

Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites.

Genomics, proteomics & bioinformatics
As a newly-identified protein post-translational modification, malonylation is involved in a variety of biological functions. Recognizing malonylation sites in substrates represents an initial but crucial step in elucidating the molecular mechanisms ...

Toward Building Protein Force Fields by Residue-Based Systematic Molecular Fragmentation and Neural Network.

Journal of chemical theory and computation
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...

Potential identification of vitamin B6 responsiveness in autism spectrum disorder utilizing phenotype variables and machine learning methods.

Scientific reports
We investigated whether machine learning methods could potentially identify a subgroup of persons with autism spectrum disorder (ASD) who show vitamin B6 responsiveness by selected phenotype variables. We analyzed the existing data from our intervent...

DeepNitro: Prediction of Protein Nitration and Nitrosylation Sites by Deep Learning.

Genomics, proteomics & bioinformatics
Protein nitration and nitrosylation are essential post-translational modifications (PTMs) involved in many fundamental cellular processes. Recent studies have revealed that excessive levels of nitration and nitrosylation in some critical proteins are...

KELM-CPPpred: Kernel Extreme Learning Machine Based Prediction Model for Cell-Penetrating Peptides.

Journal of proteome research
Cell-penetrating peptides (CPPs) facilitate the transport of pharmacologically active molecules, such as plasmid DNA, short interfering RNA, nanoparticles, and small peptides. The accurate identification of new and unique CPPs is the initial step to ...