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Databases, Protein

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DPP-PseAAC: A DNA-binding protein prediction model using Chou's general PseAAC.

Journal of theoretical biology
A DNA-binding protein (DNA-BP) is a protein that can bind and interact with a DNA. Identification of DNA-BPs using experimental methods is expensive as well as time consuming. As such, fast and accurate computational methods are sought for predicting...

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

BMC bioinformatics
BACKGROUND: Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structu...

RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.

BMC bioinformatics
BACKGROUND: Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary...

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

Journal of proteome research
Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary b...

PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

International journal of molecular sciences
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular...

PON-tstab: Protein Variant Stability Predictor. Importance of Training Data Quality.

International journal of molecular sciences
Several methods have been developed to predict effects of amino acid substitutions on protein stability. Benchmark datasets are essential for method training and testing and have numerous requirements including that the data is representative for the...

SPIN2: Predicting sequence profiles from protein structures using deep neural networks.

Proteins
Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native bac...

Prediction of Apoptosis Protein's Subcellular Localization by Fusing Two Different Descriptors Based on Evolutionary Information.

Acta biotheoretica
The apoptosis protein has a central role in the development and the homeostasis of an organism. Obtaining information about the subcellular localization of apoptosis protein is very helpful to understand the apoptosis mechanism and the function of th...

Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains.

Journal of theoretical biology
The receptor-associated protein (RAP) is an inhibitor of endocytic receptors that belong to the lipoprotein receptor gene family. In this study, a computational approach was tried to find the evolutionarily related fold of the RAP proteins. Through t...

Knowledge-Based Conformer Generation Using the Cambridge Structural Database.

Journal of chemical information and modeling
Fast generation of plausible molecular conformations is central to molecular modeling. This paper presents an approach to conformer generation that makes extensive use of the information available in the Cambridge Structural Database. By using geomet...