AIMC Topic: Amino Acid Sequence
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Amino acid composition predicts prion activity.
PLoS computational biology
Apr 10, 2017
Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, o...
Structural and Sequence Similarity Makes a Significant Impact on Machine-Learning-Based Scoring Functions for Protein-Ligand Interactions.
Journal of chemical information and modeling
Apr 5, 2017
The prediction of protein-ligand binding affinity has recently been improved remarkably by machine-learning-based scoring functions. For example, using a set of simple descriptors representing the atomic distance counts, the RF-Score improves the Pea...
Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.
Journal of theoretical biology
Mar 27, 2017
Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid ...
InterPred: A pipeline to identify and model protein-protein interactions.
Proteins
Mar 21, 2017
Protein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the numb...
Identify and analysis crotonylation sites in histone by using support vector machines.
Artificial intelligence in medicine
Mar 7, 2017
OBJECTIVE: Lysine crotonylation (Kcr) is a newly discovered histone posttranslational modification, which is specifically enriched at active gene promoters and potential enhancers in mammalian cell genomes. Although lysine crotonylation sites can be ...
Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.
Artificial intelligence in medicine
Mar 4, 2017
Computational methods are employed in bioinformatics to predict protein-protein interactions (PPIs). PPIs and protein-protein non-interactions (PPNIs) display different levels of development, and the number of PPIs is considerably greater than that o...
HemoPred: a web server for predicting the hemolytic activity of peptides.
Future medicinal chemistry
Feb 17, 2017
AIM: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.
PSPEL: In Silico Prediction of Self-Interacting Proteins from Amino Acids Sequences Using Ensemble Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Jan 10, 2017
Self interacting proteins (SIPs) play an important role in various aspects of the structural and functional organization of the cell. Detecting SIPs is one of the most important issues in current molecular biology. Although a large number of SIPs dat...
Improved protein-protein interactions prediction via weighted sparse representation model combining continuous wavelet descriptor and PseAA composition.
BMC systems biology
Dec 23, 2016
BACKGROUND: Protein-protein interactions (PPIs) are essential to most biological processes. Since bioscience has entered into the era of genome and proteome, there is a growing demand for the knowledge about PPI network. High-throughput biological te...
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.
BMC systems biology
Dec 23, 2016
BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only o...
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