scholarly journals Peptide Bioinformatics- Peptide Classification Using Peptide Machines

2008 ◽  
pp. 155-179
Author(s):  
Zheng Rong Yang
Keyword(s):  
Peptide Classification

scholarly journals NeuRiPP: Neural network identification of RiPP precursor peptides

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Emmanuel L. C. de los Santos
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Gene Clusters ◽  
Learning Tools ◽  
Data Set ◽  
Modified Peptides ◽  
The Rich ◽  
Tailoring Enzymes ◽  
Rich Data ◽  
Peptide Classification

Abstract Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both RiPP tailoring enzymes (RTEs) and RiPP precursor peptides (PPs). However, identification of PPs, particularly for novel RiPP classes remains challenging. To address this, machine learning has been used to accurately identify PP sequences. Current machine learning tools have limitations, since they are specific to the RiPPclass they are trained for and are context-dependent, requiring information about the surrounding genetic environment of the putative PP sequences. NeuRiPP overcomes these limitations. It does this by leveraging the rich data set of high-confidence putative PP sequences from existing programs, along with experimentally verified PPs from RiPP databases. NeuRiPP uses neural network archictectures that are suitable for peptide classification with weights trained on PP datasets. It is able to identify known PP sequences, and sequences that are likely PPs. When tested on existing RiPP BGC datasets, NeuRiPP was able to identify PP sequences in significantly more putative RiPP clusters than current tools while maintaining the same HMM hit accuracy. Finally, NeuRiPP was able to successfully identify PP sequences from novel RiPP classes that were recently characterized experimentally, highlighting its utility in complementing existing bioinformatics tools.

LIP Index for Peptide Classification Using MS/MS and SEQUEST Search via Logistic Regression

2004 ◽  
Vol 8 (4) ◽  
pp. 357-369 ◽  
Author(s):  
Roger Higdon ◽  
Natali Kolker ◽  
Alex Picone ◽  
Gerald Van Belle ◽  
Eugene Kolker
Keyword(s):  
Logistic Regression ◽  
Sequest Search ◽  
Peptide Classification

Machine learning multi-classifiers for peptide classification

2008 ◽  
Vol 18 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini
Keyword(s):  
Machine Learning ◽  
Peptide Classification

scholarly journals Peptide classification using optimal and information theoretic syntactic modeling

2010 ◽  
Vol 43 (11) ◽  
pp. 3891-3899 ◽  
Author(s):  
E. Aygün ◽  
B.J. Oommen ◽  
Z. Cataltepe
Keyword(s):  
Information Theoretic ◽  
Peptide Classification

A new concept in polytopic membrane proteins following from the study of band 3 protein

1998 ◽  
Vol 76 (5) ◽  
pp. 729-733 ◽  
Author(s):  
Naotaka Hamasaki ◽  
Hiroyuki Kuma ◽  
Kazuhisa Ota ◽  
Masao Sakaguchi ◽  
Katsuyoshi Mihara
Keyword(s):  
Membrane Proteins ◽  
Transmembrane Domain ◽  
Band 3 ◽  
Band 3 Protein ◽  
Transmembrane Peptides ◽  
Lipid Interactions ◽  
Transmembrane Segments ◽  
Novel Concept ◽  
Polytopic Membrane Protein ◽  
Peptide Classification

In the present communication, we introduce a novel concept in multispanning polytopic membrane proteins revealed by the study of the band 3 protein. The transmembrane domain of such proteins can be divided into three categories, that is, hydrophilic loops connecting transmembrane peptides (category 1), portions embedded by peptide-peptide interactions (category 2), and portions embedded by peptide-lipid interactions (category 3). Category 2 peptides of polytopic membrane proteins were found to stably reside in the lipid bilayer without peptide-lipid interactions that had been thought to be essential for transmembrane segments. Category 3 peptides are equivalent to single-spanning segments of bitopic membrane proteins. Three different experiments, namely proteolytic digestion, chemical modification of the band 3 protein, and cell free transcription and translation, were used to categorize the transmembrane peptides.Key words: band 3 protein, transmembrane (TM) peptide, classification of TM, category 2-TM, polytopic membrane protein.

A new encoding technique for peptide classification

2011 ◽  
Vol 38 (4) ◽  
pp. 3185-3191 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini
Keyword(s):  
Peptide Classification

Application of mutual information estimation for predicting the structural stability of pentapeptides

2020 ◽  
Vol 35 (5) ◽  
pp. 263-271
Author(s):  
A. I. Mikhalskii ◽  
I. V. Petrov ◽  
V. V. Tsurko ◽  
A. A. Anashkina ◽  
A. N. Nekrasov
Keyword(s):  
Feature Selection ◽  
Mutual Information ◽  
Structural Stability ◽  
Parametric Method ◽  
Informative Feature ◽  
Short Peptide ◽  
Regression Problems ◽  
Classification And Regression ◽  
Peptide Classification ◽  
Non Parametric

AbstractA novel non-parametric method for mutual information estimation is presented. The method is suited for informative feature selection in classification and regression problems. Performance of the method is demonstrated on problem of stable short peptide classification.

Sign in / Sign up

Export Citation Format

Share Document