Computational Methods for Predicting Post-Translational Modification Sites

ISBN-13: 9781071623169 / Angielski / Twarda / 2022

Opis

This volume describes computational approaches to predict multitudes of PTM sites. Chapters describe in depth approaches on algorithms, state-of-the-art Deep Learning based approaches, hand-crafted features, physico-chemical based features, issues related to obtaining negative training, sequence-based features, and structure-based features. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Authoritative and cutting-edge,  Computational Methods for Predicting Post-Translational Modification Sites aims to be a useful guide for researchers who are interested in the field of PTM site prediction. 

Spis treści

1.              Maximizing Depth of PTM Coverage: Generating Robust MS Datasets for Computational Prediction Modeling

Anthony A. Iannetta and Leslie M. Hicks

 

2.     PLDMS: Phosphopeptide Library Dephosphorylation followed by Mass Spectrometry Analysis to Determine the Specificity of Phosphatases for Dephosphorylation Site Sequences

Thomas Kokot, Bernhard Hoermann, Dominic Helm, Jeremy E. Chojnacki, Mikhail M. Savitski, and Maja Köhn

 

3.              FEPS: A tool for Feature Extraction from Protein Sequence

Hamid Ismail, Clarence White, Hussam AL-barakati, Robert H. Newman, and Dukka B. KC

 

4.              A pre-trained ELECTRA model for Kinase-specific Phosphorylation Site Prediction

Lei Jiang, Duolin Wang, and Dong Xu

 

5.              iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using structural-based Features

Abdollah Dehzangi, Alok Sharma, and Swakkhar Shatabda

 

6.              Functions of Glycosylation and Related Web Resources for its Prediction

Kiyoko F. Aoki-Kinoshita

 

7.              Analysis of Post-Translational Modifications in Arabidopsis Proteins and Metabolic Pathways using the FAT-PTM Database

Madison N. Blea and Ian S. Wallace

 

8.              Bioinformatic Analyses of Peroxiredoxins and RF-Prx: A RANDOM FOREST-BASED PREDICTOR and classifier for Prxs

Hussam AL-barakati, Robert H. Newman, Dukka B. KC, and Leslie B. Poole

 

9.              Computational prediction of N- and O-linked glycosylation sites for human and mouse proteins

Ghazaleh Taherzadeh, Matthew Campbell, and Yaoqi Zhou 

 

10.           iPTMnet RESTful API for Post-Translational Modification Network Analysis

Sachin Gavali, Karen E. Ross, Julie Cowart, Chuming Chen, and Cathy H. Wu

 

11.           Systematic Characterization of Lysine Post-Translational Modification Sites using MUscADEL

Zhen Chen, Xuhan Liu, Fuyi Li, Chen Li, Tatiana Marquez-Lago, André Leier, Geoffrey I. Webb, Dakang Xu, Tatsuya Akutsu, and Jiangning Song

 

12.           Enhancing the Discovery of Functional Post-Translational Modification Sites with Machine Learning Models – Development, Validation, and Interpretation

Nolan English and Matthew Torres

 

13.           Exploration of Protein Post-Translational Modification Landscape and Crosstalk with CrossTalkMapper

Arthur Grimaud, Frederik Holck, Louise Marie Buur, Rebecca Kirsch, and Veit Schwämmle

 

14.           PTM-X: Prediction of Post-Translational Modification Crosstalk Within and Across Proteins

Yuxuan Li,Yuanhua Huang, and Tingting Li

 

15.           Deep Learning-Based Advances In Protein Post-Translational Modification Site And Protein Cleavage Prediction

Subash C. Pakhrin, Suresh Pokharel, Hiroto Saigo, and Dukka B. KC