Toward Computer-Based Cleavage Site Prediction of Cysteine Endopeptidases

2003 ◽  
Vol 384 (6) ◽  
Author(s):  
T. Lohmüller ◽  
D. Wenzler ◽  
S. Hagemann ◽  
W. Kieß ◽  
C. Peters ◽  
...  
Keyword(s):  
Cleavage Site ◽  
Site Prediction ◽  
Computer Based ◽  
Cysteine Endopeptidases ◽  
Cleavage Site Prediction

HIV-1 protease cleavage site prediction based on amino acid property

2009 ◽  
Vol 30 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Bing Niu ◽  
Lin Lu ◽  
Liang Liu ◽  
Tian Hong Gu ◽  
Kai-Yan Feng ◽  
...  
Keyword(s):  
Amino Acid ◽  
Cleavage Site ◽  
Amino Acid Property ◽  
Acid Property ◽  
Site Prediction ◽  
Protease Cleavage Site ◽  
Protease Cleavage ◽  
Cleavage Site Prediction ◽  
Hiv 1

scholarly journals Twenty years of bioinformatics research for protease-specific substrate and cleavage site prediction: a comprehensive revisit and benchmarking of existing methods

2018 ◽  
Vol 20 (6) ◽  
pp. 2150-2166 ◽  
Author(s):  
Fuyi Li ◽  
Yanan Wang ◽  
Chen Li ◽  
Tatiana T Marquez-Lago ◽  
André Leier ◽  
...  
Keyword(s):  
Cleavage Site ◽  
Scoring Function ◽  
Proteolytic Cleavage ◽  
Prediction Performance ◽  
Specific Substrate ◽  
Cleavage Sites ◽  
Cell Cycle Protein ◽  
Computational Approaches ◽  
Site Prediction ◽  
Cleavage Site Prediction

Abstract The roles of proteolytic cleavage have been intensively investigated and discussed during the past two decades. This irreversible chemical process has been frequently reported to influence a number of crucial biological processes (BPs), such as cell cycle, protein regulation and inflammation. A number of advanced studies have been published aiming at deciphering the mechanisms of proteolytic cleavage. Given its significance and the large number of functionally enriched substrates targeted by specific proteases, many computational approaches have been established for accurate prediction of protease-specific substrates and their cleavage sites. Consequently, there is an urgent need to systematically assess the state-of-the-art computational approaches for protease-specific cleavage site prediction to further advance the existing methodologies and to improve the prediction performance. With this goal in mind, in this article, we carefully evaluated a total of 19 computational methods (including 8 scoring function-based methods and 11 machine learning-based methods) in terms of their underlying algorithm, calculated features, performance evaluation and software usability. Then, extensive independent tests were performed to assess the robustness and scalability of the reviewed methods using our carefully prepared independent test data sets with 3641 cleavage sites (specific to 10 proteases). The comparative experimental results demonstrate that PROSPERous is the most accurate generic method for predicting eight protease-specific cleavage sites, while GPS-CCD and LabCaS outperformed other predictors for calpain-specific cleavage sites. Based on our review, we then outlined some potential ways to improve the prediction performance and ease the computational burden by applying ensemble learning, deep learning, positive unlabeled learning and parallel and distributed computing techniques. We anticipate that our study will serve as a practical and useful guide for interested readers to further advance next-generation bioinformatics tools for protease-specific cleavage site prediction.

REDUCED BIO-BASIS FUNCTION NEURAL NETWORKS FOR PROTEASE CLEAVAGE SITE PREDICTION

2004 ◽  
Vol 02 (03) ◽  
pp. 511-531 ◽  
Author(s):  
ZHENG RONG YANG ◽  
EMILY A. BERRY
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Cleavage Site ◽  
Learning Algorithm ◽  
Site Prediction ◽  
Protease Cleavage Site ◽  
Protease Cleavage ◽  
Radial Basis ◽  
Cleavage Site Prediction

This paper presents a new neural learning algorithm for protease cleavage site prediction. The basic idea is to replace the radial basis function used in radial basis function neural networks by a so-called bio-basis function using amino acid similarity matrices. Mutual information is used to select bio-bases and a corresponding selection algorithm is developed. The algorithm has been applied to the prediction of HIV and Hepatitis C virus protease cleavage sites in proteins with success.

HIV-1 Protease Cleavage Site Prediction Based on Two-Stage Feature Selection Method

2013 ◽  
Vol 20 (3) ◽  
pp. 290-298 ◽  
Author(s):  
Bing Niu ◽  
Xiao-Cheng Yuan ◽  
Preston Roeper ◽  
Qiang Su ◽  
Chun-Rong Peng ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cleavage Site ◽  
Feature Selection Method ◽  
Selection Method ◽  
Two Stage ◽  
Site Prediction ◽  
Protease Cleavage Site ◽  
Protease Cleavage ◽  
Cleavage Site Prediction ◽  
Hiv 1
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