scholarly journals Applications of molecular modeling techniques in the design of xanthine based adenosine receptor antagonists and the development of the protein function annotation method SALSA

2014 ◽  
Author(s):  
Joslynn S. Lee
Keyword(s):  
Molecular Modeling ◽  
Adenosine Receptor ◽  
Protein Function ◽  
Receptor Antagonists ◽  
Function Annotation ◽  
Annotation Method ◽  
Protein Function Annotation ◽  
Modeling Techniques

Pyrazolo[1,5-c]quinazoline derivatives and their simplified analogues as adenosine receptor antagonists: Synthesis, structure–affinity relationships and molecular modeling studies

2013 ◽  
Vol 21 (1) ◽  
pp. 283-294 ◽  
Author(s):  
Daniela Catarzi ◽  
Vittoria Colotta ◽  
Flavia Varano ◽  
Daniela Poli ◽  
Lucia Squarcialupi ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
Adenosine Receptor ◽  
Receptor Antagonists ◽  
Modeling Studies ◽  
Molecular Modeling Studies ◽  
Quinazoline Derivatives

scholarly journals A combined approach for genome wide protein function annotation/prediction

2013 ◽  
Vol 11 (Suppl 1) ◽  
pp. S1 ◽  
Author(s):  
Alfredo Benso ◽  
Stefano Di Carlo ◽  
Hafeez ur Rehman ◽  
Gianfranco Politano ◽  
Alessandro Savino ◽  
...  
Keyword(s):  
Protein Function ◽  
Combined Approach ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Genome Wide

scholarly journals Protein function annotation using protein domain family resources

Methods ◽  
2016 ◽  
Vol 93 ◽  
pp. 24-34 ◽  
Author(s):  
Sayoni Das ◽  
Christine A. Orengo
Keyword(s):  
Protein Function ◽  
Protein Domain ◽  
Domain Family ◽  
Family Resources ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Protein Domain Family

Identifying the structural features and diversifying the chemical domain of peripherally acting CB1 receptor antagonists using molecular modeling techniques

RSC Advances ◽  
2016 ◽  
Vol 6 (2) ◽  
pp. 1466-1483 ◽  
Author(s):  
Mayank Kumar Sharma ◽  
Prashant R. Murumkar ◽  
Guanglin Kuang ◽  
Yun Tang ◽  
Mange Ram Yadav
Keyword(s):  
Molecular Modeling ◽  
Virtual Screening ◽  
3D Qsar ◽  
Structural Features ◽  
Cb1 Receptor ◽  
Receptor Antagonists ◽  
Modeling Techniques ◽  
Qsar Models ◽  
Cb1 Receptor Antagonists

A four featured pharmacophore and predictive 3D-QSAR models were developed which were used for virtual screening of the Asinex database to get chemically diverse hits of peripherally active CB1 receptor antagonists.

scholarly journals The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications

BMC Genomics ◽  
2008 ◽  
Vol 9 (Suppl 2) ◽  
pp. S2 ◽  
Author(s):  
Inbal Halperin ◽  
Dariya S Glazer ◽  
Shirley Wu ◽  
Russ B Altman
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Novel Applications

scholarly journals The development of PIPA: an integrated and automated pipeline for genome-wide protein function annotation

2008 ◽  
Vol 9 (1) ◽  
pp. 52 ◽  
Author(s):  
Chenggang Yu ◽  
Nela Zavaljevski ◽  
Valmik Desai ◽  
Seth Johnson ◽  
Fred J Stevens ◽  
...  
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation ◽  
Genome Wide ◽  
Automated Pipeline

Synthesis, Biological Studies and Molecular Modeling Investigation of 1,3-Dimethyl-2,4-dioxo-6-methyl-8-(substituted) 1,2,3,4-Tetrahydro-[1,2,4]-triazolo-[3,4-f]-purines as Potential Adenosine Receptor Antagonists.

ChemInform ◽  
2005 ◽  
Vol 36 (36) ◽  
Author(s):  
Giorgia Pastorin ◽  
Chiara Bolcato ◽  
Barbara Cacciari ◽  
Sonja Kachler ◽  
Karl-Norbert Klotz ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
Adenosine Receptor ◽  
Receptor Antagonists ◽  
Biological Studies

scholarly journals Improving Protein Function Annotation via Unsupervised Pre-training: Robustness, Efficiency, and Insights

2021 ◽  
Author(s):  
David Dohan ◽  
Andreea Gane ◽  
Maxwell L. Bileschi ◽  
David Belanger ◽  
Lucy Colwell
Keyword(s):  
Protein Function ◽  
Function Annotation ◽  
Protein Function Annotation

scholarly journals Structure–Activity Relationships and Molecular Modeling of 1,2,4-Triazoles as Adenosine Receptor Antagonists

2012 ◽  
Vol 3 (9) ◽  
pp. 715-720 ◽  
Author(s):  
Jens Carlsson ◽  
Dilip K. Tosh ◽  
Khai Phan ◽  
Zhan-Guo Gao ◽  
Kenneth A. Jacobson
Keyword(s):  
Molecular Modeling ◽  
Adenosine Receptor ◽  
Receptor Antagonists ◽  
Structure Activity Relationships ◽  
Structure Activity

scholarly journals Protein functional annotation of simultaneously improved stability, accuracy and false discovery rate achieved by a sequence-based deep learning

2019 ◽  
Vol 21 (4) ◽  
pp. 1437-1447 ◽  
Author(s):  
Jiajun Hong ◽  
Yongchao Luo ◽  
Yang Zhang ◽  
Junbiao Ying ◽  
Weiwei Xue ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Discovery Rate ◽  
Protein Function ◽  
Functional Annotation ◽  
De Novo ◽  
Learning Algorithm ◽  
Function Annotation ◽  
False Discovery ◽  
Protein Function Annotation ◽  
Annotation Accuracy

Abstract Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

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