RCSB Protein Data Bank 1D3D module: displaying positional features on macromolecular assemblies

Author:

Segura Joan1ORCID,Rose Yana1,Bittrich Sebastian1ORCID,Burley Stephen K12345,Duarte Jose M1ORCID

Affiliation:

1. Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California , La Jolla, CA 92093, USA

2. Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey , Piscataway, NJ 08854, USA

3. Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey , Piscataway, NJ 08854, USA

4. Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey , Piscataway, NJ 08854, USA

5. Cancer Institute of New Jersey, Rutgers, The State University of New Jersey , New Brunswick, NJ 08901, USA

Abstract

Abstract Motivation Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. Results We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies. Availability and implementation https://github.com/rcsb/rcsb-saguaro-3d. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Science Foundation

US Department of Energy

National Cancer Institute, National Institute of Allergy and Infectious Diseases

National Institute of General Medical Sciences

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference10 articles.

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4. Highly accurate protein structure prediction with AlphaFold;Jumper;Nature,2021

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