The Biological Structure Model Archive (BSM-Arc): an Archive for in Silico Models and Simulations

Overview

Journal Biophys Rev

Publisher Springer

Specialty Biophysics

Date 2020 Feb 7

PMID 32026396

Citations 27

Authors

Gert-Jan Bekker

Takeshi Kawabata

Genji Kurisu

Affiliations

Soon will be listed here.

Abstract

We present the Biological Structure Model Archive (BSM-Arc, https://bsma.pdbj.org), which aims to collect raw data obtained via in silico methods related to structural biology, such as computationally modeled 3D structures and molecular dynamics trajectories. Since BSM-Arc does not enforce a specific data format for the raw data, depositors are free to upload their data without any prior conversion. Besides uploading raw data, BSM-Arc enables depositors to annotate their data with additional explanations and figures. Furthermore, via our WebGL-based molecular viewer Molmil, it is possible to recreate 3D scenes as shown in the corresponding scientific article in an interactive manner. To submit a new entry, depositors require an ORCID ID to login, and to finally publish the data, an accompanying peer-reviewed paper describing the work must be associated with the entry. Submitting their data enables researchers to not only have an external backup but also provide an opportunity to promote their work via an interactive platform and to provide third-party researchers access to their raw data.

Citing Articles

Protein Data Bank Japan: Improved tools for sequence-oriented analysis of protein structures.

Bekker G, Nagao C, Shirota M, Nakamura T, Katayama T, Kihara D Protein Sci. 2025; 34(3):e70052.

PMID: 39969112 PMC: 11837027. DOI: 10.1002/pro.70052.

Effect of Organic Anion Transporting Polypeptide 1B1 on Plasma Concentration Dynamics of Clozapine in Patients with Treatment-Resistant Schizophrenia.

Sato T, Kawabata T, Kumondai M, Hayashi N, Komatsu H, Kikuchi Y Int J Mol Sci. 2024; 25(23).

PMID: 39684938 PMC: 11642635. DOI: 10.3390/ijms252313228.

Pathways to a Shiny Future: Building the Foundation for Computational Physical Chemistry and Biophysics in 2050.

Biriukov D, Vacha R ACS Phys Chem Au. 2024; 4(4):302-313.

PMID: 39069976 PMC: 11274290. DOI: 10.1021/acsphyschemau.4c00003.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations.

Matsumoto S, Ishida S, Terayama K, Okuno Y Biophys Physicobiol. 2024; 20(2):e200022.

PMID: 38496243 PMC: 10941960. DOI: 10.2142/biophysico.bppb-v20.0022.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction.

Paul R, Kasahara K, Sasaki J, Perez J, Matsunaga R, Hashiguchi T Front Mol Biosci. 2024; 10:1302737.

PMID: 38495738 PMC: 10941800. DOI: 10.3389/fmolb.2023.1302737.

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