Welcome to ConfID!

Conformational generation is a recurrent challenge in early phases of drug design, mostly due to the task of making sense between the number of conformers generated and their relevance for biological purposes.

In this sense, ConfID, a Python-based computational tool, was designed to identify and characterize conformational populations of drug-like molecules sampled through molecular dynamics simulations.

By using molecular dynamics (MD) simulations (and assuming accurate parameters are used), ConfID can identify all conformational populations sampled in the presence of solvent and quantify their relative abundance, while harnessing the benefits of MD and calculating time-dependent properties of each conformational population identified.

• ConfID homepage: http://sbcb.inf.ufrgs.br/confid
• For installation instructions, please read INSTALL.md.
• To download ConfID from snapcraft: https://snapcraft.io/confid
• For usage and configuration instructions, please read the ConfID manual.
• For tutorials please read TUTORIAL.md.
• For the application note: https://doi.org/10.1093/bioinformatics/btaa130

Have a nice "ConfIDent" analysis! =)

[Update 20/12/2020] ConfID 1.2.1 is now available! New in this version is the option to set the window length and function in the config file. For more details check the documentation.

It is a Python-based computational tool designed to identify and characterize conformational populations of small molecules sampled through molecular dynamics simulations.

ConfID was developed by:

• Bruno I. Grisci - PhD student (Institute of Informatics - UFRGS)
• Marcelo D. Polêto - Postdoctoral Researcher (General Biology Department - UFV)
• Marcio Dorn - Adjunct Professor (Institute of Informatics - UFRGS)
• Hugo Verli - Associate Professor (Center of Biotechnology - UFRGS)

Genetic algorithms and knowledge-based approaches have been employed to study molecular flexibility. However, these methods are usually based on crystallographic information, and their calculations are made in vacuum or with implicit solvent and do not take into account the influence of explicit solvent molecules on conformational preferences.

By using MD simulations (and assuming accurate parameters are used), ConfID can identify all conformational populations sampled in the presence of solvent and quantify their relative abundance, while harnessing the benefits of MD and calculating time-dependent properties of each conformational population.

a) Two analogue ligands in water

Do you have any questions? Take a look on our FAQ!

There are some papers already using ConfID! These are some:

• Feng, X., Li, F., Ding, M., Zhang, R., and Shi, T. Molecular Dynamic Simulation: Conformational Properties of Single-stranded Curdlan in Aqueous Solution, Carbohydrate Polymers 2020 116906, DOI: 10.1016/j.carbpol.2020.116906

• Pablo R. Arantes, Conrado Pedebos, Marcelo D. Polêto, Laércio Pol-Fachin, and Hugo Verli. The Lazy Life of Lipid-Linked Oligosaccharides in All Life Domains, Journal of Chemical Information and Modeling 2020 60 (2), 631-643, DOI: 10.1021/acs.jcim.9b00904

• Pablo R. Arantes, Marcelo D. Polêto, Elisa B. O. John, Conrado Pedebos, Bruno I. Grisci, Marcio Dorn, and Hugo Verli. Development of GROMOS-Compatible Parameter Set for Simulations of Chalcones and Flavonoids, The Journal of Physical Chemistry B 2019 123 (5), 994-1008, DOI: 10.1021/acs.jpcb.8b10139

• Roberta Tesch, Christian Becker, Matthias P. Müller, Michael E. Beck, Lena Quambusch, Matthäus Getlik, Jonas Lategahn, Niklas Uhlenbrock, Fanny N. Costa, Marcelo D. Polêto, Pedro S.M. Pinheiro, Daniel A. Rodrigues, Carlos M.R. Sant'Anna, Fabio F. Ferreira, Hugo Verli, Carlos A.M. Fraga, Daniel Rauh. An Unusual Intramolecular Halogen Bond Guides Conformational Selection, Angew. Chem. Int. Ed. 2018, 57, 9970, DOI: 10.1002/anie.201804917

If you use ConfID in a scientific publication, we would appreciate citations to the following paper:

Marcelo D. Polêto, Bruno I. Grisci, Marcio Dorn, Hugo Verli. ConfID: an analytical method for conformational characterization of small molecules using molecular dynamics trajectories, Bioinformatics, 2020, btaa130, DOI: 10.1093/bioinformatics/btaa130

Bibtex entry:

@article{10.1093/bioinformatics/btaa130,
    author = {Polêto, M D and Grisci, B I and Dorn, M and Verli, H},
    title = "{ConfID: an analytical method for conformational characterization of small molecules using molecular dynamics trajectories}",
    journal = {Bioinformatics},
    year = {2020},
    month = {02},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa130},
    url = {https://doi.org/10.1093/bioinformatics/btaa130},
    note = {btaa130},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa130/32677172/btaa130.pdf},
}

ConfID is registered at Instituto Nacional da Propriedade Industrial (INPI) under the number BR512019001928-8 and is freely available under the license LGPL-3.0.

Marcelo D. Polêto, Bruno Iochins Grisci, Marcio Dorn, Hugo Verli

• Centro de Biotecnologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
• Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
• Departamento de Biologia Geral, Universidade Federal de Viçosa, Viçosa, MG, Brazil

E-mail: [email protected] / [email protected]

http://sbcb.inf.ufrgs.br/confid