3BIO- BioInfo

 

The 3BIO – BioInfo group, or Computational Biology and Bioinformatics group, is specialized in structural and evolutionary bioinformatics and computational systems biology. In the latter topic, the team utilizes the formalism of deterministic and stochastic differential equations to model a variety of biological systems, such as the dysregulation of the human renin-angiotensin system upon SARS-CoV-2 infection, or the relation between intrinsic noise and complexity of generic systems.

In the field of structural bioinformatics, the team’s research is mainly centered on the development of knowledge-based, physics-driven, software tools for predicting and analyzing protein structure, stability, interactions and function, and on the application of these computational tools to biological systems of interest in collaboration with experimenters. Most of their methods are based on coarse-grained protein structure representations and force fields, which allow fast, large-scale, analyses, while maintaining a good level of accuracy. With the help of free energy evaluations using statistical potentials and artificial neural network techniques, they developed original and efficient methods for predicting protein stability (SCooP), as well as thermodynamic stability changes (PoPMuSiC), thermal stability changes (HoTMuSiC), and affinity changes (BeATMuSiC) upon point mutations. They also developed methods for identifying deleterious, disease-causing mutations (SNPMuSiC). The team applies these software tools to the rational design of modified proteins, with valuable results. The developed prediction tools are available to the scientific community on the websites dezyme.commutaframe.com and babylone.3bio.ulb.ac.be

Learn more about the lab in this video 👇

3BIO-BioInfo ULB

CONTACT

 

Website

Marianne ROOMAN
Email: marianne.rooman@ulb.ac.be
Phone: +32 2 650 2067

Business contact:

Ronnie RAEYMAEKERS
Email: ronnie@icity.brussels
Phone: +32 2 650 2002

 

 

TAGS

 

Bioinformatics, Computational systems biology, Machine learning, Molecular biophysics, Protein design, Structural bioinformatics

 

 

 

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