Research interests
Here you will find some of the major research lines explored in the lab, ranging from machine learning applied to protein dynamics, to understanding the effects of protein regulation on cellular metabolism.
Macromolecular Complex dynamics
Using dynamics to understand protein interactions and function
Protein interactions are essential for regulation of cellular processes, from the formation of multi-protein complexes to the allosteric activation of enzymes. Computational biochemistry allows us to identify the essential residues and dynamic features that regulate such interactions, paving the way for a better understanding of diverse biochemical processes. Among multiple applications, this research allows for suppression of a reaction through drug interventions or optimization of a chemical process using bio-engineered molecules.
DYnamical Network analysis
Encoding biomolecular dynamics in correlation graphs
The Dynamical Network Analysis Python package (package repo) provides all functionalities necessary to analyse Molecular Dynamics (MD) simulations by calculating generalized correlation coefficients and quantifying the correlation between the movement of neighboring residues. The resulting correlation graph can be used to characterize small-molecule binding to enzymatic active sites, the interface between complex subunits, and the mechanical properties of cellular anchoring proteins and viral capsid binders.
Hybrid QM/MM Simulations
Integrating QM and MM potentials in Molecular Dynamics simulations
The NAMD QM/MM interface extends existing NAMD features to the quantum mechanical level, presenting new features and possibilities for the world of computational chemistry. Investigations of processes occurring on a timescale usually not accessible by QM/MM methods can be performed by combining enhanced sampling and free energy calculation method already present in NAMD. Taking advantage of an easy-to-use Tcl based interface and capabilities integrated from VMD, this interface has the ability to execute multiple QM regions in parallel, thorough independent executions of your choice of quantum chemistry code.
Cellular and community Metabolism
Simulating whole-cell metabolism to understand microbial communities
Hundreds of microbial species have been identified in our gut, skin, and oral microbiome, however we still lack tools to precisely probe and engineer this community. Using systems biology tools to describe the interactions between microbial species is essential to develop predictive and quantitative models of our microbiome.
Guiding Principles
We will contribute to community efforts towards improving reproducibility, reliability, and cooperation in scientific research by following our guiding principles.
