Marcelo C. R. Melo

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Link to my Google Scholar profile.

I am currently a postdoctoral researcher in the Perelman School of Medicine at the University of Pennsylvania, advised by Professor Cesar de la Fuente. My work focuses on exploring the Gut-Brain axis from a computational biology perspective, combining machine learning with systems- and structural-biology approaches.


  • Ph.D. in Biophysics (2019) – University of Illinois at Urbana-Champaign – USA
  • M.S. in Biophysics (2013) – Federal University of Rio de Janeiro – Brazil
  • B.S. in Biophysics (2011) – Federal University of Rio de Janeiro – Brazil

Research Interests

  • Biological Systems
    • Neuroactive Peptides
    • Antimicrobial Peptides
    • Cellular Metabolism
  • Computational Methods Development
    • Machine Learning
    • Systems Biology
    • Hybrid QM/MM Simulations
    • Protein Folding

Current and Previous Research

Neuroactive Peptides

Neuropeptides are a large class of structurally diverse small proteins that control neuronal function. Understanding how such peptides bind and activate their receptors is essential for the development and improvement of (ant)agonists of multiple pharmacologically relevant GPCR targets.

Microbiome Metabolism

Hundreds of microbial species have been identified in our gut 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.

Hybrid QM/MM Simulations

The NAMD QM/MM interface extends existing NAMD features to the quantum mechanical level, presenting new features and possibilities. 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.

Melo et al. Nature Methods. 2018.

Protein Folding and Enhanced Sampling

GSAFold applies the Generalized Simulated Annealing (GSA) algorithm for ab-initio structure prediction of small proteins and intrinsically disordered regions. This new package combines a precise implementation of GSA with the broadly used NAMD Molecular Dynamics software to carry out energy calculations, allowing the user to select different force fields and parameterizations.

Melo et al. Proteins. 2012.

Bernardi et al. BBA. 2015.