Home Department: Center for Biophysics and Computational Biology.

Office Address: Chemical and Life Sciences Laboratory, Room A542.

Office Address (2): Beckman Institute, Room 3157.

Email Address: melomcr at gmail dot com or crdsdsr2 at illinois dot edu


  • PhD in Biophysics (2019) - university of Illinois at Urbana-Champaign - USA
  • Advisor: Dr. Zaida Luthey-Schulten

  • Masters in Biophysics (2013) - Federal University of Rio de Janeiro - Brazil
  • Advisor: Dr. Pedro Geraldo Pascutti

  • BS Biophysics (2011) - Federal University of Rio de Janeiro - Brazil
  • Advisor: Dr. Pedro Geraldo Pascutti

Marcelo C. R. Melo

Research Interests

  • Systems Biology
  • Cellular Metabolism
  • Hybrid QM/MM Simulations
  • Protein Folding

System Biology Approach to Cellular Metabolism


With the development of single cell and micro-colony imaging experiments, instead of measuring a single growth rate (via optical density, for example) for an entire population, we can now observe a distribution of the growth rates of individual cells. To understand or interpret the general form of the growth rate distribution, we have to dig into the metabolic behavior of the underlying subpopulations. Recent systematic genome-wide fluorescence labeling studies have provided libraries of approximately 1,000 “strains” of labeled E. coli and 4,000 “strains” of labeled yeast. Examination of these strains has shown that proteins are not expressed at a specific number across a population. Due to the well-established innate stochasticity in essentially every cellular processes (transcription, translation, DNA replication, cell division, etc.), these studies have shown that proteins are expressed in varying numbers from cell to cell. In order to understand how any given cell’s protein expression state effects its behavior, and how that behavior relates to the overall behavior at the population level, these protein distributions must be sampled and realistic subpopulations of individual cells must be modeled. Our Population FBA approach provides such a method; allowing us to carry out the generation of realistic populations of cells and subsequent analysis of their intracellular fluxes and exchanges with the environment.

We have employed our Population FBA methodology to study metabolic heterogeneity in S. cerevisiae. One of the most important result of this study is that it underscores the need for imposing biologically realistic internal constraints in flux balance models. Without the types of constraints Population FBA imposes, the yeast 7.6 model gave fluxes, growth rates, and metabolic byproducts that were qualitatively and quantitatively inconsistent with the results of a 13C fluxomics study. Our study has shown that yeast populations exhibit the same types of cell-to-cell diversity in behavior that is coming to be recognized across the microbial world, and that although the particular sets of constraints that are necessary to recover the experimental growth rate distribution are not unique, any set that does recover the growth rate distribution also recovers the main metabolic behaviors we observed, including the Crabtree effect and the noted shift toward respiration seen among our fast-growing cells.

QM/MM Simulations of Biomolecular Complexes


NAMD QM/MM interface extends existing NAMD features to the quantum mechanical level, presenting new features and possibilities. The first is the ability to execute multiple QM regions in parallel, thorough independent executions of your choice of quantum chemistry code. This allows one to account for multiple reaction centers that are known to work synergistically, for example, or even distant allosteric regulation sites and a reaction center. Investigation of processes occurring on a timescale usually not accessible by QM/MM methods can now 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, NAMD QM/MM allows the update of the QM and MM regions at every step of the QM/MM simulation.

Download the latests version of NAMD here.

Previous Projects

Protein Folding


One of the main paradigms of molecular biology tells us that the three-dimensional structure of proteins defines its function and dynamics. Such three-dimensional structures, in turn, derive from the amino acid sequence itself, through the folding process. A protein's structure determines its activity and properties, thus knowing such conformation on an atomic level is essential for both basic and applied studies of protein function and dynamics. However, the acquisition of such structures by experimental methods is slow and expensive, and current computational methods mostly depend on previously known structures to determine new ones. We developed a new software called GSAFold that applies the Generalized Simulated Annealing (GSA) algorithm on ab-initio protein structure prediction. The GSA is a stochastic search algorithm employed in energy minimization and used in global optimization problems, especially those that depend on long-range interactions, such as gravity models and conformation optimization of small molecules. This new implementation applies, for the first time in ab-initio protein structure prediction, an analytical inverse for the Visitation function of GSA. It also employs the broadly used NAMD Molecular Dynamics package to carry out energy calculations, allowing the user to select different force fields and parameterizations. Moreover, the software also allows the execution of several simulations simultaneously. Applications that depend on protein structures include rational drug design and structure-based protein function prediction.

Read more


  1. NAMD goes quantum: An integrative suite for hybrid simulations ; Melo*, M. C. R.; Bernardi*, R. C.; Rudack T.; Scheurer, M.; Riplinger, C.; Phillips, J. C.; Maia, J. D. C.; Rocha, G. D.; Ribeiro, J. V.; Stone, J. E.; Neese, F.; Schulten, K.; Luthey-Schulten, Z. ; Nature Methods, 2018

  2. Direction Matters: Monovalent Streptavidin/Biotin Complex under Load ; Sedlak*, Steffen M.; Schendel*, Leonard C.; Melo, Marcelo C. R.; Pippig, Diana A.; Luthey-Schulten, Zaida; Gaub, Hermann E.; Bernardi, Rafael C.; Nano Letters, 2018

  3. Population FBA predicts metabolic phenotypes in yeast ; P Labhsetwar*, MCR Melo*, JA Cole*, Z Luthey-Schulten ; PLOS Computational Biology, 2017

  4. Enhanced sampling techniques in molecular dynamics simulations of biological systems; RC Bernardi, MCR Melo, K Schulten; Biochimica et Biophysica Acta (BBA), 2015

  5. GSAFold: A new application of GSA to protein structure prediction; MCR Melo, RC Bernardi, TVA Fernandes, PG Pascutti; Proteins: Structure, Function, and Bioinformatics, 2012

  6. QM/MM Molecular Dynamics Methods Applied to Investigate Cellulose Fibers Hydration; RC Bernardi, MCR Melo, PG Pascutti, Biophysical Journal, 2012

  7. New Developments on Generalized Simulated Annealing Applied to ab-initio Protein Structure Prediction; MCR Melo, TV Fernandes, RC Bernardi, PG Pascutti; Biophysical Journal, 2012