(300e) Recent Contributions By Floudas Lab at the Interface of Chemical Engineering and Computational Biology

Professor Floudas' Lab applied optimization to address problems at multiple scales. A theme of the lab focused on applications in the computational biology domain [1]. In this talk, we will focus on key recent contributions in the area of modeling, simulating, and designing proteins with Post-translational modifications (PTMs) and non-canonical amino acids (NCAAs) leveraging mixed-integer and nonlinear optimization techniques. Additionally, the talk will highlight contributions made in the protein structure prediction domain.

Statistics of PTMs contained in the Swiss-Prot database were curated to identify those most frequently occurring [2]. We developed Forcefield_PTM [3], a set of AMBER charge and torsion forcefield parameters consistent with ff03 for 32 common PTMs. The parameterization methodology was extended to create charge parameters for 147 NCAAs in Forcefield_NCAA[4]. The forcefields were validated against experimental hydration free energies through thermodynamic integration calculations in both the TIP3P and TIP4P-Ew water models finding comparable correlations and errors to ff03 for natural amino acids [5]. The forcefields were integrated into a new protein design method that combines integer linear optimization and molecular simulations, and they were used to help design several new analogs of Compstatin [6]. Several analogs exhibited enhanced activity and/or solubility compared to the natural amino acid-containing starting compound. Due to their potency and solubility, these peptides are promising candidates for therapeutic development in complement-mediated diseases.

The lab contributed immensely to the protein folding problem. Recently, the lab participated in a "Coopetition", WeFold [7], collaborating with 13 labs worldwide strategically combining methods and resources to develop innovative hybrid pipelines to test in double-blind experiments. Many webtools have been created to disseminate Floudas Lab's research to a broader audience [8, 9]. The methods developed by Chris' intellectual progeny are actively being used to discover new inhibitors and to predict and refine protein structures.

References

1. Khoury, G.A., Computational methods & forcefields for protein design, structure prediction, & refinement with natural & modified amino acids. 2015, Princeton University.

2. Khoury, G.A., R.C. Baliban, and C.A. Floudas, Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database. Scientific reports, 2011. 1: p. 90.

3. Khoury, G.A., et al., Forcefield_PTM: Ab Initio Charge and AMBER Forcefield Parameters for Frequently Occurring Post-Translational Modifications. Journal of Chemical Theory and Computation, 2013. 9(12): p. 5653–5674.

4. Khoury, G.A., et al., Forcefield_NCAA: ab initio charge parameters to aid in the discovery and design of therapeutic proteins and peptides with unnatural amino acids and their application to complement inhibitors of the compstatin family. ACS synthetic biology, 2014. 3(12): p. 855-869.

5. Khoury, G.A., N. Bhatia, and C.A. Floudas, Hydration free energies calculated using the AMBER ff03 charge model for natural and unnatural amino acids and multiple water models. Computers & Chemical Engineering, 2014. 71: p. 745-752.

6. Gorham Jr, R.D., et al., New compstatin peptides containing N-terminal extensions and non-natural amino acids exhibit potent complement inhibition and improved solubility characteristics. Journal of medicinal chemistry, 2015. 58(2): p. 814.

7. Khoury, G.A., et al., WeFold: A Coopetition for Protein Structure Prediction. Proteins: Structure, Function, Bioinformatics, 2014(10.1002/prot.24538): p. In Press.

8. Khoury, G.A., et al., Princeton_TIGRESS: Protein geometry refinement using simulations and support vector machines. Proteins: Structure, Function, and Bioinformatics, 2014. 82(5): p. 794-814.

9. Smadbeck, J., et al., Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules. Journal of Visualized Experiments, 2013(77): p. e50476.