Advanced Monte Carlo growth algorithms are developed and optimized to solve the complex conformational challenges of protein folding. By designing efficient sampling techniques for both lattice and off-lattice models, this work enables the accurate determination of low-energy states and thermodynamic properties in complex polymers at low temperatures.

• Structure Optimization in Off-Lattice Models: Implementing growth-based methods to optimize continuous, coarse-grained polymer chains and locate their global energy minima.

 "Structure optimization in an off-lattice protein model", Phys. Rev. E 68, 037703 (2003).

• Lattice Heteropolymer Ground States: Designing specialized growth algorithms to navigate the rugged energy landscapes of simplified protein-folding toy models.

"Growth-based Optimization Algorithm for Lattice Heteropolymers", Phys. Rev. E 68, 021113 (2003).

• Low-Temperature Sampling: Enhancing algorithm efficiency to overcome freezing and trapping during low-temperature simulations of complex sequences.

"Growth Algorithms for Lattice Heteropolymers at Low Temperatures", J. Chem. Phys. 118, 444 (2003).

• Solvent Effects on Peptide Conformations: Evaluating how distinct solvent-accessible surface area parameterizations screen internal interactions to shift stable conformational states and alter global energy minima.

"Metropolis simulations of Met-Enkephalin with solvent-accessible area parameterizations", Phys. Rev. E 69, 026703 (2004)

"Monte Carlo Protein Folding: Simulations of Met-Enkephalin with Solvent-Accessible Area Parameterizations", ohn von Neumann Institute for Computing NIC Symposium 2004 Proceedings, 17 - 18 February 2004 | Jülich, Germany, Wolf, D., Münster, G., Kremer, M., Forschungszentrum Jülich Zentralbibliothek, NIC Series Vol. 20, pp. 323-332 (2004). e-print, arxiv:cond-mat/0408572.