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Computational Modeling of Biomolecules

Introduction: The rational design and engineering of biomolecules can be support quite often by in silico investigations leveraging powerful compute architectures. These rationale design choices steer experimental verification experiments and support the research focus synthetic biology at TU  Darmstadt. In particular, we a) help our experimental colleagues to avoid unnecessary experiments and save costs, but also b) develop new hypotheses on the molecular mechanisms underlying the living cell, which frequently cannot be investigated experimentally, yet. We used the available system to address several, related questions: - ion channel design and modularity - molecular evolution and designability of biotechnological relevant enzymes

Results: First, we implemented an effi cient, parallelized code to assess co-evolutionary measures,  such as the mutual information, effi ciently on multi-core architectures, especially GPUs. We developed an algorithm and proved its convergence properties, as well as implemented it, using it for publications and theses [1,2]. This approach allowed us to understand the molecular evolution of ion channels, e.g. like the viral Kcv channel, in detail. From there, we used reduced molecular models and transferable parametrization of these eff ective theories to scan the „design space“ of various proteins for functional optimization: for ion channels like HCN [3] or for DNA repair proteins like Rad54  [4]. Detailed methodology development [5,6] allowed us to analyze simulation results under the special  requirements of biomolecular engineering processes - as is, e.g., common in our study program „B.Sc./M.Sc. Biomolecular Engineering“ [7,8]. Eventually leading to new insight on enzymes: e.g. in
(Cordes) [9] we showed the close function-evolution relation of the omega-loop in acetylcholinesterase
(Fig. 1) - an important drug target and involved in several neurobiological processes.

Outlook: In our work, we tremendously benefi t from the available compute resources and the support by the Lichtenberg Rechner and the respective infrastructure of hard-, soft-, and most importantly brainware. We hope to leverage this in the future and collaborate even more on fundamental questions in algorithms and parallelization based upon close collaboration with the groups of Michael Goesele and Andreas Koch, both Dept. of Computer Science, TU Darmstadt. In particular, on-line reparametrization approaches of the above mentioned coarse-grained models will be the next big issues we want to address, so as to have adaptive codes for large(r) biomolecular complexes.


1. M. Waechter, K. Jaeger, D. Thuerck, S. Weissgraeber, S. Widmer, M. Goesele, K. Hamacher (2014), Using Graphics Processing Units to Investigate Molecular Coevolution, Concurrency and Computation: Practice and Experience, 26(6): 1278-1296.

2. M. Waechter (2011), Master thesis: Massively Parallel Computation of Null-Models Describing Molecular Co-Evolution.

3. S. Weißgraeber (2014), PhD thesis: Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels – Structure and Evolution.

4. S. Knorr (2014), PhD thesis: In Silico Strategies to Modulate DNA Damage Response.

5. P. Boba (2011), Diploma thesis: Correlations in Protein Backbones- A Copula Approach.

6. F. Keul (2012), Diploma thesis: Enzymatic Co-Evolution.

7. S. Pape, F. Hoff gaard, M. Dür, and K. Hamacher (2013), Distance Dependency And Minimum Amino Acid Alphabets for Decoy Scoring Potentials, J. Comp. Chem., 34: 10-20.

8. S. Jager (2013), Master thesis: Rationales Design der Fusarium Solani Cutinase.

9. H. Cordes (2011), Bachelor thesis: In silico analysis of dynamics and robustness of the glycolysis network based on reduced models.

10. S. Weißgraeber, F. Hoff gaard, and K. Hamacher (2011), Structure-Based, Biophysical Annotation of Molecular Coevolution of Acetylcholinesterase, Proteins: Structure, Function, and Bioinformatics, 79(11): 3144-3154.