Computational Biochemistry - Research
Multi-Scale Computational Approaches
Hybrid methods, typically Quantum Mechanics/Molecular Mechanics approaches (QM/MM), are at the core of our research. Currently, there is considerable interest in the development of coarse-grained (CG) force fields to improve the performance in MD simulations and geometry optimizations. We implemented a triple-layer Quantum Mechanics/Molecular Mechanics/Coarse Grained (QM/MM/CG) modeling approach to reduce computing times and extend the applicability of QM/MM calculations.
Journal of Chemical Theory and Computation (2015), 11, 1809–1818.
Machine Learning Models
Machine learning methods allow exploiting the information content in protein datasets. We introduced a procedure for the general-purpose numerical codification of polypeptides. With this, we developed a support vector machine model (PPI-Detect), which allows predicting whether two proteins will interact or not. PPI-Detect outperforms state of the art sequence-based predictors of PPI. Using PPI-Detect, we designed a peptide which biological activity was then experimentally established. PPI-Detect is freely available at https://ppi-detect.zmb.uni-due.de
Journal of Computational Chemistry (2019), DOI: 10.1002/jcc.25780
We also introduced ProtDCal-Suite, a web server comprising a set of tools for studying proteins. The main module is a software named ProtDCal, which encodes the structural information of proteins in machine-learning-friendly vectors. Secondary modules are protein analysis tools developed with ProtDCal’s descriptors: PPI-Detect, for predicting the interaction likelihood of protein-protein and protein-peptide pairs; Enzyme Identifier, for identifying enzymes from amino acid sequences or 3D structures and Pred-Nglyco, for predicting N-glycosylation sites. ProtDCal-Suite is freely available at https://protdcal.zmb.uni-due.de.