Research group Torsten Schwede
Computational structure biology
Protein structure modeling
The main interest of my group is the development of methods and algorithms for molecular modeling and simulations of three-dimensional protein structures and their interactions. One of the major limitations for using structure-based methods in biomedical research is the limited availability of experimentally determined protein structures. Prediction of the 3D structure of a protein from its amino acid sequence remains a fundamental scientific problem, and it is considered as one of the grand challenges in computational biology. Comparative or homology modeling, which uses experimentally elucidated structures of related protein family members as templates, is currently the most accurate and reliable approach to model the structure of the protein of interest. Template-based protein modeling techniques exploit the evolutionary relationship between a target protein and templates with known experimental structures, based on the observation that evolutionarily related sequences generally have similar 3D structures.
The SWISS-MODEL expert system developed by our group is a fully automated web-based workbench, which greatly facilitates the process of computing of protein structure homology models. SWISS-MODEL currently counts more than 300’000 registered users and calculates about 1 model per minute. Up to today SWISS-MODEL relevant publications have been cited more than 16’000 times by biomedical researchers world-wide.
Mean force potentials for model quality estimation
Ultimately, the quality of a model determines its usefulness for different biomedical applications such as planning mutagenesis experiments for functional analyses or studying protein-ligand interactions, e.g. in structure based drug design. The estimation of the expected quality of a predicted structural model is therefore crucial in structure prediction. Especially when the sequence identity between target and template is low, individual models may contain considerable errors. To identify such inaccuracies, scoring functions have been developed which analyze different structural features of the protein models in order to derive a quality estimate. To this end, we have introduced the composite scoring function QMEAN, which consists of four statistical potential terms and two components describing the agreement between predicted and observed secondary structure and solvent accessibility. We have shown that QMEAN can not only be used to assess the quality of theoretical protein models, but also to identify experimental structures of poor quality. Specific potentials for trans-membrane regions are required for the correct assessment of TM proteins such as receptors and channels. As membrane proteins play crucial roles in many biological processes and are important drug targets, QMEANBrane further extends our approach to membrane protein structures. Recently, we have also developed an approach for dynamically combining the knowledge-based statistical potentials of QMEAN with distance constraints derived from homologous template structures (QMEANDisCo). This method significantly increases the accuracy of the local per-residue quality estimates at a relatively small computational cost.
CASP and CAMEO: Critical assessment of structure prediction methods
Methods for structure modeling and prediction have made substantial progress of the last decades, but still fall short in accuracy compared to high-resolution experimental structures. Retrospectively assessing the quality of a blind prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets which are carefully evaluated by human experts. The “Continuous Model EvaluatiOn” (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO requires the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous operation without human intervention. CAMEO is currently assessing predictions of 3-dimensional structures, ligand binding sites, and model quality estimation.
Molecular modeling of Dengue virus RNA methyltransferase
Dengue fever is a viral disease that is transmitted between human hosts by Aedes mosquitoes, particularly Aedes aegyptii. According to the CDC, dengue virus is a leading cause of illness and death in the tropics and subtropics, with more than one-third of the world’s population are living in areas at risk for infection, and as many as 400 million people are infected yearly. There are not yet any vaccines to prevent infection with dengue virus and the most effective protective measures are those that avoid mosquito bites. One of the viral proteins encoded in the Dengue genome, RNA methyltransferase (MTase), appears as interesting target for the development of novel inhibitors of Dengue virus as it is necessary for virus replication. In a public-private partnership with Schrodinger (New York) and the Novartis Institute for Tropical Diseases in Singapore, we have used a structure based virtual screening approach to identify novel inhibitors of Dengue virus.
In order to better understand the catalytic mechanism of the MTase, we applied a diverse set of computational methodologies as well as experimental isothermal titration calorimetry (ITC) based assays. Based on a structural model of the enzyme bound to the RNA substrate and the SAM cofactor, we establish an in-silico protocol to identify the effect of single point mutations. The protocol employs MD simulations to analyze effects on the geometric arrangement between cofactor, substrate and active site residues, an MM-GBSA approach to analyze cofactor binding free energies and mixed QM/MM simulations to estimate activation barriers. With this knowledge, we hope to facilitate the rational development of novel inhibitors against dengue fever and related diseases caused by flavivirus and we believe that our protocol gives valuable contributions for future drug resistance predictions.
Structure-guided protein engineering and in-vitro evolution of enzymes
Three-dimensional models of proteins are valuable tools for the design of protein engineering and in-vitro evolution experiments. In the following, some exemplar projects involving molecular modeling of protein-ligand interactions at different levels of model resolution are briefly presented.
Conjugate vaccines in which polysaccharide antigens are covalently linked to carrier proteins belong to the most effective and safest vaccines against bacterial pathogens. The current production process of conjugate vaccines is a laborious, chemical multi-step process. The discovery of N-glycosylation in bacteria allows for protein glycosylation in recombinant bacteria by expressing the N-oligosaccharyltransferase PglB of Campylobacter jejuni in Escherichia coli. We are collaborating with GlycoVaxyn AG (Schlieren) and EMPA (St. Gallen) on a project funded by the KTI on structure-guided protein engineering of PglB in order to improve the efficiency of in vivo synthesis of novel and well characterized immunogenic polysaccharide/protein complexes for use in vaccines.
Other projects involve studying Zinc-selective inhibition of the promiscuous bacterial amide-hydrolase DapE and the implications of metal heterogeneity for evolution and anti-biotic drug design (Marc Creus), or the design of protein kinases with altered substrate specificity.