This is a short overview of the research areas we work in. Please use the menu above to navigate to individual pages or follow the links below.

The fundamental motivation for our research is understanding how life works, in particular in the context of human disease. Because we are an interdisciplinary group, our efforts branch out into several different directions. Historically, the structure and function of proteins was our primary focus area, and many of the biological questions we look for answers to are still in this area (READ MORE). Cancer, neurodegenerative disease, epitranscriptomics (m6A-RNA) and epigenetics (bromodomains) are all topics of interest to us that are also of relevance to human health. A longstanding interest of ours is the discovery of new small molecules to counteract deleterious effects of these diseases (READ MORE). We use computers to try to answer most of these questions. When faced with a tricky problem, the strategies and methods to employ may need to be modified, extended, or even newly developed, not only in relation to the problem but also in relation to feasibility on currently available computing architectures (READ MORE). Computational predictions are based on models, and thus carry intrinsic errors that are systematic in nature. To validate these predictions, we carry out a number of experiments in-house (READ MORE). Finally, most of the research carried out today, including our own, has the potential to create and store large amounts of data. We have accumulated and mastered a sizeable set of advanced data mining tools and are always interested in collaborating with researchers who are trying to tame a complex data set they have generated (READ MORE).

Past Research Accomplishments

You can find our published works on the PUBLICATIONS page. Some past highlights are as follows.

We have developed and applied molecular dynamics protocols and novel algorithms for the data-driven analysis of simulations of protein folding, amyloid aggregation [Pellarin, 2010; Vitalis, 2010], and ligand binding [Sledz, 2018]. We have pioneered the network and free-energy analysis of protein simulations which has revealed for the first time multiple folding pathways and the heterogeneity of the unfolded state [Ferrara, 2000; Rao, 2004] and the essential role of the side chains in amyloid peptide aggregation [Gsponer, 2003]. Our clustering algorithms and methods for complex network analysis, originally designed for the analysis of molecular dynamics simulations of biological macromolecules, have also been applied to data from completely different domains, most prominently in ecological networks [Kaiser, 2010] and recently in neuroscience [Garolini, 2019]. The group has also developed very efficient and accurate protocols for docking, and novel methods for de novo design of potent and selective chemical probes. These computational methods, in combination with biochemical and biophysical characterization including X-ray structures of protein/ligand complexes, have been employed to develop small-molecule antagonists of both epigenetics [Xu, 2016; Batiste, 2018] and epitranscriptomics [Bedi, 2020] reader and/or writer domains.