26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Predicting conformational ensembles of the intrinsically disordered protein α-Synuclein via molecular simulation

Intrinsically disordered proteins (IDPs) represent 30% of the human genome. They are frequently involved in a variety of diseases including neurodegenerative disorders, cancer and cardiovascular diseases. Interfering with the function of disease-associated IDPs offers a highly attractive objective for drug development. Unfortunately, the highly dynamic nature of IDPs, the presence of continuous local and global conformational rearrangements, the transient secondary and long-range tertiary structure pose serious limits to experimental approaches aimed at designing rationally new drugs. Computational methods may be of great help to characterize IDPs and their binding to drugs, yet they also faces critical challenges in sampling the wide IDPs’ conformational space, with force field and analysis tools tailored for structured proteins. My thesis addresses IDPs computational issues by adopting apt computational protocols, combined with new analysis tools developed here. I focus on human alpha-synuclein (AS), a presynaptic protein of poorly defined function, whose aggregations constitute the main component of Parkinson disease-associated Lewy bodies. The first aim of this thesis is to characterize the structural determinants of the binding between AS and an anti-aggregation ligand, dopamine (DOP), which has been shown to prevent aggregation. We developed a new conformational analysis tool based on directional statistics. The tool not only is able to detect and quantitatively analyze protein residues’ flexibility and backbone conformational transitions, it is also able to quantitative detect the effects of the ligand on the dynamical spectra of the protein, avoiding problems associated with usual computational analysis tools based on Cartesian coordinates, which are biased by the problematic alignment of extremely flexible structures, besides being affected by the movements of neighboring residues. This tool provided a rationale for experimentally observed changes in chemical shifts on passing from free AS to ASDOP adducts, distinguishing variations arising from conformational rearrangements of residues with respect to the ones due to direct contacts with the drug. The second aim of the thesis is to adapt an enhanced sampling method to investigate the changes of conformational ensemble upon a physiologically relevant chemical modification, the N-term acetylation, recently revealed in human cells. The degree of sampling of our simulations and the quality of our models, in the first and second project, has been carefully validated against experimental data. Structural experimental quantities have been successfully reproduced. The methods proposed in my thesis provide a solid computational framework, which could be of great help for the characterization of the structural determinants of IDPs of pharmacological relevance also in the presence of drugs. Computational and semi-quantitative characterizations methods, like the ones adopted here, may provide crucial insights for future therapeutic strategies in drug design

A Molecular Dynamics Simulation-Based Interpretation of Nuclear Magnetic Resonance Multidimensional Heteronuclear Spectra of α-Synuclein·Dopamine Adducts

Multidimensional heteronuclear nuclear magnetic resonance (NMR) spectroscopy provides valuable structural information about adducts between naturally unfolded proteins and their ligands. These are often highly pharmacologically relevant. Unfortunately, the determination of the contributions to observed chemical shifts changes upon ligand binding is complicated. Here we present a tool that uses molecular dynamics (MD) trajectories to help interpret two-dimensional (2D) NMR data. We apply this tool to the naturally unfolded protein human α-synuclein interacting with dopamine, an inhibitor of fibril formation, and with its oxidation products in water solutions. By coupling 2D NMR experiments with MD simulations of the adducts in explicit water, the tool confirms with experimental data that the ligands bind preferentially to 125YEMPS129 residues in the C-terminal region and to a few residues of the so-called NAC region consistently. It also suggests that the ligands might cause conformational rearrangements of distal residues located at the N-terminus. Hence, the performed analysis provides a rationale for the observed changes in chemical shifts in terms of direct contacts with the ligand and conformational changes in the protein

Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins

G protein coupled receptors (GPCRs) and intrinsic disordered proteins (IDPs) are key players for neuronal function and dysfunction. Unfortunately, their structural characterization is lacking in most cases. From one hand, no experimental structure has been determined for the two largest GPCRs subfamilies, both key proteins in neuronal pathways. These are the odorant (450 members out of 900 human GPCRs) and the bitter taste receptors (25 members) subfamilies. On the other hand, also IDPs structural characterization is highly non-trivial. They exist as dynamic, highly flexible structural ensembles that undergo conformational conversions on a wide range of timescales, spanning from picoseconds to milliseconds. Computational methods may be of great help to characterize these neuronal proteins. Here we review recent progress from our lab and other groups to develop and apply in silico methods for structural predictions of these highly relevant, fascinating and challenging systems

A Molecular Dynamics Simulation-Based Interpretation of Nuclear Magnetic Resonance Multidimensional Heteronuclear Spectra of α‑Synuclein·Dopamine Adducts

Multidimensional heteronuclear nuclear magnetic resonance (NMR) spectroscopy provides valuable structural information about adducts between naturally unfolded proteins and their ligands. These are often highly pharmacologically relevant. Unfortunately, the determination of the contributions to observed chemical shifts changes upon ligand binding is complicated. Here we present a tool that uses molecular dynamics (MD) trajectories to help interpret two-dimensional (2D) NMR data. We apply this tool to the naturally unfolded protein human α-synuclein interacting with dopamine, an inhibitor of fibril formation, and with its oxidation products in water solutions. By coupling 2D NMR experiments with MD simulations of the adducts in explicit water, the tool confirms with experimental data that the ligands bind preferentially to ¹²⁵YEMPS¹²⁹ residues in the C-terminal region and to a few residues of the so-called NAC region consistently. It also suggests that the ligands might cause conformational rearrangements of distal residues located at the N-terminus. Hence, the performed analysis provides a rationale for the observed changes in chemical shifts in terms of direct contacts with the ligand and conformational changes in the protein

Conformational ensemble of human α-synuclein physiological form predicted by molecular simulations

We perform here enhanced sampling simulations of N-terminally acetylated human \u3b1-synuclein, an intrinsically disordered protein involved in Parkinson's disease. The calculations, consistent with experiments, suggest that the post-translational modification leads to the formation of a transient amphipathic \u3b1-helix. The latter, absent in the non-physiological form, alters protein dynamics at the N-terminal and intramolecular interactions