AI3SD Video: Deep Learning enhanced prediction of protein structure and dynamics

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings showed that co-evolutionary analysis coupled with machine-learning techniques improved the prediction precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from the Multi Sequence Analysis (MSA) revealed the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on their energy scored, RMSD clustered, and the centroids locally refined. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechains orientations. Our pipeline not only consents us to retrieve the global experimental folding but also the experimental structural dynamics due to local and global conformational changes. Based on the insight of this study we are proposing a protocol that allows the in-silico investigation of protein dynamics suited for pharmacological research on catalysis and molecular recognition

Protein post-translational modifications: In silico prediction tools and molecular modeling

Post-translational modifications (PTMs) occur in almost all proteins and play an important role in numerous biological processes by significantly affecting proteins' structure and dynamics. Several computational approaches have been developed to study PTMs (e.g., phosphorylation, sumoylation or palmitoylation) showing the importance of these techniques in predicting modified sites that can be further investigated with experimental approaches. In this review, we summarize some of the available online platforms and their contribution in the study of PTMs. Moreover, we discuss the emerging capabilities of molecular modeling and simulation that are able to complement these bioinformatics methods, providing deeper molecular insights into the biological function of post-translational modified proteins

Effect of the Synaptic Plasma Membrane on the Stability of the Amyloid Precursor Protein Homodimer

The proteolytic cleavage of the transmembrane (TM) domain of the amyloid precursor protein (APP) releases amyloid-beta (A beta) peptides, which accumulation in the brain tissue is an early indicator of Alzheimer's disease. We used multiscale molecular dynamics simulations to investigate the stability of APP-TM dimer in realistic models of the synaptic plasma membrane (SPM). Between the two possible dimerization motifs proposed by NMR and EPR, namely G(709)XXXA(713) and G(700)XXXG(704)XXXG(708), our study revealed that the dimer promoted by the G(709)XXXA(713) motif is not stable in the SPM due to the competition with highly unsaturated lipids that constitute the SPM. Under the same conditions, the dimer promoted by the G(700)XXXG(704)XXXG(708) motif is instead the most stable species and likely the most biologically relevant. Independently of the dimerization state, both these motifs can be involved in the recruitment of cholesterol molecules

The In Situ Structure of Parkinson’s Disease-Linked LRRK2

Mutations in leucine-rich repeat kinase 2 (LRRK2) are the most frequent cause of familial Parkinson's disease. LRRK2 is a multi-domain protein containing a kinase and GTPase. Using correlative light and electron microscopy, in situ cryo-electron tomography, and subtomogram analysis, we reveal a 14-Å structure of LRRK2 bearing a pathogenic mutation that oligomerizes as a right-handed double helix around microtubules, which are left-handed. Using integrative modeling, we determine the architecture of LRRK2, showing that the GTPase and kinase are in close proximity, with the GTPase closer to the microtubule surface, whereas the kinase is exposed to the cytoplasm. We identify two oligomerization interfaces mediated by non-catalytic domains. Mutation of one of these abolishes LRRK2 microtubule-association. Our work demonstrates the power of cryo-electron tomography to generate models of previously unsolved structures in their cellular environment