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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.

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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 in situ cryo-electron tomography and subtomogram averaging, 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 points towards the microtubule, while 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 obtain structures of previously unsolved proteins in their cellular environment and provides insights into LRRK2 function and pathogenicity.

In situ structural biology aims to resolve macromolecular structures and collective behaviors of macromolecular machineries within the cell. In bacteria, transcription mediated by RNA polymerase is known to be functionally coupled to the leading ribosome, a process crucial for efficient gene expression. Direct ribosome-RNA polymerase interaction has been demonstrated in vitro, but how these two gigantic molecular machines coordinate is yet unclear, especially within the context of crowded and complex cellular environments. Using Mycoplasma pneumonia as model system, we integrated cellular cryo-electron tomography followed by sub-tomogram analysis (STA) and in-cell crosslinking mass spectrometry (CLMS) to investigate the coupling in native cells, without resorting to labelling or cell disruption. By averaging sub-tomograms extracted in silico from cellular volumes, we solved the 70S ribosome structure at 6.4 A resolution, so far the highest resolution reported for in-cell cryo-ET. After large-scale classification, we determined the structure of a super-complex consisting of ribosome, RNA polymerase and other auxiliary proteins. Proteinprotein interaction networks revealed by in-cell CLMS indicated an essential transcription elongation factor bridging RNA polymerase and the translating ribosome. Integrative modelling suggests a novel, indirect transcriptiontranslation coupling mechanism, which advances our understanding of key machineries of the central dogma of molecular biology in bacteria. Methodology wise, our work demonstrates the feasibility and superiority of integrative structural biology performed directly inside cells. Integration of two approaches, cryo-ET/STA and in-cell CLMS, holds great potential to delineate cellular processes in a quantitative and structural view.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article.

The proteolytic cleavage of the transmembrane (TM) domain of the amyloid precursor protein (APP) releases amyloid-β (Aβ) 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 G709XXXA713 and G700XXXG704XXXG708, our study revealed that the dimer promoted by the G709XXXA713 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 G700XXXG704XXXG708 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.

Motivation Proteins are intrinsically dynamic entities. Flexibility sampling methods, such as molecular dynamics or those arising from integrative modeling strategies are now commonplace and enable the study of molecular conformational landscapes in many contexts. Resulting structural ensembles increase in size as technological and algorithmic advancements take place, making their analysis increasingly demanding. In this regard, cluster analysis remains a go-to approach for their classification. However, many state-of-the-art algorithms are restricted to specific cluster properties. Combined with tedious parameter fine-tuning, cluster analysis of protein structural ensembles suffers from the lack of a generally applicable and easy to use clustering scheme. Results We present CLoNe, an original Python-based clustering scheme that builds on the Density Peaks algorithm of Rodriguez and Laio. CLoNe relies on a probabilistic analysis of local density distributions derived from nearest neighbors to find relevant clusters regardless of cluster shape, size, distribution and amount. We show its capabilities on many toy datasets with properties otherwise dividing state-of-the-art approaches and improves on the original algorithm in key aspects. Applied to structural ensembles, CLoNe was able to extract meaningful conformations from membrane binding events and ligand-binding pocket opening as well as identify dominant dimerization motifs or inter-domain organization. CLoNe additionally saves clusters as individual trajectories for further analysis and provides scripts for automated use with molecular visualization software. Availability www.epfl.ch/labs/lbm/resources, github.com/LBM-EPFL/CLoNe Supplementary information Supplementary data are available at Bioinformatics online.