S-nitrosylation affects TRAP1 structure and ATPase activity and modulates cell response to apoptotic stimuli.

The mitochondrial chaperone TRAP1 has been involved in several mitochondrial functions, and modulation of its expression/activity has been suggested to play a role in the metabolic reprogramming distinctive of cancer cells. TRAP1 posttranslational modifications, i.e. phosphorylation, can modify its capability to bind to different client proteins and modulate its oncogenic activity. Recently, it has been also demonstrated that TRAP1 is S-nitrosylated at Cys501, a redox modification associated with its degradation via the proteasome. Here we report molecular dynamics simulations of TRAP1, together with analysis of long-range structural communication, providing a model according to which Cys501 S-nitrosylation induces conformational changes to distal sites in the structure of the protein. The modification is also predicted to alter open and closing motions for the chaperone function. By means of colorimetric assays and site directed mutagenesis aimed at generating C501S variant, we also experimentally confirmed that selective S-nitrosylation of Cys501 decreases ATPase activity of recombinant TRAP1. Coherently, C501S mutant was more active and conferred protection to cell death induced by staurosporine. Overall, our results provide the first in silico, in vitro and cellular evidence of the relevance of Cys501 S-nitrosylation in TRAP1 biology.

Analyzing Biomolecular Ensembles.

Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.

An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass.

Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.

The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer.

SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.