MOTIVATION: Molecular dynamics (MD) simulations provide critical insights into biomolecular processes but they generate complex high-dimensional data that are often difficult to interpret directly. Dimensionality reduction methods like principal component analysis, time-lagged independent component analysis, and self-organizing maps (SOMs) have helped in extracting essential information on functional dynamics. However, there is a growing need for a user-friendly and flexible framework for SOM-based analyses of MD simulations. Such a framework should offer adaptable workflows, customizable options, and direct integration with a widely adopted analysis software. RESULTS: We designed and developed SOMMD, an R package to streamline MD analysis workflows. SOMMD facilitates the interpretation of atomistic trajectories through SOMs, providing tools for each stage of the workflow, from importing a wide range of MD trajectories data types to generating enhanced visualizations. The package also includes three example projects that demonstrate how SOM can be applied in real-world scenarios, including cluster analysis, pathways mapping and transition networks reconstruction. AVAILABILITY AND IMPLEMENTATION: SOMMD is available on CRAN (https://CRAN.R-project.org/package=SOMMD) and on GitHub (https://github.com/alepandini/SOMMD).

Hypoxia inducible factors (HIFs) are transcription factors belonging to the basic helix-loop-helix PER-ARNT-SIM (bHLH-PAS) protein family with a role in sensing oxygen levels in the cell. Under hypoxia, the HIF-α degradation pathway is blocked and dimerization with the aryl hydrocarbon receptor nuclear translocator (ARNT) makes HIF-α transcriptionally active. Due to the common hypoxic environment of tumors, inhibition of this mechanism by destabilization of HIF-α:ARNT dimerization has been proposed as a promising therapeutic strategy. Following the discovery of a druggable cavity within the PAS-B domain of HIF-2α, research efforts have been directed to identify artificial ligands that can impair heterodimerization. Although the crystallographic structures of the HIF-2α:ARNT complex have elucidated the dimer architecture and the 0X3-inhibitor placement within the HIF-2α PAS-B, unveiling the inhibition mechanism requires investigation of how ligand-induced perturbations could dynamically propagate through the structure and affect dimerization. To this end, we compared evolutionary features, intrinsic dynamics and energetic properties of the dimerization interfaces of HIF-2α:ARNT in both the apo and holo forms. Residue conservation analysis highlighted inter-domain connecting elements that have a role in dimerization. Analysis of domain contributions to the dimerization energy demonstrated the importance of bHLH and PAS-A of both partners and of HIF-2α PAS-B domain in dimer stabilization. Among quaternary structure oscillations revealed by Molecular Dynamics simulations, the hinge-bending motion of the ARNT PAS-B domain around the flexible PAS-A/PAS-B linker supports a general model for ARNT dimerization in different heterodimers. Comparison of the HIF-2α:ARNT dynamics in the apo and 0X3-bound forms indicated a model of inhibition where the HIF-2α-PAS-B interfaces are destabilised as a result of water-bridged ligand-protein interactions and these local effects allosterically propagate to perturb the correlated motions of the domains and inter-domain communication. These findings will guide the design of improved inhibitors to contrast cell survival in tumor masses.

The Aryl hydrocarbon Receptor (AhR) is a transcription factor that mediates the biochemical response to xenobiotics and the toxic effects of a number of environmental contaminants, including dioxins. Recently, endogenous regulatory roles for the AhR in normal physiology and development have also been reported, thus extending the interest in understanding its molecular mechanisms of activation. Since dimerization with the AhR Nuclear Translocator (ARNT) protein, occurring through the Helix-Loop-Helix (HLH) and PER-ARNT-SIM (PAS) domains, is needed to convert the AhR into its transcriptionally active form, deciphering the AhR:ARNT dimerization mode would provide insights into the mechanisms of AhR transformation. Here we present homology models of the murine AhR:ARNT PAS domain dimer developed using recently available X-ray structures of other bHLH-PAS protein dimers. Due to the different reciprocal orientation and interaction surfaces in the different template dimers, two alternative models were developed for both the PAS-A and PAS-B dimers and they were characterized by combining a number of computational evaluations. Both well-established hot spot prediction methods and new approaches to analyze individual residue and residue-pairwise contributions to the MM-GBSA binding free energies were adopted to predict residues critical for dimer stabilization. On this basis, a mutagenesis strategy for both the murine AhR and ARNT proteins was designed and ligand-dependent DNA binding ability of the AhR:ARNT heterodimer mutants was evaluated. While functional analysis disfavored the HIF2α:ARNT heterodimer-based PAS-B model, most mutants derived from the CLOCK:BMAL1-based AhR:ARNT dimer models of both the PAS-A and the PAS-B dramatically decreased the levels of DNA binding, suggesting this latter model as the most suitable for describing AhR:ARNT dimerization. These novel results open new research directions focused at elucidating basic molecular mechanisms underlying the functional activity of the AhR.

BACKGROUND: Molecular dynamics (MD) simulations are powerful tools to investigate the conformational dynamics of proteins that is often a critical element of their function. Identification of functionally relevant conformations is generally done clustering the large ensemble of structures that are generated. Recently, Self-Organising Maps (SOMs) were reported performing more accurately and providing more consistent results than traditional clustering algorithms in various data mining problems. We present a novel strategy to analyse and compare conformational ensembles of protein domains using a two-level approach that combines SOMs and hierarchical clustering. RESULTS: The conformational dynamics of the α-spectrin SH3 protein domain and six single mutants were analysed by MD simulations. The Cα's Cartesian coordinates of conformations sampled in the essential space were used as input data vectors for SOM training, then complete linkage clustering was performed on the SOM prototype vectors. A specific protocol to optimize a SOM for structural ensembles was proposed: the optimal SOM was selected by means of a Taguchi experimental design plan applied to different data sets, and the optimal sampling rate of the MD trajectory was selected. The proposed two-level approach was applied to single trajectories of the SH3 domain independently as well as to groups of them at the same time. The results demonstrated the potential of this approach in the analysis of large ensembles of molecular structures: the possibility of producing a topological mapping of the conformational space in a simple 2D visualisation, as well as of effectively highlighting differences in the conformational dynamics directly related to biological functions. CONCLUSIONS: The use of a two-level approach combining SOMs and hierarchical clustering for conformational analysis of structural ensembles of proteins was proposed. It can easily be extended to other study cases and to conformational ensembles from other sources.

MOTIVATION: The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset. RESULTS: The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1-4. AVAILABILITY: http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.