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Here, the authors describe the emergence and spread of a new potential SARS-CoV-2 variant of interest, B.1.620. They show that this lineage, first identified in Lithuania, has established local transmission in Europe on multiple occasions and likely emerged in Central Africa.

Voltage-gated potassium channels of the Kv7.x family are involved in a plethora of biological processes across many tissues in animals, and their misfunctioning could lead to several pathologies ranging from diseases caused by neuronal hyperexcitability, such as epilepsy, or traumatic injuries and painful diabetic neuropathy to autoimmune disorders. Among the members of this family, the Kv7.2 channel can form hetero-tetramers together with Kv7.3, forming the so-called M-channels, which are primary regulators of intrinsic electrical properties of neurons and of their responsiveness to synaptic inputs. Here, prompted by the similarity between the M-current and that in Kv7.2 alone, we perform a computational-based characterization of this channel in its different conformational states and in complex with the modulator retigabine. After validation of the structural models of the channel by comparison with experimental data, we investigate the effect of retigabine binding on the two extreme states of Kv7.2 (resting-closed and activated-open). Our results suggest that binding, so far structurally characterized only in the intermediate activated-closed state, is possible also in the other two functional states. Moreover, we show that some effects of this binding, such as increased flexibility of voltage sensing domains and propensity of the pore for open conformations, are virtually independent on the conformational state of the protein. Overall, our results provide new structural and dynamic insights into the functioning and the modulation of Kv7.2 and related channels.

The computational modeling field has vastly evolved over the past decades. The early developments of simplified protein systems represented a stepping stone towards establishing more efficient approaches to sample intricated conformational landscapes. Downscaling the level of resolution of biomolecules to coarser representations allows for studying protein structure, dynamics and interactions that are not accessible by classical atomistic approaches. The combination of different resolutions, namely hybrid modeling, has also been proved as an alternative when mixed levels of details are required. In this review, we provide an overview of coarse-grained/hybrid models focusing on their applicability in the modeling of biomolecular interactions. We give a detailed list of ready-to-use modeling software for studying biomolecular interactions allowing various levels of coarse-graining and provide examples of complexes determined by integrative coarse-grained/hybrid approaches in combination with experimental information.

Objective: The nuclear receptor PPARγ is the master regulator of adipocyte differentiation, distribution, and function. In addition, PPARγ induces terminal differentiation of several epithelial cell lineages, including colon epithelia. Loss-of-function mutations in PPARG result in familial partial lipodystrophy subtype 3 (FPDL3), a rare condition characterized by aberrant adipose tissue distribution and severe metabolic complications, including diabetes. Mutations in PPARG have also been reported in sporadic colorectal cancers, but the significance of these mutations is unclear. Studying these natural PPARG mutations provides valuable insights into structure-function relationships in the PPARγ protein. We functionally characterized a novel FPLD3-associated PPARγ L451P mutation in helix 9 of the ligand binding domain (LBD). Interestingly, substitution of the adjacent amino acid K450 was previously reported in a human colon carcinoma cell line. Methods: We performed a detailed side-by-side functional comparison of these two PPARγ mutants. Results: PPARγ L451P shows multiple intermolecular defects, including impaired cofactor binding and reduced RXRα heterodimerisation and subsequent DNA binding, but not in DBD-LBD interdomain communication. The K450Q mutant displays none of these functional defects. Other colon cancer-associated PPARγ mutants displayed diverse phenotypes, ranging from complete loss of activity to wildtype activity. Conclusions: Amino acid changes in helix 9 can differently affect LBD integrity and function. In addition, FPLD3-associated PPARγ mutations consistently cause intra- and/or intermolecular defects; colon cancer-associated PPARγ mutations on the other hand may play a role in colon cancer onset and progression, but this is not due to their effects on the most well-studied functional characteristics of PPARγ. Keywords: Lipodystrophy, FPLD3, PPARG, Nuclear receptor, Adipose tissue

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

Computational docking is a promising tool to model three-dimensional (3D) structures of protein–protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein–protein interfaces. Here, we present the software for iScore. The software provides executable scripts that fully automate the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

Viruses often subvert the host immune system using molecular decoys to prevent an effective immune response. Here, the authors examine the structural details of the viral decoy receptor GIF and its antagnosim of GM-CSF and IL-2.

The West-Life project (https://about.west-life.eu/) is a Horizon 2020 project funded by the European Commission to provide data processing and data management services for the international community of structural biologists, and in particular to support integrative experimental approaches within the field of structural biology. It has developed enhancements to existing web services for structure solution and analysis, created new pipelines to link these services into more complex higher-level workflows, and added new data management facilities. Through this work it has striven to make the benefits of European e-Infrastructures more accessible to life-science researchers in general and structural biologists in particular. Keywords: Structural biology, Virtual Research Environment, Cloud computing, Grid computing, Data management

Cryo-EM is a rapidly developing method to investigate the three dimensional structure of large macromolecular complexes. In spite of all the advances in the field, the resolution of most cryo-EM density maps is too low for de novo model building. Therefore, the data are often complemented by fitting high-resolution subunits in the density to allow for an atomic interpretation. Typically, the first step in the modeling process is placing the subunits in the density as a rigid body. An objective method for automatic placement is full-exhaustive six dimensional cross correlation search between the model and the cryo-EM data, where the three translational and three rotational degrees of freedom are systematically sampled. In this article we present PowerFit, a Python package and program for fast and sensitive rigid body fitting. We introduce a novel, more sensitive scoring function, the core-weighted local cross correlation, and show how it can be calculated using FFTs for fast translational cross correlation scans. We further improved the search algorithm by using optimized rotational sets to reduce rotational redundancy and by limiting the cryo-EM data size through resampling and trimming the density. We demonstrate the superior scoring sensitivity of our scoring function on simulated data of the 80S D. melanogaster ribosome and on experimental data for four different cases. Through these advances, a fine-grained rotational search can now be performed within minutes on a CPU and seconds on a GPU. PowerFit is free software and can be downloaded from https://github.com/haddocking/powerfit.

Molecular-docking programs coupled with suitable scoring functions are now established and very useful tools enabling computational chemists to rapidly screen large chemical databases and thereby to identify promising candidate compounds for further experimental processing. In a broader scenario, predicting binding affinity is one of the most critical and challenging components of computer-aided structure-based drug design. The development of a molecular docking scoring function which in principle could combine both features, namely ranking putative poses and predicting complex affinity, would be of paramount importance.Here, we systematically investigated the performance of the MM-PBSA approach, using two different Poisson-Boltzmann solvers (APBS and DelPhi), in the currently rising field of protein-peptide interactions (PPIs), identifying the correct binding conformations of 19 different protein–peptide complexes and predicting their binding free energies. First, we scored the decoy structures from HADDOCK calculation via the MM-PBSA approach in order to assess the capability of retrieving near-native poses in the best-scoring clusters and of evaluating the corresponding free energies of binding. MM-PBSA behaves well in finding the poses corresponding to the lowest binding free energy, however the built-in HADDOCK score shows a better performance. In order to improve the MM-PBSA–based scoring function, we dampened the MM-PBSA solvation and coulombic terms by 0.2, as proposed in the HADDOCK score and LIE approaches. The new dampened MM-PBSA (dMM-PBSA) outperforms the original MM-PBSA and ranks the decoys structures as the HADDOCK score does. Second, we found a good correlation between the dMM-PBSA and HADDOCK scores for the near-native clusters of each system and the experimental binding energies, respectively.Therefore, we propose a new scoring function, dMM-PBSA, to be used together with the built-in HADDOCK score in the context of protein-peptide docking simulations.

With the increasingly rapid growth of data in Life Sciences we are witnessing a major transition in the way research is conducted, from hypothesis-driven studies to data-driven simulations of whole systems. In the context of the European Grid Infrastructure Community Forum 2014 (Helsinki, 19–23 May 2014), a workshop was held aimed at understanding the state of the art of Grid/Cloud computing in EU research as viewed from within the field of Life Sciences. The workshop brought together Life Science researchers and infrastructure providers from around Europe and facilitated networking between them within the context of EGI. The first part of the workshop included talks from key infrastructures and projects within the Life Sciences community. This was complemented by technical talks that established the key aspects present in major research approaches. Finally, the discussion phase provided significant insights into the road ahead with proposals for possible collaborations and suggestions for future actions.

Gene duplication and sequence divergence are driving forces toward establishing protein families. To examine how sequence changes affect carbohydrate specificity, the two closely related proto-type chicken galectins CG-14 and CG-16 were selected as models. Binding properties were analyzed using a highly sensitive solid-phase assay. We tested 56 free saccharides and 34 well-defined glycoproteins. The two galectins share preference for the II (Galβ1-4GlcNAc) versus I (Galβ1-3GlcNAc) version of β-galactosides. A pronounced difference is found owing to the reactivity of CG-14 with histo-blood group ABH active oligosaccharides and A/B active glycoproteins. These experimental results prompted to determine activity–structure correlations by modeling. Computational analysis included consideration of the flexibility of binding partners and the presence of water molecules. It provided a comparative description of complete carbohydrate recognition domains, which had so far not been characterized in animal galectins. The structural models assigned II, I selectivity to a region downstream of the central Trp moiety. Docking revealed that the tetrasaccharides can be accommodated in their free-state low-energy conformations. CG-14's preference for A versus B epitopes could be attributed to a contact between His124 and the N-acetyl group of GalNAc. Regarding intergalectin comparison, the Ala53/Cys51 exchange affects the interaction potential of His54/His52. Close inspection of simulated dynamic interplay revealed reorientation of His124 at the site of the His124/Glu123 substitution, with potential impact on ligand dissociation. In summary, this study identifies activity differences and provides information on their relation to structural divergence, epitomizing the value of this combined approach beyond galectins. [ABSTRACT FROM AUTHOR]