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Infantile-onset RNaseT2 deficient leukoencephalopathy is characterised by cystic brain lesions, multifocal white matter alterations, cerebral atrophy, and severe psychomotor impairment. The phenotype is similar to congenital cytomegalovirus brain infection and overlaps with type I interferonopathies, suggesting a role for innate immunity in its pathophysiology. To date, pathophysiological studies have been hindered by the lack of mouse models recapitulating the neuroinflammatory encephalopathy found in patients. In this study, we generated Rnaset2−/− mice using CRISPR/Cas9-mediated genome editing. Rnaset2−/− mice demonstrate upregulation of interferon-stimulated genes and concurrent IFNAR1-dependent neuroinflammation, with infiltration of CD8+ effector memory T cells and inflammatory monocytes into the grey and white matter. Single nuclei RNA sequencing reveals homeostatic dysfunctions in glial cells and neurons and provide important insights into the mechanisms of hippocampal-accentuated brain atrophy and cognitive impairment. The Rnaset2−/− mice may allow the study of CNS damage associated with RNaseT2 deficiency and may be used for the investigation of potential therapies., Studies on interferon-driven brain pathology have so far been hampered by the lack of appropriate animal models. Here the authors characterize RNASET2-deficient mice and show that neuroinflammation and brain atrophy are IFNAR1-dependent.

In mammals, histone 3 lysine 4 methylation (H3K4me) is mediated by six different lysine methyltransferases. Among these enzymes, SETD1B (SET domain containing 1b) has been linked to syndromic intellectual disability in human subjects, but its role in the mammalian postnatal brain has not been studied yet. Here, we employ mice deficient for Setd1b in excitatory neurons of the postnatal forebrain, and combine neuron‐specific ChIP‐seq and RNA‐seq approaches to elucidate its role in neuronal gene expression. We observe that Setd1b controls the expression of a set of genes with a broad H3K4me3 peak at their promoters, enriched for neuron‐specific genes linked to learning and memory function. Comparative analyses in mice with conditional deletion of Kmt2a and Kmt2b histone methyltransferases show that SETD1B plays a more pronounced and potent role in regulating such genes. Moreover, postnatal loss of Setd1b leads to severe learning impairment, suggesting that SETD1B‐dependent regulation of H3K4me levels in postnatal neurons is critical for cognitive function., Comparative analyses of conditional deletion of mammalian H3K4 methyltransferases show a selective role for Setd1b in expression of neuron‐specific genes important for cognitive function.

Structural information about protein–RNA complexes supports the understanding of crucial recognition processes in the cell, and it can allow the development of high affinity ligands to interfere with these processes. In this respect, the identification of amino acid hotspots is particularly important. In contrast to protein–protein interactions, in silico approaches for protein–RNA interactions lag behind in their development. Herein, we report an analysis of available protein–RNA structures. We assembled a data set of 322 crystal and NMR structures and analyzed them regarding interface properties. In addition, we describe a computational alanine-scanning approach which provides interaction scores for interface amino acids, allowing the identification of potential hotspots in protein–RNA interfaces. We have made the computational approach available as an online tool, which allows interaction scores to be calculated for any structure of a protein–RNA complex by uploading atomic coordinates to the PRI HotScore web server (https://pri-hotscore.labs.vu.nl).

Macrocyclic peptides can interfere with challenging biomolecular targets including protein-protein interactions. Whereas there are various approaches that facilitate the identification of peptide-derived ligands, their evolution into higher affinity binders remains a major hurdle. We report a virtual screen based on molecular docking that allows the affinity maturation of macrocyclic peptides taking non-natural amino acids into consideration. These macrocycles bear large and flexible substituents that usually complicate the use of docking approaches. A virtual library containing more than 1400 structures was screened against the target focusing on docking poses with the core structure resembling a known bioactive conformation. Based on this screen, a macrocyclic peptide 22 involving two non-natural amino acids was evolved showing increased target affinity and biological activity. Predicted binding modes were verified by X-ray crystallography. The presented workflow allows the screening of large macrocyclic peptides with diverse modifications thereby expanding the accessible chemical space and reducing synthetic efforts. OA hybrid

Ligand-directed reactions allow chemical transformations at very low reactant concentrations and can thus provide access to efficient approaches for the post-translational modification of proteins. The development of these proximity-induced reactions is hampered by the number of appropriate ligands and the lack of design principles. Addressing these limitations, we report a proximity-induced labeling system which applies a moderate affinity peptide ligand. The design process was structure-guided and supported by molecular dynamics simulations. We show that selective protein labeling can be performed inside living cells enabling the subcellular translocation of a protein via ligand-directed chemistry for the first time.

Constraining a peptide in its bioactive conformation by macrocyclization represents a powerful strategy to design modulators of challenging biomolecular targets. This holds particularly true for the development of inhibitors of protein-protein interactions which often involve interfaces lacking defined binding pockets. Such flat surfaces are demanding targets for traditional small molecules rendering macrocyclic peptides promising scaffolds for novel therapeutics. However, the contribution of peptide dynamics to binding kinetics is barely understood which impedes the design process. Herein, we report unexpected trends in the binding kinetics of two closely related macrocyclic peptides that bind their receptor protein with high affinity. Isothermal titration calorimetry, 19F NMR experiments and molecular dynamics simulations reveal that increased conformational flexibility of the macrocycle–receptor complex reduces dissociation rates and contributes to complex stability. This observation has impact on macrocycle design strategies that have so far mainly focused on the stabilization of bioactive ligand conformations.

Protein engineering studies often suggest the emergence of completely new enzyme functionalities to be highly improbable. However, enzymes likely catalysed many different reactions already in the last universal common ancestor. Mechanisms for the emergence of completely new active sites must therefore either plausibly exist or at least have existed at the primordial protein stage. Here, we use resurrected Precambrian proteins as scaffolds for protein engineering and demonstrate that a new active site can be generated through a single hydrophobic-to-ionizable amino acid replacement that generates a partially buried group with perturbed physico-chemical properties. We provide experimental and computational evidence that conformational flexibility can assist the emergence and subsequent evolution of new active sites by improving substrate and transition-state binding, through the sampling of many potentially productive conformations. Our results suggest a mechanism for the emergence of primordial enzymes and highlight the potential of ancestral reconstruction as a tool for protein engineering., This work was supported by Feder Funds, Grants from the Spanish Ministry of Economy and Competitiveness BIO2015-66426-R (J.M.S.-R.), CSD2009-00088 (J.M.S.-R.), CTQ2011-29299-C02-01 (F.S.-G.), CTQ2011-22514 (M.B.), BIO2016-74875-P (J.A.G.), ‘Factoría Española de Cristalización˜’, Consolider-Ingenio 2010 (J.A.G.) and CEI BioTic V19-2015 (V.A.R.), a Wallenberg Academy Fellowship (S.C.L.K.) and DuPont Young Professor Award (E.A.G.) and Grants NNX13AI08G and NNX13AI10G (E.A.G.) from NASA Exobiology. The European Research Council has provided financial support under the European Community’s Seventh Framework Programme (FP7/2007–2013)/ERC Grant Agreement No. 306474.

A promising way to interfere with biological processes is through the control of protein-protein interactions by means of small molecules that modulate the formation of protein-protein complexes. Although the feasibility of this approach has been demonstrated in principle by recent results, many of the small-molecule modulators known to date have not been found by rational design approaches. In large part this is due to the challenges that one faces in dealing with protein binding epitopes compared to, e.g., enzyme binding pockets. Recent advances in the understanding of the energetics and dynamics of protein binding interfaces[1] and methodological developments in the field of structure-based drug design methods may open up a way to apply rational design approaches also for finding protein-protein interaction modulators.2 Here, we first show in a retrospective analysis of the well-investigated interleukin-2 system how I) potential binding sites in an interface can be identified from an unbound protein structure, II) the interface can be dissected in terms of energetic contributions of single residues, and III) one can make use of this knowledge for guiding the development of small-molecule modulators. When applied to a leukaemia-associated fusion protein in a prospective manner, the predictive character of the methodology is demonstrated [2]. Another challenge arises from the fact that protein-protein interfaces are flexible. In the second part, we thus demonstrate a novel approach for including protein flexibility into protein-ligand docking[3]. This approach is based on elastic potential grids, which provide an accurate and efficient representation of intermolecular interactions in fully-flexible docking.

Protein-protein complexes play key roles in all cellular signal transduction processes. Here, we present a fast and accurate computational approach to predict protein-protein interactions. The approach is based on DrugScorePPI, a knowledge-based scoring function for which pair potentials were derived from 851 complex structures and adapted against 309 experimental alanine scanning results. We developed the DrugScorePPI webserver [1], accessible at http://cpclab.uni-duesseldorf.de/dsppi, that is intended for identifying hotspot residues in protein-protein interfaces. For this, it allows performing computational alanine scanning of a protein-protein interface within a few minutes. Our approach has been successfully validated by application to an external test set of 22 alanine mutations in the interface of Ras/RalGDS and outperformed the widely used CC/PBSA, FoldX, and Robetta methods [1]. Next, DrugScorePPI was teamed with FRODOCK [2], a fast FFT-based protein-protein docking tool, in order to predict 3D structures of protein-protein complexes. When applied to datasets of 54 bound-bound (I) and 54 unbound-unbound (II) test cases, convincing results were obtained (docking success rate for complexes with rmsd < 10 A: I: ~80%; II: ~50%). Thus, we set out to evaluate whether our approach of deformable potential grids [3], previously developed for protein-ligand docking, also provides an accurate and efficient means for representing intermolecular interactions in fully-flexible protein-protein docking. The underlying idea is to adapt a 3D grid of potential field values, pre-calculated from an initial protein conformation by DrugScorePPI, to another conformation by moving grid intersection points in space, but keeping the potential field values constant. Protein movements are thereby translated into grid intersection displacements by coupling protein atoms to nearby grid intersection points by means of harmonic springs and modelling the irregular, deformable 3D grid as a homogeneous linear elastic body applying elasticity theory. Thus, new protein conformations can be sampled during a docking run without the need to re-calculate potential field values.

Protein-protein complexes are known to play key roles in many cellular processes. Therefore, knowledge of the three-dimensional structure of protein-complexes is of fundamental importance. A key goal in protein-protein docking is to identify near-native protein-complex structures. In this work, we address this problem by deriving a knowledge-based scoring function from protein-protein complex structures and further fine-tuning of the statistical potentials against experimentally determined alanine-scanning results. Based on the formalism of the DrugScore approach1, distance-dependent pair potentials are derived from 850 crystallographically determined protein-protein complexes 2. These DrugScorePPI potentials display quantitative differences compared to those of DrugScore, which was derived from protein-ligand complexes. When used as an objective function to score a non-redundant dataset of 54 targets with "unbound perturbation" solutions, DrugscorePPI was able to rank a near-native solution in the top ten in 89% and in the top five in 65% of the cases. Applied to a dataset of "unbound docking" solutions, DrugscorePPI was able to rank a near-native solution in the top ten in 100% and in the top five in 67% of the cases. Furthermore, Drugscore-PPI was used for computational alanine-scanning of a dataset of 18 targets with a total of 309 mutations to predict changes in the binding free energy upon mutations in the interface. Computed and experimental values showed a correlation of R2 = 0.34. To improve the predictive power, a QSAR-model was built based on 24 residue-specific atom types that improves the correlation coefficient to a value of 0.53, with a root mean square deviation of 0.89 kcal/mol. A Leave-One-Out analysis yields a correlation coefficient of 0.41. This clearly demonstrates the robustness of the model. The application to an independent validation dataset of alanine-mutations was used to show the predictive power of the method and yields a correlation coefficient of 0.51. Based on these findings, Drugscore-PPI was used to successful identify hotspots in multiple protein-interfaces. These results suggest that DrugscorePPI is an adequate method to score protein-protein interactions.