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Schizophrenia is a psychiatric disorder that is still not readily treatable. Pharmaceutical advances in the treatment of schizophrenia have mainly focused on the protein coding part of the human genome. However, the vast majority of the human transcriptome consists of non-coding RNAs. MicroRNAs are small non-coding RNAs that control the transcriptome at the systems level. In the present study we analyzed the microRNAome in blood and postmortem brains of controls and schizophrenia patients and found that miR-99b-5p was downregulated in both the prefrontal cortex and blood of patients. At the mechanistic level we show that inhibition of miR-99b-5p leads to schizophrenia-like phenotypes in mice and induced inflammatory processes in microglia linked to synaptic pruning. The miR-99b-5p-mediated inflammatory response in microglia depended onZ-DNA binding protein 1(Zbp1) which we identified as a novel miR-99b-5p target. Antisense oligos (ASOs) againstZbp1ameliorated the pathological phenotypes caused by miR-99b-5p inhibition. In conclusion, we report a novel miR-99b-5p-Zbp1pathway in microglia that contributes to the pathogenesis of schizophrenia. Our data suggest that strategies to increase the levels of miR-99b-5p or inhibitZbp1could become a novel therapeutic strategy.

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.

Histone 3 lysine 4 methylation (H3K4me) is mediated by six different lysine methyltransferases. Amongst these enzymes SET domain containing 1b (SETD1B) has been linked to syndromic intellectual disability but its role in the postnatal brain has not been studied yet. Here we employ mice that lackSetd1bfrom 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 genes with a broad H3K4me3 peak at their promoters that represent neuronal enriched genes linked to learning and memory function. Comparative analysis to corresponding data from conditionalKmt2aandKmt2bknockout mice suggests that this function is specific to SETD1B. Moreover, postnatal loss ofSetd1bleads to severe learning impairment, suggesting that SETD1B-mediated regulation of H3K4me levels in postnatal neurons is critical for cognitive function.

Neurons are highly compartmentalized cells that depend on local protein synthesis. Messenger RNAs (mRNAs) have thus been detected in neuronal dendrites, and more recently in the pre- and postsynaptic compartments as well. Other RNA species such as microRNAs have also been described at synapses where they are believed to control mRNA availability for local translation. A combined dataset analyzing the synaptic coding and non-coding RNAome via next-generation sequencing approaches is, however, still lacking. Here, we isolate synaptosomes from the hippocampus of young wild-type mice and provide the coding and non-coding synaptic RNAome. These data are complemented by a novel approach for analyzing the synaptic RNAome from primary hippocampal neurons grown in microfluidic chambers. Our data show that synaptic microRNAs control almost the entire synaptic mRNAome, and we identified several hub microRNAs. By combining the in vivo synaptosomal data with our novel microfluidic chamber system, our findings also support the hypothesis that part of the synaptic microRNAome may be supplied to neurons via astrocytes. Moreover, the microfluidic system is suitable for studying the dynamics of the synaptic RNAome in response to stimulation. In conclusion, our data provide a valuable resource and point to several important targets for further research. Supplementary Information The online version contains supplementary material available at 10.1007/s12035-021-02296-y.

Histone-3-lysine-4-methylation (H3K4me) is mediated by six different lysine methyltransferases (KMTs). Amongst these enzymes SET domain containing 1b (SETD1B) has been linked to intellectual disability but its role in the adult brain has not been studied yet. Here we show that mice lackingSetd1bfrom excitatory neurons of the adult forebrain exhibit severe memory impairment. By combining neuron-specific ChIP-seq, RNA-seq and single cell RNA-seq approaches we show thatSetd1bcontrols the expression of neuronal-identity genes with a broad H3K4me3 peak linked to learning and memory processes. Our data furthermore suggest that basal neuronal gene-expression is ensured by other H3K4 KMTs such asKmt2aandKmt2bwhile the additional presence ofSetd1bat the single cell level provides transcriptional consistency to the expression of genes important for learning & memory.

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).

The amphiphilic nature of the amyloid-β (Aβ) peptide associated with Alzheimer's disease facilitates various interactions with biomolecules such as lipids and proteins, with effects on both structure and toxicity of the peptide. Here, we investigate these peptide-amphiphile interactions by experimental and computational studies of Aβ(1-40) in the presence of surfactants with varying physicochemical properties. Our findings indicate that electrostatic peptide-surfactant interactions are required for coclustering and structure induction in the peptide and that the strength of the interaction depends on the surfactant net charge. Both aggregation-prone peptide-rich coclusters and stable surfactant-rich coclusters can form. Only Aβ(1-40) monomers, but not oligomers, are inserted into surfactant micelles in this surfactant-rich state. Surfactant headgroup charge is suggested to be important as electrostatic peptide-surfactant interactions on the micellar surface seems to be an initiating step toward insertion. Thus, no peptide insertion or change in peptide secondary structure is observed using a nonionic surfactant. The hydrophobic peptide-surfactant interactions instead stabilize the Aβ monomer, possibly by preventing self-interaction between the peptide core and C-terminus, thereby effectively inhibiting the peptide aggregation process. These findings give increased understanding regarding the molecular driving forces for Aβ aggregation and the peptide interaction with amphiphilic biomolecules.

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

Triosephosphate isomerase (TIM) is a proficient catalyst of the reversible isomerization of dihydroxyacetone phosphate (DHAP) to d-glyceraldehyde phosphate (GAP), via general base catalysis by E165. Historically, this enzyme has been an extremely important model system for understanding the fundamentals of biological catalysis. TIM is activated through an energetically demanding conformational change, which helps position the side chains of two key hydrophobic residues (I170 and L230), over the carboxylate side chain of E165. This is critical both for creating a hydrophobic pocket for the catalytic base and for maintaining correct active site architecture. Truncation of these residues to alanine causes significant falloffs in TIM’s catalytic activity, but experiments have failed to provide a full description of the action of this clamp in promoting substrate deprotonation. We perform here detailed empirical valence bond calculations of the TIM-catalyzed deprotonation of DHAP and GAP by both wild-type TIM and its I170A, L230A, and I170A/L230A mutants, obtaining exceptional quantitative agreement with experiment. Our calculations provide a linear free energy relationship, with slope 0.8, between the activation barriers and Gibbs free energies for these TIM-catalyzed reactions. We conclude that these clamping side chains minimize the Gibbs free energy for substrate deprotonation, and that the effects on reaction driving force are largely expressed at the transition state for proton transfer. Our combined analysis of previous experimental and current computational results allows us to provide an overview of the breakdown of ground-state and transition state effects in enzyme catalysis in unprecedented detail, providing a molecular description of the operation of a hydrophobic clamp in triosephosphate isomerase.

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.

Serum paraoxonase 1 (PON1) is a native lactonase capable of promiscuously hydrolyzing a broad range of substrates, including organophosphates, esters, and carbonates. Structurally, PON1 is a six-bladed beta-propeller with a flexible loop (residues 70-81) covering the active site. This loop contains a functionally critical Tyr at position 71. We have performed detailed experimental and computational analyses of the role of selected Y71 variants in the active site stability and catalytic activity in order to probe the role of Y71 in PON1's lactonase and organophosphatase activities. We demonstrate that the impact of Y71 substitutions on PON1's lactonase activity is minimal, whereas the k(cat) for the paraoxonase activity is negatively perturbed by up to 100-fold, suggesting greater mutational robustness of the native activity. Additionally, while these substitutions modulate PON1's active site shape, volume, and loop flexibility, their largest effect is in altering the solvent accessibility of the active site by expanding the active site volume, allowing additional water molecules to enter. This effect is markedly more pronounced in the organophosphatase activity than the lactonase activity. Finally, a detailed comparison of PON1 to other organophosphatases demonstrates that either a similar "gating loop" or a highly buried solvent excluding active site is a common feature of these enzymes. We therefore posit that modulating the active site hydrophobicity is a key element in facilitating the evolution of organophosphatase activity. This provides a concrete feature that can be utilized in the rational design of next-generation organophosphate hydrolases that are capable of selecting a specific reaction from a pool of viable substrates.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 12.0px 'Helvetica Neue'} Recent years have witnessed an explosion of interest in computational studies of DNA binding proteins, including both coarse grained and atomistic simulations of transcription factor-DNA recognition, in order to understand how these transcription factors recognize their binding sites on the DNA with such exquisite specificity. The present study performs μs-timescale all-atom simulations of the dimeric form of the lactose repressor (LacI), both in the absence of any DNA, and in the presence of both specific and non-specific complexes, considering three different DNA sequences. We examine, specifically, the conformational differences between specific and non-specific protein-DNA interactions, as well as the behavior of the helix-turn-helix motif of LacI when interacting with the DNA. Our simulations suggest that stable LacI binding occurs primarily to bent A-form DNA, with a loss of LacI conformational entropy and optimization of correlated conformational equilibria across the protein. In addition, binding to the specific operator sequence involves a slightly larger number of stabilizing DNA-protein hydrogen bonds (in comparison to non-specific complexes), that may account for the experimentally observed specificity for this operator. In doing so, our simulations provide a detailed atomistic description of potential structural drivers for LacI selectivity.

A Correction to this paper has been published: 10.1007/s12035-021-02349-2

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.

The Constraint Network Analysis (CNA) web server provides a user-friendly interface to the CNA approach developed in our laboratory for linking results from rigidity analyses to biologically relevant characteristics of a biomolecular structure. The CNA web server provides a refined modeling of thermal unfolding simulations that considers the temperature dependence of hydrophobic tethers and computes a set of global and local indices for quantifying biomacromolecular stability. From the global indices, phase transition points are identified where the structure switches from a rigid to a floppy state; these phase transition points can be related to a protein’s (thermo-)stability. Structural weak spots (unfolding nuclei) are automatically identified, too; this knowledge can be exploited in data-driven protein engineering. The local indices are useful in linking flexibility and function and to understand the impact of ligand binding on protein flexibility. The CNA web server robustly handles small-molecule ligands in general. To overcome issues of sensitivity with respect to the input structure, the CNA web server allows performing two ensemble-based variants of thermal unfolding simulations. The web server output is provided as raw data, plots and/or Jmol representations. The CNA web server, accessible at http://cpclab.uni-duesseldorf.de/cna or http://www.cnanalysis.de, is free and open to all users with no login requirement.

Ras-like small GTPases function as molecular switches and regulate diverse cellular events. To examine the dynamics of signaling requires spatiotemporal visualization of their activity in the cell. Current small GTPase sensors rely on specific effector domains that are available for only a small number of GTPases and compete for endogenous regulator/effector binding. Here, we describe versatile conformational sensors for GTPase activity (COSGAs) based on the conserved GTPase fold. Conformational changes upon GDP/GTP exchange were directly observed in solution, on beads, and in live cells by Forster resonance energy transfer (FRET). The COSGAs allow for monitoring of Rab1 and K-Ras activity in live cells using fluorescence lifetime imaging microscopy. We found that Rab1 is largely active in the cytoplasm and inactive at the Golgi, suggesting that the Golgi serves as the terminal of the Rab1 functional cycle. K-Ras displays polarized activity at the plasma membrane, with less activity at the edge of the cell and membrane ruffles.

Protein-protein interfaces (PPIs) are an important class of drug targets. We report on the first large-scale validation study on docking into PPIs. DrugScore-adapted AutoDock3 and Glide showed good success rates with a moderate drop-off compared to docking to "classical targets". An analysis of the binding energetics in a PPI allows identifying those interfaces that are amenable for docking. The results are important for deciding if structure-based design approaches can be applied to a particular PPI.

The NMSim web server implements a three-step approach for multiscale modeling of protein conformational changes. First, the protein structure is coarse-grained using the FIRST software. Second, a rigid cluster normal-mode analysis provides low-frequency normal modes. Third, these modes are used to extend the recently introduced idea of constrained geometric simulations by biasing backbone motions of the protein, whereas side chain motions are biased toward favorable rotamer states (NMSim). The generated structures are iteratively corrected regarding steric clashes and stereochemical constraint violations. The approach allows performing three simulation types: unbiased exploration of conformational space; pathway generation by a targeted simulation; and radius of gyration-guided simulation. On a data set of proteins with experimentally observed conformational changes, the NMSim approach has been shown to be a computationally efficient alternative to molecular dynamics simulations for conformational sampling of proteins. The generated conformations and pathways of conformational transitions can serve as input to docking approaches or more sophisticated sampling techniques. The web server output is a trajectory of generated conformations, Jmol representations of the coarse-graining and a subset of the trajectory and data plots of structural analyses. The NMSim webserver, accessible at http://www.nmsim.de, is free and open to all users with no login requirement.

The highly flexible nature of RNA provides a formidable challenge for structure-based drug design approaches that target RNA. We introduce an approach for modeling target conformational changes in RNA-ligand docking based on potential grids that are represented as elastic bodies using Navier's equation. This representation provides an accurate and efficient description of RNA-ligand interactions even in the case of a moving RNA structure. When applied to a data set of 17 RNA-ligand complexes, filtered out of the largest validation data set used for RNA-ligand docking so far, the approach is twice as successful as docking into an apo structure and still half as successful as redocking to the holo structure. The approach allows considering RNA movements of up to 6 Å rmsd and is based on a uniform and robust parametrization of the properties of the elastic potential grids, so that the approach is applicable to different RNA-ligand complex classes.

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analysed for their effectiveness in virtual screening against four different targets: angiotensin-converting enzyme (ACE), cyclooxygenase 2 (COX-2), thrombin and human immunodeficiency virus 1 (HIV-1) protease. The relative performance of the tools was compared by examining their ability to recognise known active compounds from a set of actives and nonactives. Furthermore, we investigated whether the application of different virtual-screening methods in parallel provides complementary or redundant hit lists. Docking was performed with GOLD, Glide, FlexX and Surflex. The obtained docking poses were rescored by using nine different scoring functions in addition to the scoring functions implemented as objective functions in the docking algorithms. Ligand-based virtual screening was done with ROCS (3D-similarity searching), Feature Trees and Scitegic Functional Fingerprints (2D-similarity searching). The results show that structure- and ligand-based virtual-screening methods provide comparable enrichments in detecting active compounds. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary. These results suggest that a parallel application of different structure- and ligand-based virtual-screening methods increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign.

Protein–ligand docking is the major workhorse in computeraided structure-based lead finding and optimization. Predicted protein–ligand complex configurations are used for studying protein–ligand interactions, estimating binding affinities, and as a final filter step in virtual screening. Early methods on protein–ligand docking treated either both proteins and ligands as rigid molecules or allowed for conformational flexibility of only the ligand, following a “rigid receptor hypothesis”. However, pronounced plasticity upon ligand binding has been observed for several pharmacologically important proteins, such as HIV-1 protease, aldose reductase, FK506 binding protein, renin, and dihydrofolate reductase (DHFR). Protein plasticity comprises a range of possible movements, from single side chains to drastic structural rearrangements as seen in calmodulin. Not surprisingly, if docking is performed with the assumption of a rigid active site in those cases, a dramatic decrease in docking accuracy is observed: Whereas a docking success rate of 76% was reported for docking a ligand back to the protein structure derived from the ligand’s co-crystal structure (“re-docking”), this rate dropped to only 49% if the ligands were docked against protein structures derived from other ligands’ co-crystal structures (“cross docking”). Similar drop-offs have also been reported by others. Furthermore, the drop in docking accuracy was found to be mirrored by the degree to which the protein moves upon ligand binding 32] so that docking to an empty form (“apo docking”) usually shows the largest deterioration. This clearly highlights the importance of developing strategies for taking protein plasticity into account in addition to the conformational flexibility of the ligand (henceforth referred to as “fully flexible docking”) to prevent mis-dockings of ligands to flexible proteins. At present, three major routes to include protein plasticity during docking can be identified. The classification correlates with various types of protein movements observed upon ligand binding. First, plasticity is considered implicitly following a soft-docking strategy with attenuated repulsive forces between protein and ligand. While this is simple to implement and does not compromise docking efficiency, the range of possible movements that can be covered is rather limited. Second, only side chain conformational changes in the binding pocket are modeled. These approaches assume that the protein has a rigid backbone structure, thus neglecting critical backbone shifts responsible for mis-docking of ligands. Third, large-scale conformational changes including backbone motions are taken into account. There are several types of approaches in this category: perform parallel docking into multiple protein conformations; structurally combine multiple conformations; model protein motions in reduced coordinates; apply molecular dynamics or Monte Carlo based sampling to either generate protein–ligand configurations 50] or optimize pre-computed configurations. Docking accuracy and computational efficiency determine the scope and quality of a docking approach. As for the first, fully flexible docking should ultimately become as accurate as “re-docking” pursued with a “rigid receptor hypothesis”. Preserving computational efficiency is equally important, given the short timeframe usually available for a docking run. In particular, evaluating the interaction energy between protein and ligand is expensive. A widely used approach to increase the calculation speed is based on potential fields that are pre-calculated just once in the binding pocket region of the protein, by scanning interactions between the protein and ligand atom probes. The potential field values are stored at the intersections of a regular 3D grid, providing a lookup table. The approach is applicable to all distance-dependent pairwise interactions, such as electrostatic and van der Waals interactions and interactions described by statistical pair potentials. In subsequent docking runs, interaction energies between protein and ligand are then determined in constant time from the lookup table by means of interpolation. This provides a significant rate increase relative to individually evaluating the pair interactions. However, this regular 3D grid-based approach is incompatible with fully flexible docking, because the lookup table values would need to be recalculated for every new protein conformation considered. In the present study, we therefore developed an accurate representation of intermolecular interactions that makes use of the high efficiency in evaluating protein–ligand interaction energies from lookup tables even in the case of a moving protein. The new lookup table function for potential fields that we introduce is based on irregular, deformable 3D grids (Figure 1). The underlying idea is to adapt a 3D grid with pre-calculated potential field values, which were derived from an initial protein conformation, to another conformation by moving intersection points in space, but keeping the potential field values constant. As in the case of a regular 3D grid, interaction energies between ligand and protein are then determined from this lookup table. In contrast to the established approach, however, new protein conformations can now be sampled [a] S. Kazemi, D. M. Kr ger, Prof. Dr. H. Gohlke Institut f r Pharmazeutische und Medizinische Chemie Heinrich-Heine-Universit t Universit tsstr. 1, 40225 D sseldorf (Germany) Fax: (+49)211-81-13847 E-mail : gohlke@uni-duesseldorf.de [b] Dr. F. Sirockin Novartis Pharma AG, 4002 Basel (Switzerland) Supporting information for this article is available on the WWW under http://dx.doi.org/10.1002/cmdc.200900146.

Der CNA-Webserver ist ein Online-Service, der biologisch relevante Proteineigenschaften basierend auf Rigiditatsanalysen vorhersagt und visualisiert. Er erlaubt, Struktur und Flexibilitat eines Proteins mit dessen (Thermo-)Stabilitat und Funktion zu verknupfen.

Der NMSim-Webserver ist ein Online-Service, der dabei hilft, biologisch relevante Bewegungen von Proteinen vorherzusagen und zu visualisieren.

The distance-dependent knowledge-based DrugScorePPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking >unbound perturbation> (>unbound docking>) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScorePPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScorePPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScorePPI/ FRODOCK will be successful. © 2014 Krüger et al.

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.

Der Drugscore-PPI-Webserver ahmt in silico einen Alaninscan nach und sagt so bindungsrelevante Seitenketten in Protein-Protein-Komplexen vorher. Damit lasst sich die Signalubertragung in Zellen untersuchen.

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.