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Antibody-antigen co-crystal structures are a valuable resource for the fundamental understanding of antibody-mediated immunity. Determination of structures with antibodies in complex with their antigens, however, is a laborious task without guarantee of success. Therefore, homology modeling of antibodies and docking to their respective-antigens has become a very important technique to drive antibody and vaccine design. The quality of the antibody modeling process is critical for the success of these endeavors. Here, we compare different computational protocols for predicting antibody structure from sequence in the biomolecular modeling software Rosetta - all of which use multiple existing antibody structures to guide modeling. Specifically, we compare protocols developed solely to predict antibody structure (RosettaAntibody, AbPredict) with a universal homology modeling protocol (RosettaCM). Following recent advances in homology modeling with multiple templates simultaneously, we propose that the use of multiple templates over the same antibody regions may improve modeling performance. To evaluate whether multi-template comparative modeling with RosettaCM can improve the modeling accuracy of antibodies over existing methods, this study compares the performance of the three modeling algorithms when modeling human antibodies taken from antibody-antigen co-crystal structures. In these benchmarking experiments, RosettaCM outperformed other methods when modeling antibodies with long HCDR3s and few available templates.

Inhibition of more than one cancer‐related pathway by multi‐target agents is an emerging approach in modern anticancer drug discovery. Here, based on the well‐established synergy between histone deacetylase inhibitors (HDACi) and alkylating agents, we present the discovery of a series of alkylating HDACi using a pharmacophore‐linking strategy. For the parallel synthesis of the target compounds, we developed an efficient solid‐phase‐supported protocol using hydroxamic acids immobilized on resins (HAIRs) as stable and versatile building blocks for the preparation of functionalized HDACi. The most promising compound, 3 n, was significantly more active in apoptosis induction, activation of caspase 3/7, and formation of DNA damage (γ‐H2AX) than the sum of the activities of either active principle alone. Furthermore, to demonstrate the utility of our preloaded resins, the HAIR approach was successfully extended to the synthesis of a proof‐of‐concept proteolysis‐targeting chimera (PROTAC), which efficiently degrades histone deacetylases., Hydroxamic acids immobilized on resins (HAIRs) were developed and utilized for the library synthesis of DNA‐alkylating HDAC inhibitors and a proof‐of‐concept HDAC degrader (PROTAC). A hybrid compound based on the pharmacophores of chlorambucil and panobinostat was identified as the most promising chimeric HDAC inhibitor and demonstrated improved anticancer properties compared to the sum of the activities of either pharmacophore alone.

Herein we report the structure-activity and structure-physicochemical property relationships of a series of class I selective ortho-aminoanilides targeting the "foot-pocket" in HDAC12. To balance the structural benefits and the physicochemical disadvantages of these substances, we started with a set of HDACi related to tacedinaline (CI-994) and evaluated their solubility, lipophilicity (log D

PURPOSEBi-allelic TECPR2 variants have been associated with a complex syndrome with features of both a neurodevelopmental and neurodegenerative disorder. Here, we provide a comprehensive clinical description and variant interpretation framework for this genetic locus.METHODSThrough an international collaboration, we identified 17 individuals from 15 families with bi-allelic TECPR2-variants. We systemically reviewed clinical and molecular data from this cohort and 11 cases previously reported. Phenotypes were standardized using Human Phenotype Ontology terms.RESULTSA cross-sectional analysis revealed global developmental delay/intellectual disability, muscular hypotonia, ataxia, hyporeflexia, respiratory infections and central/nocturnal hypopnea as core manifestations. A review of brain MRI scans demonstrated a thin corpus callosum in 52%. We evaluated 17 distinct variants. Missense variants in TECPR2 are predominantly located in the N- and C-terminal regions containing β-propeller repeats. Despite constituting nearly half of disease associated TECPR2 variants, classifying missense variants as (likely) pathogenic according to ACMG criteria remains challenging. We estimate a pathogenic variant carrier frequency of 1/1,221 in the general and 1/155 in the Jewish Ashkenazi populations.CONCLUSIONBased on clinical, neuroimaging and genetic data, we provide recommendations for variant reporting, clinical assessment, and surveillance/treatment of individuals with TECPR2-associated disorder. This sets the stage for future prospective natural history studies.CONFLICTS OF INTERESTAll authors involved in the study declare no conflicts of interest relevant to this study.

The Rosetta molecular modeling software package provides a large number of experimentally validated tools for modeling and designing proteins, nucleic acids, and other biopolymers, with new protocols being added continually. While freely available to academic users, external usage is limited by the need for expertise in the Unix command line environment. To make Rosetta protocols available to a wider audience, we previously created a web server called Rosetta Online Server that Includes Everyone (ROSIE), which provides a common environment for hosting web-accessible Rosetta protocols. Here we describe a simplification of the ROSIE protocol specification format, one that permits easier implementation of Rosetta protocols. Whereas the previous format required creating multiple separate files in different locations, the new format allows specification of the protocol in a single file. This new, simplified protocol specification has more than doubled the number of Rosetta protocols available under ROSIE. These new applications include pKa determination, lipid accessibility calculation, ribonucleic acid redesign, protein-protein docking, protein-small molecule docking, symmetric docking, antibody docking, cyclic toxin docking, critical binding peptide determination, and mapping small molecule binding sites. ROSIE is freely available to academic users at http://rosie.rosettacommons.org.

The neuropeptide Y2 receptor (Y2R) is involved in different pathophysiological processes such as epilepsy, mood disorders, angiogenesis and tumor growth. Therefore, the Y2R is an interesting target for drug development. A detailed understanding of the binding pocket could facilitate the development of highly selective antagonists to study the role of Y2R in vitro and in vivo. In this study, several residues crucial to the interaction of BIIE0246 and SF-11 derivatives with Y2R were investigated by signal transduction assays. Using the experimental results as constraints, the antagonists were docked into a comparative structural model of the Y2R. Despite differences in size and structure, all three antagonists display a similar binding site, including a deep hydrophobic cavity formed by transmembrane helices (TM) 5 and 6 as well as a hydrophobic patch at the top of the TM2 and 7. Additionally, we suggest that the antagonists block Q3.32, a position that has been shown to be crucial for binding of the amidated C-terminus of NPY and thus for receptor activation.

For many membrane proteins, the determination of their topology remains a challenge for methods like X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy. Electron paramagnetic resonance (EPR) spectroscopy has evolved as an alternative technique to study structure and dynamics of membrane proteins. The present study demonstrates the feasibility of membrane protein topology determination using limited EPR distance and accessibility measurements. The BCL::MP-Fold (BioChemical Library membrane protein fold) algorithm assembles secondary structure elements (SSEs) in the membrane using a Monte Carlo Metropolis (MCM) approach. Sampled models are evaluated using knowledge-based potential functions and agreement with the EPR data and a knowledge-based energy function. Twenty-nine membrane proteins of up to 696 residues are used to test the algorithm. The RMSD100 value of the most accurate model is better than 8 A for 27, better than 6 A for 22, and better than 4 A for 15 of the 29 proteins, demonstrating the algorithms' ability to sample the native topology. The average enrichment could be improved from 1.3 to 2.5, showing the improved discrimination power by using EPR data.

Small angle X-ray scattering (SAXS) is an experimental technique used for structural characterization of macromolecules in solution. Here, we introduce BCL::SAXS – an algorithm designed to replicate SAXS profiles from rigid protein models at different levels of detail. We first show our derivation of BCL::SAXS and compare our results with the experimental scattering profile of Hen Egg White Lysozyme. Using this protein we show how to generate SAXS profiles representing: 1) complete models, 2) models with approximated side chain coordinates, and 3) models with approximated side chain and loop region coordinates. We evaluated the ability of SAXS profiles to identify a correct protein topology from a non-redundant benchmark set of proteins. We find that complete SAXS profiles can be used to identify the correct protein by receiver operating characteristic (ROC) analysis with an area under the curve (AUC) > 99%. We show how our approximation of loop coordinates between secondary structure elements improves protein recognition by SAXS for protein models without loop regions and side chains. Agreement with SAXS data is a necessary but not sufficient condition for structure determination. We conclude that experimental SAXS data can be used as a filter to exclude protein models with large structural differences from the native.

Trotz jungster Fortschritte in der Strukturbestimmung von G-Protein-gekoppelten Rezeptoren (GPCR) gibt es nur wenige Daten zur Interaktion von GPCRs mit groseren Peptidliganden. In dieser Arbeit kombinieren wir Methoden der NMR-Spektroskopie, der molekularen Modellierung und der ortsgerichteten komplementaren Mutagenese und prasentieren ein Strukturmodell des Peptidhormons Neuropeptid Y (NPY) im Komplex mit seinem humanen G-Protein-gekoppelten Y2-Rezeptor (Y2R). Festkorper-NMR-Messungen von spezifisch isotopenmarkiertem NPY im Komplex mit in vitro gefaltetem und in Phospholipid-Bizellen rekonstituiertem Y2R liefern Daten zur bioaktiven Peptidstruktur. Unterstutzt von Losungs-NMR-Studien konnten Interaktionen des Liganden mit dem zweiten extrazellularen Loop des Rezeptors uber hydrophobe Kontakte nachgewiesen werden. Die C-terminale α-Helix von NPY, die sich in einer Membranumgebung in Abwesenheit des Rezeptors bildet, entfaltet sich ab T 32, um einen optimalen Kontakt tief in der Bindungstasche des transmembranen Bereichs des Y2R zu ermoglichen.

During CASP10 in summer 2012, we tested BCL::Fold for prediction of free modeling (FM) and template-based modeling (TBM) targets. BCL::Fold assembles the tertiary structure of a protein from predicted secondary structure elements (SSEs) omitting more flexible loop regions early on. This approach enables the sampling of conformational space for larger proteins with more complex topologies. In preparation of CASP11, we analyzed the quality of CASP10 models throughout the prediction pipeline to understand BCL::Fold's ability to sample the native topology, identify native-like models by scoring and/or clustering approaches, and our ability to add loop regions and side chains to initial SSE-only models. The standout observation is that BCL::Fold sampled topologies with a GDT_TS score > 33% for 12 of 18 and with a topology score > 0.8 for 11 of 18 test cases de novo. Despite the sampling success of BCL::Fold, significant challenges still exist in clustering and loop generation stages of the pipeline. The clustering approach employed for model selection often failed to identify the most native-like assembly of SSEs for further refinement and submission. It was also observed that for some β-strand proteins model refinement failed as β-strands were not properly aligned to form hydrogen bonds removing otherwise accurate models from the pool. Further, BCL::Fold samples frequently non-natural topologies that require loop regions to pass through the center of the protein. Proteins 2015; 83:547–563. © 2015 Wiley Periodicals, Inc.

Pancreatic polypeptide (PP) is a satiety-inducing gut hormone targeting predominantly the Y4 receptor within the neuropeptide Y multiligand/multireceptor family. Palmitoylated PP-based ligands have already been reported to exert prolonged satiety-inducing effects in animal models. Here, we suggest that other lipidation sites and different fatty acid chain lengths may affect receptor selectivity and metabolic stability. Activity tests revealed significantly enhanced potency of long fatty acid conjugates on all four Y receptors with a preference of position 22 over 30 at Y1 , Y2 and Y5 receptors. Improved Y receptor selectivity was observed for two short fatty acid analogues. Moreover, [K(30)(E-Prop)]hPP2-36 (15) displayed enhanced stability in blood plasma and liver homogenates. Thus, short chain lipidation of hPP at key residue 30 is a promising approach for anti-obesity therapy because of maintained selectivity and a sixfold increased plasma half-life.

Ceritinib, an advanced anaplastic lymphoma kinase (ALK) next-generation inhibitor, has been proved excellent antitumor activity in the treatment of ALK-associated cancers. However, the accumulation of acquired resistance mutations compromise the therapeutic efficacy of ceritinib. Despite abundant mutagenesis data, the structural determinants for reduced ceritinib binding in mutants remains elusive. Focusing on the G1123S and F1174C mutations, we applied molecular dynamics (MD) simulations to study possible reasons for drug resistance caused by these mutations. The MD simulations predict that the studied mutations allosterically impact the configurations of the ATP-binding pocket. An important hydrophobic cluster is identified that connects P-loop and the αC-helix, which has effects on stabilizing the conformation of ATP-binding pocket. It is suggested, in this study, that the G1123S and F1174C mutations can induce the conformational change of P-loop thereby causing the reduced ceritinib affinity and causing drug resistance.

When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge-based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton–proton distances to Cβ–Cβ distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 A versus 6.0 ± 2.0 A produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets. Proteins 2014; 82:587–595. © 2013 Wiley Periodicals, Inc.

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an α-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org.

The prolactin releasing peptide (PrRP) is involved in regulating food intake and body weight homeostasis, but molecular details on the activation of the PrRP receptor remain unclear. C-terminal segments of PrRP with 20 (PrRP20) and 13 (PrRP8-20) amino acids, respectively, have been suggested to be fully active. The data presented herein indicate this is true for the wildtype receptor only; a 5-10-fold loss of activity was found for PrRP8-20 compared to PrRP20 at two extracellular loop mutants of the receptor. To gain insight into the secondary structure of PrRP, we used CD spectroscopy performed in TFE and SDS. Additionally, previously reported NMR data, combined with ROSETTANMR, were employed to determine the structure of amidated PrRP20. The structural ensemble agrees with the spectroscopic data for the full-length peptide, which exists in an equilibrium between α- and 310-helix. We demonstrate that PrRP8-20's reduced propensity to form an α-helix correlates with its reduced biological activity on mutant receptors. Further, distinct amino acid replacements in PrRP significantly decrease affinity and activity but have no influence on the secondary structure of the peptide. We conclude that formation of a primarily α-helical C-terminal region of PrRP is critical for receptor activation. © 2012 Wiley Periodicals, Inc. Biopolymers 99: 273–281, 2013.

Predicting HIV-1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV-1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV-1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R=0.71 and SE=5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R=0.85 and SE=4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV-1 protease inhibitor development.

Glutamate, the major excitatory neurotransmitter, functions in the brain via activation of ligand gated cation channels and also the eight subtypes of Class C G protein-coupled metabotropic glutamate receptors (mGlus).[1] Selective allosteric modulation of mGlu5 has been shown to have potential for treatment of a variety of neurological disorders[2,3] including anxiety disorders[4,5], Parkinson’s disease[6–8], Fragile X syndrome[9] and schizophrenia.[10–14] The majority of mGlu5 negative allosteric modulators (NAMs) developed to date either contain an alkyne moiety 1–4 or employ the alkyne topology as basis for ligand design,[15] as in 5–8 (Figure 1). Only recently have mGlu5 NAM chemotypes been identified, through high-throughput screening (HTS) campaigns, that are structurally unrelated to the classical acetylenic derivatives, such as 9–12 (Figure 1).[16] Due to the prevalence of ‘molecular switch‘phenomenon in MPEP related scaffolds, our interest focused on the discovery and development of novel mGlu5 NAM chemotypes, by both HTS and Artificial Neural Network (ANN) virtual screens.

Basement membranes are thin extracellular protein layers, which separate endothelial and epithelial cells from the underlying connecting tissue. The main noncollagenous components of basement membranes are laminins, trimeric glycoproteins, which form polymeric networks by interactions of their N-terminal (LN) domains; however, no high-resolution structure of laminin LN domains exists so far. To construct models for laminin β(1) and γ(1) LN domains, 14 potentially suited template structures were determined using fold recognition methods. For each target/template-combination comparative models were created with Rosetta. Final models were selected based on their agreement with experimentally obtained distance constraints from natural cross-links, that is, disulfide bonds as well as chemical cross-links obtained from reactions with two amine-reactive cross-linkers. We predict that laminin β(1) and γ(1) LN domains share the galactose-binding domain-like fold.

Human ether-a-go-go-related gene (HERG) encodes the rapid, outwardly rectifying K+ current IKr that is critical for repolarization of the cardiac action potential. Congenital HERG mutations or unintended pharmaceutical block of IKr can lead to life-threatening arrhythmias. Here, we assess the functional role of the alanine at position 653 (HERG-A653) that is highly conserved among evolutionarily divergent K+ channels. HERG-A653 is close to the ‘glycine hinge’ implicated in K+ channel opening, and is flanked by tyrosine 652 and phenylalanine 656, which contribute to the drug binding site. We substituted an array of seven (I, C, S, G, Y, V and T) amino acids at position 653 and expressed individual variants in heterologous systems to assess changes in gating and drug binding. Substitution of A653 resulted in negative shifts of the V1/2 of activation ranging from −23.6 (A653S) to −62.5 (A653V) compared to −11.2 mV for wild-type (WT). Deactivation was also drastically altered: channels with A653I/C substitutions exhibited delayed deactivation in response to test potentials above the activation threshold, while A653S/G/Y/V/T failed to deactivate under those conditions and required hyperpolarization and prolonged holding potentials at −130 mV. While A653S/G/T/Y variants showed decreased sensitivity to the IKr inhibitor dofetilide, these changes could not be correlated with defects in channel closure. Homology modelling suggests that in the closed state, A653 forms tight contacts with several residues from the neighbouring subunit in the tetramer, playing a key role in S6 helix packing at the narrowest part of the vestibule. Our study suggests that A653 plays an important functional role in the outwardly rectifying gating behaviour of HERG, supporting channel closure at membrane potentials negative to the channel activation threshold.

Fits in a pocket: The binding mode of epothilone A (EpoA) to tubulin is determined by NMR spectroscopy in aqueous solution. The results explain the existing structure-activity relationship data for epothilones with both native and mutant tubulins, and support the existence of a common pharmacophore for EpoA and paclitaxel.

Protein-small molecule docking algorithms provide a means to model the structure of protein-small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side-chain flexibility for an accurate prediction of the protein-small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side-chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein- small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 A in 71 out of 100 protein-small molecule crystal structure complexes (self-docking). In cross-docking calculations in which both protein side-chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 A in 14 of 20 test cases.

We describe Rosetta predictions in the Sixth Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP), focusing on the free modeling category. Methods developed since CASP5 are described, and their application to selected targets is discussed. Highlights include improved performance on larger proteins (100–200 residues) and the prediction of a 70-residue alpha–beta protein to near-atomic resolution. Proteins 2005;Suppl 7:128–134. © 2005 Wiley-Liss, Inc.

Three new approaches for automated structure elucidations of organic molecules using NMR spectroscopic data were introduced recently. These approaches apply a neural network 13C NMR chemical shift prediction method to rank the results of structure generators by their agreement of the predicted and experimental chemical shifts. These three existing implementations are compared using realistic example molecules. The applicability and reliability of such approaches is addressed. Copyright © 2004 John Wiley & Sons, Ltd.

β-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent β-strands into β-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of β-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 ± 4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3 ± 6.1%, diverging turns with an accuracy of 73.9 ± 6.0%. Incorporation of the β-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-β-proteins and 3 αβ-proteins. The β-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003; www.jens-meiler.de/turnpred.html). Proteins 2004;54:000–000. © 2003 Wiley-Liss, Inc.

In this paper the relation between the structure of epothilones (a new class of anti-tumour agents) and their potential to influence the tubulin-microtubule equilibrium is investigated. Insights into the character of the tubulin-epothilone interactions are derived as the accuracy and reliability of support vector machines and artificial neural networks to model such relations quantitatively is compared. Both methods are well qualified to model relationships between the structure of epothilone derivatives and their anti-tumour activities. Artificial neural networks achieve lower residual standard deviations (22%) compared to support vector machines (25%) and better classification results (89% compared to 75%). However, the reproducibility of the results is greater for support vector machines, which suggests a stronger convergence. The mapping of the influence of individual structural descriptors on the three-dimensional epothilone structure suggests one side of the rather flat molecule to be more important for its activity. The "LIBSVM" software which is used for simulating the support vector machines is freely available from www.csie.ntu.edu.tw/∼cjlin/libsvm. The Program "Smart" which is used for simulating artificial neural networks is free for academic use and can be obtained together with the database of epothilones and their activities from www.jens-meiler.de.

We describe predictions of the structures of CASP5 targets using Rosetta. The Ro- setta fragment insertion protocol was used to gener- ate models for entire target domains without detect- able sequence similarity to a protein of known structure and to build long loop insertions (and N-and C-terminal extensions) in cases where a struc- tural template was available. Encouraging results were obtained both for the de novo predictions and for the long loop insertions; we describe here the successes as well as the failures in the context of current efforts to improve the Rosetta method. In particular, de novo predictions failed for large pro- teins that were incorrectly parsed into domains and for topologically complex (high contact order) pro- teins with swapping of segments between domains. However, for the remaining targets, at least one of the five submitted models had a long fragment with significant similarity to the native structure. A fully automated version of the CASP5 protocol produced results that were comparable to the human-assisted predictions for most of the targets, suggesting that automated genomic-scale, de novo protein structure prediction may soon be worthwhile. For the three targets where the human-assisted predictions were significantly closer to the native structure, we iden- tify the steps that remain to be automated. Proteins 2003;53:457- 468. © 2003 Wiley-Liss, Inc.

Residual dipolar couplings have dramatically improved the accuracy and precision of high-resolution NMR structures during the last years. This was first demonstrated for proteins. In this article, we describe, with raffinose and saccharose as examples, that dipolar couplings improve the precision of structures of carbohydrates for which usually very few structural parameters are available. The relative orientation as well as the dynamics of the monosaccharide moieties with respect to each other can be determined with the help of 13C,1H and 1H,1H dipolar couplings, which can easily be measured. Significant differences between the solution and the X-ray crystal structure exist. These results indicate that residual dipolar-coupling data may provide a more complete and dynamic model of carbohydrates in particular, and small molecules in general.

The automated structure elucidation of organic molecules from experimentally obtained properties is extended by an entirely new approach. A genetic algorithm is implemented that uses molecular constitution structures as individuals. With this approach, the structure of organic molecules can be optimized to meet experimental criteria, if in addition a fast and accurate method for the prediction of the used physical or chemical features is available. This is demonstrated using 13C NMR spectrum as readily obtainable information. 13C NMR chemical shift, intensity, and multiplicity information is available from 13C NMR DEPT spectra. By means of artificial neural networks a fast and accurate method for calculating the 13C NMR spectrum of the generated structures exists. The approach is limited by the size of the constitutional space that has to be searched and by the accuracy of the shift prediction for the unknown substance. The method is implemented and tested successfully for organic molecules with up to 20 ...

Nine different artificial neural networks were trained with the spherically encoded chemical environments of more than 500000 carbon atoms to predict their 13C NMR chemical shifts. Based on these results the PC-program "C_shift" was developed which allows the calculation of the 13C NMR spectra of any proposed molecular structure consisting of the covalently bonded elements C, H, N, O, P, S and the halogens. Results were obtained with a mean deviation as low as 1.8 ppm; this accuracy is equivalent to a determination on the basis of a large database but, in a time as short as known from increment calculations, was demonstrated exemplary using the natural agent epothilone A. The artificial neural networks allow simultaneously a precise and fast prediction of a large number of 13C NMR spectra, as needed for high throughput NMR and screening of a substance or spectra libraries.