Annotation of Human Exome Gene Variants with Consensus Pathogenicity.

A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.

Expitope 2.0: a tool to assess immunotherapeutic antigens for their potential cross-reactivity against naturally expressed proteins in human tissues.

BACKGROUND: Adoptive immunotherapy offers great potential for treating many types of cancer but its clinical application is hampered by cross-reactive T cell responses in healthy human tissues, representing serious safety risks for patients. We previously developed a computational tool called Expitope for assessing cross-reactivity (CR) of antigens based on tissue-specific gene expression. However, transcript abundance only indirectly indicates protein expression. The recent availability of proteome-wide human protein abundance information now facilitates a more direct approach for CR prediction. Here we present a new version 2.0 of Expitope, which computes all naturally possible epitopes of a peptide sequence and the corresponding CR indices using both protein and transcript abundance levels weighted by a proposed hierarchy of importance of various human tissues. RESULTS: We tested the tool in two case studies: The first study quantitatively assessed the potential CR of the epitopes used for cancer immunotherapy. The second study evaluated HLA-A*02:01-restricted epitopes obtained from the Immune Epitope Database for different disease groups and demonstrated for the first time that there is a high variation in the background CR depending on the disease state of the host: compared to a healthy individual the CR index is on average two-fold higher for the autoimmune state, and five-fold higher for the cancer state. CONCLUSIONS: The ability to predict potential side effects in normal tissues helps in the development and selection of safer antigens, enabling more successful immunotherapy of cancer and other diseases.

Assessment of cancer and virus antigens for cross-reactivity in human tissues.

MOTIVATION: Cross-reactivity (CR) or invocation of autoimmune side effects in various tissues has important safety implications in adoptive immunotherapy directed against selected antigens. The ability to predict CR (on-target and off-target toxicities) may help in the early selection of safer therapeutically relevant target antigens. RESULTS: We developed a methodology for the calculation of quantitative CR for any defined peptide epitope. Using this approach, we performed assessment of 4 groups of 283 currently known human MHC-class-I epitopes including differentiation antigens, overexpressed proteins, cancer-testis antigens and mutations displayed by tumor cells. In addition, 89 epitopes originating from viral sources were investigated. The natural occurrence of these epitopes in human tissues was assessed based on proteomics abundance data, while the probability of their presentation by MHC-class-I molecules was modelled by the method of Kesmir et al. which combines proteasomal cleavage, TAP affinity and MHC-binding predictions. The results of these analyses for many previously defined peptides are presented as CR indices and tissue profiles. The methodology thus allows for quantitative comparisons of epitopes and is suggested to be suited for the assessment of epitopes of candidate antigens in an early stage of development of adoptive immunotherapy. AVAILABILITY AND IMPLEMENTATION: Our method is implemented as a Java program, with curated datasets stored in a MySQL database. It predicts all naturally possible self-antigens for a given sequence of a therapeutic antigen (or epitope) and after filtering for predicted immunogenicity outputs results as an index and profile of CR to the self-antigens in 22 human tissues. The program is implemented as part of the iCrossR webserver, which is publicly available at http://webclu.bio.wzw.tum.de/icrossr/ CONTACT: d.frishman@wzw.tum.deSupplementary information: Supplementary data are available at Bioinformatics online.

Peak picking NMR spectral data using non-negative matrix factorization.

BACKGROUND: Simple peak-picking algorithms, such as those based on lineshape fitting, perform well when peaks are completely resolved in multidimensional NMR spectra, but often produce wrong intensities and frequencies for overlapping peak clusters. For example, NOESY-type spectra have considerable overlaps leading to significant peak-picking intensity errors, which can result in erroneous structural restraints. Precise frequencies are critical for unambiguous resonance assignments. RESULTS: To alleviate this problem, a more sophisticated peaks decomposition algorithm, based on non-negative matrix factorization (NMF), was developed. We produce peak shapes from Fourier-transformed NMR spectra. Apart from its main goal of deriving components from spectra and producing peak lists automatically, the NMF approach can also be applied if the positions of some peaks are known a priori, e.g. from consistently referenced spectral dimensions of other experiments. CONCLUSIONS: Application of the NMF algorithm to a three-dimensional peak list of the 23 kDa bi-domain section of the RcsD protein (RcsD-ABL-HPt, residues 688-890) as well as to synthetic HSQC data shows that peaks can be picked accurately also in spectral regions with strong overlap.

Effects of NMR spectral resolution on protein structure calculation.

Adequate digital resolution and signal sensitivity are two critical factors for protein structure determinations by solution NMR spectroscopy. The prime objective for obtaining high digital resolution is to resolve peak overlap, especially in NOESY spectra with thousands of signals where the signal analysis needs to be performed on a large scale. Achieving maximum digital resolution is usually limited by the practically available measurement time. We developed a method utilizing non-uniform sampling for balancing digital resolution and signal sensitivity, and performed a large-scale analysis of the effect of the digital resolution on the accuracy of the resulting protein structures. Structure calculations were performed as a function of digital resolution for about 400 proteins with molecular sizes ranging between 5 and 33 kDa. The structural accuracy was assessed by atomic coordinate RMSD values from the reference structures of the proteins. In addition, we monitored also the number of assigned NOESY cross peaks, the average signal sensitivity, and the chemical shift spectral overlap. We show that high resolution is equally important for proteins of every molecular size. The chemical shift spectral overlap depends strongly on the corresponding spectral digital resolution. Thus, knowing the extent of overlap can be a predictor of the resulting structural accuracy. Our results show that for every molecular size a minimal digital resolution, corresponding to the natural linewidth, needs to be achieved for obtaining the highest accuracy possible for the given protein size using state-of-the-art automated NOESY assignment and structure calculation methods.

Estimating structure quality trends in the Protein Data Bank by equivalent resolution.

The quality of protein structures obtained by different experimental and ab-initio calculation methods varies considerably. The methods have been evolving over time by improving both experimental designs and computational techniques, and since the primary aim of these developments is the procurement of reliable and high-quality data, better techniques resulted on average in an evolution toward higher quality structures in the Protein Data Bank (PDB). Each method leaves a specific quantitative and qualitative "trace" in the PDB entry. Certain information relevant to one method (e.g. dynamics for NMR) may be lacking for another method. Furthermore, some standard measures of quality for one method cannot be calculated for other experimental methods, e.g. crystal resolution or NMR bundle RMSD. Consequently, structures are classified in the PDB by the method used. Here we introduce a method to estimate a measure of equivalent X-ray resolution (e-resolution), expressed in units of Å, to assess the quality of any type of monomeric, single-chain protein structure, irrespective of the experimental structure determination method. We showed and compared the trends in the quality of structures in the Protein Data Bank over the last two decades for five different experimental techniques, excluding theoretical structure predictions. We observed that as new methods are introduced, they undergo a rapid method development evolution: within several years the e-resolution score becomes similar for structures obtained from the five methods and they improve from initially poor performance to acceptable quality, comparable with previously established methods, the performance of which is essentially stable.

Fast automated NMR spectroscopy of short-lived biological samples.

Faster than death: NMR techniques that make use of nonlinear sampling and hyperdimensional processing enable the recording of complete NMR data sets for the automated assignment of the backbone and side-chain resonances of short-lived protein samples of cell lysates.

A universal expression tag for structural and functional studies of proteins.

Modified ubiquitin sequences, each completed with a His tag and a TEV cleavage site, were designed to enhance the expression of protein/peptide targets. With this new system we have been able to characterize several peptide-protein interactions by ITC and by NMR and CD spectroscopic methods, including the interactions of LIR domains with autophagy modifiers.

Blind testing of routine, fully automated determination of protein structures from NMR data.

The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent "blind" assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR.

Protein structure validation by generalized linear model root-mean-square deviation prediction.

Large-scale initiatives for obtaining spatial protein structures by experimental or computational means have accentuated the need for the critical assessment of protein structure determination and prediction methods. These include blind test projects such as the critical assessment of protein structure prediction (CASP) and the critical assessment of protein structure determination by nuclear magnetic resonance (CASD-NMR). An important aim is to establish structure validation criteria that can reliably assess the accuracy of a new protein structure. Various quality measures derived from the coordinates have been proposed. A universal structural quality assessment method should combine multiple individual scores in a meaningful way, which is challenging because of their different measurement units. Here, we present a method based on a generalized linear model (GLM) that combines diverse protein structure quality scores into a single quantity with intuitive meaning, namely the predicted coordinate root-mean-square deviation (RMSD) value between the present structure and the (unavailable) "true" structure (GLM-RMSD). For two sets of structural models from the CASD-NMR and CASP projects, this GLM-RMSD value was compared with the actual accuracy given by the RMSD value to the corresponding, experimentally determined reference structure from the Protein Data Bank (PDB). The correlation coefficients between actual (model vs. reference from PDB) and predicted (model vs. "true") heavy-atom RMSDs were 0.69 and 0.76, for the two datasets from CASD-NMR and CASP, respectively, which is considerably higher than those for the individual scores (-0.24 to 0.68). The GLM-RMSD can thus predict the accuracy of protein structures more reliably than individual coordinate-based quality scores.

Automatic assignment of protein backbone resonances by direct spectrum inspection in targeted acquisition of NMR data.

The necessity to acquire large multidimensional datasets, a basis for assignment of NMR resonances, results in long data acquisition times during which substantial degradation of a protein sample might occur. Here we propose a method applicable for such a protein for automatic assignment of backbone resonances by direct inspection of multidimensional NMR spectra. In order to establish an optimal balance between completeness of resonance assignment and losses of cross-peaks due to dynamic processes/degradation of protein, assignment of backbone resonances is set as a stirring criterion for dynamically controlled targeted nonlinear NMR data acquisition. The result is demonstrated with the 12 kDa (13)C,(15) N-labeled apo-form of heme chaperone protein CcmE, where hydrolytic cleavage of 29 C-terminal amino acids is detected. For this protein, 90 and 98% of manually assignable resonances are automatically assigned within 10 and 40 h of nonlinear sampling of five 3D NMR spectra, respectively, instead of 600 h needed to complete the full time domain grid. In addition, resonances stemming from degradation products are identified. This study indicates that automatic resonance assignment might serve as a guiding criterion for optimal run-time allocation of NMR resources in applications to proteins prone to degradation.

Hyperdimensional NMR spectroscopy with nonlinear sampling.

An approach is described for joint interleaved recording, real-time processing, and analysis of NMR data sets. The method employs multidimensional decomposition to find common information in a set of conventional triple-resonance spectra recorded in the nonlinear sampling mode, and builds a model of hyperdimensional (HD) spectrum. While preserving sensitivity per unit of measurement time and allowing for maximal spectral resolution, the approach reduces data collection time on average by 2 orders of magnitude compared to the conventional method. The 7-10 dimensional HD spectrum, which is represented as a set of deconvoluted 1D vectors, is easy to handle and amenable for automated analysis. The method is exemplified by automated assignment for two protein systems of low and high spectral complexity: ubiquitin (globular, 8 kDa) and zetacyt (naturally disordered, 13 kDa). The collection and backbone assignment of the data sets are achieved in real time after approximately 1 and 10 h, respectively. The approach removes the most critical time bottlenecks in data acquisition and analysis. Thus, it can significantly increase the value of NMR spectroscopy in structural biology, for example, in high-throughput structural genomics applications.

Targeted acquisition for real-time NMR spectroscopy.

A target-oriented approach for the acquisition of information in biomolecular NMR spectroscopy is being developed. This approach combines concurrent data accumulation, processing, and monitoring of spectral quality. Real-time estimation of parameters allows acquisition to be stopped when results are complete and have a specified precision. The technique is based on multidimensional decomposition, which can process incomplete data. An incremental nonuniform sampling scheme ensures the optimization of resolution sensitivity. To validate this method, 3D HNCO spectra of three biomolecular systems (8 kDa ubiquitin, 22 kDa barstar-barnase complex, and 82 kDa malate synthase G) are processed incrementally at small acquisition time steps. The range of molecular sizes illustrates applicability in both sample- and sensitivity-limited regimes. In each case, the target was to acquire all backbone resonances in the spectra. For the three systems, the targets are achieved after 4.5 min, 1.6 h, and 22 h of acquisition time, respectively. A number of other targets that can be similarly monitored as a function of time are discussed.

Removal of a time barrier for high-resolution multidimensional NMR spectroscopy.

We introduce the recursive multidimensional decomposition (R-MDD) method to speed recording of high-resolution NMR spectra. The measurement time is logarithmically dependent on the sizes of indirect spectral dimensions. R-MDD has the sensitivity and resolution advantages of optimized nonuniform acquisition schemes and is applicable to all types of biomolecular spectra. We demonstrated it for triple resonance experiments on three globular proteins (ubiquitin, azurin and the barstar-barnase complex) of 8-22 kDa.

Quantification of H/D isotope effects on protein hydrogen-bonds by h3JNC' and 1JNC' couplings and peptide group 15N and 13C' chemical shifts.

The effect of hydrogen/deuterium exchange on protein hydrogen bond coupling constants (h3)J(NC') has been investigated in the small globular protein ubiquitin. The couplings across deuterated or protonated hydrogen bonds were measured by a long-range quantitative HA(CACO)NCO experiment. The analysis is combined with a determination of the H(N)/D(N) isotope effect on the amide group (1)J(NC') couplings and the (15)N and (13)C' chemical shifts. On average, H-bond deuteration exchange weakens (h3)J(NC') and strengthens (1)J(NC') couplings. A correlation is found between the size of the (15)N isotope shift, the (15)N chemical shift, and the (h3)J(NC') coupling constants. The data are consistent with a reduction of donor-acceptor overlap as expected from the classical Ubbelohde effect and the common understanding that H(N)/D(N) exchange leads to a shortening of the N-hydron bond length.

From structure and dynamics of protein L7/L12 to molecular switching in ribosome.

Based on the (1)H-(15)N NMR spectroscopy data, the three-dimensional structure and internal dynamic properties of ribosomal protein L7 from Escherichia coli were derived. The structure of L7 dimer in solution can be described as a set of three distinct domains, tumbling rather independently and linked via flexible hinge regions. The dimeric N-terminal domain (residues 1-32) consists of two antiparallel alpha-alpha-hairpins forming a symmetrical four-helical bundle, whereas the two identical C-terminal domains (residues 52-120) adopt a compact alpha/beta-fold. There is an indirect evidence of the existence of transitory helical structures at least in the first part (residues 33-43) of the hinge region. Combining structural data for the ribosomal protein L7/L12 from NMR spectroscopy and x-ray crystallography, it was suggested that its hinge region acts as a molecular switch, initiating "ratchet-like" motions of the L7/L12 stalk with respect to the ribosomal surface in response to elongation factor binding and GTP hydrolysis. This hypothesis allows an explanation of events observed during the translation cycle and provides useful insights into the role of protein L7/L12 in the functioning of the ribosome.