A major problem in structural biology is the recognition of errors in experimental and theoretical models of protein structures. The ProSA program (Protein Structure Analysis) is an established tool which has a large user base and is frequently employed in the refinement and validation of experimental protein structures and in structure prediction and modeling. The analysis of protein structures is generally a difficult and cumbersome exercise. The new service presented here is a straightforward and easy to use extension of the classic ProSA program which exploits the advantages of interactive web-based applications for the display of scores and energy plots that highlight potential problems spotted in protein structures. In particular, the quality scores of a protein are displayed in the context of all known protein structures and problematic parts of a structure are shown and highlighted in a 3D molecule viewer. The service specifically addresses the needs encountered in the validation of protein structures obtained from X-ray analysis, NMR spectroscopy and theoretical calculations. ProSA-web is accessible at https://prosa.services.came.sbg.ac.at.

ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins.

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-262v4shee8.pdf

Comparative Protein Structure Modeling Using MODELLER

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-1ye86wsiv7.pdf

Comparative protein structure modeling using Modeller.

Motivation: Quality assessment of protein structures is an important part of experimental structure validation and plays a crucial role in protein structure prediction, where the predicted models may contain substantial errors. Most current scoring functions are primarily designed to rank alternative models of the same sequence supporting model selection, whereas the prediction of the absolute quality of an individual protein model has received little attention in the field. However, reliable absolute quality estimates are crucial to assess the suitability of a model for specific biomedical applications.

Results: In this work, we present a new absolute measure for the quality of protein models, which provides an estimate of the ‘degree of nativeness’ of the structural features observed in a model and describes the likelihood that a given model is of comparable quality to experimental structures. Model quality estimates based on the QMEAN scoring function were normalized with respect to the number of interactions. The resulting scoring function is independent of the size of the protein and may therefore be used to assess both monomers and entire oligomeric assemblies. Model quality scores for individual models are then expressed as ‘Z-scores’ in comparison to scores obtained for high-resolution crystal structures. We demonstrate the ability of the newly introduced QMEAN Z-score to detect experimentally solved protein structures containing significant errors, as well as to evaluate theoretical protein models.

In a comprehensive QMEAN Z-score analysis of all experimental structures in the PDB, membrane proteins accumulate on one side of the score spectrum and thermostable proteins on the other. Proteins from the thermophilic organism Thermatoga maritima received significantly higher QMEAN Z-scores in a pairwise comparison with their homologous mesophilic counterparts, underlining the significance of the QMEAN Z-score as an estimate of protein stability.

Availability: The Z-score calculation has been integrated in the QMEAN server available at: http://swissmodel.expasy.org/qmean.

Contact: torsten.schwede@unibas.ch

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/toward-the-estimation-of-the-absolute-quality-of-individual-28ar23ticc.pdf

Toward the estimation of the absolute quality of individual protein structure models

The ability to separate correct models of protein structures from less correct models is of the greatest importance for protein structure prediction methods. Several studies have examined the ability of different types of energy function to detect the native, or native-like, protein structure from a large set of decoys. In contrast to earlier studies, we examine here the ability to detect models that only show limited structural similarity to the native structure. These correct models are defined by the existence of a fragment that shows significant similarity between this model and the native structure. It has been shown that the existence of such fragments is useful for comparing the performance between different fold recognition methods and that this performance correlates well with performance in fold recognition. We have developed ProQ, a neural-network-based method to predict the quality of a protein model that extracts structural features, such as frequency of atom-atom contacts, and predicts the quality of a model, as measured either by LGscore or MaxSub. We show that ProQ performs at least as well as other measures when identifying the native structure and is better at the detection of correct models. This performance is maintained over several different test sets. ProQ can also be combined with the Pcons fold recognition predictor (Pmodeller) to increase its performance, with the main advantage being the elimination of a few high-scoring incorrect models. Pmodeller was successful in CASP5 and results from the latest LiveBench, LiveBench-6, indicating that Pmodeller has a higher specificity than Pcons alone.

Can correct protein models be identified

Summary: Progress in structural biology depends on several key technologies. In particular tools for alignment and superposition of protein structures are indispensable. Here we describe the use of the TopMatch web service, an effective computational tool for protein structure alignment, for the visualization of structural similarities, and for highlighting relationships found in protein classifications. We provide several instructive examples.

Availability: TopMatch is available as a public web service at http://services.came.sbg.ac.at

Contact: sippl@came.sbg.ac.at

A note on difficult structure alignment problems

We present the assessment of the CASP4 fold recognition category. The tasks we had to execute include the splitting of multidomain targets into single domains, the classification of target domains in terms of prediction categories, the numerical evaluation of predictions, the mapping of numerical scores to quality indices, the ranking of predictors, the selection of top-performing groups, and the analysis and critical discussion of the state of the art in this field. The 125 fold recognition groups were assessed by a total score that summarizes their performance over all targets and a quality score reflecting the average quality of the submitted models. Most of the top-performing groups achieved respectable results on both scores simultaneously. Several groups submitted models that were much closer to the respective target structures than any of the known folds in the Protein Data Bank. The CASP4 assessment included the automated servers of the parallel CAFASP experiment. For the total score, the highest rank achieved by a fully automated server is 12. Two thirds of the predictors have rather low scores.

Assessment of the CASP4 fold recognition category

https://www.cell.com/structure/pdf/S0969-2126(12)00055-X.pdf

Detection of Spatial Correlations in Protein Structures and Molecular Complexes

Engineered nanomaterials (ENMs) interact with different biomolecules as soon as they are in contact, resulting in the formation of a biomolecule ‘corona’. Hence, the ‘corona’ defines the biological identity of the ENMs and could affect the response of the immune system to ENM exposure. With up to 40 % of the world population suffering from type I allergy, a possible modulation of allergen effects by binding to ENMs is highly relevant with respect to work place and consumer safety. Therefore, the aim of this present study was to gain an insight into the interactions of gold nanoparticles with different seasonally and perennially occurring outdoor and indoor allergens. Gold nanoparticles (AuNPs) were conjugated with the major allergens of birch pollen (Bet v 1), timothy grass pollen (Phl p 5) and house dust mite (Der p 1). The AuNP-allergen conjugates were characterized by means of TEM negative staining, dynamic light scattering (DLS), z-potential measurements and hyperspectral imaging. Furthermore, 3D models were constructed, based on the characterization data, to visualize the interaction between the allergens and the AuNPs surface. Differences in the activation of human basophil cells derived from birch/grass pollen- and house dust mite-allergic patients in response to free allergen and AuNP-allergen conjugates were determined using the basophil activation assay (BAT). Potential allergen corona replacement during BAT was controlled for using Western blotting. The protease activity of AuNP-Der p 1 conjugates compared to free Der p 1 was assessed, by an enzymatic activity assay and a cellular assay pertaining to lung type II alveolar epithelial cell tight junction integrity. The formation of a stable corona was found for all three allergens used. Our data suggest, that depending on the allergen, different effects are observed after binding to ENMs, including enhanced allergic responses against Der p 1 and also, for some patients, against Bet v 1. Moreover elevated protease activity of AuNP-Der p 1 conjugates compared to free Der p 1 was found. In summary, this study presents that conjugation of allergens to ENMs can modulate the human allergic response, and that protease activity can be increased.

/pdf/nanoparticle-allergen-interactions-mediate-human-allergic-1mfs5z4pvq.pdf

Markus Wiederstein

Papers

ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins.

A note on difficult structure alignment problems

Assessment of the CASP4 fold recognition category

Detection of Spatial Correlations in Protein Structures and Molecular Complexes

Nanoparticle-allergen interactions mediate human allergic responses: protein corona characterization and cellular responses