Protein Structure Similarity Research Articles

TOPS++FATCAT: Fast flexible structural alignment using constraints derived from TOPS+ Strings Model

BackgroundProtein structure analysis and comparison are major challenges in structural bioinformatics. Despite the existence of many tools and algorithms, very few of them have managed to capture the intuitive understanding of protein structures developed in structural biology, especially in the context of rapid database searches. Such intuitions could help speed up similarity searches and make it easier to understand the results of such analyses.ResultsWe developed a TOPS++FATCAT algorithm that uses an intuitive description of the proteins' structures as captured in the popular TOPS diagrams to limit the search space of the aligned fragment pairs (AFPs) in the flexible alignment of protein structures performed by the FATCAT algorithm. The TOPS++FATCAT algorithm is faster than FATCAT by more than an order of magnitude with a minimal cost in classification and alignment accuracy. For beta-rich proteins its accuracy is better than FATCAT, because the TOPS+ strings models contains important information of the parallel and anti-parallel hydrogen-bond patterns between the beta-strand SSEs (Secondary Structural Elements). We show that the TOPS++FATCAT errors, rare as they are, can be clearly linked to oversimplifications of the TOPS diagrams and can be corrected by the development of more precise secondary structure element definitions.Software AvailabilityThe benchmark analysis results and the compressed archive of the TOPS++FATCAT program for Linux platform can be downloaded from the following web site: ConclusionTOPS++FATCAT provides FATCAT accuracy and insights into protein structural changes at a speed comparable to sequence alignments, opening up a possibility of interactive protein structure similarity searches.

Protein structure alignment considering phenotypic plasticity

Protein structure comparison exhibits differences and similarities of proteins and protein families and may help to elucidate protein sequence and structure evolution. Despite many methods to score protein structure similarity with and without flexibility and to align proteins accurately based on their structures, a meaningful evolutionary distance measure and alignment method which models the cost of mutations, insertions and deletions occurring in protein sequences on the structure level is still missing. Here, we introduce a new measure for protein structure similarity and propose a novel method called phenotypic plasticity method (PPM) which explicitly tries to model the evolutionary distance of two proteins on the structure level by measuring the cost of 'morphing' one structure into the other one. PPM aligns protein structures taking variations naturally observed in groups of structures ('phenotypic plasticity') into account while preserving the overall topological arrangement of the structures. The performance of PPM in detecting similarities between protein structures is evaluated against well-known structure classification methods on two benchmark sets. The larger set consists of more than 3.6 million structure pairs from the SCOP database which are also consistently classified in CATH. In the current parameterization, PPM already performs comparable or better than other methods such as TM-Align and Vorolign on those two sets according to various evaluation criteria showing that the method is able to reliably classify known protein structures, to detect their similarities and to compute accurate alignments despite phenotypic plasticity. Executables are available upon request. Datasets and supplementary data (datasets and superpositions) can be accessed on http://www.bio. ifi.lmu.de/PPM.

Statistics of Random Protein Superpositions: p-Values for Pairwise Structure Alignment

Quantification of statistical significance is essential for the interpretation of protein structural similarity. To address this, a random model for protein structure comparison was developed. Novelty of the model is threefold. First, a sample of random structure comparisons is restricted to molecules of the same size and shape as the superposition of interest. Second, careful selection of the sample and accurate modeling of shape allows approximation of the root mean square deviation (RMSD) distribution of random comparisons with a Nakagami probability density function. Third, through convolution, a second probability density function is obtained that describes the coordinate difference vector projections underlying the random distribution of RMSD. This last feature allows sampling random distributions of not only RMSD, but also any similarity score that depends on difference vector projections, such as GDT_TS score, TM score, and LiveBench 3D score. Probabilities estimated from the method correlate well with common measures of structural similarity, such as the Dali Z-score and the GDT_TS score. As a result, the p-value for a given superposition can be calculated using simple formulae depending on RMSD, radius of gyration, and thinnest molecular dimension. In addition to scoring structural similarity, p-values computed by this method can be applied to evaluation of homology modeling techniques, providing a statistically sound alternative to scores used in reference-independent evaluation of alignment quality.

Synthesis of a dysidiolide-inspired compound library and discovery of acetylcholinesterase inhibitors based on protein structure similarity clustering (PSSC)

Biologically relevant compound collections are a major prerequisite for efficient protein ligand development and ultimately for drug discovery. We herein describe the development of a compound collection inspired by the decalin core motif of two natural products, dysidiolide 1 and sulfiricin 2, both inhibitors of the Cdc25A phosphatase. Several keto-functionalized decalinols were synthesized in solution, immobilized on Merrifield resin equipped with a dihydropyranyl linker, and then subjected to aldol condensation reactions with different aldehydes leading to exocyclic E-configured olefins. Further diversity-increasing transformations on the solid support included Sonogashira, Suzuki, and Heck reactions, Cu-catalyzed conjugate addition and Grignard reactions, alkylation reactions in the α-position to a ketone, Wittig reactions, and reductive animations. In total, 483 compounds were synthesized. Cdc25A and AChE exhibit structural similarity in their ligand-sensing cores and were thus grouped into a protein structure similarity cluster (PSSC). A screen for AChE inhibition of a subset of 162 compounds yielded three micromolar inhibitors of AChE with IC 50 values <20 μM.

BIOS: Similarity-based design of natural product derived compound collections

Computational approaches are frequently used in ligand design. We have developed two complementary techniques, the Protein Structure Similarity Clustering (PSSC) [1] and the Scaffold Tree [2,3]. The PSSC is based on subfold similarity around the binding sites of proteins. Its central hypothesis is that structurally similar binding sites bind structurally similar ligands. Thus PSSC leads to target clusters and structural templates for library design. The Scaffold Tree provides a structure based classifiction of chemical compounds based on their scaffolds. These scaffolds are then further deconstructed according to a set of rules yielding a hierarchy of chemically meaningful entities. This genealogy based on substructure relationships can be used to chart chemical spaces and to mine large datasets, e.g., of screening data. The concept of “Biology Oriented Synthesis” (BIOS) describes the synthesis of new biologically active compounds starting from prevalidated structures and also incorporates the joint application of PSSC and the Scaffold Tree. It offers new routes to biologically prevalidated compounds and corresponding target cluster promising more and selective hits.

Accuracy of structure-based sequence alignment of automatic methods

BackgroundAccurate sequence alignments are essential for homology searches and for building three-dimensional structural models of proteins. Since structure is better conserved than sequence, structure alignments have been used to guide sequence alignments and are commonly used as the gold standard for sequence alignment evaluation. Nonetheless, as far as we know, there is no report of a systematic evaluation of pairwise structure alignment programs in terms of the sequence alignment accuracy.ResultsIn this study, we evaluate CE, DaliLite, FAST, LOCK2, MATRAS, SHEBA and VAST in terms of the accuracy of the sequence alignments they produce, using sequence alignments from NCBI's human-curated Conserved Domain Database (CDD) as the standard of truth. We find that 4 to 9% of the residues on average are either not aligned or aligned with more than 8 residues of shift error and that an additional 6 to 14% of residues on average are misaligned by 1–8 residues, depending on the program and the data set used. The fraction of correctly aligned residues generally decreases as the sequence similarity decreases or as the RMSD between the Cα positions of the two structures increases. It varies significantly across CDD superfamilies whether shift error is allowed or not. Also, alignments with different shift errors occur between proteins within the same CDD superfamily, leading to inconsistent alignments between superfamily members. In general, residue pairs that are more than 3.0 Å apart in the reference alignment are heavily (>= 25% on average) misaligned in the test alignments. In addition, each method shows a different pattern of relative weaknesses for different SCOP classes. CE gives relatively poor results for β-sheet-containing structures (all-β, α/β, and α+β classes), DaliLite for "others" class where all but the major four classes are combined, and LOCK2 and VAST for all-β and "others" classes.ConclusionWhen the sequence similarity is low, structure-based methods produce better sequence alignments than by using sequence similarities alone. However, current structure-based methods still mis-align 11–19% of the conserved core residues when compared to the human-curated CDD alignments. The alignment quality of each program depends on the protein structural type and similarity, with DaliLite showing the most agreement with CDD on average.

Multivariate statistical analysis of large-scale IgE antibody measurements reveals allergen extract relationships in sensitized individuals

Many allergenic sources are reportedly cross-reactive because of protein structural similarities. Although several aggregations are well characterized, no holistic mapping of IgE reactivity has hitherto been reported. The aim of this study was to disclose relevant associations within a large set of allergen preparations, as revealed by specific IgE antibody levels in blood sera of multireactive human donors. A dataset of recorded IgE antibody serum concentrations of 1011 nonidentifiable multireactive individuals (devoid of clinical records) to 89 allergen extracts was compiled for in silico analysis. Various algorithms were used to identify specific multivariate dependencies between the IgE antibody levels. Exhaustive cluster analysis demonstrates that IgE antibody responses to the 89 extracts can be aggregated into 12 stable formations. These clusters hold both well-known relationships, unexpected patterns, and unknown patterns, the latter categories being exemplified by the coclustering of wasp and certain seafood and a clear differentiation among pollen allergens. Identified relationships within several well-known groups of cross-reactive allergen extracts confirm the applicability of dedicated multivariate data analysis within the allergology field. Moreover, some of the unexpected IgE reactivity associations in sensitized human subjects might help in identifying new relationships with potential importance to allergy. Although clinical implications from this study should be validated in subsequent investigations with documentation on symptoms included, we believe this seminal approach is a key step toward the development of new analysis tools for interpretation of allergy data generated by using high-throughput recording systems.

Protein structural similarity search by Ramachandran codes

BackgroundProtein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases.ResultsWe propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms.ConclusionAs a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era.

Reticulon-like proteins in Arabidopsis thaliana: Structural organization and ER localization

Reticulons are proteins that have been found predominantly associated with the endoplasmic reticulum in yeast and mammalian cells. While their functions are still poorly understood, recent findings suggest that they participate in the shaping of the tubular endoplamic reticulum (ER). Although reticulon-like proteins have been identified in plants, very little is known about their cellular localization and functions. Here, we characterized the reticulon-like protein family of Arabidopsis thaliana. Three subfamilies can be distinguished on the basis of structural organization and sequence homology. We investigated the subcellular localization of two members of the largest subfamily, i.e. AtRTNLB2 and AtRTNLB4, using fluorescent protein tags. The results demonstrate for the first time that plant reticulon-like proteins are associated with the ER. Both AtRTNLB proteins are located in the tubular ER but AtRTNLB4 is also found in the lamellar ER cisternae, and in ER tubules in close association with the chloroplasts. Similarity in protein structure and subcellular localization between AtRTNLB2 and mammalian reticulons suggests that they could assume similar basic functions inside the cell.

Knowledge-Driven Multidimensional Indexing Structure for Biomedical Media Database Retrieval

Today, biomedical media data are being generated at rates unimaginable only years ago. Content-based retrieval of biomedical media from large databases is becoming increasingly important to clinical, research, and educational communities. In this paper, we present the recently developed entropy balanced statistical (EBS) k-d tree and its applications to biomedical media, including a high-resolution computed tomography (HRCT) lung image database and the first real-time protein tertiary structure search engine. Our index utilizes statistical properties inherent in large-scale biomedical media databases for efficient and accurate searches. By applying concepts from pattern recognition and information theory, the EBS k-d tree is built through top-down decision tree induction. Experimentation shows similarity searches against a protein structure database of 53 363 structures consistently execute in less than 8.14 ms for the top 100 most similar structures. Additionally, we have shown improved retrieval precision over adaptive and statistical k-d trees. Retrieval precision of the EBS k-d tree is 81.6% for content-based retrieval of HRCT lung images and 94.9% at 10% recall for protein structure similarity search. The EBS k-d tree has enormous potential for use in biomedical applications embedded with ground-truth knowledge and multidimensional signatures.

Chemometric Approach in Quantification of Structural Identity/Similarity of Proteins in Biopharmaceuticals

We present a chemometrics study in which we show the identity or degree of similarity of 3D protein structures of various G-CSF (Granulocyte Colony-Stimulating Factor) isolates. The G-CSF isolates share the same amino acid sequence, but the preparation was carried out by somehow diverse technologies. The comparison of 3D structures was made on the basis of 2D NMR NOESY (Nuclear Overhauser Enhancement Spectroscopy) spectra of proteins. In searching for the most appropriate criteria to determine the identity or degree of similarity of selected spectral regions of different isolates, two methods for quantitative evaluation of identity/similarity were used. The first method compares all peaks in the two investigated protein spectral regions; the extent of peaks that overlap is determined. The second method includes spectral invariants originating from graph theory. The criteria of identity/similarity were calculated from graphs, derived from a collection of up to 200 peaks of investigated 2D NMR spectral region. The peaks were linked into a graph according to the sequential nearest neighborhoods. According to the first method all peaks were relevant, considering that spectral noise was previously removed; the largest similarity was found between the protein of a commercially available G-CSF drug and one of the three new isolates produced in the laboratory. The second method indicated that the pairwise similarity of the three new isolates is larger than the similarity of any of the new isolates with the commercially available drug. This is an expected result taking into account that the new isolates are produced by the same technology, while the commercial product has additives for long-term storage that could not be completely compensated. The proposed measure of similarity may help the developers of biosimilar products to optimize the controllable parameters of the production technology and eventually to argue the identity of the new isolate in comparison with the originator commercial product.

PROTMAP2D: visualization, comparison and analysis of 2D maps of protein structure

Protein structure comparison is a fundamental problem in structural biology and bioinformatics. Two-dimensional maps of distances between residues in the structure contain sufficient information to restore the 3D representation, while maps of contacts reveal characteristic patterns of interactions between secondary and super-secondary structures and are very attractive for visual analysis. The overlap of 2D maps of two structures can be easily calculated, providing a sensitive measure of protein structure similarity. PROTMAP2D is a software tool for calculation of contact and distance maps based on user-defined criteria, quantitative comparison of pairs or series of contact maps (e.g. alternative models of the same protein, model versus native structure, different trajectories from molecular dynamics simulations, etc.) and visualization of the results. PROTMAP2D for Windows / Linux / MacOSX is freely available for academic users from http://genesilico.pl/protmap2d.htm

Detection of Legume Proteins Cross-reactivity by Immunoblot Using Human Plasma of Individuals with Food Allergies to Peanut and/or Soybean

RATIONALE: Peanut and soybean are well recognized allergenic food crops. Allergy to other legumes is becoming more widely reported and is thought to be partly due to cross-reactivity of structurally similar storage proteins. We have initiated IgE-binding studies to evaluate the extent of cross-reactivity, which may be due to similarities in protein or glycan structure. METHODS: Extracts of 11 species of commonly consumed legumes were separated by SDS polyacrylamide gel electrophoresis (SDS-PAGE) under reducing and non-reducing conditions. Proteins were stained with colloidal blue or glycoprotein stain, or blotted onto membranes for IgE-binding using plasma of subjects with food allergies to peanut and/or soybean. Inhibition of IgE-binding was conducted by preincubation of inhibitors, legume extracts and horseradish peroxidase (glycan inhibitor), in plasma dilutions during immunoblotting. RESULTS: Protein profiles in stained gels were different under reducing and non-reducing conditions. Glycoproteins were present in all legume samples. Most plasma samples exhibited positive IgE-binding to major proteins in a few legume extracts, with the molecular weights ranging from 10 to 75 kDa. IgE-binding of some plasma to legume proteins was inhibited by peanut protein extract. Most of the IgE-binding was not totally inhibited by any legume extract. Some plasma showed strong IgE-binding to HRP, a glycoprotein containing cross-reactive carbohydrate determinants (CCD), and is thought to be clinical irrelevant. CONCLUSIONS: These results suggest that cross-reactivity may be present among various legume species. However, these preliminary results demonstrate that careful experimental design and controls are required to differentiate the specificity of IgE-binding, which in some cases is due to CCD.

Protein Structure Similarity Clustering: Dynamic Treatment of PDB Structures Facilitates Clustering

In the family: The introduction of ligand docking, molecular dynamics, and the VAST algorithm into protein structure similarity clustering greatly streamlines the process and opens up otherwise unseen connections to new protein-cluster partners. VAST=vector alignment search tool.

The Ig Doublet Z1Z2: A Model System for the Hybrid Analysis of Conformational Dynamics in Ig Tandems from Titin

Titin is a gigantic elastic filament that determines sarcomere ultrastructure and stretch response in vertebrate muscle. It folds into numerous Ig and FnIII domains connected in tandem. Data on interdomain arrangements and dynamics are essential for understanding the function of this filament. Here, we report a mechanistic analysis of the conformational dynamics of two Ig domains from the N terminus of titin, Z1Z2, by using X-ray crystallography, SAXS, NMR relaxation data, and residual dipolar couplings in combination. Z1Z2 preferentially adopts semiextended conformations in solution, with close-hinge arrangements representing low-probability states. Although interdomain contacts are not observed, the linker appears to acquire moderate rigidity via small, local hydrophobic interactions. Thus, Z1Z2 constitutes an adaptable modular system with restricted dynamics. We speculate that its preexistent conformation contributes to the selective recruitment of the binding partner telethonin onto the repetitive surface of the filament. The structural interconversion of four Z1Z2 conformers is analyzed.

StructSorter: A Method for Continuously Updating a Comprehensive Protein Structure Alignment Database

Advances in protein crystallography and homology modeling techniques are producing vast amounts of high resolution protein structure data at ever increasing rates. As such, the ability to quickly and easily extract structural similarities is a key tool in discovering important functional relationships. We report on an approach for creating and maintaining a database of pairwise structure alignments for a comprehensive database comprising the PDB and homology models for the human and select pathogen genomes. Our approach consists of a novel, multistage method for determining pairwise structural similarity coupled with an efficient clustering protocol that approximates a full NxN assessment in a fraction of the time. Since biologists are commonly interested in recently released structures, and the homology models built from them, an automatically updating database of structural alignments has great value. Our approach yields a querying system that allows scientists to retrieve databank-wide protein structure similarities as easily as retrieving protein sequence similarities via BLAST or PSI-BLAST. Basic, noncommercial access to the database can be requested at https://tip.eidogen-sertanty.com/.

Charting Biological and Chemical Space: PSSC and SCONP as Guiding Principles for the Development of Compound Collections Based on Natural Product Scaffolds

AbstractFinding small molecules that selectively modulate protein function remains one of the major challenges in the fields of chemogenomics and drug development. Natural products interact with proteins in their biosynthesis and when displaying biological activity, and can therefore be considered as “privileged structures”. Thus, natural products may serve as biologically validated starting points for the design of focused compound collections. In recent years, effective synthesis methods for natural‐product‐based focused compound collections have been developed, including multi‐component reactions. This review discusses recent advances in the synthesis of natural‐product‐inspired compound collections as well as the application of Protein Structure Similarity Clustering (PSSC) and a Structural Classification Of Natural Products (SCONP) as guiding principles for the design of focused compound collections based on natural products.

Protein structure similarity from Principle Component Correlation analysis.

BackgroundOwing to rapid expansion of protein structure databases in recent years, methods of structure comparison are becoming increasingly effective and important in revealing novel information on functional properties of proteins and their roles in the grand scheme of evolutionary biology. Currently, the structural similarity between two proteins is measured by the root-mean-square-deviation (RMSD) in their best-superimposed atomic coordinates. RMSD is the golden rule of measuring structural similarity when the structures are nearly identical; it, however, fails to detect the higher order topological similarities in proteins evolved into different shapes. We propose new algorithms for extracting geometrical invariants of proteins that can be effectively used to identify homologous protein structures or topologies in order to quantify both close and remote structural similarities.ResultsWe measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins.ConclusionThe PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.

Flexible Structural Neighborhood--a database of protein structural similarities and alignments

Protein structures are flexible, changing their shapes not only upon substrate binding, but also during evolution as a collective effect of mutations, deletions and insertions. A new generation of protein structure comparison algorithms allows for such flexibility; they go beyond identifying the largest common part between two proteins and find hinge regions and patterns of flexibility in protein families. Here we present a Flexible Structural Neighborhood (FSN), a database of structural neighbors of proteins deposited in PDB as seen by a flexible protein structure alignment program FATCAT, developed previously in our group. The database, searchable by a protein PDB code, provides lists of proteins with statistically significant structural similarity and on lower menu levels provides detailed alignments, interactive superposition of structures and positions of hinges that were identified in the comparison. While superficially similar to other structural protein alignment resources, FSN provides a unique resource to study not only protein structural similarity, but also how protein structures change. FSN is available from a server and by direct links from the PDB database.

Charting biologically relevant chemical space: A structural classification of natural products (SCONP)

The identification of small molecules that fall within the biologically relevant subfraction of vast chemical space is of utmost importance to chemical biology and medicinal chemistry research. The prerequirement of biological relevance to be met by such molecules is fulfilled by natural product-derived compound collections. We report a structural classification of natural products (SCONP) as organizing principle for charting the known chemical space explored by nature. SCONP arranges the scaffolds of the natural products in a tree-like fashion and provides a viable analysis- and hypothesis-generating tool for the design of natural product-derived compound collections. The validity of the approach is demonstrated in the development of a previously undescribed class of selective and potent inhibitors of 11beta-hydroxysteroid dehydrogenase type 1 with activity in cells guided by SCONP and protein structure similarity clustering. 11beta-hydroxysteroid dehydrogenase type 1 is a target in the development of new therapies for the treatment of diabetes, the metabolic syndrome, and obesity.