Vorolign--fast structural alignment using Voronoi contacts

Evolutionary and Molecular Characterization of liver-enriched gene 1

Liver-enriched gene 1 (Leg1) is a newly identified gene with little available functional information. To evolutionarily and molecularly characterize Leg1 genes, a phylogenetic study was first conducted, which indicated that Leg1 is a conserved gene that exists from bacteria to mammals. During the evolution of mammals, Leg1s underwent tandem duplications, which gave rise to Leg1a, Leg1b, and Leg1c clades. Analysis of the pig genome showed the presence of all three paralogs of pig Leg1 genes (pLeg1s), whereas only Leg1a could be found in the human (hLeg1a) or mouse (mLeg1a) genomes. Purifying force acts on the evolution of Leg1 genes, likely subjecting them to functional constraint. Molecularly, pLeg1a and its coded protein, pig LEG1a (pLEG1a), displayed high similarities to its human and mouse homologs in terms of gene organization, expression patterns, and structures. Hence, pLeg1a, hLeg1a, and mLeg1a might preserve similar functions. Additionally, expression analysis of the three Leg1as suggested that eutherian Leg1as might have different functions from those of zebrafish and platypus due to subfunctionalization. Therefore, pLeg1a might provide essential information about eutherian Leg1a. Moreover, a preliminary functional study using RNA-seq suggested that pLeg1a is involved in the lipid homeostasis. In conclusion, our study provides some basic information on the aspects of evolution and molecular function, which could be applied for further validation of Leg1 using pig models.

Cluster expansion of apolipoprotein D (ApoD) genes in teleost fishes

BACKGROUND

Gene and genome duplication play important roles in the evolution of gene function. Compared to individual duplicated genes, gene clusters attract particular attention considering their frequent associations with innovation and adaptation. Here, we report for the first time the expansion of the apolipoprotein D (ApoD) ligand-transporter genes in a cluster manner specific to teleost fishes.

RESULTS

Based on comparative genomic and transcriptomic analyses, protein 3D structure comparison, positive selection detection and breakpoints detection, the single ApoD gene in the ancestor expanded into two clusters following a dynamic evolutionary pattern in teleost fishes. Orthologous genes show conserved expression patterns, whereas lineage-specific duplicated genes show tissue-specific expression patterns and even evolve new gene expression profiles. Positive selection occurred in branches before and after gene duplication, especially for lineage-specific duplicated genes. Cluster analyses based on protein 3D structure comparisons, especially comparisons of the four loops at the opening side, show gene duplication-segregating patterns. Duplicated ApoD genes are predicted to be associated with forkhead transcription factors and MAPK genes. ApoD clusters are located next to the breakpoints of genome rearrangements.

CONCLUSIONS

Here, we report the expansion of ApoD genes specific to teleost fishes in a cluster manner for the first time. Neofunctionalization and subfunctionalization were observed at both the protein and expression levels after duplication. Evidence from different aspects-i.e., abnormal expression-induced disease in humans, fish-specific expansion, predicted associations with forkhead transcription factors and MAPK genes, specific expression patterns in tissues related to sexual selection and adaptation, duplicated genes under positive selection and their location next to the breakpoints of genome rearrangements-suggests the potentially advantageous roles of ApoD genes in teleost fishes. The cluster expansion of ApoD genes specific to teleost fishes provides thus an ideal evo-devo model for studying gene duplication, cluster maintenance and new gene function emergence.

Relationships between residue Voronoi volume and sequence conservation in proteins

BACKGROUND

Functional and biophysical constraints can cause different levels of sequence conservation in proteins. Previously, structural properties, e.g., relative solvent accessibility (RSA) and packing density of the weighted contact number (WCN), have been found to be related to protein sequence conservation (CS). The Voronoi volume has recently been recognized as a new structural property of the local protein structural environment reflecting CS. However, for surface residues, it is sensitive to water molecules surrounding the protein structure. Herein, we present a simple structural determinant termed the relative space of Voronoi volume (RSV); it uses the Voronoi volume and the van der Waals volume of particular residues to quantify the local structural environment.

METHODS

RSV (range, 0-1) is defined as (Voronoi volume-van der Waals volume)/Voronoi volume of the target residue. The concept of RSV describes the extent of available space for every protein residue.

RESULTS

RSV and Voronoi profiles with and without water molecules (RSVw, RSV, VOw, and VO) were compared for 554 non-homologous proteins. RSV (without water) showed better Pearson's correlations with CS than did RSVw, VO, or VOw values. The mean correlation coefficient between RSV and CS was 0.51, which is comparable to the correlation between RSA and CS (0.49) and that between WCN and CS (0.56).

CONCLUSIONS

RSV is a robust structural descriptor with and without water molecules and can quantitatively reflect evolutionary information in a single protein structure. Therefore, it may represent a practical structural determinant to study protein sequence, structure, and function relationships.

Calculating and scoring high quality multiple flexible protein structure alignments

MOTIVATION

Calculating multiple protein structure alignments (MSAs) is important for understanding functional and evolutionary relationships between protein families, and for modeling protein structures by homology. While incorporating backbone flexibility promises to circumvent many of the limitations of rigid MSA algorithms, very few flexible MSA algorithms exist today. This article describes several novel improvements to the Kpax algorithm which allow high quality flexible MSAs to be calculated. This article also introduces a new Gaussian-based MSA quality measure called 'M-score', which circumvents the pitfalls of RMSD-based quality measures.

RESULTS

As well as calculating flexible MSAs, the new version of Kpax can also score MSAs from other aligners and from previously aligned reference datasets. Results are presented for a large-scale evaluation of the Homstrad, SABmark and SISY benchmark sets using Kpax and Matt as examples of state-of-the-art flexible aligners and 3DCOMB as an example of a state-of-the-art rigid aligner. These results demonstrate the utility of the M-score as a measure of MSA quality and show that high quality MSAs may be achieved when structural flexibility is properly taken into account.

AVAILABILITY AND IMPLEMENTATION

Kpax 5.0 may be downloaded for academic use at http://kpax.loria.fr/

CONTACT

dave.ritchie@inria.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Conformational Sampling Reveals Amino Acids with a Steric Influence on Specificity

Flexible representations of protein structures can enable structure comparison algorithms to find remotely homologous proteins, even when they have been crystallized in different conformations. By compensating for large spatial variations, these representations can enable these algorithms to better detect remote similarities in the space of protein structures. Subtle variations in protein structures can also have a substantial impact structure comparison. For example, the motion of a single side chain into a binding cavity can make the cavity appear totally dissimilar to identical binding sites, even though, in reality, the presence of the side chain does not affect binding. To address the impact of subtle conformational variations, this article describes FAVA (Flexible Aggregate Volumetric Analysis), an algorithm that enables comparisons of ligand binding sites while compensating for subtle, localized flexibility. FAVA integrates hundreds of conformational samples, sourced from any molecular simulation software that provides all-atom detail, to characterize the geometry of ligand binding sites as they frequently appear. This representation enables rare conformations, as defined by the user, to be excluded from the structural comparison. In our results, on three families of serine proteases and three families of enolases, we show that despite substantial binding site variations, FAVA is able to correctly classify families with different binding preferences. We also demonstrate that FAVA can examine the motion of individual amino acids to identify those that influence ligand binding specificity. Together, these capabilities demonstrate that comparison errors associated with small conformational variations, which can substantially alter the geometry of ligand binding sites and other local features, can be mitigated by an analysis of many conformational samples.

VLDP web server: a powerful geometric tool for analysing protein structures in their environment

Protein structures are an ensemble of atoms determined experimentally mostly by X-ray crystallography or Nuclear Magnetic Resonance. Studying 3D protein structures is a key point for better understanding protein function at a molecular level. We propose a set of accurate tools, for analysing protein structures, based on the reliable method of Voronoi–Laguerre tessellations. The Voronoi Laguerre Delaunay Protein web server (VLDPws) computes the Laguerre tessellation on a whole given system first embedded in solvent. Through this fine description, VLDPws gives the following data: (i) Amino acid volumes evaluated with high precision, as confirmed by good correlations with experimental data. (ii) A novel definition of inter-residue contacts within the given protein. (iii) A measure of the residue exposure to solvent that significantly improves the standard notion of accessibility in some cases. At present, no equivalent web server is available. VLDPws provides output in two complementary forms: direct visualization of the Laguerre tessellation, mostly its polygonal molecular surfaces; files of volumes; and areas, contacts and similar data for each residue and each atom. These files are available for download for further analysis. VLDPws can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/vldp.

Classifying the metal dependence of uncharacterized nitrogenases

Nitrogenase enzymes have evolved complex iron–sulfur (Fe–S) containing cofactors that most commonly contain molybdenum (MoFe, Nif) as a heterometal but also exist as vanadium (VFe, Vnf) and heterometal-independent (Fe-only, Anf) forms. All three varieties are capable of the reduction of dinitrogen (N₂) to ammonia (NH₃) but exhibit differences in catalytic rates and substrate specificity unique to metal type. Recently, N₂ reduction activity was observed in archaeal methanotrophs and methanogens that encode for nitrogenase homologs which do not cluster phylogenetically with previously characterized nitrogenases. To gain insight into the metal cofactors of these uncharacterized nitrogenase homologs, predicted three-dimensional structures of the nitrogenase active site metal-cofactor binding subunits NifD, VnfD, and AnfD were generated and compared. Dendrograms based on structural similarity indicate nitrogenase homologs cluster based on heterometal content and that uncharacterized nitrogenase D homologs cluster with NifD, providing evidence that the structure of the enzyme has evolved in response to metal utilization. Characterization of the structural environment of the nitrogenase active site revealed amino acid variations that are unique to each class of nitrogenase as defined by heterometal cofactor content; uncharacterized nitrogenases contain amino acids near the active site most similar to NifD. Together, these results suggest that uncharacterized nitrogenase homologs present in numerous anaerobic methanogens, archaeal methanotrophs, and firmicutes bind FeMo-co in their active site, and add to growing evidence that diversification of metal utilization likely occurred in an anoxic habitat.

Selecting fast-folding proteins by their rate of convergence

We propose a general method for predicting potentially good folders from a given number of amino acid sequences. Our approach is based on the calculation of the rate of convergence of each amino acid chain towards the native structure using only the very initial parts of the dynamical trajectories. It does not require any preliminary knowledge of the native state and can be applied to different kinds of models, including atomistic descriptions. We tested the method within both the lattice and off-lattice model frameworks and obtained several so far unknown good folders. The unbiased algorithm also allows one to determine the optimal folding temperature and takes at least 3-4 orders of magnitude fewer time steps than those needed to compute folding times.

Optimal simultaneous superpositioning of multiple structures with missing data

MOTIVATION

Superpositioning is an essential technique in structural biology that facilitates the comparison and analysis of conformational differences among topologically similar structures. Performing a superposition requires a one-to-one correspondence, or alignment, of the point sets in the different structures. However, in practice, some points are usually 'missing' from several structures, for example, when the alignment contains gaps. Current superposition methods deal with missing data simply by superpositioning a subset of points that are shared among all the structures. This practice is inefficient, as it ignores important data, and it fails to satisfy the common least-squares criterion. In the extreme, disregarding missing positions prohibits the calculation of a superposition altogether.

RESULTS

Here, we present a general solution for determining an optimal superposition when some of the data are missing. We use the expectation-maximization algorithm, a classic statistical technique for dealing with incomplete data, to find both maximum-likelihood solutions and the optimal least-squares solution as a special case.

AVAILABILITY AND IMPLEMENTATION

The methods presented here are implemented in THESEUS 2.0, a program for superpositioning macromolecular structures. ANSI C source code and selected compiled binaries for various computing platforms are freely available under the GNU open source license from http://www.theseus3d.org.

CONTACT

dtheobald@brandeis.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Progressive structure-based alignment of homologous proteins: Adopting sequence comparison strategies

Comparison of multiple protein structures has a broad range of applications in the analysis of protein structure, function and evolution. Multiple structure alignment tools (MSTAs) are necessary to obtain a simultaneous comparison of a family of related folds. In this study, we have developed a method for multiple structure comparison largely based on sequence alignment techniques. A widely used Structural Alphabet named Protein Blocks (PBs) was used to transform the information on 3D protein backbone conformation as a 1D sequence string. A progressive alignment strategy similar to CLUSTALW was adopted for multiple PB sequence alignment (mulPBA). Highly similar stretches identified by the pairwise alignments are given higher weights during the alignment. The residue equivalences from PB based alignments are used to obtain a three dimensional fit of the structures followed by an iterative refinement of the structural superposition. Systematic comparisons using benchmark datasets of MSTAs underlines that the alignment quality is better than MULTIPROT, MUSTANG and the alignments in HOMSTRAD, in more than 85% of the cases. Comparison with other rigid-body and flexible MSTAs also indicate that mulPBA alignments are superior to most of the rigid-body MSTAs and highly comparable to the flexible alignment methods.

Evolutionary inaccuracy of pairwise structural alignments

Motivation: Structural alignment methods are widely used to generate gold standard alignments for improving multiple sequence alignments and transferring functional annotations, as well as for assigning structural distances between proteins. However, the correctness of the alignments generated by these methods is difficult to assess objectively since little is known about the exact evolutionary history of most proteins. Since homology is an equivalence relation, an upper bound on alignment quality can be found by assessing the consistency of alignments. Measuring the consistency of current methods of structure alignment and determining the causes of inconsistencies can, therefore, provide information on the quality of current methods and suggest possibilities for further improvement.

Results: We analyze the self-consistency of seven widely-used structural alignment methods (SAP, TM-align, Fr-TM-align, MAMMOTH, DALI, CE and FATCAT) on a diverse, non-redundant set of 1863 domains from the SCOP database and demonstrate that even for relatively similar proteins the degree of inconsistency of the alignments on a residue level is high (30%). We further show that levels of consistency vary substantially between methods, with two methods (SAP and Fr-TM-align) producing more consistent alignments than the rest. Inconsistency is found to be higher near gaps and for proteins of low structural complexity, as well as for helices. The ability of the methods to identify good structural alignments is also assessed using geometric measures, for which FATCAT (flexible mode) is found to be the best performer despite being highly inconsistent. We conclude that there is substantial scope for improving the consistency of structural alignment methods.

Contact:msadows@nimr.mrc.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

Cloning and characterization of a pectin lyase gene from Colletotrichum lindemuthianum and comparative phylogenetic/structural analyses with genes from phytopathogenic and saprophytic/opportunistic microorganisms

Background

Microorganisms produce cell-wall-degrading enzymes as part of their strategies for plant invasion/nutrition. Among these, pectin lyases (PNLs) catalyze the depolymerization of esterified pectin by a β-elimination mechanism. PNLs are grouped together with pectate lyases (PL) in Family 1 of the polysaccharide lyases, as they share a conserved structure in a parallel β-helix. The best-characterized fungal pectin lyases are obtained from saprophytic/opportunistic fungi in the genera Aspergillus and Penicillium and from some pathogens such as Colletotrichum gloeosporioides.

The organism used in the present study, Colletotrichum lindemuthianum, is a phytopathogenic fungus that can be subdivided into different physiological races with different capacities to infect its host, Phaseolus vulgaris. These include the non-pathogenic and pathogenic strains known as races 0 and 1472, respectively.

Results

Here we report the isolation and sequence analysis of the Clpnl2 gene, which encodes the pectin lyase 2 of C. lindemuthianum, and its expression in pathogenic and non-pathogenic races of C. lindemuthianum grown on different carbon sources. In addition, we performed a phylogenetic analysis of the deduced amino acid sequence of Clpnl2 based on reported sequences of PNLs from other sources and compared the three-dimensional structure of Clpnl2, as predicted by homology modeling, with those of other organisms. Both analyses revealed an early separation of bacterial pectin lyases from those found in fungi and oomycetes. Furthermore, two groups could be distinguished among the enzymes from fungi and oomycetes: one comprising enzymes from mostly saprophytic/opportunistic fungi and the other formed mainly by enzymes from pathogenic fungi and oomycetes. Clpnl2 was found in the latter group and was grouped together with the pectin lyase from C. gloeosporioides.

Conclusions

The Clpnl2 gene of C. lindemuthianum shares the characteristic elements of genes coding for pectin lyases. A time-course analysis revealed significant differences between the two fungal races in terms of the expression of Clpnl2 encoding for pectin lyase 2. According to the results, pectin lyases from bacteria and fungi separated early during evolution. Likewise, the enzymes from fungi and oomycetes diverged in accordance with their differing lifestyles. It is possible that the diversity and nature of the assimilatory carbon substrates processed by these organisms played a determinant role in this phenomenon.

SProt: sphere-based protein structure similarity algorithm

Background

Similarity search in protein databases is one of the most essential issues in computational proteomics. With the growing number of experimentally resolved protein structures, the focus shifted from sequences to structures. The area of structure similarity forms a big challenge since even no standard definition of optimal structure similarity exists in the field.

Results

We propose a protein structure similarity measure called SProt. SProt concentrates on high-quality modeling of local similarity in the process of feature extraction. SProt’s features are based on spherical spatial neighborhood of amino acids where similarity can be well-defined. On top of the partial local similarities, global measure assessing similarity to a pair of protein structures is built. Finally, indexing is applied making the search process by an order of magnitude faster.

Conclusions

The proposed method outperforms other methods in classification accuracy on SCOP superfamily and fold level, while it is at least comparable to the best existing solutions in terms of precision-recall or quality of alignment.

Alignment of distantly related protein structures: algorithm, bound and implications to homology modeling

MOTIVATION

Building an accurate alignment of a large set of distantly related protein structures is still very challenging.

RESULTS

This article presents a novel method 3DCOMB that can generate a multiple structure alignment (MSA) with not only as many conserved cores as possible, but also high-quality pairwise alignments. 3DCOMB is unique in that it makes use of both local and global structure environments, combined by a statistical learning method, to accurately identify highly similar fragment blocks (HSFBs) among all proteins to be aligned. By extending the alignments of these HSFBs, 3DCOMB can quickly generate an accurate MSA without using progressive alignment. 3DCOMB significantly excels others in aligning distantly related proteins. 3DCOMB can also generate correct alignments for functionally similar regions among proteins of very different structures while many other MSA tools fail. 3DCOMB is useful for many real-world applications. In particular, it enables us to find out that there is still large improvement room for multiple template homology modeling while several other MSA tools fail to do so.

AVAILABILITY

3DCOMB is available at http://ttic.uchicago.edu/~jinbo/software.htm.

CONTACT

jinboxu@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improvement of protein structure comparison using a structural alphabet

The three dimensional structure of a protein provides major insights into its function. Protein structure comparison has implications in functional and evolutionary studies. A structural alphabet (SA) is a library of local protein structure prototypes that can abstract every part of protein main chain conformation. Protein Blocks (PBs) is a widely used SA, composed of 16 prototypes, each representing a pentapeptide backbone conformation defined in terms of dihedral angles. Through this description, the 3D structural information can be translated into a 1D sequence of PBs. In a previous study, we have used this approach to compare protein structures encoded in terms of PBs. A classical sequence alignment procedure based on dynamic programming was used, with a dedicated PB Substitution Matrix (SM). PB-based pairwise structural alignment method gave an excellent performance, when compared to other established methods for mining. In this study, we have (i) refined the SMs and (ii) improved the Protein Block Alignment methodology (named as iPBA). The SM was normalized in regards to sequence and structural similarity. Alignment of protein structures often involves similar structural regions separated by dissimilar stretches. A dynamic programming algorithm that weighs these local similar stretches has been designed. Amino acid substitutions scores were also coupled linearly with the PB substitutions. iPBA improves (i) the mining efficiency rate by 6.8% and (ii) more than 82% of the alignments have a better quality. A higher efficiency in aligning multi-domain proteins could be also demonstrated. The quality of alignment is better than DALI and MUSTANG in 81.3% of the cases. Thus our study has resulted in an impressive improvement in the quality of protein structural alignment.

Improving structure alignment-based prediction of SCOP families using Vorolign kernels

MOTIVATION

The slow growth of expert-curated databases compared to experimental databases makes it necessary to build upon highly accurate automated processing pipelines to make the most of the data until curation becomes available. We address this problem in the context of protein structures and their classification into structural and functional classes, more specifically, the structural classification of proteins (SCOP). Structural alignment methods like Vorolign already provide good classification results, but effectively work in a 1-Nearest Neighbor mode. Model-based (in contrast to instance-based) approaches so far have been shown to be of limited values due to small classes arising in such classification schemes.

RESULTS

In this article, we describe how kernels defined in terms of Vorolign scores can be used in SVM learning, and explore variants of combined instance-based and model-based learning, up to exclusively model-based learning. Our results suggest that kernels based on Vorolign scores are effective and that model-based learning can yield highly competitive classification results for the prediction of SCOP families.

AVAILABILITY

The code is made available at: http://wwwkramer.in.tum.de/research/applications/vorolign-kernel.

Vorescore--fold recognition improved by rescoring of protein structure models

Summary: The identification of good protein structure models and their appropriate ranking is a crucial problem in structure prediction and fold recognition. For many alignment methods, rescoring of alignment-induced models using structural information can improve the separation of useful and less useful models as compared with the alignment score. Vorescore, a template-based protein structure model rescoring system is introduced. The method scores the model structure against the template used for the modeling using Vorolign. The method works on models from different alignment methods and incorporates both knowledge from the prediction method and the rescoring.

Results: The performance of Vorescore is evaluated in a large-scale and difficult protein structure prediction context. We use different threading methods to create models for 410 targets, in three scenarios: (i) family members are contained in the template set; (ii) superfamily members (but no family members); and (iii) only fold members (but no family or superfamily members). In all cases Vorescore improves significantly (e.g. 40% on both Gotoh and HHalign at the fold level) on the model quality, and clearly outperforms the state-of-the-art physics-based model scoring system Rosetta. Moreover, Vorescore improves on other successful rescoring approaches such as Pcons and ProQ. In an additional experiment we add high-quality models based on structural alignments to the set, which allows Vorescore to improve the fold recognition rate by another 50%.

Availability: All models of the test set (about 2 million, 44 GB gzipped) are available upon request.

Contact:csaba@bio.ifi.lmu.de; ralf.zimmer@ifi.lmu.de

A novel evaluation of residue and protein volumes by means of Laguerre tessellation

Amino acids control the protein folding process and maintain its functional fold. This study underlines the interest of the Laguerre tessellation to determine relevant amino acid volumes in proteins. Previous studies used a limited number of proteins and only buried residues. The present computations improve the method and results on three main points: (i) a large, high-quality updated and refined data bank of proteins is used; (ii) all residues are taken into account, including those at the protein surface, thanks to (iii) the addition of a realistic solvent. The new values of the average and standard deviation of amino acid volumes show significant corrections with respect to previous studies. Another issue of the method is the polyhedral protein/water interface area (PIA) which quantifies the exposure of atoms or residues to the solvent. We propose this PIA as a new, parameter-free, alternative for measuring accessibility. The comparison with NACCESS is satisfactory; however, the methods disagree in pointing out buried residues: where NACCESS evaluates to zero, the exposure given by PIA ranges from 0 to 20%. Variations of average residue volumes have been analyzed under several conditions, e.g., how they depend on protein size and on secondary structure environments. As it is based on strong mathematical grounds and on numerous high-quality protein structures, our work gives a reliable methodology and up-to-date values of amino acid volumes and surface accessibility.

Alignment-free local structural search by writhe decomposition

Motivation: Rapid methods for protein structure search enable biological discoveries based on flexibly defined structural similarity, unleashing the power of the ever greater number of solved protein structures. Projection methods show promise for the development of fast structural database search solutions. Projection methods map a structure to a point in a high-dimensional space and compare two structures by measuring distance between their projected points. These methods offer a tremendous increase in speed over residue-level structural alignment methods. However, current projection methods are not practical, partly because they are unable to identify local similarities.

Results: We propose a new projection-based approach that can rapidly detect global as well as local structural similarities. Local structural search is enabled by a topology-inspired writhe decomposition protocol that produces a small number of fragments while ensuring that similar structures are cut in a similar manner. In benchmark tests, we show that our method, writher, improves accuracy over existing projection methods in terms of recognizing scop domains out of multi-domain proteins, while maintaining accuracy comparable with existing projection methods in a standard single-domain benchmark test.

Availability: The source code is available at the following website: http://compbio.berkeley.edu/proj/writher/

Contact:dzhi@compbio.berkeley.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Identification of tyrosine-phosphorylated proteins associated with metastasis and functional analysis of FER in human hepatocellular carcinoma cells

BACKGROUND

Aberrant activity of tyrosine-phosphorylated proteins is commonly associated with HCC metastasis. Cell signaling events driven by these proteins are implicated in numerous processes that alter cancer cell behavior. Exploring the activities and signaling pathways of these proteins in HCC metastasis may help in identifying new candidate molecules for HCC-targeted therapy.

METHODS

Hep3B (a nonmetastatic HCC cell line) and MHCC97H (a highly metastatic HCC cell line) were used in this study, and the tyrosine-phosphorylated proteins expressed in these cell lines were profiled by a phosphoproteomics technique based on LC-MS/MS. Protein-protein interaction and functional clustering analyses were performed to determine the activities of the identified proteins and the signaling pathways closely related to HCC metastasis.

RESULTS

In both cell lines, a total of 247 phosphotyrosine (pTyr) proteins containing 281 pTyr sites were identified without any stimulation. The involvement of almost 30% of these in liver or liver cancer has not been reported previously. Biological process clustering analysis indicated that pTyr proteins involved in cell motility, migration, protein autophosphorylation, cell-cell communication, and antiapoptosis functions were overexpressed during metastasis. Pathway clustering analysis revealed that signaling pathways such as those involved in EGFR signaling, cytokine- and chemokine-mediated signal transduction, and the PI3K and JAK-STAT cascades were significantly activated during HCC metastasis. Moreover, noncanonical regulation of the JNK cascade might also provide new targets for HCC metastasis. After comparing the pTyr proteins that were differentially expressed during HCC cell metastasis, we selected FER, a nonreceptor tyrosine kinase, and validated its role in terms of both expression and function. The data confirmed that FER might play a critical role in the invasion and metastasis of HCC.

CONCLUSION

The identification of pTyr proteins and signaling pathways associated with HCC metastasis could provide useful information for selecting new molecular intervention targets. Moreover, FER might serve as a novel drug target in future HCC therapy.

Advances and pitfalls of protein structural alignment

Structure comparison opens a window into the distant past of protein evolution, which has been unreachable by sequence comparison alone. With 55,000 entries in the Protein Data Bank and about 500 new structures added each week, automated processing, comparison, and classification are necessary. A variety of methods use different representations, scoring functions, and optimization algorithms, and they generate contradictory results even for moderately distant structures. Sequence mutations, insertions, and deletions are accommodated by plastic deformations of the common core, retaining the precise geometry of the active site, and peripheral regions may refold completely. Therefore structure comparison methods that allow for flexibility and plasticity generate the most biologically meaningful alignments. Active research directions include both the search for fold invariant features and the modeling of structural transitions in evolution. Advances have been made in algorithmic robustness, multiple alignment, and speeding up database searches.

Flexible structural protein alignment by a sequence of local transformations

MOTIVATION

Throughout evolution, homologous proteins have common regions that stay semi-rigid relative to each other and other parts that vary in a more noticeable way. In order to compare the increasing number of structures in the PDB, flexible geometrical alignments are needed, that are reliable and easy to use.

RESULTS

We present a protein structure alignment method whose main feature is the ability to consider different rigid transformations at different sites, allowing for deformations beyond a global rigid transformation. The performance of the method is comparable with that of the best ones from 10 aligners tested, regarding both the quality of the alignments with respect to hand curated ones, and the classification ability. An analysis of some structure pairs from the literature that need to be matched in a flexible fashion are shown. The use of a series of local transformations can be exported to other classifiers, and a future golden protein similarity measure could benefit from it.

AVAILABILITY

A public server for the program is available at http://dmi.uib.es/ProtDeform/.

SUPPLEMENTARY INFORMATION

All data used, results and examples are available at http://dmi.uib.es/people/jairo/bio/ProtDeform.

Systematic comparison of SCOP and CATH: a new gold standard for protein structure analysis

Background

SCOP and CATH are widely used as gold standards to benchmark novel protein structure comparison methods as well as to train machine learning approaches for protein structure classification and prediction. The two hierarchies result from different protocols which may result in differing classifications of the same protein. Ignoring such differences leads to problems when being used to train or benchmark automatic structure classification methods. Here, we propose a method to compare SCOP and CATH in detail and discuss possible applications of this analysis.

Results

We create a new mapping between SCOP and CATH and define a consistent benchmark set which is shown to largely reduce errors made by structure comparison methods such as TM-Align and has useful further applications, e.g. for machine learning methods being trained for protein structure classification. Additionally, we extract additional connections in the topology of the protein fold space from the orthogonal features contained in SCOP and CATH.

Conclusion

Via an all-to-all comparison, we find that there are large and unexpected differences between SCOP and CATH w.r.t. their domain definitions as well as their hierarchic partitioning of the fold space on every level of the two classifications. A consistent mapping of SCOP and CATH can be exploited for automated structure comparison and classification.

Availability

Benchmark sets and an interactive SCOP-CATH browser are available at .

Integrated search and alignment of protein structures

MOTIVATION

Identification and comparison of similar three-dimensional (3D) protein structures has become an even greater challenge in the face of the rapidly growing structure databases. Here, we introduce Vorometric, a new method that provides efficient search and alignment of a query protein against a database of protein structures. Voronoi contacts of the protein residues are enriched with the secondary structure information and a metric substitution matrix is developed to allow efficient indexing. The contact hits obtained from a distance-based indexing method are extended to obtain high-scoring segment pairs, which are then used to generate structural alignments.

RESULTS

Vorometric is the first to address both search and alignment problems in the protein structure databases. The experimental results show that Vorometric is simultaneously effective in retrieving similar protein structures, producing high-quality structure alignments, and identifying cross-fold similarities. Vorometric outperforms current structure retrieval methods in search accuracy, while requiring com-parable running times. Furthermore, the structural superpositions produced are shown to have better quality and coverage, when compared with those of the popular structure alignment tools.

AVAILABILITY

Vorometric is available as a web service at http://bio.cse.ohio-state.edu/Vorometric

Protein structure search and local structure characterization

Background

Structural similarities among proteins can provide valuable insight into their functional mechanisms and relationships. As the number of available three-dimensional (3D) protein structures increases, a greater variety of studies can be conducted with increasing efficiency, among which is the design of protein structural alphabets. Structural alphabets allow us to characterize local structures of proteins and describe the global folding structure of a protein using a one-dimensional (1D) sequence. Thus, 1D sequences can be used to identify structural similarities among proteins using standard sequence alignment tools such as BLAST or FASTA.

Results

We used self-organizing maps in combination with a minimum spanning tree algorithm to determine the optimum size of a structural alphabet and applied the k-means algorithm to group protein fragnts into clusters. The centroids of these clusters defined the structural alphabet. We also developed a flexible matrix training system to build a substitution matrix (TRISUM-169) for our alphabet. Based on FASTA and using TRISUM-169 as the substitution matrix, we developed the SA-FAST alignment tool. We compared the performance of SA-FAST with that of various search tools in database-scale search tasks and found that SA-FAST was highly competitive in all tests conducted. Further, we evaluated the performance of our structural alphabet in recognizing specific structural domains of EGF and EGF-like proteins. Our method successfully recovered more EGF sub-domains using our structural alphabet than when using other structural alphabets. SA-FAST can be found at .

Conclusion

The goal of this project was two-fold. First, we wanted to introduce a modular design pipeline to those who have been working with structural alphabets. Secondly, we wanted to open the door to researchers who have done substantial work in biological sequences but have yet to enter the field of protein structure research. Our experiments showed that by transforming the structural representations from 3D to 1D, several 1D-based tools can be applied to structural analysis, including similarity searches and structural motif finding.

Alternative splicing and protein structure evolution

Alternative splicing is thought to be one of the major sources for functional diversity in higher eukaryotes. Interestingly, when mapping splicing events onto protein structures, about half of the events affect structured and even highly conserved regions i.e. are non-trivial on the structure level. This has led to the controversial hypothesis that such splice variants result in nonsense-mediated mRNA decay or non-functional, unstructured proteins, which do not contribute to the functional diversity of an organism. Here we show in a comprehensive study on alternative splicing that proteins appear to be much more tolerant to structural deletions, insertions and replacements than previously thought. We find literature evidence that such non-trivial splicing isoforms exhibit different functional properties compared to their native counterparts and allow for interesting regulatory patterns on the protein network level. We provide examples that splicing events may represent transitions between different folds in the protein sequence–structure space and explain these links by a common genetic mechanism. Taken together, those findings hint to a more prominent role of splicing in protein structure evolution and to a different view of phenotypic plasticity of protein structures.

Conformational analysis of alternative protein structures

MOTIVATION

Alternative structural models determined experimentally are available for an increasing number of proteins. Structural and functional studies of these proteins need to take these models into consideration as they can present considerable structural differences. The characterization of the structural differences and similarities between these models is a fundamental task in structural biology requiring appropriate methods.

RESULTS

We propose a method for characterizing sets of alternative structural models. Three types of analysis are performed: grouping according to structural similarity, visualization and detection of structural variation and comparison of subsets for identifying and locating distinct conformational states. The alpha carbon atoms are used in order to analyse the backbone conformations. Alternatively, side-chain atoms are used for detailed conformational analysis of specific sites. The method takes into account estimates of atom coordinate uncertainty. The invariant regions are used to generate optimal superpositions of these models. We present the results obtained for three proteins showing different degrees of conformational variability: relative motion of two structurally conserved subdomains, a disordered subdomain and flexibility in the functional site associated with ligand binding. The method has been applied in the analysis of the alternative models available in SCOP. Considerable structural variability can be observed for most proteins.

AVAILABILITY

The results of the analysis of the SCOP alternative models, the estimates of coordinate uncertainty as well as the source code of the implementation are available in the STRuster web site: http://struster.bioinf.mpi-inf.mpg.de.

AutoPSI: a database for automatic structural classification of protein sequences and structures

In protein research, structural classifications of protein domains provided by databases such as SCOP play an important role. However, as such databases have to be curated and prepared carefully, they update only up to a few times per year, and in between newly entered PDB structures cannot be used in cases where a structural classification is required. The Automated Protein Structure Identification (AutoPSI) database delivers predicted SCOP classifications for several thousand yet unclassified PDB entries as well as millions of UniProt sequences in an automated fashion. In order to obtain predictions, we make use of two recently published methods, namely AutoSCOP (sequence-based) and Vorolign (structure-based) and the consensus of both. With our predictions, we bridge the gap between SCOP versions for proteins with known structures in the PDB and additionally make structure predictions for a very large number of UniProt proteins. AutoPSI is freely accessible at http://www.bio.ifi.lmu.de/AutoPSIDB.

AutoSCOP: automated prediction of SCOP classifications using unique pattern-class mappings

MOTIVATION

The sequence patterns contained in the available motif and hidden Markov model (HMM) databases are a valuable source of information for protein sequence annotation. For structure prediction and fold recognition purposes, we computed mappings from such pattern databases to the protein domain hierarchy given by the ASTRAL compendium and applied them to the prediction of SCOP classifications. Our aim is to make highly confident predictions also for non-trivial cases if possible and abstain from a prediction otherwise, and thus to provide a method that can be used as a first step in a pipeline of prediction methods. We describe two successful examples for such pipelines. With the AutoSCOP approach, it is possible to make predictions in a large-scale manner for many domains of the available sequences in the well-known protein sequence databases.

RESULTS

AutoSCOP computes unique sequence patterns and pattern combinations for SCOP classifications. For instance, we assign a SCOP superfamily to a pattern found in its members whenever the pattern does not occur in any other SCOP superfamily. Especially on the fold and superfamily level, our method achieves both high sensitivity (above 93%) and high specificity (above 98%) on the difference set between two ASTRAL versions, due to being able to abstain from unreliable predictions. Further, on a harder test set filtered at low sequence identity, the combination with profile-profile alignments improves accuracy and performs comparably even to structure alignment methods. Integrating our method with structure alignment, we are able to achieve an accuracy of 99% on SCOP fold classifications on this set. In an analysis of false assignments of domains from new folds/superfamilies/families to existing SCOP classifications, AutoSCOP correctly abstains for more than 70% of the domains belonging to new folds and superfamilies, and more than 80% of the domains belonging to new families. These findings show that our approach is a useful additional filter for SCOP classification prediction of protein domains in combination with well-known methods such as profile-profile alignment.

AVAILABILITY

A web server where users can input their domain sequences is available at http://www.bio.ifi.lmu.de/autoscop.