DBAli: a database of protein structure alignments

SUMMARY: The DBAli database includes approximately 35000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions. AVAILABILITY: http://guitar.rockefeller.edu/DBAli/     

Multiple structure alignment with msTALI

ABSTRACT: BACKGROUND: Multiple structure alignments have received increasing attention in recent years as an alternative to multiple sequence alignments. Although multiple structure alignment algorithms can potentially be applied to a number of problems, they have primarily been used for protein core identification. A method that is capable of solving a variety of problems using structure comparison is still absent. Here we introduce a program msTALI for aligning multiple protein structures. Our algorithm uses several informative features to guide its alignments: torsion angles, backbone Calpha atom positions, secondary structure, residue type, surface accessibility, and properties of nearby atoms. The algorithm allows the user to weight the types of information used to generate the alignment, which expands its utility to a wide variety of problems. RESULTS: msTALI exhibits competitive results on 824 families from the Homstrad and SABmark databases when compared to Matt and Mustang. We also demonstrate success at building a database of protein cores using 341 randomly selected CATH domains and highlight the contribution of msTALI compared to the CATH classifications. Finally, we present an example applying msTALI to the problem of detecting hinges in a protein undergoing rigid-body motion. CONCLUSIONS: msTALI is an effective algorithm for multiple structure alignment. In addition to its performance on standard comparison databases, it utilizes clear, informative features, allowing further customization for domain-specific applications. The C++ source code for msTALI is available for Linux on the web at http://ifestos.cse.sc.edu/mstali.     

Reconstructing protein networks of epithelial differentiation from histological sections

MOTIVATION: For systems biology of complex stratified epithelia like human epidermis, it will be of particular importance to reconstruct the spatiotemporal gene and protein networks regulating keratinocyte differentiation and homeostasis. RESULTS: Inside the epidermis, the differentiation state of individual keratinocytes is correlated with their respective distance from the connective tissue. We here present a novel method to profile this correlation for multiple epithelial protein biomarkers in the form of quantitative spatial profiles. Profiles were computed by applying image processing algorithms to histological sections stained with tri-color indirect immunofluorescence. From the quantitative spatial profiles, reflecting the spatiotemporal changes of protein expression during cellular differentiation, graphs of protein networks were reconstructed. CONCLUSION: Spatiotemporal networks can be used as a means for comparing and interpreting quantitative spatial protein expression profiles obtained from different tissue samples. In combination with automated microscopes, our new method supports the large-scale systems biological analysis of stratified epithelial tissues.     

Moonlighting glyceraldehyde-3-phosphate dehydrogenase: posttranslational modification,protein and nucleic acid interactions in normal cells and in human pathology

Abstract

Moonlighting glyceraldehyde-3-phosphate dehydrogenase (GAPDH) exhibits multiple functions separate and distinct from its historic role in energy production. Further, it exhibits dynamic changes in its subcellular localization which is an a priori requirement for its multiple activities. Separately, moonlighting GAPDH may function in the pathology of human disease, involved in tumorigenesis, diabetes, and age-related neurodegenerative disorders. It is suggested that moonlighting GAPDH function may be related to specific modifications of its protein structure as well as the formation of GAPDH protein: protein or GAPDH protein: nucleic acid complexes.     

Integrative approach for computationally inferring protein domain interactions

MOTIVATION: The current need for high-throughput protein interaction detection has resulted in interaction data being generated en masse through such experimental methods as yeast-two-hybrids and protein chips. Such data can be erroneous and they often do not provide adequate functional information for the detected interactions. Therefore, it is useful to develop an in silico approach to further validate and annotate the detected protein interactions. RESULTS: Given that protein-protein interactions involve physical interactions between protein domains, domain-domain interaction information can be useful for validating, annotating, and even predicting protein interactions. However, large-scale, experimentally determined domain-domain interaction data do not exist. Here, we describe an integrative approach to computationally derive putative domain interactions from multiple data sources, including protein interactions, protein complexes, and Rosetta Stone sequences. We further prove the usefulness of such an integrative approach by applying the derived domain interactions to predict and validate protein-protein interactions. AVAILABILITY: A database of putative protein domain interactions derived using the method described in this paper is available at http://interdom.lit.org.sg.     

FASMA: a service to format and analyze sequences in multiple alignments

Multiple sequence alignments are successfully applied in many studies for under- standing the structural and functional relations among single nucleic acids and protein sequences as well as whole families. Because of the rapid growth of sequence databases, multiple sequence alignments can often be very large and difficult to visualize and analyze. We offer a new service aimed to visualize and analyze the multiple alignments obtained with different external algorithms, with new features useful for the comparison of the aligned sequences as well as for the creation of a final image of the alignment. The service is named FASMA and is available at http://bioinformatica.isa.cnr.it/FASMA/.     

Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

ABSTRACT: BACKGROUND: Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions. RESULTS: Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map. CONCLUSIONS: With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.     

SHARP2: protein-protein interaction predictions using patch analysis

SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY: http://www.bioinformatics.sussex.ac.uk/SHARP2.     

TRMP: a database of therapeutically relevant multiple pathways

SUMMARY: Disease processes often involve crosstalks between proteins in different pathways. Different proteins have been used as separate therapeutic targets for the same disease. Synergetic targeting of multiple targets has been explored in combination therapy of a number of diseases. Potential harmful interactions of multiple targeting have also been closely studied. To facilitate mechanistic study of drug actions and a more comprehensive understanding the relationship between different targets of the same disease, it is useful to develop a database of known therapeutically relevant multiple pathways (TRMPs). Information about non-target proteins and natural small molecules involved in these pathways also provides useful hint for searching new therapeutic targets and facilitate the understanding of how therapeutic targets interact with other molecules in performing specific tasks. The TRMPs database is designed to provide information about such multiple pathways along with related therapeutic targets, corresponding drugs/ligands, targeted disease conditions, constituent individual pathways, structural and functional information about each protein in the pathways. Cross links to other databases are also introduced to facilitate the access of information about individual pathways and proteins. AVAILABILITY: This database can be accessed at http://bidd.nus.edu.sg/group/trmp/trmp.asp and it currently contains 11 entries of multiple pathways, 97 entries of individual pathways, 120 targets covering 72 disease conditions together with 120 sets of drugs directed at each of these targets. Each entry can be retrieved through multiple methods including multiple pathway name, individual pathway name and disease name. SUPPLEMENTARY INFORMATION: http://bidd.nus.edu.sg/group/trmp/sm.pdf     

Integration of genomic datasets to predict protein complexes in yeast

The ultimate goal of functional genomics is to define the function of all the genes in the genome of an organism. A large body of information of the biological roles of genes has been accumulated and aggregated in the past decades of research, both from traditional experiments detailing the role of individual genes and proteins, and from newer experimental strategies that aim to characterize gene function on a genomic scale.It is clear that the goal of functional genomics can only be achieved by integrating information and data sources from the variety of these different experiments. Integration of different data is thus an important challenge for bioinformatics.The integration of different data sources often helps to uncover non-obvious relationships between genes, but there are also two further benefits. First, it is likely that whenever information from multiple independent sources agrees, it should be more valid and reliable. Secondly, by looking at the union of multiple sources, one can cover larger parts of the genome. This is obvious for integrating results from multiple single gene or protein experiments, but also necessary for many of the results from genome-wide experiments since they are often confined to certain (although sizable) subsets of the genome.In this paper, we explore an example of such a data integration procedure. We focus on the prediction of membership in protein complexes for individual genes. For this, we recruit six different data sources that include expression profiles, interaction data, essentiality and localization information. Each of these data sources individually contains some weakly predictive information with respect to protein complexes, but we show how this prediction can be improved by combining all of them. Supplementary information is available at http://bioinfo.mbb.yale.edu/integrate/interactions/.Abbreviations: TP: true possitive; TN: true negative; FP: false positive; FN: false negative; Y2H: yeast two-hybrid.     

A multiple reaction monitoring method for absolute quantification of the human liver alcohol dehydrogenase ADH1C1 isoenzyme

Although significant progress has been made in protein quantification using mass spectrometry during recent years, absolute protein quantification in complex biological systems remains a challenging task in proteomics. The use of stable isotope-labeled standard peptide is the most commonly used strategy for absolute quantification, but it might not be suitable in all instances. Here we report an alternative strategy that employs a stable isotope-labeled intact protein as an internal standard to absolutely quantify the alcohol dehydrogenase (ADH) expression level in a human liver sample. In combination with a new targeted proteomics approach employing the method of multiple reaction monitoring (MRM), we precisely and quantitatively measured the absolute protein expression level of an ADH isoenzyme, ADH1C1, in human liver. Isotope-labeled protein standards are predicted to be particularly useful for measurement of highly homologous isoenzymes such as ADHs where multiple signature peptides can be examined by MRM in a single experiment.     

Automated multiple structure alignment and detection of a common substructural motif

While a number of approaches have been geared toward multiple sequence alignments, to date there have been very few approaches to multiple structure alignment and detection of a recurring substructural motif. Among these, none performs both multiple structure comparison and motif detection simultaneously. Further, none considers all structures at the same time, rather than initiating from pairwise molecular comparisons. We present such a multiple structural alignment algorithm. Given an ensemble of protein structures, the algorithm automatically finds the largest common substructure (core) of C(alpha) atoms that appears in all the molecules in the ensemble. The detection of the core and the structural alignment are done simultaneously. Additional structural alignments also are obtained and are ranked by the sizes of the substructural motifs, which are present in the entire ensemble. The method is based on the geometric hashing paradigm. As in our previous structural comparison algorithms, it compares the structures in an amino acid sequence order-independent way, and hence the resulting alignment is unaffected by insertions, deletions and protein chain directionality. As such, it can be applied to protein surfaces, protein-protein interfaces and protein cores to find the optimally, and suboptimally spatially recurring substructural motifs. There is no predefinition of the motif. We describe the algorithm, demonstrating its efficiency. In particular, we present a range of results for several protein ensembles, with different folds and belonging to the same, or to different, families. Since the algorithm treats molecules as collections of points in three-dimensional space, it can also be applied to other molecules, such as RNA, or drugs.     

Variations on probabilistic suffix trees: statistical modeling and prediction of protein families

MOTIVATION: We present a method for modeling protein families by means of probabilistic suffix trees (PSTs). The method is based on identifying significant patterns in a set of related protein sequences. The patterns can be of arbitrary length, and the input sequences do not need to be aligned, nor is delineation of domain boundaries required. The method is automatic, and can be applied, without assuming any preliminary biological information, with surprising success. Basic biological considerations such as amino acid background probabilities, and amino acids substitution probabilities can be incorporated to improve performance. RESULTS: The PST can serve as a predictive tool for protein sequence classification, and for detecting conserved patterns (possibly functionally or structurally important) within protein sequences. The method was tested on the Pfam database of protein families with more than satisfactory performance. Exhaustive evaluations show that the PST model detects much more related sequences than pairwise methods such as Gapped-BLAST, and is almost as sensitive as a hidden Markov model that is trained from a multiple alignment of the input sequences, while being much faster.     

Aquerium: A web application for comparative exploration of domain‐based protein occurrences on the taxonomically clustered genome tree

Gene duplication and loss are major driving forces in evolution. While many important genomic resources provide information on gene presence, there is a lack of tools giving equal importance to presence and absence information as well as web platforms enabling easy visual comparison of multiple domain‐based protein occurrences at once. Here, we present Aquerium, a platform for visualizing genomic presence and absence of biomolecules with a focus on protein domain architectures. The web server offers advanced domain organization querying against the database of pre‐computed domains for ～26,000 organisms and it can be utilized for identification of evolutionary events, such as fusion, disassociation, duplication, and shuffling of protein domains. The tool also allows alternative inputs of custom entries or BLASTP results for visualization. Aquerium will be a useful tool for biologists who perform comparative genomic and evolutionary analyses. The web server is freely accessible at http://aquerium.utk.edu . Proteins 2016; 85:72–77. © 2016 Wiley Periodicals, Inc.     

Rational design of solution additives for the prevention of protein aggregation

We have developed a statistical-mechanical model of the effect of solution additives on protein association reactions. This model incorporates solvent radial distribution functions obtained from all-atom molecular dynamics simulations of particular proteins into simple models of protein interactions. In this way, the effects of additives can be computed along the entire association/dissociation reaction coordinate. We used the model to test our hypothesis that a class of large solution additives, which we term "neutral crowders," can slow protein association and dissociation by being preferentially excluded from protein-protein encounter complexes, in a manner analogous to osmotic stress. The magnitude of this proposed "gap effect" was probed for two simple model systems: the association of two spheres and the association of two planes. Our results suggest that for a protein of 20 A radius, an 8 A additive can increase the free energy barrier for association and dissociation by as much as 3-6 kcal/mol. Because the proposed gap effect is present only for reactions involving multiple molecules, it can be exploited to develop novel additives that affect protein association reactions although having little or no effect on unimolecular reactions such as protein folding. This idea has many potential applications in areas such as the stabilization of proteins against aggregation during folding and in pharmaceutical formulations.     

Transformation strategies for stable expression of complex hetero‐multimeric proteins like secretory immunoglobulin A in plants

Plant expression systems have proven to be exceptional in producing high‐value complex polymeric proteins such as secretory IgAs (SIgAs). However, polymeric protein production requires the expression of multiple genes, which can be transformed as single or multiple T‐DNA units to generate stable transgenic plant lines. Here, we evaluated four strategies to stably transform multiple genes and to obtain high expression of all components. Using the in‐seed expression of a simplified secretory IgA (sSIgA) as a reference molecule, we conclude that it is better to spread the genes over two T‐DNAs than to contain them in a single T‐DNA, because of the presence of homologous recombination events and gene silencing. These T‐DNAs can be cotransformed to obtain transgenic plants in one transformation step. However, if time permits, more transformants with high production levels of the polymeric protein can be obtained either by sequential transformation or by in‐parallel transformation followed by crossing of transformants independently selected for excellent expression of the genes in each T‐DNA.     

Absolute quantification of protein and post-translational modification abundance with stable isotope-labeled synthetic peptides

In the analysis of biological systems, it is of interest to identify the components of the system and to monitor their changes in abundance under different conditions. The AQUA (for 'absolute quantification') method allows sensitive and specific targeted quantification of protein and post-translational modifications in complex protein mixtures using stable isotope-labeled peptides as internal standards. Each AQUA experiment is composed of two stages: method development and application to a biological scenario. In the method development stage, peptides from the protein of interest are chosen and then synthesized with stable isotopes such as (13)C, (2)H or (15)N. The abundance of these internal standards and their endogenous counterparts can be measured by mass spectrometry with selected reaction monitoring or selected ion monitoring methods. Once an AQUA method is established, it can be rapidly applied to a wide range of biological samples, from tissue culture cells to human plasma and tissue. After AQUA peptide synthesis, the development, optimization and application of AQUA analyses to a specific biological problem can be achieved in ~1 week. Here we demonstrate the usefulness of this method by monitoring both Polo-like kinase 1 (Plk1) protein abundance in multiple lung cancer cell lines and the extent of Plk1 activation loop phosphorylation (pThr-210) during release from S phase.     

The Protein 4.1 family: Hub proteins in animals for organizing membrane proteins

Proteins of the 4.1 family are characteristic of eumetazoan organisms. Invertebrates contain single 4.1 genes and the Drosophila model suggests that 4.1 is essential for animal life. Vertebrates have four paralogues, known as 4.1R, 4.1N, 4.1G and 4.1B, which are additionally duplicated in the ray-finned fish. Protein 4.1R was the first to be discovered: it is a major mammalian erythrocyte cytoskeletal protein, essential to the mechanochemical properties of red cell membranes because it promotes the interaction between spectrin and actin in the membrane cytoskeleton. 4.1R also binds certain phospholipids and is required for the stable cell surface accumulation of a number of erythrocyte transmembrane proteins that span multiple functional classes; these include cell adhesion molecules, transporters and a chemokine receptor. The vertebrate 4.1 proteins are expressed in most tissues, and they are required for the correct cell surface accumulation of a very wide variety of membrane proteins including G-Protein coupled receptors, voltage-gated and ligand-gated channels, as well as the classes identified in erythrocytes. Indeed, such large numbers of protein interactions have been mapped for mammalian 4.1 proteins, most especially 4.1R, that it appears that they can act as hubs for membrane protein organization. The range of critical interactions of 4.1 proteins is reflected in disease relationships that include hereditary anaemias, tumour suppression, control of heartbeat and nervous system function. The 4.1 proteins are defined by their domain structure: apart from the spectrin/actin-binding domain they have FERM and FERM-adjacent domains and a unique C-terminal domain. Both the FERM and C-terminal domains can bind transmembrane proteins, thus they have the potential to be cross-linkers for membrane proteins. The activity of the FERM domain is subject to multiple modes of regulation via binding of regulatory ligands, phosphorylation of the FERM associated domain and differential mRNA splicing. Finally, the spectrum of interactions of the 4.1 proteins overlaps with that of another membrane-cytoskeleton linker, ankyrin. Both ankyrin and 4.1 link to the actin cytoskeleton via spectrin, and we hypothesize that differential regulation of 4.1 proteins and ankyrins allows highly selective control of cell surface protein accumulation and, hence, function. This article is part of a Special Issue entitled: Reciprocal influences between cell cytoskeleton and membrane channels, receptors and transporters. Guest Editor: Jean Claude Hervé     

Venus: Elucidating the Impact of Amino Acid Variants on Protein Function Beyond Structure Destabilisation

Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection.Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelanglo and the results can be exported to it, allowing them to be annotated and shared with other researchers.Venus is freely accessible at https://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.