Protein Data Bank Japan (PDBj): updated user interfaces, resource description framework, analysis tools for large structures.

The Protein Data Bank Japan (PDBj, http://pdbj.org), a member of the worldwide Protein Data Bank (wwPDB), accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins. We herein outline the updated web user interfaces together with RESTful web services and the backend relational database that support the former. To enhance the interoperability of the PDB data, we have previously developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, which is now a wwPDB standard called wwPDB/RDF. We have enhanced the connectivity of the wwPDB/RDF data by incorporating various external data resources. Services for searching, comparing and analyzing the ever-increasing large structures determined by hybrid methods are also described.

Neuro-symbolic representation learning on biological knowledge graphs.

MOTIVATION: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. AVAILABILITY AND IMPLEMENTATION: https://github.com/bio-ontology-research-group/walking-rdf-and-owl. CONTACT: robert.hoehndorf@kaust.edu.sa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A new look at an old view of denaturant induced protein unfolding.

We re-examine a site-binding approach independently proposed by Schellman (Schellman, J.A. (1958) Compt. rend. Lab. Carlsberg Ser. Chim. 30, 439-449) and Aune and Tanford (Aune, K.C. and Tanford, D. (1969) Biochemistry, 8, 4586-4590) for explicitly including the denaturant concentration within the protein unfolding equilibrium. We extend and formalize the approach through development of a multi-dimensional analytical model in which the folding reaction coordinate is defined by the number of denaturant molecules bound to sites located on either the initially folded, or unfolded, states of the protein. We use the developed method to re-examine the mechanistic determinants underlying the sigmoidal shape of the unfolding transition. A natural feature of our method is that it presents a landscape picture of the denaturant induced protein unfolding reaction.

Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families.

In the protein sequence space, natural proteins form clusters of families which are characterized by their unique native folds whereas the great majority of random polypeptides are neither clustered nor foldable to unique structures. Since a given polypeptide can be either foldable or unfoldable, a kind of "folding transition" is expected at the boundary of a protein family in the sequence space. By Monte Carlo simulations of a statistical mechanical model of protein sequence alignment that coherently incorporates both short-range and long-range interactions as well as variable-length insertions to reproduce the statistics of the multiple sequence alignment of a given protein family, we demonstrate the existence of such transition between natural-like sequences and random sequences in the sequence subspaces for 15 domain families of various folds. The transition was found to be highly cooperative and two-state-like. Furthermore, enforcing or suppressing consensus residues on a few of the well-conserved sites enhanced or diminished, respectively, the natural-like pattern formation over the entire sequence. In most families, the key sites included ligand binding sites. These results suggest some selective pressure on the key residues, such as ligand binding activity, may cooperatively facilitate the emergence of a protein family during evolution. From a more practical aspect, the present results highlight an essential role of long-range effects in precisely defining protein families, which are absent in conventional sequence models.

VaProS: a database-integration approach for protein/genome information retrieval.

Life science research now heavily relies on all sorts of databases for genome sequences, transcription, protein three-dimensional (3D) structures, protein-protein interactions, phenotypes and so forth. The knowledge accumulated by all the omics research is so vast that a computer-aided search of data is now a prerequisite for starting a new study. In addition, a combinatory search throughout these databases has a chance to extract new ideas and new hypotheses that can be examined by wet-lab experiments. By virtually integrating the related databases on the Internet, we have built a new web application that facilitates life science researchers for retrieving experts' knowledge stored in the databases and for building a new hypothesis of the research target. This web application, named VaProS, puts stress on the interconnection between the functional information of genome sequences and protein 3D structures, such as structural effect of the gene mutation. In this manuscript, we present the notion of VaProS, the databases and tools that can be accessed without any knowledge of database locations and data formats, and the power of search exemplified in quest of the molecular mechanisms of lysosomal storage disease. VaProS can be freely accessed at http://p4d-info.nig.ac.jp/vapros/ .

Rigid-body motions of interacting proteins dominate multispecific binding of ubiquitin in a shape-dependent manner.

To understand the dynamic aspects of multispecificity of ubiquitin, we studied nine ubiquitin-ligand (partner protein) complexes by normal mode analysis based on an elastic network model. The coupling between ubiquitin and ligand motions was analyzed by decomposing it into rigid-body (external) and vibrational (internal) motions of each subunit. We observed that in total the external motions in one of the subunits largely dominated the coupling. The combination of external motions of ubiquitin and the ligands showed general trends of rotations and translations. Moreover, we observed that the rotational motions of ubiquitin were correlated to the ligand orientations. We also identified ubiquitin atomic vibrations that differentiated the orientation of the ligand molecule. We observed that the extents of coupling were correlated to the shapes of the ligands, and this trend was more pronounced when the coupling involved vibrational motions of the ligand. In conclusion, an intricate interplay between internal and external motions of ubiquitin and the ligands help understand the dynamics of multispecificity, which is mostly guided by the shapes of the ligands and the complex.

Protein Data Bank Japan (PDBj): maintaining a structural data archive and resource description framework format.

The Protein Data Bank Japan (PDBj, http://pdbj.org) is a member of the worldwide Protein Data Bank (wwPDB) and accepts and processes the deposited data of experimentally determined macromolecular structures. While maintaining the archive in collaboration with other wwPDB partners, PDBj also provides a wide range of services and tools for analyzing structures and functions of proteins, which are summarized in this article. To enhance the interoperability of the PDB data, we have recently developed PDB/RDF, PDB data in the Resource Description Framework (RDF) format, along with its ontology in the Web Ontology Language (OWL) based on the PDB mmCIF Exchange Dictionary. Being in the standard format for the Semantic Web, the PDB/RDF data provide a means to integrate the PDB with other biological information resources.

Plasticity-led and mutation-led evolutions are different modes of the same developmental gene regulatory network.

The standard theory of evolution proposes that mutations cause heritable variations, which are naturally selected, leading to evolution. However, this mutation-led evolution (MLE) is being questioned by an alternative theory called plasticity-led evolution (PLE). PLE suggests that an environmental change induces adaptive phenotypes, which are later genetically accommodated. According to PLE, developmental systems should be able to respond to environmental changes adaptively. However, developmental systems are known to be robust against environmental and mutational perturbations. Thus, we expect a transition from a robust state to a plastic one. To test this hypothesis, we constructed a gene regulatory network (GRN) model that integrates developmental processes, hierarchical regulation, and environmental cues. We then simulated its evolution over different magnitudes of environmental changes. Our findings indicate that this GRN model exhibits PLE under large environmental changes and MLE under small environmental changes. Furthermore, we observed that the GRN model is susceptible to environmental or genetic fluctuations under large environmental changes but is robust under small environmental changes. This indicates a breakdown of robustness due to large environmental changes. Before the breakdown of robustness, the distribution of phenotypes is biased and aligned to the environmental changes, which would facilitate rapid adaptation should a large environmental change occur. These observations suggest that the evolutionary transition from mutation-led to plasticity-led evolution is due to a developmental transition from robust to susceptible regimes over increasing magnitudes of environmental change. Thus, the GRN model can reconcile these conflicting theories of evolution.

Plasticity-led evolution as an intrinsic property of developmental gene regulatory networks.

The modern evolutionary synthesis seemingly fails to explain how a population can survive a large environmental change: the pre-existence of heritable variants adapted to the novel environment is too opportunistic, whereas the search for new adaptive mutations after the environmental change is so slow that the population may go extinct. Plasticity-led evolution, the initial environmental induction of a novel adaptive phenotype followed by genetic accommodation, has been proposed to solve this problem. However, the mechanism enabling plasticity-led evolution remains unclear. Here, we present computational models that exhibit behaviors compatible with plasticity-led evolution by extending the Wagner model of gene regulatory networks. The models show adaptive plastic response and the uncovering of cryptic mutations under large environmental changes, followed by genetic accommodation. Moreover, these behaviors are consistently observed over distinct novel environments. We further show that environmental cues, developmental processes, and hierarchical regulation cooperatively amplify the above behaviors and accelerate evolution. These observations suggest plasticity-led evolution is a universal property of complex developmental systems independent of particular mutations.

Computational modelling of plasticity-led evolution.

Plasticity-led evolution is a form of evolution where a change in the environment induces novel traits via phenotypic plasticity, after which the novel traits are genetically accommodated over generations under the novel environment. This mode of evolution is expected to resolve the problem of gradualism (i.e., evolution by the slow accumulation of mutations that induce phenotypic variation) implied by the Modern Evolutionary Synthesis, in the face of a large environmental change. While experimental works are essential for validating that plasticity-led evolution indeed happened, we need computational models to gain insight into its underlying mechanisms and make qualitative predictions. Such computational models should include the developmental process and gene-environment interactions in addition to genetics and natural selection. We point out that gene regulatory network models can incorporate all the above notions. In this review, we highlight results from computational modelling of gene regulatory networks that consolidate the criteria of plasticity-led evolution. Since gene regulatory networks are mathematically equivalent to artificial recurrent neural networks, we also discuss their analogies and discrepancies, which may help further understand the mechanisms underlying plasticity-led evolution.

Functional characterization of protein domains common to animal viruses and mouse.

BACKGROUND: Many viruses contain genes that originate from their hosts. Some of these acquired genes give viruses the ability to interfere with host immune responses by various mechanisms. Genes of host origin that appear commonly in viruses code for proteins that span a wide range of functions, from kinases and phosphotases, to cytokines and their receptors, to ubiquitin ligases and proteases. While many important cases of such lateral gene transfer in viruses have been documented, there has yet to be a genome-wide survey of viral-encoded genes acquired from animal hosts. RESULTS: Here we carry out such a survey in order to gain insight into the host immune system. We made the results available in the form of a web-based tool that allows viral-centered or host-centered queries to be performed (http://imm.ifrec.osaka-u.ac.jp/musvirus/). We examine the relationship between acquired genes and immune function, and compare host-virus homology with gene expression data in stimulated dendritic cells and T-cells. We found that genes whose expression changes significantly during the innate antiviral immune response had more homologs in animal virus than genes whose expression did not change or genes involved in the adaptive immune response. CONCLUSIONS: Statistics gathered from the MusVirus database support earlier reports of gene transfer from host to virus and indicate that viruses are more likely to acquire genes involved in innate antiviral immune responses than those involved in acquired immune responses.

SeSAW: balancing sequence and structural information in protein functional mapping.

MOTIVATION: Functional similarity between proteins is evident at both the sequence and structure levels. SeSAW is a web-based program for identifying functionally or evolutionarily conserved motifs in protein structures by locating sequence and structural similarities, and quantifying these at the level of individual residues. Results can be visualized in 2D, as annotated alignments, or in 3D, as structural superpositions. An example is given for both an experimentally determined query structure and a homology model. AVAILABILITY AND IMPLEMENTATION: The web server is located at http://www.pdbj.org/SeSAW/.

Comprehensive structural classification of ligand-binding motifs in proteins.

Comprehensive knowledge of protein-ligand interactions should provide a useful basis for annotating protein functions, studying protein evolution, engineering enzymatic activity, and designing drugs. To investigate the diversity and universality of ligand-binding sites in protein structures, we conducted the all-against-all atomic-level structural comparison of over 180,000 ligand-binding sites found in all the known structures in the Protein Data Bank by using a recently developed database search and alignment algorithm. By applying a hybrid top-down-bottom-up clustering analysis to the comparison results, we determined approximately 3000 well-defined structural motifs of ligand-binding sites. Apart from a handful of exceptions, most structural motifs were found to be confined within single families or superfamilies, and to be associated with particular ligands. Furthermore, we analyzed the components of the similarity network and enumerated more than 4000 pairs of structural motifs that were shared across different protein folds.

Protein structure databases with new web services for structural biology and biomedical research.

The Protein Data Bank Japan (PDBj) curates, edits and distributes protein structural data as a member of the worldwide Protein Data Bank (wwPDB) and currently processes approximately 25-30% of all deposited data in the world. Structural information is enhanced by the addition of biological and biochemical functional data as well as experimental details extracted from the literature and other databases. Several applications have been developed at PDBj for structural biology and biomedical studies: (i) a Java-based molecular graphics viewer, jV; (ii) display of electron density maps for the evaluation of structure quality; (iii) an extensive database of molecular surfaces for functional sites, eF-site, as well as a search service for similar molecular surfaces, eF-seek; (iv) identification of sequence and structural neighbors; (v) a graphical user interface to all known protein folds with links to the above applications, Protein Globe. Recent examples are shown that highlight the utility of these tools in recognizing remote homologies between pairs of protein structures and in assigning putative biochemical functions to newly determined targets from structural genomics projects.

BioHackathon 2015: Semantics of data for life sciences and reproducible research.

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Properties of contact matrices induced by pairwise interactions in proteins.

The properties of contact matrices ( C matrices) needed for native proteins to be the lowest-energy conformations are considered in relation to a contact energy matrix ( E matrix). The total conformational energy is assumed to consist of pairwise interaction energies between atoms or residues, each of which is expressed as a product of a conformation-dependent function (an element of the C matrix) and a sequence-dependent energy parameter (an element of the E matrix). Such pairwise interactions in proteins force native C matrices to be in a relationship as if the interactions are a Go-like potential [N. Go, Annu. Rev. Biophys. Bioeng. 12, 183 (1983)] for the native C matrix, because the lowest bound of the total energy function is equal to the total energy of the native conformation interacting in a Go-like pairwise potential. This relationship between C and E matrices corresponds to (a) a parallel relationship between the eigenvectors of the C and E matrices and a linear relationship between their eigenvalues and (b) a parallel relationship between a contact number vector and the principal eigenvectors of the C and E matrices, where the E matrix is expanded in a series of eigenspaces with an additional constant term. The additional constant term in the spectral expansion of the E matrix is indicated by the lowest bound of the total energy function to correspond to a threshold of contact energy that approximately separates native contacts from non-native ones. Inner products between the principal eigenvector of the C matrix, that of the E matrix, and a contact number vector have been examined for 182 proteins, each of which is a representative from each family of the SCOP database [Murzin, J. Mol. Biol. 247, 536 (1995)], and the results indicate the parallel tendencies between those vectors. A statistical contact potential [S. Miyazawa and R. L. Jernigan, Proteins 34, 49 (1999); S. Miyazawa and R. L. Jernigan, Proteins50, 35 (2003)] estimated from protein crystal structures was used to evaluate pairwise residue-residue interactions in the proteins. In addition, the spectral representation of C and E matrices reveals that pairwise residue-residue interactions, which depend only on the types of interacting amino acids, but not on other residues in a protein, are insufficient and other interactions including residue connectivities and steric hindrance are needed to make native structures unique lowest-energy conformations.

Mechanism of evolution by genetic assimilation : Equivalence and independence of genetic mutation and epigenetic modulation in phenotypic expression.

Conrad H. Waddington discovered the phenomenon of genetic assimilation through a series of experiments on fruit flies. In those experiments, artificially exerted environmental stress induced plastic phenotypic changes in the fruit flies, but after some generations, the same phenotypic variant started to appear without the environmental stress. Both the initial state (where the phenotypic changes were environmentally induced and plastic) and the final state (where the phenotypic changes were genetically fixed and constitutive) are experimental facts. However, it remains unclear how the environmentally induced phenotypic change in the first generation becomes genetically fixed in the central process of genetic assimilation itself. We have argued that the key to understanding the mechanism of genetic assimilation lies in epigenetics, and proposed the "cooperative model" in which the evolutionary process depends on both genetic and epigenetic factors. Evolutionary simulations based on the cooperative model reproduced the process of genetic assimilation. Detailed analysis of the trajectories has revealed genetic assimilation is a process in which epigenetically induced phenotypic changes are incrementally and statistically replaced with multiple minor genetic mutations through natural selection. In this scenario, epigenetic and genetic changes may be considered as mutually independent but equivalent in terms of their effects on phenotypic changes. This finding rejects the common (and confused) hypothesis that epigenetically induced phenotypic changes depend on genetic mutations. Furthermore, we argue that transgenerational epigenetic inheritance is not required for evolution by genetic assimilation.

New tools and functions in data-out activities at Protein Data Bank Japan (PDBj).

The Protein Data Bank Japan (PDBj), a member of the worldwide Protein Data Bank (wwPDB), accepts and processes the deposited data of experimentally determined biological macromolecular structures. In addition to archiving the PDB data in collaboration with the other wwPDB partners, PDBj also provides a wide range of original and unique services and tools, which are continuously improved and updated. Here, we report the new RDB PDBj Mine 2, the WebGL molecular viewer Molmil, the ProMode-Elastic server for normal mode analysis, a virtual reality system for the eF-site protein electrostatic molecular surfaces, the extensions of the Omokage search for molecular shape similarity, and the integration of PDBj and BMRB searches.

Human transcription factors contain a high fraction of intrinsically disordered regions essential for transcriptional regulation.

Human transcriptional regulation factors, such as activators, repressors, and enhancer-binding factors are quite different from their prokaryotic counterparts in two respects: the average sequence in human is more than twice as long as that in prokaryotes, while the fraction of sequence aligned to domains of known structure is 31% in human transcription factors (TFs), less than half of that in bacterial TFs (72%). Intrinsically disordered (ID) regions were identified by a disorder-prediction program, and were found to be in good agreement with available experimental data. Analysis of 401 human TFs with experimental evidence from the Swiss-Prot database showed that as high as 49% of the entire sequence of human TFs is occupied by ID regions. More than half of the human TFs consist of a small DNA binding domain (DBD) and long ID regions frequently sandwiching unassigned regions. The remaining TFs have structural domains in addition to DBDs and ID regions. Experimental studies, particularly those with NMR, revealed that the transactivation domains in unbound TFs are usually unstructured, but become structured upon binding to their partners. The sequences of human and mouse TF orthologues are 90.5% identical despite a high incidence of ID regions, probably reflecting important functional roles played by ID regions. In general ID regions occupy a high fraction in TFs of eukaryotes, but not in prokaryotes. Implications of this dichotomy are discussed in connection with their functional roles in transcriptional regulation and evolution.

Wang-Landau molecular dynamics technique to search for low-energy conformational space of proteins.

Multicanonical molecular dynamics (MD) is a powerful technique for sampling conformations on rugged potential surfaces such as protein. However, it is notoriously difficult to estimate the multicanonical temperature effectively. Wang and Landau developed a convenient method for estimating the density of states based on a multicanonical Monte Carlo method. In their method, the density of states is calculated autonomously during a simulation. In this paper, we develop a set of techniques to effectively apply the Wang-Landau method to MD simulations. In the multicanonical MD, the estimation of the derivative of the density of states is critical. In order to estimate it accurately, we devise two original improvements. First, the correction for the density of states is made smooth by using the Gaussian distribution obtained by a short canonical simulation. Second, an approximation is applied to the derivative, which is based on the Gaussian distribution and the multiple weighted histogram technique. A test of this method was performed with small polypeptides, Met-enkephalin and Trp-cage, and it is demonstrated that Wang-Landau MD is consistent with replica exchange MD but can sample much larger conformational space.