Reactome describes biological pathways as chemical reactions that closely mirror the actual physical interactions that occur in the cell. Recent extensions of our data model accommodate the annotation of cancer and other disease processes. First, we have extended our class of protein modifications to accommodate annotation of changes in amino acid sequence and the formation of fusion proteins to describe the proteins involved in disease processes. Second, we have added a disease attribute to reaction, pathway, and physical entity classes that uses disease ontology terms. To support the graphical representation of “cancer” pathways, we have adapted our Pathway Browser to display disease variants and events in a way that allows comparison with the wild type pathway, and shows connections between perturbations in cancer and other biological pathways. The curation of pathways associated with cancer, coupled with our efforts to create other disease-specific pathways, will interoperate with our existing pathway and network analysis tools. Using the Epidermal Growth Factor Receptor (EGFR) signaling pathway as an example, we show how Reactome annotates and presents the altered biological behavior of EGFR variants due to their altered kinase and ligand-binding properties, and the mode of action and specificity of anti-cancer therapeutics. The development of a malignantly transformed cell from a normal cell is a complex multi-step process that remains incompletely understood ,2. “BottPathway databases have been effectively used to annotate our “bottom-up” understanding of molecular details of processes relevant to cell growth, differentiation, migration, and death. Here, we describe one such database, Reactome, focusing on extensions to this basic annotation strategy to allow the capture of details of disease processes, and on the development of data analysis tools to support the annotation and interpretation of gene sets identified in top-down studies.Reactome is an open-source, open access, curated and peer-reviewed biological knowledgebase of human reactions, pathways and processes that serves as a platform for pathway visualization and analysis ,10,11,12Reactome curators, in collaboration with outside expert researchers, annotate new pathways. The molecular details of every reaction are traceable to experimental evidence in the primary literature. If an event has not been directly studied in human systems, the appropriate non-human reaction is annotated and the homologous human one is inferred from it. Every pathway module is peer-reviewed by an additional expert. New and revised modules are publicly released to the Reactome website every quarter. Pathways, reactions, protein and small molecule entities are cross referenced with accession numbers and identifiers to a number of well-established databases, including NCBI Gene , EnsemblCurrently, the pathways in Reactome cover about 25% of the gene products encoded in the human genome, and contain the normal versions of many pathways that can be abnormally activated in cancer, such as “Signaling by EGFR” , “SignalHere, we use the epidermal growth factor receptor (EGFR), fibroblast growth factor receptor (FGFR) and PI3K/AKT signaling pathways to illustrate Reactome annotation of cancer pathways. EGFR and FGFR are transmembrane receptor tyrosine kinases. EGFR is activated by several growth factors, including the epidermal growth factor (EGF) . FGFR faEGFR, FGFRs, PIK3CA, PIK3R1 and AKT1 are proto-oncogenes, frequently activated in cancer through gain-of-function mutations and/or overexpression. PTEN is an established tumor suppressor gene, with a frequent loss of function in cancer . Gain-ofSmall molecule therapeutics and recombinant antibodies are being developed as potential treatments for cancers driven by increased activity of EGFR, FGFR and/or PI3K/AKT. Gefitinib and erlotinib, small tyrosine kinase inhibitors, are approved for the clinical treatment of cancers harboring specific EGFR mutations. A recombinant antibody, cetuximab, is approved for the clinical treatment of cancers that overexpress wild-type EGFR . Small mWe have extended the Reactome data model and enhanced the web tools to permit the annotation and visualization of the altered biological behavior of protein variants. These enhancements can be applied to any molecular abnormality due to germline or somatic mutation, as well as to abnormalities due to expression of foreign proteins encoded by genomes of infectious agents like viruses or intracellular parasites.Pathways that stimulate cell growth, cell division and survival, and maintenance of undifferentiated state are activated in cancer through gain-of-function mutations in participating proto-oncogenes and/or their overexpression. On the other hand, pathways that negatively regulate cell division, growth and survival, or promote cellular differentiation are inactivated through loss-of-function mutations in tumor suppressor genes and/or their downregulation. To capture these two groups of cancer effectors, we have added new classes of data to the Reactome database.The protein modification class in the Reactome data model was constructed to support annotation of covalent co- and post-translational modifications of proteins such as the phosphorylation of serine residues. To allow for annotation of mutant proteins, two new subclasses of modifications were introduced: Replaced Residue and Fragment Modification a. The ReThe FragmentModification subclass includes two subclasses, FragmentInsertionModification and FragmentDeletionModification. FragmentInsertionModification is used to annotate insertions of amino acid residues in a protein sequence. FragmentDeletionModification is used to annotate removal of amino acid residues. PIK3R1 Y463_L466del is a variant of the PI3K regulatory subunit p85alpha found in endometrial cancer c. This PThe FragmentModification class can also be used to annotate fusion proteins. For example, the translocation t in chronic myeloid leukemia produces a BCR-FGFR1 fusion that consists of the first four exons of BCR and exons 9–18 of FGFR1 . The BCROn the Reactome website, selecting a physical entity or an event node by clicking on a pathway diagram brings up a record for that particular instance in the details pane, which appears by clicking on the yellow triangle at the bottom of the Pathway Browser page. Selecting EGFRvIII in the diagram a, bringsAll physical entities related to disease variants, such as proteins, sets of proteins, and protein complexes are tagged with disease attributes , using aBesides providing information on disease involvement of specific proteins and directing users to more detailed disease descriptions, a disease attribute annotation enables users to search Reactome database for proteins and events associated with a specific disease. For example, in The Reactome data model allows for annotation of small molecules and antibodies used as anti-cancer therapeutics, as well as the annotation of their specific mode of action. We have annotated nine small tyrosine kinase inhibitors (TKIs) used to inhibit EGFR kinase activity in cancer ,69, as wFor each anti-EGFR TKI, we specify whether it associates with the EGFR catalytic domain through formation of a covalent (irreversible) bond or through a non-covalent interaction (reversible). We also specify whether a TKI is EGFR-specific or whether it can inhibit other receptor tyrosine kinases besides EGFR (EGFRplus). Each small molecule instance we annotate is associated with the Chemical Entities of Biological Interest (ChEBI) database identifier . On the EGFR cancer mutants in Reactome are classified into sets based on their sensitivity to various TKIs a. LigandIn addition to cancer, Reactome also collects and provides information on communicable diseases. Currently featured infection-related Reactome pathways are “HIV Infection”, “Influenza Infection”, “Botulinum Neurotoxicity”, and “Latent Infection with Mycobacterium tuberculosis”. The pathway “Signaling by FGFR in Disease” contains, besides information on FGFR in cancer, the information on FGFR mutations and their functional implication in various developmental disorders, such as Pfeiffer syndrome and Crouzon syndrome. Reactome has recently published “Abnormal Metabolism in Phenylketonuria” and “Mucopolysaccharidoses” pathways, thereby introducing metabolic genetic diseases.The Reactome Pathway Browser, based upon the Systems Biology Graphical Notation (SBGN) , permitsPhysical entity and reaction nodes within the pathway diagrams are interactive. Clicking on either feature displays specific information and additional links out to external databases in the “Details” Panel, which opens by clicking on the yellow triangle at the bottom of the Pathway Browser page . ContextThe Pathway Browser provides an intuitive and interactive pathway visualization system, promoting a variety of web-based data analyses of user-supplied experimental data. The Pathway Analysis tool provides two alternate functions to analyze lists of genes. First, in the identifier (ID) mapping mode, a user-supplied set of gene or protein identifiers can be mapped to Reactome events. Second, in the overrepresentation analysis mode, users can determine which pathways are statistically overrepresented in a gene/protein list. The Expression Analysis tool will aid with the biological interpretation of large-scale cancer genome sequencing, genomics and proteomics experiments. For example, this tool allows users to visualize expression data  superimposed on the Reactome pathway diagram. Reactome applies an orthology-based computational algorithm to curated human data to infer pathways in 22 diverse model organisms. The Species Comparison tool allows users to visually compare and contrast human pathways with these predicted model organism pathways. As additional cancer-perturbed pathways are added to Reactome, this method of “inferred” curation will provide a platform from which to study molecular disease mechanisms across the evolutionary spectrum. Reactome data is available for downloading and manipulation by third party visualization and analysis tools, including Cytoscape, Vanted and CellDesigner ,77,78.Using the previously curated human EGFR pathway, which included a number of annotations for EGFR and downstream signaling by SHC1, GRB2, PLCG1 and CBL, as a template from which to extend the EGFR pathway, we imported this dataset into the Reactome Curator Tool . BrieflyReactome is a highly reliable, curated database of biological pathways. Through our website, we provide access to pathway and network data analysis tools for visualizing pathway data and interpreting experimental data sets. All Reactome data and software is openly available with no licensing required.In view of the potential applicability of pathway and network analyses to identify and characterize novel cancer targets, Reactome has integrated and expanded the pathway gene product-function annotation and pathway curation to promote comprehensive and effective characterization of cancer targets, their related relationships and pathways. Our curation efforts thus far have focused on the EGFR pathway , FGFR and PI3K-AKT signaling and their downstream effector genes. Reactome curators will enhance our curation of other cancer-perturbed pathways, such as apoptosis, cell cycle checkpoints, and other signaling pathways, including BMP, PDGF, NOTCH, VEGF, WNT, Rho-GTPase, and TGF-beta. Furthermore, as the Ontario Institute for Cancer Research and its partners in the International Cancer Genome Consortium (ICGC) ,80 sequeReactome is not the only pathway database to curate pathway data relevant to cancer and disease. Cancer-perturbed signaling pathways can be found in KEGG, Panther, MetaCyc, and NCI-PID ,82,83,84A number of other bioinformatics databases such as Mouse Genome Informatics (MGI)  and CompWe are working on further improvements to the Reactome pathway browser to produce more compact images and to be able to share one diagram between the wild-type pathway and several disease pathways with different etiologies. Furthermore, we are making additions to the Molecular Interaction Overlay to promote visual linkages between pathway entities and disease annotations, such as OMIM. Network-based methods have been used extensively in genomic and proteomic studies to analyze challenging and complex datasets. Reactome provides the Functional Interaction (FI) network plug-in for Cytoscape, which can identify network patterns related to diseases, including cancer . Future


Characteristics of individual transport pathways, including their geometry, physico-chemical properties and dynamics are instrumental for understanding of structure-function relationships of these proteins, for the design of new inhibitors and construction of improved biocatalysts. CAVER is a software tool widely used for the identification and characterization of transport pathways in static macromolecular structures. Herein we present a new version of CAVER enabling automatic analysis of tunnels and channels in large ensembles of protein conformations. CAVER 3.0 implements new algorithms for the calculation and clustering of pathways. A trajectory from a molecular dynamics simulation serves as the typical input, while detailed characteristics and summary statistics of the time evolution of individual pathways are provided in the outputs. To illustrate the capabilities of CAVER 3.0, the tool was applied for the analysis of molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. CAVER 3.0 safely identified and reliably estimated the importance of all previously published DhaA tunnels, including the tunnels closed in DhaA crystal structures. Obtained results clearly demonstrate that analysis of molecular dynamics simulation is essential for the estimation of pathway characteristics and elucidation of the structural basis of the tunnel gating. CAVER 3.0 paves the way for the study of important biochemical phenomena in the area of molecular transport, molecular recognition and enzymatic catalysis. The software is freely available as a multiplatform command-line application at  PLOS Computational Biology Software ArticleThis is a Proteins are highly complex systems containing a variety of clefts, grooves, protrusions and empty space in the proteins interior. Besides many tiny cavities, this empty internal spa`ce may form cavities of specific functions, as well as tunnels and channels (or pores), representing potential transport pathways for small molecules, ions and water molecules Recently, several geometry-based tools for rapid identification and analysis of pathways in protein structures have been developed Some of the limitations of CAVER 1.0 were overcome by MOLE 1.2 Here, we present a new version of CAVER suitable for the effective analysis of tunnels and channels in large ensembles of protein structures. For this purpose, CAVER 3.0 implements new algorithms for both the calculation and clustering of pathways. For the construction of the Voronoi diagram, individual atoms of the analyzed structure are approximated by balls of a fixed size, thus minimizing calculation errors. Similarities between pathways are evaluated based on their geometrical distance. The implemented hierarchical average-link clustering algorithm ensures robust clustering results and enables an easy adjustment of the clustering parameters. We demonstrate that CAVER 3.0 outperforms existing software for geometry-based analysis of pathways in MD simulations. It provides detailed characteristics of individual transport pathways and their time evolution, enables to identify pathways invisible in a static structure and to investigate the structural basis of pathway gating mechanism. This way, CAVER 3.0 opens up new possibilities for the study of important biochemical phenomena, design of biomolecules with appropriate catalytic properties as well as design of inhibitors acting via the blockage of the transport pathways.CAVER 3.0 is written in the Java programming language and runs on all operating systems with installed Java Runtime Environment 6.0 or higher. The algorithm of CAVER 3.0 consists of three separable steps: (i) identification of pathways in each provided structure, e.g., each snapshot of a MD simulation; (ii) clustering of pathways identified in all snapshots; and (iii) calculation and generation of output data. Due to the separation of the identification and clustering steps, it is possible to run the calculation of pathways in different snapshots in parallel. Moreover, the results of each step can be saved and processed in the subsequent steps with varying parameters, thus accelerating the search for optimal parameters for the studied system.r1−r2, where r1 is the radius of the largest atom in the system and r2 radius of the smallest one. To take differences in atom radii into account and still have molecule represented by balls of equal radii, CAVER 3.0 approximates all atoms in the input structure which are larger than the smallest atom in the structure by a user-specified number of balls with the van der Waals (VDW) radius equal to the VDW radius of the smallest atom, analogously to the solution used in MolAxis 1.4 representation of input structure (RIS). Furthermore, the distance between a point and a ball will always denote the distance of the point and the closest point on the surface of the ball. Finally, Delaunay triangulation Representation and processing of Voronoi diagram of balls of equal radii, i.e., ordinary Voronoi diagram of points, is more effective than working with Voronoi diagram of balls with variable radii, i.e., weighted Voronoi diagram, and algorithms for its construction are also better studied L and constant radius r, the cost of the path isn is a non-negative real number. Therefore, if two paths have equal radii, the shorter has a lower cost. If they have the same length, then the wider has a lower cost, unless n is zero. The parameter n controls the balance between width and length and can be set to a real number from 0 to 100. If set to 0, only the length of the path is taken into account. On the other hand, if n is a high number, a path that is only slightly narrower than the widest path will have a higher cost unless it is many times longer. The default is set to an established value of n = 2 to give priority to wide paths with a reasonable length The axes of the pathways are identified as simple paths in a graph composed of Voronoi vertices and edges, i.e., the axis of a pathway is a sequence of Voronoi edges, where each of the two consecutive edges share a vertex. The pathway is then composed of balls with center on the pathway axis and a maximum radius at which the ball does not collide with the RIS. We define the cost of a path so as to reflect the probability that the pathway is actually used as a route for transportation of the substances. In the simplified case of a path of length r(l) defines the radius of the largest ball which does not collide with the RIS and is centered at the point on the pathway axis in the distance l from the starting vertex measured along the path. The user-defined threshold value maxr is used for r(l), if the actual radius of the ball is larger than maxr. The minimum value 0.1 Å is used for r(l) if the actual radius of the ball is smaller than 0.1 Å. The value L is the total path length. The cost of the pathway is computed by summing the costs of individual edges forming the pathway axis. The cost of each edge is integrated numerically using trapezoidal rule with a uniform grid. The minimum number of trapezoids is 8 and minimum grid distance 0.1 Å. Prior to the search for the lowest cost paths, all edges which cannot be traversed by a probe of radius of the user-specified value Br are removed. Because of the approximated VDW molecular surface, some edges appear to be wider than they are in reality. Consequently, some edges are not removed from the Voronoi diagram even though their real width is slightly narrower than Br. This can happen only if the real edge width is similar to Br, and may lead to false positive results, i.e., identification of pathways which are in some pathway segment narrower than Br  the target cavity, or intersects with RIS, thus minimizing the effects of the user-selected starting point location on the calculation results. On the other hand, if a completely wrong initial starting point is selected, the starting point optimization procedure does not help and consequently no or irrelevant pathways are found. It is also important to note that the position of the starting point directly influences overall length of identified pathways and hence also their costs and throughputs. The starting point used for analysis of pathways in a particular macromolecule should be therefore always reported together with the output data to allow reproduction of the calculations.A proper setting of bulk solvent vertex if it can be accessed from the structure exterior by a shell probe with a radius Sr (e.g. Sr = 3 Å). Then, a ball called a bulk solvent ball is placed into each bulk solvent vertex. The radius of each bulk solvent ball is the largest possible, but satisfying the condition that the ball does not intersect RIS. Next, surface vertices are found by a search in the neighborhood of each bulk solvent ball. Starting in a bulk solvent ball of a center A and radius r, a vertex is called a surface vertex if it can be accessed by a probe of the radius Br traveling from A through Voronoi edges so that the probe never intersects RIS and always lies inside a ball of the center A and the radius r+sd, where sd is by default set to the value of 4 Å. This way, a set of surface vertices and a set of bulk solvent vertices are defined. Furthermore, a surface boundary vertex is defined as a surface vertex connected by an edge to a vertex that does not belong to surface vertices. Similarly, a bulk solvent boundary vertex is a bulk solvent vertex connected by an edge to a vertex that does not belong to bulk solvent vertices.Pathways are identified as the paths in a graph composed of Voronoi vertices and Voronoi edges. First, two groups of vertices are identified: bulk solvent vertices and surface vertices. A vertex is called a Sr is reached.The pathway search itself consists of two steps. In the first step, the lowest cost path to every surface boundary vertex is found by Dijkstra's algorithm P is selected and all pathways within a user specified distance from P are discarded. The procedure is repeated with the next remaining lowest cost pathway, until all pathways are either selected or discarded.Several pathways with nearly identical axes may be identified within one snapshot. To remove such redundant pathways, the following iterative procedure is employed. The lowest cost pathway N points and then compute only distances between pairs of points with the corresponding order, thus performing only N distance computations for each pair of pathways. First, a point S is constructed as the center of gravity of the starting Voronoi vertices from all snapshots. Then, a set of points P is constructed iteratively by processing the pathways ordered by their cost in ascending order. For the pathway being processed, an extreme point X is identified as the pathway axis point of the greatest distance from S. The distance |SX| will be called a straight length of a pathway. Then, the point Y from P is identified such that the angle XSY is minimal. If P is still empty or the angle size is greater than the user specified value, the extreme point X is added to P and its weight is set to one. Otherwise, X is discarded and the point Y is moved along the SY half-line so that its new distance to S is |SX|+Yw·|SY|, where Yw is the weight of the point Y. The weight of the point Y is then increased by one. In the end, each point in the set P captures averaged straight lengths of pathways whose end points are close to each other.Previously, we proposed a method for estimation of a distance (i.e. dissimilarity) of two pathways, which evaluated the distance between each ball from the first pathway and its respective closest ball from the second pathway Q is identified within P such that for the extreme point X of the actually processed pathway and any point Y from Q, the size of the angle XSY is not greater than a parameter called smoothness. Then, the distance endd is computed asNext, all the pathways are processed once more. First, a set of points N points by the following procedure. For each pathway, the distance endd is divided into a sequence of N+2 intervals startI, 1I, 2I, …, NI and endI. The size of the intervals startI and endI is startz and endz, where values endz and startz may be specified by the user and the intervals themselves determine the pathway beginning and end sections that are not used for the evaluation of pathway similarities. For each i from 1 to N, the center of gravity iG of all points whose distance from S falls within the interval iI is computed. Thus, each pathway is characterized by a sequence of points 1G, 2G, …, NG. The distance of two pathways A and B is defined asAiGBiG| denotes the Euclidean distance of two corresponding points from pathways A and B and w is a linear function w(x) = ax+b. The coefficients a and b are derived from the user defined weighting parameter q so that the ratio between the weights of the end point w(1) and starting point w(0) equals q and w(0.5) equals 1. The weighting of the point distances allows the user to emphasize either the importance of the pathway end section by setting q>1, or of the pathway beginning section by setting q<1. In practice, the weighting is performed by identification of such points iK lying on the halfline iSG thatA and B is calculated asNext, the geometry of each pathway axis is converted into N) of coordinates, which allows fast clustering of pathways; and (iii) pairwise distances of pathways can be calculated using only selected pathway sections and/or the importance of the pathway beginning or end sections can be emphasized according to the needs of individual users.Pathway distances are calculated on the fly during the above described algorithm for the removal of redundant pathways, as well as for all pairs of non-redundant pathways identified throughout the MD simulation, which is used to establish the correspondence between pathways in different snapshots. The matrix of pairwise distances of all non-redundant pathways is stored on the hard disk for further processing. The presented algorithm for the calculation of pathway distances has several advantageous properties: (i) the algorithm is fast and scalable as only a small and adjustable number of distances need be evaluated for each pair of pathways; (ii) each pathway is characterized by the same number . These pathway profiles, calculated as the dependence of the pathway radius on the distance measured along a straight line, are more straightforwardly aligned to one another than the profiles calculated along the pathway axis. The aligned pathway profiles are visualized as heat map images, where the color of the point with coordinates x and y expresses the radius of a given pathway in the x-th snapshot and the y-th distance interval. Additionally, the time evolution of pathway bottlenecks radii can be visualized as heat map where the color of the point with coordinates x and y expresses the bottleneck radius of the y-th pathway in the x-th snapshot.Pathway profiles and characteristics are provided in the comma-separated values (.csv) file format. Radii of each pathway are measured in regular intervals along the pathway axis, thus providing information on the pathway profile. Besides that, the pathway radius is also estimated in regular intervals of the distance from the point CAVER 3.0 was primarily developed for the analysis of protein structures, but it can be used for analysis of any molecular structure containing tunnel-like features. The user has to provide a structure or a set of aligned structures with consistently numbered atoms and residues in PDB format as an input and specify the starting point of the calculation. For the clustering of the identified pathways, the user can choose either an accurate hierarchical average-link clustering algorithm or the combination of this clustering with supervised machine learning classification, which enables fast processing of very large datasets containing hundreds of thousands of pathways. A number of additional settings are available, enabling users to adjust the calculation and clustering parameters based on their needs. All results are presented in a comprehensive way. Output data from the calculation includes: (i) characteristics of individual pathways summarized over all analyzed snapshots—frequency and priority of a given collective pathway, mean and maximum radius of the narrowest region (bottleneck), mean length, curvature and throughput of a collective pathway; (ii) characteristics of individual pathways in individual snapshots, enabling to track the changes of the collective pathways over time—bottleneck radius, length, curvature, throughput, cost; (iii) data for plotting of the pathway profiles—radius over distance from the start; (iv) data for plotting of bottleneck radius and throughput histograms; (v) list of atoms and residues lining individual collective pathways; (vi) list of the residues making up the bottleneck; (vii) heat maps showing time evolution of the bottleneck radii and pathway profiles; and (viii) scripts for visualization of the results in PyMOL and VMD. The individual pathways making up the collective pathways can either be visualized in a single frame, which is useful for the evaluation of the clustering results and for a general overview of the collective pathway variability, or as a MD trajectory tracking the changes of the collective pathways over time.CAVER 3.0 was tested on a variety of biomolecular structures , which hCAVER 3.0 and MOLE 1.2 were used for the analysis of 1,000 snapshots from a 10 ns MD simulation of haloalkane dehalogenase DhaA using various settings of clustering parameters  and S3. Rhodococcus spp. Haloalkane dehalogenase DhaA isolated from various strains of et al.CAVER 3.0 was used for the analysis of 10,000 snapshots from a 10 ns classical MD simulation of DhaA in explicit water solvent  and S3. We found a good agreement between the results of CAVER 3.0 and the previous MD and RAMD study of DhaA product release pathways It is important to note that RAMD and other methods specialized in the simulation of ligand migration through protein structure provide information specific to a particular ligand The capability of CAVER 3.0 to analyze dynamical systems is essential for the identification of biochemically relevant tunnels. All previously known DhaA tunnels were reliably identified by the analysis of the 10 ns MD simulation snapshots, while only two of them—p1 and p2ab—were found in the crystal structures of DhaA  under the same calculation settings . By decrThe residues forming the tunnel bottleneck represent promising hotspots for the modification of tunnel properties, since their substitutions have potentially the most pronounced impact on the tunnel geometry. Similarly to other tunnel characteristics, the analysis of the bottlenecks is more informative when the dynamics of the protein is considered. The list of the bottleneck residues obtained by the analysis of the MD trajectory using CAVER 3.0 suggested the existence of two distinct bottlenecks along the DhaA p1 tunnel, while only one of them can be identified in the crystal structures . The anaThe most frequent bottleneck in the DhaA p1 tunnel is formed mainly by Phe149 (71% of p1 tunnels), Cys176 (59%), Ala172 (50%) and Ala145 (38%) residues, mostly in combinations Ala145-Phe149-Cys176 and Phe149-Ala172-Cys176 . Comparison of structures with an open and closed p1 tunnels suggested that the gating in this bottleneck is mediated by the movement of the N-terminal part of the cap domain (Glu139-Phe149) carrying the bottleneck residues Ala145 and Phe149, movement of the C-terminal part of the α5-helix with the bottleneck residue Cys176, and by the conformational change of the bottleneck residues Phe149 and Cys176 . The proThe second bottleneck is positioned at the tunnel entrance from the bulk solvent and is formed predominantly by Lys175 , Thr148 (23%) and Ala172 , most often in the combination Thr148-Ala172-Lys175 . The gating in the second bottleneck seems to be mediated mainly by the conformational change of Lys175 . This reThe importance of the bottleneck residues identified by CAVER 3.0 for the catalytic properties of DhaA had already been demonstrated experimentally , Table 2CAVER 3.0 is a new tool for geometry-based analysis of pathways in both static and dynamic protein structures. For the analysis of dynamical systems, the software implements several new algorithms for accurate calculation and clustering of pathways. CAVER 3.0 requires a protein structure or a set of aligned structures and the specification of the calculation starting point as the only obligatory inputs. A number of additional settings are available, enabling users to customize the calculation based on their needs. In the output, CAVER 3.0 provides all necessary data for the analysis of the time evolution of individual pathways.To demonstrate the capabilities of CAVER 3.0, we used it for the analysis of tunnels in a molecular dynamics simulation of the microbial enzyme haloalkane dehalogenase DhaA. We were able to identify and reliably estimate the importance of all previously published DhaA tunnels, including tunnels closed in DhaA crystal structures as well as to correctly predict the bottleneck residues important for the catalytic function of this enzyme. Detailed investigation of the dynamics of the p1 tunnel revealed the structural basis of tunnel gating. The obtained results demonstrate that the analysis of static structures can lead to overlooking of relevant auxiliary tunnels or prediction of tunnels whose relevancy is disputable.In summary, CAVER 3.0 enables an effective analysis of pathways in dynamical protein structures, which provides important insights into the structure-function relationships of proteins and facilitates the design of improved biocatalysts and new inhibitors.http://www.caver.cz or as supplementary material accompanying this paper (Software S1).CAVER 3.0 is a multiplatform command-line Java application. The software is licensed under the GNU General Public License v3.0 and is freely available at Figure S1Clusters of pathways calculated in the molecular dynamics simulation of haloalkane dehalogenase DhaA by CAVER 3.0 (A–C) and MOLE 1.2 (D–F). Pathways identified throughout the simulation are shown in one frame as pathway centerlines; if more pathways from the same snapshot are grouped to the same cluster, only the pathway with the lowest cost is shown. The variants of the p1 (main) tunnel are in different shades of blue, variants of the p2 tunnel in yellow, green and magenta and the p3 tunnel is in red. (A–C) CAVER 3.0 results for different settings of the clustering threshold. (A) Clustering was performed with constant weights along the entire pathway and low clustering threshold of 3.5. The p1 pathways with dispersed openings as well as the p2a and p2b pathways which have a common opening are separated into different clusters. (B) Increasing the clustering threshold led to the joining of the p2a and p2b pathway clusters. (C) Further increase of the clustering threshold led to the grouping of all the p1 pathways into a single cluster. Note that some of the previously visible p1 pathways are not visible after the change of threshold since in individual snapshots, only the pathway with the lowest cost is shown for each cluster. (D–F) MOLE 1.2 results for different settings of the clustering parameters. (D) The parameters were set to distinguish the known variants of the p2 tunnel; the p2a and p2b pathway clusters are not well defined as they largely overlap along the entire tunnel length. The p1 tunnel was divided into multiple clusters. (E) Recalculation with a lower value of the bound parameter led to the grouping of a portion of the p1 pathways into one cluster, while other p1 pathways remained separated. The p2a and p2b clusters are not well defined—part of the p2b cluster overlaps with the p2a cluster and part with the p1b cluster. (F) The bound parameter was optimized to join all the p1 pathways into a single cluster. This led to also the p2a and p2b pathways being clustered together; part of the p2ab cluster overlaps with the p2c cluster. Note that many of the previously visible p1 pathways are not visible, since in individual snapshots, only the pathway with the lowest cost is retained for each cluster.(TIF)Click here for additional data file.Protocol S1Comparison of CAVER 3.0, MOLE 1.2 and MolAxis 1.4.(PDF)Click here for additional data file.Protocol S2Molecular dynamics simulation of haloalkane dehalogenase DhaA.(PDF)Click here for additional data file.Protocol S3Analysis of molecular dynamics simulation of DhaA.(PDF)Click here for additional data file.Protocol S4Analysis of crystal structures of DhaA.(PDF)Click here for additional data file.Software S1http://www.caver.cz.CAVER 3.0 package containing CAVER 3.0 executable, source code, license, documentation and examples. The latest release of CAVER 3.0 can be downloaded from (ZIP)Click here for additional data file.Table S1Comparison of pathways calculated by CAVER 3.0, MOLE 1.2 and MolAxis 1.4.(PDF)Click here for additional data file.Table S2Characteristics of the pathways identified in 10,000 snapshots of the 10 ns molecular dynamics trajectory of DhaA using the probe radius of 0.9 Å and the clustering threshold of 4.3.(PDF)Click here for additional data file.Table S3Characteristics of the pathways identified in DhaA crystal structures using the probe radius of 0.8 Å.(PDF)Click here for additional data file.Table S4Comparison of characteristics of the DhaA p1 tunnel obtained by the analysis of the molecular dynamics trajectory and crystal structures.(PDF)Click here for additional data file.Table S5Bottleneck residues of the top ranked tunnels of DhaA identified by CAVER 3.0 in molecular dynamics trajectory using the probe radius of 0.9 Å and the clustering threshold of 3.5.(PDF)Click here for additional data file.Text S1Evaluation of potential false positive results.(PDF)Click here for additional data file.Text S2Comparison of tunnels identified by CAVER 3.0 with known DhaA tunnels.(PDF)Click here for additional data file.


Molecular pathways represent an ensemble of interactions occurring among molecules within the cell and between cells. The identification of similarities between molecular pathways across organisms and functions has a critical role in understanding complex biological processes. For the inference of such novel information, the comparison of molecular pathways requires to account for imperfect matches (flexibility) and to efficiently handle complex network topologies. To date, these characteristics are only partially available in tools designed to compare molecular interaction maps.a priori biological information. It then addresses the second issue by relying directly on the rich graph topology described in the Systems Biology Markup Language (SBML) standard, and uses multidigraphs to efficiently handle multiple queries on biological graph databases. The algorithm has been here successfully used to highlight the contact point between various human pathways in the Reactome database.Our approach MIMO (Molecular Interaction Maps Overlap) addresses the first problem by allowing the introduction of gaps and mismatches between query and template pathways and permits -when necessary- supervised queries incorporating MIMO offers a flexible and efficient graph-matching tool for comparing complex biological pathways. In the post-genomic era the analysis of biological networks plays a crucial role in computational and systems biology. Consequently, biological network databases, tools for biological graph modeling and approaches for the management and standardization of the large amount of generated data are under continuous evolution. As an example, the PathGuide  repositosubgraph matching methods arises in a number of applications. The main goal of biological graph matching is to detect the template-subgraphs that share similarity with noisy pathways built from the analysis of experimental data or built by collecting various sources of information. The goals are numerous and span over a large amount of topics: inference of metabolic pathways  and roughly measures how much of the graph G1 is contained in G2 according to E .where Our software has been implemented in C language by using the libSBML  interfacOur implementation takes as input two valid SBML documents (no conversion required into intermediate formats) and provides also the possibility to obtain as output, additionally to the comparison score, the computed overlap in SBML format. In particular, if requested by the user, the executable saves three SBML files related to the highest-scoring solution found. Two of these documents correspond to the SBML documents in which the non-matched reactions have been removed. In these files the matched reactions are saved in their full definition, i.e. all reactants, products and modifiers of the reaction are saved, even if they have not been matched in the computed overlap. The third SBML file contains the computed overlap between the two molecular interaction maps. In this file, every matched pair of reaction pathways is saved as a unique reaction. Only those reactants, products and modifiers that have been matched are saved into the document. In addition, in order to simplify the inspection of the output SBML documents, a text file listing the mappings between species, reactions and compartments is also saved.fast queries on large databases.The running time of our algorithm is bound by the running time of the BKv2 procedure , used inrelated to the query.We present the performances of MIMO in detecting functional relationships among the 56 human pathways of the manually-curated Reactome  databaseWe compared the performances of MIMO with those of SAGA , which hid attribute, the a priori amount of overlap between two maps can be assessed by simply counting the fraction of common reactions . This set of 36 maps is used to assess the performances of MIMO and SAGA in detecting similarities between biological pathways , with a time ratio between MIMO and SAGA of ∼4 when the necessary format conversion time (dataset upload into the postrgreSQL database) is included and ∼5 when excluded. This, however, comes at a cost in terms of the quality of the overlaps identified, including a number that is totally or partially missed by SAGA Tables  and 2.P-value ≤0.01) and (ii) the fragment procedure which forbids the match between linear pathways in two graphs. As a consequence, non-trivial overlaps (i.e. >16% common reactions) such as the ones related to the pairs ,  and  are lost  and R158, R152 aWhen querying map R111083  comprises a series of enzyme-catalysed chemical reactions of central importance in all living cells that use oxygen as part of cellular respiration. In eukaryotic cells, the citric acid cycle occurs in the matrix of the mitochondrion  and starThe results of the biological inference test are summarized in Table computational analyses of biological networks can represent indeed an issue as it distorts the evaluation of topological network parameters such as centrality or pathway length[biological interest of such overlaps may not be discarded a priori, and since non-interesting overlaps can be easily detected by visual inspection of the outputs we here chose to maintain them (like tha authors of SAGA did), and give the end-user the option to keep or remove them. Nevertheless, in this inference test at least in one case, namely for maps R11045 and R152, MIMO identifies a non-trivial overlap, which is not identified by SAGA  whose substrates can be divided into two main groups: direct regulators of CDKs, regulators of gene transcription or both. β-catenin is the substrate of SCF- β-TrCP1 in the Wnt signaling pathway [β-catenin is recognized and ubiquitinated by the SCF- β TrCP ubiquitin ligase complex and is subsequently degraded by the proteasome [β TrCP in the cell cycle. From there, once they have been identified by SCF- β TrCP, all substrates enter the process of proteasomal degradation, the identified common process.The Wnt signaling pathway (R11045) is critically involved in the early development of complex, multi-cellular organisms controlling early axis formation, limb patterning and organogenesis . Abnorma pathway  where itoteasome . SimilarEasy-to-use: MIMO takes as input biological networks encoded with the Systems Biology Markup Language (SBML) standard. The SBML standard is widely adopted for biological network modeling and is flexible enough to allow the encoding of quite complex molecular interactions. Most importantly, the choice to adopt a standard format as input avoids the pre-processing phase needed to convert molecular interaction maps in a non-standard format removing all the consequent burden. (ii) Flexibility: MIMO implements a flexible procedure for sub-graph matching, which naturally allows the introduction of gaps and mismatches and permits (if required) supervised queries incorporating a priori biological information. (iii) Computational efficiency: while the subgraph matching problem is computationally intractable, MIMO implementation is fast enough to allow multiple queries on graph databases.MIMO (Molecular Interaction Map Overlap) is a tool for biological graph matching. The main features of MIMO are: (i) The capabilities of MIMO have been highlighted by performing a one-to-one comparison on all human pathways in the Reactome database. The experimental tests prove that MIMO is flexible and efficient enough to make it a suitable tool for biological pathway comparisons.Project Name: Molecular Interaction Map Overlap;Project home page: http://www.picb.ac.cn/ClinicalGeno micNTW/software.htm;Operating system(s): Linux, Mac Os X;Programming language: C;Other requirements: libSBML;Licence: GPL3;Any restrictions to use by non-academics: No.The authors declare that they have no competing interests.PDL and CN designed the project and wrote the manuscript. PDL designed and implemented the algorithmic procedure, and carried out all computational experiments. GW interpreted the biological results. All the authors read and approved the final manuscript.Detailed description of the algorithmic procedure.Click here for fileList of Reactome’s biological pathways in human.Click here for fileFull list of comparison scores.Click here for file


Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for  MetaCyc (MetaCyc.org) is a highly curated nonredundant reference database of small-molecule metabolism. It contains metabolic pathway and enzyme data that have been experimentally validated and reported in the scientific literature . Owing tIn conjunction with its role as a general reference on metabolism, MetaCyc can be used as a reference database for the PathoLogic component of the Pathway Tools software  to compuMetaCyc family of databases . Examples of organisms that were studied during the previous 2 years using Pathway Tools include archaea   , we addears ago) .Along with the increase in pathway number, the number of enzymes, reactions, chemical compounds and citations in the database grew by 20, 19, 11 and 21%, respectively; the number of referenced organisms increased by 18% (currently at 2460). See A reaction atom mapping describes for each atom of a reactant (excluding hydrogens) its corresponding atom in the product. Implicitly, an atom mapping illustrates which bonds are broken and created during the reaction. Atom mapping information is depicted in PGDB reactions by coloring conserved chemical moieties within a reaction (currently available only in the Firefox and Chrome browsers). In addition, if the user hovers the mouse over an atom in a reactant, the corresponding atom is highlighted in the product .http://biocyc.org/PGDBConceptsGuide.shtml#node_sec_3.5. The atom mappings were computed by a technique described in  for this reaction’ command from the right side bar. All atom mappings for MetaCyc are stored in one flat file, atom-mappings.dat, and the Mol files for all compounds involved in the atom mappings are stored in MetaCyc-MOLfiles.tgz. The atom mapping encoding, as stored in atom-mappings.dat, is described at ribed in . The errIn MetaCyc version 17.5, >10 100 reactions have computed atom mappings. The vast majority of reactions that lack atom-mapping data are either not completely mass balanced or they include substrates without an atomic structure. In a few rare cases, reactions were not processed because the computation would be too time consuming. In contrast, ∼5% of the reactions have multiple atom mappings, often due to symmetries in the compound structure. We tried to eliminate such duplicates, but we kept the cases where the enzyme might operate in more than one way.The atom mappings are also used by the RouteSearch Tool (See Section below).The degradation of large and complex glycan polymers poses a challenge for standard pathway diagrams. Rather than a linear process, the degradation of such polymers often consists of multiple types of enzymes simultaneously attacking different types of bonds within the polymer. The enzymes work in parallel, resulting in the liberation of small fragments. Because no particular order exists for these attacks, an attempt to show the process as linear, involving specific intermediates, is misleading. To overcome this limitation, we developed a new type of pathway diagram that shows the precise location of the sites attacked by the different enzymes by using color-coded arrows pointing to the cleaved bonds within the polymer structure . To makePathway Tools now supports the symbolic representation of glycans recommended by the Consortium for Functional Glycomics (CFG) and uses the Glyco-CT format for the import/export of such structures. To enable the curation of glycan structures, we developed a Pathway Tools interface for the GlycanBuilder software ,72. GlycThe introduction of the symbolic structures did not replace the existing atomic structures—glycan molecules in MetaCyc may contain both the regular atomic structure that is used for all chemical compounds, and the CFG symbolic representation.m, Vmax, Kcat and specific activity are now collected separately for each reactant, including for alternative substrates. Ki values for inhibitors are collected as before.We have recently revised the types of enzyme kinetic data that can be captured in Pathway Tools PGDBs, the interface used to enter these data and the presentation of the data. When the reaction is reversible, capturing the optimal temperature and pH for each direction is now possible. KVersion 17.5 of MetaCyc includes 3883 enzymatic reactions with Km data , 242 enzymatic reactions with Vmax values, 390 enzymatic reactions with Kcat values and 201 enzymatic reactions with specific activity values.In addition, all kinetic data are now presented in a table format that makes it much easier to read .Figure http://www.chem.qmul.ac.uk/iubmb/enzyme/rules.html). Since its creation, Pathway Tools has encoded this information by assigning the EC number to the reaction catalyzed by the enzyme, with a limitation that only one EC number could be assigned to each reaction . However, this approach gave limited compatibility with the many-to-many relationship between EC numbers and reactions that is used by the EC system. To increase compatibility with the EC system we have implemented a new way of encoding EC numbers. A new object type (EC-number) was added to the database to represent EC numbers (The Enzyme Commission (EC) classifies enzymes based on the reaction(s) that they catalyze (see  numbers . These Ewww.enzyme-database.org) , which includes new and modified EC entries. The data are retrieved from the ExplorEnz database (ase.org) . The EC The full NCBI Taxonomy database  is integhttp://www.geneontology.org/external2go/metacyc2go.The mapping between MetaCyc reactions and Gene Ontology (GO) process and function terms  is beingDuring the past 2 years we added new links from MetaCyc to several external databases, which are listed in Tier 1 PGDBs have received at least 1 year of manual curation. Although some Tier 1 PGDBs (e.g. MetaCyc and EcoCyc) have received decades of manual curation and are updated continuously, others are less well curated and are still in need of significant curation.Tier 2 PGDBs have received moderate amounts of review (less than a year), and may or may not be updated on an ongoing basis.Tier 3 PGDBs were created computationally and received no subsequent manual review or updating.The BioCyc databases are organized into three tiers.S. cerevisiae), which has been curated for many years by the saccharomyces genome database, is now hosted at BioCyc.org and has undergone significant curation in the past year. The number of pathways in YeastCyc has grown from 154 in December 2012 to 259 in October 2013. The curation of fungal pathways will be one of our priorities for the next few years.During the past 2 years, the number of BioCyc PGDBs increased from 1129 (version 15.1) to 2988 (version 17.1). The Tier 1 PGDB YeastCyc , the AraCyc PGDB  and the LeishCyc PGDB  have been upgraded to Tier 1 status, bringing the total of Tier 1 PGDBs to six . As of version 17.1, Tier 2 includes 35 PGDBs, and Tier 3 includes 2947 PGDBs. Some Tier 2 PGDBs were provided by groups outside SRI. The database authors are identified on the database summary page .The HumanCyc PGDB , they are integrated into the BioCyc collection. Version 17.5 includes 891 such genomes.As fully sequenced and annotated genomes become available from the Human Microbiome Project (The following sections describe significant enhancements to Pathway Tools (which powers the BioCyc Web site) during the past 2 years.A new right-sidebar appears on BioCyc web pages . This siThe graphic display of the chemical structures on the compound and reaction pages has been re-implemented using the scalable vector graphics web standard, resulting in higher quality graphics. This improvement is currently visible only when using the recent versions of the Firefox and Chrome web browsers.A Web Group is a spreadsheet-like structure that can contain both Pathway Tools objects and other values such as numbers or strings. Like a spreadsheet, it is organized by rows and columns, and the user can add or delete any of them. A typical group contains a set of Pathway Tools objects in the first column (e.g. a set of genes generated by a search). The other columns contain properties of the object , or the result of a transformation .Web Groups can be created from search results, by importing data from external text files, and by adding objects individually from either their web pages or from another group. For example, a Web Group can contain a column of genes and columns of gene expression values, and the contents of the group can be painted onto a BioCyc metabolic map diagram using the Cellular Omics Viewer. Web Groups can be shared either publicly or with selected users.Group transformations facilitate converting an existing group into a new group or into a new column in an existing group. Many new transformations have been added during the past 2 years, including several regulation-related transformations for genes (e.g. transforming a list of genes into a list of the transcription factors that regulate their expression), the ability to transform a group of genes into a group of the upstream promoters of those genes, to transform a protein into a list of regulatory DNA sites it binds to and to transform a compound into a list of proteins it is known to either bind to, activate or inhibit.A relatively recent innovation is the ability to incorporate nucleotide and amino-acid sequence data as group objects. Such sequence data can be automatically added to groups that contain genes or proteins. Genes, promoters and transcription-factor binding sites can be transformed not only into their sequence but also into a list of their coordinates in the genome. A list of DNA regions or point locations (e.g. mutation locations) can be imported from a file to form a group, which could then be transformed into the set of genes nearest those regions.The Web Groups interface also enables users to apply an enrichment/depletion analysis to the contents of a group .RouteSearch is a new web-based tool (accessible from the top menu command Metabolism → Metabolic Route Search) that generates reaction pathways connecting starting and ending metabolites specified by the user. Optional parameters include the number of routes to return, the maximum route length, the cost of using a native reaction , the cost of losing an atom along the way and the atom species to take into account. Specifying the maximum amount of time allowed for searching for routes is also possible.RouteSearch only returns linear pathways from the starting to the ending metabolite. Along that linear pathway, it computes the weighted sum of the atoms lost, based on the cost of atom loss provided by the user. To do so, it uses the atom-mapping data already computed for MetaCyc (see section Atom Mapping) because the atom mappings define which atoms are transferred between compounds. The objective of RouteSearch is to minimize this sum. RouteSearch also simultaneously minimizes the length of the pathways found, as the weighted sum of all reactions used to reach the ending metabolite adds to the overall cost.Notice that searching for such optimal routes may not return well-known routes from a starting to an ending metabolite because the objective is to minimize both the number of reactions used and the number of atoms lost.RouteSearch is a new tool and is still undergoing evaluation and potential redesign.The Cellular Omics Viewer enables users to paint an organism-specific metabolic map diagram for any BioCyc PGDB with multicolor highlighting to represent large-scale omics data sets, as well as to animate the highlighting to show temporal changes in the data. Any type of data that can be mapped to a compound, a reaction, an enzyme or a gene is supported, although the most common data types are gene-expression, reaction flux and metabolomics data. During the previous year, we reengineered the web-based Omics Viewer to improve its performance. Both speed and browser resource use were improved by one to two orders of magnitude. Currently the initial times to load data and display the resulting highlights are on the order of a few to the low tens of seconds.New functionality makes it possible to display per-node omics data in a pop-up window as a column chart, an x-y plot or a heat map. This new functionality is invoked by hovering the mouse over a reaction or metabolite of interest and selecting the ‘Omics’ option in the menu of the resulting pop-up window, which will graph the omics data for that object in column chart (bar) mode. Clicking different tabs converts the graph to an x-y plot or a heat map. Customizing the data labels is also possible.The same type of pop-up can be generated to show all the data for a given pathway. Right-clicking a reaction within the pathway opens a pop-up that includes the option ‘Display Omics Data for Every Node in Pathway’ .Figure A new function enables the painting of omics data on a full-scale pathway diagram. The new functionality is invoked from the right-side bar by using the command ‘Customize Pathway Diagram’, which displays a window that includes an option for painting omics data onto the pathway diagram .Figure Generating a table displaying omics data painted onto small diagrams of all individual pathways is now possible. A ‘Show data’ selector has been added to the Omics Viewer dialog, which enables users to select whether they want the omics data painted on the cellular overview, the table of individual pathways or both.The Flux Balance Analysis (FBA) module of Pathway Tools, called MetaFlux, enables the creation of steady-state quantitative metabolic flux models. MetaFlux is capable of solving FBA models, performing multiple gap-filling and performing multiple gene or reaction knockouts. Its latest enhancements include (i) the ability to specify compartments for the metabolites in the biomass reaction, the list of nutrients and the list of secreted metabolites; (ii) a much faster instantiation of generic reactions; (iii) some enhancements to the graphical user interface; and (iv) a new development mode called Fast-Gap.In the regular development mode, gap-filling can be done simultaneously on reactions, nutrients, secretions and the biomass reaction. This regular development mode uses Mixed-Integer Linear Programming, which can be computationally time-consuming: it may require several hours of computing. The new Fast-Gap mode is limited to gap-filling only reactions, but it executes in a short time, typically in less than 1 min (at most a few minutes). Therefore, Fast-Gap can be used instead of the regular development mode when a fast answer is desired. Fast-Gap can also provide, in some cases, a more meaningful reaction gap-filling solution than the regular mode due to its use of a different optimization technique.The following enhancements only apply to the desktop version of the Pathway Tools software.File → Save Display State to File.The display state of Pathway Tools can now be saved to a file, which could be used for later restoration. Examples of display states that can be saved include the state of the omics viewers (including omics pop-ups), genome-browser tracks and cloned windows. The display-state file can be e-mailed to another user, who could then restore the exact same state on a different computer. Saving a display state to a file is invoked via the command Users that install Pathway Tools on their computer can download and install any of the PGDBs available on the BioCyc Web site by using an embedded utility called PGDB Registry (accessible from the command Tools → Browse PGDB Registry). This utility enables downloading and installing a PGDB with a few mouse clicks. However, the proliferation in the number of PGDBs available for download had resulted in a major slowdown of the utility. The interface of the utility has been completely redesigned, so that finding PGDBs within the registry is now fast.http://biocyc.org/subscribe.shtml. A list of our publications is available online  ; Department of Energy [DE-SC0004878 toward Bioenergy-related pathway curation]; National Science Foundation [IOS-1026003 and DBI-0640769 toward MetaCyc curation performed by the Plant Metabolic Network]. Funding for open access: NIH.Conflict of interest statement. None declared.