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Boundary conditions widen twice the band of the matrix of a system of linear equations in the method of interpolation by splines in tension (a generalization of Briggs’ method). A node numbering scheme for a finite difference grid is proposed, which significantly narrows down the band and reduces the matrix profile. The method belongs to cognitive graphics methods and is not based on graph theory concepts. The scheme or its modifications can be extended to other similar problems.

While numerous architectures exist for solving multidisciplinary design optimization (MDO) problems, there is currently no standard way of describing these architectures. In particular, a standard visual representation of the solution process would be particularly useful as a communication medium among practitioners and those new to the field. This paper presents the extended design structure matrix (XDSM), a new diagram for visualizing MDO processes. The diagram is based on extending the standard design structure matrix (DSM) to simultaneously show data dependency and process flow on a single diagram. Modifications include adding special components to define iterative processes, defining different line styles to show data and process connections independently, and adding a numbering scheme to define the order in which the components are executed. This paper describes the rules for constructing XDSMs along with many examples, including diagrams of several MDO architectures. Finally, this paper discusses potential applications of the XDSM in other areas of MDO and the future development of the diagrams.

Comparisons of residues between sub-types of influenza virus is increasingly used to assess the zoonotic potential of a circulating strain and for comparative studies across subtypes. An analysis of N-terminal cleavage sites for thirteen subtypes of influenza A hemagglutinin (HA) sequences, has previously been described by Nobusawa and colleagues. We have expanded this analysis for the eighteen known subtypes of influenza. Due to differences in the length of HA, we have included strains from multiple clades of H1 and H5, as well as strains of H5 and H7 subtypes with both high and low pathogenicity. Analysis of known structures of influenza A HA enables us to define amino acids which are structurally and functionally equivalent across all HA subtypes using a numbering system based on the mature HA sequence. We provide a list of equivalences for amino acids which are known to affect the phenotype of the virus.

G-protein-coupled receptors (GPCRs) are divided phylogenetically into six classes 1 , 2 , denoted A to F. More than 370 structures of vertebrate GPCRs (belonging to classes A, B, C and F) have been determined, leading to a substantial understanding of their function 3 . By contrast, there are no structures of class D GPCRs, which are found exclusively in fungi where they regulate survival and reproduction. Here we determine the structure of a class D GPCR, the Saccharomyces cerevisiae pheromone receptor Ste2, in an active state coupled to the heterotrimeric G protein Gpa1–Ste4–Ste18. Ste2 was purified as a homodimer coupled to two G proteins. The dimer interface of Ste2 is formed by the N terminus, the transmembrane helices H1, H2 and H7, and the first extracellular loop ECL1. We establish a class D1 generic residue numbering system (CD1) to enable comparisons with orthologues and with other GPCR classes. The structure of Ste2 bears similarities in overall topology to class A GPCRs, but the transmembrane helix H4 is shifted by more than 20 Å and the G-protein-binding site is a shallow groove rather than a cleft. The structure provides a template for the design of novel drugs to target fungal GPCRs, which could be used to treat numerous intractable fungal diseases 4 . A cryo-electron microscopy structure of the yeast pheromone receptor Ste2, a class D G-protein-coupled receptor, in its active state reveals that Ste2 is a homodimer that couples to two G proteins.

We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.

Microtubules are polymeric filaments, constructed of α-β tubulin heterodimers that underlie critical subcellular structures in eukaryotic organisms. Four homologous proteins (γ-, δ-, ε- and ζ-tubulin) additionally contribute to specialized microtubule functions. Although there is an immense volume of publicly available data pertaining to tubulins, it is difficult to assimilate all potentially relevant information across diverse organisms, isotypes, and categories of data. We previously assembled an extensive web-based catalogue of published missense mutations to tubulins with >1,500 entries that each document a specific substitution to a discrete tubulin, the species where the mutation was described and the associated phenotype with hyperlinks to the amino acid sequence and citation(s) for research. This report describes a significant update and expansion of our online resource ( TubulinDB.bio.uci.edu ) to nearly 18,000 entries. It now encompasses a cross-referenced catalog of post-translational modifications (PTMs) to tubulin drawn from public datasets, primary literature, and predictive algorithms. In addition, tubulin protein structures were used to define local interactions with bound ligands (GTP, GDP and diverse microtubule-targeting agents) and amino acids at the intradimer interface, within the microtubule lattice and with associated proteins. To effectively cross-reference these datasets, we established a universal tubulin numbering system to map entries into a common framework that accommodates specific insertions and deletions to tubulins. Indexing and cross-referencing permitted us to discern previously unappreciated patterns. We describe previously unlinked observations of loss of PTM sites in the context of cancer cells and tubulinopathies. Similarly, we expanded the set of clinical substitutions that may compromise MAP or microtubule-motor interactions by collecting tubulin missense mutations that alter amino acids at the interface with dynein and doublecortin. By expanding the database as a curated resource, we hope to relate model organism data to clinical findings of pathogenic tubulin variants. Ultimately, we aim to aid researchers in hypothesis generation and design of studies to dissect tubulin function.

Nowadays, electronic computers use a “ binary ” numbering system, as opposed to “ ternary ” logic, which is closer to the way the human brain thinks. In this paper, the symmetric ternary system is applied to membrane computing for the first time. By combining the symmetric ternary system with membrane computing, this paper provides a more suitable arithmetic operation method for bio-computers, which breaks through the limitations of the traditional binary system in complex operations, and has a great potential for application in artificial intelligence and automatic learning in particular. The P System we designed include: Π + for symmetric ternary addition, Π* for symmetric ternary multiplication, and Π / for symmetric ternary division. The operation process of each P System was explained through examples, and their feasibility and effectiveness were verified through simulation software, UPSimulator. The system we designed can be further applied to symmetric ternary applications.

Introduction: A core mission of the Institute of Pharmacovigilance (IPV), a non-profit non-governmental organization (NGO), is to develop a Global Pharmacovigilance Professional Certification (GPPC) which, when fully developed, will be using a pool of 5000 questions reflecting the wide scope of pharmacovigilance knowledge. A key component of the knowledge to be covered is safety regulatory compliance. To gather the relevant information with a global scope, the comprehensive global pharmacovigilance requirements profiling tool (PVReg-profiler) described in this paper was developed. Objective: The purpose of the PVReg-profiler includes: (a) Constitute a core element of the Knowlege Management System supporting the GPPC; (b) Master the evolution of the diverse global safety regulatory environment; (c) Measure the impact of regulatory strengthening policies, serving the needs of Regional and National Regulatory Authorities, International Public Health Stakeholders and Market Authorisation Holders. Methods: The concept was designed for collecting any compliancecritical pharmacovigilance-relevant requirement from any national or regional regulatory system, and to be able to evolve as regulatory frameworks are rapidly strengthening. Questions were formulated in order to serve as basis for generating multiple choice questions and standardised country safety requirement profiles for making comparative analyses across countries. A unique item numbering system was developed to allow for comparing items across versions. An initial set of 350 questions was created involving a panel of pilot experts invited to test it and provide feedback. The main challenge faced during the development phase was to address the actual diversity of requirement across countries by capturing compliance relevant details without inflating the number of items to be assessed. This was resolved by allowing for structured comments. Results: The PVReg-profiler has entered its production phase including (i) the collection by appointed country experts of the relevant information, (ii) the validation of the profile by the National Regulatory Authority, (iii) the use of the information by item makers, (iv) the use of the available profiles by stakeholders interested such as holders of medicinal product licenses and public health stakeholders involved in regulatory convergence and strengthening. Conclusion: Beyond its primary utility as a source of safety regulatory knowledge for the GPPC, this key component of IPV's ecosystem is intended to be used by Public Health stakeholders to facilitate identifying priorities for regulatory convergence and strengthening and monitor its progress.

The GPCR autoproteolysis inducing (GAIN) domain is an ancient protein fold ubiquitous in adhesion G protein-coupled receptors (aGPCR). It contains a tethered agonist necessary and sufficient for receptor activation. The GAIN domain is a hotspot for pathological mutations. However, the low primary sequence conservation of GAIN domains has thus far hindered the knowledge transfer across different GAIN domains in human receptors as well as species orthologs. Here, we present a scheme for generic residue numbering of GAIN domains, based on structural alignments of over 14,000 modeled GAIN domain structures. This scheme is implemented in the GPCR database (GPCRdb) and elucidates the domain topology across different aGPCRs and their homologs in a large panel of species. We identify conservation hotspots and statistically cancer-enriched positions in human aGPCRs and show the transferability of positional and structural information between GAIN domain homologs. The GAIN-GRN scheme provides a robust strategy to allocate structural homologies at the primary and secondary levels also to GAIN domains of polycystic kidney disease 1/PKD1-like proteins, which now renders positions in both GAIN domain types comparable to one another. In summary, our work enables researchers to generate hypothesis and rationalize experiments related to GAIN domain function and pathology. Transferring information on the GAIN domain is hampered by low sequence conservation. Here, authors introduce a generic residue numbering scheme for GAIN domains to facilitate information integration as demonstrated on cancer data.

The Immunoglobulin fold (Ig-fold) is found in proteins from all domains of life and represents the most populous fold in the human genome, with current estimates ranging from 2 to 3% of protein coding regions. That proportion is much higher in the surfaceome where Ig and Ig-like domains orchestrate cell-cell recognition, adhesion and signaling. The ability of Ig-domains to reliably fold and self-assemble through highly specific interfaces represents a remarkable property of these domains, making them key elements of molecular interaction systems: the immune system, the nervous system, the vascular system and the muscular system. We define a universal residue numbering scheme, common to all domains sharing the Ig-fold in order to study the wide spectrum of Ig-domain variants constituting the Ig-proteome and Ig-Ig interactomes at the heart of these systems . The “IgStrand numbering scheme” enables the identification of Ig structural proteomes and interactomes in and between any species, and comparative structural, functional, and evolutionary analyses. We review how Ig-domains are classified today as topological and structural variants and highlight the “Ig-fold irreducible structural signature” shared by all of them. The IgStrand numbering scheme lays the foundation for the systematic annotation of structural proteomes by detecting and accurately labeling Ig-, Ig-like and Ig-extended domains in proteins, which are poorly annotated in current databases and opens the door to accurate machine learning. Importantly, it sheds light on the robust Ig protein folding algorithm used by nature to form beta sandwich supersecondary structures. The numbering scheme powers an algorithm implemented in the interactive structural analysis software iCn3D to systematically recognize Ig-domains, annotate them and perform detailed analyses comparing any domain sharing the Ig-fold in sequence, topology and structure, regardless of their diverse topologies or origin. The scheme provides a robust fold detection and labeling mechanism that reveals unsuspected structural homologies among protein structures beyond currently identified Ig- and Ig-like domain variants. Indeed, multiple folds classified independently contain a common structural signature, in particular jelly-rolls. Examples of folds that harbor an “Ig-extended” architecture are given. Applications in protein engineering around the Ig-architecture are straightforward based on the universal numbering.