A comparison of proteins and peptides as substrates for microsomal and solubilized oligosaccharyltransferase

A chemoenzymatic synthesis of homogeneous neoglycoproteins and glycopeptides was explored using oligosaccharyltransferase isolated from yeast. Neither the microsomal form nor the solubilized form of the enzyme catalyzed the transfer of the core Glc3Man9(GlcNAc)2 oligosaccharide to chemically modified ribonuclease A or [alpha]-lactalbumin. Similarly, no transfer was observed to the 32-amino acid peptide hormone, calcitonin, by either the membrane-bound or soluble form of oligosaccharyltransferase. However, a 17-amino acid fragment of yeast invertase with the unusual sequence containing two overlapping glycosylation sequons proved to be a good substrate, slightly less effective than the well studied tripeptide, Bz-Asn-Leu-Thr-NH2. Product analysis using gel permeation chromatography showed that the expected glycopeptides were formed and endo H-catalyzed cleavage of the oligosaccharide portion from the glycopeptides demonstrated that the glycopeptides contained the same carbohydrate moiety.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31237/1/0000142.pd

The Vehicle, June 1960, Vol. 2 no. 3

Vol. 2, No. 3 To the ReaderRobert Mills Frenchpage 2 Blue-Nosed RobinThomas McPeakpage 3 Forest EtudeJames M. Jenkinsonpage 7 Chant For The MenJerry Whitepage 8 It\u27s OK Now, Chief J.B. Youngpage 9 Magic WordsKathleen Ferreepage 11 SpurnedRay Hoopspage 12 Danger!A. Seerpage 13 GenecideGeorge Fosterpage 14 To a Stern ParentC.E.S.page 14 ReservationNeil O. Parkerpage 14 The Worm and IRichard Blairpage 15 One Way -- Non-TransferableRobert Mills Frenchpage 15 NorthlightEDSpage 16https://thekeep.eiu.edu/vehicle/1007/thumbnail.jp

Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/

Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer

Background Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease. Result Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as S1 Archive

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/

Reactive centre loop mutants of α-1-antitrypsin reveal position-specific effects on intermediate formation along the polymerization pathway

The common severe Z mutation (E342K) of α1-antitrypsin forms intracellular polymers that are associated with liver cirrhosis. The native fold of this protein is well-established and models have been proposed from crystallographic and biophysical data for the stable inter-molecular configuration that terminates the polymerization pathway. Despite these molecular 'snapshots', the details of the transition between monomer and polymer remain only partially understood. We surveyed the RCL (reactive centre loop) of α1-antitrypsin to identify sites important for progression, through intermediate states, to polymer. Mutations at P14P12 and P4, but not P10P8 or P2P1', resulted in a decrease in detectable polymer in a cell model that recapitulates the intracellular polymerization of the Z variant, consistent with polymerization from a near-native conformation. We have developed a FRET (Förster resonance energy transfer)-based assay to monitor polymerization in small sample volumes. An in vitro assessment revealed the position-specific effects on the unimolecular and multimolecular phases of polymerization: the P14P12 region self-inserts early during activation, while the interaction between P6P4 and β-sheet A presents a kinetic barrier late in the polymerization pathway. Correspondingly, mutations at P6P4, but not P14P12, yield an increase in the overall apparent activation energy of association from ~360 to 550 kJ mol-1

BioSimulators: a central registry of simulation engines and services for recommending specific tools

Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations

A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components