Automated Chloroform-Methanol Protein Extraction on the Biomek-FX Liquid Handler System v2

This protocol details steps to extract protein from Gram-negative bacterial or fungal cells (that have been pretreated with zymolyase) in quantitative proteomic workflows by using a Biomek FX liquid handler system. It is a semi-automated protocol that includes several 'pause' steps for centrifugation steps that are conducted manually "off-deck". This protocol works best as part of an automated proteomic sample preparation workflow with: Automated Protein Quantitation with the Biomek-FX liquid handler system and Automated Protein Normalization and Tryptic Digestion on a Biomek-NX Liquid Handler System

Right Care, First Time: Developing a Theory-Based Automated Protocol to Help Clinically Stage Young People Based on Severity and Persistence of Mental Illness

Most mental disorders emerge before the age of 25 years and, if left untreated, have the potential to lead to considerable lifetime burden of disease. Many services struggle to manage high demand and have difficulty matching individuals to timely interventions due to the heterogeneity of disorders. The technological implementation of clinical staging for youth mental health may assist the early detection and treatment of mental disorders. We describe the development of a theory-based automated protocol to facilitate the initial clinical staging process, its intended use, and strategies for protocol validation and refinement. The automated clinical staging protocol leverages the clinical validation and evidence base of the staging model to improve its standardization, scalability, and utility by deploying it using Health Information Technologies (HIT). Its use has the potential to enhance clinical decision-making and transform existing care pathways, but further validation and evaluation of the tool in real-world settings is needed.

A new straightforward method for semi-automated segmentation of trabecular bone from cortical bone in diverse and challenging morphologies

Many physiological, biomechanical, evolutionary and clinical studies that explore skeletal structure and function require successful separation of trabecular from cortical compartments of a bone that has been imaged by X-ray micro-computed tomography (micro-CT) prior to analysis. Separation often involves manual subdivision of these two similarly radio-opaque compartments, which can be time-consuming and subjective. We have developed an objective, semi-automated protocol which reduces user bias and enables straightforward, user-friendly segmentation of trabecular from the cortical bone without requiring sophisticated programming expertise. This method can conveniently be used as a ‘recipe’ in commercial programmes (Avizo herein) and applied to a variety of datasets. Here, we characterize and share this recipe, and demonstrate its application to a range of murine and human bone types, including normal and osteoarthritic specimens, and bones with distinct embryonic origins and spanning a range of ages. We validate the method by testing inter-user bias during the scan preparation steps and confirm utility in the architecturally challenging analysis of growing murine epiphyses. We also report details of the recipe, so that other groups can readily re-create a similar method in open access programmes. Our aim is that this method will be adopted widely to create a reproducible and time-efficient method of segmenting trabecular and cortical bone.

A New Straightforward Method for Automated Segmentation of Trabecular Bone from Cortical Bone in Diverse and Challenging Morphologies

Many physiological, biomechanical, evolutionary and clinical studies that explore skeletal structure and function require successful separation of trabecular from cortical compartments of a bone that has been imaged by X-ray micro-computed tomography (microCT) prior to analysis. Separation is often time-consuming, involves user bias and needs manual sub-division of these two similarly radio-opaque compartments. We have developed an objective, automated protocol which reduces user bias and enables straightforward, user-friendly segmentation of trabecular from cortical bone without requiring sophisticated programming expertise. This method can conveniently be used as a “recipe” in commercial programmes (Avizo herein) and applied to a variety of datasets. Here, we characterise and share this recipe, and demonstrate its application to a range of murine and human bone types, including normal and osteoarthritic specimens, and bones with distinct embryonic origins and spanning a range of ages. We validate the method by testing inter-user bias during the scan preparation steps and confirm utility in the architecturally challenging analysis of growing murine epiphyses. We also report details of the recipe, so that other groups can readily re-create a similar method in open access programs. Our aim is that this method will be adopted widely to create a more standardized and time efficient method of segmenting trabecular and cortical bone.

An Extensive Formal Analysis of Multi-factor Authentication Protocols

Passwords are still the most widespread means for authenticating users, even though they have been shown to create huge security problems. This motivated the use of additional authentication mechanisms in so-called multi-factor authentication protocols. In this article, we define a detailed threat model for this kind of protocol: While in classical protocol analysis attackers control the communication network, we take into account that many communications are performed over TLS channels, that computers may be infected by different kinds of malware, that attackers could perform phishing, and that humans may omit some actions. We formalize this model in the applied pi calculus and perform an extensive analysis and comparison of several widely used protocols—variants of Google 2-step and FIDO’s U2F (Yubico’s Security Key token). The analysis is completely automated, generating systematically all combinations of threat scenarios for each of the protocols and using the P ROVERIF tool for automated protocol analysis. To validate our model and attacks, we demonstrate their feasibility in practice, even though our experiments are run in a laboratory environment. Our analysis highlights weaknesses and strengths of the different protocols. It allows us to suggest several small modifications of the existing protocols that are easy to implement, as well as an extension of Google 2-step that improves security in several threat scenarios.

A combined Chemical, Computational and invitro approach identifies SBL 105 as novel DHODH inhibitor in Acute Myeloid Leukemia cells.

Inhibition of the dihydroorotate dehydrogenase (DHODH) has been successful at the preclinical level in controlling myeloid leukemia. However, poor clinical trials warrant the search for new potent DHODH inhibitors. Herein we present a novel DHODH inhibitor SBL-105 effective against myeloid leukemia. Chemical characteristics were identified by 1H NMR, 13C NMR, and Mass-spectroscopy. Virtual docking and molecular dynamic simulation analysis were performed using the automated protocol with AutoDock -VINA, GROMACS program. Human-recombinant (rh) DHODH was used for enzyme inhibition study. THP-1, TF-1, HL-60 and SKM-1 cell lines were used. MTT assay was used to assess cell viability. Flow cytometry was employed for cell cycle, apoptosis, and differentiation analysis. Chemical analysis identified the compound to be 3-benzylidene-6,7-benz-chroman-4-one (SBL-105). The compound showed high binding efficacy towards DHODH with a ΔGbinding score of -10.9 kcal/mol. Trajectory analysis indicated conserved interactions of SBL-105-DHODH to be stable throughout the 200ns simulation. SBL-105 inhibited rh DHODH with an IC50 value of 48.48 nM. The GI50 values of SBL-105 in controlling THP-1, TF-1, HL-60 and SKM-1 cell proliferations were 60.66 nM 45.33 nM, 73.98 nM and 86.01 nM respectively. A dose-dependent increase in S-phase cell cycle arrest and total apoptosis was observed by SBL-105 treatment in both cell types, which were reversed in the presence of uridine. The compound also increased the differentiation marker CD11b positive populations in both THP-1 and TF-1 cells, which were decreased under uridine influence. SBL-105, a novel DHODH inhibitor, identified using computational and in vitro analysis, was effective controlled AML cells and needs attention for further preclinical developments.

CGMD Platform: Integrated Web Servers for the Preparation, Running, and Analysis of Coarse-Grained Molecular Dynamics Simulations

Advances in coarse-grained molecular dynamics (CGMD) simulations have extended the use of computational studies on biological macromolecules and their complexes, as well as the interactions of membrane protein and lipid complexes at a reduced level of representation, allowing longer and larger molecular dynamics simulations. Here, we present a computational platform dedicated to the preparation, running, and analysis of CGMD simulations. The platform is built on a completely revisited version of our Martini coarsE gRained MembrAne proteIn Dynamics (MERMAID) web server, and it integrates this with other three dedicated services. In its current version, the platform expands the existing implementation of the Martini force field for membrane proteins to also allow the simulation of soluble proteins using the Martini and the SIRAH force fields. Moreover, it offers an automated protocol for carrying out the backmapping of the coarse-grained description of the system into an atomistic one.

An automated protocol for modelling peptide substrates to proteases

Background Proteases are key drivers in many biological processes, in part due to their specificity towards their substrates. However, depending on the family and molecular function, they can also display substrate promiscuity which can also be essential. Databases compiling specificity matrices derived from experimental assays have provided valuable insights into protease substrate recognition. Despite this, there are still gaps in our knowledge of the structural determinants. Here, we compile a set of protease crystal structures with bound peptide-like ligands to create a protocol for modelling substrates bound to protease structures, and for studying observables associated to the binding recognition. Results As an application, we modelled a subset of protease–peptide complexes for which experimental cleavage data are available to compare with informational entropies obtained from protease–specificity matrices. The modelled complexes were subjected to conformational sampling using the Backrub method in Rosetta, and multiple observables from the simulations were calculated and compared per peptide position. We found that some of the calculated structural observables, such as the relative accessible surface area and the interaction energy, can help characterize a protease’s substrate recognition, giving insights for the potential prediction of novel substrates by combining additional approaches. Conclusion Overall, our approach provides a repository of protease structures with annotated data, and an open source computational protocol to reproduce the modelling and dynamic analysis of the protease–peptide complexes.