Three dimensional structure prediction and ligand-protein interaction study of expansin protein ATEXPA23 from Arabidopsis thaliana L.

Arabidopsis thaliana L. is a small flowering plant that is widely used as a model organism in plant biology. In the present study, we study the peripheral membrane protein ATEXPA23 from Arabidopsis thaliana L. using homology modelling and molecular docking. The microarray analysis shows expression of ATEXPA23 (AT5G39280) protein, which leads to loosening and extension of plant cell walls. This protein is differentially expressed during different stages of plant embryogenesis. It contains one expansin-like CBD domain and one expansin-like EG45 domain. ATEXPA23 belongs to the expansin family in expansin a subfamily. The 3D model after refinement is used to explore the xyloglucan binding characteristics of ATEXPA23 using AutoDock. The docking analysis shows that the surface exposed aromatic amino acid residues Phe 193 and Phe 265 interact with ligand xyloglucan through CH-л interaction. The binding energy values of docking reflect a stable conformation of the docked complex. The interaction of expansin protein with carbohydrate xyloglucan, present in hemicellulose structures of plant cell wall, is thoroughly analysed with cellotetrose and xyloglucan heptasaccharide using electrostatic potential calculation. This CH-л non-covalent interaction predominates on the cellulose-xyloglucan interaction in plant cell wall during cell growth

The One Health European Joint Programme (OHEJP), 2018-2022: an exemplary One Health initiative

OverviewOne Health is an increasingly popular approach used to tackle complex health problems. The One Health concept recognizes that human health is tightly connected to the health of animals and the environment. Although the related fields are now more aware of the benefits of collaborative working, the full benefits have not yet been realized as research efforts are often focussed on just one of these health domains. To address regional and global issues such as foodborne zoonoses (FBZ), antimicrobial resistance (AMR) and emerging infectious threats (ET), there must be transdisciplinary collaboration between the health domains, in addition to active dialogue between scientists and international policy makers. This editorial introduces the One Health European Joint Programme (OHEJP) as an example of a One Health initiative.Zoonoses, AMR and their global burdenZoonoses are infectious diseases that can be transmitted directly or indirectly between humans and animals. Although the severity of zoonotic infections varies, their global impact is undisputable. The World Bank estimates that just six zoonotic disease outbreaks between 1997 and 2009 led to a global economic loss of US$80 billion [1]. This high cost is due to medical costs, loss of individual productivity and restrictions on trade and movement during outbreaks. Despite improvements in the management and treatment of zoonotic outbreaks, high disease burdens caused by zoonotic pathogens continue to be reported globally. These problems have been amply demonstrated recently by the SARS-CoV-2 pandemic. Although it is still too soon to fully assess the total economic and societal cost of this virus, recent publications, such as Nicola et al. [2] have begun to highlight just how widespread the impact of a truly global zoonotic disease can be.Alongside zoonoses, AMR is a growing international issue. The World Health Organization (WHO) has listed AMR as one of the ten greatest global health threats in 2019 [3]. AMR is defined as the ability of microorganisms to survive the effect of antimicrobial drugs, hindering not only our ability to treat infectious diseases, but also to perform medical procedures requiring prophylactic antibiotic administration. It has been predicted that by 2050, the number of deaths due to unresponsive infections will reach 10 million annually, with the associated costs being estimated at US$ 100 trillion [4]. Increased and inappropriate use of antimicrobials has contributed to the development and spread of AMR, which can be transmitted between humans, animals and the environment.The history of the ‘One Health’ conceptThe origins of One Health go as far back as 1855, when Rudolf Virchow founded comparative pathology, which could be seen as the origin of the One Health concept. Building upon this, Calvin W. Schwabe argued in the twentieth century against compartmentalization in medical research, using the term ‘One Medicine’. The term One Health was then popularized in 2004 by the Wildlife Conservation Society at a conference in New York [5], and its use has continued to evolve since then, fostering the revival of comparative medicine (Fig. 1, and reviewed in Gibbs [6]). One Health has now been adopted by the WHO [7], the Food and Agriculture Organization (FAO) [8] and the World Organization for Animal Health (OIE) [9]

EXACT2: the semantics of biomedical protocols

© 2014 Soldatova et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.This article has been made available through the Brunel Open Access Publishing Fund.Background: The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods: We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously ‘unseen’ (not used for the construction of EXACT2) protocols. Results: The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions: The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format.This work has been partially funded by the Brunel University BRIEF award and a grant from Occams Resources

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: http://sysbio.icm.edu.pl/secstruct and http://code.google.com/p/cmater-bioinfo

A novel whole system integrated genomics approach to identify key genetic components which facilitate synthetic design of a genetically engineered strain of Escherichia coli K12 with enhanced isobutanol tolerance

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.There has been an increased global interest in biofuels which provide a renewable and sustainable alternative to fossil fuels. Isobutanol is an attractive and superior alternative to the currently produced bioethanol possessing several key advantages. Previous work focuses on strategies for metabolic optimisation of carbon utilisation. However, existing solutions reach a stage where the amount of alcohol produced reaches toxic thresholds for bacteria. This inhibits growth and reduces carbohydrate consumption resulting in lower product yields rendering the biofuel production process uneconomical. In this project, a novel strategy has been adopted which uses a whole system integrated genomics approach consisting of expression profiling, selection to create isobutanol-adapted lineages, next generation sequencing, and comparative behavioural genomics to interrogate the system thoroughly and identify critical determinants of resistance to isobutanol. These were used in the highly-defined model species, E. coli K12 to deliver results of the adaptive mechanisms which take place across the entire genome. 41 gene candidates (4 previously identified in literature) were identified to play a role in isobutanol tolerance. These candidates belong to a range of functional groups such as carbohydrate metabolism, oxidative stress response, osmotic stress response; but also identified novel membrane-associated functions such as the Tol-Pal system, BAM complex and colanic acid production. The results also identify critical genes with unknown functions. The results support previous notions that central carbon metabolism shifts from aerobic to anaerobic metabolism in the presence of isobutanol, but also shows there is a transitionary phase where mixed acid fermentation pathways are utilised. This shift was previously thought to be mediated by the ArcA-ArcB two-component system. However, these results suggest the inactive 2Fe-2S core of the anaerobic-regulator Fnr is re-activated by Fe2+ to form the 4Fe-4S core transported by the FeoAB ferrous iron transport system. The strategy also identified the Tol-Pal system and show it is essential to grow in the presence of isobutanol, which is responsible for the maintaining the integrity of the cell envelope structure and increasing the rate of cell division. The BAM complex is responsible for folding and assembly of outer membrane proteins (OMP) and OMP membrane permeability- this system was found to be important for growth in isobutanol, and SurA, which is the primary OMP assembly pathway provided tolerance which was specific to isobutanol. Colanic acid, an extracellular polysaccharide is produced when the cell experiences stress, and provides protection by forming a physical barrier around the cell. The results show that the presence of colanic acid plays a large role in allowing E. coli to grow in presence of isobutanol, and its role becomes essential at critical concentrations. The results also show deletion of the negative regulator of the colanic acid gene cluster improves growth at critical and growth-inhibiting concentrations. When consolidated, these results facilitated knowledge-led based design and subsequently led to the identification of components for a synthetic design schedule, which lists the genetic manipulations proposed to exploit E. coli to enhance isobutanol tolerance

Three dimensional structure prediction and ligand-protein interaction study of expansin protein ATEXPA23 from Arabidopsis thaliana L.

20-27Arabidopsis thaliana L. is a small flowering plant that is widely used as a model organism in plant biology. In the present study, we study the peripheral membrane protein ATEXPA23 from Arabidopsis thaliana L. using homology modelling and molecular docking. The microarray analysis shows expression of ATEXPA23 (AT5G39280) protein, which leads to loosening and extension of plant cell walls. This protein is differentially expressed during different stages of plant embryogenesis. It contains one expansin-like CBD domain and one expansin-like EG45 domain. ATEXPA23 belongs to the expansin family in expansin a subfamily. The 3D model after refinement is used to explore the xyloglucan binding characteristics of ATEXPA23 using AutoDock. The docking analysis shows that the surface exposed aromatic amino acid residues Phe 193 and Phe 265 interact with ligand xyloglucan through CH-л interaction. The binding energy values of docking reflect a stable conformation of the docked complex. The interaction of expansin protein with carbohydrate xyloglucan, present in hemicellulose structures of plant cell wall, is thoroughly analysed with cellotetrose and xyloglucan heptasaccharide using electrostatic potential calculation. This CH-л non-covalent interaction predominates on the cellulose-xyloglucan interaction in plant cell wall during cell growth

Detection of spreader nodes in human-SARS-CoV protein-protein interaction network

The entire world is witnessing the coronavirus pandemic (COVID-19), caused by a novel coronavirus (n-CoV) generally distinguished as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 promotes fatal chronic respiratory disease followed by multiple organ failure, ultimately putting an end to human life. International Committee on Taxonomy of Viruses (ICTV) has reached a consensus that SARS-CoV-2 is highly genetically similar (up to 89%) to the Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), which had an outbreak in 2003. With this hypothesis, current work focuses on identifying the spreader nodes in the SARS-CoV-human protein–protein interaction network (PPIN) to find possible lineage with the disease propagation pattern of the current pandemic. Various PPIN characteristics like edge ratio, neighborhood density, and node weight have been explored for defining a new feature spreadability index by which spreader proteins and protein–protein interaction (in the form of network edges) are identified. Top spreader nodes with a high spreadability index have been validated by Susceptible-Infected-Susceptible (SIS) disease model, first using a synthetic PPIN followed by a SARS-CoV-human PPIN. The ranked edges highlight the path of entire disease propagation from SARS-CoV to human PPIN (up to level-2 neighborhood). The developed network attribute, spreadability index, and the generated SIS model, compared with the other network centrality-based methodologies, perform better than the existing state-of-art