Coronaviruses infect animals and humans causing a wide range of diseases. The diversity of coronaviruses in many mammalian species is contributed by relatively high mutation and recombination rates during replication. This dynamic nature of coronaviruses may facilitate cross-species transmission and shifts in tissue or cell tropism in a host, resulting in substantial change in virulence. Feline enteric coronavirus (FECV) causes inapparent or mild enteritis in cats, but a highly fatal disease, called feline infectious peritonitis (FIP), can arise through mutation of FECV to FIP virus (FIPV). The pathogenesis of FIP is intimately associated with immune responses and involves depletion of T cells, features shared by some other coronaviruses like Severe Acute Respiratory Syndrome Coronavirus. The increasing risks of highly virulent coronavirus infections in humans or animals call for effective antiviral drugs, but no such measures are yet available. Previously, we have reported the inhibitors that target 3C-like protease (3CLpro) with broad-spectrum activity against important human and animal coronaviruses. Here, we evaluated the therapeutic efficacy of our 3CLpro inhibitor in laboratory cats with FIP. Experimental FIP is 100% fatal once certain clinical and laboratory signs become apparent. We found that antiviral treatment led to full recovery of cats when treatment was started at a stage of disease that would be otherwise fatal if left untreated. Antiviral treatment was associated with a rapid improvement in fever, ascites, lymphopenia and gross signs of illness and cats returned to normal health within 20 days or less of treatment. Significant reduction in viral titers was also observed in cats. These results indicate that continuous virus replication is required for progression of immune-mediated inflammatory disease of FIP. These findings may provide important insights into devising therapeutic strategies and selection of antiviral compounds for further development for important coronaviruses in animals and humans.

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Reversal of the Progression of Fatal Coronavirus Infection in Cats by a Broad-Spectrum Coronavirus Protease Inhibitor

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.

Homology modeling in drug discovery: Overview, current applications, and future perspectives.

Background
In recent years, an exponential growing number of tools for protein sequence analysis, editing and modeling tasks have been put at the disposal of the scientific community. Despite the vast majority of these tools have been released as open source software, their deep learning curves often discourages even the most experienced users.

/pdf/pymod-sequence-similarity-searches-multiple-sequence-1l3e7o7d66.pdf

PyMod: sequence similarity searches, multiple sequence-structure alignments, and homology modeling within PyMOL

Adipose tissue is the primary site of storage for excess energy as triglyceride and it helps in synthesizing a number of biologically active compounds that regulate metabolic homeostasis. Consumpti...

In silico and in vivo analysis to identify the antidiabetic activity of beta sitosterol in adipose tissue of high fat diet and sucrose induced type-2 diabetic experimental rats

https://www.usask.ca/toxicology/jgiesy/pdf/publications/JA-687.pdf

Amino acid sequence of the ligand-binding domain of the aryl hydrocarbon receptor 1 predicts sensitivity of wild birds to effects of dioxin-like compounds

MODELLER is a program for automated protein Homology Modeling. It is one of the most widely used tool for homology or comparative modeling of protein three-dimensional structures, but most users find it a bit difficult to start with MODELLER as it is command line based and requires knowledge of basic Python scripting to use it efficiently. The study was designed with an aim to develop of "EasyModeller" tool as a frontend graphical interface to MODELLER using Perl/Tk, which can be used as a standalone tool in windows platform with MODELLER and Python preinstalled. It helps inexperienced users to perform modeling, assessment, visualization, and optimization of protein models in a simple and straightforward way. EasyModeller provides a graphical straight forward interface and functions as a stand-alone tool which can be used in a standard personal computer with Microsoft Windows as the operating system.

/pdf/easymodeller-a-graphical-interface-to-modeller-2xand5b119.pdf

EasyModeller: A graphical interface to MODELLER

In this study, we have developed a standalone tool called as ANAMBS (Analysis of Metal Binding Site) to derive metal neighbourhood information using PERL as the programming language. The tool accepts the structures in the pdb format. The cut off distance to define the metal binding region can be specified. The metal binding site composition, orientation of various amino acids and atoms along with the Hydropathy index within the metal binding site region can be measured. Its speed and efficiency makes it a beneficial tool for various structural biology projects, especially when the characterization of the metal binding domain is needed. Additionally, the database MEDB (Metal Environment Database) was developed which presents quantitative information on metal-binding sites in protein structures. It can be used for identification of trends or patterns in the metal-binding sites. The information obtained can be used to generate structural templates from metal binding sites of known enzymes and to develop constraints for computational modeling of metalloproteins. The tool and database are available at http://www.uohyd.ernet.in/anambs/

Development of tools and database for analysis of metal binding sites in protein.

The community of different types of microbes present in a biological niche plays a very important role in functioning of the system. The crosstalk or interactions among the different microbes contributes to the building blocks of such microbial community structures. Evidence reported in biomedical text serves as a reliable source for predicting such interactions. However, going through the vast and ever-increasing volume of biomedical literature is an intimidating and time consuming process. This necessitates development of automated methods capable of accurately extracting bacterial relations reported in biomedical literature. In this paper, we introduce a method for automated extraction of microbial interactions (specifically between bacteria) from biomedical literature along with ways of using transfer learning to improve its accuracy. We also describe a pipeline using which relations among specific bacteria groups can be mined. Additionally, we introduce the first publicly available dataset which can be used to develop bacterial interaction extraction methods.

Bhusan Kumar Kuntal

Papers

EasyModeller: A graphical interface to MODELLER

Development of tools and database for analysis of metal binding sites in protein.

BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text