scholarly journals Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Stephen J. Goodswen ◽  
Paul J. Kennedy ◽  
John T. Ellis
Keyword(s):  
Machine Learning ◽  
Secondary Structure ◽  
Structural Properties ◽  
Protein Secondary Structure ◽  
Accessible Surface Area ◽  
Machine Learning Algorithms ◽  
Babesia Bovis ◽  
Solvent Accessible Surface Area ◽  
Babesia Bigemina ◽  
Bovine Babesiosis

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

scholarly journals Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis

Pathogens ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 660
Author(s):  
Stephen J. Goodswen ◽  
Paul J. Kennedy ◽  
John T. Ellis
Keyword(s):  
Machine Learning ◽  
Severe Disease ◽  
Economic Loss ◽  
Babesia Bovis ◽  
Training Data ◽  
Babesia Bigemina ◽  
Bovine Babesiosis ◽  
Canine Babesiosis ◽  
Clinically Significant ◽  
Significant Disease

Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train–validation–test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.

scholarly journals Hermes: an ensemble machine learning architecture for protein secondary structure prediction

2019 ◽  
Author(s):  
Larry Bliss ◽  
Ben Pascoe ◽  
Samuel K Sheppard
Keyword(s):  
Machine Learning ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Cross Validation ◽  
Secondary Structure Prediction ◽  
Protein Structures ◽  
Lower Boundary ◽  
Protein Secondary Structure ◽  
Homologous Proteins

AbstractMotivationProtein structure predictions, that combine theoretical chemistry and bioinformatics, are an increasingly important technique in biotechnology and biomedical research, for example in the design of novel enzymes and drugs. Here, we present a new ensemble bi-layered machine learning architecture, that directly builds on ten existing pipelines providing rapid, high accuracy, 3-State secondary structure prediction of proteins.ResultsAfter training on 1348 solved protein structures, we evaluated the model with four independent datasets: JPRED4 - compiled by the authors of the successful predictor with the same name, and CASP11, CASP12 & CASP13 - assembled by the Critical Assessment of protein Structure Prediction consortium who run biannual experiments focused on objective testing of predictors. These rigorous, pre-established protocols included 7-fold cross-validation and blind testing. This led to a mean Hermes accuracy of 95.5%, significantly (p<0.05) better than the ten previously published models analysed in this paper. Furthermore, Hermes yielded a reduction in standard deviation, lower boundary outliers, and reduced dependency on solved structures of homologous proteins, as measured by NEFF score. This architecture provides advantages over other pipelines, while remaining accessible to users at any level of bioinformatics experience.Availability and ImplementationThe source code for Hermes is freely available at: https://github.com/HermesPrediction/Hermes. This page also includes the cross-validation with corresponding models, and all training/testing data presented in this study with predictions and accuracy.

scholarly journals Diagnostic Tools for the Identification of Babesia sp. in Persistently Infected Cattle

Pathogens ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 143 ◽  
Author(s):  
J. Antonio Alvarez ◽  
Carmen Rojas ◽  
Julio V. Figueroa
Keyword(s):  
Clinical Signs ◽  
International Health ◽  
Babesia Bovis ◽  
Diagnostic Tools ◽  
Infected Cattle ◽  
Babesia Bigemina ◽  
Bovine Babesiosis ◽  
Culture Methods ◽  
Persistently Infected

Bovine babesiosis is a tick-borne disease of cattle caused by the protozoan parasites of the genus Babesia. Babesia bovis, Babesia bigemina and Babesia divergens are considered by International health authorities (OIE) as the principal species of Babesia that cause bovine babesiosis. Animals that recover from a babesial primo infection may remain as persistent carriers with no clinical signs of disease and can be the source of infection for ticks that are able to acquire Babesia parasites from infected cattle and to transmit Babesia parasites to susceptible cattle. Several procedures that have been developed for parasite detection and diagnosis of this infectious carrier state constitute the basis for this review: A brief description of the direct microscopic detection of Babesia-infected erytrocytes; PCR-based diagnostic assays, which are very sensitive particularly in detecting Babesia in carrier cattle; in-vitro culture methods, used to demonstrate presence of carrier infections of Babesia sp.; animal inoculation, particularly for B. divergens isolation are discussed. Alternatively, persistently infected animals can be tested for specific antibabesial antibodies by using indirect serological assays. Serological procedures are not necessarily consistent in identifying persistently infected animals and have the disadvantage of presenting with cross reactions between antibodies to Babesia sp.

scholarly journals Assessing the Immunochromatographic Test Strip for Serological Detection of Bovine Babesiosis in Uganda

2020 ◽  
Vol 8 (8) ◽  
pp. 1110 ◽  
Author(s):  
Dickson Stuart Tayebwa ◽  
Amany Magdy Beshbishy ◽  
Gaber El-Saber Batiha ◽  
Mariam Komugisha ◽  
Byaruhanga Joseph ◽  
...  
Keyword(s):  
Test Strip ◽  
Spherical Body ◽  
Babesia Bovis ◽  
Diagnostic Tools ◽  
Immunochromatographic Test ◽  
Babesia Bigemina ◽  
Bovine Babesiosis ◽  
Body Protein ◽  
Eastern Uganda ◽  
Good Agreement

In Uganda, bovine babesiosis continues to cause losses to the livestock industry because of shortages of cheap, quick, and reliable diagnostic tools to guide prescription measures. In this study, the presence of antibodies to Babesia bigemina and Babesia bovis in 401 bovine blood samples obtained from eastern and central areas of Uganda were detected using enzyme-linked immunosorbent assays (ELISAs) and immunochromatographic test strips (ICTs). The ELISA and ICT test used targeted the B. bigemina C-terminal rhoptry-associated protein (RAP-1/CT17) and B. bovis spherical body protein-4 (SPB-4). Using ELISA, single-ICT and dual-ICT, positive samples for B. bovis were detected in 25 (6.2%), 17 (4.3%), and 14 (3.7%) samples respectively, and positive samples for B. bigemina were detected in 34 (8.4%), 27 (6.7%), and 25 (6.2%), respectively. Additionally, a total of 13 animals (3.2%) had a mixed infection. The correlation between ELISA and single-ICT strips results revealed slight agreement with kappa values ranging from 0.088 to 0.191 between both methods, while the comparison between dual-ICT and single-ICT results showed very good agreement with kappa values >0.80. This study documented the seroprevalence of bovine babesiosis in central and eastern Uganda, and showed that ICT could, after further optimization, be a useful rapid diagnostic test for the diagnosis of bovine babesiosis in field settings.

First Report on Molecular Identification of Babesia bigemina in a Nervous Signs Evident Naturally Infected Holstein-Friesian Cattle from Central India

2021 ◽  
Author(s):  
V. Agrawal ◽  
G. Das ◽  
A. Jaiswal ◽  
A.K. Jayraw ◽  
G.P. Jatav ◽  
...  
Keyword(s):  
Blood Sample ◽  
Genomic Dna ◽  
Blood Smear ◽  
Clinical Signs ◽  
Central India ◽  
Babesia Bovis ◽  
Thin Blood Smear ◽  
Babesia Bigemina ◽  
Bovine Babesiosis ◽  
Holstein Friesian

Background: Bovine babesiosis caused by an intraerythrocytic apicomplexan protozoon responsible for the most prevalent and costly tick borne diseases (TBD’s) of cattle throughout the globe. Cerebral babesiosis of bovine is fatal and mainly caused by Babesia bovis. To the knowledge of author, there is no confirm molecular report of Babeisa bigemina caused cerebral babesiosis in cattle. Therefore, authors want to report Babesia bigemina caused cerebral babesiosis on record. Methods: In the year 2015, a Holstein-Friesian cow aged 3 years and weighing approximately 300 kg, was attended at Jabalpur, (M.P.) with the clinical signs of high rise in temperature (104°F), recumbency, severe dysponea, peculiar sound during open mouth breathing, pale color of eye conjunctiva and mucous membrane of vagina, convulsions, sever anaemia, paddling of legs at frequent interval. After preparation of peripheral thin blood smear from animal at the site of collection and fixation with methanol, blood sample brought to Department of Veterinary Parasitology, College of Veterinary Science and A.H, Jabalpur and stained by standard protocol for Giemsa staining. Genomic DNA was isolated from the collected blood sample using QIAamp® DNA blood mini kit following the manufacturer’s recommendations and PCR was performed. Conclusion: The thin blood smear examination revealed the presence of Babesia parasite. The species of Babesia was confirmed by molecular amplification of genomic DNA as B. bigemina. This might be the first confirmed report of cerebral babesiosis caused by B. bigemina from Central India.

Silk Fibroin: A Promising Biomaterial

2011 ◽  
Vol 409 ◽  
pp. 99-104 ◽  
Author(s):  
Mariana Agostini de Moraes ◽  
Mariana Ferreira Silva ◽  
Raquel Farias Weska ◽  
Marisa Masumi Beppu
Keyword(s):  
Tissue Engineering ◽  
Controlled Release ◽  
Secondary Structure ◽  
Bombyx Mori ◽  
Structural Properties ◽  
Silk Fibroin ◽  
Protein Secondary Structure ◽  
Processing Conditions ◽  
Protein Fiber

Silk fibroin (SF) is a protein fiber spun by Bombyx mori silkworm. SF fibers are about 10-25 μm wide in diameter and a single cocoon may provide over 1000 m of SF fibers. SF can present several conformations regarding protein secondary structure which ultimately define the structural properties of SF-based materials. For this reason, a rigorous control on its processing conditions shall be performed. It is known that SF has excellent properties to be used in biomaterials field, controlled release and scaffolds for tissue engineering. In addition, SF can be processed in several forms, such as films, fibers, hydrogels or microparticles. This work seeks to provide an overview on SF processing conditions, regarding the preparation of SF membranes (dense and porous), hydrogels and biocomposites, focusing on biomaterials application.

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