Computational approach for protein structure prediction

A multi-epitope chimeric protein elicited a strong antibody response and partial protection against Edwardsiella ictaluri in Nile tilapia

Edwardsiella ictaluri infects several fish species and protection of the all the susceptible fish hosts from the pathogen using a monovalent vaccine is impossible because the species is composed of host-based genotypes that are genetic, serological and antigenic heterogenous. Here, immunoinformatic approach was employed to design a cross-immunogenic chimeric EiCh protein containing multi-epitopes. The chimeric EiCh protein is composed of 11 B-cell epitopes and 7 major histocompatibility complex class II epitopes identified from E. ictaluri immunogenic proteins previously reported. The 49.32 kDa recombinant EiCh protein was expressed in vitro in Escherichia coli BL-21 (DE3) after which inclusion bodies were successfully solubilized and refolded. Ab initio protein modelling revealed secondary and tertiary structures. Secondary structure was confirmed by circular dichroism spectroscopy. Antigenicity of the chimeric EiCh protein was exhibited by strong reactivity with serum from striped catfish and Nile tilapia experimentally infected with E. ictaluri. Furthermore, immunogenicity of the chimeric EiCh protein was investigated in vivo in Nile tilapia juveniles and it was found that the protein could strongly induce production of specific antibodies conferring agglutination activity and partially protected Nile tilapia juveniles with a relative survival percentage (RPS) of 42%. This study explored immunoinformatics as reverse vaccinology approach in vaccine design for aquaculture to manage E. ictaluri infections.

An overview of databases and bioinformatics tools for plant antimicrobial peptides

Antimicrobial peptides (AMPs) are small, ribosomally synthesized proteins found in nearly all forms of life. In plants, AMPs play a central role in plant defense due to their distinct physicochemical properties. Due to their broad-spectrum antimicrobial activity and rapid killing action, plant AMPs have become important candidates for the development of new drugs to control plant and animal pathogens that are resistant to multiple drugs. Further research is required to explore the potential uses of these natural compounds. Computational strategies have been increasingly used to understand key aspects of antimicrobial peptides. These strategies will help to minimize the time and cost of "wet-lab" experimentation. Researchers have developed various tools and databases to provide updated information on AMPs. However, despite the increased availability of antimicrobial peptide resources in biological databases, finding AMPs from plants can still be a difficult task. The number of plant AMP sequences in current databases is still small and yet often redundant. To facilitate further characterization of plant AMPs, we have summarized information on the location, distribution, and annotations of plant AMPs available in the most relevant databases for AMPs research. We also mapped and categorized the bioinformatics tools available in these databases. We expect that this will allow researchers to advance in the discovery and development of new plant AMPs with potent biological properties. We hope to provide insights to further expand the application of AMPs in the fields of biotechnology, pharmacy, and agriculture.

Computational Chemistry to Repurposing Drugs for the Control of COVID-19

Thus far, in 2021, 219 countries with over 175 million people have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is a positive sense, single-stranded RNA virus, and is the causal agent for coronavirus disease (COVID-19). Due to the urgency of the situation, virtual screening as a computational modeling method offers a fast and effective modality of identifying drugs that may be effective against SARS-CoV-2. There has been an overwhelming abundance of molecular docking against SARS-CoV-2 in the last year. Due to the massive volume of computational studies, this systematic review has been created to evaluate and summarize the findings of existing studies. Herein, we report on computational articles of drugs which target, (1) viral protease, (2) Spike protein-ACE 2 interaction, (3) RNA-dependent RNA polymerase, and (4) other proteins and nonstructural proteins of SARS-CoV-2. Based on the studies presented, there are 55 identified natural or drug compounds with potential anti-viral activity. The next step is to show anti-viral activity in vitro and translation to determine effectiveness into human clinical trials.

Virtual screening of a MDR-TB WhiB6 target identified by gene expression profiling

Multidrug resistance in M. tb has become a huge global problem due to drug resistance. Hence, the treatment remains a challenge, even though short term chemotherapy is available. Therefore, it is of interest to identify novel drug targets in M.tb through gene expression profiling complimented by a subtractive proteome model. WhiB6 is a transcriptional regulator protein and a known drug resistant marker that is critical in the secretion dependent regulation of ESX-1, which is specialized for the deployment of host membrane-targeting proteins. The WhiB6 protein structure was modelled ab initio and was docked with a library of 173 phytochemicals with potential antituberculosis activity to the identified drug marker to find novel lead molecules. UDP-galactopyranose and GDP-L-galactose were identified to be potential lead molecules to inhibit the target WhiB6. The results were compared with the first line drugs for MDR-TB by docking with WhiB6. Data showed that Ethambutol showed better binding ability to WhiB6 but the afore mentioned top ranked phytochemicals were found to be better candidate molecules. The chosen candidate lead molecules should be further validated by suitable in vitro or in vivo investigation.

A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model

Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization.

An algorithm of discovering signatures from DNA databases on a computer cluster

Background

Signatures are short sequences that are unique and not similar to any other sequence in a database that can be used as the basis to identify different species. Even though several signature discovery algorithms have been proposed in the past, these algorithms require the entirety of databases to be loaded in the memory, thus restricting the amount of data that they can process. It makes those algorithms unable to process databases with large amounts of data. Also, those algorithms use sequential models and have slower discovery speeds, meaning that the efficiency can be improved.

Results

In this research, we are debuting the utilization of a divide-and-conquer strategy in signature discovery and have proposed a parallel signature discovery algorithm on a computer cluster. The algorithm applies the divide-and-conquer strategy to solve the problem posed to the existing algorithms where they are unable to process large databases and uses a parallel computing mechanism to effectively improve the efficiency of signature discovery. Even when run with just the memory of regular personal computers, the algorithm can still process large databases such as the human whole-genome EST database which were previously unable to be processed by the existing algorithms.

Conclusions

The algorithm proposed in this research is not limited by the amount of usable memory and can rapidly find signatures in large databases, making it useful in applications such as Next Generation Sequencing and other large database analysis and processing. The implementation of the proposed algorithm is available athttp://www.cs.pu.edu.tw/~fang/DDCSDPrograms/DDCSD.htm.