The prolonged conventional approaches of drug screening and vaccine designing prerequisite patience, vigorous effort, outrageous cost as well as additional manpower. Screening and experimentally validating thousands of molecules for a specific therapeutic property never proved to be an easy task. Similarly, traditional way of vaccination includes administration of either whole or attenuated pathogen, which raises toxicity and safety issues. Emergence of sequencing and recombinant DNA technology led to the epitope-based advanced vaccination concept, i.e., small peptides (epitope) can stimulate specific immune response. Advent of bioinformatics proved to be an adjunct in vaccine and drug designing. Genomic study of pathogens aid to identify and analyze the protective epitope. A number of in silico tools have been developed to design immunotherapy as well as peptide-based drugs in the last two decades. These tools proved to be a catalyst in drug and vaccine designing. This review solicits therapeutic peptide databases as well as in silico tools developed for designing peptide-based vaccine and drugs.
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http://dx.doi.org/10.1016/bs.apcsb.2018.01.006 | DOI Listing |
Front Cell Infect Microbiol
January 2025
Department of Haematology and Oncology, Shenzhen Children's Hospital, Shenzhen, China.
Background: Methicillin-resistant (MRSA) poses a significant challenge in clinical environments due to its resistance to standard antibiotics. Protein A (SpA), a crucial virulence factor of MRSA, undermines host immune responses, making it an attractive target for vaccine development. This study aimed to identify potential epitopes within SpA that could elicit robust immune responses, ultimately contributing to the combat against multidrug-resistant (MDR) MRSA.
View Article and Find Full Text PDFObjectives: To analyse and compare the functionality of extraluminal and intraluminal artificial urinary sphincters (AUSs), an in silico procedure has been defined and applied. Design and reliability assessments of the AUS are typically performed using a clinical approach, which does not provide data on mechanical stimulation of urethral tissues. Mechanical stimulation may determine tissue degeneration, such as urethral atrophy or erosion, the main causes of AUS failure.
View Article and Find Full Text PDFPlant Signal Behav
December 2025
Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco.
Various metabolic and cell signaling processes impact the functions of sugarcane plant cells. MicroRNAs (miRNAs) play critical regulatory roles in enhancing yield and providing protection against various stressors. This study seeks to identify and partially characterize several novel miRNAs in sugarcane using tools, while also offering a preliminary assessment of their functions.
View Article and Find Full Text PDFAsian Pac J Cancer Prev
January 2025
Parul Institute of Applied Sciences, Parul University, Vadodara, India.
Background: Breast cancer remains a significant global health challenge, requiring innovative therapeutic strategies. In silico methods, which leverage computational tools, offer a promising pathway for vaccine development. These methods facilitate antigen identification, epitope prediction, immune response modelling, and vaccine optimization, accelerating the design process.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
NovaMechanics Ltd, Nicosia 1070, Cyprus.
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced models, approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems.
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