Review on bibliography related to antimicrobials

In this report, a bibliographic research has been done in the field of antimicrobials.In this report, a bibliographic research has been done in the field of antimicrobials. Not all antimicrobials have been included, but those that are being subject of matter in the group GBMI in Terrassa, and others of interest. It includes chitosan and other biopolymers. The effect of nanoparticles is of great interest, and in this sense, the effect of Ag nanoparticles and antibiotic nanoparticles (nanobiotics) has been revised. The report focuses on new publications and the antimicrobial effect of peptides has been considered. In particular, the influence of antimicrobials on membranes has deserved much attention and its study using the Langmuir technique, which is of great utility on biomimetic studies. The building up of antimicrobials systems with new techniques (bottom-up approach), as the Layer-by-Layer technique, can also be found in between the bibliography. It has also been considered the antibiofilm effect, and the new ideas on quorem sensing and quorum quenching.Preprin

Foldamers in Medicinal Chemistry

International audienc

Use of Peptide Libraries for Identification and Optimization of Novel Antimicrobial Peptides.

The increasing rates of resistance among bacteria and to a lesser extent fungi have resulted in an urgent need to find new molecules that hold therapeutic promise against multidrug-resistant strains. Antimicrobial peptides have proven very effective against a variety of multidrug-resistant bacteria. Additionally, the low levels of resistance reported towards these molecules are an attractive feature for antimicrobial drug development. Here we summarise information on diverse peptide libraries used to discover or to optimize antimicrobial peptides. Chemical synthesized peptide libraries, for example split and mix method, tea bag method, multi-pin method and cellulose spot method are discussed. In addition biological peptide library screening methods are summarized, like phage display, bacterial display, mRNA-display and ribosomal display. A few examples are given for small peptide libraries, which almost exclusively follow a rational design of peptides of interest rather than a combinatorial approach

APPLICATION OF MACHINE LEARNING APPROACHES TO EMPOWER DRUG DEVELOPMENT

Human health, one of the major topics in Life Science, is facing intensified challenges, including cancer, pandemic outbreaks, and antimicrobial resistance. Thus, new medicines with unique advantages, including peptide-based vaccines and permeable small molecule antimicrobials, are in urgent need. However, the drug development process is long, complex, and risky with no guarantee of success. Also, the improvements in techniques applied in genomics, proteomics, computational biology, and clinical trials significantly increase the data complexity and volume, which imposes higher requirements on the drug development pipeline. In recent years, machine learning (ML) methods were employed to support drug development in various aspects and were shown to be highly effective. Here, we explored the application of advanced ML approaches to empower the development of peptide-based vaccines and permeable antimicrobials. First, the peptide-based vaccines targeting pancreatic cancer and COVID-19 were predicted and screened via multiple approaches. Next, novel structure-based methods to improve the performance of peptide: MHC binding affinity prediction were developed, including an HLA modeling pipeline that provides structures for docking-based peptide binder validation, and hierarchical clustering of HLA I into supertypes and subtypes that have similar peptide binding specificity. Finally, the physicochemical properties governing the permeability of small molecules into multidrug-resistant Pseudomonas aeruginosa cells were selected using a random forest model. In conclusion, the use of machine learning methods could accelerate the drug development process at a lower cost and promote data-based decision-making if used properly

Neoteric advancement in TB drugs and an overview on the anti-tubercular role of peptides through computational approaches

Peptides of varied origins such as human immune cells and non-immune cells, bacteria, fungi, and venoms have been widely investigated as anti-tubercular agents for the replacement of existing anti-tubercular drugs in future. In the present review, we spotlighted not only on the mechanisms of action and mode of administration of currently available anti-tubercular drugs but also the recent comprehensive report of World Health Organization (WHO) on TB epidemic, diagnosis, prevention, and treatment. The major excerpt of the study also inspects the direct contribution of different computational tools during drug designing strategies against M. tuberculosis in order to grasp the interplay between anti-tubercular peptides and targeted bacterial protein. The potentiality of some of these anti-tubercular peptides as therapeutic agents unlocks a new portal for achieving the goal of end TB strategy.Tuberculosis (TB) is a devastating threat to human health whose treatment without the emergence of drug resistant Mycobacterium tuberculosis (M. tuberculosis) is the million-dollar question at present. The pathogenesis of M. tuberculosis has been extensively studied which represents unique defence strategies by infecting macrophages. Several anti-tubercular drugs with varied mode of action and administration from diversified sources have been used for the treatment of TB that later contributed to the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). However, few of potent anti-tubercular drugs are scheduled for clinical trials status in 2017–2018