Design, Screening, and Testing of Non-Rational Peptide Libraries with Antimicrobial Activity: In Silico and Experimental Approaches

Abstract

One of the challenges of modern biotechnology is to find new routes to mitigate the resistance to conventional antibiotics. Antimicrobial peptides (AMPs) are an alternative type of biomolecules, naturally present in a wide variety of organisms, with the capacity to overcome the current microorganism resistance threat. Here, we reviewed our recent efforts to develop a new library of non-rationally produced AMPs that relies on bacterial genome inherent diversity and compared it with rationally designed libraries. Our approach is based on a four-stage workflow process that incorporates the interplay of recent developments in four major emerging technologies: artificial intelligence, molecular dynamics, surface-display in microorganisms, and microfluidics. Implementing this framework is challenging because to obtain reliable results, the in silico algorithms to search for candidate AMPs need to overcome issues of the state-of-the-art approaches that limit the possibilities for multi-space data distribution analyses in extremely large databases. We expect to tackle this challenge by using a recently developed classification algorithm based on deep learning models that rely on convolutional layers and gated recurrent units. This will be complemented by carefully tailored molecular dynamics simulations to elucidate specific interactions with lipid bilayers. Candidate AMPs will be recombinantly-expressed on the surface of microorganisms for further screening via different droplet-based microfluidic-based strategies to identify AMPs with the desired lytic abilities. We believe that the proposed approach opens opportunities for searching and screening bioactive peptides for other applications.

Gelatin-Graphene Oxide Nanocomposite Hydrogels for Kluyveromyces lactis Encapsulation: Potential Applications in Probiotics and Bioreactor Packings

“…The widespread and sometimes misguided use of antibiotics in a large portion of the population has led to the increasing emergence of pathogens with a worrisome resistance to such molecules [36,37]. In contrast, other microorganisms play a fundamental role in developing and strengthening the immune system against such pathogens.…”

Understanding In-Silico Approaches to Design Synthetic Antifungal Peptides to Combat Fungal Infections like Mucormycosis

“…In VAEs multiple molecular properties are imposed on a latent space in which these molecules are expressed as real vectors [171], it takes the input data which is the basis of continuous latent space. The addition of two known AMPs helps in generating novel alternatives which represent a progression between the two already known peptides [172]. While in GANs, the input peptides are subjected to distribution and sampling to form a new set of novel peptides using predominantly two machine learning networks namely, generator and discriminator.…”

Microfluidics for Multiphase Mixing and Liposomal Encapsulation of Nanobioconjugates: Passive vs. Acoustic Systems

“…In this context, microfluidic systems have permitted the high reproducibility of different processes involving the precise encapsulation of bioactive compounds with unique properties for the potential treatment of several conditions ranging from cancer to neurodegeneration. Some include mixing and interaction of liquid phases, sorting of particles, high-throughput screening, and novel drug delivery systems [2][3][4]. The growing interest in microfluidic systems can be attributed to the low volume samples, the size of the devices, and the ease of use, allowing a reduction in costs and fast prototyping and testing [5,6].…”