Streamlining Natural Products Biomanufacturing With Omics and Machine Learning Driven Microbial Engineering

Towards consolidated bioprocessing of biomass and plastic substrates for semi-synthetic production of bio-poly(ethylene furanoate) (PEF) polymer using omics-guided construction of artificial microbial consortia

Poly(ethylene furanoate) (PEF) plastic is a 100% renewable polyester that is currently being pursued for commercialization as the next-generation bio-based plastic. This is in line with growing demand for circular bioeconomy and new plastics economy that is aimed at minimizing plastic waste mismanagement and lowering carbon footprint of plastics. However, the current catalytic route for the synthesis of PEF is impeded with technical challenges including high cost of pretreatment and catalyst refurbishment. On the other hand, the semi-biosynthetic route of PEF plastic production is of increased biotechnological interest. In particular, the PEF monomers (Furan dicarboxylic acid and ethylene glycol) can be synthesized via microbial-based biorefinery and purified for subsequent catalyst-mediated polycondensation into PEF. Several bioengineering and bioprocessing issues such as efficient substrate utilization and pathway optimization need to be addressed prior to establishing industrial-scale production of the monomers. This review highlights current advances in semi-biosynthetic production of PEF monomers using consolidated waste biorefinery strategies, with an emphasis on the employment of omics-driven systems biology approaches in enzyme discovery and pathway construction. The roles of microbial protein transporters will be discussed, especially in terms of improving substrate uptake and utilization from lignocellulosic biomass, as well as from depolymerized plastic waste as potential bio-feedstock. The employment of artificial bioengineered microbial consortia will also be highlighted to provide streamlined systems and synthetic biology strategies for bio-based PEF monomer production using both plant biomass and plastic-derived substrates, which are important for circular and new plastics economy advances.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae

Saccharomyces cerevisiae has a long-standing history of biotechnological applications even before the dawn of modern biotechnology. The field is undergoing accelerated advancement with the recent systems and synthetic biology approaches. In this review, we highlight the recent findings in the field with a focus on omics studies of S. cerevisiae to investigate its stress tolerance in different industries. The latest advancements in S. cerevisiae systems and synthetic biology approaches for the development of genome-scale metabolic models (GEMs) and molecular tools such as multiplex Cas9, Cas12a, Cpf1, and Csy4 genome editing tools, modular expression cassette with optimal transcription factors, promoters, and terminator libraries as well as metabolic engineering. Omics data analysis is key to the identification of exploitable native genes/proteins/pathways in S. cerevisiae with the optimization of heterologous pathway implementation and fermentation conditions. Through systems and synthetic biology, various heterologous compound productions that require non-native biosynthetic pathways in a cell factory have been established via different strategies of metabolic engineering integrated with machine learning.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Medicinal Plants: Guests and Hosts in the Heterologous Expression of High-Value Products

Medicinal plants play an important dual role in the context of the heterologous expression of high-value pharmaceutical products. On the one hand, the classical biochemical and modern omics approaches allowed for the discovery of various genes encoding biosynthetic pathways in medicinal plants. Recombinant DNA technology enabled introducing these genes and regulatory elements into host organisms and enhancing the heterologous production of the corresponding secondary metabolites. On the other hand, the transient expression of foreign DNA in plants facilitated the production of numerous proteins of pharmaceutical importance. This review summarizes several success stories of the engineering of plant metabolic pathways in heterologous hosts. Likewise, a few examples of recombinant protein expression in plants for therapeutic purposes are also highlighted. Therefore, the importance of medicinal plants has grown immensely as sources for valuable products of low and high molecular weight. The next step ahead for bioengineering is to achieve more success stories of industrial-scale production of secondary plant metabolites in microbial systems and to fully exploit plant cell factories' commercial potential for recombinant proteins.

Machine learning approaches for biomolecular, biophysical, and biomaterials research

A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.

Microbial pathways for advanced biofuel production

Decarbonisation of the transport sector is essential to mitigate anthropogenic climate change. Microbial metabolisms are already integral to the production of renewable, sustainable fuels and, building on that foundation, are being re-engineered to generate the advanced biofuels that will maintain mobility of people and goods during the energy transition. This review surveys the range of natural and engineered microbial systems for advanced biofuels production and summarises some of the techno-economic challenges associated with their implementation at industrial scales.

Force in numbers: high-throughput screening approaches to unlock microbial transport

Biological membranes are inherently complex, making transport processes in microbial cell factories a significant bottleneck. Lack of knowledge on transport proteins' characteristics and the need for advanced technical equipment often hamper transporter identification and optimization. For these reasons, moving away from individual characterization and towards high-throughput mining, engineering, and screening of transporters is an increasingly attractive approach. Superior transporters can be selected from large libraries by coupling their activity to growth, for substrates that function as feedstocks or toxic compounds. Other compounds can be screened thanks to recent advances in the design and deployment of synthetic genetic circuits (biosensors). Furthermore, novel strategies are rapidly increasing the repertoire of biomolecule transporters susceptible to high-throughput selection methods.

Forty Years of Directed Evolution and its Continuously Evolving Technology Toolbox - A Review of the Patent Landscape

Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for decades. Rational design and directed evolution are two major pathways to achieve the desired ends. Whilst rational protein design approach has made substantial progress, the idea of using a method based on cycles of mutagenesis and natural selection to develop novel binding proteins, enzymes and structures has attracted great attention. Laboratory evolution of proteins/enzymes requires new tools and analytical approaches to create genetic diversity and identifying variants with desired traits. In this pursuit, construction of sufficiently large libraries of target molecules to search for improved variants and the need for new protocols to alter the properties of target molecules has been a continuing challenge in the directed evolution experiments. This review will discuss the in vivo and in vitro gene diversification tools, library screening or selection approaches, and artificial intelligence/machine-learning-based strategies to mutagenesis developed in the last forty years to accelerate the natural process of evolution in creating new functional protein variants, optimization of microbial strains and transformation of enzymes into industrial machines. Analyzing patent position over these techniques and mechanisms also constitutes an integral and distinctive part of this review. The aim is to provide an up-to-date resource/technology toolbox for research-based and pharmaceutical companies to discover the boundaries of competitor's intellectual property (IP) portfolio, their freedom-to-operate in the relevant IP landscape, and the need for patent due diligence analysis to rule out whether use of a particular patented mutagenesis method, library screening/selection technique falls outside the safe harbor of experimental use exemption. While so doing, we have referred to some recent cases that emphasize the significance of selecting a suitable gene diversification strategy in directed evolution experiments. This article is protected by copyright. All rights reserved.

Bioproduction process of natural products and biopharmaceuticals: Biotechnological aspects

Decades of research have been put in place for developing sustainable routes of bioproduction of high commercial value natural products (NPs) on the global market. In the last few years alone, we have witnessed significant advances in the biotechnological production of NPs. The development of new methodologies has resulted in a better understanding of the metabolic flux within the organisms, which have driven manipulations to improve production of the target product. This was further realised due to the recent advances in the omics technologies such as genomics, transcriptomics, proteomics, metabolomics and secretomics, as well as systems and synthetic biology. Additionally, the combined application of novel engineering strategies has made possible avenues for enhancing the yield of these products in an efficient and economical way. Invention of high-throughput technologies such as next generation sequencing (NGS) and toolkits for genome editing Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated 9 (CRISPR/Cas9) have been the game changers and provided unprecedented opportunities to generate rationally designed synthetic circuits which can produce complex molecules. This review covers recent advances in the engineering of various hosts for the production of bioactive NPs and biopharmaceuticals. It also highlights general approaches and strategies to improve their biosynthesis with higher yields in a perspective of plants and microbes (bacteria, yeast and filamentous fungi). Although there are numerous reviews covering this topic on a selected species at a time, our approach herein is to give a comprehensive understanding about state-of-art technologies in different platforms of organisms.