The limitations of the current protein classification tools in identifying lipolytic features in putative bacterial lipase sequences

Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics

Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.

Production of octyl butyrate using psychrophilic mutant lipase from Croceibacter atlanticus LipCA lipase developed by a molecular evolution technique

Lipases are used to synthesize a variety of industrially useful compounds. Among them, psychrophilic lipase can be used to synthesize thermo-labile compounds at low temperatures. In this study, random mutagenesis was introduced into Antarctic Croceibacter atlanticus lipase gene using error-prone PCR, resulting in changes in its protein sequence. Through two rounds of mutagenesis and screening, we found that a mutant R1 showed an enhanced activity at low temperatures. Mutant R1 had five mutations (F43L, S48G, S49G, D141K, and K297R) and higher kcat/KM value than the wild type (WT) at 10 °C. We immobilized this enzyme on methacrylate divinylbenzene resin and used it to synthesize octyl butyrate, a flavor compound. The esterification reaction proceeded even at 10 °C. Mutant R1 synthesized the ester compound faster than the WT. To determine which amino acids were responsible for the increase of activity, site-directed mutagenesis was performed to introduce five back mutations into mutant R1. Three back mutants (L43F, G48S, G49S) showed significant decreases of activity at low temperatures, indicating that these amino acids were closely related to the increase in activity. This psychrophilic mutant R1 is expected to be used in low-temperature enzyme conversion reactions in the food industry.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

The ESTHER database on alpha/beta hydrolase fold proteins - An overview of recent developments

The ESTHER database, dedicated to ESTerases and alpha/beta-Hydrolase Enzymes and Relatives (https://bioweb.supagro.inra.fr/ESTHER/general?what=index), offers online access to a continuously updated, sequence-based classification of proteins harboring the alpha/beta hydrolase fold into families and subfamilies. In particular, the database proposes links to the sequences, structures, ligands and huge diversity of functions of these proteins, and to the related literature and other databases. Taking advantage of the promiscuity of enzymatic function, many engineered esterases, lipases, epoxide-hydrolases, haloalkane dehalogenases are used for biotechnological applications. Finding means for detoxifying those protein members that are targeted by insecticides, herbicides, antibiotics, or for reactivating human cholinesterases when inhibited by nerve gas, are still active areas of research. Using or improving the capacity of some enzymes to breakdown plastics with the aim to recycle valuable material and reduce waste is an emerging challenge. Most hydrolases in the superfamily are water-soluble and act on or are inhibited by small organic compounds, yet in a few subfamilies some members interact with other, unrelated proteins to modulate activity or trigger functional partnerships. Recent development in 3D structure prediction brought by AI-based programs now permits analysis of enzymatic mechanisms for a variety of hydrolases with no experimental 3D structure available. Finally, mutations in as many as 34 of the 120 human genes compiled in the database are now linked to genetic diseases, a feature fueling research on early detection, metabolic pathways, pharmacological treatment or enzyme replacement therapy. Here we review those developments in the database that took place over the latest decade and discuss potential new applications and recent and future expected research in the field.

Computer-Aided Lipase Engineering for Improving Their Stability and Activity in the Food Industry: State of the Art

As some of the most widely used biocatalysts, lipases have exhibited extreme advantages in many processes, such as esterification, amidation, and transesterification reactions, which causes them to be widely used in food industrial production. However, natural lipases have drawbacks in terms of organic solvent resistance, thermostability, selectivity, etc., which limits some of their applications in the field of foods. In this systematic review, the application of lipases in various food processes was summarized. Moreover, the general structure of lipases is discussed in-depth, and the engineering strategies that can be used in lipase engineering are also summarized. The protocols of some classical methods are compared and discussed, which can provide some information about how to choose methods of lipase engineering. Thermostability engineering and solvent tolerance engineering are highlighted in this review, and the basic principles for improving thermostability and solvent tolerance are summarized. In the future, comput er-aided technology should be more emphasized in the investigation of the mechanisms of reactions catalyzed by lipases and guide the engineering of lipases. The engineering of lipase tunnels to improve the diffusion of substrates is also a promising prospect for further enhanced lipase activity and selectivity.