With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Lignocellulosic biomass is a most promising feedstock in the production of second-generation biofuels. Efficient degradation of lignocellulosic biomass requires a synergistic action of several cellulases and hemicellulases. Cellulases depolymerize cellulose, the main polymer of the lignocellulosic biomass, to its building blocks. The production of cellulase cocktails has been widely explored, however, there are still some main challenges that enzymes need to overcome in order to develop a sustainable production of bioethanol. The main challenges include low activity, product inhibition, and the need to perform fine-tuning of a cellulase cocktail for each type of biomass. Protein engineering and directed evolution are powerful technologies to improve enzyme properties such as increased activity, decreased product inhibition, increased thermal stability, improved performance in non-conventional media, and pH stability, which will lead to a production of more efficient cocktails. In this review, we focus on recent advances in cellulase cocktail production, its current challenges, protein engineering as an efficient strategy to engineer cellulases, and our view on future prospects in the generation of tailored cellulases for biofuel production.

Expanding synthetic capabilities to routinely employ enzymes in organic solvents (OSs) is a dream for protein engineers and synthetic chemists. Despite significant advances in the field of protein engineering, general and transferable design principles to improve the OS resistance of enzymes are poorly understood. Herein, we report a combined computational and directed evolution study of Bacillus subtlis lipase A (BSLA) in three OSs (i. e., 1,4‐dioxane, dimethyl sulfoxide, 2,2,2‐trifluoroethanol) to devise a rational strategy to guide engineering OS resistant enzymes. Molecular dynamics simulations showed that OSs reduce BSLA activity and resistance in OSs by (i) stripping off essential water molecules from the BLSA surface mainly through H‐bonds binding; and (ii) penetrating the substrate binding cleft leading to inhibition and conformational change. Interestingly, integration of computational results with “BSLA‐SSM” variant library (3439 variants; all natural diversity with amino acid exchange) revealed two complementary rational design strategies: (i) surface charge engineering, and (ii) substrate binding cleft engineering. These strategies are most likely applicable to stabilize other lipases and enzymes and assist experimentalists to design organic solvent resistant enzymes with reduced time and screening effort in lab experiments.