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Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on datadriven strategies. However, the lack of interpretability and transparency in these models limits their trustworthiness and applicability in real-world scenarios. Explainable Artificial Intelligence addresses these challenges by providing insights into the decision-making processes of machine learning models, enhancing their reliability and interpretability. Explainable strategies has been successfully applied in various biotechnology fields, including drug discovery, genomics, and medicine, yet its application in protein engineering remains underexplored. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. This perspective work explores the principles and methodologies of explainable artificial intelligence, highlighting its relevance in biotechnology and its potential to enhance protein design. Additionally, three theoretical pipelines integrating predictive models with explainable strategies are proposed, focusing on their advantages, disadvantages, and technical requirements. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed.
Publications
Hornstedtia scyphifera (J.Koenig) Steud. represents a lesser-known member of the ginger family (Zingiberaceae) that is used in Malaysia as spice and traditional medicine. The phytochemical investigation of leaves from this species utilizing diverse analytical methods has provided comprehensive insights into its chemical profile for the first time. Headspace gas chromatography-mass spectrometry (HS-GCMS) and GCMS analyses of essential oil and nonpolar extracts verified α-pinene, camphene, p-cymene, and camphor as main volatile compounds. Metabolite profiling of the crude extract by ultra-high-performance-liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) unveiled terpenoids, flavonoids and other phenolics as major compound classes. Isolation and follow-up structure elucidation, involving 1D and 2D NMR, HRMS, UV and CD analysis, yielded two new sesquiterpenoids, (1R,5S,6S,7R,10R)-mustak-14-oic acid (1) and (1R,6S,7S,10R)-6-hydroxy-anhuienosol (2), along with 24 known compounds (seven terpenoids, seven flavonoids, ten phenolics), 21 of these never reported for H. scyphifera. Additionally, the crude extract and fractions from the purification process were screened for antibacterial and antifungal activity. This is supplemented by an extensive literature research for described bioactivities of all isolated compounds. Our results support and explain previously detected antimicrobial, antifungal and neuroprotective effects of H. scyphifera extracts and provide evidence for its potential pharmacological importance.