I aimed to participate in the upcoming Hong Kong Laureate Forum to share my research and discuss science with other young scientific leaders.
I have excelled in my doctorate. I leveraged bioscience knowledge and computational methods in the interrogation of human genomic variants. By applying machine learning, I have participated in five research projects to annotate variants with unknown clinical significance to target cancer and Alzheimer’s Disease. This not only allowed me to obtain three first-author manuscripts published in Briefings in Bioinformatics and Protein Science, but provided me opportunities to engage in collaboration with clinicians across Australia, driving protein structure methodology for clinical translation. I want to explore more international collaborations in Hong Kong through this forum to seek a better solution on complex genetic diseases.
I have huge enthusiasm for science communications. In my PhD, I have presented over 15 oral and poster presentations in various conferences. I believe my interdisciplinary skill sets make me a good fit for this forum to share insights with Astronomy scientists and Mathematicians. I developed a friendly and supportive culture within the group. This has helped me to take on leadership on research supervision of four students’ projects, promoting science education for next-generation.

Qisheng2025_BostjanKobe.pdf

Enzyme engineering offers a multitude of modifications and improvements on enzyme physico-chemical properties, which brings huge benefits to the food and pharmaceutical industry. My methodology on enzyme engineering and design can be discussed horizontally and vertically.
In a horizontal way, I characterise different effects of missense mutation on enzyme structures. Changes in protein can have dramatic effects on how proteins fold, their stability and dynamics. I systematically evaluated methods to quantify the change of protein biophysical properties upon mutations, such as protein stability and enzyme-substrate binding affinity. Further to that, I leveraged these tools to study the pathogenic genetic variants in human Aldehyde Dehydrogenase (ALDH). I found that cancer-related mutations have strong association with the ALDH conservation, while other disease-causing mutations significantly destabilised ALDH structures. My work provides new drug design strategies to develop small-molecule stabilisers to recover protein function and target cancers.
From a vertical perspective, I incorporated evolutional knowledge and tried to reconstruct the ancestral state of extant enzymes. I used protein representations from language models to cluster similar protein domains, followed by the inference of ancestral sequences using generative AI. The ancestral enzymes usually have enhanced properties, such as higher thermostability, making them favourable for industrial uses.