CHEMICAL BIOTECHNOLOGY AND BIOCATALYSIS

Chemical Biotechnology and Biocatalysis (CBB) division focuses on advancing synthetic biology and biocatalysis to drive sustainable chemicals manufacturing and bioproducts development. Aligned with ISCE2’s mission and the national sustainability strategy, we aim to transform the chemicals value chain by reducing reliance on crude oil through innovative bioconversion of low-carbon feedstocks into value-added products. We are committed to developing solutions for the mid to long-term transformation of the energy and chemicals (E&C) sectors, leveraging our key competencies in biocatalysis, digital enablers (informatics, data, AI/ML), and chemical biotechnology.

Biocatalysis is the use of natural catalysts, such as enzymes, to perform chemical transformations, offering a sustainable alternative to traditional chemical processes. It provides several advantages, including mild reaction conditions, high selectivity, and reduced environmental impact. Our biocatalysis research focuses on harnessing these benefits to develop enzyme-based solutions tailored to the energy and chemicals (E&C) sectors. By combining computational and experimental approaches, we accelerate enzyme discovery and optimization, enabling efficient, scalable, and eco-friendly chemical production. This positions us to contribute to the long-term sustainability and transformation of the E&C industries.

Biotechnology uses biological systems, such as microorganisms, to create sustainable solutions for chemical production and other industrial processes. Our biotechnology research focuses on developing new value chains by transforming next-generation feedstocks into valuable chemicals through advanced bioconversion processes. This approach reduces reliance on fossil resources and aligns with Singapore’s commitment to building a future bioeconomy. By integrating cutting-edge synthetic biology tools with sustainable bioprocesses, we aim to create scalable, efficient, and environmentally friendly alternatives to conventional chemical manufacturing.

Our Digital Enablers research focuses on utilizing advanced computational tools and AI to accelerate the development of synthetic biology and bioproducts. We are building a multi-modal, chem/bio-informatics platform that integrates chemical data with machine learning algorithms to unlock the value of Singapore’s microbial biodiversity. This platform enables rapid identification, prediction, and optimization of bio-product design and development. By leveraging protein language models (PLMs), we streamline the search and retrieval of protein sequences linked to biosynthetic gene clusters, allowing for targeted experimental validation and efficient enzyme generation, ultimately driving innovation in sustainable biomanufacturing.

We have developed an integrative computational/experimental workflow1 to create predictive enzyme models that have reduced experimental effort by >100-fold and built a panel of galactose oxidase enzymes2 with better profiles and activities toward an expanded scope of bulky benzylic secondary alcohols, increasing their industrial applicability.

We broaden the scope and enhance precision of two unique standalone halogenases: PrnC and RadH, to expand their potential in medicinal chemistry for optimizing bioactivity and potency by integrating computational and experimental approaches.

Expression and engineering of new PET degrading enzymes from Microbispora, Nonomuraea, and Micromonospora

We have uncovered and characterized a new thermostable PETase amongst four representative sequences (SIBER 1 – 4) in the genera of Microbispora, Nonomuraea, and Micromonospora. Engineered version of SIBER 1 has more than doubled its activity against lcPET. The objective of this study is to enhance our understanding of this group of enzymes by identifying and characterizing novel enzymes that can facilitate the breakdown of PET waste. This data will expand the enzymatic repertoire and provide valuable insights into the prerequisites for successful PET degradation.

Natural products possess immense structural diversity and are a valuable resource for various applications such as specialty chemicals, food additives and therapeutics. Tremendous effort must be invested for the biosynthesis, curation and characterization of natural product libraries, resulting in only approximately 400K fully characterized natural products known to-date.1 Following our developed in silico pipeline (Figure 1), we have generated an expansive, curated database of >68 million natural product-like molecules2 representing a 167-fold expansion from the roughly 400K known natural products. This generated natural product-like database covering broad chemical space may serve as starting points for high throughput in silico screening for functional natural products.