Zhang, Y. J. | Search bioRxiv

bioRxiv2022

LLPSWise - fast and accurate prediction of LLPS constituents

The recent discovery of different types of biological liquid-liquid phase separation (LLPS) systems presents enormous opportunities to uncover underlying biological mechanisms from a single-molecule level to mesoscopic scales and beyond. While progress has been made on the front of computational prediction of LLPS propensity of proteins, the constituent identification of LLPS systems continues to be a pertinent issue that has yet to be addressed. Compiled evidence indicates that the biological mechanism of LLPS involves the employment of different biomolecules into their liquid-liquid separation phase. Since constituent identification relies on experimental approaches, the process is arduous and inefficient. Here, we propose a sequence based LLPS propensity prediction method and scan the human proteome and biochemical pathways to establish a systematic biological view of LLPS. Additionally, we present a fast, accurate method for identifying the constituents of LLPS systems. The source code for our method is available via https://github.com/promethiume/LLPSWise

bioinformatics↗

bioRxiv2021

Deep learning redesign of PETase for practical PET degrading applications

Plastic waste poses an ecological challenge1. While current plastic waste management largely relies on unsustainable, energy-intensive, or even hazardous physicochemical and mechanical processes, enzymatic degradation offers a green and sustainable route for plastic waste recycling2. Poly(ethylene terephthalate) (PET) has been extensively used in packaging and for the manufacture of fabrics and single-used containers, accounting for 12% of global solid waste3. The practical application of PET hydrolases has been hampered by their lack of robustness and the requirement for high processing temperatures. Here, we use a structure-based, deep learning algorithm to engineer an extremely robust and highly active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives, (including a leaf-branch compost cutinase and its mutant4) and possesses enhanced thermostability and pH tolerance. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours at 50 {degrees}C. Finally, we demonstrate two paths for closed-loop PET recycling and valorization. First, we re-synthesize virgin PET from the monomers recovered after enzymatic depolymerization. Second, we enable in situ microbially-enabled valorization using a Pseudomonas strain together with FAST-PETase to degrade PET and utilize the evolved monomers as a carbon source for growth and polyhydroxyalkanoate production. Collectively, our results demonstrate the substantial improvements enabled by deep learning and a viable route for enzymatic plastic recycling at the industrial scale.

bioengineering↗