Tootfinder

lmao.
"who's going to try [the experimental body horror enzyme]? some intern?" "yes!"
#monsterdon #XtheManWithTheXrayEyes

Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
Yanzi Zhang, Felix Moorhoff, Sizhe Qiu, Wenjuan Dong, David Medina-Ortiz, Jing Zhao, Mehdi D. Davari
https://arxiv.org/abs/2507.07666

Machine Learning-Driven Enzyme Mining: Opportunities, Challenges, and Future Perspectives
Enzyme mining is rapidly evolving as a data-driven strategy to identify biocatalysts with tailored functions from the vast landscape of uncharacterized proteins. The integration of machine learning into these workflows enables high-throughput prediction of enzyme functions, including Enzyme Commission numbers, Gene Ontology terms, substrate specificity, and key catalytic properties such as kinetic parameters, optimal temperature, pH, solubility, and thermophilicity. This review provides a syste…

“Astounding” Results: Blocking One Enzyme Brings Parkinson’s-Damaged Cells Back to Life
https://scitechdaily.com/astounding-results-blocking-one-enzyme-brings-parkinsons-damaged-cells-back-to-life/

"Plastic-Recycling Enzyme Discovered in Compost Heap Close to Commercialization Through German Startup"
#Plastic #Plastics #Germany

Plastic-Recycling Enzyme Discovered in Compost Heap Close to Commercialization Through German Startup
If successful, their 2030 plan will be four 350 cubic-meter reactors capable of processing 45,000 metric tons of PET plastic every year.

Enzyme-based plastics recycling at an industrial scale could be cost-effective, analysis finds
https://phys.org/news/2025-06-enzyme-based-plastics-recycling-industrial.html

OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
Zhiwei Nie, Hongyu Zhang, Hao Jiang, Yutian Liu, Xiansong Huang, Fan Xu, Jie Fu, Zhixiang Ren, Yonghong Tian, Wen-Bin Zhang, Jie Chen
https://arxiv.org/abs/2506.17963

OmniESI: A unified framework for enzyme-substrate interaction prediction with progressive conditional deep learning
Understanding and modeling enzyme-substrate interactions is crucial for catalytic mechanism research, enzyme engineering, and metabolic engineering. Although a large number of predictive methods have emerged, they do not incorporate prior knowledge of enzyme catalysis to rationally modulate general protein-molecule features that are misaligned with catalytic patterns. To address this issue, we introduce a two-stage progressive framework, OmniESI, for enzyme-substrate interaction prediction thro…

Thermodynamics of multiple Maxwell demons
Sandipan Dutta
https://arxiv.org/abs/2506.12159 https://arxiv.org/pdf/2506.12159

Thermodynamics of multiple Maxwell demons
In many assembly line processes like metabolic and signaling networks in biological systems, the products of the first enzyme is the reactant for the next enzyme in the network. Working of multiple machines leads to efficient utilization of resources. Motivated by this, we investigate if multiple Maxwell demons lead to more efficient information processing. We study the phase space of multiple demons acting on an information tape based on the model of Mandal and Jarzynski [1, 2]. Their model is…

Targeting Melanoma-Specific Tyrosinase: Cyclic Peptide Disrupts Actin Dynamics for Precision Apoptosis Induction
Ruoyang Zhao, Xiaowei Wang, Jiajia Hu, Qingqing Sun, Xinmin Zhao, Jun Guo, Feng Zhang, Min Wu
https://arxiv.org/abs/2507.06534

Targeting Melanoma-Specific Tyrosinase: Cyclic Peptide Disrupts Actin Dynamics for Precision Apoptosis Induction
Melanoma is an aggressive and highly metastatic cancer that exhibits stubborn resistance to conventional therapies, highlighting the need for novel treatments. Existing therapeutic strategies often suffer from systemic toxicity, poor efficacy and fast-gained drug resistance. In this study, we designed a cyclic peptide system (c-RGDKYQ) that takes the advantage of the overexpression of tyrosinase in melanoma cells to trigger enzyme-mediated oxidation and self-assembly. The assembled peptide nano…