Results 1 to 10 of about 7,621,216 (146)
Artificial intelligence-aided protein engineering: from topological data analysis to deep protein language models [PDF]
Briefings Bioinform., 2023 Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more.
Yuchi Qiu, Guo-Wei Wei
semanticscholar +1 more source
Machine Learning-Guided Protein Engineering
ACS Catalysis, 2023 Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in ...
Petr Kouba +9 more
semanticscholar +1 more source
Protein Engineering with Lightweight Graph Denoising Neural Networks
bioRxiv, 2023 Protein engineering faces challenges in finding optimal mutants from the massive pool of candidate mutants. In this study, we introduce a deep learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes
Bingxin Zhou +7 more
semanticscholar +1 more source
A human bispecific antibody neutralizes botulinum neurotoxin serotype A
Scientific Reports, 2023 Botulinum neurotoxin (BoNT) shows high lethality and toxicity, marking it as an important biological threat. The only effective post-exposure therapy is botulinum antitoxin; however, such products have great potential for improvement. To prevent or treat
Jiansheng Lu +10 more
doaj +1 more source
Data‐Driven Protein Engineering for Improving Catalytic Activity and Selectivity
ChemBioChem, 2023 Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in
Yu-Fei Ao +5 more
semanticscholar +1 more source
Cell Communication and Signaling, 2023 Fibroblast growth factors (FGFs) and their receptors (FGFRs) constitute complex signaling hubs that are crucial for the development and homeostasis of the human body.
Aleksandra Gedaj +9 more
doaj +1 more source
Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering.
Industrial & Engineering Chemistry Research, 2022 Proteins are Nature's molecular machinery and comprise diverse roles while consisting of chemically similar building blocks. In recent years, protein engineering and design have become important research areas, with many applications in the ...
Jesse Horne, D. Shukla
semanticscholar +1 more source
ECNet is an evolutionary context-integrated deep learning framework for protein engineering
Nature Communications, 2021 Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited ...
Yunan Luo +9 more
semanticscholar +1 more source
FGF12 is a novel component of the nucleolar NOLC1/TCOF1 ribosome biogenesis complex
Cell Communication and Signaling, 2022 Among the FGF proteins, the least characterized superfamily is the group of fibroblast growth factor homologous factors (FHFs). To date, the main role of FHFs has been primarily seen in the modulation of voltage-gated ion channels, but a full picture of ...
Martyna Sochacka +6 more
doaj +1 more source
Molecular Medicine, 2021 Background Antibody drug conjugates (ADCs) represent one of the most promising approaches in the current immuno-oncology research. The precise delivery of cytotoxic drugs to the cancer cells using ADCs specific for tumor-associated antigens enables ...
Marta Poźniak +9 more
doaj +1 more source