Protein design
Dotaz
Zobrazit nápovědu
elektronický časopis
- Konspekt
- Biotechnologie. Genetické inženýrství
- NLK Obory
- biomedicínské inženýrství
- biochemie
- NLK Publikační typ
- elektronické časopisy
elektronický časopis
- Konspekt
- Biotechnologie. Genetické inženýrství
- NLK Obory
- biomedicínské inženýrství
- NLK Publikační typ
- elektronické časopisy
... Contents -- Preface xi -- 1 Introduction to Protein Engineering 1 -- Jeffrey L. Cleland, Andrew J. ... ... Craik -- 2 Protein Conformation 33 -- Fred E. Cohen and David P. ... ... Hearst -- 3 Predicting the Conformation of Proteins from Sequence Data 71 -- Steven A. ... ... on Protein Folding: Methodology, Application, and Interpretation 249 -- Mark R. ... ... Structure-Function Relationships for Protein Design 317 -- Craig S. ...
x, 518 s. : il.
ix, 682 s. : il., tab.
- Klíčová slova
- Klonování, Proteiny,
- MeSH
- klonování DNA MeSH
- molekulární biologie MeSH
- proteiny MeSH
- vazba proteinů fyziologie MeSH
- Publikační typ
- laboratorní příručky MeSH
243 s.
- Klíčová slova
- Laboratorní technika, Proteiny,
- MeSH
- klinické laboratorní techniky MeSH
- proteiny MeSH
- western blotting MeSH
- Publikační typ
- monografie MeSH
- Konspekt
- Biochemie. Molekulární biologie. Biofyzika
- NLK Obory
- biochemie
- MeSH
- alosterická regulace * MeSH
- alosterické místo * MeSH
- benzodiazepiny farmakokinetika farmakologie MeSH
- farmaceutická chemie MeSH
- farmakokinetika MeSH
- kalcimimetika farmakokinetika farmakologie MeSH
- lékové transportní systémy MeSH
- lidé MeSH
- neurotransmiterové látky farmakokinetika farmakologie MeSH
- racionální návrh léčiv MeSH
- receptory GABA-A * fyziologie MeSH
- receptory spřažené s G-proteiny fyziologie MeSH
- sirtuin 1 farmakokinetika farmakologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- práce podpořená grantem MeSH
Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol, that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.
[1st ed.] xi, 362 s. : il.
