Margaret E Ackerman

Oct 18, 2024 | bostonglobe.com | Margaret E Ackerman

When I became a geriatric nurse practitioner, Pop, who had always taken an interest in my work, became my adviser on how to talk to elderly people. “You’re old, right?” I’d begin a conversation, in our flippant style of communicating. I was writing my doctoral thesis on end-of-life conversations with the elderly, and I had asked Pop for tips.

Aug 21, 2024 | onlinelibrary.wiley.com | Katherine McCoy |Margaret E Ackerman |Gevorg Grigoryan

Supporting Information Filename Description pro5127-sup-0001-supinfo.docxWord 2007 document , 2.5 MB Data S1. Supporting Information. REFERENCES , , , , , . ImmuneBuilder: deep-learning models for predicting the structures of immune proteins. Commun Biol. 2023; 6(1): 1–8. https://doi.org/10.1038/s42003-023-04927-7 , , , , , . Efficient and accurate prediction of protein structure using RoseTTAFold2. BioRxiv. 2023;2023.05.24.542179.

Jun 28, 2024 | biorxiv.org | Katherine McCoy |Margaret E Ackerman |Gevorg Grigoryan

AbstractThe ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML).

Mar 17, 2024 | biorxiv.org | Katherine McCoy |Margaret E Ackerman |Gevorg Grigoryan

AbstractThe ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML).

Dec 11, 2023 | onlinelibrary.wiley.com | Katherine McCoy |Margaret E Ackerman |Gevorg Grigoryan

Supporting Information Filename Description pro4853-sup-0001-Supinfo.pdfPDF document, 735.5 KB Figure S1. Visual inspection of docking randomness. One alanine residue was docked randomly upon a sphere of randomly generated atoms, unbiased (left) and biased (right). The biased dockings were required to be at an angle of 30° or less to a randomly selected patch on the sphere. Figure S2. Grid search for best step size standard deviation and number of clashes allowed for each random dock.