L
Li Mei
Researcher at Pfizer
Publications - 7
Citations - 703
Li Mei is an academic researcher from Pfizer. The author has contributed to research in topics: Medicine & Chemistry. The author has an hindex of 3, co-authored 3 publications receiving 585 citations.
Papers
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Journal ArticleDOI
Precise determination of the diversity of a combinatorial antibody library gives insight into the human immunoglobulin repertoire
Jacob Glanville,Wenwu Zhai,Jan Berka,Dilduz Telman,Gabriella Huerta,Gautam R. Mehta,Irene Ni,Li Mei,Purnima Sundar,Giles Day,David Cox,Arvind Rajpal,Jaume Pons +12 more
TL;DR: A general method for assessing human antibody sequence diversity displayed on phage using massively parallel pyrosequencing, a novel application of Kabat column-labeled profile Hidden Markov Models, and translated complementarity determining region (CDR) capture-recapture analysis is presented.
Journal ArticleDOI
Anti–IL-7 receptor-α reverses established type 1 diabetes in nonobese diabetic mice by modulating effector T-cell function
Li-Fen Lee,Kathryn Logronio,Guang Huan Tu,Wenwu Zhai,Irene Ni,Li Mei,Jeanette Dilley,Jessica Yu,Arvind Rajpal,Colleen Brown,Charles Takeshi Appah,Sherman Michael Chin,Bora Han,Timothy Affolter,John C. Lin +14 more
TL;DR: The durable efficacy and the multipronged tolerogenic mechanisms of IL-7Rα antibody therapy suggest a unique disease-modifying approach to T1D.
Journal ArticleDOI
Synthetic Antibodies Designed on Natural Sequence Landscapes
Wenwu Zhai,Jacob Glanville,Markus Fuhrmann,Li Mei,Irene Ni,Purnima Sundar,Thomas Van Blarcom,Yasmina Noubia Abdiche,Kevin Lindquist,Ralf Strohner,Dilduz Telman,Guido Cappuccilli,William J.J. Finlay,Jan Van den Brulle,David R. Cox,Jaume Pons,Arvind Rajpal +16 more
TL;DR: The obtained library diversity explores a comparable sequence space as the donor-derived natural repertoire and, at the same time, is able to access novel recombined diversity due to lack of segmental linkage.
Journal ArticleDOI
Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level
TL;DR: A general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level and examines the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation.
Journal ArticleDOI
Systematic Modeling, Prediction, and Comparison of Domain–Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?
TL;DR: It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues.