Author
David Bryant Sauer
University of OxfordORCID 0000-0001-9291-4640Unclaimed ORCID profile · follow to get notified of new preprints
Biology Disciplines
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2019-2026 · peak 2/yrCo-Author Collaboration Network
10 top collaborators · 18 mutual co-author links7 papers
Recent·Most cited
- BiophysicsStructurePeer-reviewed
Structures of the human sodium-citrate cotransporter NaCT with and without substrates
David Bryant Sauer, Jinmei Song, Jennifer J. Marden…·Da‐Neng Wang
This study determines the structures of the human NaCT protein in three states to elucidate the mechanism of sodium-substrate coupling in the DASS family.
- Synthetic BiologyNature Chemical BiologyPeer-reviewed
Di-Gluebodies as covalently-rigidified, modular protein assemblies enable simultaneous determination of high-resolution, low-size, cryo-EM structures
Gangshun Yi, Dimitrios Mamalis, Mingda Ye…·Robert J.C. Gilbert
The paper introduces Di-Gluebodies, covalently-dimerized nanobodies, to improve cryo-EM structure determination of small proteins by providing rigidity and modularity.
12 citations - Systems BiologyMolecular Systems BiologyPeer-reviewed
Metabolic mapping of the human solute carrier superfamily
Tabea Wiedmer, Shao Thing Teoh, Eirini Christodoulaki…·Giulio Superti‐Furga
The paper systematically evaluates the metabolic and transcriptional effects of SLC superfamily members, identifying new substrates and functional groups.
20 citationstop 2% - NeurosciencebioRxivPreprint
Structures of the human adult muscle-type nicotinic receptor in resting and desensitised states
Anna Li, Ashley C. W. Pike, Richard G. Webster…·Yin Yao Dong
The paper presents structures of human adult muscle-type nicotinic receptors in resting and desensitised states and examines the effects of genetic variants on channel function.
DOI 10.1101/2024.12.13.624530 - BioinformaticsbioRxivPreprint
Using machine learning to predict quantitative phenotypes from protein and nucleic acid sequences
David Bryant Sauer, Da-Neng Wang
The paper uses machine learning to predict quantitative phenotypes like optimal growth temperature, pH, and oxygen tolerance from protein and nucleic acid sequences.
- BioinformaticsBioinformaticsPeer-reviewed
Prediction of Optimal Growth Temperature using only Genome Derived Features
David Bryant Sauer, Da‐Neng Wang
This paper predicts the optimal growth temperature of prokaryotes using only genomic sequences with high accuracy.
100 citations