Author

T. Burnley

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 2 Biochemistry· 1

Signature Assays, Organisms & Methods

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Assay:cryo-EM· 3 Approach:deep learning· 2 Research Focus:protein structure prediction· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Organism:human· 1 Approach:simulation· 1 Research Focus:software / tool· 1

Frequent Co-Authors

A. Anandkumar· 1 shared A. Brace· 1 shared A. Kryshtafovych· 1 shared A. P. Joseph· 1 shared A. Patwardhan· 1 shared A. Perez· 1 shared A. R. Clyde· 1 shared A. Ramanathan· 1 shared

Activity by Year

2020-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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1 shared paperBiophysics

A. Anandkumar

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

A. Trifan, D. Gorgun, Z. Li, A. Brace, M. Zvyagin, H. Ma, A. R. Clyde, D. A. Clark, M. Salim, D. Hardy, T. Burnley, L. Huang, J. McCalpin, M. Emani, H. Yoo, , A. Ramanathan

This study develops an AI-driven multi-resolution simulation workflow to study SARS-CoV-2 RTC dynamics, combining cryo-EM with mesoscale simulations and AI methods.

bioRxiv · Oct 9, 2021 · PreprintBiophysics

Cryo-EM single particle structure refinement and map calculation using Servalcat

K. Yamashita, C. M. Palmer, T. Burnley, G. N. Murshudov

This paper introduces Servalcat, a Python package for refining atomic models and calculating difference maps in cryo-EM single particle analysis.

bioRxiv · May 4, 2021Biophysics

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

C. L. Lawson, A. Kryshtafovych, P. D. Adams, P. Afonine, M. L. Baker, B. A. Barad, P. Bond, T. Burnley, R. Cao, J. Cheng, G. Chojnowski, K. Cowtan, K. A. Dill, F. DiMaio, D. Farrell, , Wan

The paper evaluates cryo-EM model quality using benchmark maps, showing high accuracy and reproducibility across 13 teams and recommending multiple metrics for model assessment.

bioRxiv · Jun 12, 2020Biochemistry