Author

K. M. Dalton

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· 4

Signature Assays, Organisms & Methods

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Approach:experimental assay· 3 Assay:structural biology· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Organism:E. coli· 1 Research Focus:protein folding & dynamics· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

D. R. Hekstra· 4 shared J. B. Greisman· 3 shared C. J. Sheehan· 2 shared M. A. Klureza· 2 shared D. E. Brookner· 1 shared I.-S. Kim· 1 shared J. Greisman· 1 shared R. W. Henning· 1 shared

Activity by Year

2021-2023 · peak 3/yr

Co-Author Collaboration Network

9 top collaborators · 29 mutual co-author links

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4 shared papersBiophysics

D. R. Hekstra

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Resolving conformational changes that mediate a two-step catalytic mechanism in a model enzyme

J. B. Greisman, K. M. Dalton, D. E. Brookner, M. A. Klureza, C. J. Sheehan, I.-S. Kim, R. W. Henning, S. Russi, D. R. Hekstra

This study identifies conformational changes in DHFR that mediate its two-step catalytic mechanism through experimental perturbations and X-ray diffraction.

bioRxiv · Jun 2, 2023Biophysics

Native SAD Phasing at Room Temperature

J. B. Greisman, K. M. Dalton, C. J. Sheehan, M. A. Klureza, D. R. Hekstra

This paper introduces a room-temperature data collection strategy for native SAD phasing, demonstrating its effectiveness in solving macromolecular structures with high-quality datasets.

bioRxiv · Dec 13, 2021Biophysics

Reciprocalspaceship: A Python Library for Crystallographic Data Analysis

J. B. Greisman, K. M. Dalton, D. R. Hekstra

Reciprocalspaceship is a Python library that provides tools for analyzing crystallographic data by extending pandas with methods for handling diffraction data.

bioRxiv · Feb 3, 2021Biophysics

A Unifying Bayesian Framework for Merging X-ray Diffraction Data

K. M. Dalton, J. Greisman, D. R. Hekstra

This paper introduces a Bayesian framework using deep learning and variational inference to merge X-ray diffraction data, improving the detection of conformational changes and anomalous scattering.

bioRxiv · Jan 5, 2021Biophysics