About

Doeke Romke Hekstra is an Associate Professor of Molecular and Cellular Biology at Harvard University, affiliated with the Harvard John A. Paulson School of Engineering and Applied Sciences. His primary teaching areas include Applied Physics, Biophysics, Soft Matter, Materials, and Mechanical Engineering. His research focuses on biophysics and applied physics, with particular interest in soft matter and materials science. He is based at the Northwest Lab Building in Harvard's Allston campus and can be contacted via email at doeke_hekstra@harvard.edu or by phone at (617) 496-4740. His work involves exploring the physical principles underlying biological systems and soft materials, contributing to the understanding of complex biological and physical phenomena.

Research topics

• Computer Science
• Physics
• Optics
• Artificial Intelligence
• Crystallography
• Chemistry
• Biology
• Materials science
• Evolutionary biology
• Computational physics
• Algorithm
• Mathematics
• Statistical physics
• Quantum mechanics
• Programming language
• Computational science
• Computational biology
• Nanotechnology
• Biochemistry
• Biophysics
• Biological system

Selected publications

• Emerging Time-Resolved X-Ray Diffraction Approaches for Protein Dynamics

  Annual Review of Biophysics · 2023 · 42 citations

  1st authorCorresponding
  • Computer Science
  • Biological system
  • Biophysics

  Proteins guide the flows of information, energy, and matter that make life possible by accelerating transport and chemical reactions, by allosterically modulating these reactions, and by forming dynamic supramolecular assemblies. In these roles, conformational change underlies functional transitions. Time-resolved X-ray diffraction methods characterize these transitions either by directly triggering sequences of functionally important motions or, more broadly, by capturing the motions of which proteins are capable. To date, most successful have been experiments in which conformational change is triggered in light-dependent proteins. In this review, I emphasize emerging techniques that probe the dynamic basis of function in proteins lacking natively light-dependent transitions and speculate about extensions and further possibilities. In addition, I review how the weaker and more distributed signals in these data push the limits of the capabilities of analytical methods. Taken together, these new methods are beginning to establish a powerful paradigm for the study of the physics of protein function.

  DOI

• A unifying Bayesian framework for merging X-ray diffraction data

  Nature Communications · 2022 · 32 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Novel X-ray methods are transforming the study of the functional dynamics of biomolecules. Key to this revolution is detection of often subtle conformational changes from diffraction data. Diffraction data contain patterns of bright spots known as reflections. To compute the electron density of a molecule, the intensity of each reflection must be estimated, and redundant observations reduced to consensus intensities. Systematic effects, however, lead to the measurement of equivalent reflections on different scales, corrupting observation of changes in electron density. Here, we present a modern Bayesian solution to this problem, which uses deep learning and variational inference to simultaneously rescale and merge reflection observations. We successfully apply this method to monochromatic and polychromatic single-crystal diffraction data, as well as serial femtosecond crystallography data. We find that this approach is applicable to the analysis of many types of diffraction experiments, while accurately and sensitively detecting subtle dynamics and anomalous scattering.

  DOI

• <i>reciprocalspaceship</i> : a Python library for crystallographic data analysis

  Journal of Applied Crystallography · 2021 · 32 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Crystallography

  library with built-in methods for handling space groups, unit cells and symmetry-based operations. As is illustrated, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development and release of new methods.

  DOI

Recent grants

• Identifying Principles of Protein Mechanics by Applying Force and Observing Motion

  NIH · $2.4M · 2020–2025

Frequent coauthors

• Kevin M. Dalton

  SLAC National Accelerator Laboratory

  44 shared

• Jack B. Greisman

  Harvard University

  29 shared

• Robert Henning

  University of Chicago

  16 shared

• Margaret A. Klureza

  Harvard University

  14 shared

• Rama Ranganathan

  University of Chicago

  12 shared

• Stanislas Leibler

  Center for Systems Biology

  12 shared

• V. Šrajer

  University of Chicago

  10 shared

• K. Ian White

  Stanford University

  10 shared

Education

• Ph.D.

  Rockefeller University

  2009

Similar researchers at Harvard University

• Walter C Willetth-429
• JoAnn E Mansonh-359
• Randy L. Bucknerh-268
• Edward Giovannuccih-267
• Kristen Andersonh-263