From c5044055e803594c18784b3367e06b39668270e2 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Wed, 31 Jul 2024 10:05:41 -0400 Subject: [PATCH 1/5] Starting multi Wilson blog post --- _posts/2024-08-01-multivariate-wilson.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 _posts/2024-08-01-multivariate-wilson.md diff --git a/_posts/2024-08-01-multivariate-wilson.md b/_posts/2024-08-01-multivariate-wilson.md new file mode 100644 index 0000000..c48cbfe --- /dev/null +++ b/_posts/2024-08-01-multivariate-wilson.md @@ -0,0 +1,21 @@ +--- +layout: post +title: Multivariate Wilson Priors +subtitle: New Pre-prints Demonstrate Multivariate Priors for Time-Resolved Crystallography +author: Kevin Dalton +usemathjax: true +--- + +### Careless +Diffraction data are heavily contaminated with systematic errors which frustrates the inference of time-resolved changes. To address this problem, we built [careless](https://github.com/rs-station/careless), a flexible tool for merging diffraction data which supports both monochromatic and Laue (polychromatic) time-resolved measurements. Careless uses modern concepts from machine learning such as variational inference and deep learning to correct the systematic errors which can be much larger than time-resolved changes. In the original [publication](https://doi.org/10.1038/s41467-022-35280-8) we demonstrated that careless works well for time-resolved Laue diffraction data collected at the Advanced Photon Source. This week, we're excited to announce a new feature which takes careless to the next level in this application domain. + + 1) [Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.](https://doi.org/10.1101/2024.07.22.604476) + 2) [Resolving DJ-1 Glyoxalase Catalysis Using Mix-and-Inject Serial Crystallography at a Synchrotron](https://doi.org/10.1101/2024.07.19.604369) + 3) [Scaling and Merging Time-Resolved Laue Data with Variational Inference](https://doi.org/???????/?????????????????) + +### Multivariate Wilson Prior +In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and of course anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be many hundreds of times larger than the true differences we seek. + +In Reference 1, Hekstra et al. derive a mathematical formalism for comparative crystallography. The key insight in their approach is to add some structure into the Bayesian prior distribution used for merging data in careless. Specifically, the default implementation in careless treats related structures as statistically __independent__. By allowing users to specify that subsequent time-points should be statistically related, this work is able to tease much more signal out of diffraction data. Specific applications demonstrated in the manuscript + +### Time-resolved Diffraction of an Enzyme, DJ-1 From e868cde82e1df52a0b8d6dcd44c8ff6566c9b70d Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Wed, 31 Jul 2024 16:09:04 -0400 Subject: [PATCH 2/5] draft blog post --- _posts/2024-07-31-multivariate-wilson.md | 54 ++++++++++++++++++ _posts/2024-08-01-multivariate-wilson.md | 21 ------- .../comparative_xtal_graphs.png | Bin 0 -> 294061 bytes .../dj1_ablation_study.png | Bin 0 -> 121173 bytes .../dj1_allostery.png | Bin 0 -> 661526 bytes .../lc_intermediate.png | Bin 0 -> 116147 bytes .../systematic_error.png | Bin 0 -> 438877 bytes 7 files changed, 54 insertions(+), 21 deletions(-) create mode 100644 _posts/2024-07-31-multivariate-wilson.md delete mode 100644 _posts/2024-08-01-multivariate-wilson.md create mode 100644 assets/posts/2024-07-31-multivariate-wilson/comparative_xtal_graphs.png create mode 100644 assets/posts/2024-07-31-multivariate-wilson/dj1_ablation_study.png create mode 100644 assets/posts/2024-07-31-multivariate-wilson/dj1_allostery.png create mode 100644 assets/posts/2024-07-31-multivariate-wilson/lc_intermediate.png create mode 100644 assets/posts/2024-07-31-multivariate-wilson/systematic_error.png diff --git a/_posts/2024-07-31-multivariate-wilson.md b/_posts/2024-07-31-multivariate-wilson.md new file mode 100644 index 0000000..0e7d8c0 --- /dev/null +++ b/_posts/2024-07-31-multivariate-wilson.md @@ -0,0 +1,54 @@ +--- +layout: post +title: Multivariate Wilson Priors +subtitle: Pre-prints Demonstrate Multivariate Priors for Time-Resolved Crystallography +author: Kevin Dalton +usemathjax: true +--- + + +{:image: style="margin-right: 25%;margin-left: 25%;width: 50%"} +{:caption: style="display: inline-block;padding: 0 15%;"} + +### Comparative Crystallography and Careless +In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be hundreds of times larger than the true structural differences. + +![Systematic errors in rotation data](/assets/posts/2024-08-01-multivariate-wilson/systematic_error.png){: image} +*Example of systematic errors in conventional diffraction data from [Dalton et al](https://doi.org/10.1038/s41467-022-35280-8). [(CC-BY license)](https://creativecommons.org/licenses/by/4.0/)*{: caption} + + +To address the problem of systematic errors in diffraction data, we built [careless](https://github.com/rs-station/careless), a flexible tool for merging reflection intensities. Careless uses modern concepts from machine learning like variational inference and deep learning to correct the systematic errors. In our original [publication](https://doi.org/10.1038/s41467-022-35280-8) we demonstrated that careless works well for one important application of comparative crystallography which is time-resolved diffraction. Specifically, we showed state of the art inference of time-resolved structural changes in photoactive yellow protein. This week, we're excited to announce a new feature which takes careless to the next level in the comparative crystallography setting. We shared our insights in a series of 3 closely related pre-prints. + + 1. [Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.](https://doi.org/10.1101/2024.07.22.604476) + 2. [Resolving DJ-1 Glyoxalase Catalysis Using Mix-and-Inject Serial Crystallography at a Synchrotron](https://doi.org/10.1101/2024.07.19.604369) + 3. [Scaling and Merging Time-Resolved Laue Data with Variational Inference](https://doi.org/10.1101/2024.07.30.605871) + +### Multivariate Wilson Prior +In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and of course anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be many hundreds of times larger than the true differences we seek. + +![Applications of the multivariate Wilson prior](/assets/posts/2024-08-01-multivariate-wilson/comparative_xtal_graphs.png){: image} +*Applications of the multivariate Wilson prior from [Hekstra et al.](https://doi.org/10.1101/2024.07.22.604476) [(CC-BY-NC license)](https://creativecommons.org/licenses/by-nc/4.0/)*{: caption} + +Hekstra et al. (1) derive a mathematical formalism for comparative crystallography. The key insight in their approach is to add some structure into the Bayesian prior distribution used for merging data in careless. Specifically, the default implementation in careless treats related structures as statistically *independent*. By allowing users to specify that subsequent time-points should be statistically *dependent*, this work is able to tease much more signal out of diffraction data. Specific applications demonstrated in the manuscript include + - polychromatic anomalous diffraction + - time-resolved polychromatic diffraction + - anomalous diffraction at an X-ray free electron laser (XFEL) + - drug fragment screen + + In every case, the structured prior is able to increase the amount of signal observed in the data. + +### Time-resolved Diffraction of an Enzyme, DJ-1 +Zielinski and Dolamore et al. (2) applied the Multivariate Wilson prior to DJ-1, an enzyme involved in Parkinson's disease. In this study, Lois Pollack's [team](https://pollack.research.engineering.cornell.edu/) from Cornell University developed sample mixers which allowed DJ-1 to be rapidly mixed with a substrate, methylglyoxal. Mark Wilson's [group](https://redoxbiologycenter.unl.edu/markwilsonphd) from University of Nebraska Lincoln grew the DJ-1 crystals which were recorded in in a time-resolved fashion at [BioCARS](https://biocars.uchicago.edu/), a polychromatic beamline at the Advanced Photon Source. This configuration allowed the collaborators to observe the conversion of toxic methylgloxal into non-toxic lactate. The exceptional clarity of the time-resolved difference electron density maps produced by Careless enabled new insights into the catalytic mechanism of DJ-1 including an explanation of the enantio-purity of the products. + +![Stereo chemistry of DJ-1 Lactoyl-cysteine intermediate](/assets/posts/2024-08-01-multivariate-wilson/lc_intermediate.png){: image} +*The mechanism of enantioselective hydrolysis in DJ-1 catalysis as explained by [Zielinski and Dolamore et al.](https://doi.org/10.1101/2024.07.19.604369). Water can only access the Lactoylcysteine intermediate from the solvent-exposed face of the active site. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)*{: caption} + + +![Long-range allostery in DJ-1](/assets/posts/2024-08-01-multivariate-wilson/dj1_allostery.png){: image} +*[Zielinski and Dolamore et al.](https://doi.org/10.1101/2024.07.19.604369) show evidence of long-range allostery between the active site and Cystein-53. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)*{: caption} + +### Variational Inference Best Practices +Finally, Zielinski et al (3), details the application of Careless to time-resolved data using DJ-1 as an example. This manuscript offers helpful guidance to users seeking to get the most of their time-resolved diffraction data. Furthermore, it contains a thorough ablation study which demonstrates the impact of many components of the model. + +![DJ-1 merging ablation study](/assets/posts/2024-08-01-multivariate-wilson/dj1_ablation_study.png){: style="width: 100%"} +*[Zielinski et al.](https://doi.org/10.1101/2024.07.30.605871) studied the importance of careless model components by conducting ablation studies. Among other important observations, they showed the multivariate Wilson prior accounts for a nearly 10 sigma increase in time-resolved difference signal. [(CC-BY-NC license)](https://creativecommons.org/licenses/by-nc/4.0/)* diff --git a/_posts/2024-08-01-multivariate-wilson.md b/_posts/2024-08-01-multivariate-wilson.md deleted file mode 100644 index c48cbfe..0000000 --- a/_posts/2024-08-01-multivariate-wilson.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -layout: post -title: Multivariate Wilson Priors -subtitle: New Pre-prints Demonstrate Multivariate Priors for Time-Resolved Crystallography -author: Kevin Dalton -usemathjax: true ---- - -### Careless -Diffraction data are heavily contaminated with systematic errors which frustrates the inference of time-resolved changes. To address this problem, we built [careless](https://github.com/rs-station/careless), a flexible tool for merging diffraction data which supports both monochromatic and Laue (polychromatic) time-resolved measurements. Careless uses modern concepts from machine learning such as variational inference and deep learning to correct the systematic errors which can be much larger than time-resolved changes. In the original [publication](https://doi.org/10.1038/s41467-022-35280-8) we demonstrated that careless works well for time-resolved Laue diffraction data collected at the Advanced Photon Source. This week, we're excited to announce a new feature which takes careless to the next level in this application domain. - - 1) [Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography.](https://doi.org/10.1101/2024.07.22.604476) - 2) [Resolving DJ-1 Glyoxalase Catalysis Using Mix-and-Inject Serial Crystallography at a Synchrotron](https://doi.org/10.1101/2024.07.19.604369) - 3) [Scaling and Merging Time-Resolved Laue Data with Variational Inference](https://doi.org/???????/?????????????????) - -### Multivariate Wilson Prior -In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and of course anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be many hundreds of times larger than the true differences we seek. - -In Reference 1, Hekstra et al. derive a mathematical formalism for comparative crystallography. The key insight in their approach is to add some structure into the Bayesian prior distribution used for merging data in careless. Specifically, the default implementation in careless treats related structures as statistically __independent__. By allowing users to specify that subsequent time-points should be statistically related, this work is able to tease much more signal out of diffraction data. 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z)~G+&VE}RiU{kueaET$N`uP6m5X7&4&~ecBNlD*{=R$Tn`PC~egg>m21{Y1cBe+?p zN%}wVJQv-5!beNP7MM{Kw5bW7^NW T?e079lfF|&Q(vQ$XcPK>OdiaW literal 0 HcmV?d00001 From 9be41103de6749b37b757aaf2696c811182b0ef7 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Wed, 31 Jul 2024 16:09:47 -0400 Subject: [PATCH 3/5] fix broken link --- _posts/2024-07-31-multivariate-wilson.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_posts/2024-07-31-multivariate-wilson.md b/_posts/2024-07-31-multivariate-wilson.md index 0e7d8c0..d8c23e1 100644 --- a/_posts/2024-07-31-multivariate-wilson.md +++ b/_posts/2024-07-31-multivariate-wilson.md @@ -50,5 +50,5 @@ Zielinski and Dolamore et al. (2) applied the Multivariate Wilson prior to DJ-1, ### Variational Inference Best Practices Finally, Zielinski et al (3), details the application of Careless to time-resolved data using DJ-1 as an example. This manuscript offers helpful guidance to users seeking to get the most of their time-resolved diffraction data. Furthermore, it contains a thorough ablation study which demonstrates the impact of many components of the model. -![DJ-1 merging ablation study](/assets/posts/2024-08-01-multivariate-wilson/dj1_ablation_study.png){: style="width: 100%"} +![DJ-1 merging ablation study](/assets/posts/2024-07-31-multivariate-wilson/dj1_ablation_study.png){: style="width: 100%"} *[Zielinski et al.](https://doi.org/10.1101/2024.07.30.605871) studied the importance of careless model components by conducting ablation studies. Among other important observations, they showed the multivariate Wilson prior accounts for a nearly 10 sigma increase in time-resolved difference signal. [(CC-BY-NC license)](https://creativecommons.org/licenses/by-nc/4.0/)* From 06c3c9c69eb94d4b6618af9717c5217563e945a0 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Wed, 31 Jul 2024 16:39:06 -0400 Subject: [PATCH 4/5] fix image links --- _posts/2024-07-31-multivariate-wilson.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/_posts/2024-07-31-multivariate-wilson.md b/_posts/2024-07-31-multivariate-wilson.md index d8c23e1..39d7776 100644 --- a/_posts/2024-07-31-multivariate-wilson.md +++ b/_posts/2024-07-31-multivariate-wilson.md @@ -13,7 +13,7 @@ usemathjax: true ### Comparative Crystallography and Careless In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be hundreds of times larger than the true structural differences. -![Systematic errors in rotation data](/assets/posts/2024-08-01-multivariate-wilson/systematic_error.png){: image} +![Systematic errors in rotation data](/assets/posts/2024-07-31-multivariate-wilson/systematic_error.png){: image} *Example of systematic errors in conventional diffraction data from [Dalton et al](https://doi.org/10.1038/s41467-022-35280-8). [(CC-BY license)](https://creativecommons.org/licenses/by/4.0/)*{: caption} @@ -26,7 +26,7 @@ To address the problem of systematic errors in diffraction data, we built [carel ### Multivariate Wilson Prior In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and of course anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be many hundreds of times larger than the true differences we seek. -![Applications of the multivariate Wilson prior](/assets/posts/2024-08-01-multivariate-wilson/comparative_xtal_graphs.png){: image} +![Applications of the multivariate Wilson prior](/assets/posts/2024-07-31-multivariate-wilson/comparative_xtal_graphs.png){: image} *Applications of the multivariate Wilson prior from [Hekstra et al.](https://doi.org/10.1101/2024.07.22.604476) [(CC-BY-NC license)](https://creativecommons.org/licenses/by-nc/4.0/)*{: caption} Hekstra et al. (1) derive a mathematical formalism for comparative crystallography. The key insight in their approach is to add some structure into the Bayesian prior distribution used for merging data in careless. Specifically, the default implementation in careless treats related structures as statistically *independent*. By allowing users to specify that subsequent time-points should be statistically *dependent*, this work is able to tease much more signal out of diffraction data. Specific applications demonstrated in the manuscript include @@ -40,11 +40,11 @@ Hekstra et al. (1) derive a mathematical formalism for comparative crystallograp ### Time-resolved Diffraction of an Enzyme, DJ-1 Zielinski and Dolamore et al. (2) applied the Multivariate Wilson prior to DJ-1, an enzyme involved in Parkinson's disease. In this study, Lois Pollack's [team](https://pollack.research.engineering.cornell.edu/) from Cornell University developed sample mixers which allowed DJ-1 to be rapidly mixed with a substrate, methylglyoxal. Mark Wilson's [group](https://redoxbiologycenter.unl.edu/markwilsonphd) from University of Nebraska Lincoln grew the DJ-1 crystals which were recorded in in a time-resolved fashion at [BioCARS](https://biocars.uchicago.edu/), a polychromatic beamline at the Advanced Photon Source. This configuration allowed the collaborators to observe the conversion of toxic methylgloxal into non-toxic lactate. The exceptional clarity of the time-resolved difference electron density maps produced by Careless enabled new insights into the catalytic mechanism of DJ-1 including an explanation of the enantio-purity of the products. -![Stereo chemistry of DJ-1 Lactoyl-cysteine intermediate](/assets/posts/2024-08-01-multivariate-wilson/lc_intermediate.png){: image} +![Stereo chemistry of DJ-1 Lactoyl-cysteine intermediate](/assets/posts/2024-07-31-multivariate-wilson/lc_intermediate.png){: image} *The mechanism of enantioselective hydrolysis in DJ-1 catalysis as explained by [Zielinski and Dolamore et al.](https://doi.org/10.1101/2024.07.19.604369). Water can only access the Lactoylcysteine intermediate from the solvent-exposed face of the active site. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)*{: caption} -![Long-range allostery in DJ-1](/assets/posts/2024-08-01-multivariate-wilson/dj1_allostery.png){: image} +![Long-range allostery in DJ-1](/assets/posts/2024-07-31-multivariate-wilson/dj1_allostery.png){: image} *[Zielinski and Dolamore et al.](https://doi.org/10.1101/2024.07.19.604369) show evidence of long-range allostery between the active site and Cystein-53. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)*{: caption} ### Variational Inference Best Practices From d2cbd4aca51422aedb7412a3195a65c6608522b1 Mon Sep 17 00:00:00 2001 From: Kevin Dalton Date: Thu, 1 Aug 2024 10:39:58 -0400 Subject: [PATCH 5/5] incorporate suggestions from KZ --- _posts/2024-07-31-multivariate-wilson.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/_posts/2024-07-31-multivariate-wilson.md b/_posts/2024-07-31-multivariate-wilson.md index 39d7776..184a908 100644 --- a/_posts/2024-07-31-multivariate-wilson.md +++ b/_posts/2024-07-31-multivariate-wilson.md @@ -24,12 +24,10 @@ To address the problem of systematic errors in diffraction data, we built [carel 3. [Scaling and Merging Time-Resolved Laue Data with Variational Inference](https://doi.org/10.1101/2024.07.30.605871) ### Multivariate Wilson Prior -In modern structural biology, we are often more interested in the small differences between related structures rather than the structures themselves. There are many applications of this concept in the domain of comparative crystallography. For instance, equilibrium biophysical perturbations such as multitemperature crystallography, non-equilibrium perturbations like electric-field or temperature jumps, drug fragment screening, and of course anomalous diffraction all fall under the umbrella of comparative crystallography. While comparative data have been successfully analyzed using conventional tools, the fidelity of experiments are often challenged by the magnitude of systematic errors present in diffraction data. These can be many hundreds of times larger than the true differences we seek. - ![Applications of the multivariate Wilson prior](/assets/posts/2024-07-31-multivariate-wilson/comparative_xtal_graphs.png){: image} *Applications of the multivariate Wilson prior from [Hekstra et al.](https://doi.org/10.1101/2024.07.22.604476) [(CC-BY-NC license)](https://creativecommons.org/licenses/by-nc/4.0/)*{: caption} -Hekstra et al. (1) derive a mathematical formalism for comparative crystallography. The key insight in their approach is to add some structure into the Bayesian prior distribution used for merging data in careless. Specifically, the default implementation in careless treats related structures as statistically *independent*. By allowing users to specify that subsequent time-points should be statistically *dependent*, this work is able to tease much more signal out of diffraction data. Specific applications demonstrated in the manuscript include +Hekstra et al. (1) derive a mathematical formalism for comparative crystallography. The key insight in their approach is to add some structure into the Bayesian prior distribution used for merging data in Careless. Specifically, the default implementation in careless treats related structures as statistically *independent*. By allowing users to specify that subsequent time-points should be statistically *dependent*, this work is able to tease much more signal out of diffraction data. Specific applications demonstrated in the manuscript include - polychromatic anomalous diffraction - time-resolved polychromatic diffraction - anomalous diffraction at an X-ray free electron laser (XFEL) @@ -48,7 +46,10 @@ Zielinski and Dolamore et al. (2) applied the Multivariate Wilson prior to DJ-1, *[Zielinski and Dolamore et al.](https://doi.org/10.1101/2024.07.19.604369) show evidence of long-range allostery between the active site and Cystein-53. [(CC-BY-NC-ND license)](https://creativecommons.org/licenses/by-nc-nd/4.0/)*{: caption} ### Variational Inference Best Practices -Finally, Zielinski et al (3), details the application of Careless to time-resolved data using DJ-1 as an example. This manuscript offers helpful guidance to users seeking to get the most of their time-resolved diffraction data. Furthermore, it contains a thorough ablation study which demonstrates the impact of many components of the model. +Finally, Zielinski et al (3), details the application of Careless to time-resolved data using DJ-1 as an example. This manuscript offers helpful guidance to users seeking to get the most of their time-resolved diffraction data. Furthermore, it contains a thorough ablation study which demonstrates the impact of many components of the model. As judged from the ablation, the multivariate Wilson prior was extremely important for maximizing time-resolved signal. In our estimation, the prior contributes a nearly 10 sigma boost in signal to noise! ![DJ-1 merging ablation study](/assets/posts/2024-07-31-multivariate-wilson/dj1_ablation_study.png){: style="width: 100%"} *[Zielinski et al.](https://doi.org/10.1101/2024.07.30.605871) studied the importance of careless model components by conducting ablation studies. Among other important observations, they showed the multivariate Wilson prior accounts for a nearly 10 sigma increase in time-resolved difference signal. [(CC-BY-NC license)](https://creativecommons.org/licenses/by-nc/4.0/)* + +### The Future of Variational Inference in Crystallography +Together, these pre-prints highlight the flexibility of variational inference as a framework for modeling crystallographic data. New insights like the multivariate prior are easy to incorporate and test. Furthermore, they translate to meaningful biological results! The rs-station devs are looking forward to continuing advancing the state of the art as we support and develop this important technology.

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