Biology subjects

Dalton, K. M.

Publications and source records attributed to Dalton, K. M..

12 recordsLinked to original sources

bioRxiv2026

Symmetry Breaking under Vectorial Perturbations in Macromolecular X-ray Crystallography

Proteins are molecular machines whose dynamics govern their function. Using time-resolved crystallography, the basis of protein mechanics can be uncovered by physical perturbation on protein crystals. Perturbations are often vector fields that, in crystals, break symmetry. Lacking existing tools for analyzing symmetry breaking, we built open-source software to do so. We applied this method to three time-resolved crystallography experiments and analyzed the effect of vectorial perturbations on macromolecular crystal structure. We demonstrate that our method can handle symmetry reduction in all 65 Sohncke space groups.

biophysics↗

bioRxiv2026

Daraxonrasib (RMC-6236) is an effective targeted therapy for RAS-mutant neuroblastoma

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Relapsed or refractory (R/R) high-risk (HR) NB tumors continue to exhibit poor outcomes despite intensive and protractive multimodal therapy. Activating mutations in the RAS- mitogen-activated protein kinase (MAPK) pathway are frequently observed in R/R HRNB. The early promise of ALK inhibitors to treat ALK-mutant NB underscores the ability of appropriate targeted therapies to improve outcomes for HRNB patients. While MAPK pathway activation is prominent in HRNB, FDA-approved MEK inhibitors and KRAS G12C inhibitors have failed to demonstrate significant preclinical single-agent activity. Daraxonrasib (RMC-6236), a potent and selective RAS(ON) inhibitor, has demonstrated activity in both preclinical models and early phase clinical trials of RAS-mutant adult cancers. A subset of R/R HRNB tumors is noteworthy for containing diverse RAS-mutations, providing rationale for RMC-6236 investigation. In this study, we evaluated the therapeutic efficacy and oncogenic signaling modulation of RMC-6236 across NB models harboring RAS pathway activation. RMC-6236 as a single-agent treatment led to a significant decrease in cell viability, suppression of downstream MAPK signaling, upregulation of the MAPK pathway effector protein BIM, and increased cell death in RAS-mutant NB models as well as in NF1-mutant NB models. In vivo studies evidenced that RMC-6236 had on-target activity that significantly reduced tumor growth and extended survival in RAS-mutant HRNB mouse models. Furthermore, RMC-6236-induced both BCL-2 and BIM upregulation and enhancement of BIM:BCL-2 complexes in RAS-mutant NB. As such, the BCL-2 inhibitor venetoclax further enhanced RMC-6236-mediated killing by disrupting RMC-6236 enhanced BIM:BCL-2 complexes. These findings demonstrate that RMC-6236 is a rationale targeted therapy for RAS-mutant NB, a subset of NB that is progressively understood as conferring particularly poor outcomes. RMC-6236 is a clinically relevant drug that can successfully target the MAPK pathway in these cancers. This study supports expanded clinical testing of this novel therapy to this important subset of neuroblastoma.

cancer biology↗

bioRxiv2025

SFCalculator: connecting deep generative models and crystallography

Proteins drive biochemical transformations by transitioning through distinct conformational states. Understanding these states is essential for modulating protein function. Although X-ray crystallography has enabled revolutionary advances in protein structure prediction by machine learning, this connection was made at the level of atomic models, not the underlying data. This lack of connection to crystallographic data limits the potential for further advances in both the accuracy of protein structure prediction and the application of machine learning to experimental structure determination. Here, we present SFCalculator, a differentiable pipeline that generates crystallographic observables from atomistic molecular structures with bulk solvent correction, bridging crystallographic data and neural network-based molecular modeling. We validate SFCalculator against conventional methods and demonstrate its utility by establishing three important proof-of-concept applications. First, SFCalculator enables accurate placement of molecular models relative to crystal lattices (known as phasing). Second, SFCalculator enables the search of the latent space of generative models for conformations that fit crystallographic data and are, therefore, also implicitly constrained by the information encoded by the model. Finally, SFCalculator enables the use of crystallographic data during training of generative models, enabling these models to generate an ensemble of conformations consistent with crystallographic data. SFCalculator, therefore, enables a new generation of analytical paradigms integrating crystallographic data and machine learning.

biophysics↗

bioRxiv2024

Scaling and Merging Time-Resolved Laue Data with Variational Inference

Time-resolved X-ray crystallography (TR-X) at synchrotrons and free electron lasers is a promising technique for recording dynamics of molecules at atomic resolution. While experimental methods for TR-X have proliferated and matured, data analysis is often difficult. Extracting small, time-dependent changes in signal is frequently a bottleneck for practitioners. Recent work demonstrated this challenge can be addressed when merging redundant observations by a statistical technique known as variational inference (VI). However, the variational approach to time-resolved data analysis requires identification of successful hyperparameters in order to optimally extract signal. In this case study, we present a successful application of VI to time-resolved changes in an enzyme, DJ-1, upon mixing with a substrate molecule, methylglyoxal. We present a strategy to extract high signal-to-noise changes in electron density from these data. Furthermore, we conduct an ablation study, in which we systematically remove one hyperparameter at a time to demonstrate the impact of each hyperparameter choice on the success of our model. We expect this case study will serve as a practical example for how others may deploy VI in order to analyze their time-resolved diffraction data.

biophysics↗

bioRxiv2024

Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography

Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.

biophysics↗

bioRxiv2024

Laue-DIALS: open-source software for polychromatic X-ray diffraction data

Most X-ray sources are inherently polychromatic. Polychromatic ("pink") X-rays provide an efficient way to conduct diffraction experiments as many more photons can be used and large regions of reciprocal space can be probed without sample rotation during exposure--ideal conditions for time-resolved applications. Analysis of such data is complicated, however, causing most X-ray facilities to discard >99% of X-ray photons to obtain monochromatic data. Key challenges in analyzing polychromatic diffraction data include lattice searching, indexing and wavelength assignment, correction of measured intensities for wavelength-dependent effects, and deconvolution of harmonics. We recently described an algorithm, Careless, that can perform harmonic deconvolution and correct measured intensities for variation in wavelength when presented with integrated diffraction intensities and assigned wavelengths. Here, we present Laue-DIALS, an open-source software pipeline that indexes and integrates polychromatic diffraction data. Laue-DIALS is based on the dxtbx toolbox, which supports the DIALS software commonly used to process monochromatic data. As such, Laue-DIALS provides many of the same advantages: an open-source, modular, and extensible architecture, providing a robust basis for future development. We present benchmark results showing that Laue-DIALS, together with Careless, provides a suitable approach to the analysis of polychromatic diffraction data, including for time-resolved applications.

molecular biology↗

bioRxiv2024

Resolving DJ-1 Glyoxalase Catalysis Using Mix-and-Inject Serial Crystallography at a Synchrotron

DJ-1 (PARK7) is an intensively studied protein whose cytoprotective activities are dysregulated in multiple diseases. DJ-1 has been reported as having two distinct enzymatic activities in defense against reactive carbonyl species that are difficult to distinguish in conventional biochemical experiments. Here, we establish the mechanism of DJ-1 using a synchrotron-compatible version of mix-and-inject-serial crystallography (MISC), which was previously performed only at XFELs, to directly observe DJ-1 catalysis. We designed and used new diffusive mixers to collect time-resolved Laue diffraction data of DJ-1 catalysis at a pink beam synchrotron beamline. Analysis of structurally similar methylglyoxal-derived intermediates formed through the DJ-1 catalytic cycle shows that the enzyme catalyzes nearly two turnovers in the crystal and defines key aspects of its glyoxalase mechanism. In addition, DJ-1 shows allosteric communication between a distal site at the dimer interface and the active site that changes during catalysis. Our results rule out the widely cited deglycase mechanism for DJ-1 action and provide an explanation for how DJ-1 produces L-lactate with high chiral purity.

biochemistry↗

bioRxiv2024

Targeting of SUMOylation leads to cBAF complex stabilization and disruption of the SS18::SSX transcriptome in Synovial Sarcoma

Synovial Sarcoma (SS) is driven by the SS18::SSX fusion oncoprotein. and is ultimately refractory to therapeutic approaches. SS18::SSX alters ATP-dependent chromatin remodeling BAF (mammalian SWI/SNF) complexes, leading to the degradation of canonical (cBAF) complex and amplified presence of an SS18::SSX-containing non-canonical BAF (ncBAF or GBAF) that drives an SS-specific transcription program and tumorigenesis. We demonstrate that SS18::SSX activates the SUMOylation program and SSs are sensitive to the small molecule SAE1/2 inhibitor, TAK-981. Mechanistically, TAK-981 de-SUMOylates the cBAF subunit SMARCE1, stabilizing and restoring cBAF on chromatin, shifting away from SS18::SSX-ncBAF-driven transcription, associated with DNA damage and cell death and resulting in tumor inhibition across both human and mouse SS tumor models. TAK-981 synergized with cytotoxic chemotherapy through increased DNA damage, leading to tumor regression. Targeting the SUMOylation pathway in SS restores cBAF complexes and blocks the SS18::SSX-ncBAF transcriptome, identifying a therapeutic vulnerability in SS, positioning the in-clinic TAK-981 to treat SS.

cancer biology↗

bioRxiv2023

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

Enzymes catalyze biochemical reactions through precise positioning of substrates, cofactors, and amino acids to modulate the transition-state free energy. However, the role of conformational dynamics remains poorly understood due to lack of experimental access. This shortcoming is evident with E. coli dihydro-folate reductase (DHFR), a model system for the role of protein dynamics in catalysis, for which it is unknown how the enzyme regulates the different active site environments required to facilitate proton and hydride transfer. Here, we present ligand-, temperature-, and electric-field-based perturbations during X-ray diffraction experiments that enable identification of coupled conformational changes in DHFR. We identify a global hinge motion and local networks of structural rearrangements that are engaged by substrate protonation to regulate solvent access and promote efficient catalysis. The resulting mechanism shows that DHFRs two-step catalytic mechanism is guided by a dynamic free energy landscape responsive to the state of the substrate.

biophysics↗

bioRxiv2021

Native SAD Phasing at Room Temperature

AO_SCPLOWBSTRACTC_SCPLOWSingle-wavelength anomalous diffraction (SAD) is a routine method for overcoming the phase problem when solving a new macromolecular structure. This technique requires the accurate measurement of intensities to sensitively determine differences across Bijvoet pairs, making it a stringent test for the reliability of a data collection method. Although SAD experiments are commonly conducted at cryogenic temperatures to mitigate the effects of radiation damage, such temperatures can alter the conformational ensemble of the protein crystal and may impede the merging of data from multiple crystals due to non-uniform freezing. Here, we propose a data collection strategy to obtain high-quality data from room temperature samples. To illustrate the strengths of this approach we use native SAD phasing at 6.5 keV to solve four structures of three model systems at 295 K. The resulting datasets allow for automatic phasing and model building, and exhibit alternate conformations that are well-supported by the electron density. The high-redundancy data collection method demonstrated here enables the routine collection of high-quality, room-temperature diffraction to improve the study of protein conformational ensembles.

biophysics↗

bioRxiv2021

Reciprocalspaceship: A Python Library for Crystallographic Data Analysis

AO_SCPLOWBSTRACTC_SCPLOWX-ray crystallography is an invaluable technique for studying the atomic structure of macromolecules. Much of crystallographys success is due to the software packages developed to enable the automated processing of diffraction data. However, the analysis of unconventional diffraction experiments can still pose significant challenges--many existing programs are closed-source, sparsely documented, or are challenging to integrate with modern libraries for scientific computing and machine learning. Here we describe reciprocalspaceship, a Python library for exploring reciprocal space. It provides a tabular representation for reflection data from diffraction experiments that extends the widely-used pandas library with built-in methods for handling space group, unit cell, and symmetry-based operations. As we illustrate, this library facilitates new modes of exploratory data analysis while supporting the prototyping, development, and release of new methods.

biophysics↗

bioRxiv2021

Careless: A Variational Bayesian Model for Merging X-ray Diffraction Data

AO_SCPLOWBSTRACTC_SCPLOWNovel 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.

biophysics↗