Publications

Nucleocytoplasmic transport is essential for cellular function, presenting a canonical example of rapid molecular sorting inside cells. It consists of a coordinated interplay between import/export of molecules in/out the cell nucleus. Here, we investigate the role of spatio-temporal dynamics of the nucleocytoplasmic transport and its regulation. We develop a biophysical model that captures the main features of the nucleocytoplasmic transport, in particular, its regulation through the Ran cycle. Our model yields steady-state profiles for the molecular components of the Ran cycle, their relaxation times, as well as the nuclear-to-cytoplasmic molecule ratio. We show that these quantities are affected by their spatial dynamics and heterogeneity within the nucleus. Specifically, we find that the spatial nonuniformity of Ran Guanine Exchange Factor (RanGEF) -- particularly its proximity to the nuclear envelope -- enhances the Ran cycle's efficiency. We further show that RanGEF's accumulation near the nuclear envelope results from its intrinsic dynamics as a nuclear cargo, transported by the Ran cycle itself. Overall, our work highlights the critical role of molecular spatial dynamics in cellular processes, and proposes new avenues for theoretical and experimental inquiries into the nucleocytoplasmic transport.

Coiled-coil protein motifs have become widely employed in the design of biomaterials. Some of these designs have been studied for use in drug delivery due to the unique ability of coiled-coils to impart stability, oligomerization, and supramolecular assembly. To leverage these properties and improve drug delivery, release, and targeting, a variety of nano- to mesoscale architectures have been adopted. Coiled-coil drug delivery and therapeutics have been developed by using the coiled-coil alone, designing for higher-order assemblies such as fibers and hydrogels, and combining coiled-coil proteins with other biocompatible structures such as lipids and polymers. We review the recent development of these structures and the design criteria used to generate functional proteins of varying sizes and morphologies.

Deciphering the regulatory code of gene expression and interpreting the transcriptional effects of genome variation are critical challenges in human genetics. Modern experimental technologies have resulted in an abundance of data, enabling the development of sequence-based deep learning models that link patterns embedded in DNA to the biochemical and regulatory properties contributing to transcriptional regulation, including modeling epigenetic marks, 3D genome organization, and gene expression, with tissue and cell-type specificity. Such methods can predict the functional consequences of any noncoding variant in the human genome, even rare or never-before-observed variants, and systematically characterize their consequences beyond what is tractable from experiments or quantitative genetics studies alone. Recently, the development and application of interpretability approaches have led to the identification of key sequence patterns contributing to the predicted tasks, providing insights into the underlying biological mechanisms learned and revealing opportunities for improvement in future models.

Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?

In cells, transcription is tightly regulated on multiple layers. The condensation of the transcription machinery into distinct phases is hypothesized to spatio-temporally fine tune RNA polymerase II behaviour during two key stages, transcription initiation and the elongation of the nascent RNA transcripts. However, it has remained unclear whether these phases would mix when present at the same time or remain distinct chemical environments; either as multi-phase condensates or by forming entirely separate condensates. Here we combine particle-based multi-scale simulations and experiments in the model organism C. elegans to characterise the biophysical properties of RNA polymerase II condensates. Both simulations and the in vivo work describe a lower critical solution temperature (LCST) behaviour of RNA Polymerase II, with condensates dissolving at lower temperatures whereas higher temperatures promote condensate stability, which highlights that these condensates are physio-chemically distinct from heterochromatin condensates. The LCST behavior of CTD correlates with gradual shifts in the transcription program but is largely uncoupled from the classical stress response. Expanding the simulations we model how the degree of phosphorylation of the disordered C-terminal domain of RNA polymerase II (CTD), which is characteristic for each step of transcription, controls the existence and morphology of multi-phasic condensates. We show that the two phases putatively underpinning the initiation of transcription and transcription elongation constitute distinct chemical environments and are in agreement with RNA polymerase II condensates observed in C. elegans embryos by super resolution microscopy. Our analysis shows how depending on its post transcriptional modifications and its interaction partner a single protein can form multiple partially engulfed condensates, potentially promoting the selective recruitment of additional factors to these two phases.

Short interfering RNA (siRNA) therapeutics have soared in popularity due to their highly selective and potent targeting of faulty genes, providing a non-palliative approach to address diseases. Despite their potential, effective transfection of siRNA into cells requires the assistance of an accompanying vector. Vectors constructed from non-viral materials, while offering safer and non-cytotoxic profiles, often grapple with lackluster loading and delivery efficiencies, necessitating substantial milligram quantities of expensive siRNA to confer the desired downstream effects. We detail the recombinant synthesis of a diverse series of coiled-coil supercharged protein (CSP) biomaterials systematically designed to investigate the impact of two arginine point mutations (Q39R and N61R) and decahistidine tags on liposomal siRNA delivery. The most efficacious variant, N8, exhibits a twofold increase in its affinity to siRNA and achieves a twofold enhancement in transfection activity with minimal cytotoxicity in vitro. Subsequent analysis unveils the destabilizing effect of the Q39R and N61R supercharging mutations and the incorporation of C-terminal decahistidine tags on α-helical secondary structure. Cross-correlational regression analyses reveal that the amount of helical character in these mutants is key in N8's enhanced siRNA complexation and downstream delivery efficiency.

Our ability to calculate rates of biochemical processes using molecular dynamics simulations is severely limited by the fact that the time scales for reactions, or changes in conformational state, scale exponentially with the relevant free-energy barriers. In this work, we improve upon a recently proposed rate estimator that allows us to predict transition times with molecular dynamics simulations biased to rapidly explore one or several collective variables. This approach relies on the idea that not all bias goes into promoting transitions, and along with the rate, it estimates a concomitant scale factor for the bias termed the collective variable biasing efficiency γ. First, we demonstrate mathematically that our new formulation allows us to derive the commonly used Infrequent Metadynamics (iMetaD) estimator when using a perfect collective variable, γ=1. After testing it on a model potential, we then study the unfolding behavior of a previously well characterized coarse-grained protein, which is sufficiently complex that we can choose many different collective variables to bias, but which is sufficiently simple that we are able to compute the unbiased rate dire ctly. For this system, we demonstrate that our new Exponential Average Time-Dependent Rate (EATR) estimator converges to the true rate more rapidly as a function of bias deposition time than does the previous iMetaD approach, even for bias deposition times that are short. We also show that the γ parameter can serve as a good metric for assessing the quality of the biasing coordinate. Finally, we demonstrate that the approach works when combining multiple less-than-optimal bias coordinates.

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta’s protein engineering toolbox that allow for the rational design of PTMs.

Synthetic biology is creating genetically engineered organisms at an increasing rate for many potentially valuable applications, but this potential comes with the risk of misuse or accidental release. To begin to address this issue, we have developed a system called GUARDIAN that can automatically detect signatures of engineering in DNA sequencing data, and we have conducted a blinded test of this system using a curated Test and Evaluation (T&E) data set. GUARDIAN uses an ensemble approach based on the guiding principle that no single approach is likely to be able to detect engineering with perfect accuracy. Critically, ensembling enables GUARDIAN to detect sequence inserts in 13 target organisms with a high degree of specificity that requires no subject matter expert (SME) review.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.