Biology subjects

Paraan, M.

Publications and source records attributed to Paraan, M..

9 recordsLinked to original sources

bioRxiv2026

Characterization Standard for In-situ Cryo-electron Tomography

Standardized biological specimens are essential for optimizing cryoEM workflows and benchmarking instrument performance. While apoferritin fulfills this role for single-particle analysis, no equivalent exists for cryo-electron tomography. Ribosomes are frequently used but require large datasets due to C1 symmetry and structural heterogeneity, limiting rapid optimization and standardized comparison of workflows. Here, we present PP7 virus-like particles (VLPs) overexpressed in E. coli as a scalable in situ benchmark. VLPs have high orders of symmetry enabling rapid, high-resolution validation of tomographic pipelines from minimal datasets, while their distinct structural features across low to high resolutions provide a practical resolution metric.

cell biology↗

bioRxiv2025

Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography

The difficulty of particle picking in cryo-electron tomography remains a barrier to routine in situ structure determination. Machine learning is well-suited to overcome this bottleneck with efficient algorithms that generalize across molecular species. To spur new algorithm development, we held a three-month Kaggle challenge that tasked contestants with annotating five molecular species across hundreds of experimental tomograms. This competition successfully engaged >1000 participants from diverse fields and delivered particle pickers that outperformed existing state-of-the-art. Systematic comparisons of the contestants submissions revealed the tolerance of subtomogram averaging to moderate but not severe over-picking and underscored the need for more robust measures of annotation quality. The winning models also highlighted the importance of data augmentation to overcome limited training data. All competition tomograms along with the ground truth and winning teams annotations have been released on the CryoET Data Portal as a resource to benchmark current and future tools for particle picking.

cell biology↗

bioRxiv2025

Cationic amino acid identity and net charge influence condensate properties in E. coli

Understanding the formation of biomolecular condensates (BMC) in biological systems has proven to be a paradigm shift in our understanding of the subcellular organization of biomacromolecules. From RNA metabolism, stress response mechanisms, and amyloidogenic pathologies, condensates have been implicated to play a role in a myriad of cellular phenomena. Despite their near ubiquity, we still do no wholly understand how the primary sequence of biomolecules influences their biophysical and rheological properties. Here, we aim to understand the impact of primary cationic amino acid composition on the properties of condensates. Using engineered recombinant proteins, we show that the formation and phase boundaries of coacervates formed between proteins and RNA is dependent on the cationic amino acid identity, as well as the net charge of the protein involved in condensation. Despite the equivalent charge between arginine and lysine at physiological pH, arginine has been shown to promote increased encapsulation efficiency and salt stability, as well as reduced protein mobility within condensates. We show that arginine-tagged globular proteins also have a higher salt resistance in vitro when compared to similar lysine-tagged globular proteins. This translates to a cellular context in which arginine tagged proteins promote increased condensate formation in model E. coli cells. We were also able to observe a reduction in the total fluorescent recovery and protein mobility within arginine-based condensates via FRAP. Together, these results suggest that in addition to electrostatic interactions and disorder as the main driving forces of phase separation in biological contexts, the primary sequence and side chain composition of proteins plays a significant role in dictating dynamics of coacervates.

biophysics↗

bioRxiv2025

AreTomoLive: Automated reconstruction of comprehensively-corrected and denoised cryo-electron tomograms in real-time and at high throughput

A high throughput processing pipeline that performs comprehensive corrections is needed to realize the full potential of cryo-electron tomography and subtomogram averaging. The fields fragmented software landscape remains a significant hurdle to this end. Here we present AreTomoLive, an automated real-time pipeline composed of two GPU-accelerated packages. The first, AreTomo3, streamlines tomographic alignment and reconstruction, with new features to fully account for sample geometry, locally correct the contrast transfer function, and curate data for downstream tasks. The second package, DenoisET, is a new implementation of the machine learning algorithm Noise2Noise and runs in parallel with AreTomo3 to perform contrast enhancement. To reduce barriers to routine use, AreTomoLive prioritizes automation: AreTomo3 autonomously pauses and reactivates processing depending on the status of data collection, while DenoisET algorithmically determines when to transition from training to inference. AreTomoLive endeavors to advance cryoET for in situ structural analysis with its comprehensive corrections and full automation.

cell biology↗

bioRxiv2024

Annotating CryoET Volumes: A Machine Learning Challenge

Cryo-electron tomography (cryoET) has emerged as a powerful structural biology tool for understanding protein complexes in their native cellular environments. Presently, 3D volumes of cellular environments can be acquired in the thousands in a few days where each volume provides a rich and complex cellular landscape. Despite numerous innovations, localizing and identifying the vast majority of protein species in these volumes remains prohibitively difficult. Machine learning based methods provide an opportunity to automate the process of labeling and annotating cryoET volumes. Due to current bottlenecks in the annotation process, and a lack of large standardized datasets, training datasets for machine learning algorithms have been scarce. Here, we present a defined "phantom" sample, along with "ground truth" annotations, that will be the basis of a machine learning challenge to bring cryoET and ML experts together and spur creativity to address this annotation problem. We have also set up a cryoET data portal that provides additional diverse sets of annotated 3D volumes from cryoET experts across the world for the machine learning challenge.

cell biology↗

bioRxiv2024

Open-source Tools for CryoET Particle Picking Machine Learning Competitions

We are launching a machine learning (ML) competition focused on particle picking in cryo-electron tomography (cryoET) data, a crucial task in structural biology. To support this, we have created a comprehensive suite of open-source tools to develop resources for our competition, including copick for dataset management, napari plugins for interactive visualization, utilities for converting particle picks to segmentation masks, and PyTorch tools for custom dataset sampling. These resources streamline the processes of data handling, labeling, and visualization, allowing participants to focus on model development. By leveraging these tools, competitors will be better equipped to tackle the unique challenges of cryoET data and push forward advancements in particle picking techniques.

bioinformatics↗

bioRxiv2024

Dose-Efficient Cryo-Electron Microscopy for Thick Samples using Tilt-Corrected Scanning Transmission Electron Microscopy, Demonstrated on Cells and Single Particles

Cryo-EM is a powerful tool in structural biology, providing insights through techniques like single-particle analysis (SPA) and cryogenic electron tomography (cryo-ET). In thick specimens, challenges arise as an exponentially larger fraction of the transmitted electrons lose energy from inelastic scattering and can no longer be properly focused as a result of chromatic aberrations in the post-specimen optics. Rather than filtering out the inelastic scattering at the price of reducing potential signal, as is done in energy-filtered transmission electron microscopy (EFTEM), we show how a dose-efficient and unfiltered image can be rapidly obtained using tilt-corrected bright-field scanning-TEM (tcBF-STEM) data collected on a pixelated detector. Enhanced contrast and a 3-5x improvement in collection efficiency are observed for 2D images of intact bacterial cells and large organelles using tcBF-STEM compared to EFTEM for thicknesses beyond 500 nm. As a proof of concept for the techniques performance in structural determination, we present an SPA map at subnanometer resolution for a highly symmetric virus-like particle (VLP) with 789 particles. These findings suggest applications for tcBF-STEM in cryo-EM of thicker cellular volumes where current approaches struggle.

cell biology↗

bioRxiv2022

The structure of a Type III-A CRISPR-Cas effector complex reveals conserved and idiosyncratic contacts to target RNA and crRNA among Type III-A systems

Type III CRISPR-Cas systems employ multiprotein effector complexes bound to small CRISPR RNAs (crRNAs) to detect foreign RNA transcripts and elicit a complex immune response that leads to the destruction of invading RNA and DNA. Type III systems are among the most widespread in nature, and emerging interest in harnessing these systems for biotechnology applications highlights the need for detailed structural analyses of representatives from diverse organisms. We performed cryo-EM reconstructions of the Type III-A Cas10-Csm effector complex from S. epidermidis bound to an intact, cognate target RNA and identified two oligomeric states, a 276 kDa complex and a 318 kDa complex. 3.1 [A] density for the well-ordered 276 kDa complex allowed construction of atomic models for the Csm2, Csm3, Csm4 and Csm5 subunits within the complex along with the crRNA and target RNA. We also collected small-angle X-ray scattering data which was consistent with the 276 kDa Cas10-Csm architecture we identified. Detailed comparisons between the S. epidermidis Cas10-Csm structure and the well-resolved bacterial (S. thermophilus) and archaeal (T. onnurineus) Cas10-Csm structures reveal differences in how the complexes interact with target RNA and crRNA which are likely to have functional ramifications. These structural comparisons shed light on the unique features of Type III-A systems from diverse organisms and will assist in improving biotechnologies derived from Type III-A effector complexes.

molecular biology↗

bioRxiv2020

The structures of natively assembled clathrin coated vesicles

Vesicle trafficking by clathrin coated vesicles (CCVs) is one of the major mechanisms by which proteins and nutrients are absorbed by the cell and transported between organelles. The individual proteins comprising the coated vesicles include clathrin heavy chain, clathrin light chain, and a variety of adaptor protein complexes. Much is known about the structures of the individual CCV components, but data are lacking about the structures of the fully assembled complexes together with membrane and in complex with cargo. Here we determined the structures of natively assembled CCVs in a variety of geometries. We show that the adaptor {beta}2-appendages crosslink adjacent CHC {beta}-propellers and that the appendage densities reside almost exclusively in CCV hexagonal faces. We resolve how AP2 and other associated factors in hexagonal faces form an assembly hub with an extensive web of interactions between neighboring {beta}-propellers and propose a structural model that explains how adaptor binding can direct the formation of pentagonal and hexagonal faces.

biophysics↗