Biology subjects

Tang, W. S.

Publications and source records attributed to Tang, W. S..

5 recordsLinked to original sources

bioRxiv2026

Quantifying RNA Folding Equilibria from Unclassified Cryo-EM Particle Images

Functional RNA structures are typically far more dynamic than most proteins solved by cryo-electron microscopy (cryo-EM). Flexible junctions and weak tertiary contacts produce broad conformational ensembles, and cryo-EM particle classification analyses discard unaligned particles, which inherently contain the information needed to quantify such broad ensembles. Here, we infer compositional and conformational distributions from unclassified particle images by Bayesian ensemble reweighting. We tested our approach on the P4-P6 domain of the Tetrahymena thermophila group I intron, a classic model system of Mg2+-dependent RNA folding. While stable monomeric and dimeric states yielded high-resolution reconstructions, analyses of abundant, flexible conformations required Bayesian ensemble inference. Our approach quantitatively captured the Mg2+-dependent folding transition, indicating that cryo-EM can report the abundance of coexisting RNA states in agreement with bulk solution measurements. These results showcase how quantitative studies of RNA folding can be achieved by combining cryo-EM measurements with statistical ensemble-inference tools.

biochemistry↗

bioRxiv2024

CryoLike: A python package for cryo-electron microscopy image-to-structure likelihood calculations

Extracting conformational heterogeneity from cryo-electron microscopy (cryo-EM) images is particularly challenging for flexible biomolecules, where traditional 3D classification approaches often fail. Over the past few decades, advancements in experimental and computational techniques have been made to tackle this challenge, especially Bayesian-based approaches that provide physically interpretable insights into cryo-EM heterogeneity. To reduce the computational cost for Bayesian approaches, we introduce CryoLike, a computationally efficient algorithm for evaluating image-to-structure (or image-to-volume) likelihoods across large image datasets, which is built on Fourier-Bessel representations of the images and packaged in a user-friendly Python workflow.

biophysics↗

bioRxiv2024

A Viral-Encoded Homologue of IPS-1 Modulates Innate Immune Signaling During KSHV Lytic Replication

Modulation of innate immunity is critical for virus persistence in a host. In particular, viral-encoded disruption of type I interferon, a major antiviral cytokine induced to fight viral infection, is a key component in the repertoire of viral pathogenicity genes. We have identified a previously undescribed open reading frame within the Kaposis sarcoma-associated herpesvirus (KSHV) genome that encodes a homologue of the human IPS-1 (also referred to as MAVS) protein that we have termed viral-IPS-1 (v-IPS-1). This protein is expressed during the lytic replication program of KSHV, and expression of v-IPS-1 blocks induction of type I interferon upstream of the TRAF3 signaling node including signaling initiated via both the RLR and TLR3/4 signaling axes. This disruption of signaling coincides with destabilization of the cellular innate signaling adaptors IPS-1 and TRIF along with a concatenate stabilization of the TRAF3 protein. Additionally, expression of v-IPS-1 leads to decreased antiviral responses indicating a blot to type I interferon induction during viral infection. Taken together, v-IPS-1 is the first described viral homologue of IPS-1 and this viral protein leads to reprogramming of innate immunity through modulation of type I interferon signaling during KSHV lytic replication.

microbiology↗

bioRxiv2021

A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Author SummaryStructural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.

biophysics↗

bioRxiv2020

Identifying Sequence Perturbations to an Intrinsically Disordered Protein that Determine Its Phase Separation Behavior

Phase separation of intrinsically disordered proteins (IDPs) commonly underlies the formation of membraneless organelles, which compartmentalize molecules intracellularly in the absence of a lipid membrane. Identifying the protein sequence features responsible for IDP phase separation is critical for understanding physiological roles and pathological consequences of biomolecular condensation, as well as for harnessing phase separation for applications in bio-inspired materials design. To expand our knowledge of sequence determinants of IDP phase separation, we characterized variants of the intrinsically disordered RGG domain from LAF-1, a model protein involved in phase separation and a key component of P granules. Based on a predictive coarse-grained IDP model, we identified a region of the RGG domain that has high contact probability and is highly conserved between species; deletion of this region significantly disrupts phase separation in vitro and in vivo. We determined the effects of charge patterning on phase behavior through sequence shuffling. By altering the wild-type sequence, which contains well-mixed charged residues, to increase charge segregation, we designed sequences with significantly increased phase separation propensity. This result indicates the natural sequence is under negative selection to moderate this mode of interaction. We measured the contributions of tyrosine and arginine residues to phase separation experimentally through mutagenesis studies and computationally through direct interrogation of different modes of interaction using all-atom simulations. Finally, we show that in spite of these sequence perturbations, the RGG-derived condensates remain liquid-like. Together, these studies advance a predictive framework and identify key biophysical principles of sequence features important to phase separation. Significance StatementMembraneless organelles are assemblies of highly concentrated biomolecules that form through a liquid-liquid phase separation process. These assemblies are often enriched in intrinsically disordered proteins, which play an important role in driving phase separation. Understanding the sequence-to-phase behavior relationship of these disordered proteins is important for understanding the biochemistry of membraneless organelles, as well as for designing synthetic organelles and biomaterials. In this work, we explore a model protein, the disordered N-terminal domain of LAF-1, and highlight how three key features of the sequence control the proteins propensity to phase separate. Combining predictive simulations with experiments, we find that phase behavior of this model IDP is dictated by the presence of a short conserved domain, charge patterning, and arginine-tyrosine interactions.

biophysics↗