Biology subjects

Bell, E. W.

Publications and source records attributed to Bell, E. W..

7 recordsLinked to original sources

bioRxiv2026

Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1

Compound ranking in structure-based virtual screening notoriously yields highly ranked false positive binders due to variable poses or biases in scoring terms. We developed a compound prioritization strategy that utilizes sampled docked poses from contrasting docking approaches (targeted physics-based docking and blind docking with a generative model) against multiple models of the target protein to train a multi-layer perceptron (MLP). The model predicts binders at the orthosteric ligand-binding pocket of the nuclear receptor LRH-1 (NR5A2). Our approach circumvents the reliance on a single docked pose for scoring compounds or individual scoring metrics for compound ranking. In a separate benchmarking set, we observed that the MLP identifies known binders that are chemically dissimilar from the compounds in the training set and is sensitive to single scaffold modifications, making it a potential tool for lead optimization. We applied our strategy to a prospective virtual screening campaign, which resulted in the discovery of four putative LRH-1 binders. We found that a combination of scoring and prediction metrics enriches for the hit compounds across library sizes. In all, this implementation presents a method to leverage structural and experimental data to aid virtual screening for a challenging protein target.

bioinformatics↗

bioRxiv2026

Architecture of the Gβγ-prefusion SNARE complex reveals the molecular mechanism of inhibition of vesicle fusion

Presynaptic inhibitory GPCR (Gi/o GPCR) signaling is an essential regulatory mechanism in vertebrate physiology. Near the presynaptic active zone, Gi/o GPCR activation releases G-protein {beta}{gamma} heterodimers (G{beta}{gamma}) which act to inhibit synaptic vesicle fusion through either modulation of Ca2+ entry via voltage-gated Ca2+ channels, or by direct interactions with the core exocytotic machinery comprised of the ternary SNARE complex downstream of Ca2+ influx. The precise molecular mechanism underlying G{beta}{gamma}-SNARE mediated inhibition has remained unclear due to lack of structural data for the G{beta}{gamma}-SNARE complex. We address this long-standing question here by stabilizing the interaction between G{beta}1{gamma}2 and a pre-fusion ternary SNARE mimetic and determining the structure using single-particle cryo-EM. We used our cryo-EM envelope to build an atomic level prediction of the interaction interface. We validated key interaction sites predicted by our model at the C-terminus of SNAP-25 using site directed mutagenesis and biochemical affinity measurements. Additionally, we found that G{beta}1{gamma}2 and a fragment of the regulatory protein complexin can engage the pre-fusion SNARE complex simultaneously. On the basis of these results, we propose a model in which G{beta}1{gamma}2 acts on the partially zipped SNARE complex at a late stage in the vesicle docking and priming cycle. In the model, the amino-terminal coiled-coil of G{beta}1{gamma}2 forms an interface with the C-terminus of the target membrane SNARE (t-SNARE) complex to prevent complete incorporation of the vesicle SNARE (v-SNARE) into the core SNARE helical bundle, thus blocking vesicle approach to the plasma membrane. The {beta}-propeller domain of G{beta}1 may also sterically hinder vesicle approach. Together these results provide crucial structural insights into the mechanism of binding of G{beta}{gamma} to the SNARE complex, and lends essential insights into the critical role of GPCR signaling to the SNARE complex in modulating synaptic vesicle fusion.

biophysics↗

bioRxiv2025

Classification models distinguish functional and trafficking effects of KCNQ1 variants to enhance variant interpretation

Missense mutations compromise protein fitness by altering stability and function, which can lead to various clinical disease states. The potassium ion channel KCNQ1 underlies the majority of congenital long QT syndrome (LQTS) cases, one of the most common genetic arrhythmia syndromes. During genetic testing for LQTS, variants of uncertain significance (VUS) confound diagnosis and clinical management. KCNQ1 protein fitness metrics enable mechanistic classification of variants, directly informing the molecular basis for dysfunction and providing clinical interpretation of variants linked to LQTS and other channelopathies. We developed structure-aware random forest classifier models to predict seven metrics of KCNQ1 fitness, four functional electrophysiology measurements (peak current density, voltage-dependence, gating kinetics), and three trafficking values measured by flow cytometry. Our trained models outperformed AlphaMissense in predicting protein fitness, enhancing interpretation of ClinVar VUS and variants classified as ambiguous by AlphaMissense. We demonstrate the classifiers distinguish benign and pathogenic variants from ClinVar and gnomAD and identify systematic patterns of dysfunction and mistrafficking along the functionally critical S4 helix. Our method advances variant effect prediction with a mechanistic classifier that reliably links missense mutations in KCNQ1 to their specific disease-causing mechanisms. As a resource for precision medicine approaches for LQTS or other KCNQ1 channelopathies, we provide the predictions and scores for all KCNQ1 missense variants across the structured region of the protein.

biophysics↗

bioRxiv2025

FakeRotLib: expedient non-canonical amino acid parameterization in Rosetta

Non canonical amino acids (NCAAs) occupy an important place, both in natural biology and synthetic applications. However, modeling these amino acids still lies outside the capabilities of most deep learning methods due to sparse training datasets for this task. Instead, biophysical methods such as Rosetta can excel in modeling NCAAs. We discuss the various aspects of parameterizing a NCAA for use in Rosetta, identifying rotamer distribution modeling as one of the most impactful factors of NCAA parameterization on Rosetta performance. To this end, we also present FakeRotLib, a method which uses statistical fitting of small molecule conformer to create rotamer distributions. We find that FakeRotLib outperforms existing methods in a fraction of the time and is able to parameterize NCAA types previously unmodeled by Rosetta.

bioinformatics↗

bioRxiv2021

PEPPI: Whole-proteome protein-protein interaction prediction through structure and sequence similarity, functional association, and machine learning

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naive Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of noninteractions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

systems biology↗

bioRxiv2020

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP and CAMEO experiments, and outperformed other state-of-the-art methods by at least 58.4% for the CASP 11&12 and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library. AvailabilityThe training and testing data, standalone package, and the online server for TripletRes are available at https://zhanglab.ccmb.med.umich.edu/TripletRes/. Author SummaryAb initio protein folding has been a major unsolved problem in computational biology for more than half a century. Recent community-wide Critical Assessment of Structure Prediction (CASP) experiments have witnessed exciting progress on ab initio structure prediction, which was mainly powered by the boosting of contact-map prediction as the latter can be used as constraints to guide ab initio folding simulations. In this work, we proposed a new open-source deep-learning architecture, TripletRes, built on the residual convolutional neural networks for high-accuracy contact prediction. The large-scale benchmark and blind test results demonstrate significant advancement of the proposed methods over other approaches in predicting medium- and long-range contact-maps that are critical for guiding protein folding simulations. Detailed data analyses showed that the major advantage of TripletRes lies in the unique protocol to fuse multiple evolutionary feature matrices which are directly extracted from whole-genome and metagenome databases and therefore minimize the information loss during the contact model training.

bioinformatics↗

bioRxiv2020

Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al. (https://doi.org/10.1002/jmv.25682) and by Pradhan et al. (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the "novel insertions" observed in the spike protein are naturally evolved from bat coronaviruses.

bioinformatics↗