Biology subjects

Dokholyan, N. V.

Publications and source records attributed to Dokholyan, N. V..

At least 19 recordsLinked to original sources

bioRxiv2026

ALSmiR: a curated evidence resource for ALS-associated microRNAs for biomarker discovery

Amyotrophic lateral sclerosis is a multifactorial, late-onset neurodegenerative disease with incompletely understood pathogenesis. Limited survival after symptom onset creates an urgent need for biomarkers to support diagnosis, prognosis, and patient stratification. MicroRNAs are among the candidate biomarkers investigated in this disease. Although reviews have synthesized microRNA expression findings, differences in patient populations, biospecimens, and methods complicate comparisons. In the age of data-driven research, we need an updated, searchable resource that links each finding to its context and incorporates growing evidence. Here, we present ALSmiR, a public, manually curated database of polymerase chain reaction-based microRNA expression findings from human biological fluids and their components. ALSmiR currently contains 1,620 expression records from 49 publications comparing patients with amyotrophic lateral sclerosis with healthy controls, covering 816 microRNA names across 13 biospecimen categories. We link publication, cohort, and microRNA-result tables to preserve each finding's clinical and experimental context. We retain significant and non-significant results and provide searchable records and comma-separated values exports for independent analysis. We update ALSmiR annually and verify author submissions against source publications before inclusion. Researchers can use ALSmiR to assess candidate biomarkers, formulate hypotheses, and design independent validation studies. ALSmiR is freely accessible worldwide without registration at https://alsmir.dokhlab.org/.

bioinformatics↗

bioRxiv2026

Modular Deep Learning for Direct RNA Sequence Design via Self-Contained RNA Units

RNA sequence design is a pivotal challenge in synthetic biology, yet state-of-the-art deep learning methods face a fundamental bottleneck: the scarcity of high-resolution 3D structures. To compensate for limited training data, existing approaches like NA-MPNN and RiboDiffusion employ computationally expensive autoregressive or iterative diffusion sampling, substantially limiting their throughput and scalability. In this work, we propose that this data limitation is largely a problem of accessibility and granularity. We introduce SCRU-DB, a comprehensive database that systematically decomposes complex RNAs into over 61,000 Self-contained RNA Units (SCRUs). This scale far exceeds previous RNA motif libraries, capturing over 8,200 unique structural clusters. Crucially, SCRUs are rigorously defined as structurally autonomous modules identified via tertiary contact clustering, ensuring they act as self-stabilizing, foldable physical units. Leveraging this massive, modular prior, we present SCRU-Seq (a direct, O(1) prediction GNN) and SCRU-Diff (an iterative diffusion model). On our high-fidelity set112 benchmark, SCRU-Seq achieves a native sequence recovery (NSR) of 63.7%, while SCRU-Diff reaches a superior Best NSR of 79.2%. We demonstrate high structural fidelity via 3D backbone superposition using the C4 RMSD (reaching 1.5[A] for complex targets) and validate the structural isomorphism of our modular fragments. This framework provides a scalable, physically grounded solution for generating diverse and structurally accurate RNA sequences.

bioinformatics↗

bioRxiv2026

ChironRNA: Steric Clashes Resolution in RNA Structures via E(3)-Equivariant Diffusion

Due to the limited resolution of experimental data, many determined RNA structures contain physically implausible geometries, such as severe steric clashes and missing atoms. Resolving these defects during RNA structure refinement remains a fundamental challenge. Structure dictates the function, so the geometric accuracy of RNA structure is critical for understanding biological mechanisms. However, traditional algorithms for correction have limitations because of the complexity of RNA structures. We propose ChironRNA, an all-atom diffusion model with E(3)-equivariant graph neural networks to perform RNA refinement by resolving steric clashes and completing missing atoms. In ChironRNA, we adopt a hierarchical approach, including both an all-atom diffusion model and a coarse-grained diffusion model where each nucleotide is represented by a five-point representation. Our pipeline consists of two stages: a training stage and a generation stage. The diffusion model regenerates clashing nucleotide atoms step by step by removing the noise predicted by EGNN. ChironRNA achieves an 80% clash reduction on more than 80% of the test set. It performs better on structures of less than 200 nucleotides, resulting in a high percentage of cases having over 80% clash reduction rate and 100% atom reconstruction rate. Our results demonstrate that ChironRNA successfully resolves steric clashes and rebuilds missing atoms with high precision, offering a robust solution where traditional fine-tuning or enumerative approaches fail.

biophysics↗

bioRxiv2026

ATLAS: Graph-based 3D RNA Motif Library Incorporating non-Watson-Crick Interactions

The structures of RNA exhibit recurrent patterns, which are defined as motifs. Motifs are crucial for the biological functions of RNA molecules. The 3D RNA motif library includes 3D RNA structures, providing a resource for motif-based RNA 3D structure prediction and design. We built the Advanced Template Library for Assembly and Structure (ATLAS) that features graph representations of RNA structures, atomic 3D structures from the Protein Data Bank (PDB), and an isomorphism graph searching algorithm. ATLAS includes nucleotide-level graph representations of RNA motifs and corresponding PDB IDs from the PDB and 3D atomic structures retrieved from the PDB. We provide the web service to search for and download RNA motifs and user-defined structures, supporting non-WC interactions. Graph representations include two types of graphs: motifs with non-Watson-Crick (non-WC) and without non-WC. Compared to existing motif libraries, ATLAS includes graph representations with non-Watson-Crick interactions and pseudoknots and integrates the latest RNA structures from the PDB. We also develop a measure of similarity between RNAs based on shared motifs. We proposed a physics-inspired, time-dependent mechanistic model for RNA evolution, whose steady-state solution fits the global distribution of RNA structural similarity.

biophysics↗

bioRxiv2025

PSP-0119: Targeted IRAK4 Degradation as a Novel Therapeutic Strategy for FLT3-Mutant AML

Acute Myeloid Leukemia (AML) is a life-threatening hematologic malignancy. Despite recent therapeutic advances, rising incidence rates emphasize the urgent need for identification of new targets and therapies. Roles of interleukin receptor-associated kinases IRAK1/4 are emerging in hematologic and solid malignancies. In AML, IRAK4 mRNA is overexpressed at diagnosis, relapses, in residual disease, and in FLT3-ITD-mutant cells, MDS, MPN, and MDS/MPN-negative subtypes. Compared with hematopoietic stem cells, IRAK4 is elevated in t(15;17), inv(16)/t(16;16), and t(11q23)/MLL subtypes, correlating with poor survival. Here, we disclose anti-AML activity of PSP-0119, a novel IRAK4 PROTAC degrader. PSP-0119, inhibited IRAK4 kinase activity, NF-{kappa}{beta} activity, and IL-1{beta}-induced IRAK4 phosphorylation. In-silico docking revealed interactions in CRBN/IRAK4/PSP-0119 ternary complex. PSP-0119 degraded IRAK4 in FLT3-mutant AML cell lines sparing FLT3-wild-type AML cells, FLT3-wild-type patient samples, and normal bone-marrow. Bulk-seq of PSP-0119 treated MOLM-13 cells revealed downregulation of eNOS, a poor AML prognosticator. PSP-0119 suppressed colony formation, cell viability, and MOLM-13 xenograft growth, and synergized with IRAK1 covalent inhibitor JH-X-119-01. PSP-0119 is metabolically stable, retaining 71% of parent compound at 60 minutes in human liver microsomes. In summary, IRAK4 degradation via PSP-0119 as a promising therapeutic strategy for treatment of FLT3-mutant AML.

cancer biology↗

bioRxiv2025

Unified Protein-Small Molecule Graph Neural Networks for Binding Site Prediction

Predicting small molecule binding sites on proteins remains a key challenge in structure-based drug discovery. While AlphaFold3 has transformed protein structure prediction, accurate identification of functional sites such as ligand binding pockets remains a distinct and unresolved problem. Graph neural networks have emerged as promising tools for this task, but most current approaches focus on local structural features and are trained on relatively small datasets, limiting their ability to model long-range protein-ligand interactions. Here, we develop YuelPocket, a new graph neural network that addresses these limitations through an innovative global graph design featuring a virtual joint node connecting all protein residues and small molecule atoms to capture long-range interactions while maintaining linear computational complexity. Trained on the comprehensive MOAD dataset containing over 38,000 protein-small molecule complexes, YuelPocket achieves exceptional performance with AUC-ROC values of 0.85 and 0.89 on COACH420 and Holo4k datasets, respectively.

bioinformatics↗

bioRxiv2025

A Diffusion-Based Framework for Designing Molecules in Flexible Protein Pockets

The design of molecules for flexible protein pockets represents a significant challenge in structure-based drug discovery, as proteins often undergo conformational changes upon ligand binding. While deep learning-based approaches have shown promise in molecular generation, they typically treat protein pockets as rigid structures, limiting their ability to capture the dynamic nature of protein-ligand interactions. Here, we introduce YuelDesign, a novel diffusion-based framework specifically developed to address this challenge. YuelDesign employs a new protein encoding scheme with a fully connected graph representation to encode protein pocket flexibility, a systematic denoising process that refines both atomic properties and coordinates, and a specialized bond reconstruction module tailored for de novo generated molecules. Our results demonstrate that YuelDesign generates molecules with favorable drug-likeness and low synthetic complexity. The generated molecules also exhibit diverse chemical functional groups, including some not even present in the training set. Redocking analysis reveals that the generated molecules exhibit docking energies comparable to native ligands. Additionally, a detailed analysis of the denoising process shows how the model systematically refines molecular structures through atom type transitions, bond dynamics, and conformational adjustments. Overall, YuelDesign presents a versatile framework for generating novel molecules tailored to flexible protein pockets, with promising implications for drug discovery applications.

bioinformatics↗

bioRxiv2025

Multimodal Bonds Reconstruction Towards Generative Molecular Design

Generative models such as diffusion-based approaches have transformed de novo drug design by enabling rapid generation of novel molecular structures in both 2D and 3D formats. However, accurate reconstruction of chemical bonds, especially from distorted geometries produced by generative models, remains a critical challenge. Here, we present YuelBond, a multimodal graph neural network framework for robust bonds reconstruction across three key scenarios: (i) recovery of bonds from accurate 3D atomic coordinates, (ii) reconstruction of chemically valid bonds in crude de novo generated compounds (CDGs) with perturbed geometries, and (iii) reassignment of bond orders in 2D topological graphs. YuelBond outperforms traditional rule-based methods such as RDKit, achieving 98.4% F1-score on standard 3D structures and maintaining strong performance (92.7% F1-score) on distorted CDGs, even when RDKit fails on most cases. Our results demonstrate that YuelBond enables accurate and reliable bond reconstruction from imperfect molecular data, bridging a critical gap in generative drug discovery pipelines.

bioinformatics↗

bioRxiv2025

Novel extracellular vesicle release pathway facilitated by toxic superoxide dismutase 1 oligomers

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease resulting in paralysis and death within three to five years. Mutations in over forty different proteins have been linked to ALS, leading to controversy whether ALS is one disease or many diseases with a similar phenotype. Mutations in Cu,Zn superoxide dismutase 1 (SOD1) are only found in 2-3% of ALS cases, yet misfolded SOD1 is found in both sporadic (sALS) and familial (fALS) patients. Yet, mutations in TDP-43 or FUS increase the level of misfolded SOD1 on extracellular vesicles (EVs). Additionally, small EVs isolated from ALS patient samples caused cell death of wild type motor neurons and myotubules. The toxicity and protein alterations of ALS EVs have led to the theory that EVs are responsible for the spread of ALS. We hypothesize that previously-identified toxic trimeric SOD1 is spreading on EVs in ALS and altering the spread of other ALS-related proteins, linking them to a common mechanism. To test our hypothesis, we isolate EVs from motor neuron-like cells expressing trimer stabilizing mutations and perform a sandwich enzyme-linked immunoassay (ELISA) (CD9 capture antibody) to quantify whether misfolded SOD1 and 17 other ALS-related proteins increase or decrease on EVs with trimer stabilization. We identify which EV release pathway is being affected by trimeric SOD1 utilizing endocytosis and exocytosis inhibitors, and determine if any specific EV-related proteins are altered with trimer stabilization. We establish that VAPB, VCP, and Stathmin-2 increase on EVs with trimer stabilization. The common pathway between SOD1 and three other ALS-associated proteins is affected by multiple pathways, including the Caveolae endocytosis pathway, suggesting a novel hybrid pathway of EV release present in ALS.

molecular biology↗

bioRxiv2024

Targeting IL-1/IRAK1/4 signaling in Acute Myeloid Leukemia Stem Cells Following Treatment and Relapse

Therapies for acute myeloid leukemia (AML) face formidable challenges due to relapse, often driven by leukemia stem cells (LSCs). Strategies targeting LSCs hold promise for enhancing outcomes, yet paired comparisons of functionally defined LSCs at diagnosis and relapse remain underexplored. We present transcriptome analyses of functionally defined LSC populations at diagnosis and relapse, revealing significant alterations in IL-1 signaling. Interleukin-1 receptor type I (IL1R1) and interleukin-1 receptor accessory protein (IL1RAP) were notably upregulated in leukemia stem and progenitor cells at both diagnosis and relapse. Knockdown of IL1R1 and IL1RAP reduced the clonogenicity and/or engraftment of primary human AML cells. In leukemic MLL-AF9 mice, Il1r1 knockout reduced LSC frequency and extended survival. To target IL-1 signaling at both diagnosis and relapse, we developed UR241-2, a novel interleukin-1 receptor-associated kinase 1 and 4 (IRAK1/4) inhibitor. UR241-2 robustly suppressed IL-1/IRAK1/4 signaling, including NF-{kappa}B activation and phosphorylation of p65 and p38, following IL-1 stimulation. UR241-2 selectively inhibited LSC clonogenicity in primary human AML cells at both diagnosis and relapse, while sparing normal hematopoietic stem and progenitor cells. It also reduced AML engraftment in leukemic mice. Our findings highlight the therapeutic potential of UR241-2 in targeting IL-1/IRAK1/4 signaling to eradicate LSCs and improve AML outcomes.

cancer biology↗

bioRxiv2024

Leveraging Transfer Learning for Predicting Protein-Small Molecule Interactions

A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: [~]104 proteins in a typical human cell offer a plausible 108 interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete datasets (103-104 protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small datasets. Yuel 2 is pre-trained on a large-scale dataset to learn intricate structural features and then fine-tuned on specialized datasets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics - Kd, Ki, and IC50 - between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.

bioinformatics↗

bioRxiv2024

Comprehensive mapping of the Interaction of levodopa and iron metabolism in Parkinson's disease

Levodopa remains the primary treatment for Parkinsons disease (PD), yet its long-term use has been associated with iron accumulation in the brain, a phenomenon linked to neurodegeneration. We utilize deep machine learning to determine plausible molecular mechanisms that may underlie the effects of levodopa on iron metabolism. Using the DRIFT platform, we performed a proteome-wide target identification of levodopa and uncovered significant interactions potentially involved in cellular iron transport. Pathway analysis revealed that levodopa may influence critical iron-related pathways, including the response of EIF2AK1 to heme deficiency, heme signaling, and ABC-family protein-mediated transport. These findings suggest that levodopa may contribute to iron dysregulation in PD by interacting with iron transporters and modulating iron-related pathways. Because levodopa is used at relatively high doses in PD, our findings provide new insight into secondary effects unrelated to being a precursor of dopamine. This highlights the need for careful consideration of its effects on iron metabolism as a consequence of use in the long-term management of PD. Further experimental validation is required to confirm these interactions, and also to explore potential strategies to mitigate iron-related side effects while preserving therapeutic efficacy.

bioinformatics↗

bioRxiv2024

APOE regulates the transport of GM1

Apolipoprotein E (APOE) is responsible for lipid transport, including cholesterol transport and clearance. While the {varepsilon}4 allele of APOE (APOE4) is associated with a significant genetic risk factor for late-onset Alzheimers disease (AD), no mechanistic understanding of its contribution to AD etiology has been established yet. In addition to cholesterol, monosialotetrahexosylganglioside (GM1) is a crucial lipid component in cell membranes and has been implicated in promoting the aggregation of amyloid beta protein (A{beta}), a key protein associated with AD. Here, we ask whether there are direct interactions between APOE and GM1 that further impact AD pathology. We find that both APOE3 and APOE4 exhibit superior binding affinity to GM1 compared to cholesterol and have an enhanced cellular uptake to GM1 lipid structures than cholesterol lipid structures. APOE regulates the transport process of GM1 depending on the cell type, which is influenced by the expression of APOE receptors in different cell lines and alters GM1 contents in cell membranes. We also find that the presence of GM1 alters the secondary structure of APOE3 and APOE4 and enhances the binding affinity between APOE and its receptor low-density lipoprotein receptor (LDLR), consequently promoting the cellular uptake of lipid structures in the presence of APOE. To understand the enhanced cellular uptake observed in lipid structures containing 20% GM1, we determined the distribution of GM1 on the membrane and found that GM1 clustering in lipid rafts, thereby supporting the physiological interaction between APOE and GM1. Overall, we find that APOE plays a regulatory role in GM1 transport, and the presence of GM1 on the lipid structures influences this transport process. Our studies introduce a plausible direct link between APOE and AD etiology, wherein APOE regulates GM1, which, in turn, promotes A{beta} oligomerization and aggregation.

neuroscience↗

bioRxiv2024

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells

Misfolded soluble trimeric species of superoxide dismutase 1 (SOD1) are associated with increased death in neuron-like cell models and greater disease severity in amyotrophic lateral sclerosis (ALS) patients compared to insoluble protein aggregates. The mechanism by which structurally independent SOD1 trimers cause cellular toxicity is unknown but may be a driver of disease pathology. Here, we uncovered the SOD1 trimer interactome - a map of potential tissue-selective protein binding partners in the brain, spinal cord, and skeletal muscle. We identified binding partners and key pathways associated with SOD1 trimers, comparing them to those of wild-type SOD1 dimers. We found that trimers may affect normal cellular functions such as dendritic spine morphogenesis and synaptic function in the central nervous system and cellular metabolism in skeletal muscle. We also identified key pathways using transcriptomic data from motor neuron-like cells (NSC-34s) expressing SOD1 trimers. We discovered differential gene expression in cells that express SOD1 trimers with selective enrichment of genes responsible for protein localization to membranes and a global upregulation of cellular senescence pathways. We performed detailed computational and biochemical characterization of protein binding for septin-7, an SOD1 trimer binding partner. We found that septin-7 preferentially binds SOD1 trimers and co-localizes in neuron-like cells. We explore a double-edged sword theory regarding the toxicity of SOD1 trimers. These trimers are implicated in causing dysfunction not only in the central nervous system but also in muscle tissues. Our investigation highlights key protein factors and pathways within each system, revealing a plausible intersection of genetic and pathophysiological mechanisms in ALS through interactions involving SOD1 trimers. SummaryIn amyotrophic lateral sclerosis (ALS), misfolded soluble species of superoxide dismutase 1 (SOD1) are associated with disease severity and, specifically, trimeric forms of SOD1 are toxic in neuron-like cells compared to insoluble aggregates. The role of toxic SOD1 trimers in cells is unknown. Using molecular engineering and pull-down experiments, we found that SOD1 trimers have tissue-selective protein interactions that affect pathways such as dendritic spine morphogenesis and synaptic function in the nerves, energy, and amino acid metabolism in skeletal muscle. We investigated the SOD1 trimer transcriptome to reveal a global upregulation of genes associated with cellular senescence compared to SOD1 dimers. We further validated septin-7, a shared brain and spinal cord protein binding hit, using integrative computational and biochemical approaches, and confirmed that septin-7 binds SOD1 trimers and not native dimers. Taken together, we show evidence that SOD1 trimers play a central role in the convergence of ALS pathophysiology. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=117 SRC="FIGDIR/small/586186v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@35e9d9org.highwire.dtl.DTLVardef@18bbc3corg.highwire.dtl.DTLVardef@f30b02org.highwire.dtl.DTLVardef@57e51b_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Septins Enable T Cell Contact Guidance via Amoeboid-Mesenchymal Switch

Lymphocytes exit circulation and enter in-tissue guided migration toward sites of tissue pathologies, damage, infection, or inflammation. By continuously sensing and adapting to the guiding chemo-mechano-structural properties of the tissues, lymphocytes dynamically alternate and combine their amoeboid (non-adhesive) and mesenchymal (adhesive) migration modes. However, which mechanisms guide and balance different migration modes are largely unclear. Here we report that suppression of septins GTPase activity induces an abrupt amoeboid-to-mesenchymal transition of T cell migration mode, characterized by a distinct, highly deformable integrin-dependent immune cell contact guidance. Surprisingly, the T cell actomyosin cortex contractility becomes diminished, dispensable and antagonistic to mesenchymal-like migration mode. Instead, mesenchymal-like T cells rely on microtubule stabilization and their non-canonical dynein motor activity for high fidelity contact guidance. Our results establish septins GTPase activity as an important on/off switch for integrin-dependent migration of T lymphocytes, enabling their dynein-driven fluid-like mesenchymal propulsion along the complex adhesion cues. SIGNIFICANCE STATEMENTDeciphering mechanisms of guided lymphocyte migration paves the way towards effective immunotherapies for the extracellular matrix-rich tissues, such as solid tumors. Here we demonstrate that T cell septins GTPase activity regulates both actomyosin and microtubules, alternately enhancing either of these two major motor systems. Surprisingly, the suppression of septin GTPase activity also induces a highly guided integrin-dependent mesenchymal-like migration directed by the extracellular matrix proteins. The phenomenon of guided mesenchymal-like migration of T cells relies on the microtubules and microtubule-based dynein motors that are responsible for the force generation, powering guided T cell motility. This finding opens a new perspective for future studies of septin GTPases in a context of the optimisation of T cell-based immunotherapies for the solid tissues.

cell biology↗

bioRxiv2023

Allosteric regulation of kinase activity in living cells

The dysregulation of protein kinases is associated with multiple diseases due to the kinases involvement in a variety of cell signaling pathways. Manipulating protein kinase function, by controlling the active site, is a promising therapeutic and investigative strategy to mitigate and study diseases. Kinase active sites share structural similarities making it difficult to specifically target one kinase, allosteric control allows specific regulation and study of kinase function without directly targeting the active site. Allosteric sites are distal to the active site but coupled via a dynamic network of inter-atomic interactions between residues in the protein. Establishing an allosteric control over a kinase requires understanding the allosteric wiring of the protein. Computational techniques offer effective and inexpensive mapping of the allosteric sites on a protein. Here, we discuss methods to map and regulate allosteric communications in proteins, and strategies to establish control over kinase functions in live cells and organisms. Protein molecules, or "sensors" are engineered to function as tools to control allosteric activity of the protein as these sensors have high spatiotemporal resolution and help in understanding cell phenotypes after immediate activation or inactivation of a kinase. Traditional methods used to study protein functions, such as knockout, knockdown, or mutation, cannot offer a sufficiently high spatiotemporal resolution. We discuss the modern repertoire of tools to regulate protein kinases as we enter a new era in deciphering cellular signaling and developing novel approaches to treat diseases associated with signal dysregulation.

biophysics↗

bioRxiv2023

Quantifying network behavior in the rat prefrontal cortex: a reproducibility crisis

The question of how consciousness and behavior arise from neural activity is fundamental to understanding the brain, and to improving the diagnosis and treatment of neurological and psychiatric disorders. There is significant murine and primate literature on how behavior is related to the electrophysiological activity of the medial prefrontal cortex and its role in working memory processes such as planning and decision-making. Existing experimental designs, however, have insufficient statistical power to unravel the complex processes of the prefrontal cortex. We therefore examined the theoretical limitations of such experiments, providing concrete guidelines for robust and reproducible science. We piloted the use of dynamic time warping and associated statistical tests to data from neuron spike trains and local field potentials, to quantify neural network synchronicity and correlate neuroelectrophysiology with rat behavior. Our results indicate the statistical limitations of existing data, making meaningful comparison between dynamic time warping with traditional Fourier and wavelet analysis currently impossible until larger and cleaner datasets are available. Significance StatementThe prefrontal cortex is important in decision-making, yet no robust method currently exists to correlate neuron firing in the PFC to behavior. We argue that existing experimental designs are ill-suited to addressing these scientific questions, and we propose a potential method using dynamic time warping to analyze PFC neural electrical activity. We conclude that careful curation of experimental controls is needed to separate true neural signals from noise accurately.

neuroscience↗

bioRxiv2022

Adaptation-proof SARS-CoV-2 vaccine design

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface spike glycoprotein - a major antibody target - is critical for virus entry via engagement of human angiotensin-converting enzyme 2 (ACE2) receptor. Despite successes with existing vaccines and therapies that primarily target the receptor binding domain (RBD) of the spike protein, the susceptibility of RBD to mutations provides escape routes for the SARS-CoV-2 from neutralizing antibodies. On the other hand, structural conservation in the spike protein can be targeted to reduce escape mutations and achieve broad protection. Here, we designed candidate stable immunogens that mimic surface features of selected conserved regions of spike protein through epitope grafting, in which we present the target epitope topology on diverse heterologous scaffolds that can structurally accommodate the spike epitopes. Structural characterization of the epitope-scaffolds showed stark agreement with our computational models and target epitopes. The sera from mice immunized with engineered designs display epitope-scaffolds and spike binding activity. We also demonstrated the utility of the designed epitope-scaffolds in diagnostic applications. Taken all together, our study provides important methodology for targeting the conserved, non-RBD structural motifs of spike protein for SARS-CoV-2 epitope vaccine design and demonstrates the potential utility of epitope grafting in rational vaccine design.

bioengineering↗