Biology subjects

Tuszynski, J. A.

Publications and source records attributed to Tuszynski, J. A..

12 recordsLinked to original sources

bioRxiv2025

Potent broad-spectrum anti-coronaviral frameshift inhibitors from virtual screen of RNA binding

Coronavirus genomes contain an RNA pseudoknot that directs -1 programmed ribosomal frame-shifting (-1 PRF) to control expression of viral proteins crucial for replication. Ligands that inhibit -1 PRF can thus attenuate viral propagation and have potential as drugs for limiting corona-virus infections. To search for novel small-molecule frameshift inhibitors with anticoronaviral activity, we computationally screened over 14 million compounds for binding to the SARS-CoV-2 pseudoknot, followed by experimental validation of the top hits for inhibition of -1 PRF and viral replication. We identified multiple potent -1 PRF inhibitors, effective at nM concentrations, some of which significantly suppressed SARS-CoV-2 replication in cell culture. Several compounds also inhibited -1 PRF in multiple representative bat coronaviruses, indicating broad-spectrum activity. These results showcase the promise of viral RNA structures like frameshift-stimulatory pseudoknots as targets for broad-spectrum antiviral drugs.

biophysics↗

bioRxiv2024

Application of Supervised Machine Learning Models for Drug-Action Prediction Towards Nuclear Type I Receptors

1.Interactions between drugs can lead to adverse side effects for patients taking combination therapies to treat complex diseases such as cancer. Knowledge of drug-action towards a receptor would allow these drug-drug interactions to be predicted, and in this study, we trained a total of 5 different machine learning models to classify whether a given drug was an agonist (activator), antagonist (blocker), or a decoy (non-binder) to each of the androgen, estrogen, glucocorticoid, and progesterone receptors. The classification performance and efficiency, measured in training time, of the decision tree, naive Bayes, neural network, random forest, and support vector machine models for each receptor were then compared. The results showed that the decision tree and naive Bayes models were best suited for drug-action prediction across all receptors while only requiring minutes of training time at most. Future work will focus on increasing the prediction accuracy of antagonist drugs, integrating experimental data during training, and using other targets outside of nuclear type I receptors.

bioinformatics↗

bioRxiv2024

Supervised Machine Learning for Bioelectrical Cellular Networks

1.Cells utilize bioelectricity to form networks as well as regulate and control a variety of processes such as apoptosis, tumor suppression, and voltage-gated ion channels. In-silico modeling of bioelectrical networks can be performed using BETSE, an application that models gap junctions and ion channel activity of networked cells, but its usage of matrix-based differential equations to estimate these properties limits simulations based on the amount of computational resources available. To alleviate this issue, we trained a total of 8 machine learning models to replace three core functions of BETSE, that is, 1) predicting the average transmembrane potential (Vmem) of an entire cellular network, 2) predicting the Vmem of each individual cell within the network, and finally, 3) predicting the average ion concentrations of sodium, potassium, chloride, and calcium within the cell network. For objective 1, the random forest model was shown to be most performant over all 4 scoring metrics, in objective 2 both the decision tree and k-nearest neighbors models scored best in half of all metrics, and for objective 3 the super learner, a meta-learner comprised of multiple base learners, scored best among all scoring metrics. Overall, these models provide a more resource efficient method of predicting properties of bioelectric cellular networks, and future work will include further properties such as temperature and pressure.

bioinformatics↗

bioRxiv2024

A Systems Biology Analysis of Chronic Lymphocytic Leukemia

Whole-genome sequencing has revealed that TP53, NOTCH1, ATM, SF3B1, BIRC3, ABL, NXF1, BCR, ZAP70 are often mutated in CLL, but not consistently across all CLL patients. This paper employs a statistical thermo-dynamics approach in combination with the systems biology of the CLL protein-protein interaction networks to identify the most significant participant proteins in the cancerous transformation. Betti number (a topology of complexity) estimates highlight a protein hierarchy, primarily in the Wnt pathway known for aberrant CLL activation. These individually identified proteins suggest a network-targeted strategy over single-target drug development. The findings advocate for a multi-target inhibition approach, limited to several key proteins to minimize side effects, thereby providing a foundation for designing therapies. This study emphasizes a shift towards a comprehensive, multi-scale analysis to enhance personalized treatment strategies for CLL, which could be experimentally validated using siRNA or small molecule inhibitors. The result is not just the identification of these proteins but their rank-order, offering a potent signal amplification in the context of the 20,000 proteins produced by the human body, thus providing a strategic basis for therapeutic intervention in CLL, underscoring the necessity for a more holistic, cellular, chromosomal, and genome-wide study to develop tailored treatments for CLL patients. Author SummaryChronic Lymphocytic Leukemia (CLL) is a unique and slowly progressing cancer affecting white blood cells, and research on CLL has highlighted the inconsistency of gene mutations across patients. Using a novel approach that merges statistical thermodynamics and systems biology, this research examines the CLL protein-protein interaction networks to pinpoint proteins integral to the onset of the disease. Betti number (a topology of complexity) estimates, which measure the importance of individual proteins when removed from the network, helped identify numerous potential therapeutic targets, notably within the Wnt signaling pathway, a pathway implicated in various cellular processes and known for its defective expression in CLL. The finding advocates for a multi-target inhibition approach, focusing on several key proteins to minimize side effects, thereby laying a foundation for designing more effective therapies for CLL. This paper emphasizes the potential benefits of a comprehensive study, spanning cellular to genome-wide scales, to design personalized treatments for CLL patients.

systems biology↗

bioRxiv2020

Modeling the structure of the frameshift stimulatory pseudoknot in SARS-CoV-2 reveals multiple possible conformers

The coronavirus causing the COVID-19 pandemic, SARS-CoV-2, uses -1 programmed ribosomal frameshifting (-1 PRF) to control the relative expression of viral proteins. As modulating -1 PRF can inhibit viral replication, the RNA pseudoknot stimulating -1 PRF may be a fruitful target for therapeutics treating COVID-19. We modeled the unusual 3-stem structure of the stimulatory pseudoknot of SARS-CoV-2 computationally, using multiple blind structural prediction tools followed by s-long molecular dynamics simulations. The results were compared for consistency with nuclease-protection assays and single-molecule force spectroscopy measurements of the SARS-CoV-1 pseudoknot, to determine the most likely conformations. We found several possible conformations for the SARS-CoV-2 pseudoknot, all having an extended stem 3 but with different packing of stems 1 and 2. Several conformations featured rarely-seen threading of a single strand through the junction formed between two helices. These structural models may help interpret future experiments and support efforts to discover ligands inhibiting -1 PRF in SARS-CoV-2.

biophysics↗

bioRxiv2020

Multifractal Nature of Microtubule Dynamic Instability Process

The irregularity of growing and shortening patterns observed experimentally in microtubules reflects a dynamical system that fluctuates stochastically between assembly and disassembly phases. The observed time series of microtubule lengths have been extensively analyzed to shed light on structural and dynamical properties of microtubules. Here, for the first time, Multifractal Detrended Fluctuation analysis (MFDFA) has been employed to investigate the multifractal and topological properties of both experimental and simulated microtubule time series. We find that the time dependence of microtubule length possesses true multifractal characteristics and cannot be described by monofractal distributions. Based on the multifractal spectrum profile, a set of multifractal indices have been calculated that can be related to the level of dynamical activities of microtubules. We also show that the resulting multifractal spectra for the simulated data might not be comparable with experimental data. Statement of SignificanceMicrotubules are some of the most important subcellular structures involved in a multitude of functions in all eukaryotic cells. In addition to their cylindrical geometry, their polymerization/depolymerization dynamics, termed dynamic instability, is unique among all protein polymers. In this paper we demonstrate that there is a very specific mathematical representation of microtubule growth and shrinkage time series in terms of multifractality. We further show that using this characteristic, one can distinguish real experimental data from synthetic time series generated from computer simulations.

biophysics↗

bioRxiv2020

Molecular Dynamics Simulations Indicate the COVID-19 Mpro Is Not a Viable Target for Small-Molecule Inhibitors Design

The novel coronavirus whose outbreak took place in December 2019 continues to spread at a rapid rate worldwide. In the absence of an effective vaccine, inhibitor repurposing or de novo drug design may offer a longer-term strategy to combat this and future infections due to similar viruses. Here, we report on detailed classical and mix-solvent molecular dynamics simulations of the main protease (Mpro) enriched by evolutionary and stability analysis of the protein. The results were compared with those for a highly similar SARS Mpro protein. In spite of a high level of sequence similarity, the active sites in both proteins show major differences in both shape and size indicating that repurposing SARS drugs for COVID-19 may be futile. Furthermore, analysis of the binding sites conformational changes during the simulation time indicates its flexibility and plasticity, which dashes hopes for rapid and reliable drug design. Conversely, structural stability of the protein with respect to flexible loop mutations indicates that the virus mutability will pose a further challenge to the rational design of small-molecule inhibitors. However, few residues contribute significantly to the protein stability and thus can be considered as key anchoring residues for Mpro inhibitor design.

molecular biology↗

bioRxiv2019

How signals of calcium ions initiate the beats of cilia and flagella

Cilia and flagella are cell organelles serving basic roles in cellular motility. Ciliary movement is performed by a sweeping-like repeated bending motion, which gives rise to a self-propagating \"ciliary beat\". The hallmark structure in cilia is the axoneme, a stable architecture of microtubule doublets. The motion of axoneme is powered by the axonemal dynein motor family powered by ATP hydrolysis. It is still unclear how the organized beat of cilium and flagella emerges from the combined action of hundreds of dynein molecules. It has been hypothesized that such coordination is mediated by mechanical stress due to transverse, radial or sliding deformations. The beating asymmetry is crucial for airway ciliary function and it requires tubulin glutamination a unique posttranslational modification of C-termini of constituent microtubules that is highly abundant in cilia and flagella. The exact role of tubulin glutamination in ciliary or flagellar function is still unclear. Here we examine the role of calcium (Ca2+) ions based on the experimental evidences that the flagellar asymmetry can be increased due to the entry of extracellular Ca2+ through, for example, nimodipine-sensitive pathway located in the flagella. We propose a new scenario based on the polyelectrolyte properties of cellular microtubules (MTs) such that dynamic influx of Ca2+ ions provides the initiation and synchronization of dynein sliding along microtubules. We also point out the possible interplay between tubulin polyglutaminated C-termini and localized pulses of Ca2+ ions along microtubules.

biophysics↗

bioRxiv2019

Tubulin as a sensitive target of nanosecond-scale intense electric field: quantitative insights from molecular dynamics simulations

Intense pulsed electric fields are known to act at the cell membrane level and are already being exploited in biomedical and biotechnological applications. However, it is not clear if intra-cellular components such as cytoskeletal proteins could be directly influenced by electric pulses within biomedically-attainable parameters. If so, a molecular mechanism of action could be uncovered for therapeutic applications of such electric fields. To help clarify this question, we first identified that a tubulin heterodimer is a natural biological target for intense electric fields due to its exceptional electric properties and crucial roles played in cell division. Using molecular dynamics simulations, we then demonstrated that an intense - yet experimentally attainable - electric field of nanosecond duration can affect the {beta}-tubulins C-terminus conformations and also influence local electrostatic properties at the GTPase as well as the binding sites of major tubulin drugs site. Our results suggest that intense nanosecond electric pulses could be used for physical modulation of microtubule dynamics. Since a nanosecond pulsed electric field can penetrate the tissues and cellular membranes due to its broadband spectrum, our results are also potentially significant for the development of novel therapeutic protocols. Author summary/{beta}-tubulin heterodimers are the basic building blocks of microtubules, that form diverse cellular structures responsible for essential cell functions such as cell division and intracellular transport. The ability of tubulin protein to adopt distinct conformations contributes to control the architecture of microtubule networks, microtubule-associated proteins, and motor proteins; moreover, it regulates microtubule growth, shrinkage, and the transitions between these states. Previous recent molecular dynamics simulations demonstrated that the interaction of the tubulin protein macrodipole with external electric field modifies orientation and conformations of key loops involved in lateral contacts: as a result, the stability of microtubules can be modulated by such fields. In this study, we seek to exploit these findings by investigating the possibility of fine-tuning the dipolar properties of binding sites of major drugs, by means of the action of electric fields. This may open the way to control tubulin-drug interactions using electric fields, thus modulating and altering the biological functions relative to the molecular vectors of microtubule assembly or disassembly. The major finding of our study reveals that intense (> 20 MV/m) ultra-short (30 ns) electric fields induce changes in the major residues of selected binding sites in a field strength-dependent manner.

biophysics↗

bioRxiv2018

Gibbs Free Energy of Protein-Protein Interactions Correlates with ATP Production in Cancer Cells

In this paper we analyze several cancer cell types from two seemingly independent angles: (a) the over-expression of various proteins participating in protein-protein interaction networks and (b) a metabolic shift from oxidative phosphorylation to glycolysis. We use large data sets to obtain a thermodynamic measure of the protein-protein interaction network, namely the associated Gibbs free energy. We find a reasonably strong inverse correlation between the percentage of energy production via oxidative phosphorylation and the Gibbs free energy of the protein networks. The latter is a measure of functional dysregulation within the cell. Our findings corroborate earlier indications that signaling pathway upregulation in cancer cells are linked to the metabolic shift known as the Warburg effect, hence these two seemingly independent characteristics of cancer phenotype may be interconnected.

biophysics↗

bioRxiv2018

Nonlinear calcium ion waves along actin filaments control active hair-bundle motility

Actin filaments are highly dynamic semiflexible cellular biopolymers with diverse functions, such as cell motility. They also play the role of conduits for propagation of calcium ion waves. In this paper, we propose a new biophysical model that describes how actin filaments with their polyelectrolyte properties serve as pathways for calcium ion flows in hair cells. We show this can be utilized for the tuning of force-generating myosin motors. In this model, we unify the calcium nonlinear dynamics involved in the control of the myosin adaptation motors with mechanical displacements of hair- bundles. The model shows that the characteristic time scales fit reasonably well with the available experimental data for spontaneous oscillations in the inner ear. This model offers promises to fill a gap in our understanding of the role of calcium ion nonlinear dynamics in the regulation of processes in the auditory cells of the inner ear.

biophysics↗

bioRxiv2017

Monitoring Microtubule Mechanical Vibrations via Optomechanical Coupling

The possible disruption of a microtubule during mitosis can control the duplication of a cancer cell. Cancer detection and treatment may be possible based on the detection and control of microtubule mechanical oscillations in cells throughexternal fields (e.g. electromagnetic or ultrasound). However, little is known about the dynamic (high-frequency) mechanical properties of microtubules. Here we propose to control the vibrations of a doubly clamped microtubule by tip electrodes and to detect its motion via the optomechanical coupling between the vibrational modes of the microtubule and an optical cavity. In the presence of a red-detuned strong pump laser, this coupling leads to optomechanical induced transparency of an optical probe field, which can be detected with state-of-the art technology. The center frequency and linewidth of the transparency peak give the resonance frequency and damping rate of the microtubule respectively, while the height of the peak reveals information about the microtubule-cavity field coupling. Our method should yield new knowledge about the physical properties of microtubules, which will enhance our capability to design physical cancer treatment protocols as alternatives to chemotherapeutic drugs.

biophysics↗