Biology subjects

Kolomeisky, A.

Publications and source records attributed to Kolomeisky, A..

16 recordsLinked to original sources

bioRxiv2026

A Minimal Stochastic Model of Microbial Ecological Dynamics in a Single-Species-Single-Resource Setting

Microbes exhibit complex dynamic behavior as the result of a large number of biochemical processes, spatial and temporal interactions, environmental variations, and evolutionary pressure. Although significant progress has been achieved in understanding microbial ecological dynamics, multiple open questions remain, including the microscopic mechanisms of growth and the roles of nutrients and stochasticity. In this work, we present a minimal theoretical approach to clarify the link between consumption of resources by microbes and their growth. A stochastic model that accounts for a single microbial species consuming a single type of resource while growing via cell division is studied analytically and via Monte Carlo computer simulations. We identify three distinct dynamical regimes of microbial growth determined by the relative magnitudes of resource uptake and division rates and initial conditions. We also show that stochasticity influences the dynamic behavior when the amounts of microbes or resources are low. The model recovers Monod growth kinetics and provides a mechanistic interpretation of the Monod constant and maximal growth rate. The theoretical framework presented captures a wide spectrum of dynamic behaviors in microbial systems, providing a clearer microscopic picture to explain their underlying complex mechanisms. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/735782v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1395856org.highwire.dtl.DTLVardef@1d68f59org.highwire.dtl.DTLVardef@15d328borg.highwire.dtl.DTLVardef@1a14d9b_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Stochastic Mechanism of Dominant Follicle Selection: Selection of One Suppresses Selection of Others

One of the most critical steps in human reproduction is the selection of the dominant follicle when a single follicle is chosen from a large group of follicles to ovulate. Although this process involves complex hormonal regulation, the complete microscopic picture of unique selectivity remains unclear. We propose a novel stochastic mechanism for dominant follicle selection that incorporates the actions of the most relevant hormones, follicle-stimulating hormone (FSH) and estradiol. Our theoretical picture suggests the following sequence of events. As soon as the FSH concentration reaches the critical threshold, one of the available follicles is randomly selected, which immediately stimulates the production of estradiol, which, via a negative feedback mechanism, suppresses further FSH production, lowering its concentration below the critical threshold. This suppression limits the time window for the possible second follicle selection event, allowing only a single follicle to be selected. Based on this picture, a minimal quantitative theoretical model of dominant follicle selection is developed and analyzed using analytical calculations and computer simulations. Theoretical analysis shows how the interplay between different parameters that govern follicle selection leads to high selectivity. Our theoretical approach can explain some key known observations, providing a quantitative tool for analyzing biological reproduction phenomena.

biophysics↗

bioRxiv2024

Covalent Stabilization of Collagen Mimetic Triple Helices and Assemblies by Dopa Crosslinking

Creating thermally stable collagen mimetic peptides (CMPs) is a persistent challenge. Nature leverages covalent crosslinkings to stabilize collagens signature triple helical tertiary structure and higher-order assemblies. Herein, we demonstrate that crosslinkings between levodopa (Dopa) and lysine, amino acids present in native collagen, can covalently stabilize the triple helix in collagen mimetic peptides. Since alkaline conditions catalyze the oxidation of the catechol on Dopa to a benzoquinone, while being in proximity to the nucleophilic lysine, we hypothesized that this reaction could be a facile method to covalently capture the supramolecular structure of CMPs by simply increasing the pH of the aqueous solvent with the addition of sodium hydroxide. This covalent capture strategy successfully stabilizes CMP homotrimers and a de novo designed ABC-type heterotrimer demonstrating that the Lysine-Dopa covalent bond is best templated by a supramolecular, axial cation-{pi} pairwise interaction. In nature, collagen can hierarchically assemble into fibers. This behavior can be mimicked with the self-assembly of CMPs, but the resulting nanofibers typically exhibit thermal stability below body temperature. In a final application, we demonstrate that Dopa-Lysine covalent capture also enhances the thermal stability of CMP nanofibers well above 37 {degrees}C. This biomimetic covalent capture strategy can stabilize a wide variety of CMP systems and potentially enable the biomedical application of these materials.

biochemistry↗

bioRxiv2024

Heterotrimeric Collagen Helix with High Specificity of Assembly Results in a Rapid Rate of Folding

The most abundant natural collagens form heterotrimeric triple helices. Synthetic mimics of collagen heterotrimers have been found to fold slowly, even compared to the already slow rates of homotrimeric helices. These prolonged folding rates are not understood and have not been studied. This work compares three heterotrimeric collagen mimics stabilities, specificities and folding rates. One of these was designed through a computational-assisted approach, resulting in a well-controlled composition and register, in addition to providing increased amino acid diversity and excellent specificity. The crystal structure of this heterotrimer elucidates the composition, register and geometry of pairwise cation-{pi} and axial and lateral salt bridges. Complementary experimental methods of circular dichroism and NMR suggest the folding paradigm is frustrated by unproductive, competing heterotrimer species and these species must completely unwind to the monomeric state before refolding into the thermodynamically favored assembly. This collagen heterotrimer, which displays the best reported thermal specificity, was also found to fold much faster (hours vs days) than comparable, well-designed systems. The heterotrimeric collagen folding rate was observed to be both concentration and temperature-independent, suggesting a complex, multi-step mechanism. These results suggest heterotrimer folding kinetics are dominated by frustration of the energy landscape caused by competing triple helices.

biophysics↗

bioRxiv2023

Differences in Relevant Physicochemical Properties Correlate with Synergistic Activity of Antimicrobial Peptides

With the urgent need for new medical approaches due to increased bacterial resistance to antibiotics, antimicrobial peptides (AMPs) have been considered as potential treatments for infections. Experiments indicate that combinations of several types of AMPs might be more effective at inhibiting bacterial growth with reduced toxicity and a lower likelihood of inducing bacteria resistance. The molecular mechanisms of AMP-AMP synergistic antimicrobial activity, however, remain not well understood. Here, we present a theoretical approach that allows us to relate the physicochemical properties of AMPs and their antimicrobial cooperativity. A concept of physicochemical similarity is introduced, and it is found that less similar AMPs with respect to certain physicochemical properties lead to greater synergy because of their complementary antibacterial actions. The analysis of correlations between the similarity and the antimicrobial properties allows us to effectively separate synergistic from non-synergistic AMPs pairs. Our theoretical approach can be used for the rational design of more effective AMPs combinations for specific bacterial targets, for clarifying the mechanisms of bacterial elimination, and for a better understanding of cooperativity phenomena in biological systems. Author summaryIt is impossible to imagine modern medicine without antibiotics. But there is a growing problem of increased bacterial resistance to them. These considerations stimulated a search for novel methods to defend against infections. Antimicrobial peptides (AMPs) came out as powerful antibacterial agents. It was also found that combinations of AMPs are even more efficient than individual peptides. The mechanisms of such synergistic activities, however, are not understood. We developed a computational framework that allows us to connect the physicochemical properties of AMPs and their abilities to cooperatively eliminate infections. It is found that less similar peptides might exhibit synergy because of their complementary antibacterial properties. Our theoretical approach might lead to a better rational design of new antimicrobial drugs.

biophysics↗

bioRxiv2023

Dynamics of Single-Base Editing: Theoretical Analysis

Recent experimental advances led to the development of DNA base editors (BEs) with a single-nucleotide precision that is critical for future progress in various scientific and technological fields. The molecular mechanisms of single-base discrimination, however, remain not well understood. Using a recently developed stochastic approach, we theoretically investigated the dynamics of single-base editing. More specifically, transient and mean times to edit "TC" motifs by cytosine BEs are explicitly evaluated for correct (target) and incorrect (bystander) locations on DNA. In addition, the effect of mutations on the dynamics of the single-base edition is also analyzed. It is found that for most ranges of parameters, it is possible to temporarily separate target and bystander products of base editing, supporting the idea of dynamic selectivity as a method of improving the precision of single-base editing. We conclude that to improve the efficiency of single-base editing, selecting the probability or selecting the time requires different strategies. Physical-chemical arguments to explain the observed dynamic properties are presented. The theoretical analysis clarifies some important aspects of molecular mechanisms of selective base editing.

biophysics↗

bioRxiv2023

Assembly of Synaptic Protein-DNA Complexes: Critical Role of Non-Specific Interactions

The synaptic protein-DNA complexes, formed by specialized proteins that bridge two or more distant sites on DNA, are critically involved in various genetic processes. However, the molecular mechanism by which the protein searches for these sites and how it brings them together is not well understood. Our previous studies directly visualized search pathways used by SfiI, and we identified two pathways, DNA threading and site-bound transfer pathways, specific to the site search process for synaptic DNA-protein systems. To investigate the molecular mechanism behind these site search pathways, we assembled complexes of SfiI with various DNA substrates corresponding to different transient states and measured their stability using a single-molecule fluorescence approach. These assemblies corresponded to specific-specific (synaptic), non-specific-non-specific (non-specific), and specific-non-specific (pre-synaptic) SfiI-DNA states. Unexpectedly, there was an elevated stability in pre-synaptic complexes assembled with specific and non-specific DNA substrates has been found. To explain these surprising observations, a theoretical approach that describes the assembly of these complexes and compares the predictions with the experiment is developed. The theory explains this effect by utilizing entropic arguments, according to which, after the partial dissociation, the non-specific DNA template has multiple possibilities of rebinding, effectively increasing the stability. Such difference in the stabilities of SfiI complexes with specific and non-specific DNA explains the utilization of threading and site-bound transfer pathways in the search process of synaptic protein-DNA complexes discovered in the time-lapse AFM experiments.

biophysics↗

bioRxiv2023

Theoretical understanding of evolutionary dynamics on inhomogeneous networks

Evolution is the main feature of all biological systems that allows populations to change their characteristics over successive generations. A powerful approach to understand evolutionary dynamics is to investigate fixation probabilities and fixation times of novel mutations on networks that mimic biological populations. It is now well established that the structure of such networks can have dramatic effects on evolutionary dynamics. In particular, there are population structures that might amplify the fixation probabilities while simultaneously delaying the fixation events. However, the microscopic origins of such complex evolutionary dynamics remain not well understood. We present here a theoretical investigation of the microscopic mechanisms of mutation fixation processes on inhomogeneous networks. It views evolutionary dynamics as a set of stochastic transitions between discrete states specified by different numbers of mutated cells. By specifically considering star networks, we obtain a comprehensive description of evolutionary dynamics. Our approach allows us to employ physics-inspired free-energy landscape arguments to explain the observed trends in fixation times and fixation probabilities, providing a better microscopic understanding of evolutionary dynamics in complex systems.

biophysics↗

bioRxiv2022

Predicting Antimicrobial Activity for Untested Peptide-Based Drugs Using Collaborative Filtering and Link Prediction

The increase of bacterial resistance to currently available antibiotics has underlined the urgent need to develop new antibiotic drugs. Antimicrobial peptides (AMPs), alone or in combination with other peptides and/or existing antibiotics, have emerged as promising candidates for this task. However, given that there are thousands of known AMPs and an even larger number can be synthesized, it is inefficient to comprehensively test all of them using standard wet lab experimental methods. These observations stimulated an application of machine-learning methods to identify promising AMPs. Currently, machine learning studies frequently combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets of antimicrobial activity disqualifies the application of traditional machine-learning methods or renders the results unreliable. Here we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacterias response to untested AMPs, AMP-AMP combinations, and AMP-antibiotic combinations based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to suggest new combinations that are likely to be effective. Our theoretical analysis of AMP physico-chemical features suggests that there is an optimal similarity between two different AMPs that exhibit strong synergistic behavior. This principle, alongside with our specific results, can be applied to find or design effective AMP-AMP combinations that are target-specific. Author summaryIt is well known that combinations of different antimicrobial peptides (AMPs), in comparison to single AMP species, can lead to more efficient antimicrobial activity, but the large number of possible combinations requires the application of efficient machine-learning methods. We developed a new approach consisting of collaborative filtering, link prediction, and AMP feature analysis to predict previously-unknown, bacteria-specific activity of AMP combinations, suggest novel synergistic AMP-antibiotic combinations, and guide future design of effective AMP-AMP combinations.

biophysics↗

bioRxiv2022

Sequence Dependent Internucleosomal Interactions Dominate Array Assembly

The organization of the nucleosome array is a critical component of the chromatin assembly into higher order structure as well as its function. Here we investigated the contribution of the DNA sequence and internucleosomal interactions to the organization of the nucleosomal arrays in compact structures using Atomic Force Microscopy. We assembled nucleosomes on DNA substrates allowing for the formation of tetranucleosomes. We found that nucleosomes are capable of forming constructs with the close positioning of nucleosomes with no discernible space between them, even in the case of assembled dinucleosomes. This morphology of the array is in contrast with that observed for arrays assembled with repeats of the nucleosome positioning motifs separated by uniform spacers. Simulated assembly of tetranucleosomes by random placement along the substrates revealed that nucleosome array compaction is promoted by the interaction of the nucleosomes. We developed a theoretical model to account for the role of DNA sequence and internucleosomal interactions in the formation of the nucleosome structures. These findings suggest that, in the chromatin assembly, the affinity of the nucleosomes to the DNA sequence and the strengths of the internucleosomal interactions are the two major factors defining the compactness of the chromatin.

biophysics↗

bioRxiv2021

DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins

AO_SCPLOWBSTRACTC_SCPLOWPrediction of side chain conformations of amino acids in proteins (also termed packing) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.

biophysics↗

bioRxiv2021

Single-Cell Stochastic Modeling of the Action of Antimicrobial Peptides on Bacteria

Antimicrobial peptides (AMPs) produced by multi-cellular organisms as their immune systems defense against microbes are actively considered as natural alternatives to conventional antibiotics. Although a substantial progress has been achieved in studying the AMPs, the microscopic mechanisms of their functioning remain not well understood. Here, we develop a new theoretical framework to investigate how the AMPs are able to efficiently neutralize the bacteria. In our minimal theoretical model, the most relevant processes, AMPs entering into and the following inhibition of the single bacterial cell, are described stochastically. Using complementary master equations approaches, all relevant features of bacteria clearance dynamics by AMPs, such as the probability of inhibition and the mean times before the clearance, are explicitly evaluated. It is found that both processes, entering and inhibition, are equally important for the efficient functioning of AMPs. Our theoretical method naturally explains a wide spectrum of efficiencies of existing AMPs and their heterogeneity at the single-cell level. Theoretical calculations are also consistent with existing single-cell measurements. Thus, the presented theoretical approach clarifies some microscopic aspects of the action of AMPs on bacteria.

biophysics↗

bioRxiv2020

Trade-Offs between Speed, Accuracy, and Dissipation in tRNAIle Aminoacylation

Living systems maintain a high fidelity in information processing through kinetic proofreading, a mechanism to preferentially remove incorrect substrates at the cost of energy dissipation and slower speed. Proofreading mechanisms must balance their demand for higher speed, fewer errors, and lower dissipation, but it is unclear how rates of individual reaction steps are evolutionary tuned to balance these needs, especially when multiple proofreading mechanisms are present. Here, using a discrete-state stochastic model, we analyze the optimization strategies in Escherichia coli isoleucyl-tRNA synthetase. Surprisingly, this enzyme adopts an economic proofreading strategy and improves speed and dissipation as long as the error is tolerable. Through global parameter sampling, we reveal a fundamental dissipation-error relation that bounds the enzymes optimal performance and explains the importance of the post-transfer editing mechanism. The proximity of native system parameters to this bound demonstrates the importance of energy dissipation as an evolutionary force affecting fitness. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/027615v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@f4f4e1org.highwire.dtl.DTLVardef@1f8e45forg.highwire.dtl.DTLVardef@1591e16org.highwire.dtl.DTLVardef@1c650b0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2020

Mesoscopic Liquid Clusters Represent a Distinct Condensate of Mutant p53

The oncogenic properties of mutant p53 have been ascribed to destabilization of the p53 conformation, followed by aggregation into insoluble fibrils. Here we combine immunofluorescent 3D confocal microscopy of breast cancer cells expressing the p53 mutant Arg248Gln (R248Q) with light scattering from solutions of the purified protein and molecular simulations to probe the mechanisms that govern phase behaviors of the mutant across multiple length scales, from cellular to molecular. We establish that p53 R248Q forms mesoscopic protein-rich clusters, an anomalous liquid phase with several unique properties. We demonstrate that the clusters host and facilitate the nucleation of amyloid fibrils. The distinct characteristics of the clusters of R248Q and wild-type p53 and theoretical models indicate that p53 condensation into clusters is driven by the structural destabilization of the core domain and not by interactions of its extensive disordered region. Two-step nucleation of mutant p53 amyloids suggests means to control fibrillization and the associated pathologies through modifying the cluster behaviors. In a broader context, our findings exemplify interactions between distinct protein phases that activate complex physicochemical mechanisms operating in biological systems.

biophysics↗

bioRxiv2019

Kinetic control of stationary flux ratios for a wide range of biochemical processes

One of the most intriguing features of biological systems is their ability to regulate the steady-state fluxes of the underlying biochemical reactions, however the regulatory mechanisms and their physicochemical properties are not fully understood. Fundamentally, flux regulation can be explained with a chemical kinetic formalism describing the transitions between discrete states with the reaction rates defined by an underlying free-energy landscape. Which features of the energy landscape affect the flux distribution? Here we prove that the ratios of the steady-state fluxes of quasi-first-order biochemical processes are invariant to energy perturbations of the discrete states and are only affected by the energy barriers. In other words, the non-equilibrium flux distribution is under kinetic and not thermodynamic control. We illustrate the generality of this result for three biological processes. For the network describing protein folding along competing pathways, the probabilities of proceeding via these pathways are shown to be invariant to the stability of the intermediates or to the presence of additional misfolded states. For the network describing protein synthesis, the error rate and the energy expenditure per peptide bond is proven to be independent of the stability of the intermediate states. For molecular motors such as myosin-V, the ratio of forward to backward steps and the number of ATP molecules hydrolyzed per step is demonstrated to be invariant to energy perturbations of the intermediate states. These findings place important constraints on the ability of mutations and drug perturbations to affect the steady-state flux distribution for a wide class of biological processes.

biophysics↗

bioRxiv2017

Mechanism of genome interrogation: How CRISPR RNA-guided Cas9 proteins locate specific targets on DNA

The ability to precisely edit and modify genome opens endless opportunities to investigate fundamental properties of living systems as well as to advance various medical techniques and bioengineering applications. This possibility is now close to reality due to a recent discovery of the adaptive bacterial immune system, which is based on clustered regularly interspaced short palindromic repeats (CRISPR)-associated proteins (Cas) that utilize RNA to find and cut the double-stranded DNA molecules at specific locations. Here we develop a quantitative theoretical approach to analyze the mechanism of target search on DNA by CRISPR RNA-guided Cas9 proteins, which is followed by a selective cleavage of nucleic acids. It is based on a discrete-state stochastic model that takes into account the most relevant physical-chemical processes in the system. Using a method of first-passage processes, a full dynamic description of the target search is presented. It is found that the location of specific sites on DNA by CRISPR Cas9 proteins is governed by binding first to protospacer adjacent motif (PAM) sequences on DNA, which is followed by reversible transitions into DNA interrogation states. In addition, the search dynamics is strongly influenced by the off-target cutting. Our theoretical calculations allows us to explain the experimental observations and to give experimentally testable predictions. Thus, the presented theoretical model clarifies some molecular aspects of the genome interrogation by CRISPR RNA-guided Cas9 proteins.\n\nInsert Received for publication Date and in final form Date.

biophysics↗