Biology subjects

Kolomeisky, A. B.

Publications and source records attributed to Kolomeisky, A. B..

16 recordsLinked to original sources

bioRxiv2026

Quantitative Model of Transcriptional Noise Regulation by mRNA Condensates

A fundamental biological process of transcription occurs in the cell nucleus, which is a complex medium that also contains multiple heterogeneous structures known as biomolecular condensates. Interestingly, some of these condensates contain mRNA molecules in addition to proteins, suggesting an important cellular role in transcription that is not yet well understood. In this work, we develop a minimal theoretical framework for quantitative investigation of the role of reversible mRNA condensation in transcription. Our discrete-state stochastic approach accounts for the most relevant processes, allowing us to explicitly evaluate the properties of the system and clarify the effects of condensation. Analytical calculations supported by computer simulations suggest that reversible mRNA condensation influences the transcription processes by maintaining a constant level of free mRNA in the nucleoplasm while lowering the degree of stochastic noise and increasing the robustness against external perturbations. Physicochemical arguments are presented to explain these observations. The proposed theoretical framework elucidates important microscopic aspects of transcription, providing a convenient quantitative tool for investigating complex biological phenomena.

biophysics↗

bioRxiv2025

Physical-Chemical Approach to Identify Local Structural Determinants of Molecular Mechanisms: Case Study of Antimalarial Drug Pyronaridine and Crystal-Growth Inhibition

Understanding how specific molecular substructures control chemical behavior is central to rational molecular design and the development of new materials. However, most current predictive models offer limited mechanistic resolution at the fragmental level. We present a novel physical-chemical computational framework, which is based on systematically perturbing a parent molecule, to quantify fragment-level contributions to both a specific mechanistic action and a broader functional outcome. To test our theoretical approach, we investigated local structural contributions to pyronaridine (PY), a clinically used antimalarial drug with a mechanistically distinctive mode of inhibition of hematin crystal growth via step-bunching. Chemically plausible PY molecular analogs have been computationally generated by selectively removing or substituting functional groups hypothesized to influence either step-bunching mechanisms or whole-parasite blood-stage activity. For each analog, we predicted the probability of four different crystal-growth inhibition mechanisms using a centroid-based similarity model based on a small dataset of experimentally verified crystal-growth inhibitors. The blood-stage antimalarial activity has also been estimated using the MAIP platform. A systematic comparison of molecular analogs revealed that step-bunching mechanisms depend primarily on two protonated pyrrolidines, with chlorobenzene as a strong secondary contributor. In contrast, antimalarial activity is more distributed, relying on coordinated interactions between aromatic-heteroatom scaffolds and an amine linker. The obtained results demonstrate that our approach can disentangle position-specific and cooperative fragmental effects, offering mechanistically interpretable guidance for the design of mechanism-optimized inhibitors. The framework might be broadly applicable across chemical and materials domains where linking local structure to specific mechanisms is essential.

biochemistry↗

bioRxiv2025

Kinetic mechanisms for the sequence dependence of transcriptional errors

The fidelity of template-dependent mRNA synthesis during transcription elongation is the primary determinant of accurate gene expression and the maintenance of functional RNA transcripts. However, the mechanisms governing transcription fidelity remain incompletely understood. While previous studies have characterized how error rates vary with nucleotide identity at upstream and downstream positions from the incorporation site, the comprehensive microscopic explanation of this sequence dependence has not been elucidated. In this study, we develop a novel theoretical approach that integrates transcription proofreading mechanisms and inhomogenous DNA sequence effects. Using first-passage analysis validated by Monte Carlo simulations, we quantitatively characterize nucleotide-specific error rates during RNA polymerase II transcription. The model accurately reproduces experimental error rates and predicts kinetic parameters influencing transcriptional fidelity. Analysis reveals nucleotide incorporation rates follow the hierarchy U<C<G<A, consistent with independent experimental observations. Notably, our model not only explains how the error rates depend on the nature of the base immediately down-stream (+1) but also predicts that the identity of the nucleotide at the second downstream position (+2) also plays an important role. Pyrimidines at position +2 contribute to lower error rates than purines, whereas the third downstream base (+3) has no effect. These previously unreported correlations are corroborated by bioinformatic analysis of existing datasets. In addition, using the BRCA1 gene as an example, we explore the physiological implications of sequence-dependent error rates, identifying an increased likelihood of premature stop codon errors. These findings clarify how DNA sequence context modulates nucleotide incorporation kinetics, advancing our understanding of transcriptional fidelity and its functional consequences.

biophysics↗

bioRxiv2024

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

biophysics↗

bioRxiv2024

Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions

The ability to accurately predict protein-protein interactions is critically important for our understanding of major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes a concept of physical-chemical similarity to determine which interactions will most probably occur. In our approach, the physical-chemical features of protein are extracted using bioinformatics tools for different organisms, and then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It is found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid compositions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators. Our theoretical approach provides a simple and robust method for quantifying the important details of complex mechanisms of biological processes.

bioinformatics↗

bioRxiv2022

Optimal pathways control fixation of multiple mutations during cancer initiation

Cancer starts after initially healthy tissue cells accumulate several specific mutations or other genetic alterations. The dynamics of tumor formation is a very complex phenomenon due to multiple involved biochemical and biophysical processes. It leads to a very large number of possible pathways on the road to final fixation of all mutations that marks the beginning of the cancer, complicating the understanding of microscopic mechanisms of tumor formation. We present a new theoretical framework of analyzing the cancer initiation dynamics by exploring the properties of effective free-energy landscape of the process. It is argued that although there are many possible pathways for the fixation of all mutations in the system, there are only few dominating pathways on the road to tumor formation. The theoretical approach is explicitly tested in the system with only two mutations using analytical calculations and Monte Carlo computer simulations. Excellent agreement with theoretical predictions is found for a large range of parameters, supporting our hypothesis and allowing us to better understand the mechanisms of cancer initiation. Our theoretical approach clarifies some important aspects of microscopic processes that lead to tumor formation.

biophysics↗

bioRxiv2021

The Role of Extended Range of Interactions in the Dynamics of Interacting Molecular Motors

Motor proteins, also known as biological molecular motors, play important roles in various intracellular processes. Experimental investigations suggest that molecular motors interact with each other during the cellular transport, but the nature of such interactions remains not well understood. Stimulated by these observations, we present a theoretical study aimed to understand the effect of the range of interactions on dynamics of interacting molecular motors. For this purpose, we develop a new version of the totally asymmetric simple exclusion processes in which nearest-neighbor as well as the next nearest-neighbor interactions are taken into account in a thermodynamically consistent way. A theoretical framework based on a cluster mean-field approximation, which partially takes correlations into account, is developed to evaluate the stationary properties of the system. It is found that fundamental current-density relations in the system strongly depend on the strength and the sign of interactions, as well as on the range of interactions. For repulsive interactions stronger than some critical value, increasing the range of interactions leads to a change from unimodal to trimodal dependence in the flux-density fundamental diagram. Theoretical calculations are tested with extensive Monte Carlo computer simulations. Although in most ranges of parameters excellent agreement between theoretical predictions and computer simulations is observed, there are situations when the cluster mean-field approach fails to describe properly the dynamics in the system. Theoretical arguments to explain these observations are presented. Our theoretical analysis clarifies the microscopic picture of how the range of interactions influences the dynamics of interacting molecular motors.

biophysics↗

bioRxiv2021

Temporal order of mutations influences cancer initiation dynamics

Cancer is a set of genetic diseases that are driven by mutations. It was recently discovered that the temporal order of genetic mutations affects the cancer evolution and even the nature of the decease itself. The mechanistic origin of these observations, however, remain not well understood. Here we present a theoretical model for cancer initiation dynamics that allows us to quantify the impact of the temporal order of mutations. In our approach, the cancer initiation process is viewed as a set of stochastic transitions between discrete states defined by the different numbers of mutated cells. Using a first-passage analysis, probabilities and times before the cancer initiation are explicitly evaluated for two alternative sequences of two mutations. It is found that the probability of cancer initiation is determined only by the first mutation, while the dynamics is specified by both mutations. In addition, it is shown that the acquisition of a mutation with higher fitness before mutation with lower fitness increases the probability of the tumor formation but delays the cancer initiation. Theoretical results are explained using effective free-energy landscapes.

biophysics↗

bioRxiv2019

Relaxation times of ligand-receptor complex formation control T cell activation

One of the most important functions of immune T cells is to recognize the presence of the pathogen-derived ligands and to quickly respond to them while at the same time not to respond to its own ligands. This is known as an absolute discrimination, and it is one of the most challenging phenomena to explain. The effectiveness of pathogen detection by T cell receptor (TCR) is limited by the chemical similarity of foreign and self-peptides and very low concentrations of foreign ligands. We propose a new mechanism of the absolute discrimination by T cells. It is suggested that the decision to activate or not to activate the immune response is controlled by the time to reach the stationary concentration of the TCR-ligand activated complex, which transfers the signal to downstream cellular biochemical networks. Our theoretical method models T-cell receptor phosphorylation events as a sequence of stochastic transitions between discrete biochemical states, and this allows us to explicitly describe the dynamical properties of the system. It is found that the proposed criterion on the relaxation times is able to explain available experimental observations. In addition, our theoretical approach explicitly analyzes the relationships between speed, sensitivity and specificity of the T cell functioning, which are the main characteristics of the process. Thus, it clarifies the molecular picture of the T cell activation processes in immune response.

biophysics↗

bioRxiv2019

Mechanochemical Model of Transcriptional Bursting

Populations of genetically identical cells generally show a large variability in cell phenotypes, which is typically associated with the stochastic nature of gene expression processes. It is widely believed that a significant source of such randomness is the transcriptional bursting, which is when periods of active production of RNA molecules alternate with periods of RNA degradation. However, the molecular mechanisms of such strong fluctuations remain unclear. Recent studies suggest that DNA supercoiling, which happens during transcription, might be directly related to the bursting behavior. Stimulated by these observations, we developed a stochastic mechano-chemical model of supercoiling-induced transcriptional bursting where the RNA synthesis leads to the buildup of torsion in DNA. This slows down the RNA production until binding of an enzyme gyrase to DNA, which releases the stress and allows for the RNA synthesis to restart with the original rate. Using a thermodynamically consistent coupling between mechanical and chemical processes, dynamic properties of transcription are explicitly evaluated. In addition, a first-passage method to evaluate the dynamics of transcription is developed. Theoretical analysis shows that the transcriptional bursting is observed when both the supercoiling and the mechanical stress-release due to gyrase are present in the system. It is also found that the overall RNA production rate is not constant and depends on the number of previously synthesized RNA molecules. A comparison with experimental data on bacteria allows us to evaluate the energetic cost of supercoiling during transcription. It is argued that the relatively weak mechanochemical coupling allows transcription to be regulated most effectively.\n\nSIGNIFICANCETranscriptional bursting has been cited as one of the probable causes of phenotypic differences in cells with identical genomes. However, the microscopic origin of noisy dynamics in RNA production remains unclear. We developed a thermodynamically-consistent mechano-chemical stochastic model, which, via explicit calculations of dynamic properties, provides a consistent physical-chemical description of how the supercoiling of DNA together with enzymatic activity of gyrases produce transcriptional bursting. It also allows us to explain that the coupling between mechanical and chemical processes might be the reason for efficient regulation of transcription.

biophysics↗

bioRxiv2019

Direct detection of molecular intermediates from first-passage times

All natural phenomena are governed by energy landscapes. However, the direct measurement of this fundamental quantity remains challenging, particularly in complex systems involving intermediate states. Here, we uncover key details of the energy landscapes that underpin a range of experimental systems through quantitative analysis of first-passage time distributions. By combined study of colloidal dynamics in confinement, transport through a biological pore and the folding kinetics of DNA hairpins, we demonstrate conclusively how a short-time, power-law regime of the first-passage time distribution universally reflects the number of intermediate states associated with each process, irrespective of the lengthscales, timescales or interactions in the system. We thereby establish a powerful method for investigating the underlying mechanisms of complex molecular processes.

biophysics↗

bioRxiv2019

When will the cancer start? Elucidating the correlations between cancer initiation times and lifetime cancer risks

Cancer is a genetic disease that results from accumulation of unfavorable mutations. As soon as genetic and epigenetic modifications associated with these mutations become strong enough, the uncontrolled tumor cell growth is initiated, eventually spreading through healthy tissues. Clarifying the dynamics of cancer initiation is thus critically important for understanding the molecular mechanisms of tumorigenesis. Here we present a new theoretical method to evaluate the dynamic processes associated with the cancer initiation. It is based on a discrete-state stochastic description of the formation of tumors as a fixation of unfavorable mutations in tissues. Using a first-passage analysis the probabilities for the cancer to appear and the times before it happens, which are viewed as fixation probabilities and fixation times, respectively, are explicitly calculated. It is predicted that the slowest cancer initiation dynamics is observed for neutral mutations, while it is fast for both advantageous and, surprisingly, disadvantageous mutations. The method is applied for estimating the cancer initiation times from experimentally available lifetime cancer risks for different types of cancer. It is found that the higher probability of the cancer to occur does not necessary lead to the fast times of starting the cancer. Our theoretical analysis helps to clarify microscopic aspects of cancer initiation processes.

biophysics↗

bioRxiv2018

Theoretical investigation of stochastic clearance of bacteria: First-passage analysis

Understanding mechanisms of bacterial eradication is critically important for overcoming failures of antibiotic treatments. Current studies suggest that the clearance of large bacterial populations proceeds deterministically, while for smaller populations the stochastic effects become more relevant. Here we develop a theoretical approach to investigate the bacterial population dynamics under the effect of antibiotic drugs using a method of first-passage processes. It allows us to explicitly evaluate the most important characteristics of the bacterial clearance dynamics such as extinction probabilities and extinction times. The new meaning of minimal inhibitory concentrations for stochastic clearance of bacterial populations is also discussed. In addition, we investigate the effect of fluctuations in the population growth rates on dynamics of bacterial eradication. It is found that extinction probabilities and extinction times generally do not correlate with each other when random fluctuations in the growth rates are taking place. Unexpectedly, for a significant range of parameters the extinction times increase due to these fluctuations, indicating a slowing in the bacterial clearance dynamics. It is argued that this might be one of the initial steps in the pathway for the development of antibiotic resistance. Furthermore, it is suggested that extinction times is a convenient measure of bacterial tolerance.

biophysics↗

bioRxiv2018

A molecular model of the surface-assisted protein aggregation process

The importance of cell surfaces in the self-assembly of proteins is widely accepted. One biologically significant event is the assembly of amyloidogenic proteins into aggregates, which leads to neurodegenerative disorders like Alzheimers and Parkinsons. The interaction of amyloidogenic proteins with cellular membranes appears to dramatically facilitate the aggregation process. Recent findings indicate that, in the presence of surfaces, aggregation occurs at physiologically low concentrations, suggesting interaction with surfaces plays a critical role in the disease-prone aggregation process. However, the molecular mechanisms behind on-surface aggregation remain unclear. Here we provide a theoretical model that offers a molecular explanation. According to this model, monomers transiently immobilized to surfaces increase the local monomer protein concentration and thus work as nuclei to dramatically accelerate the entire aggregation process. This theory was verified by experimental studies, using mica surfaces, to examine the aggregation kinetics of amyloidogenic-synuclein protein (-Syn) and non-amyloidogenic cytosine deaminase APOBEC3G (A3G).

biophysics↗

bioRxiv2018

Kinetic network model to explain gain-of-function mutations in ERK2 enzymes

ERK2 is a kinase protein that belongs to a Ras/Raf/MEK/ERK signalling pathway, which is activated in response to a range of extracellular signals. Malfunctioning of this cascade leads to variety of serious diseases, including cancers. This is often caused by mutations in proteins belonging to the cascade, frequently leading to abnormally high activity of the cascade even in the absence of external signal. One such gain-of-function mutation in ERK2 protein, called a sevenmaker mutation (D319N), was discovered in 1994 in Drosophila. This mutation leads to disruption of interactions of other proteins with D-site of ERK2 and results, contrary to expectations, in increase of its activity in vivo. However, no molecular mechanism to explain this effect has been presented so far. The difficulty is that this mutation should equally negatively affect interactions of ERK2 with all substrates, activators and deactivators. In this paper, we present a quantitative kinetic network model that gives a possible explanation of the increased activity of mutant ERK2 species. A simplified biochemical network for ERK2, viewed as a system of coupled Michaelis-Menten processes, is presented. Its dynamic properties are calculated explicitly using the method of first-passage processes. The effect of mutation is associated with changes in the strength of interaction energy between the enzyme and the substrates. It is found that the dependence of kinetic properties of the protein on the interaction energy is non-monotonic, suggesting that some mutations might lead to more efficient catalytic properties, despite weakening inter-molecular interactions. Our theoretical predictions agree with experimental observations for the sevenmaker mutation in ERK2. It is also argued that the effect of mutations might depend on the concentrations of substrates.

biophysics↗

bioRxiv2017

Elucidating interplay of speed and accuracy in biological error correction

One of the most fascinating features of biological systems is the ability to sustain high accuracy of all major cellular processes despite the stochastic nature of underlying chemical processes. It is widely believed that such low errors are the result of the error correcting mechanism known as kinetic proofreading. However, it is usually argued that enhancing the accuracy should result in slowing down the process leading to so-called speed-accuracy trade-off. We developed a discrete-state stochastic framework that allowed us to investigate the mechanisms of the proofreading using the method of first-passage processes. With this framework, we simultaneously analyzed speed and accuracy of the two fundamental biological processes, DNA replication and tRNA selection during the translation. The results indicate that speed-accuracy trade-off is not always observed. However, when the trade-off is present, the biological systems tend to optimize the speed rather than the accuracy of the processes, as long as the error level is tolerable. Additional constraints due to the energetic cost of proofreading also play a role in the error correcting process. Our theoretical findings provide a new microscopic picture of how complex biological processes are able to function so fast with a high accuracy.

biophysics↗