R-3 Repository :: Browsing by Author "Kolomeisky, Anatoly"

Browsing by Author "Kolomeisky, Anatoly"

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Antimicrobial Peptides Activity and Efficacy Prediction by Stochastic Models and Machine Learning Methods
(2024-04-25) Nguyen, Thao; Kolomeisky, Anatoly; Onuchic, José; Tabor, Jeffrey
The development of new antimicrobial drugs is becoming more urgent than ever due to the rapid emergence of antibiotic resistance and limitations in bacteria targets. A promising alternative that received considerable scientific attention is antimicrobial peptides (AMPs), also known as host defense peptides. In this work, we aim to facilitate the design of more effective peptides using computational tools by solving the following two main challenges in the field. First, the underlying microscopic mechanisms of how AMPs interact with bacteria and other pathogens remain inadequately understood. Second, the infinite possibilities in engineering new peptides is a time-consuming task. We developed a theoretical framework for the interactions of AMPs and bacteria on the single-cell and population levels. We also investigated the effect of AMP cooperativity on efficacy as measured by minimal inhibitory concentrations (MIC), fractional inhibitory concentrations (FIC), and our acceleration parameter R by looking at cases with 1, 2, 3, and eventually an arbitrary number m types of AMPs. Our results explained the broad concentration spectrum where different types of AMP operate more optimally, offering a mechanistic explanation of the bacterial clearance dynamics and AMP cooperativity mechanisms. Increasing the number of AMP components in a mixture while keeping the total amount fixed enhances their synergistic activities, and strong cooperativity can be achieved for weak intermolecular interactions, providing a qualitative measure for the degree of cooperativity applicable in natural systems. We also used feature selection methods to build our machine learning pipeline to extract features that make peptides antimicrobial. This model produced decent accuracy with manual hyperparameter tuning, and the results can be applied to engineer better AMPs.
Contrast Agent Development for Molecular Imaging of Cancer
(2021-12-03) Pollard, Alyssa Camille; Pagel, Mark D; Kolomeisky, Anatoly
This dissertation focuses on the design, synthesis, and application of contrast agents for molecular imaging. Specifically, these agents have been designed to probe specific tumor biomarkers such as pH or cancer metabolism to provide information related to tumor biology. Two sets of positron emission tomography/magnetic resonance imaging (PET/MRI) co-agents have been designed to measure tumor extracellular pH. One set of agents is metal-based using gadolinium or a radiometal, and the other introduces a fluorine-18 or fluorine-19 tag. While both the radiometal and fluorine-based co-agents were able to measure pH in solution, the fluorine-based co-agents reached a lower standard error of 0.06 pH units. In addition, the fluorine-based co-agents demonstrated high stability in solution and in vivo, while the radiometal-based agents underwent dechelation of the radiometal upon injection. Finally, the fluorine-based agents were able to measure tumor pH in a 4T1 breast cancer cell line. To apply the PET/MRI co-agents, extensive work had to first be performed to characterize the small animal PET/MRI system and determine how the two modalities affected the other’s image quality. After correcting for PET quantification, radioactivity could be accurately measured by the PET detector with errors of less than 5% for fluorine-18 and less than 20% for gallium-68. Despite the larger errors for gallium-68, they did not largely influence pH measurements using 68Ga-labeled PET co-agents. The power of PET/MRI was also utilized to identify lung tumor metastases and measure [18F](2S,4R)-4-fluoroglutamine ([18F]FGln) uptake in a clear cell renal cell carcinoma lung metastasis model. Due to the high anatomical contrast of PET/MRI, lung tumors were clearly identified versus normal lung tissue, and 1.5-fold higher [18F]FGln uptake was observed in lung tumors versus the healthy lungs in a set of control mice. In addition, we found that [18F]FGln uptake was decreased after treatment with the glutaminase inhibitor CB-839. Finally, a novel fullerene-based nanomaterial with potential biomedical applications was radiolabeled with copper-64 to answer questions about its in vivo biodistribution and pharmacokinetic profile using PET imaging. Interestingly, this highly water-soluble fullerene conjugated with serinolamide groups displayed rapid renal clearance in mice.
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Covalent and Non-Covalent Functionalization of ssDNA-Wrapped Single-Wall Carbon Nanotubes: Computational and Experimental Studies
(2023-04-13) Alizadehmojarad, Ali A.; Weisman, Bruce; Kolomeisky, Anatoly
Single-wall nanotubes (SWCNTs) are nanomaterials with a wide range of optical and electronic properties that depend on their physical structure, which is indexed by a pair of integers (n,m). The discovery and interpretation of nanotube fluorescence has taken SWCNT research to a new level by enabling novel studies of structure-specific reactions and processes. These require the suspension of individualized SWCNTs in liquids through non-covalent functionalization by dispersants such as polymers, conventional surfactants, and single-stranded DNA (ssDNA). Understanding the specific interactions between nanotubes and coatings is important for advancing both basic and applied research. Both computational and experimental investigations have been conducted to understand the mechanism of separation and sorting of specific (n,m) species coated by DNA oligonucleotides. In addition to non-covalent SWCNT functionalization, covalent functionalization has also added a new research dimension and the ability to modify important properties of pristine nanotubes. In this thesis, novel computational and experimental methods were developed to further understand non-covalent and covalent functionalization of SWCNTs dispersed in DNA oligonucleotides and in a conventional surfactant. Computational studies were performed to understand how a DNA oligo distinguishes two enantiomers of an (n,m) species. Replica exchange molecular dynamics (REMD) simulations revealed that this recognition is directly correlated with the nanotube surface area exposed to the environment when wrapped by a DNA oligo. In the case of covalent functionalization of guanine nucleobases to SWCNTs, steered MD (SMD) simulations showed that the nucleotides in the middle of a DNA strand are found in closer proximity to nanotube surface than those at the strand end. These observations are aligned with experimental findings suggesting that a greater degree of guanine functionalization is achieved with 31-nucleotide DNA oligos containing only one guanine in the middle than DNA oligos with one guanine near the end of a DNA strand. A novel experimental method was developed to measure extinction coefficient of SWCNTs in the UV region. These results have enabled the simple determination of total SWCNT concentrations in aqueous dispersions. The DNA/SWCNT mass ratio was then quantitatively evaluated to provide an important parameter for understanding conformations of DNA oligos wrapped around SWCNTs. Through this method, the dependence of DNA/SWCNT mass ratio on DNA oligo base sequence was experimentally determined. Those conformations around different (n,m) species were then further explored using standard MD simulations. In this way, the strength of interactions between nanotubes and DNA oligos was investigated, revealing that thymine-rich DNA oligos interact with nanotube surfaces more strongly than cytosine-rich oligos. The guanine functionalization of SWCNTs was conducted using excitation of metalloporphyrin photosensitizers with violet light. Guanine functionalization of nanotubes was monitored by red-shifts in their absorption and fluorescence spectra. The great advantage of using metalloporphyrin dyes instead of rose bengal as a photosensitizer is that they are more photostable and cause less interference with SWCNT absorption and fluorescence spectra. Consequently, kinetic studies of guanine functionalization of SWCNTs were performed to find the correlation between kinetic parameters and either dye concentration or guanine contents of the DNA oligos. Quantum chemistry methods were used to predict find the most stable product of covalent attachment of guanine to SWCNT side walls and to correlate the formation enthalpy of each adduct with nanotube diameter. The semi-empirical quantum chemistry results show that 4,5-guanine peroxide (GPO) attached to either ortho L30 or ortho L-30 positions on the nanotubes’ surface give the most energetically stable products of guanine functionalization. Excited state calculations then predict that 4,5-GPO attached at the ortho L30 positions results in the most red-shifted (6,5) absorption peak compared to other adducts. In a separate experimental study, the first structure-selective guanine functionalization of SWCNTs was achieved using near-infrared or visible photochemistry. Specific (n,m) SWCNT species were excited by monochromatic excitation at their characteristic E11 or E22 transitions. Energy transfer to dissolved O2 then generated singlet oxygen, which led to formation of guanine peroxide (GPO) and its bonding to SWCNT side walls. The guanine functionalization was selective for the excited nanotubes. Finally, MD simulations were used to investigate the noncovalent adsorption of the surfactant sodium dodecyl sulfate (SDS) on SWCNT surfaces. This study uncovered four different SDS morphologies, characterized their structures, and found the combinations of SDS concentrations and nanotube diameters leading to the different morphologies.
Data-driven Construction of Coarse-grained Protein Models
(2023-09-26) Yang, Wangfei; Clementi, Cecilia; Kolomeisky, Anatoly
With the rapid development of computational power over the past few decades, computational simulation has grown increasingly important in protein-related research due to its high resolution and predictability. However, simulating large proteins over extended time scales using all-atomic resolution models and explicit solvents remains infeasible due to the substantial computational burden. Consequently, researchers have turned to developing coarse-grained (CG) models, which reduce degrees of freedom to accelerate simulations. To develop a CG model, two tasks must be completed. One involves determining which degrees of freedom will be retained in the CG model, known as the CG mapping. The other task is accurately capturing equivalent interactions at the CG resolution using an appropriate force field. In this dissertation, two data-driven methods are developed for the aforementioned tasks. The first method, Variational Approach for Markov Processes (VAMP)-based coarse-graining, provides a criterion to evaluate different CG mappings under a specific resolution directly from finer-grained simulation data. This evaluation is accomplished without constructing a complete CG model and running CG simulations. Additionally, based on this method, a Markov Chain Monte Carlo sampling method is developed to attain the optimal CG mapping without enumerating all possibilities. The second method optimizes the CG force field according to the native structure based on the energy landscape theory. Specifically, this method is used to extend an existing coarse-grained model, the Associative memory, Water-mediated, Structure, and Energy Model (AWSEM), to include protein-metal ion interactions. The use of these methods enables the construction of accurate coarse-grained protein models, making it possible to simulate larger protein systems over longer time scales.
Exploring the structure-function relationship of biomacromolecules: simulation and prediction of structural behavior of viral proteins and chromatin
(2024-07-01) Dodero Rojas, Esteban; Onuchic, José Nelson; Hazzard, Kaden; Kolomeisky, Anatoly
Biomacromolecules are the main functional constituents in living systems. They exhibit a great diversity of tasks depending on the composition of their monomers. Most protein and chromatin functions emerge from the interactions with other biomacromolecules. This dissertation focuses on viral proteins and chromatin systems, describing the relationship between their structure, composition and function using computational and theoretical models. Chapter 1 presents the motivation and describes the two main studied systems: the SARS-CoV-2 Spike protein and the eukaryote interphase genome. Chapter 2 focuses on the implementation of Structure Based Models to simulate the conformational change of the S2 subunit of the SARS-CoV-2 Spike protein associated with the membrane fusion process. We determined the transition states of the Spike protein, and predicted relevant intermediate states readily available to serve as druggable or vaccine targets. The simulated transitions highlight the role of post-translational modification (branched glycans) during viral entry. This model was further expanded to explore how the neutralizing CV3-25 antibody blocks viral entry by inhibiting the full transition of the Spike protein. Chapter 3 describes a pipeline to infer the efficiency of SARS-CoV-2 epitopes in scaffold vaccine strategies. Using explicit solvent simulations, we observed the dynamics of the target epitopes on exposed environments, similar to their context in scaffold-driven vaccines. When compared with experiments, we noticed that the most experimentally efficient epitope (S1Ep4) correlates with high thermal stability around the Wuhan-1 Spike protein conformation. To assess whether the S1Ep4 epitope would trigger immune response for SARS-CoV-2 variants, we performed explicit solvent simulations of the variant local environment of the epitope. The target epitope showed high conformational stability for all the variants around the Wuhan-1 strain structure. We determined that there is high likelihood the S1Ep4 epitope to incite immunoreponse on all the SARS-CoV-2 variants. Lastly, using the aforementioned simulated transitions of Spike during the membrane fusion, we identify new epitopes in the S2 subunit and the conformational study pipeline was implemented. Two new epitopes on the S2 subunit were proposed in highly conserved regions of the Spike protein, stepping towards pan-coronavirus vaccine strategies. Chapter 4 delves into the correlation between the biochemical composition of the DNA and its structural behavior. We expanded upon previous models to predict the subcompartment annotations of the chromatin based on biomarker enrichment along the genomes; including Histone Modification frequencies, transcriptor factor binding profiles and transcription activities. The prediction method, called PyMEGABASE (PYMB), is based on a graph model with a trainable Potts model. Within the model one node corresponds to the locus subcompartment and the remaining nodes are associated with the biomarker enrichment profiles. Using PYMB, we inferred the subcompartment annotations for hundreds of cell lines in multiple eukaryotes, which allow us to determine the cell identity from the subcompartment profile. In Chapter 5 we aim to increase the accuracy at predicting subcompartments by training a transformer architecture, called TECSAS. The new model is able to outperform PYMB's accuracy by more than 20%. From the new predictions, we determined that the transition region in sequence between subcompartments is approximately 150kbp. Finally, we expanded the model to predict the likelihood of genome loci to bind to nuclear bodies (Lamina, Nucleoli, and Speckles). We demonstrated based on the projection of the predicted likelihoods upon 3D chromatin data on the cell IMR-90 that both Lamina and Speckles create a stronger structural bias than the nucleoli. In summary, we explored the relationship between structure and function in the Spike protein's refolding pathway and demonstrated the significance of the conformational stability of epitopes around the target protein structure for vaccine efficiency. Further, the biochemical composition to structural behavior was examined for chromatin systems by the prediction of subcompartments from biochemical data, as well as the impact of the nuclear bodies, such as the lamina, in the overall ensemble behavior of chromatin systems. Overall, we determined that the mechanisms driving function of biomacromolecules are tightly correlated with their composition and structure.
Measuring forces at the leading edge: a force assay for cell motility
(Royal Society of Chemistry, 2013) Farrell, Brenda; Qian, Feng; Kolomeisky, Anatoly; Anvari, Bahman; Brownell, William E.
Cancer cells become mobile by remodelling their cytoskeleton to form migratory structures. This transformation is dominated by actin assembly and disassembly (polymerisation and depolymerisation) in the cytoplasm. Synthesis of filamentous actin produces a force at the leading edge that pushes the plasma membrane forward. We describe an assay to measure the restoring force of the membrane in response to forces generated within the cytoplasm adjacent to the membrane. A laser trap is used to form a long membrane nanotube from a living cell and to measure the axial membrane force at the end of the tube. When the tube, resembling a filopodium, is formed and in a relaxed state the axial membrane force exhibits a positive stationary value. This value reflects the influence of the cytoskeleton that acts to pull the tube back to the cell. A dynamic sawtooth force that rides upon the stationary value is also observed. This force is sensitive to a toxin that affects actin assembly and disassembly, but not affected by agents that influence microtubules and myosin light chain kinase. We deduce from the magnitude and characteristics of dynamic force measurements that it originates from depolymerisation and polymerisation of F-actin. The on- and off-rates, the number of working filaments, and the force per filament (2.5 pN) are determined. We suggest the force-dependent transitions are thermodynamically uncoupled as both the on- and off-rates decrease exponentially with a compressive load. We propose kinetic schemes that require attachment of actin filaments to the membrane during depolymerisation. This demonstrates that actin kinetics can be monitored in a living cell by measuring force at the membrane, and used to probe the mobility of cells including cancer cells.
Embargo
Molecular coarse-graining for classical and quantum systems
(2024-04-19) Zaporozhets, Iryna; Kolomeisky, Anatoly; Clementi, Cecilia
Understanding the intricate molecular mechanisms underlying biological processes is crucial for tackling multiple biomedical challenges. Molecular dynamics serves as a "computational microscope", offering insights into biomolecular processes with unparalleled spatial and temporal resolution. Yet capturing these processes on biologically relevant scales poses significant computational challenges, especially when additional phenomena, such as nuclear quantum effects (NQEs), must be considered. However, many processes of interest can be described by a smaller set of collective variables instead of the intractably large number of degrees of freedom arising in atomistic simulation. The idea behind coarse-graining is to integrate out the irrelevant degrees of freedom and model the target system at a lower resolution while preserving the target properties. This thesis contributes to the development and application of coarse-grained models to increase the computational efficiency of biomolecular simulation and extend the range of molecular processes that can be investigated computationally. First, we applied a structure-based coarse-grained model combined with all-atom simulations to elucidate the helix formation mechanism following the chromophore isomerization in cyanobacteriochrome Slr1393-g3. Our findings indicate a destabilization of the helical state in the 15-Z configuration compared to the 15-E configuration, which has implications for future experimental investigations. This project also highlights the need for improved coarse-grained models. Second, the ODEM optimization framework was used to parameterize protein structure-based models using experimental data. The results suggest that incorporating many-body terms to describe nonbonded interactions is crucial to accurately reproduce the protein thermodynamics. This result underscores the importance of using neural networks' potential in approximating coarse-grained force-fields for future research. Next, a combination of coarse-graining, path integral quantum mechanics, and machine learning was used to develop potentials that incorporate NQEs into all-atom simulation at the cost of classical molecular dynamics. We developed separate models to approximate quantum dynamics and quantum statistics, which demonstrated good performance when applied to test systems. These approaches have the potential to obtain an accurate incorporation of NQEs in biomolecular simulation. Finally, we discuss how the developed approaches contribute to the bigger goal of effective and accurate methods for computational elucidation of biomolecular processes.
Nanopores: Modeling the Separation Mechanism and Application of Carbon Nanotubes in Sensing
(2018-11-12) Agah, Shaghayegh; Kolomeisky, Anatoly; Pasquali, Matteo
Transport and separation through biological membranes exhibit fast, efficient and selective behaviors. These unique features have brought a significant interest recently in using artificial and biological nanopore membranes for separation and sensing pur- poses. Molecular transport through nanopores is a complex process that involves various interactions between the molecules and the channel. These interactions lead to a highly efficient and selective separation. The goal of this thesis is to under- stand this complicated phenomenon and develop a model that captures this behav- ior. Based on this modeling, the mechanism can be mimicked in different applications varying from molecular separation in nanopore sensing and drug delivery technologies to chemical and pharmaceutical separations. We construct a theoretical framework which models the general mechanism of selectivity in the translocation dynamics of molecular mixtures through a nanopore. By developing a discrete-state stochastic model, the effects of the molecule-channel interactions on molecular selectivity and flux are demonstrated. Our framework shows that the amount and the position of the interaction inside the channel has a significant effect on the separation efficiency. In addition, an efficient algorithm is developed to implement our theoretical framework for long channels. The proposed algorithm optimizes the magnitude and the position of the interaction in channels with two to ten binding sites. Due to the exponential nature of the problem, it is computationally infeasible to apply our algorithm for channels with more than ten binding sites. To address this issue, we use machine-learning techniques and extend our results for longer channels and predict the best position for molecular-pore interaction. Our results demonstrate that the best interaction location depends on the length of the nanopore and the mass transport rates inside and outside of the channel. Depending on these factors, the best interaction location may vary between the entrance and the middle of the pore. Sensing is another important application of nanopores. Nanopores have been shown to have the ability of single-molecule sensing of various biological molecules in a rapid fashion and at a low cost. We discuss the recent progress in the nanopore- sequencing field with a focus on the nature of nanopores as well as sensing mechanisms during the translocation. The current challenges such as the fast translocation speed and low sensitivity of the sensing techniques, and alternative methods based on using Single Walled Carbon Nanotubes (SWCNTs) are also explored. Carbon nanotubes (CNTs) demonstrate enhanced water and ion flow, have wide variety of lengths and diameters, and manifest excellent electrical and optical properties. These features make CNTs one of the best candidates for artificial biochannels and nanopore devices. Our molecular dynamic simulation study shows that it is beneficial to use SWCNT for nanopore sequencing application. This is due to the possibility of slowing down the DNA transport through the tube by two orders of magnitude. Additionally, we propose to use the photoluminescence (PL) properties of SWCNTs as a sensing method. Therefore, the PL spectrum characteristics of SWCNTs at the presence of different defect densities are studied to investigate whether the carbon nanotube photoluminescence quantum yield can be enhanced at the single molecular level for future sensing technologies.

Browsing by Author "Kolomeisky, Anatoly"

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