Chemoinformatics Insights on Molecular Jackhammers and Cancer Cells.

One of the most challenging tasks in modern medicine is to find novel efficient cancer therapeutic methods with minimal side effects. The recent discovery of several classes of organic molecules known as "molecular jackhammers" is a promising development in this direction. It is known that these molecules can directly target and eliminate cancer cells with no impact on healthy tissues. However, the underlying microscopic picture remains poorly understood. We present a study that utilizes theoretical analysis together with experimental measurements to clarify the microscopic aspects of jackhammers' anticancer activities. Our physical-chemical approach combines statistical analysis with chemoinformatics methods to design and optimize molecular jackhammers. By correlating specific physical-chemical properties of these molecules with their abilities to kill cancer cells, several important structural features are identified and discussed. Although our theoretical analysis enhances understanding of the molecular interactions of jackhammers, it also highlights the need for further research to comprehensively elucidate their mechanisms and to develop a robust physical-chemical framework for the rational design of targeted anticancer drugs.

Molecular Mechanisms of Precise Timing in Cell Lysis.

Many biological systems exhibit precise timing of events, and one of the most known examples is cell lysis, which is a process of breaking bacterial host cells in the virus infection cycle. However, the underlying microscopic picture of precise timing remains not well understood. We present a novel theoretical approach to explain the molecular mechanisms of effectively deterministic dynamics in biological systems. Our hypothesis is based on the idea of stochastic coupling between relevant underlying biophysical and biochemical processes that lead to noise cancellation. To test this hypothesis, we introduced a minimal discrete-state stochastic model to investigate how holin proteins produced by bacteriophages break the inner membranes of Gram-negative bacteria. By explicitly solving this model, the dynamic properties of cell lysis are fully evaluated, and theoretical predictions quantitatively agree with available experimental data for both wild-type and holin mutants. It is found that the observed threshold-like behavior is a result of the balance between holin proteins entering the membrane and leaving the membrane during the lysis. Theoretical analysis suggests that the cell lysis achieves precise timing for wild-type species by maximizing the number of holins in the membrane and narrowing their spatial distribution. In contrast, for mutated species, these conditions are not satisfied. Our theoretical approach presents a possible molecular picture of precise dynamic regulation in intrinsically random biological processes.

Insights into Error Control Mechanisms in Biological Processes: Copolymerization and Enzyme-Kinetics Revisited.

The high fidelity observed in biological information processing ranging from replication to translation has stimulated significant research efforts to clarify the underlying microscopic picture. Theoretically, several approaches to analyze the error rates have been proposed. The copolymerization theory describes the addition and removal of monomers at the growing tip of a copolymer, leading to a closed set of nonlinear equations. On the other hand, enzyme-kinetics approaches formulate linear equations of biochemical networks, describing transitions between discrete chemical states. However, it is still unclear whether the error values computed by the two approaches agree. Moreover, there are conflicting interpretations on whether the error is under thermodynamic or kinetic discrimination control. In this work, we examine the error rate in persistent copying biochemical processes by specifically analyzing both theoretical approaches. The initial disagreement of the results between the two theories motivated us to rederive the formula for the error rate in the kinetic model. The error computed with the new method resulted in excellent agreement between both theoretical approaches and with Monte Carlo simulations. Furthermore, our theoretical analysis shows that the kinetic discrimination controls the error, even when the energy difference between adding the right and wrong products is very small. Our theoretical investigation gives important insights into the physical-chemical properties of complex biological processes by providing the quantitative framework to evaluate them.

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.

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 even more effective at inhibiting bacterial growth with reduced toxicity and a lower likelihood of inducing bacterial 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. It utilizes correlation and bioinformatics analysis. 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 nonsynergistic AMP pairs. Our theoretical approach can be used for the rational design of more effective AMP combinations for specific bacterial targets, for clarifying the mechanisms of bacterial elimination, and for a better understanding of cooperativity phenomena in biological systems.

Physical-Chemical Approach to Designing Drugs with Multiple Targets.

Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.

Why Are Nucleosome Breathing Dynamics Asymmetric?

In eukaryotic cells, DNA is bound to nucleosomes, but DNA segments occasionally unbind in the process known as nucleosome breathing. Although DNA can unwrap simultaneously from both ends of the nucleosome (symmetric breathing), experiments indicate that DNA prefers to dissociate from only one end (asymmetric breathing). However, the molecular origin of the asymmetry is not understood. We developed a new theoretical approach that gives microscopic explanations of asymmetric breathing. It is based on a stochastic description that leads to a comprehensive evaluation of dynamics by using effective free-energy landscapes. It is shown that asymmetric breathing follows the kinetically preferred pathways. In addition, it is also found that asymmetric breathing leads to a faster target search by transcription factors. Theoretical predictions, supported by computer simulations, agree with experiments. It is proposed that nature utilizes the symmetry of nucleosome breathing to achieve a better dynamic accessibility of chromatin for more efficient genetic regulation.

How to Build Plasmon-Driven Molecular Jackhammers that Disassemble Cell Membranes and Cytoskeletons in Cancer.

Plasmon-driven molecular machines with ultrafast motion at the femtosecond scale are effective for the treatment of cancer and other diseases. It is recently shown that cyanine dyes act as molecular jackhammers (MJH) through vibronic (vibrational and electronic mode coupling) driven activation that causes the molecule to stretch longitudinally and axially through concerted whole molecule vibrations. However, the theoretical and experimental underpinnings of these plasmon-driven motions in molecules are difficult to assess. Here the use of near-infrared (NIR) light-activated plasmons in a broad array of MJH that mechanically disassemble membranes and cytoskeletons in human melanoma A375 cells is described. The characteristics of plasmon-driven molecular mechanical disassembly of supramolecular biological structures are observed and recorded using real-time fluorescence confocal microscopy. Molecular plasmon resonances in MJH are quantified through a new experimental plasmonicity index method. This is done through the measurement of the UV-vis-NIR spectra in various solvents, and quantification of the optical response as a function of the solvent polarity. Structure-activity relationships are used to optimize the synthesis of plasmon-driven MJH, applying them to eradicate human melanoma A375 cells at low lethal concentrations of 75 nm and 80 mW cm-2 of 730 nm NIR-light for 10 min.

Brownian Diffusion of Hexagonal Boron Nitride Nanosheets and Graphene in Two Dimensions.

Two-dimensional (2D) nanomaterials have numerous interesting chemical and physical properties that make them desirable building blocks for the manufacture of macroscopic materials. Liquid-phase processing is a common method for forming macroscopic materials from these building blocks including wet-spinning and vacuum filtration. As such, assembling 2D nanomaterials into ordered functional materials requires an understanding of their solution dynamics. Yet, there are few experimental studies investigating the hydrodynamics of disk-like materials. Herein, we report the lateral diffusion of hexagonal boron nitride nanosheets (h-BN and graphene) in aqueous solution when confined in 2-dimensions. This was done by imaging fluorescent surfactant-tagged nanosheets and visualizing them by using fluorescence microscopy. Spectroscopic studies were conducted to characterize the interactions between h-BN and the fluorescent surfactant, and atomic force microscopy (AFM) was conducted to characterize the quality of the dispersion. The diffusion data under different gap sizes and viscosities displayed a good correlation with Kramers' theory. We propose that the yielded activation energies by Kramers' equation express the magnitude of the interaction between fluorescent surfactant tagged h-BN and glass because the energies remain constant with changing viscosity and decrease with increasing confinement size. The diffusion of graphene presented a similar trend with similar activation energy as the h-BN. This relationship suggests that Kramers' theory can also be applied to simulate the diffusion of other 2D nanomaterials.

How Heterogeneity Affects Cooperative Communications within Single Nanocatalysts.

Catalysis remains one of the most essential methods in chemical research and industry. Recent experiments have discovered an unusual phenomenon of catalytic cooperativity, when a reaction at one active site can stimulate reactions at neighboring sites within single nanoparticles. While theoretical analysis established that the transport of charged holes is responsible for this phenomenon, it does not account for inhomogeneity in the structural and dynamic properties of single nanocatalysts. Here, we investigate the effect of heterogeneity on catalytic communications by extending a discrete-state stochastic framework to random distributions of the transition rates. Our explicit calculations of spatial and temporal properties of heterogeneous systems in comparison with homogeneous systems predict that the strength of cooperativity increases, while the communication lifetimes and distances decrease. Monte Carlo computer simulations support theoretical calculations, and microscopic arguments to explain these observations are also presented. Our theoretical analysis clarifies some important aspects of molecular mechanisms of catalytic processes.

Increasing Heterogeneity in Antimicrobial Peptide Combinations Enhances Their Synergistic Activities.

Antimicrobial peptides (AMPs) are short biopolymers produced by living organisms as an immune system defense against infections. They have been considered as potential alternatives to conventional antibiotics. Experiments suggest that combining several types of different AMPs might enhance their antimicrobial activity more effectively than using single-component AMPs. However, a clear understanding of the underlying microscopic mechanisms is still lacking. We present a theoretical investigation of antibacterial cooperativity mechanisms involving several types of AMPs. It is argued that synergy results from intermolecular interactions when the presence of one type of AMP stimulates the association of another type of AMP to bacteria. It is found that increasing the number of different AMPs in the mixtures increases the number of such interactions, making them more efficient in eliminating infections. Our theoretical framework provides valuable insights into the mechanisms of antimicrobial action.

Role of Nucleosome Sliding in the Protein Target Search for Covered DNA Sites.

Associations of transcription factors (TFs) with specific sites on DNA initiate major cellular processes. But DNA in eukaryotic cells is covered by nucleosomes which prevent TFs from binding. However, nucleosome structures on DNA are not static and exhibit breathing and sliding. We develop a theoretical framework to investigate the effect of nucleosome sliding on a protein target search. By analysis of a discrete-state stochastic model of nucleosome sliding, search dynamics are explicitly evaluated. It is found that for long sliding lengths the target search dynamics are faster for normal TFs that cannot enter the nucleosomal DNA. But for more realistic short sliding lengths, the so-called pioneer TFs, which can invade nucleosomal DNA, locate specific sites faster. It is also suggested that nucleosome breathing, which is a faster process, has a stronger effect on protein search dynamics than that of nucleosome sliding. Theoretical arguments to explain these observations are presented.

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 impossible 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 combine very different bacteria without considering bacteria-specific features or interactions with AMPs. In addition, the sparsity of current AMP data sets disqualifies the application of traditional machine-learning methods or makes the results unreliable. Here, we present a new approach, featuring neighborhood-based collaborative filtering, to predict with high accuracy a given bacteria's response to untested AMPs based on similarities between bacterial responses. Furthermore, we also developed a complementary bacteria-specific link prediction approach that can be used to visualize networks of AMP-antibiotic combinations, enabling us to propose new combinations that are likely to be effective.

Dynamics of single-base editing: Theoretical analysis.

Recent experimental advances led to the development of DNA base editors (BEs) with single-nucleotide precision, which is critical for future progress in various scientific and technological fields. The molecular mechanisms of single-base discrimination, however, remain poorly 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 the molecular mechanisms of selective base editing.

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, an elevated stability in pre-synaptic complexes assembled with specific and non-specific DNA substrates was found. To explain these surprising observations, a theoretical approach that describes the assembly of these complexes and compares the predictions with the experiment was 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.

Nucleosome Breathing Facilitates the Search for Hidden DNA Sites by Pioneer Transcription Factors.

Transfer of genetic information starts with transcription factors (TFs) binding to specific sites on DNA. But in living cells, DNA is mostly covered by nucleosomes. There are proteins, known as pioneer TFs, that can efficiently reach the DNA sites hidden by nucleosomes, although the underlying mechanisms are not understood. Using the recently proposed idea of interaction-compensation mechanism, we develop a stochastic model for the target search on DNA with nucleosome breathing. It is found that nucleosome breathing can significantly accelerate the search by pioneer TFs in comparison to situations without breathing. We argue that this is the result of the interaction-compensation mechanism that allows proteins to enter the inner nucleosome region through the outer DNA segment. It is suggested that nature optimized pioneer TFs to take advantage of nucleosome breathing. The presented theoretical picture provides a possible microscopic explanation for the successful invasion of nucleosome-buried genes.

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.

Synergy among Pausing, Intrinsic Proofreading, and Accessory Proteins Results in Optimal Transcription Speed and Tolerable Accuracy.

Cleavage of dinucleotides after the misincorporational pauses serves as a proofreading mechanism that increases transcriptional elongation accuracy. The accuracy is further improved by accessory proteins such as GreA and TFIIS. However, it is not clear why RNAP pauses and why cleavage-factor-assisted proofreading is necessary despite transcriptional errors in vitro being of the same order as those in downstream translation. Here, we developed a chemical-kinetic model that incorporates most relevant features of transcriptional proofreading and uncovers how the balance between speed and accuracy is achieved. We found that long pauses are essential for high accuracy, whereas cleavage-factor-stimulated proofreading optimizes speed. Moreover, in comparison to the cleavage of a single nucleotide or three nucleotides, RNAP backtracking and dinucleotide cleavage improve both speed and accuracy. Our results thereby show how the molecular mechanism and the kinetic parameters of the transcriptional process were evolutionarily optimized to achieve maximal speed and tolerable accuracy.

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.

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods.

There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.