Computational Biophysics

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

Aging is one of the chief biomedical problems of the 21st century. After decades of basic research on biogerontology
(the science of aging), the aging process still remains an enigma. Although hundreds of "theories" on aging have
been formulated and many fundamental insights about age-related changes and genetic as well as environmental
interventions that change the pace of aging have been discovered, the actual why and how we age remain enigmatic.
In the post-genomic era there is an exponential increase in data. As a consequence it is a challenge to utilize all
information based on it and derive meaningful knowledge about biological phenomena. No individual scientist, no
group, nor consortium is capable of keeping up within their own field and are overwhelmed by the explosion of data
increase. Machine learning applied on biological data has the potential to solve this and cause a paradigm shift from
hypothesis-driven research (which predominates biological research including biogerontology) to data-driven
research.
This dissertation addresses this problem. In particular it proposes and executes the use of machine learning on
current existing data to predict drivers of aging (and therefore helps to distinguish causes from consequences),
interventions to counteract aging, and specific hypotheses to fill in research gaps that require experimental
validation.
The objective of this project is therefore to build computational models that are based on data relevant to the
phenomenon of aging and to predict as many of its aspects and dimensions as possible (thus elucidate their relations
to each other). For converting between and sorting within dimensions which are relevant to aging, different machine
learning models are evaluated. Ones models are built, it can be determined how much they can explain different
aspects of aging. Those models will also be capable of specifying which features are most relevant for prediction (in
both classification or regression). It is possible to train models that incorporate age-related changes based on
transcriptomic, proteomic, metabolomic, epigenomic as well as morphological data and their combinations. Machine
learning is further used to convert between and within them.
This work focuses on three types of predictors. Subsequently, discoveries are made with the statistical and learning
algorithms. The first model (lifespan predictor) is trained on predicting the lifespan based on genotype, environment
and combinations thereof. It is useful for predicting lifespan extending interventions on the population level. The
second model (age predictor) is trained on predicting the age given features measured on individuals. This is useful
for identifying biomarkers of aging and to determine the effects of interventions on the level of individuals. The third
model predicts functions/regulations of biological entities in regard to the aging process based on heterogeneous data
such as ontologies and diverse omics including time-series gene expression profiles (which can be visualized as
plots), and linked data. It is used to understand the role of genes and proteins as well as perhaps other entities such as
small molecules including lipids and other metabolites. Functions of proteins, which are still unknown, especially
those involved in yeast lipid metabolism and its regulation, can be predicted.
For this purpose we use primarily yeast as model organism as well as data on humans. Other biomedical model
organisms might be added if found beneficial.
The novel aspects of this research are for instance that 1) aging is investigated systematically in an unbiased
data-driven approach, 2) lifespan is predicted as continuous values, 3) age is predicted by combining multiple omics
data, 4) functions and regulations of biological entities like genes are predicted with high confidence from
heterogeneous data sources.
This thesis discovered that genetics is the most important feature of lifespan determination. Phenotypic features
related to lipid and membranes such as vacuolar morphology and autophagy activity are important for lifespan
determination according to the best performing models. A age predictor based on transcriptomics and proteomics can
highly accurately determine the age. It selected features are associated with both translation and lipid metabolism.
Among the top selected features are transcripts of genes when deleted exhibit abnormal vacuolar morphology as well
as targets of Opi1. Opi1 itself and its regulators were found to be differentially regulated post-transcriptional or
post-translational. Lastly, a function predictor for genes was created that achieved exceptional accuracy of
classifying aging genes. It learned for instance that piecemeal autophagy of the nucleus is strongly predictive for
aging-suppressor genes while cytoplasmic translation is strongly predictive for gerontogenes

The priming dose effect, called also the Raper-Yonezawa effect or simply the Yonezawa effect, is a special case of the radiation adaptive response phenomenon (radioadaptation), which refers to: (a) faster repair of direct DNA lesions (damage), and (b) DNA mutation frequency reduction after irradiation, by applying a small priming (conditioning) dose prior to the high detrimental (challenging) one. This effect is observed in many (but not all) radiobiological experiments which present the reduction of lesion, mutation or even mortality frequency of the irradiated cells or species. Additionally, the multi-parameter model created by Dr. Yonezawa and collaborators tried to explain it theoretically based on experimental data on the mortality of mice with chronic internal irradiation. The presented paper proposes a new theoretical approach to understanding and explaining the priming dose effect: it starts from the radiation adaptive response theory and moves to the three-parameter model, separately for two previously mentioned situations: creation of fast (lesions) and delayed damage (mutations). The proposed biophysical model was applied to experimental data-lesions in human lymphocytes and chromosomal inversions in mice-and was shown to be able to predict the Yonezawa effect for future investigations. It was also found that the strongest radioadaptation is correlated with the weakest cellular radiosensitivity. Additional discussions were focussed on more general situations where many small priming doses are used.

It is well known that ionizing radiation can cause damages to cells that interact with it directly. However, many studies have shown that damages also occur in cells that have not experienced direct interaction. This is due to the so-called bystander effect, which is observed when the irradiated cell sends signals that can damage neighboring cells. Due to the complexity of this effect, it is not easy to strictly describe it biophysically, and thus it is also difficult to simulate. This article reviews various approaches to modeling and simulating the bystander effect from the point of view of radiation biophysics. In particular, the last model presented within this article is part of a larger project of modeling the response of a group of cells to ionizing radiation using Monte Carlo methods. The new approach presented here is based on the probability tree, the Poisson distribution of signals and the saturated dose-related probability distribution of the bystander effect’s appearance, which makes the model very broad and universal.

Biocomputing is a recently growing and highly interdisciplinary field of research that investigates models and computational techniques inspired by biology and related sciences. Here, cyclic behavior (redox cycling) of purified horseradish peroxidase protein among native enzyme and its two electron-oxidized and single electron-oxidized intermediates known as Compounds I and II was algebraically expressed as a cyclic additive group Z 3 = {C 0 , C 2 , C 1 } = {C 0 , 1C 2 , 2C 2 } = {0, 2, 1}, and a cyclic multiplicative group Z * 3 = {C 1 , C 2 } = {C 1 , C 1 2 } = {1, 2}, with C 2 as the common generator. Above algebraically expressed features of the enzyme's redox cycle was applied to help determining the coefficients in polynomials formed after additive and/or multiplicative operations between polynomial rings f (x) and g(x) over a coefficient field derived from Z 3. Similarly, use of a pair of small DNA molecules was proposed for determining the coefficients for additively and multiplicatively obtained polynomials over F , (Z 2 ; +, ×); where Z 2 = {C 0 , C 1 } = {0, 1}. Discussion includes the required designs of two distinct DNA molecules for performing binary logical conjunction (AND) and exclusive disjunc-tion (XOR), upon polymerase-chain reactions.

Recent experimental studies have shown that astrocytes respond to external stimuli with a transient increase
of the intracellular calcium concentration or can exhibit self-
sustained spontaneous activity. Both evoked and spontaneous astrocytic calcium oscillations are accompanied by exocytosis of glutamate caged in astrocytes leading to paroxysmal depolarization shifts (PDS) in neighboring neurons. Here, we present a simple mathematical model of the interaction between astrocytes and neurons that is able to numerically reproduce the experimental results concerning the initiation of the PDS. The timing of glutamate release from the astrocyte is studied by means of a combined modeling of a vesicle cycle and the dynamics of SNARE-proteins. The neuronal slow inward currents (SICs), induced by the astrocytic glutamate and leading to PDS, are modeled via the activation of presynaptic glutamate receptors. The dependence of the bi-directional  communication between neurons and astrocytes on
the concentration of glutamate transporters is analyzed, as
well. Our numerical results are in line with experimental
findings showing that astrocyte can induce synchronous PDSs
in neighboring neurons, resulting in a transient synchronous
spiking activity.

The innate flexibility of a DNA sequence is quantified by the Jacobson-Stockmayer's J-factor, which measures the propensity for DNA loop formation. Recent studies of ultra-short DNA sequences revealed a discrepancy of up to six orders of magnitude between experimentally measured and theoretically predicted J-factors. These large differences suggest that, in addition to the elastic moduli of the double helix, other factors contribute to loop formation. Here, we develop a new theoretical model that explores how coherent delocalized phonon-like modes in DNA provide single-stranded " flexible hinges " to assist in loop formation. We combine the Czapla-Swigon-Olson structural model of DNA with our extended Peyrard-Bishop-Dauxois model and, without changing any of the parameters of the two models, apply this new computational framework to 86 experimentally characterized DNA sequences. Our results demonstrate that the new computational framework can predict J-factors within an order of magnitude of experimental measurements for most ultra-short DNA sequences, while continuing to accurately describe the J-factors of longer sequences. Further, we demonstrate that our computational framework can be used to describe the cyclization of DNA sequences that contain a base pair mismatch. Overall, our results support the conclusion that coherent delocalized phonon-like modes play an important role in DNA cyclization.

With drug resistance becoming widespread in Plasmodium falciparum infections, the development of the alternative drugs is the desired strategy for prevention and cure of malaria. Three drug targets were selected to screen promising drug molecules from the GSK library of 13469 molecules. Using an in silico structure based drug designing approach, the differences in binding energies of the substrate and inhibitor were exploited between target sites of parasite and human to design a drug molecule against Plasmodium. The docking studies have shown several promising molecules from GSK library with more effective binding as compared to the already known inhibitors for the drug targets. Though stronger interaction has been shown by several molecules as compared to the reference, few
molecules have shown the potential as drug candidates though in vitro studies are required to validate the results. In case of thymidylate synthase-dihydrofolate reductase (TS-DHFR), three compounds have shown promise for future studies as potential drugs.

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

We show how it is possible to use the plethystic program in order to compute baryonic generating functions that count BPS operators in the chiral ring of quiver gauge theories living on the world volume of D branes probing a non compact CY manifold. Special attention is given to the conifold theory and the orbifold C 2 Z 2 × C, where exact expressions for generating functions are given in detail. This paper solves a long standing problem for the combinatorics of quiver gauge theories with baryonic moduli spaces. It opens the way to a statistical analysis of quiver theories on baryonic branches. Surprisingly, the baryonic charge turns out to be the quantized Kähler modulus of the geometry.

Keywords: A. Fe 1.06 Te 0.6 Se 0.4 single crystal B. Critical current density C. Flux pinning mechanism a b s t r a c t The temperature and magnetic field dependence of the magnetization and critical current density of Fe Te Se 1.06 0.6 0.4 single crystal have been investigated, and the flux pinning mechanism has been analyzed. The critical current density results indicate that there are different pinning mechanisms in this crystal. The pinning mechanisms are studied in terms of the pinning model where the normalized volume pinning force, f p , versus = h H H / irr , where H irr is the irreversibility, were studied systematically. It was found that a variety of pinning mechanisms including normal point pinning, normal surface pinning, and pinning based on spatial variation in the Ginzburg-Landau parameter (Δk pinning) pinning mechanisms coexist. The effects each of the different pinning mechanisms were obtained. The results show that the contributions of the real pinning mechanisms are dependent on the temperature and magnetic field in this the single crystal.

Electron microscopy and atomic force microscopy images of cholesterol-dependent cytolysins and related proteins that form large pores in lipid membranes have revealed the presence of incomplete rings, or arcs. Some evidence indicates that these arcs are inserted into the membrane and induce membrane leakage, but other experiments seem to refute that. Could such pores, only partially lined by protein, be kinetically and thermodynamically stable? How would the lipids be structured in such a pore? Using the antimicrobial peptide protegrin-1 as a model, we test the stability of pores only partially lined by peptide using all-atom molecular dynamics simulations in POPC and POPE/POPG membranes. The data show that, whereas pure lipid pores close rapidly, pores partially lined by protegrin arcs are stable for at least 300 ns. Estimates of the thermodynamic stability of these arcs using line tension data and implicit solvent calculations show that these arcs can be marginally stable in both zwitterionic and anionic membranes. Arcs provide an explanation for the observed ion selectivity in protegrin electrophysiology experiments and could possibly be involved in other membrane permeabilization processes where lipids are thought to participate, such as those induced by antimicrobial peptides and colicins, as well as the Bax apoptotic pore.

The nucleation and growth theory, described by the Avrami equation (also called Johnson-Mehl-Avrami-Kolmogorov equation), and usually used to describe crystallization and nucleation processes in condensed matter physics, was applied in the present paper to cancer physics. This can enhance the popular multi-hit model of carcinogenesis to volumetric processes of single cell's DNA neoplastic transformation. The presented approach assumes the transforming system as a DNA chain including many oncogenic mutations. Finally, the probability function of the cell's cancer transformation is directly related to the number of oncogenic mutations. This creates a universal sigmoidal probability function of cancer transformation of single cells, as observed in the kinetics of nucleation and growth, a special case of a phase transition process. The proposed model, which represents a different view on the multi-hit carcinogenesis approach, is tested on clinical data concerning gastric cancer. The results also show that cancer transformation follows DNA fractal geometry.

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

The membrane environment, its composition, dynamics, and remodeling, have been shown to participate in the function and organization of a wide variety of transmembrane (TM) proteins, making it necessary to study the molecular mechanisms of such proteins in the context of their membrane settings. We review some recent conceptual advances enabling such studies, and corresponding computational models and tools designed to facilitate the concerted experimental and computational investigation of protein-membrane interactions. To connect productively with the high resolution achieved by cognate experimental approaches, the computational methods must offer quantitative data at an atomistically detailed level. We show how such a quantitative method illuminated the mechanistic importance of a structural characteristic of multihelical TM proteins, that is, the likely presence of adjacent polar and hydrophobic residues at the protein-membrane interface. Such adjacency can preclude the complete alleviation of the well-known hydrophobic mismatch between TM proteins and the surrounding membrane, giving rise to an energy cost of residual hydrophobic mismatch. The energy cost and biophysical formulation of hydrophobic mismatch and residual hydrophobic mismatch are reviewed in the context of their mechanistic role in the function of prototypical members of multihelical TM protein families: 1), LeuT, a bacterial homolog of mammalian neurotransmitter sodium symporters; and 2), rhodopsin and the b1and b2-adrenergic receptors from the G-protein coupled receptor family. The type of computational analysis provided by these examples is poised to translate the rapidly growing structural data for the many TM protein families that are of great importance to cell function into ever more incisive insights into mechanisms driven by protein-ligand and protein-protein interactions in the membrane environment.

are cytoskeletal polymers that exhibit dynamic instability, the random alternation between growth and shrinkage. MT dynamic instability plays an essential role in cell development, division, and motility. To investigate dynamic instability, simulation models have been widely used. However, conditions under which the concentration of free tubulin fluctuates as a result of growing or shrinking MTs have not been studied before. Such conditions can arise, for example, in small compartments, such as neuronal growth cones. Here we investigate by means of computational modeling how concentration fluctuations caused by growing and shrinking MTs affect dynamic instability. We show that these fluctuations shorten MT growth and shrinkage times and change their distributions from exponential to non-exponential, gamma-like. Gamma-like distributions of MT growth and shrinkage times, which allow optimal stochastic searching by MTs, have been observed in various cell types and are believed to require structural changes in the MT during growth or shrinkage. Our results, however, show that these distributions can already arise as a result of fluctuations in the concentration of free tubulin due to growing and shrinking MTs. Such fluctuations are possible not only in small compartments but also when tubulin diffusion is slow or when many MTs (de)polymerize synchronously. Volume and all other factors that influence these fluctuations can affect MT dynamic instability and, consequently, the processes that depend on it, such as neuronal growth cone behavior and cell motility in general.

The ability to detect a chemical gradient is fundamental to many cellular processes. In multi-cellular organisms gradient sensing plays an important role in many physiological processes such as wound healing and development. Unicellular organisms use gradient sensing to move (chemotaxis) or grow (chemotropism) towards a favorable environment. Some cells are capable of detecting extremely shallow gradients, even in the presence of significant molecular-level noise. For example, yeast have been reported to detect pheromone gradients as shallow as 0.1 nM/μm. Noise reduction mechanisms, such as time-averaging and the internalization of pheromone molecules, have been proposed to explain how yeast cells filter fluctuations and detect shallow gradients. Here, we use a Particle-Based Reaction-Diffusion model of ligand-receptor dynamics to test the effectiveness of these mechanisms and to determine the limits of gradient sensing. In particular, we develop novel simulation methods for establishing chemical gradients that not only allow us to study gradient sensing under steady-state conditions, but also take into account transient effects as the gradient forms. Based on reported measurements of reaction rates, our results indicate neither time-averaging nor receptor endocytosis significantly improves the cell's accuracy in detecting gradients over time scales associated with the initiation of polarized growth. Additionally, our results demonstrate the physical barrier of the cell membrane sharpens chemical gradients across the cell. While our studies are motivated by the mating response of yeast, we believe our results and simulation methods will find applications in many different contexts.

The forward-reverse or FR method is an efficient bidirectional work method for determining the potential of mean force w(z) and also supposedly gives in principle the position-dependent diffusion coefficient D(z). Results from a variation called the OFR (oscillating FR) method suggest inconsistencies in the D(z) values when calculated as prescribed by the FR method. A new steering protocol has thus been developed and applied to the OFR method for the accurate determination of D(z) and also provides greater convergence for w(z) in molecular dynamics simulations. The bulk diffusion coefficient for water was found to be (6.03 ± 0.16) × 10 −5 cm 2 /s at 350 K with system size dependence within the statistical error bars. Using this steering protocol, D(z) and w(z) for water permeating a dipalmitoylphosphatidylcholine (DPPC) bilayer were determined. The potential of mean force is shown to have a barrier of peak height, w max /(k B T) = 8.4, with a width of about 10Å on either side from the membrane center. The diffusion constant is shown to be highest in the core region of the membrane [peak value ∼ (8.0 ± 0.8) × 10 −5 cm 2 /s], lowest in the head-group region [minimum value ∼ (2.0 ± 0.3) × 10 −5 cm 2 /s], and to tend toward the bulk value as the water molecule leaves the membrane. The permeability coefficient P for H 2 O in DPPC was determined using the simulated D(z) and w(z) to give values of (0.129 ± 0.075) cm/s at 323 K and (0.141 ± 0.043) cm/s at 350 K. The results show more spatial detail than results presented in previous work while reducing the computational and user effort.

We present a method that enables the use of the forward-reverse (FR) method of Kosztin et al. on a broader range of problems in soft matter physics. Our method, which we call the oscillating forward-reverse (OFR) method, adds an oscillatory steering potential to the constant velocity steering potential of the FR method. This enables the calculation of the potential of mean force (PMF) in a single unidirectional oscillatory drift, rather than multiple drifts in both directions as required by the FR method. By following small forward perturbations with small reverse perturbations, the OFR method is able to generate a piecewise reverse path that follows the piecewise forward path much more closely than any practical set of paths used in the FR method. We calculate the PMF for four different systems: a dragged Brownian oscillator, a pair of atoms in a Lennard-Jones liquid, a Na +-Cl − ion pair in an aqueous solution, and a deca-alanine molecule being stretched in an implicit solvent. In all cases, the PMF results are in good agreement with those published previously using various other methods, and, to our knowledge, we give for the first time PMFs calculated by nonequilibrium methods for the Lennard-Jones and Na +-Cl − systems.

The effect of salt concentration on association properties of caffeine molecule was investigated by employing molecular dynamics simulations in isothermal-isobaric ensemble of eight caffeine molecules in pure water and three different salt (NaCl) concentrations, at 300 K temperature and 1 atm pressure. The concentration of caffeine was taken almost at the solubility limit. With increasing salt concentration, we observe enhancement of first peak height and appearance of a second peak in the caffeine-caffeine distribution function. Furthermore, our calculated solvent accessible area values and cluster structure analyses suggest formation of higher order caffeine cluster on addition of salt. The calculated hydrogen bond properties reveal that there is a modest decrease in the average number of water-caffeine hydrogen bonds on addition of NaCl salt. Also observed are: (i) decrease in probability of salt contact ion pair as well as decrease in the solvent separated ion pair formation with increasing salt concentration, (ii) a modest second shell collapse in the water structure, and (iii) dehydration of hydrophobic atomic sites of caffeine on addition of NaCl. © 2013 AIP Publishing LLC. [http://dx.

ABSTRACTCell penetration after recognition of the SARS-CoV-2 virus by the ACE2 receptor, and the fusion of its viral envelope membrane with cellular membranes, are the early steps of infectivity. A region of the Spike protein (S) of the virus, identified as the “fusion peptide” (FP), is liberated at its N-terminal site by a specific cleavage occurring in concert with the interaction of the receptor binding domain of the Spike. Studies have shown that penetration is enhanced by the required binding of Ca2+ ions to the FPs of corona viruses, but the mechanisms of membrane insertion and destabilization remain unclear. We have predicted the preferred positions of Ca2+ binding to the SARS-CoV-2-FP, the role of Ca2+ ions in mediating peptide-membrane interactions, the preferred mode of insertion of the Ca2+-bound SARS-CoV-2-FP and consequent effects on the lipid bilayer from extensive atomistic molecular dynamics (MD) simulations and trajectory analyses. In a systematic sampling of the in...

Ions play key mechanistic roles in the gating dynamics of Neurotransmitter:sodium symporters (NSS). In recent microsecond scale molecular dynamics (MD) simulations of a complete model of the dopamine transporter (DAT), a NSS protein, we observed a partitioning of K(+) ions from the intracellular side towards the unoccupied Na2 site of DAT following the release of the Na2-bound Na(+) Here we evaluate with computational simulations and experimental measurements of ion affinities under corresponding conditions, the consequences of K(+) binding in the Na2 site of LeuT, a bacterial homolog of NSS, when both Na(+) ions and substrate have left, and the transporter prepares for a new cycle. We compare the results to the consequences of binding Na(+) in the same apo system. Analysis of >50 µs atomistic MD and enhanced sampling trajectories of constructs with E290 either charged or neutral, point to the E290 protonation state as a main determinant in the structural reconfiguration of the e...

ABSTRACTComputational modeling and simulation of biomolecular systems at their functional pH ranges requires an accurate approach to exploring the pH dependence of conformations and interactions. Here we present a new approach – the Equilibrium Constant pH (ECpH) method – to perform conformational sampling of protein systems in the framework of molecular dynamics simulations in an N, P, T-thermodynamic ensemble. The performance of ECpH is illustrated for two proteins with experimentally determined conformational responses to pH change: the small globular water-soluble bovine b-lactoglobulin (BBL), and the dimer transmembrane antiporter CLC-ec1 Cl−/H+. We show that with computational speeds comparable to equivalent canonical MD simulations we performed, the ECpH trajectories reproduce accurately the pH-dependent conformational changes observed experimentally in these two protein systems, some of which were not seen in the corresponding canonical MD simulations. FigureTable of Content...

The allergic response in humans results from the crosslinking of IgE-Fc< RI receptor complexes via the binding of IgE antibodies to antigens, which results in cell degranulation. The relationship between cell degranulation and antigen-antibody aggregation was investigated for the shrimp allergen Pen a 1. A biological rule-based model was developed to simulate aggregation of IgE antibodies and the Pen a 1 antigen. The forward rate constant and the crosslinking factor were varied to demonstrate how the model output changes as these model parameters are changed. It was found that the peak of the dose-response curve becomes greater and more defined as the crosslinking factor increases, and that the peak shifts to lower doses as the forward rate constant increases. Parameter scanning was performed to fit the model output to experimental cell secretion data obtained by collaborators, assuming a directly proportional relationship between the two quantities. Four concentrations of allergenspecific IgE were examined: 15 ng/mL, 30 ng/mL, 60 ng/mL, and 120 ng/mL. For each IgE concentration, nine doses of Pen a 1 were examined ranging from 0.0001 ng/mL to 10,000 ng/mL. The average aggregate size was used as the measure of aggregation to compare to the experimental data. It was found that the output of the biological rule-based model fit well to the cell degranulation data.

Induction and proliferation of cancer cells are complex processes whose ab initio mathematical description is virtually impossible. Nevertheless, some integral characteristics such as the Gompertz law, which is generally used to describe cancer development, can result from a simple mathematical consideration of biophysical processes. The simplified response to a single-source lesion created in a cell's DNA consists in the repair of the lesion or induction of a mutation which can lead to neoplastic transformation. This approach makes it possible to propose a single mathematical formula connecting the Gompertz curve with the simplified biophysical characteristics of tumor growth. Such an approach, which merges the process of neoplastic transformation with cancer development, is shown in the present paper based on the relatively simple analytical and Monte-Carlo models. The models operate with a small number of biophysical parameters and account, at least in part, for the mechanisms that operate in an organism exposed to low doses of ionizing radiation.

Rotary sequential hydrolysis of the metabolic machine F 1-ATPase is a prominent manifestation of high coordination among multiple chemical sites in ring-shaped molecular machines, and it is also functionally essential for F 1 to tightly couple chemical reactions and central g-shaft rotation. High-speed AFM experiments have identified that sequential hydrolysis is maintained in the F 1 stator ring even in the absence of the g-rotor. To explore the origins of intrinsic sequential performance, we computationally investigated essential inter-subunit couplings on the hexameric ring of mitochondrial and bacterial F 1. We first reproduced in stochastic Monte Carlo simulations the experimentally determined sequential hydrolysis schemes by kinetically imposing inter-subunit couplings and following subsequent tri-site ATP hydrolysis cycles on the F 1 ring. We found that the key couplings to support the sequential hydrolysis are those that accelerate neighbor-site ADP and Pi release upon a certain ATP binding or hydrolysis reaction. The kinetically identified couplings were then examined in atomistic molecular dynamics simulations at a coarse-grained level to reveal the underlying structural mechanisms. To do that, we enforced targeted conformational changes of ATP binding or hydrolysis to one chemical site on the F 1 ring and monitored the ensuing conformational responses of the neighboring sites using structure-based simulations. Notably, we found asymmetrical neighbor-site opening that facilitates ADP release upon enforced ATP binding. We also captured a complete charge-hopping process of the Pi release subsequent to enforced ATP hydrolysis in the neighbor site, confirming recent single-molecule analyses with regard to the role of ATP hydro-lysis in F 1. Our studies therefore elucidate both the coordinated chemical kinetics and structural dynamics mechanisms underpinning the sequential operation of the F 1 ring.

We present a new sampling and analysis scheme for calculating the 8 potential of mean force (PMF) of systems studied by steered molecular dynamics 9 simulations. This scheme, which we call the bin-passing method, is based on the 10 forward−reverse (FR) method (due to I. Kosztin and co-workers, Kosztin et al. J. 11 Chem. Phys. 2006, 124(6), 064106) and arguments based on the second law of 12 thermodynamics. Applying the bin-passing method results in enhanced sampling, 13 better separation of the reversible and irreversible work distributions, and faster 14 convergence to the underlying PMF of the system under study. Post-simulation 15 analysis is performed using a purpose-built software that we have made publicly 16 available at https://github.com/1particle/bin-passing_analyzer under the terms of 17 the GNU General Public License (version 3). Three examples are provided, for 18 systems of varying sizes and complexities, to demonstrate the efficiency of this 19 method and the quality of the results: for the dissociation PMF of NaCl in water, the bin-passing method obtains PMFs in 20 excellent agreement with that obtained for the same system and using the same force-field through static (equilibrium) methods. 21 The bin-passing method gives a very symmetric PMF for passage of a single water molecule through a DPPC bilayer, and the 22 resultant PMF leads to permeability values in better agreement with experiments than those obtained through previous 23 simulation studies. Finally, we consider the interaction of the antimicrobial peptide HHC-36 with two model membranes and 24 employ the bin-passing method to obtain the PMFs for peptide adsorption to the membranes. The characteristics of these PMFs 25 are consistent with the qualities established for the HHC-36 peptide through in vivo and in vitro experiments, as a non-toxic 26 strong antimicrobial agent. 27

We present a computational framework tailored for the modeling of the complex, dynamic relationships that are encountered in splicing regulation. The starting point is whole-genome transcriptomic data from high-throughput array or sequencing methods that are used to quantify gene expression and alternative splicing across multiple contexts. This information is used as input for state of the art methods for Graphical Model Selection in order to recover the structure of a composite network that simultaneously models exon co-regulation and their cognate regulators. Community structure detection and social network analysis methods are used to identify distinct modules and key actors within the network. As a proof of concept for our framework we studied the splicing regulatory network for Drosophila development using the publicly available modENCODE data. The final model offers a comprehensive view of the splicing circuitry that underlies fly development. Identified modules are associated with major developmental hallmarks including maternally loaded RNAs, onset of zygotic gene expression, transitions between life stages and sex differentiation. Within-module key actors include well-known developmental-specific splicing regulators from the literature while additional factors previously unassociated with developmental-specific splicing are also highlighted. Finally we analyze an extensive battery of Splicing Factor knock-down transcriptome data and demonstrate that our approach captures true regulatory relationships.