Quantitative Biology - Biomolecules Publications (50)

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Quantitative Biology - Biomolecules Publications

Cytosine methylation has been found to play a crucial role in various biological processes, including a number of human diseases. The detection of this small modification remains challenging. In this work, we computationally explore the possibility of detecting methylated DNA strands through direct electrical conductance measurements. Read More


We review the status of protein-based molecular electronics. First we discuss fundamental concepts of electron transfer and transport in and across proteins and proposed mechanisms for these processes. We then describe the immobilization of proteins to solid-state surfaces in both nanoscale and macroscopic approaches, and highlight how different methodologies can alter protein electronic properties. Read More


Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we show how to adapt some of these techniques to create a novel chained convolutional architecture with next-step conditioning for improving performance on protein sequence prediction problems. We explore its value by demonstrating its ability to improve performance on eight-class secondary structure prediction. Read More


PHAST is a software package written in standard Fortran, with MPI and CUDA extensions, able to efficiently perform parallel multicanonical Monte Carlo simulations of single or multiple heteropolymeric chains, as coarse-grained models for proteins. The outcome data can be straightforwardly analyzed within its microcanonical Statistical Thermodynamics module, which allows for computing the entropy, caloric curve, specific heat and free energies. As a case study, we investigate the aggregation of heteropolymers bioinspired on $A\beta_{25-33}$ fragments and their cross-seeding with $IAPP_{20-29}$ isoforms. Read More


The 70 kDa Heat Shock Proteins Hsp70 have several essential functions in living systems, such as protecting proteins against protein aggregation, assisting protein folding, remodeling protein complexes and driving the translocation into organelles. These functions require high affinity for non-specific amino-acid sequences that are ubiquitous in proteins. It has been recently shown that this high affinity, called ultra-affinity, depends on a process driven out of equilibrium by ATP hydrolysis. Read More


Emergence of antibiotic resistance due to New Delhi Metallo $\beta$-lactamase (NDM-1) bacterial enzymes is of great concern due to their ability to hydrolyze wide range of antibiotics. Efforts are ongoing to obtain the atomistic details of the hydrolysis mechanism in order to develop novel drugs and inhibitors against NDM-1. Especially, it remains elusive how drug molecules of different family of antibiotics are hydrolyzed by NDM-1 in an efficient manner. Read More


Ribonucleic acid (RNA) is involved in many regulatory and catalytic processes in the cell. The function of any RNA molecule is intimately related with its structure. In-line probing experiments provide valuable structural datasets for a variety of RNAs and are used to characterize conformational changes in riboswitches. Read More


Recent experiments have shown that trivalent ion, spermidine$^{3+}$, can provoke lateral microphase segregation in DNA brushes. Using molecular simulations and simple theoretical arguments, we explore the effects of trivalent counterions on polyelectrolyte brushes. At a proper range of grafting density, polymer size, and ion concentration, the brush polymers collapse heterogeneously into octopus-like surface micelles. Read More


The percentage and sequence of AT and GC base pairs and charges on the DNA backbone contribute significantly to the stiffness of DNA. This elastic property of DNA also changes with small interacting ligands. The single-molecule force spectroscopy technique shows different interaction modes by measuring the mechanical properties of DNA bound with small ligands. Read More


Free energy perturbation (FEP) is frequently used to evaluate the free energy change of a biological process, e.g. the drug binding free energy or the ligand solvation free energy. Read More


As high-throughput biological sequencing becomes faster and cheaper, the need to extract useful information from sequencing becomes ever more paramount, often limited by low-throughput experimental characterizations. For proteins, accurate prediction of their functions directly from their primary amino-acid sequences has been a long standing challenge. Here, machine learning using artificial recurrent neural networks (RNN) was applied towards classification of protein function directly from primary sequence without sequence alignment, heuristic scoring or feature engineering. Read More


Nonnative residual interactions have attracted increasing attention in recent protein folding researches. Experimental and theoretical investigations had been set out to catch nonnative contacts that might dominate key events in protein folding. However, energetic frustrations caused by nonnative inter-residue interactions are not systematically characterized, due to the complicated folding mechanism. Read More


Identifying protein functional sites (PFSs) and protein-ligand interactions (PLIs) are critically important in understanding the protein function and the involved biochemical reactions. As large amount of unknown proteins are quickly accumulated in this post-genome era, an urgent task arises to predict PFSs and PLIs at residual level. Nowadays many knowledge-based methods have been well developed for prediction of PFSs, however, accurate methods for PLI prediction are still lacking. Read More


Ordered chains (such as chains of amino acids) are ubiquitous in biological cells, and these chains perform specific functions contingent on the sequence of their components. Using the existence and general properties of such sequences as a theoretical motivation, we study the statistical physics of systems whose state space is defined by the possible permutations of an ordered list, i.e. Read More


Chemical or enzymatic cross-linking of casein micelles (CMs) increases their stability against dissociating agents. In this paper, a comparative study of stability between native CMs and CMs cross-linked with genipin (CMs-GP) as a function of pH is described. Stability to temperature and ethanol were investigated in the pH range 2. Read More


Using a structure-based coarse-grained model of proteins, we study the mechanism of unfolding of knotted proteins through heating. We find that the dominant mechanisms of unfolding depend on the temperature applied and are generally distinct from those identified for folding at its optimal temperature. In particular, for shallowly knotted proteins, folding usually involves formation of two loops whereas unfolding through high-temperature heating is dominated by untying of single loops. Read More


The rotary sequential hydrolysis of metabolic machine F1-ATPase is a prominent feature to reveal high coordination among multiple chemical sites on the stator F1 ring, which also contributes to tight coupling between the chemical reaction and central {\gamma}-shaft rotation. High-speed AFM experiments discovered that the sequential hydrolysis was maintained on the F1 ring even in the absence of the {\gamma} rotor. To explore how the intrinsic sequential performance arises, we computationally investigated essential inter-subunit couplings on the hexameric ring of mitochondrial and bacterial F1. Read More


The principles behind the computation of protein-ligand binding free energies by Monte Carlo integration are described in detail. The simulation provides gas-phase binding free energies that can be converted to aqueous energies by solvation corrections. The direct integration simulation has several characteristics beneficial to free-energy calculations. Read More


About half of human cancers show normal TP53 gene and aberrant overexpression of Mdm2 and/or MdmX. This fact promotes a promising cancer therapeutic strategy which targeting the interactions between p53 and Mdm2/MdmX. For developing the inhibitors to disrupt the p53-Mdm2/MdmX interactions, we systematically investigate structural and interaction characteristics of p53 and inhibitors with Mdm2 and MdmX from atomistic level by exploiting stochastic molecular dynamics simulations. Read More


Proteins are biological polymers that underlie all cellular functions. The first high-resolution protein structures were determined by x-ray crystallography in the 1960s. Since then, there has been continued interest in understanding and predicting protein structure and stability. Read More


The rapid expansion in the spectrum of two-dimensional (2D) materials has driven the efforts of research on the fabrication of 2D composites and heterostructures. Highly ordered structure of 2D materials provides an excellent platform for controlling the ultimate structure and properties of the composite material with precision. However, limited control over the structure of the adherent material and its interactions with highly ordered 2D materials results in defective composites with inferior performance. Read More


Trapping nanoscopic objects to observe their dynamic behaviour for extended periods of time is an ongoing quest. Particularly, sub-100nm transparent objects are hard to catch and most techniques rely on immobilisation or transient diffusion through a confocal laser focus. We present an Anti-Brownian ELectrokinetic trap (pioneered by A. Read More


We show how active transport of ions can be interpreted as an entropy facilitated process. In this interpretation, the pore geometry through which substrates are transported can give rise to a driving force. This gives a direct link between the geometry and the changes in Gibbs energy required. Read More


Sedimentation velocity analytical ultracentrifugation with fluorescence detection has emerged as a powerful method for the study of interacting systems of macromolecules. It combines picomolar sensitivity with high hydrodynamic resolution, and can be carried out with photoswitchable fluorophores for multi-component discrimination, to determine the stoichiometry, affinity, and shape of macromolecular complexes with dissociation equilibrium constants from picomolar to micromolar. A popular approach for data interpretation is the determination of the binding affinity by isotherms of weight-average sedimentation coefficients, sw. Read More


Understanding the operation of biological molecular motors, nanoscale machines that transduce electrochemical energy into mechanical work, is enhanced by bottom-up strategies to synthesize novel motors. Read More


Circular polarization spectroscopy has proven to be an indispensable tool in photosynthesis research and (bio)-molecular research in general. Oxygenic photosystems typically display an asymmetric Cotton effect around the chlorophyll absorbance maximum with a signal $\leq 1 \%$. In vegetation, these signals are the direct result of the chirality of the supramolecular aggregates. Read More


We investigate dynamical coupling between water and amino acid side-chain residues in solvation dynamics by selecting residues often used as natural probes, namely tryptophan, tyrosine and histidine, located at different positions on protein surface and having various degrees of solvent exposure. Such differently placed residues are found to exhibit different timescales of relaxation. The total solvation response, as measured by the probe is decomposed in terms of its interactions with (i) protein core, (ii) side-chain atoms and (iii) water molecules. Read More


Monitoring the kinetics and conformational dynamics of single enzymes is crucial in order to better understand their biological functions as these motions and structural dynamics are usually unsynchronized among the molecules. Detecting the enzyme-reactant interactions and associated conformational changes of the enzyme on a single molecule basis, however, remain as a challenge with established optical techniques due to the commonly required labeling of the reactants or the enzyme itself. The labeling process is usually non-trivial and the labels themselves might skew the physical properties of the enzyme. Read More


Hybrid quantum mechanical-molecular mechanical (QM/MM) simulations are widely used in enzyme simulation. Over ten convergence studies of QM/MM methods have revealed over the past several years that key energetic and structural properties approach asymptotic limits with only very large (ca. 500-1000 atom) QM regions. Read More


Assuming that mutation and fixation processes are reversible Markov processes, we prove that the equilibrium ensemble of sequences obeys a Boltzmann distribution with $\exp(4N_e m (1 - 1/(2N)))$, where $m$ is a Malthusian fitness and $N_e$ and $N$ are the effective and actual population sizes. Combining this finding with the knowledge of protein folding, we derive a correspondence between protein fitness and folding free energy, i.e. Read More


The probability distribution of sequences with maximum entropy that satisfies a given amino acid composition at each site and a given pairwise amino acid frequency at each site pair is a Boltzmann distribution with $\exp(-\psi_N)$, where the total interaction $\psi_N$ is represented as the sum of one body and pairwise interactions. A protein folding theory based on the random energy model (REM) indicates that the equilibrium ensemble of natural protein sequences is a canonical ensemble characterized by $\exp(-\Delta G_{ND}/k_B T_s)$ or by $\exp(- G_{N}/k_B T_s)$ if an amino acid composition is kept constant, meaning $\psi_N = \Delta G_{ND}/k_B T_s +$ constant, where $\Delta G_{ND} \equiv G_N - G_D$, $G_N$ and $G_D$ are the native and denatured free energies, and $T_s$ is the effective temperature of natural selection. Here, we examine interaction changes ($\Delta \psi_N$) due to single nucleotide nonsynonymous mutations, and have found that the variance of their $\Delta \psi_N$ over all sites hardly depends on the $\psi_N$ of each homologous sequence, indicating that the variance of $\Delta G_N (= k_B T_s \Delta \psi_N)$ is nearly constant irrespective of protein families. Read More


Extensive molecular dynamics simulations reveal that the interactions between proteins and poly(ethylene glycol)(PEG) can be described in terms of the surface composition of the proteins. PEG molecules accumulate around non-polar residues while avoiding polar ones. A solvent-accessible-surface-area model of protein adsorption on PEGylated nanoparticles accurately fits a large set of data on the composition of the protein corona recently obtained by label-free proteomic mass spectrometry. Read More


Interaction with divalent cations is of paramount importance for RNA structural stability and function. We here report a detailed molecular dynamics study of all the possible binding sites for Mg$^{2+}$ on a RNA duplex, including both direct (inner sphere) and indirect (outer sphere) binding. In order to tackle sampling issues, we develop a modified version of bias-exchange metadynamics which allows us to simultaneously compute affinities with previously unreported statistical accuracy. Read More


Efficient replication and assembly of virus particles are integral to the establishment of infection. In addition to the primary role of the capsid protein (CP) in encapsidating the RNA progeny, experimental evidence on positive sense single-stranded RNA viruses suggests that the CP also regulates RNA synthesis. Here, we demonstrate that replication of Satellite tobacco mosaic virus (STMV) is controlled by the cooperative interaction between STMV CP and the helper virus (HV) Tobacco mosaic virus (TMV) replicase. Read More


In this paper, we introduce multiscale persistent functions for biomolecular structure characterization. The essential idea is to combine our multiscale rigidity functions with persistent homology analysis, so as to construct a series of multiscale persistent functions, particularly multiscale persistent entropies, for structure characterization. To clarify the fundamental idea of our method, the multiscale persistent entropy model is discussed in great detail. Read More


Nowadays different experimental techniques, such as single molecule or relaxation experiments, can provide dynamic properties of biomolecular systems, but the amount of detail obtainable with these methods is often limited in terms of time or spatial resolution. Here we use state-of-the-art computational techniques, namely atomistic molecular dynamics and Markov state models, to provide insight into the rapid dynamics of short RNA oligonucleotides, in order to elucidate the kinetics of stacking interactions. Analysis of multiple microsecond-long simulations indicates that the main relaxation modes of such molecules can consist of transitions between alternative folded states, rather than between random coils and native structures. Read More


We obtain a closed form of generating functions of RNA substructure using hermitian matrix model with the Chebyshev polynomial of the second kind, which has the form of the hypergeometric function. To match the experimental findings of the statistical behavior, we regard the substructure as a grand canonical ensemble and find its fugacity value corresponding to the number of stems. We also suggest a hierarchical picture based on the planar structure so that the non-planar structure such as pseudoknot are included. Read More


We propose a model for the formation of chromatin loops based on the diffusive sliding of a DNA-bound factor which can dimerise to form a molecular slip-link. Our slip-links mimic the behaviour of cohesin-like molecules, which, along with the CTCF protein, stabilize loops which organize the genome. By combining 3D Brownian dynamics simulations and 1D exactly solvable non-equilibrium models, we show that diffusive sliding is sufficient to account for the strong bias in favour of convergent CTCF-mediated chromosome loops observed experimentally. Read More


N-methyl-D-aspartate receptors (NMDARs) are glycoproteins in the brain central to learning and memory. The effects of glycosylation on the structure and dynamics of NMDARs are largely unknown. In this work, we use extensive molecular dynamics simulations of GluN1 and GluN2B ligand binding domains (LBDs) of NMDARs to investigate these effects. Read More


Background: Guanine quadruplexes (GQs) play vital roles in many cellular processes and are of much interest as drug targets. In contrast to the availability of many structural studies, there is still limited knowledge on GQ folding. Scope of review: We review recent molecular dynamics (MD) simulation studies of the folding of GQs, with an emphasis paid to the human telomeric DNA GQ. Read More


Theoretical analysis, which maps single molecule time trajectories of a molecular motor onto unicyclic Markov processes, allows us to evaluate the heat dissipated from the motor and to elucidate its dependence on the mean velocity and diffusivity. Unlike passive Brownian particles in equilibrium, the velocity and diffusion constant of molecular motors are closely inter-related to each other. In particular, our study makes it clear that the increase of diffusivity with the heat production is a natural outcome of active particles, which is reminiscent of the recent experimental premise that the diffusion of an exothermic enzyme is enhanced by the heat released from its own catalytic turnover. Read More


2016Dec
Affiliations: 1University of Pittsburgh, 2University of Pittsburgh, 3The College of New Jersey, 4University of Pittsburgh, 5University of Pittsburgh

Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. Read More


Geometric, topological and graph theory modeling and analysis of biomolecules are of essential importance in the conceptualization of molecular structure, function, dynamics, and transport. On the one hand, geometric modeling provides molecular surface and structural representation, and offers the basis for molecular visualization, which is crucial for the understanding of molecular structure and interactions. On the other hand, it bridges the gap between molecular structural data and theoretical/mathematical models. Read More


2016Dec
Affiliations: 1School of Science and Technology, University of Camerino, 2School of Science and Technology, University of Camerino, 3School of Science and Technology, University of Camerino

We propose a new approach for modelling the process of RNA folding as a graph transformation guided by the global value of free energy. Since the folding process evolves towards a configuration in which the free energy is minimal, the global behaviour resembles the one of a self-adaptive system. Each RNA configuration is a graph and the evolution of configurations is constrained by precise rules that can be described by a graph grammar. Read More


We propose to combine a mean field approach with all atom molecular dynamics into a multistage algorithm that can model protein folding and dynamics over very long time periods yet with atomic level precision. As an example we investigate an isolated monomeric Myc oncoprotein that has been implicated in carcinomas including those in colon, breast and lungs. Under physiological conditions a monomeric Myc is presumed to be an example of intrinsically disordered proteins, that pose a serious challenge to existing modelling techniques. Read More


A global optimization method called Greedy Neighborhood Search (GNS) and a novel conformational sampling method using a spherical distribution is proposed to find the minimum energy conformation of a protein-like heteropolymer model called AB model. The AB model consists of hydrophobic (A) and hydrophilic (B) monomers analogous to the real proteins. The AB model in three-dimensional space is represented by simple bead-rod chain system which is identical to the one-bead protein model. Read More


Context-free and context-sensitive formal grammars are often regarded as more appropriate to model proteins than regular level models such as finite state automata and Hidden Markov Models. In theory, the claim is well founded in the fact that many biologically relevant interactions between residues of protein sequences have a character of nested or crossed dependencies. In practice, there is hardly any evidence that grammars of higher expressiveness have an edge over old good HMMs in typical applications including recognition and classification of protein sequences. Read More


The pathogenesis and progression of many tumors, including hematologic malignancies is highly dependent on enhanced lipogenesis. De novo fatty-acid synthesis permits accelerated proliferation of tumor cells by providing structural components to build the membranes. It may also lead to alterations of physicochemical properties of the formed membranes, which can have an impact on signaling or even increase resistance to drugs in cancer cells. Read More


Characterization of B-cell protein epitope and developing critical parameters for its identification is one of the long standing interests. Using Layers algorithm, we introduced the concept of anchor residues to identify epitope. We have shown that majority of the epitope is composed of anchor residues and have significant bias in epitope for these residues. Read More


Single molecule detection, sequencing and conformational mapping of aptamers are important for improving medical and biosensing technologies and for better understanding of biological processes at the molecular level. We obtain vibrational signals of single aptamers immobilized on gold substrates using tip-enhanced Raman spectroscopy (TERS). We compare topographic and optical signals and investigate the fluctuations of the position-dependent TERS spectra. Read More