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arXiv2001

Optimality of the genetic code with respect to protein stability and amino acid frequencies

How robust is the natural genetic code with respect to mistranslation errors? It has long been known that the genetic code is very efficient in limiting the effect of point mutation. A misread codon will commonly code either for the same amino acid or for a similar one in terms of its biochemical properties, so the structure and function of the coded protein remain relatively unaltered. Previous studies have attempted to address this question more quantitatively, namely by statistically estimating the fraction of randomly generated codes that do better than the genetic code regarding its overall robustness. In this paper, we extend these results by investigating the role of amino acid frequencies in the optimality of the genetic code. When measuring the relative fitness of the natural code with respect to a random code, it is indeed natural to assume that a translation error affecting a frequent amino acid is less favorable than that of a rare one, at equal mutation cost. We find that taking the amino acid frequency into account accordingly decreases the fraction of random codes that beat the natural code, making the latter comparatively even more robust. This effect is particularly pronounced when more refined measures of the amino acid substitution cost are used than hydrophobicity. To show this, we devise a new cost function by evaluating with computer experiments the change in folding free energy caused by all possible single-site mutations in a set of known protein structures. With this cost function, we estimate that of the order of one random code out of 100 millions is more fit than the natural code when taking amino acid frequencies into account. The genetic code seems therefore structured so as to minimize the consequences of translation errors on the 3D structure and stability of proteins.

physics.bio-ph↗

arXiv2001

Hamming distance geometry of a protein conformational space. Application to the clustering of a 4 ns molecular dynamics trajectory of the HIV-1 integrase catalytic core

Protein structures can be encoded into binary sequences, these are used to define a Hamming distance in conformational space: the distance between two different molecular conformations is the number of different bits in their sequences. Each bit in the sequence arises from a partition of conformational space in two halves. Thus, the information encoded in the binary sequences is also used to characterize the regions of conformational space visited by the system. We apply this distance and their associated geometric structures, to the clustering and analysis of conformations sampled during a 4 ns molecular dynamics simulation of the HIV-1 integrase catalytic core. The cluster analysis of the simulation shows a division of the trajectory into two segments of 2.6 and 1.4 ns length, which are qualitatively different: the data points to the fact that equilibration is only reached at the end of the first segment. Some length of the paper is devoted to compare the Hamming distance to the r.m.s. deviation measure. The analysis of the cases studied so far, shows that under the same conditions the two measures behave quite differently, and that the Hamming distance appears to be more robust than the r.m.s. deviation.

physics.bio-ph↗

arXiv2002

Dynamical investigation of macromolecular hybridization bioassays

A novel sensoric technique for the dynamical in situ investigation of a hybridization bio assay is presented, which utilizes a metal bead labeling method. Therein, hybridization results in an increased metal coverage on parts of a substrate, where it takes place. Our sensing principle relies on the measurement of the radio frequency impedance of the hybridization spots. We propose several examples for sensor devices.

physics.ins-det↗

arXiv2004

Degeneracy in the genetic code and its symmetries by base substitutions

Why is the genetic code the way it is? The most successful theory states that the codon assignments minimise the effects of errors arising in primordial living systems. Here a transversion is reported that leaves invariant degeneracy in the genetic code. A model was established to address the question: How should sets of synonymous codons be chosen to minimise the deleterious effects of non synonymous substitutions? It is shown that the arithmetic theory of quadratic forms provides the framework for degeneracy in the genetic code.

physics.bio-ph↗

arXiv1998

Scaling Laws and Similarity Detection in Sequence Alignment with Gaps

We study the problem of similarity detection by sequence alignment with gaps, using a recently established theoretical framework based on the morphology of alignment paths. Alignments of sequences without mutual correlations are found to have scale-invariant statistics. This is the basis for a scaling theory of alignments of correlated sequences. Using a simple Markov model of evolution, we generate sequences with well-defined mutual correlations and quantify the fidelity of an alignment in an unambiguous way. The scaling theory predicts the dependence of the fidelity on the alignment parameters and on the statistical evolution parameters characterizing the sequence correlations. Specific criteria for the optimal choice of alignment parameters emerge from this theory. The results are verified by extensive numerical simulations.

physics.bio-ph↗

arXiv1998

A dynamic programming algorithm for RNA structure prediction including pseudoknots

We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of ${\cal O}(N^6)$ in time and ${\cal O}(N^4)$ in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the algorithm that generates the optimal minimum energy structure for a single RNA sequence, using standard RNA folding thermodynamic parameters augmented by a few parameters describing the thermodynamic stability of pseudoknots. We demonstrate the properties of the algorithm by using it to predict structures for several small pseudoknotted and non-pseudoknotted RNAs. Although the time and memory demands of the algorithm are steep, we believe this is the first algorithm to be able to fold optimal (minimum energy) pseudoknotted RNAs with the accepted RNA thermodynamic model.

physics.bio-ph↗

arXiv2007

Measurability of side chain rotational isomer populations: NMR and molecular mechanics of cobalt glycyl-leucine dipeptide model system

The cobalt glycyl-leucine dipeptide is a model system for studying the effects of Karplus equation calibration, molecular mechanics accuracy, backbone conformation, and thermal motions on the measurability of side chain rotational isomer populations. We analyze measurements of 8 vicinal coupling constants about the alpha to beta-carbon and beta to gamma-carbon bonds of the leucine side chain and of 10 NOESY cross relaxation rates across these bonds. Molecular mechanics and peptide and protein crystallographic databases are an essential part of this analysis because they independently suggest that the trans gauche-plus and gauche-minus trans rotational isomers of the leucine side chain predominate. They also both suggest that puckering of the cobalt dipeptide ring system reduces the gauche-plus gauche-plus rotational isomer population to less than about 10%. At the present +/- 1 Hz calibration accuracy of Karplus equations for vicinal coupling constants, the predominant trans gauche-plus and gauche-minus trans rotational isomer populations can be measured with about 5% accuracy, but the population of the gauche-plus gauche-plus rotational isomer is probably very near or just below the limit of measurability. These estimates also depend upon qualitative assessments of the accuracy of the molecular mechanics energy wells. We introduce gel graphics that are ideally suited to presenting qualitative error and measurability estimates.

physics.bio-ph↗

arXiv1999

Physical Parameters for Biconcave Shape Vesicles

The Helfrich's shape equation of axisymmetric vesicles is studied. A sufficient condition on the physical parameters and some geometric properties are discovered for the biconcave shape.

physics.bio-ph↗

arXiv2004

Semiclassical Neural Network

We have constructed a simple semiclassical model of neural network where neurons have quantum links with one another in a chosen way and affect one another in a fashion analogous to action potentials. We have examined the role of stochasticity introduced by the quantum potential and compare the system with the classical system of an integrate-and-fire model by Hopfield. Average periodicity and short term retentivity of input memory are noted.

quant-ph↗

arXiv1994

Learning and Complexity in Genetic Auto-Adaptive Systems

We describe and investigate the learning capablities displayed by a population of self-replicating segments of computer-code subject to random mutation: the tierra environment. We find that learning is achieved through phase transitions that adapt the population to whichever environment it encounters, with a learning rate characterized by the environmental variables. Our results suggest that most effective learning is achieved close to the edge of chaos.

adap-org↗

arXiv1995

Protein Folding and Spin Glass

We explicitly show the connection between the protein folding problem and spin glass transition. This is then used to identify appropriate quantities that are required to describe the transition. A possible way of observing the spin glass transition is proposed.

cond-mat↗

arXiv2001

Generalized-ensemble simulations of spin systems and protein systems

In complex systems such as spin systems and protein systems, conventional simulations in the canonical ensemble will get trapped in states of energy local minima. We employ the generalized-ensemble algorithms in order to overcome this multiple-minima problem. Three well-known generalized-ensemble algorithms, namely, multicanonical algorithm, simulated tempering, and replica-exchange method, are described. We then present three new generalized-ensemble algorithms based on the combinations of the three methods. Effectiveness of the new methods are tested with a Potts model and protein systems.

cond-mat.stat-mech↗

arXiv1999

On the structure pf genealogical trees in the presence of selection

We investigate through numerical simulations the effect of selection on two summary statistics for nucleotide variation in a sample of two genes from a population of N asexually reproducing haploid individuals. One is the mean time since two individuals had their most recent common ancestor ($\bar{T_s}$), and the other is the mean number of nucleotide differences between two genes in the sample ($\bar{d_s}$). In the case of diminishing epistasis, in which the deleterious effect of a new mutation is attenuated, we find that the scale of $\bar{d_s}$ with the population size depends on the mutation rate, leading then to the onset of a sharp threshold phenomenon as N becomes large.

cond-mat.stat-mech↗

arXiv1998

Hydrodynamics of bacterial motion

In this paper we present a hydrodynamic approach to describe the motion of migrating bacteria as a special class of self-propelled systems. Analytical and numerical calculations has been performed to study the behavior of our model in the turbulent-like regime and to show that a phase transition occurs as a function of noise strength. Our results can explain previous experimental observations as well as results of numerical simulations.

cond-mat.soft↗

arXiv1998

Realistic Models of Biological Motion

The origin of biological motion can be traced back to the function of molecular motor proteins. Cytoplasmic dynein and kinesin transport organelles within our cells moving along a polymeric filament, the microtubule. The motion of the myosin molecules along the actin filaments is responsible for the contraction of our muscles. Recent experiments have been able to reveal some important features of the motion of individual motor proteins, and a new statistical physical description - often referred to as ``thermal ratchets'' - has been developed for the description of motion of these molecules. In this approach the motors are considered as Brownian particles moving along one-dimensional periodic structures due to the effect of nonequilibrium fluctuations. Assuming specific types of interaction between the particles the models can be made more realistic. We have been able to give analytic solutions for our model of kinesin with elastically coupled Brownian heads and for the motion of the myosin filament where the motors are connected through a rigid backbone. Our theoretical predictions are in a very good agreement with the various experimental results. In addition, we have considered the effects arising as a result of interaction among a large number of molecular motors, leading to a number of novel cooperative transport phenomena.

cond-mat.stat-mech↗

arXiv1998

A hierarchical scheme for cooperativity and folding in proteins

We propose a protein model based on a hierarchy of constraints that force the protein to follow certain pathways when changing conformation. The model exhibits a first order phase transition, cooperativity and is exactly solvable. It also shows an unexpected symmetry between folding and unfolding pathways as suggested by a recent experiment.

cond-mat↗

arXiv1997

Wilson renormalization of a reaction-diffusion process

Healthy and sick individuals (A and B particles) diffuse independently with diffusion constants D_A and D_B. Sick individuals upon encounter infect healthy ones (at rate k), but may also spontaneously recover (at rate 1/τ). The propagation of the epidemic therefore couples to the fluctuations in the total population density. Global extinction occurs below a critical value ρ_{c} of the spatially averaged total density. The epidemic evolves as the diffusion--reaction--decay process A + B --> 2B, B --> A , for which we write down the field theory. The stationary state properties of this theory when D_A=D_B were obtained by Kree et al. The critical behavior for D_A D_B remains unsolved.

cond-mat.stat-mech↗

arXiv1999

SOC in a population model with global control

We study a plant population model introduced recently by J. Wallinga [OIKOS {\bf 74}, 377 (1995)]. It is similar to the contact process (`simple epidemic', `directed percolation'), but instead of using an infection or recovery rate as control parameter, the population size is controlled directly and globally by removing excess plants. We show that the model is very closely related to directed percolation (DP). Anomalous scaling laws appear in the limit of large populations, small densities, and long times. These laws, associated critical exponents, and even some non-universal parameters, can be related to those of DP. As in invasion percolation and in other models where the rôles of control and order parameters are interchanged, the critical value $p_c$ of the wetting probability $p$ is obtained in the scaling limit as singular point in the distribution of infection rates. We show that a mean field type approximation leads to a model studied by Y.C. Zhang et al. [J. Stat. Phys. {\bf 58}, 849 (1990)]. Finally, we verify the claim of Wallinga that family extinction in a marginally surviving population is governed by DP scaling laws, and speculate on applications to human mitochondrial DNA.

cond-mat.stat-mech↗