SEARCH · Searcharxiv

Results for “q-bio”

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18Linked to original sources

arXiv1998

The Folding Funnel Landscape of the Peptide Met-enkephalin

We study the free energy landscape of the small peptide Met-enkephalin. Our data were obtained from a generalized-ensemble Monte Carlo simulation taking the interactions among all atoms into account. We show that the free energy landscape resembles that of a funnel, indicating that this peptide is a good folder. Our work demonstrates that the energy landscape picture and folding concept, developed in the context of simplified protein models, can also be used to describe the folding in more realistic models.

cond-mat↗

arXiv1998

Optimizing Smith-Waterman alignments

Mutual correlation between segments of DNA or protein sequences can be detected by Smith-Waterman local alignments. We present a statistical analysis of alignment of such sequences, based on a recent scaling theory. A new fidelity measure is introduced and shown to capture the significance of the local alignment, i.e., the extent to which the correlated subsequences are correctly identified. It is demonstrated how the fidelity may be optimized in the space of penalty parameters using only the alignment score data of a single sequence pair.

cond-mat↗

arXiv1998

The role of the energy gap in protein folding dynamics

The dynamics of folding of proteins is studied by means of a phenomenological master equation. The energy distribution is taken as a truncated exponential for the misfolded states plus a native state sitting below the continuum. The influence of the gap on the folding dynamics is studied, for various models of the transition probabilities between the different states of the protein. We show that for certain models, the relaxation to the native state is accelerated by increasing the gap, whereas for others it is slowed down .

cond-mat.soft↗

arXiv1998

Neutral evolution of model proteins: diffusion in sequence space and overdispersion

We simulate the evolution of model protein sequences subject to mutations. A mutation is considered neutral if it conserves 1) the structure of the ground state, 2) its thermodynamic stability and 3) its kinetic accessibility. All other mutations are considered lethal and are rejected. We adopt a lattice model, amenable to a reliable solution of the protein folding problem. We prove the existence of extended neutral networks in sequence space -- sequences can evolve until their similarity with the starting point is almost the same as for random sequences. Furthermore, we find that the rate of neutral mutations has a broad distribution in sequence space. Due to this fact, the substitution process is overdispersed (the ratio between variance and mean is larger than one). This result is in contrast with the simplest model of neutral evolution, which assumes a Poisson process for substitutions, and in qualitative agreement with biological data.

cond-mat.stat-mech↗

arXiv1998

Protein Foldability and Designability: General Physics and Pretty Chemistry

Making use of a simplified model for protein folding, it can be shown that conformations which are particularly stable when their energy is minimized with respect to amino acid sequence (in the sense that they display a large energy gap to the lowest structrally dissimilar conformation), aside from leading to fast folding, are highly designable (in the sense that many sequences target onto it in the folding process). These results are quite general, do not depend on the particular simmetries displayed by the compact conformation chosen as native, and can be obtained making use of a large class of contact energies. On the other hand, the design of sequences which fold onto native conformations displaying secondary structures and eventually tertiary simmetries, is a difficult task requiring a delicate tuning of the contact energies.

cond-mat.soft↗

arXiv1998

Identifying the protein folding nucleus using molecular dynamics

Molecular dynamics simulations of folding in an off-lattice protein model reveal a nucleation scenario, in which a few well-defined contacts are formed with high probability in the transition state ensemble of conformations. Their appearance determines folding cooperativity and drives the model protein into its folded conformation.

cond-mat.stat-mech↗

arXiv1998

Discrete molecular dynamics studies of the folding of a protein-like model

Background: Many attempts have been made to resolve in time the folding of model proteins in computer simulations. Different computational approaches have emerged. Some of these approaches suffer from the insensitivity to the geometrical properties of the proteins (lattice models), while others are computationally heavy (traditional MD). Results: We use a recently-proposed approach of Zhou and Karplus to study the folding of the protein model based on the discrete time molecular dynamics algorithm. We show that this algorithm resolves with respect to time the folding --- unfolding transition. In addition, we demonstrate the ability to study the coreof the model protein. Conclusion: The algorithm along with the model of inter-residue interactions can serve as a tool to study the thermodynamics and kinetics of protein models.

cond-mat.stat-mech↗

arXiv1999

Collectivity in the Brain Sensory Response

A question of cooperative effects in auditory brain processing on various space- and time-scales is addressed. The experimental part of our study is based on Multichannel Magnetoencephalography recordings in normal human subjects. Left, right and binaural stimulations were used, in separate runs, for each subject. The resulting time-series representing left and right auditory cortex activity provide a clear evidence for two levels of neuronal cooperation. One is the local hemispheric collective response, termed M100 for its maximum at around 100ms after a stimulus onset. Its only global characteristics turn out to be time-locked to a stimulus, however, which means that the detailed neuronal evolution is largely stochastic. This, together with the $1/f$ character of the corresponding power spectrum indicates self-organized criticality as an underlying mechanism. The second level is communication between the two hemispheres with clear laterality effects: as a rule, the contralateral hemisphere leads by $\sim$10ms. Mutual information analysis points to a direct information transport as a way of this communication.

cond-mat↗

arXiv1999

Fixed points of Hopfield type neural networks

The set of the fixed points of the Hopfield type network is under investigation. The connection matrix of the network is constructed according the Hebb rule from the set of memorized patterns which are treated as distorted copies of the standard-vector. It is found that the dependence of the set of the fixed points on the value of the distortion parameter can be described analytically. The obtained results are interpreted in the terms of neural networks and the Ising model at T=0.

cond-mat.dis-nn↗

arXiv1999

Entropic Elasticity, Cooperative Extensibility and Supercoiling Property of DNA: A Unified Viewpoint

A unified model is constructed to study the recently observed DNA entropic elasticity, cooperative extensibility, and supercoiling property. With the introduction of a new structural parameter (the folding angle $ϕ$), bending deformations of sugar-phosphate backbones, steric effects of nucleotide basepairs, and short-range basestacking interactions are considered. The comprehensive agreement of theoretical results with experimental observations on both torsionally relaxed and negatively supercoiled DNAs strongly indicates that, basestacking interactions, although short-ranged in nature, dominate the elasticity of DNA and hence are of vital biological significance.

cond-mat.soft↗

arXiv1999

Cortical Potential Distributions and Cognitive Information Processing

The use of cortical field potentials rather than the details of spike trains as the basis for cognitive information processing is proposed. This results in a space of cognitive elements with natural metrics. Sets of spike trains may also be considered to be points in a multidimensional metric space. The closeness of sets of spike trains in such a space implies the closeness of points in the resulting function space of potential distributions.

cond-mat.dis-nn↗

arXiv1999

Protein Design is a Key Factor for Subunit-subunit Association

Fundamental questions about the role of the quaternary structures are addressed using a statistical mechanics off-lattice model of a dimer protein. The model, in spite of its simplicity, captures key features of the monomer-monomer interactions revealed by atomic force experiments. Force curves during association and dissociation are characterized by sudden jumps followed by smooth behavior and form hysteresis loops. Furthermore, the process is reversible in a finite range of temperature stabilizing the dimer. It is shown that in the interface between the two monomeric subunits the design procedure naturally favors those amino acids whose mutual interaction is stronger. Furthermore it is shown that the width of the hysteresis loop increases as the design procedure improves, i.e. stabilizes more the dimer.

cond-mat.stat-mech↗

arXiv1999

Folding Lennard-Jones proteins by a contact potential

We studied the possibility to approximate a Lennard Jones interaction by a pairwise contact potential. First we used a Lennard-Jones potential to design off-lattice, protein-like heteropolymer sequences, whose lowest energy (native) conformations were then identified by Molecular Dynamics. Then we turned to investigate whether one can find a pairwise contact potential, whose ground states are the contact maps associated with these native conformations. We show that such a requirement cannot be satisfied exactly - i.e. no such contact parameters exist. Nevertheless, we found that one can find contact energy parameters for which an energy minimization procedure, acting in the space of contact maps, yields maps whose corresponding structures are close to the native ones. Finally we show that when these structures are used as the initial point of a Molecular Dynamics energy minimization process, the correct native folds are recovered with high probability.

cond-mat.stat-mech↗

arXiv1999

Determination of optimal effective interactions between amino acids in globular proteins

An optimization technique is used to determine the pairwise interactions between amino acids in globular proteins. A numerical strategy is applied to a set of proteins for maximizing the native fold stability with respect to alternative structures obtained by gapless threading. The extracted parameters are shown to be very reliable for identifying the native states of proteins (unrelated to those in the training set) among thousands of conformations. The only poor performers are proteins with heme groups and/or poor compactness whose complexity cannot be captured by standard pairwise energy functionals.

cond-mat.soft↗

arXiv1999

Cold and Warm Denaturation of Hydrophobic Polymers

We introduce a polymer model where the transition from swollen to compact configurations is due to interactions between the monomers and the solvent. These interactions are the origin of the effective attractive interactions between hydrophobic amminoacids in proteins. We find that in the low and high temperature phases polymers are swollen, and there is an intermediate phase where the most favorable configurations are compact. We argue that such a model captures in a single framework both the cold and the warm denaturation experimentally detected for proteins. Some consequences for protein folding are discussed.

cond-mat.stat-mech↗

arXiv1999

Synchronous clusters in a noisy inhibitory neural network

We study the stability and information encoding capacity of synchronized states in a neuronal network model that represents part of thalamic circuitry. Our model neurons have a Hodgkin-Huxley-type low threshold Calcium channel, display post inhibitory rebound, and are connected via GABAergic inhibitory synapses. We find that there is a threshold in synaptic strength, $τ_c$, below which there are no stable spiking network states. Above threshold the stable spiking state is a cluster state, where different groups of neurons fire consecutively, and each neuron fires with the same cluster each time. Weak noise destabilizes this state, but stronger noise drives the system into a different, self-organized, stochastically synchronized state. Neuronal firing is still organized in clusters, but individual neurons can hop from cluster to cluster. Noise can actually induce and sustain such a state below the threshold of synaptic strength. We do find a qualitative difference in the firing patterns between small ($\sim 10$ neurons) and large ($\sim 1000$ neurons) networks. We determine the information content of the spike trains in terms of two separate contributions: the spike time jitter around cluster firing times, and the hopping from cluster to cluster. We quantify the information loss due to temporally correlated interspike intervals. Recent experiments on the locust olfactory system and striatal neurons suggest that the nervous system may actually use these two channels to encode separate and unique information.

cond-mat.stat-mech↗

arXiv1999

Fast global oscillations in networks of integrate-and-fire neurons with low firing rates

We study analytically the dynamics of a network of sparsely connected inhibitory integrate-and-fire neurons in a regime where individual neurons emit spikes irregularly and at a low rate. In the limit when the number of neurons N tends to infinity,the network exhibits a sharp transition between a stationary and an oscillatory global activity regime where neurons are weakly synchronized. The activity becomes oscillatory when the inhibitory feedback is strong enough. The period of the global oscillation is found to be mainly controlled by synaptic times, but depends also on the characteristics of the external input. In large but finite networks, the analysis shows that global oscillations of finite coherence time generically exist both above and below the critical inhibition threshold. Their characteristics are determined as functions of systems parameters, in these two different regimes. The results are found to be in good agreement with numerical simulations.

cond-mat.dis-nn↗

arXiv1999

Hiking in the energy landscape in sequence space: a bumpy road to good folders

With the help of a simple 20 letters, lattice model of heteropolymers, we investigate the energy landscape in the space of designed good-folder sequences. Low-energy sequences form clusters, interconnected via neutral networks, in the space of sequences. Residues which play a key role in the foldability of the chain and in the stability of the native state are highly conserved, even among the chains belonging to different clusters. If, according to the interaction matrix, some strong attractive interactions are almost degenerate (i.e. they can be realized by more than one type of aminoacid contacts) sequence clusters group into a few super-clusters. Sequences belonging to different super-clusters are dissimilar, displaying very small ($\approx 10%$) similarity, and residues in key-sites are, as a rule, not conserved. Similar behavior is observed in the analysis of real protein sequences.

cond-mat.soft↗