arXiv subjects

Chao Tang

Publications and source records attributed to Chao Tang.

At least 109 records · Page 6Linked to original sources

arXiv2006

Dynamic Studies of Scaffold-dependent Mating Pathway in Yeast

The mating pathway in \emph{Saccharomyces cerevisiae} is one of the best understood signal transduction pathways in eukaryotes. It transmits the mating signal from plasma membrane into the nucleus through the G-protein coupled receptor and the mitogen-activated protein kinase (MAPK) cascade. According to the current understandings of the mating pathway, we construct a system of ordinary differential equations to describe the process. Our model is consistent with a wide range of experiments, indicating that it captures some main characteristics of the signal transduction along the pathway. Investigation with the model reveals that the shuttling of the scaffold protein and the dephosphorylation of kinases involved in the MAPK cascade cooperate to regulate the response upon pheromone induction and to help preserving the fidelity of the mating signaling. We explored factors affecting the dose-response curves of this pathway and found that both negative feedback and concentrations of the proteins involved in the MAPK cascade play crucial role. Contrary to some other MAPK systems where signaling sensitivity is being amplified successively along the cascade, here the mating signal is transmitted through the cascade in an almost linear fashion.

q-bio.MN↗

arXiv2006

Stochastic Model of Yeast Cell Cycle Network

Biological functions in living cells are controlled by protein interaction and genetic networks. These molecular networks should be dynamically stable against various fluctuations which are inevitable in the living world. In this paper, we propose and study a stochastic model for the network regulating the cell cycle of the budding yeast. The stochasticity in the model is controlled by a temperature-like parameter $β$. Our simulation results show that both the biological stationary state and the biological pathway are stable for a wide range of "temperature". There is, however, a sharp transition-like behavior at $β_c$, below which the dynamics is dominated by noise. We also define a pseudo energy landscape for the system in which the biological pathway can be seen as a deep valley.

q-bio.MN↗

arXiv2005

Specificity of Trypsin and Chymotrypsin: Loop Motion Controlled Dynamic Correlation as a Determinant

Trypsin and chymotrypsin are both serine proteases with high sequence and structural similarities, but with different substrate specificity. Previous experiments have demonstrated the critical role of the two loops outside the binding pocket in controlling the specificity of the two enzymes. To understand the mechanism of such a control of specificity by distant loops, we have used the Gaussian Network Model to study the dynamic properties of trypsin and chymotrypsin and the roles played by the two loops. A clustering method was introduced to analyze the correlated motions of residues. We have found that trypsin and chymotrypsin have distinct dynamic signatures in the two loop regions which are in turn highly correlated with motions of certain residues in the binding pockets. Interestingly, replacing the two loops of trypsin with those of chymotrypsin changes the motion style of trypsin to chymotrypsin-like, whereas the same experimental replacement was shown necessary to make trypsin have chymotrypsin's enzyme specificity and activity. These results suggest that the cooperative motions of the two loops and the substrate-binding sites contribute to the activity and substrate specificity of trypsin and chymotrypsin.

q-bio.BM↗

arXiv2004

The Yeast Cell-Cycle Network Is Robustly Designed

The interactions between proteins, DNA, and RNA in living cells constitute molecular networks that govern various cellular functions. To investigate the global dynamical properties and stabilities of such networks, we studied the cell-cycle regulatory network of the budding yeast. With the use of a simple dynamical model, it was demonstrated that the cell-cycle network is extremely stable and robust for its function. The biological stationary state--the G1 state--is a global attractor of the dynamics. The biological pathway--the cell-cycle sequence of protein states--is a globally attracting trajectory of the dynamics. These properties are largely preserved with respect to small perturbations to the network. These results suggest that cellular regulatory networks are robustly designed for their functions.

q-bio.MN↗

arXiv2004

Finding regulatory modules through large-scale gene-expression data analysis

The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Based on the Iterative Signature Algorithm [Bergmann, S., Ihmels, J. and Barkai, N. (2002) Phys. Rev. E 67, 031902], we present an algorithm - the Progressive Iterative Signature Algorithm (PISA) - that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology annotation database as a reference, found that our algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics.

q-bio.QM↗

arXiv2003

Correlation between sequence hydrophobicity and surface-exposure pattern of database proteins

Hydrophobicity is thought to be one of the primary forces driving the folding of proteins. On average, hydrophobic residues occur preferentially in the core, whereas polar residues tends to occur at the surface of a folded protein. By analyzing the known protein structures, we quantify the degree to which the hydrophobicity sequence of a protein correlates with its pattern of surface exposure. We have assessed the statistical significance of this correlation for several hydrophobicity scales in the literature, and find that the computed correlations are significant but far from optimal. We show that this less than optimal correlation arises primarily from the large degree of mutations that naturally occurring proteins can tolerate. Lesser effects are due in part to forces other than hydrophobicity and we quantify this by analyzing the surface exposure distributions of all amino acids. Lastly we show that our database findings are consistent with those found from an off-lattice hydrophobic-polar model of protein folding.

q-bio.BM↗

arXiv2003

Flexibility of beta-sheets: Principal-component analysis of database protein structures

Protein folds are built primarily from the packing together of two types of structures: alpha-helices and beta-sheets. Neither structure is rigid, and the flexibility of helices and sheets is often important in determining the final fold ({\it e.g.}, coiled coils and beta-barrels). Recent work has quantified the flexibility of alpha-helices using a principal-component analysis (PCA) of database helical structures (Emberly, 2003). Here, we extend the analysis to beta-sheet flexibility using PCA on a database of beta-sheet structures. For sheets of varying dimension and geometry, we find two dominant modes of flexibility: twist and bend. The distributions of amplitudes for these modes are found to be Gaussian and independent, suggesting that the PCA twist and bend modes can be identified as the soft elastic normal modes of sheets. We consider the scaling of mode eigenvalues with sheet size and find that parallel beta-sheets are more rigid than anti-parallel sheets over the entire range studied. Lastly, we discuss the application of our PCA results to modeling and design of beta-sheet proteins.

cond-mat.soft↗

arXiv2003

Statistical mechanics of RNA folding: importance of alphabet size

We construct a minimalist model of RNA secondary-structure formation and use it to study the mapping from sequence to structure. There are strong, qualitative differences between two-letter and four or six-letter alphabets. With only two kinds of bases, there are many alternate folding configurations, yielding thermodynamically stable ground-states only for a small set of structures of high designability, i.e., total number of associated sequences. In contrast, sequences made from four bases, as found in nature, or six bases have far fewer competing folding configurations, resulting in a much greater average stability of the ground state.

cond-mat.stat-mech↗

arXiv2003

Designability and Thermal Stability of Protein Structures

Only about 1,000 qualitatively different protein folds are believed to exist in nature. Here, we review theoretical studies which suggest that some folds are intrinsically more designable than others, {\it i.e.} are lowest energy states of an unusually large number of sequences. The sequences associated with these folds are also found to be unusually thermally stable. The connection between highly designable structures and highly stable sequences is generally known as the "designability principle". The designability principle may help explain the small number of natural folds, and may also guide the design of new folds.

cond-mat.stat-mech↗

arXiv2003

Simulation and analysis of in vitro DNA evolution

We study theoretically the in vitro evolution of a DNA sequence by binding to a transcription factor. Using a simple model of protein-DNA binding and available binding constants for the Mnt protein, we perform large-scale, realistic simulations of evolution starting from a single DNA sequence. We identify different parameter regimes characterized by distinct evolutionary behaviors. For each regime we find analytical estimates which agree well with simulation results. For small population sizes, the DNA evolutional path is a random walk on a smooth landscape. While for large population sizes, the evolution dynamics can be well described by a mean-field theory. We also study how the details of the DNA-protein interaction affect the evolution.

cond-mat↗

arXiv2003

Origin of Scaling Behavior of Protein Packing Density: A Sequential Monte Carlo Study of Compact Long Chain Polymers

Single domain proteins are thought to be tightly packed. The introduction of voids by mutations is often regarded as destabilizing. In this study we show that packing density for single domain proteins decreases with chain length. We find that the radius of gyration provides poor description of protein packing but the alpha contact number we introduce here characterize proteins well. We further demonstrate that protein-like scaling relationship between packing density and chain length is observed in off-lattice self-avoiding walks. A key problem in studying compact chain polymer is the attrition problem: It is difficult to generate independent samples of compact long self-avoiding walks. We develop an algorithm based on the framework of sequential Monte Carlo and succeed in generating populations of compact long chain off-lattice polymers up to length $N=2,000$. Results based on analysis of these chain polymers suggest that maintaining high packing density is only characteristic of short chain proteins. We found that the scaling behavior of packing density with chain length of proteins is a generic feature of random polymers satisfying loose constraint in compactness. We conclude that proteins are not optimized by evolution to eliminate packing voids.

cond-mat.soft↗

arXiv2002

Flexibility of $α$-helices: Results of a statistical analysis of database protein structures

$α$-helices stand out as common and relatively invariant secondary structural elements of proteins. However, $α$-helices are not rigid bodies and their deformations can be significant in protein function ({\it e.g.} coiled coils). To quantify the flexibility of $α$-helices we have performed a structural principal-component analysis of helices of different lengths from a representative set of protein folds in the Protein Data Bank. We find three dominant modes of flexibility: two degenerate bend modes and one twist mode. The data are consistent with independent Gaussian distributions for each mode. The mode eigenvalues, which measure flexibility, follow simple scaling forms as a function of helix length. The dominant bend and twist modes and their harmonics are reproduced by a simple spring model, which incorporates hydrogen-bonding and excluded volume. As an application, we examine the amount of bend and twist in helices making up several coiled-coil proteins. Incorporation of $α$-helix flexibility into structure refinement and design is discussed.

cond-mat.stat-mech↗

arXiv2002

Structure Space of Model Proteins --A Principle Component Analysis

We study the space of all compact structures on a two-dimensional square lattice of size $N=6\times6$. Each structure is mapped onto a vector in $N$-dimensions according to a hydrophobic model. Previous work has shown that the designabilities of structures are closely related to the distribution of the structure vectors in the $N$-dimensional space, with highly designable structures predominantly found in low density regions. We use principal component analysis to probe and characterize the distribution of structure vectors, and find a non-uniform density with a single peak. Interestingly, the principal axes of this peak are almost aligned with Fourier eigenvectors, and the corresponding Fourier eigenvalues go to zero continuously at the wave-number for alternating patterns ($q=π$). These observations provide a stepping stone for an analytic description of the distribution of structural points, and open the possibility of estimating designabilities of realistic structures by simply Fourier transforming the hydrophobicities of the corresponding sequences.

physics.bio-ph↗

arXiv2002

Designability of alpha-helical Proteins

A typical protein structure is a compact packing of connected alpha-helices and/or beta-strands. We have developed a method for generating the ensemble of compact structures a given set of helices and strands can form. The method is tested on structures composed of four alpha-helices connected by short turns. All such natural four-helix bundles that are connected by short turns seen in nature are reproduced to closer than 3.6 Angstroms per residue within the ensemble. Since structures with no natural counterpart may be targets for ab initio structure design, the designability of each structure in the ensemble -- defined as the number of sequences with that structure as their lowest energy state -- is evaluated using a hydrophobic energy. For the case of four alpha-helices, a small set of highly designable structures emerges, most of which have an analog among the known four-helix fold families, however several novel packings and topologies are identified.

cond-mat.stat-mech↗

arXiv2002

The Designability of Protein Structures: A Lattice-Model Study using the Miyazawa-Jernigan Matrix

We study the designability of all compact 3x3x3 and 6x6 lattice-protein structures using the Miyazawa-Jernigan (MJ) matrix. The designability of a structure is the number of sequences that design the structure, i.e. sequences that have that structure as their unique lowest-energy state. Previous studies of hydrophobic-polar (HP) models showed a wide distribution of structure designabilities. Recently, questions were raised concerning the use of a 2-letter (HP) code in such studies. Here we calculate designabilities using all 20 amino acids, with empirically determined interaction potentials (MJ matrix), and compare with HP model results. We find good qualitative agreement between the two models. In particular, highly designable structures in the HP model are also highly designable in the MJ model--and vice versa--with the associated sequences having enhanced thermodynamic stability.

cond-mat.stat-mech↗

arXiv2001

Identifying Proteins of High Designability via Surface-Exposure Patterns

Using an off-lattice model, we fully enumerate folded conformations of polypeptide chains of up to N = 19 monomers. Structures are found to differ markedly in designability, defined as the number of sequences with that structure as a unique lowest-energy conformation. We find that designability is closely correlated with the pattern of surface exposure of the folded structure. For longer chains, complete enumeration of structures is impractical. Instead, structures can be randomly sampled, and relative designability estimated either from designability within the random sample, or directly from surface-exposure pattern. We compare the surface-exposure patterns of those structures identified as highly designable to the patterns of naturally occurring proteins.

cond-mat.soft↗

arXiv2001

Fast Tree Search for Enumeration of a Lattice Model of Protein Folding

Using a fast tree-searching algorithm and a Pentium cluster, we enumerated all the sequences and compact conformations (structures) for a protein folding model on a cubic lattice of size $4\times3\times3$. We used two types of amino acids -- hydrophobic (H) and polar (P) -- to make up the sequences, so there were $2^{36} \approx 6.87 \times 10^{10}$ different sequences. The total number of distinct structures was 84,731,192. We made use of a simple solvation model in which the energy of a sequence folded into a structure is minus the number of hydrophobic amino acids in the ``core'' of the structure. For every sequence, we found its ground state or ground states, i.e., the structure or structures for which its energy is lowest. About 0.3% of the sequences have a unique ground state. The number of structures that are unique ground states of at least one sequence is 2,662,050, about 3% of the total number of structures. However, these ``designable'' structures differ drastically in their designability, defined as the number of sequences whose unique ground state is that structure. To understand this variation in designability, we studied the distribution of structures in a high dimensional space in which each structure is represented by a string of 1's and 0's, denoting core and surface sites, respectively.

cond-mat.stat-mech↗

arXiv2000

Symmetry and designability for lattice protein models

Native protein folds often have a high degree of symmetry. We study the relationship between the symmetries of native proteins, and their designabilities -- how many different sequences encode a given native structure. Using a two-dimensional lattice protein model based on hydrophobicity, we find that those native structures that are encoded by the largest number of different sequences have high symmetry. However only certain symmetries are enhanced, e.g. x/y-mirror symmetry and $180^o$ rotation, while others are suppressed. If it takes a large number of mutations to destabilize the native state of a protein, then, by definition, the state is highly designable. Hence, our findings imply that insensitivity to mutation implies high symmetry. It appears that the relationship between designability and symmetry results because protein substructures are also designable. Native protein folds may therefore be symmetric because they are composed of repeated designable substructures.

cond-mat.stat-mech↗