Hydrophobic-polar Lattice Model Research Articles

Surface Patterning for Enhanced Protein Stability: Insights from Molecular Simulations.

Reduced activity of enzymes upon immobilization is a major challenge for the industrial use of enzymes. Enzyme-surface interactions and interactions between the immobilized enzymes are thought of as primary reasons for the reduced activity. In the current paper, we study the thermal and structural stability of proteins on a patterned hydrophobic surface in the framework of a hydrophobic-polar lattice model. Our results indicate that, while a homogeneous hydrophobic surface denatures the proteins, carefully patterned surfaces can dramatically increase the stability of adsorbed proteins. The size, shape, and the distance between surface patterns play a significant role in determining the stability of proteins. When the spacing between the patterns is large, maximum stability is observed when the surface pattern is complementary to the exposed hydrophobic domain of the protein, while at smaller spacing, patterns with lower hydrophobicity stabilize the protein more compared to the complementary pattern. The findings from the paper can be rationalized to design novel enzyme-specific surfaces for immobilization with enhanced enzymatic activity.

A Review of Protein Structure Prediction Using Lattice Model.

The prediction of the tertiary structure of a protein from its primary sequence is a long-standing problem for life science researchers. Solutions to this problem have a direct impact on other research areas such as design and discovery of new drugs, understanding the sequenced genomic data, solving the puzzle of protein structure, and understanding the folding mechanism. Solving protein structure prediction (PSP) is considered a cornerstone for life science research to reveal the mechanism of cellular processes; it has been an open problem for past six decades. Globally, various research institutes are working toward a solution to PSP with different methods. In this paper, we present a review of de novo PSP using hydrophobic-polar lattice model. In the hydrophobic-polar model, the PSP problem is defined as a combinatorial optimization problem with an objective of finding the protein structure having the lowest free energy. This study is bounded to two key factors. The first is application of the evolutionary search algorithms for PSP, and the second is the evolution of the modeling method and its corresponding potential energy function. The goal of this paper is to give sufficient biological background to understand the fundamentals of proteins, their structure, and available computational methods.

Structure optimisation by thermal cycling for the hydrophobic-polar lattice model of protein folding

The function of a protein depends strongly on its spatial structure. Therefore the transition from an unfolded stage to the functional fold is one of the most important problems in computational molecular biology. Since the corresponding free energy landscapes exhibit huge numbers of local minima, the search for the lowest-energy configurations is very demanding. Because of that, efficient heuristic algorithms are of high value. In the present work, we investigate whether and how the thermal cycling (TC) approach can be applied to the hydrophobic-polar (HP) lattice model of protein folding. Evaluating the efficiency of TC for a set of two- and three-dimensional examples, we compare the performance of this strategy with that of multi-start local search (MSLS) procedures and that of simulated annealing (SA). For this aim, we incorporated several simple but rather efficient modifications into the standard procedures: in particular, a strong improvement was achieved by also allowing energy conserving state modifications. Furthermore, the consideration of ensembles instead of single samples was found to greatly improve the efficiency of TC. In the framework of different benchmarks, for all considered HP sequences, we found TC to be far superior to SA, and to be faster than Wang-Landau sampling.

Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.

Generic folding and transition hierarchies for surface adsorption of hydrophobic-polar lattice model proteins

The thermodynamic behavior and structural properties of hydrophobic-polar (HP) lattice proteins interacting with attractive surfaces are studied by means of Wang-Landau sampling. Three benchmark HP sequences (48mer, 67mer, and 103mer) are considered with different types of surfaces, each of which attract either all monomers, only hydrophobic (H) monomers, or only polar (P) monomers, respectively. The diversity of folding behavior in dependence of surface strength is discussed. Analyzing the combined patterns of various structural observables, such as, e.g., the derivatives of the numbers of surface contacts, together with the specific heat, we are able to identify generic categories of folding and transition hierarchies. We also infer a connection between these transition categories and the relative surface strengths, i.e., the ratio of the surface attractive strength to the interchain attraction among H monomers. The validity of our proposed classification scheme is reinforced by the analysis of additional benchmark sequences. We thus believe that the folding hierarchies and identification scheme are generic for HP proteins interacting with attractive surfaces, regardless of chain length, sequence, or surface attraction.

Protein Folding in Hydrophobic-Polar Lattice Model: A Flexible Ant-Colony Optimization Approach

This paper proposes a flexible ant colony (FAC) algorithm for solving protein folding problems based on the hydrophobic-polar square lattice model. Collaborations of novel pheromone and heuristic strategies in the proposed algorithm make it more effective in predicting structures of proteins compared with other state-of-the-art algorithms.

A thermodynamic study of peptides binding to carbon nanotubes based on a hydrophobic-polar lattice model using Monte Carlo simulations

Carbon nanotubes (CNTs) are outstanding novel materials that have great potential for a variety of chemical and biomedical applications. However, the mechanism of their interactions with biomaterials is still not fully understood, and more insightful research work is needed. In this work, we use the 2D hydrophobic-polar lattice model and the Monte Carlo simulation method to study the interactions between model peptides and CNTs. The energy parameters of the coarse-grained lattice model are qualitatively determined based on experimental data and molecular dynamics simulation results. Our model is capable of reproducing the essential phenomena of peptides folding in bulk water and binding to CNTs, as well as providing new insights into the thermodynamics and conformational properties of peptides interacting with nanotubes. The results suggest that both the internal energy and the peptide conformational entropy contribute to the binding process. Upon binding to the CNTs, peptides generally unfold into their denatured structures before they reach the lowest-accessible energy states of the system. Temperature has a significant influence on the adsorption process.

Exploration of two-dimensional hydrophobic-polar lattice model by combining local search with elastic net algorithm

Determining the functional conformation of a protein from its amino acid sequence remains a central problem in computational biology. In this paper, we establish the mathematical optimal model of protein folding problem (PFP) on two-dimensional space based on the minimal energy principle. A novel hybrid of elastic net algorithm and local search method (ENLS) is applied successfully to simulations of protein folding on two-dimensional hydrophobic-polar (HP) lattice model. Eight HP benchmark instances with up to 64 amino acids are tested to verify the effectiveness of proposed approach and model. In several cases, the ENLS method finds new lower energy states. The numerical results show that it is drastically superior to other methods in finding the ground state of a protein.

Designable structures are easy to unfold

We study the structural stability of models of proteins for which the selected folds are unusually stable to mutation, that is, designable. A two-dimensional hydrophobic-polar lattice model was used to determine designable folds and these folds were investigated through Langevin dynamics. We find that the phase diagram of these proteins depends on their designability. In particular, highly designable folds are found to be weaker, i.e., easier to unfold, than low designable ones. We expect this to be related to protein flexibility.

Unfolding designable structures

Among an infinite number of possible folds, nature has chosen only about 1000 distinct folds to form protein structures. Theoretical studies suggest that selected folds are intrinsically more designable than others; these selected folds are unusually stable, a property called the designability principle. In this paper we use the 2D hydrophobic-polar lattice model to classify structures according to their designability, and Langevin dynamics to account for their time evolution in the presence of shear flow. We demonstrate that, among all possible folds, the more designable ones are easier to unfold due to their large number of surface-core bonds.

Secondary-structure-favored hydrophobic-polar lattice model of protein folding

Protein folding is studied using a two-dimensional lattice model with the Hamiltonian including both hydrophobic interactions and main chain hydrogen bond interactions of amino acids. Since compact conformations have different designabilities and only highly designable conformations can act as native structural candidates [H. Li, R. Helling, C. Tang, and N. Wingreen, Science 273, 666 (1996)], it is shown that hydrophobic interaction alone is insufficient to explain the appearance of a high proportion of regular secondary structures, especially beta sheets whose content decreases with increasing designability, but interactions of main chain hydrogen bonds can account for this. Thus the emergence of only a small number of structure types (folds) among all possible structures can be understood to some extent.

Geometry selects highly designable structures

By enumerating all sequences of length 20, we study the designability of structures in a two-dimensional hydrophobic-polar (HP) lattice model in a wide range of intermonomer interaction parameters. We find that although the histogram of designability depends on interaction parameters, the set of highly designable structures is invariant. So in the HP lattice model the high designability should be a purely geometrical feature. Our results suggest two geometrical properties for highly designable structures; they have the maximum number of contacts and a unique neighborhood vector representation. Also we show that the contribution of perfectly stable sequences in the designability of structures plays a major role in making them highly designable.

Protein ground state candidates in a simple model: an enumeration study.

The concept of the reduced set of contact maps is introduced. Using this concept we find the ground state candidates for a hydrophobic-polar lattice model on a two-dimensional square lattice. Using these results we exactly enumerate the native states of all proteins for a wide range of energy parameters. In this way, we show that there are some sequences which have an absolute native state. Moreover, we study the scale dependence of the number of members of the reduced set, the number of ground state candidates, and the number of perfectly stable sequences by comparing the results for sequences with lengths of 6 up to 20.

Geometrically Reduced Number of Protein Ground State Candidates

Geometrical properties of protein ground states are studied using an algebraic approach. It is shown that independent from inter-monomer interactions, the collection of ground state candidates for any folded protein is unexpectedly small: For the case of a two-parameter Hydrophobic-Polar lattice model for $L$-mers, the number of these candidates grows only as $L^2$. Moreover, the space of the interaction parameters of the model breaks up into well-defined domains, each corresponding to one ground state candidate, which are separated by sharp boundaries. In addition, by exact enumeration, we show there are some sequences which have one absolute unique native state. These absolute ground states have perfect stability against change of inter-monomer interaction potential.