A balanced secondary structure predictor.

Secondary structure (SS) refers to the local spatial organization of a polypeptide backbone atoms of a protein. Accurate prediction of SS can provide crucial features to form the next higher level of 3D structure of a protein accurately. SS has three different major components, helix (H), beta (E) and coil (C). Most of the SS predictors express imbalanced accuracies by claiming higher prediction performances in predicting H and C, and on the contrary having low accuracy in E predictions. E component being in low count, a predictor may show very good overall performance by over-predicting H and C and under predicting E, which can make such predictors biologically inapplicable. In this work we are motivated to develop a balanced SS predictor by incorporating 33 physicochemical properties into 15-tuble peptides via Chou׳s general PseAAC, which allowed obtaining higher accuracies in predicting all three SS components. Our approach uses three different support vector machines for binary classification of the major classes and then form optimized multiclass predictor using genetic algorithm (GA). The trained three binary SVMs are E versus non-E (i.e., E/¬E), C/¬C and H/¬H. This GA based optimized and combined three class predictor, called cSVM, is further combined with SPINE X to form the proposed final balanced predictor, called MetaSSPred. This novel paradigm assists us in optimizing the precision and recall. We prepared two independent test datasets (CB471 and N295) to compare the performance of our predictors with SPINE X. MetaSSPred significantly increases beta accuracy (QE) for both the datasets. QE score of MetaSSPred on CB471 and N295 were 71.7% and 74.4% respectively. These scores are 20.9% and 19.0% improvement over the QE scores given by SPINE X alone on CB471 and N295 datasets respectively. Standard deviations of the accuracies across three SS classes of MetaSSPred on CB471 and N295 datasets were 4.2% and 2.3% respectively. On the other hand, for SPINE X, these values are 12.9% and 10.9% respectively. These findings suggest that the proposed MetaSSPred is a well-balanced SS predictor compared to the state-of-the-art SPINE X predictor.

Aldolases Utilize Different Oligomeric States To Preserve Their Functional Dynamics.

Aldolases are essential enzymes in the glycolysis pathway and catalyze the reaction cleaving fructose/tagatose 1,6-bisphosphate into dihydroxyacetone phosphate and glyceraldehyde 3-phosphate. To determine how the aldolase motions relate to its catalytic process, we studied the dynamics of three different class II aldolase structures through simulations. We employed coarse-grained elastic network normal-mode analyses to investigate the dynamics of Escherichia coli fructose 1,6-bisphosphate aldolase, E. coli tagatose 1,6-bisphosphate aldolase, and Thermus aquaticus fructose 1,6-bisphosphate aldolase and compared their motions in different oligomeric states. The first one is a dimer, and the second and third are tetramers. Our analyses suggest that oligomerization not only stabilizes the aldolase structures, showing fewer fluctuations at the subunit interfaces, but also allows the enzyme to achieve the required dynamics for its functional loops. The essential mobility of these loops in the functional oligomeric states can facilitate the enzymatic mechanism, substrate recruitment in the open state, bringing the catalytic residues into their required configuration in the closed bound state, and moving back to the open state to release the catalytic products and repositioning the enzyme for its next catalytic cycle. These findings suggest that the aldolase global motions are conserved among aldolases having different oligomeric states to preserve its catalytic mechanism. The coarse-grained approaches taken permit an unprecedented view of the changes in the structural dynamics and how these relate to the critical structural stabilities essential for catalysis. The results are supported by experimental findings from many previous studies.

The use of experimental structures to model protein dynamics.

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high-for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods-Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them.

The critical role of the loops of triosephosphate isomerase for its oligomerization, dynamics, and functionality.

Triosephosphate isomerase (TIM) catalyzes the reaction to convert dihydroxyacetone phosphate into glyceraldehyde 3-phosphate, and vice versa. In most organisms, its functional oligomeric state is a homodimer; however, tetramer formation in hyperthermophiles is required for functional activity. The tetrameric TIM structure also provides added stability to the structure, enabling it to function at more extreme temperatures. We apply Principal Component Analysis to find that the TIM structure space is clearly divided into two groups--the open and the closed TIM structures. The distribution of the structures in the open set is much sparser than that in the closed set, showing a greater conformational diversity of the open structures. We also apply the Elastic Network Model to four different TIM structures--an engineered monomeric structure, a dimeric structure from a mesophile--Trypanosoma brucei, and two tetrameric structures from hyperthermophiles Thermotoga maritima and Pyrococcus woesei. We find that dimerization not only stabilizes the structures, it also enhances their functional dynamics. Moreover, tetramerization of the hyperthermophilic structures increases their functional loop dynamics, enabling them to function in the destabilizing environment of extreme temperatures. Computations also show that the functional loop motions, especially loops 6 and 7, are highly coordinated. In summary, our computations reveal the underlying mechanism of the allosteric regulation of the functional loops of the TIM structures, and show that tetramerization of the structure as found in the hyperthermophilic organisms is required to maintain the coordination of the functional loops at a level similar to that in the dimeric mesophilic structure.

The importance of slow motions for protein functional loops.

Loops in proteins that connect secondary structures such as alpha-helix and beta-sheet, are often on the surface and may play a critical role in some functions of a protein. The mobility of loops is central for the motional freedom and flexibility requirements of active-site loops and may play a critical role for some functions. The structures and behaviors of loops have not been studied much in the context of the whole structure and its overall motions, especially how these might be coupled. Here we investigate loop motions by using coarse-grained structures (C(α) atoms only) to solve the motions of the system by applying Lagrange equations with elastic network models to learn about which loops move in an independent fashion and which move in coordination with domain motions, faster and slower, respectively. The normal modes of the system are calculated using eigen-decomposition of the stiffness matrix. The contribution of individual modes and groups of modes is investigated for their effects on all residues in each loop by using Fourier analyses. Our results indicate overall that the motions of functional sets of loops behave in similar ways as the whole structure. But overall only a relatively few loops move in coordination with the dominant slow modes of motion, and these are often closely related to function.

Structural interpretation of protein-protein interaction network.

BACKGROUND: Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is. METHODS: In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built--the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction. RESULTS: The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported. CONCLUSIONS: Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.