To understand how protein reduces the conformational space to be searched for the native structure, it is crucial to characterize ensembles of conformations on the way of folding processes, in particular ensembles of relatively long-range structures connecting between an extensively unfolded state and a state with a native-like overall chain topology. To analyze such intermediate conformations, we performed multiple unfolding molecular dynamics simulations of barnase at 498 K. Some short-range structures such as part of helix and turn were well sustained while most of the secondary structures and the hydrophobic cores were eventually lost, which is consistent with the results by other experimental and computational studies. The most important novel findings were persistence of long-range relatively compact substructures, which was captured by exploiting the concept of module. Module is originally introduced to describe the hierarchical structure of a globular protein in the native state. Modules are conceptually such relatively compact substructures that are resulted from partitioning the native structure of a globular protein completely into several contiguous segments with the least extended conformations. We applied this concept of module to detect a possible hierarchical structure of each snapshot structure in unfolding processes as well. Along with this conceptual extension, such detected relatively compact substructures are named quasi-modules. We found almost perfect persistence of quasi-module boundaries that are positioned close to the native module boundaries throughout the unfolding trajectories. Relatively compact conformations of the quasi-modules seemed to be retained mainly by hydrophobic interactions formed between residues located at both terminal regions within each module. From these results, we propose a hypothesis that hierarchical folding with the early formation of quasi-modules effectively reduces search space for the native structure.

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Protein-protein interactions support most biological processes, and it is important to find specifically interacting partner proteins among homologous proteins in order to elucidate cellular functions such as signal transduction systems. Various high-throughput experimental methods for identifying these interactions have been invented, and used to generate a huge amount of data. Because these experiments have been applied to only a few organisms, and their accuracy is believed to be limited, it would be valuable to develop computational methods for predicting protein-protein interactions from their amino acid sequences or tertiary structural information. In this study, we describe a prediction method of interacting proteins based on homology-modeled complex structures. We employed the statistical residue-residue contact energy used in a previous study, and two types of new scores, simple electrostatic energy and sequence similarity between target sequences and template structures. The validity of each protein-protein complex model was measured using their single and combined scores. We applied our method to all the protein heterodimers of Saccharomyces cerevisiae. To evaluate the prediction performance of our method, we prepared two types of protein-protein interaction dataset: a complete dataset and high confidence dataset. The complete dataset (10,325 protein dimer models) contains all the yeast protein heterodimers whose complex structures can be modeled. Among them, pairs registered in the DIP database are defined as interacting pairs, and those not registered are defined as non-interacting protein pairs. The high confidence dataset (3,219 protein dimer models) is a more reliable subset of the complete dataset extracted using the criteria of the common subcellular localization. Both datasets show that sequence similarity has a much higher discrimination power than the other structure-based scores, but that the inclusion of contact energy results in significant improvement over predictions using sequence similarity alone. These results suggest that the sequence similarity is indispensable for the prediction, whereas structure scores can play supporting roles.

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Hubs are highly connected proteins in a protein-protein interaction network. Previous work has implicated disordered domains and high surface charge as the properties significant in the ability of hubs to bind multiple proteins. While conformational flexibility of disordered domains plays an important role in the binding ability of large hubs, high surface charge is the dominant property in small hubs. In this study, we further investigate the role of the high surface charge in the binding ability of small hubs in the absence of disordered domains. Using multipole expansion, we find that the charges are highly distributed over the hub surfaces. Residue enrichment studies show that the charged residues in hubs are more prevalent on the exposed surface, with the exception of Arg, which is predominantly found at the interface, as compared to non-hubs. This suggests that the charged residues act primarily from the exposed surface rather than the interface to affect the binding ability of small hubs. They do this through (i) enhanced intra-molecular electrostatic interactions to lower the desolvation penalty, (ii) indirect long – range intermolecular interactions with charged residues on the partner proteins for better complementarity and electrostatic steering, and (iii) increased solubility for enhanced diffusion-controlled rate of binding. Along with Arg, we also find a high prevalence of polar residues Tyr, Gln and His and the hydrophobic residue Met at the interfaces of hubs, all of which have the ability to form multiple types of interactions, indicating that the interfaces of hubs are optimized to participate in multiple interactions.

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The numbers of membrane proteins in the current genomes of various organisms provide an important clue about how the protein world has evolved from the aspect of membrane proteins. Numbers of membrane proteins were estimated by analyzing the total proteomes of 248 prokaryota, using the SOSUI system for membrane proteins (Hirokawa et al., Bioinformatics, 1998) and SOSUIsignal for signal peptides (Gomi et al., CBIJ, 2004). The results showed that the ratio of membrane proteins to total proteins in these proteomes was almost constant: 0.228. When amino acid sequences were randomized, setting the probability of occurrence of all amino acids to 5%, the membrane protein/total protein ratio decreased to about 0.085. However, when the same simulation was carried out, but using the amino acid composition of the above proteomes, this ratio was 0.218, which is nearly the same as that of the real proteomic systems. This fact is consistent with the birth, death and innovation (BDI) model for membrane proteins, in which transmembrane segments emerge and disappear in accordance with random mutation events.

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High-density oligonucleotide arrays are powerful tools for the analysis of genome-wide expression of genes and for genome-wide screens of genetic variation in living organisms. One of the critical problems in high-density oligonucleotide arrays is how to identify the actual amounts of a transcript due to noise and cross-hybridization involved in the observed signal intensities. Although mismatch (MM) probes are spotted on Affymetrix GeneChips to evaluate the noise and cross-hybridization embedded in perfect match (PM) probes, the behavior of probe-level signal intensities remains unclear. In the present study, we hybridized only one complement 25-mer oligonucleotide to characterize the behavior of duplex formation between target and probe in the complete absence of cross-hybridization. Titration experiments using only one oligonucleotide demonstrated that a substantial amount of intact target was hybridized not only to the PM but also the MM probe and that duplex formation between intact target and MM probe was efficiently reduced by increasing the stringency of hybridization conditions and shortening probe length. In addition, we discuss the correlation between potential for secondary structure of target oligonucleotide and hybridization intensity. These findings will be useful for the development of genome-wide analysis of gene expression and genetic variations by optimization of hybridization and probe conditions.

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We have made a stereoscopic viewing system for a large assembly of proteins using OpenRasmol. The stable version 2.7.1 of OpenRasmol is modified for the system, which uses an eye-ware instead of trained bare-eyes. Software rendering and other benefits in OpenRasmol are reserved. A 3-D graphic board is used just for the active stereo method, not for the acceleration of rendering. Our modification is simple one. In the results, an actin filament of 16-mers, where one actin monomer has about 400 residues, in space filling model can be rendered in stereoscopic viewing mode and can be made one turn within 10 seconds as quick as non-stereoscopic mode. Other 3-D molecular graphics programs with 3-D accelerator boards cannot render such a large assembly of molecules in stereoscopic usage mode as quickly as the modified OpenRasmol. An attractive application of our system is stereoscopic viewing with a large 200 inch screen in passive stereo method. Simultaneous usage is available for more than 100 persons with inexpensive eye-wares. The large screen allows us to investigate an interior of a groove in an actin filament in detail. Our modified OpenRasmol is distributed following the license, RASLIC, as an open source code at our web site (www.irisa-lab.bio.kyutech.ac.jp/openrasmol), where video files showing rendering speeds of our modified OpenRasmol are also available.

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RhoA and Rac1 are small GTPases primarily involved in cytoskeletal remodeling. Many biochemical studies have suggested that they are also key organizers of cell-substrate adhesion. Recently, fluorescence resonance energy transfer (FRET)-based indicators have been developed to visualize RhoA and Rac1 activity in living cells [Yoshizaki et al., J. Cell Biol. 162, 223 (2003); Pertz et al., Nature 440, 1069 (2006)]. These indicators use one of the interactions between RhoA (Rac1) and the RhoA (Rac1)-binding domain of their effector proteins. However, distribution of RhoA activity in single cells has not yet been observed with micrometer-scale resolution. Here, we employed an approach that detects GDP/GTP exchange on small GTPases by using FRET from YFP-fused small GTPases to a fluorescent analogue of GTP, BODIPY(TR)-GTP. This approach allowed us to visualize confined localization of active (GTP-bound forms of) RhoA and Rac1 in individual focal adhesions. Activated RhoA accumulated in immobile and long-lived focal adhesions but was not evident in unstable and temporary adhesions, while activated Rac1 was observed at every adhesion. Our results suggest that RhoA is the major regulator determining the stability of individual cell adhesion structures.

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A method to search for local structural similarities in proteins at atomic resolution is presented. It is demonstrated that a huge amount of structural data can be handled within a reasonable CPU time by using a conventional relational database management system with appropriate indexing of geometric data. This method, which we call geometric indexing, can enumerate ligand binding sites that are structurally similar to sub-structures of a query protein among more than 160,000 possible candidates within a few hours of CPU time on an ordinary desktop computer. After detecting a set of high scoring ligand binding sites by the geometric indexing search, structural alignments at atomic resolution are constructed by iteratively applying the Hungarian algorithm, and the statistical significance of the final score is estimated from an empirical model based on a gamma distribution. Applications of this method to several protein structures clearly shows that significant similarities can be detected between local structures of non-homologous as well as homologous proteins.

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