Ion specific protein assembly and hydrophobic surface forces

Large anions are attracted to hydrophobic surfaces while smaller, well solvated ions are repelled. Using a combination of explicit solvent and continuum model simulations we show that this leads to significant ion-specific protein-protein interactions due to hydrophobic patches on the protein surfaces. In solutions of NaI and NaCl we calculate the potentials of mean force and find that the resulting second virial coefficients for lysozyme correspond well with experiment. We argue that ionic interactions with nonpolar surface groups may play an important role for biomolecular assembly and Hofmeister-type effects.     

蛋白质支链动力学快运动的核磁共振研究

蛋白质的功能不仅取决于其结构,而且受到其构像及其变化的影响. 许多生物化学过程就是由于蛋白质结构的一些动力学变化而完成,如蛋白质-蛋白质,蛋白质-药物配体之间的相互作用. 因此分析蛋白质的动力学变化,就能够对其参与的生化过程进行分析. 作为动力学研究的有力工具之一,核磁共振能够分辨到原子范围内的从千秒到皮秒时间范围的运动过程,因此在动力学研究中有着不可替代的作用. 本文仅就核磁共振在蛋白质支链快运动方法（ps-ns）研究方面的进展进行总结,以期阐明核磁共振的在支链动力学研究中的发展现状.     

Computational design of proteins with novel structure and functions

Computational design of proteins is a relatively new field, where scientists search the enormous sequence space for sequences that can fold into desired structure and perform desired functions. With the computational approach, proteins can be designed, for example, as regulators of biological processes, novel enzymes, or as biotherapeutics. These approaches not only provide valuable information for understanding of sequence–structure–function relations in proteins, but also hold promise for applications to protein engineering and biomedical research. In this review, we briefly introduce the rationale for computational protein design, then summarize the recent progress in this field, including de novo protein design, enzyme design, and design of protein–protein interactions. Challenges and future prospects of this field are also discussed.     

Nonlinear pressure dependence of the interaction potential of dense protein solutions

The influence of pressure on the structure and protein-protein interaction potential of dense protein solutions was studied and analyzed using small-angle x-ray scattering in combination with a liquid state theoretical approach. The structural as well as the interaction parameters of dense lysozyme solutions are affected by pressure in a nonlinear way. The structural properties of water lead to a modification of the protein-protein interactions below 4?kbar, which might have significant consequences for the stability of proteins in extreme natural environments.     

Predicting disease-related genes by topological similarity in human protein-protein interaction network

Predicting genes likely to be involved in human diseases is an important task in bioinformatics field. Nowadays, the accumulation of human protein-protein interactions (PPIs) data provides us an unprecedented opportunity to gain insight into human diseases. In this paper, we adopt the topological similarity in human protein-protein interaction network to predict disease-related genes. As a computational algorithm to speed up the identification of disease-related genes, the topological similarity has substantial advantages over previous topology-based algorithms. First of all, it provides a global measurement of similarity between two vertices. Secondly, quantity which can measure new topological feature has been integrated into the notion of topological similarity. Our method is specially designed for predicting disease-related genes of single disease-gene family. The proposed method is applied to human protein-protein interaction and hepatocellular carcinoma (HCC) data. The results show a significant enrichment of disease-related genes that are characterized by higher topological similarity than other genes.     

Spin labeling studies of the interactions of potentially useful therapeutic agents for the treatment of Alzheimer’s disease with cytoskeletal proteins in erythrocyte ghosts and brain synaptosomal membranes

Alzheimer’s disease (AD) is the major dementing disorder of the elderly with four million victims of this disease in the United States. The molecular basis of AD remains unknown, but the biochemical and biophysical state of cytoskeletal proteins is reportedly altered in AD cortical neurons. No approved effective therapy for AD is available. We have used electron spin resonance (ESR) and a protein-specific spin label to investigate the interactions of three potential therapeutic agents in AD: tacrine, velnacrine, and acetylcarnitine, with cytoskeletal proteins in erythrocyte membranes. Further, we report for the first time the effects of tacrine on the physical state of membrane proteins in brain neocortex synaptosomal membranes. All three agents lead to decreased segmental motion and increased cytoskeletal protein-protein interactions in erythrocyte membranes in order of effectiveness: tacrine > velnacrine > acetylcarnitine. In addition, we have synthesized N-methylacridinium methosulfate which has a positive charge on the opposite side of the molecule relative to tacrine. This former agent gave a less pronounced diminution of the relevant ESR parameter than that caused by tacrine, implying that the orientation of the molecule with its interaction site is important in the increased cytoskeletal protein-protein interactions induced by tacrine. With synaptosomal membranes tacrine also significantly decreased segmental motion of membrane proteins. Our ESR results on erythrocyte and brain membranes suggest that in addition to their biochemical effects, these potential AD therapeutic agents function to strengthen cytoskeletal protein-protein interactions. These results are discussed with reference to possible molecular mechanisms involving cytoskeletal proteins in AD.     

"New-version-fast-multipole-method" accelerated electrostatic interactions in biomolecular systems

In this paper, we present an efficient and accurate numerical algorithm for calculating the electrostatic interactions in biomolecular systems. In our scheme, a boundary integral equation (BIE) approach is applied to discretize the linearized Poisson-Boltzmann (PB) equation. The resulting integral formulas are well conditioned for single molecule cases as well as for systems with more than one macromolecule, and are solved efficiently using Krylov subspace based iterative methods such as generalized minimal residual (GMRES) or bi-conjugate gradients stabilized (BiCGStab) methods. In each iteration, the convolution type matrix-vector multiplications are accelerated by a new version of the fast multipole method (FMM). The implemented algorithm is asymptotically optimal O(N) both in CPU time and memory usage with optimized prefactors. Our approach enhances the present computational ability to treat electrostatics of large scale systems in protein-protein interactions and nano particle assembly processes. Applications including calculating the electrostatics of the nicotinic acetylcholine receptor (nAChR) and interactions between protein Sso7d and DNA are presented.     

The TRPC2 channel forms protein-protein interactions with Homer and RTP in the rat vomeronasal organ

Background  

The signal transduction cascade operational in the vomeronasal organ (VNO) of the olfactory system detects odorants important for prey localization, mating, and social recognition. While the protein machinery transducing these external cues has been individually well characterized, little attention has been paid to the role of protein-protein interactions among these molecules. Development of an in vitro expression system for the transient receptor potential 2 channel (TRPC2), which establishes the first electrical signal in the pheromone transduction pathway, led to the discovery of two protein partners that couple with the channel in the native VNO.     

Preferential attachment in the protein network evolution

The Saccharomyces cerevisiae protein-protein interaction map, as well as many natural and man-made networks, shares the scale-free topology. The preferential attachment model was suggested as a generic network evolution model that yields this universal topology. However, it is not clear that the model assumptions hold for the protein interaction network. Using a cross-genome comparison, we show that (a) the older a protein, the better connected it is, and (b) the number of interactions a protein gains during its evolution is proportional to its connectivity. Therefore, preferential attachment governs the protein network evolution. Evolutionary mechanisms leading to such preference and some implications are discussed.     

Statistically enhanced self-attraction of random patterns

In this work we develop a theory of interaction of randomly patterned surfaces as a generic prototype model of protein-protein interactions. The theory predicts that pairs of randomly superimposed identical (homodimeric) random patterns have always twice as large magnitude of the energy fluctuations with respect to their mutual orientation, as compared with pairs of different (heterodimeric) random patterns. The amplitude of the energy fluctuations is proportional to the square of the average pattern density, to the square of the amplitude of the potential and its characteristic length, and scales linearly with the area of surfaces. The greater dispersion of interaction energies in the ensemble of homodimers implies that strongly attractive complexes of random surfaces are much more likely to be homodimers, rather than heterodimers. Our findings suggest a plausible physical reason for the anomalously high fraction of homodimers observed in real protein interaction networks.     

Molecular level dynamics of genetic oscillator—The effect of protein-protein interaction

Uncovering how interactions of a set of molecular components influence the system’s dynamic behavior is important for understanding intracellular processes and elucidating design principles, but unfortunately, there are limited efforts for studying this issue. Here, we study the effect of distinct post-translational dynamics controlled by protein dimerization on oscillations in the repressilator. For this, we propose three biologically motivated model scenarios of the repressilator with monomer or dimer being the active form of repressor, and with protein-protein interactions. It is found that the dimer dissociation constant can tune oscillatory regions, frequency and amplitude. Introducing a modified linear noise approximation to evaluate fluctuations of amplitude and period in the oscillatory systems, we show that different dimerization leads to a different effect on period and amplitude in reducing noise. The manipulation of the circuit’s biochemical properties provides a practical strategy for designing a robust and tunable oscillator.     

Atomic force microscopy: determination of unbinding force, off rate and energy barrier for protein-ligand interaction

Recently, atomic force microscopy (AFM) based force measurements have been applied biophysically and clinically to the field of molecular recognition as well as to the evaluation of dynamic parameters for various interactions between proteins and ligands in their native environment. The aim of this review is to describe the use of the AFM to measure the forces that control biological interaction, focusing especially on protein-ligand and protein-protein interaction modes. We first considered the measurements of specific and non-specific unbinding forces which together control protein-ligand interactions. As such, we will look at the theoretical background of AFM force measurement curves for evaluating the unbinding forces of protein-ligand complexes. Three AFM model dynamic parameters developed recently for use in protein-ligand interactions are reviewed: (i) unbinding forces, (ii) off rates, and (iii) binding energies. By reviewing the several techniques developed for measuring forces between biological structures and intermolecular forces in the literature, we show that use of an AFM for these applications provides an excellent tool in terms of spatial resolution and lateral resolution, especially for protein-protein and protein-ligand interactions.     

High-field (275 GHz) spin-label EPR for high-resolution polarity determination in proteins

The polarity of protein surfaces is one of the factors driving protein-protein interactions. High-field, spin-label EPR at 95 GHz, i.e., 10 times higher than conventional EPR, is an upcoming technique to determine polarity parameters of the inside of proteins. Here we show that by 275 GHz EPR even the small polarity differences of sites at the protein surface can be discriminated. To do so, four single cysteine mutations were introduced at surface sites (positions 12, 27, 42, and 118) of azurin and spin labeled. By 275 GHz EPR in frozen solution, polarity/proticity differences between all four sites can be resolved, which is impossible by 95 GHz EPR. In addition, by 275 GHz EPR, two spectral components are observed for all mutants. The difference between them corresponds to one additional hydrogen bond.     

In silico analysis of microdomain-mediated trimer formation in the T cell membrane

We consider stochastic reaction-diffusion dynamics involved in the formation of a trimeric protein receptor complex, where diffusion is modulated by the presence of small, fixed membrane microdomains. Compartmentalisation of cell membrane signalling proteins may optimise signal transduction but previous modelling work suggests that signalling is only augmented if microdomains are highly mobile. Using a Gillespie algorithm-based spatial numerical simulation, we examine the effect of the presence, size and total coverage of microdomains, which either slow protein diffusion or trap proteins at their boundary. We examine scenarios where protein-protein interactions take place within microdomains, and also where interactions are favoured at the microdomain boundary. This model is motivated by the formation of the high-affinity receptor for the cytokine IL-2. Proliferation requires a threshold number of bound receptors, but pleiotropic effects of IL-2 on other cell types means that high ligand concentrations are undesirable. Hence, optimising T cell sensitivity to IL-2 is essential. In agreement with earlier models, we find that small microdomain sizes result in the greatest augmentation in receptor formation, but that static microdomains can also confer an increased sensitivity in the case of heterotrimeric receptor complex formation.     

A unifying motif of intermolecular cooperativity in protein associations

At the molecular level, most biological processes entail protein associations which in turn rely on a small fraction of interfacial residues called hot spots. Our theoretical analysis shows that hot spots share a unifying molecular attribute: they provide a third-body contribution to intermolecular cooperativity. Such motif, based on the wrapping of interfacial electrostatic interactions, is essential to maintain the integrity of the interface. Thus, our main result is to unravel the molecular nature of the protein association problem by revealing its underlying physics and thus by casting it in simple physical grounds. Such knowledge could then be exploited in rational drug design since the regions here indicated may serve as blueprints to engineer small molecules disruptive of protein-protein interfaces.     

Identifying dynamical bottlenecks of stochastic transitions in biochemical networks

In biochemical networks, identifying key proteins and protein-protein reactions that regulate fluctuation-driven transitions leading to pathological cellular function is an important challenge. Using large deviation theory, we develop a semianalytical method to determine how changes in protein expression and rate parameters of protein-protein reactions influence the rate of such transitions. Our formulas agree well with computationally costly direct simulations and are consistent with experiments. Our approach reveals qualitative features of key reactions that regulate stochastic transitions.     

Energy Fluctuations Shape Free Energy of Nonspecific Biomolecular Interactions

Understanding design principles of biomolecular recognition is a key question of molecular biology. Yet the enormous complexity and diversity of biological molecules hamper the efforts to gain a predictive ability for the free energy of protein-protein, protein-DNA, and protein-RNA binding. Here, using a variant of the Derrida model, we predict that for a large class of biomolecular interactions, it is possible to accurately estimate the relative free energy of binding based on the fluctuation properties of their energy spectra, even if a finite number of the energy levels is known. We show that the free energy of the system possessing a wider binding energy spectrum is almost surely lower compared with the system possessing a narrower energy spectrum. Our predictions imply that low-affinity binding scores, usually wasted in protein-protein and protein-DNA docking algorithms, can be efficiently utilized to compute the free energy. Using the results of Rosetta docking simulations of protein-protein interactions from Andre et al. (Proc. Natl. Acad. Sci. USA 105:16148, 2008), we demonstrate the power of our predictions.     

High density peptide microarrays. In situ synthesis and applications

The technologies enabling the creation of large scale, miniaturized peptide or protein microarrays are emerging. The focuses of this review are the synthesis and applications of peptide and peptidomimetic microarrays, especially the light directed parallel synthesis of individually addressable high density peptide microarrays using a novel photogenerated reagent chemistry and digital photolithography (Gao et al., 1998, J. Am. Chem. Soc. 120, 12698; Pellois et al. 2002, Nat. Biotechnol. 20, 922). Concepts related to the synthesis are discussed, such as the reactions of photogenerated acids in the deprotection step of peptide synthesis or oligonucleotide synthesis, and the applications of high density peptide chips in antibody binding assays are discussed. Peptide chips provide versatile tools for probing antigen-antibody, protein-protein, peptide-ligand interactions and are basic components for miniaturization, automation, and system integration in research and clinical diagnosis applications.     

Detection of the homo- and hetero-interaction of proteins using fluorescence resonance energy transfer spectrum method

Fluorescence spectrometry based on fluorescence resonance energy transfer (FRET) principle is a simple but effective tool for investigating protein-protein interactions. In this paper, we report a spectrometry to quantify FRET efficiency based on our home-designed spectral probe system and spectral data-processing procedure. In our method, the fluorescence spectrum from each specimen is recorded at two wavelengths 454 and 502 nm. Least-squares linear fitting algorithm is applied directly to decompose the spectra of donor and acceptor under these two wavelengths to obtain FRET efficiency, which takes both spectral intensity and spectral profile into account compared with traditional three-step analysis. This system and the data-processing procedure enabled us to detect the homo-interaction and hetero-interaction of proteins in living cell.