The Influence of 150-Cavity Binders on the Dynamics of Influenza A Neuraminidases as Revealed by Molecular Dynamics Simulations and Combined Clustering

Neuraminidase inhibitors are the main pharmaceutical agents employed for treatments of influenza infections. The neuraminidase structures typically exhibit a 150-cavity, an exposed pocket that is adjacent to the catalytic site. This site offers promising additional contact points for improving potency of existing pharmaceuticals, as well as generating entirely new candidate inhibitors. Several inhibitors based on known compounds and designed to interact with 150-cavity residues have been reported. However, the dynamics of any of these inhibitors remains unstudied and their viability remains unknown. This work reports the outcome of long-term, all-atom molecular dynamics simulations of four such inhibitors, along with three standard inhibitors for comparison. Each is studied in complex with four representative neuraminidase structures, which are also simulated in the absence of ligands for comparison, resulting in a total simulation time of 9.6µs. Our results demonstrate that standard inhibitors characteristically reduce the mobility of these dynamic proteins, while the 150-binders do not, instead giving rise to many unique conformations. We further describe an improved RMSD-based clustering technique that isolates these conformations – the structures of which are provided to facilitate future molecular docking studies – and reveals their interdependence. We find that this approach confers many advantages over previously described techniques, and the implications for rational drug design are discussed.     

Enzymes: An integrated view of structure, dynamics and function

Microbes utilize enzymes to perform a variety of functions. Enzymes are biocatalysts working as highly efficient machines at the molecular level. In the past, enzymes have been viewed as static entities and their function has been explained on the basis of direct structural interactions between the enzyme and the substrate. A variety of experimental and computational techniques, however, continue to reveal that proteins are dynamically active machines, with various parts exhibiting internal motions at a wide range of time-scales. Increasing evidence also indicates that these internal protein motions play a role in promoting protein function such as enzyme catalysis. Moreover, the thermodynamical fluctuations of the solvent, surrounding the protein, have an impact on internal protein motions and, therefore, on enzyme function. In this review, we describe recent biochemical and theoretical investigations of internal protein dynamics linked to enzyme catalysis. In the enzyme cyclophilin A, investigations have lead to the discovery of a network of protein vibrations promoting catalysis. Cyclophilin A catalyzes peptidyl-prolyl cis/trans isomerization in a variety of peptide and protein substrates. Recent studies of cyclophilin A are discussed in detail and other enzymes (dihydrofolate reductase and liver alcohol dehydrogenase) where similar discoveries have been reported are also briefly discussed. The detailed characterization of the discovered networks indicates that protein dynamics plays a role in rate-enhancement achieved by enzymes. An integrated view of enzyme structure, dynamics and function have wide implications in understanding allosteric and co-operative effects, as well as protein engineering of more efficient enzymes and novel drug design.     

The complexity of the overlap method for sequencing biopolymers

The problem of trying to reconstruct the sequence of a biopolymer by using overlapping fragments obtained from cleaving agents is shown to be computationally intractable. This strongly suggests that any computer program for overlap sequencing, even though it may work well for a limited number of inputs, will not work sufficiently for all inputs. However, if the problem is restricted so that certain crucial fragments are known, called prime strings, a sequence can be found efficiently in all cases. Graph theory techniques for doing so can also be used to count the number of sequences consistent with the fragment data to determine whether a unique sequence has been obtained.     

War of attrition with implicit time cost

In the game-theoretic model war of attrition, players are subject to an explicit cost proportional to the duration of contests. We construct a model where the time cost is not explicitly given, but instead depends implicitly on the strategies of the whole population. We identify and analyse the underlying mechanisms responsible for the implicit time cost. Each player participates in a series of games, where those prepared to wait longer win with higher certainty but play less frequently. The model is characterized by the ratio of the winner's score to the loser's score, in a single game. The fitness of a player is determined by the accumulated score from the games played during a generation. We derive the stationary distribution of strategies under the replicator dynamics. When the score ratio is high, we find that the stationary distribution is unstable, with respect to both evolutionary and dynamical stability, and the dynamics converge to a limit cycle. When the ratio is low, the dynamics converge to the stationary distribution. For an intermediate interval of the ratio, the distribution is dynamically but not evolutionarily stable. Finally, the implications of our results for previous models based on the war of attrition are discussed.     

Vibrational Stark effects calibrate the sensitivity of vibrational probes for electric fields in proteins

Infrared spectroscopy is widely used to probe local environments and dynamics in proteins. The introduction of a unique vibration at a specific site of a protein or more complex assembly offers many advantages over observing the spectra of an unmodified protein. We have previously shown that infrared frequency shifts in proteins can arise from differences in the local electric field at the probe vibration. Thus, vibrational frequencies can be used to map electric fields in proteins at many sites or to measure the change in electric field due to a perturbation. The Stark tuning rate gives the sensitivity of a vibrational frequency to an electric field, and for it to be useful, the Stark tuning rate should be as large as possible. Vibrational Stark effect spectroscopy provides a direct measurement of the Stark tuning rate and allows a quantitative interpretation of frequency shifts. We present vibrational Stark spectra of several bond types, extending our work on nitriles and carbonyls and characterizing four additional bond types (carbon-fluorine, carbon-deuterium, azide, and nitro bonds) that are potential probes for electric fields in proteins. The measured Stark tuning rates, peak positions, and extinction coefficients provide the primary information needed to design amino acid analogues or labels to act as probes of local environments in proteins.     

Surface structure of ionic liquids under an external electric field

Abstract

Surface structure and properties play an important role in many applications of ionic liquids (ILs). ILs can form unique surface structures that are very different from the bulk. In imidazolium-based ILs, for example, polar groups form ordered layer structure, while cationic alkyl chains are bundled together and point out from the surface. In many applications, ILs work under an external electric field. However, the effect of external electric field on the surface structure of ILs is still not clear. Here by using coarse-grained molecular dynamics simulation, we found that an electric field as strong as 1 V/nm is required to alter the surface structure of 1-dodecyl-3-methylimidazolium nitrate. Under a strong external electric field, layered structure disappears, and instead a sparser, more homogeneous and less orientationally ordered surface develops.     

Dynamical structure of αB-crystallin

The human small heat-shock protein αB-crystallin is an extremely difficult molecule to study, with its inherent structural dynamics posing unique challenges to all biophysical and structural biology techniques. Here we highlight how the polydispersity and quaternary dynamics of αB-crystallin are intrinsically inter-twined, and how this can impact on measurements of the oligomeric distribution. We show that, in spite of these difficulties, considerable understanding of the varied fluctuations αB-crystallin undergoes at equilibrium has emerged in the last few years. By reporting on data obtained from a variety of biophysical techniques, we demonstrate how the αB-crystallin solution ensemble is governed by molecular motions of varying amplitude and time-scales spanning several orders of magnitude. We describe how these diverse measurements are being used to construct an integrated view of the dynamical structure of αB-crystallin, and highlight areas that require further interrogation. With its study motivating the refinement of experimental techniques, and the development of new approaches to combine the hybrid datasets, we conclude that αB-crystallin continues to represent a paradigm for dynamical biology.     

Modeling of bias for the analysis of receptor signaling in biochemical systems

Ligand bias is a recently introduced concept in the receptor signaling field that underlies innovative strategies for targeted drug design. Ligands, as a consequence of conformational selectivity, produce signaling bias in which some downstream biochemical pathways are favored over others, and this contributes to variability in physiological responsiveness. Though the concept of bias and its implications for receptor signaling have become more important, its working definition in biochemical signaling is sufficiently imprecise as to impede the use of bias as an analytical tool. In this work, we provide a precise mathematical definition for receptor signaling bias using a formalism expressly applied to logistic response functions, models of most physiological behaviors. We show that signaling-response bias of biological processes may be represented by hyperbolae, or more generally as families of bias coordinates that index hyperbolae. Furthermore, we show bias is a property of a parametric mapping of these indexes into vertical strings that reside within a cylinder of stacked Poincare disks and that bias factors representing signaling probabilities are the radial distance of the strings from the cylinder axis. The utility of the formalism is demonstrated with logistic hyperbolic plots, by transducer ratio modeling, and with novel examples of Poincare disk plots of Gi and β-arrestin biased dopamine 2 receptor signaling. Our results provide a platform for categorizing compounds using distance relationships in the Poincare disk, indicate that signaling bias is a relatively common phenomenon at low ligand concentrations, and suggest that potent partial agonists and signaling pathway modulators may be preferred leads for signal bias-based therapies.     

Quantitative proteomics and phosphoproteomics reveal novel insights into complexity and dynamics of the EGFR signaling network

The epidermal growth factor receptor (EGFR/ErbB1/Her1) belongs to the ErbB family of receptor tyrosine kinases (RTKs) and is a key player in the regulation of cell proliferation, differentiation, survival, and migration. Overexpression and mutational changes of EGFR have been identified in a variety of human cancers and the regulation of EGFR signaling plays a critical role in tumor development and progression. Due to its biological significance the EGFR signaling network is a widely used model system for the development of analytical techniques. Novel quantitative proteomics and phosphoproteomics approaches play an important role in the characterization of signaling pathways in a time and stimulus dependent manner. Recent studies discussed in this review provide new insights into different aspects of EGFR signal transduction, such as regulation and dynamics of its phosphorylation sites, association with interaction partners and identification of regulated phosphoproteins. Correlation of data from functional proteomics studies with results from other fields of signal transduction research by systems biology will be necessary to integrate and translate these findings into successful clinical applications.     

Solution structure and backbone dynamics of the Trypanosoma cruzi cysteine protease inhibitor chagasin

A Trypanosoma cruzi cysteine protease inhibitor, termed chagasin, is the first characterized member of a new family of tight-binding cysteine protease inhibitors identified in several lower eukaryotes and prokaryotes but not present in mammals. In the protozoan parasite T.cruzi, chagasin plays a role in parasite differentiation and in mammalian host cell invasion, due to its ability to modulate the endogenous activity of cruzipain, a lysosomal-like cysteine protease. In the present work, we determined the solution structure of chagasin and studied its backbone dynamics by NMR techniques. Structured as a single immunoglobulin-like domain in solution, chagasin exerts its inhibitory activity on cruzipain through conserved residues placed in three loops in the same side of the structure. One of these three loops, L4, predicted to be of variable length among chagasin homologues, is flexible in solution as determined by measurements of (15)N relaxation. The biological implications of structural homology between chagasin and other members of the immunoglobulin super-family are discussed.     

A differential phosphoproteomic analysis of retinoic acid-treated P19 cells

External stimuli trigger internal signaling events within a cell that may represent either a temporary or permanent shift in the phosphorylation state of its proteome. Numerous reports have elucidated phosphorylation sites from a variety of biological samples and more recent studies have monitored the temporal dynamics of protein phosphorylation as a given system is perturbed. Understanding which proteins are phosphorylated as well as when they are phosphorylated may indicate novel functional roles within a system and allow new therapeutic avenues to be explored. To elucidate the dynamics of protein phosphorylation within differentiating murine P19 embryonal carcinoma cells, we induced P19 cells to differentiate using all-trans-retinoic acid and developed a strategy that combines isotopically labeled methyl esterification, immobilized metal affinity chromatography, mass spectrometric analysis, and a rigorous and unique data evaluation approach. We present the largest differential phosphoproteomic analysis using isotopically labeled methyl esterification to date, identifying a total of 472 phosphorylation sites on 151 proteins; 56 of these proteins had altered abundances following treatment with retinoic acid and approximately one-third of these have been previously associated with cellular differentiation. A series of bioinformatic tools were used to extract information from the data and explore the implications of our findings. This study represents the first global gel-free analysis that elucidates protein phosphorylation dynamics during cellular differentiation.     

Modeling capsid self-assembly: design and analysis

A series of simulations aimed at elucidating the self-assembly dynamics of spherical virus capsids is described. This little-understood phenomenon is a fascinating example of the complex processes that occur in the simplest of organisms. The fact that different viruses adopt similar structural forms is an indication of a common underlying design, motivating the use of simplified, low-resolution models in exploring the assembly process. Several versions of a molecular dynamics approach are described. Polyhedral shells of different sizes are involved, the assembly pathways are either irreversible or reversible and an explicit solvent is optionally included. Model design, simulation methodology and analysis techniques are discussed. The analysis focuses on the growth pathways and the nature of the intermediate states, properties that are hard to access experimentally. Among the key observations are that efficient growth proceeds by means of a cascade of highly reversible stages, and that while there are a large variety of possible partial assemblies, only a relatively small number of strongly bonded configurations are actually encountered.     

Monogonont rotifers as model systems for the study of micro-evolutionary adaptation and its eco-evolutionary implications

A better understanding of the ability of organisms to adapt to local selection conditions is essential for a better insight in their ecological dynamics. The study of micro-evolutionary adaptation and its eco-evolutionary consequences is challenging for many reasons and the choice of a suitable model organism is particularly important. In this paper, we explain why monogonont rotifers, through their unique combination of traits, are ideal study organisms for this purpose. With a literature review, we demonstrate the capacity of monogonont populations to adapt to a variety of selection conditions (e.g., salinity, food shortage, elemental limitation, and disturbance regimes) within very short-time frames and highlight some potential eco-evolutionary implications. Although monogononts are increasingly used in eco-evolution-oriented studies, their potential is still underappreciated compared to other model organisms. No doubt the high prevalence of cryptic species complexes and the lack of genomic tools form important obstacles that may discourage researchers to work with this group. Here, we argue that none of these difficulties should prevent monogonont rotifers from becoming commonly used model organisms in micro-evolutionary studies and make suggestions for future research.     

Biophysical aspects of neutron scattering from vibrational modes of proteins

This review describes a major portion of the published work on neutron scattering experiments aimed at measuring large scale motions in proteins. The importance of these motions for enzyme function and oxygen transport is indicated. The theory applicable to each type of neutron scattering measurement is given and results are discussed with a view to biological relevance. New experiments are suggested and a comparison of neutron scattering data is made with results from other techniques such as raman scattering, infrared absorption, photolysis and molecular dynamics simulations.     

Extracellular enzymes and the pathogenesis of nematophagous fungi

Nematophagous fungi are an important group of soil microorganisms that can suppress the populations of plant-parasitic nematodes. The pathogenic mechanisms of nematophagous fungi are diverse: They can be parasitical–mechanical through producing specialized capturing devices, or toxin-dependent. During infections, a variety of virulence factors may be involved against nematodes by nematophagous fungi. In this review, we present up-to-date information on the modes of infection by nematophagous fungi. The roles of extracellular hydrolytic enzymes and other virulence factors involved in infection against nematodes were summarized. The biochemical properties and peptide sequences of a special group of enzymes, the serine proteases, were compared, and their implications in infections were discussed. We also discussed the impact of emerging new techniques on our understanding of this unique group of fungi.     

Representational difference analysis,high-resolution physical mapping,and transcript identification of the zebrafish genomic region for a motor behavior

Zebrafish is one of the best model organisms for investigating gene functions in vertebrates. By 4,5',8-trimethylpsoralen mutagenesis, we isolated a zebrafish mutant, vibrato, with defects in the spontaneous contraction and touch response. Whole genome subtraction between the wild-type and the mutant genomes by representational difference analysis yielded polymorphic markers tightly linked to the vibrato locus. Using these markers, we constructed a high-resolution physical map and localized the vibrato locus within a genomic region of 720 kb. Direct cDNA selection with the contig led to the identification of a novel gene, solo, encoding a protein with SEC14 and spectrin repeat domains. These domains of Solo shared significant amino acid sequence identities with those of mammalian Trio and Karilin. In addition, we found the zebrafish orthologs for mammalian TTN, COL5A2, and CED-6 in the vibrato region. Mapping of these genes localized human chromosomal regions possibly involved in motor disorders. Our results suggest that representational difference analysis provides an efficient way to isolate mutated genomic regions in zebrafish.     

DBAli: a database of protein structure alignments

SUMMARY: The DBAli database includes approximately 35000 alignments of pairs of protein structures from SCOP (Lo Conte et al., Nucleic Acids Res., 28, 257-259, 2000) and CE (Shindyalov and Bourne, Protein Eng., 11, 739-747, 1998). DBAli is linked to several resources, including Compare3D (Shindyalov and Bourne, http://www.sdsc.edu/pb/software.htm, 1999) and ModView (Ilyin and Sali, http://guitar.rockefeller.edu/ModView/, 2001) for visualizing sequence alignments and structure superpositions. A flexible search of DBAli by protein sequence and structure properties allows construction of subsets of alignments suitable for a number of applications, such as benchmarking of sequence-sequence and sequence-structure alignment methods under a variety of conditions. AVAILABILITY: http://guitar.rockefeller.edu/DBAli/     

Volume signalling in real and robot nervous systems

Summary This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. A variety of interesting and significant properties of such four dimensional neural signalling systems are demonstrated. It is shown that the morphology of the neuromodulator source plays a highly significant role in the diffusion patterns observed. The paper goes on to describe work in adaptive autonomous systems directly inspired by this: an exploration of the use of virtual diffusing modulators in robot nervous systems built from non-standard artificial neural networks. These virtual chemicals act over space and time modulating a variety of node and connection properties in the networks. A wide variety of rich dynamics are possible in such systems; in the work described here, evolutionary robotics techniques have been used to harness the dynamics to produce autonomous behaviour in mobile robots. Detailed comparative analyses of evolutionary searches, and search spaces, for robot controllers with and without the virtual gases are introduced. The virtual diffusing modulators are found to provide significant advantages.     

The role of internal duplication in the evolution of multi-domain proteins

Many proteins consist of several structural domains. These multi-domain proteins have likely been generated by selective genome growth dynamics during evolution to perform new functions as well as to create structures that fold on a biologically feasible time scale. Domain units frequently evolved through a variety of genetic shuffling mechanisms. Here we examine the protein domain statistics of more than 1000 organisms including eukaryotic, archaeal and bacterial species. The analysis extends earlier findings on asymmetric statistical laws for proteome to a wider variety of species. While proteins are composed of a wide range of domains, displaying a power-law decay, the computation of domain families for each protein reveals an exponential distribution, characterizing a protein universe composed of a thin number of unique families. Structural studies in proteomics have shown that domain repeats, or internal duplicated domains, represent a small but significant fraction of genome. In spite of its importance, this observation has been largely overlooked until recently. We model the evolutionary dynamics of proteome and demonstrate that these distinct distributions are in fact rooted in an internal duplication mechanism. This process generates the contemporary protein structural domain universe, determines its reduced thickness, and tames its growth. These findings have important implications, ranging from protein interaction network modeling to evolutionary studies based on fundamental mechanisms governing genome expansion.