Alpan Raval

Refinement of protein structure homology models via long, all-atom molecular dynamics simulations

Accurate computational prediction of protein structure represents a longstanding challenge in molecular biology and structure‐based drug design. Although homology modeling techniques are widely used to produce low‐resolution models, refining these models to high resolution has proven difficult. With long enough simulations and sufficiently accurate force fields, molecular dynamics (MD) simulations should in principle allow such refinement, but efforts to refine homology models using MD have for the most part yielded disappointing results. It has thus far been unclear whether MD‐based refinement is limited primarily by accessible simulation timescales, force field accuracy, or both. Here, we examine MD as a technique for homology model refinement using all‐atom simulations, each at least 100 μs long—more than 100 times longer than previous refinement simulations—and a physics‐based force field that was recently shown to successfully fold a structurally diverse set of fast‐folding proteins. In MD simulations of 24 proteins chosen from the refinement category of recent Critical Assessment of Structure Prediction (CASP) experiments, we find that in most cases, simulations initiated from homology models drift away from the native structure. Comparison with simulations initiated from the native structure suggests that force field accuracy is the primary factor limiting MD‐based refinement. This problem can be mitigated to some extent by restricting sampling to the neighborhood of the initial model, leading to structural improvement that, while limited, is roughly comparable to the leading alternative methods. Proteins 2012;.

Identifying Hubs in Protein Interaction Networks

Background In spite of the scale-free degree distribution that characterizes most protein interaction networks (PINs), it is common to define an ad hoc degree scale that defines “hub” proteins having special topological and functional significance. This raises the concern that some conclusions on the functional significance of proteins based on network properties may not be robust. Methodology In this paper we present three objective methods to define hub proteins in PINs: one is a purely topological method and two others are based on gene expression and function. By applying these methods to four distinct PINs, we examine the extent of agreement among these methods and implications of these results on network construction. Conclusions We find that the methods agree well for networks that contain a balance between error-free and unbiased interactions, indicating that the hub concept is meaningful for such networks.

Thermodynamics of Neutral Protein Evolution

Naturally evolving proteins gradually accumulate mutations while continuing to fold to stable structures. This process of neutral evolution is an important mode of genetic change and forms the basis for the molecular clock. We present a mathematical theory that predicts the number of accumulated mutations, the index of dispersion, and the distribution of stabilities in an evolving protein population from knowledge of the stability effects (ΔΔG values) for single mutations. Our theory quantitatively describes how neutral evolution leads to marginally stable proteins and provides formulas for calculating how fluctuations in stability can overdisperse the molecular clock. It also shows that the structural influences on the rate of sequence evolution observed in earlier simulations can be calculated using just the single-mutation ΔΔG values. We consider both the case when the product of the population size and mutation rate is small and the case when this product is large, and show that in the latter case the proteins evolve excess mutational robustness that is manifested by extra stability and an increase in the rate of sequence evolution. All our theoretical predictions are confirmed by simulations with lattice proteins. Our work provides a mathematical foundation for understanding how protein biophysics shapes the process of evolution.

Evolution favors protein mutational robustness in sufficiently large populations

BackgroundAn important question is whether evolution favors properties such as mutational robustness or evolvability that do not directly benefit any individual, but can influence the course of future evolution. Functionally similar proteins can differ substantially in their robustness to mutations and capacity to evolve new functions, but it has remained unclear whether any of these differences might be due to evolutionary selection for these properties.ResultsHere we use laboratory experiments to demonstrate that evolution favors protein mutational robustness if the evolving population is sufficiently large. We neutrally evolve cytochrome P450 proteins under identical selection pressures and mutation rates in populations of different sizes, and show that proteins from the larger and thus more polymorphic population tend towards higher mutational robustness. Proteins from the larger population also evolve greater stability, a biophysical property that is known to enhance both mutational robustness and evolvability. The excess mutational robustness and stability is well described by mathematical theory, and can be quantitatively related to the way that the proteins occupy their neutral network.ConclusionOur work is the first experimental demonstration of the general tendency of evolution to favor mutational robustness and protein stability in highly polymorphic populations. We suggest that this phenomenon could contribute to the mutational robustness and evolvability of viruses and bacteria that exist in large populations.

PathSys: integrating molecular interaction graphs for systems biology

BackgroundThe goal of information integration in systems biology is to combine information from a number of databases and data sets, which are obtained from both high and low throughput experiments, under one data management scheme such that the cumulative information provides greater biological insight than is possible with individual information sources considered separately.ResultsHere we present PathSys, a graph-based system for creating a combined database of networks of interaction for generating integrated view of biological mechanisms. We used PathSys to integrate over 14 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and Gene Ontology. A number of exploratory questions were formulated as a combination of relational and graph-based queries to the integrated database. Thus, PathSys is a general-purpose, scalable, graph-data warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping.ConclusionResults from several test studies demonstrate the effectiveness of the approach in retrieving biologically interesting relations between genes and proteins, the networks connecting them, and of the utility of PathSys as a scalable graph-based warehouse for interaction-network integration and a hypothesis generator system. The PathSyss client software, named BiologicalNetworks, developed for navigation and analyses of molecular networks, is available as a Java Web Start application at http://brak.sdsc.edu/pub/BiologicalNetworks.

Mining protein networks for synthetic genetic interactions

BackgroundThe local connectivity and global position of a protein in a protein interaction network are known to correlate with some of its functional properties, including its essentiality or dispensability. It is therefore of interest to extend this observation and examine whether network properties of two proteins considered simultaneously can determine their joint dispensability, i.e., their propensity for synthetic sick/lethal interaction. Accordingly, we examine the predictive power of protein interaction networks for synthetic genetic interaction in Saccharomyces cerevisiae, an organism in which high confidence protein interaction networks are available and synthetic sick/lethal gene pairs have been extensively identified.ResultsWe design a support vector machine system that uses graph-theoretic properties of two proteins in a protein interaction network as input features for prediction of synthetic sick/lethal interactions. The system is trained on interacting and non-interacting gene pairs culled from large scale genetic screens as well as literature-curated data. We find that the method is capable of predicting synthetic genetic interactions with sensitivity and specificity both exceeding 85%. We further find that the prediction performance is reasonably robust with respect to errors in the protein interaction network and with respect to changes in the features of test datasets. Using the prediction system, we carried out novel predictions of synthetic sick/lethal gene pairs at a genome-wide scale. These pairs appear to have functional properties that are similar to those that characterize the known synthetic lethal gene pairs.ConclusionOur analysis shows that protein interaction networks can be used to predict synthetic lethal interactions with accuracies on par with or exceeding that of other computational methods that use a variety of input features, including functional annotations. This indicates that protein interaction networks could plausibly be rich sources of information about epistatic effects among genes.

Proteomic profiling of aging in the mouse heart: Altered expression of mitochondrial proteins.

Using two-dimensional gel electrophoresis and liquid chromatography-tandem mass spectrometry, we have used a systems biology approach to study the molecular basis of aging of the mouse heart. We have identified 8 protein spots whose expression is up-regulated due to aging and 36 protein spots whose expression is down-regulated due to aging (p0.05 as judged by Wilcoxon Rank Sum test). Among the up-regulated proteins, we have characterized 5 protein spots and 2 of them, containing 3 different enzymes, are mitochondrial proteins. Among the down-regulated proteins, we have characterized 27 protein spots and 16 of them are mitochondrial proteins. Mitochondrial damage is believed to be a key factor in the aging process. Our current study provides molecular evidence at the level of the proteome for the alteration of structural and functional parameters of the mitochondria that contribute to impaired activity of the mouse heart due to aging.

Detection of genomic islands via segmental genome heterogeneity

While the recognition of genomic islands can be a powerful mechanism for identifying genes that distinguish related bacteria, few methods have been developed to identify them specifically. Rather, identification of islands often begins with cataloging individual genes likely to have been recently introduced into the genome; regions with many putative alien genes are then examined for other features suggestive of recent acquisition of a large genomic region. When few phylogenetic relatives are available, the identification of alien genes relies on their atypical features relative to the bulk of the genes in the genome. The weakness of these ‘bottom–up’ approaches lies in the difficulty in identifying robustly those genes which are atypical, or phylogenetically restricted, due to recent foreign ancestry. Herein, we apply an alternative ‘top–down’ approach where bacterial genomes are recursively divided into progressively smaller regions, each with uniform composition. In this way, large chromosomal regions with atypical features are identified with high confidence due to the simultaneous analysis of multiple genes. This approach is based on a generalized divergence measure to quantify the compositional difference between segments in a hypothesis-testing framework. We tested the proposed genome island prediction algorithm on both artificial chimeric genomes and genuine bacterial genomes.

Stochastic theory of accelerated detectors in a quantum field.

We analyze the statistical mechanical properties of n-detectors in arbitrary states of motion interacting with each other via a quantum field. We use the open system concept and the influence functional method to calculate the influence of quantum fields on detectors in motion, and the mutual influence of detectors via fields. We discuss the difference between self and mutual impedance and advanced and retarded noise. The mutual effects of detectors on each other can be studied from the Langevin equations derived from the influence functional, as it contains the backreaction of the field on the system self-consistently. We show the existence of general fluctuation- dissipation relations, and for trajectories without event horizons, correlation-propagation relations, which succinctly encapsulate these quantum statistical phenomena. These findings serve to clarify some existing confusions in the accelerated detector problem. The general methodology presented here could also serve as a platform to explore the quantum statistical properties of particles and fields, with practical applications in atomic and optical physics problems.

Proteome profiling of aging in mouse models : Differential expression of proteins involved in metabolism, transport, and stress response in kidney

Aging is a time‐dependent complex biological phenomenon observed in various organs and organelles of all living organisms. To understand the molecular mechanism of age‐associated functional loss in aging kidneys, we have analyzed the expression of proteins in the kidneys of young (19–22 wk) and old (24 months) C57/BL6 male mice using 2‐DE followed by LC‐MS/MS. We found that expression levels of 49 proteins were upregulated (p ≤ 0.05), while that of only ten proteins were downregulated (p ≤ 0.05) due to aging. The proteins identified belong to three broad functional categories: (i) metabolism (e.g., aldehyde dehydrogenase family, ATP synthase β‐subunit, malate dehydrogenase, NADH dehydrogenase (ubiquinone), hydroxy acid oxidase 2), (ii) transport (e.g., transferrin), and (iii) chaperone/stress response (e.g., Ig‐binding protein, low density lipoprotein receptor‐related protein associated protein 1, selenium‐binding proteins (SBPs)). Some proteins with unknown functions were also identified as being differentially expressed. ATP synthase β subunit, transferrin, fumarate hydratase, SBPs, and albumin are present in multiple forms, possibly arising due to proteolysis or PTMs. The above functional categories suggest specific mechanisms and pathways for age‐related kidney degeneration.