Assessment of genome annotation using gene function similarity within the gene neighborhood.

BACKGROUND: Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. RESULTS: We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. CONCLUSIONS: Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations.

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces.

BACKGROUND: Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB). RESULTS: We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section). CONCLUSION: Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

Protein docking using surface matching and supervised machine learning.

Computational prediction of protein complex structures through docking offers a means to gain a mechanistic understanding of protein interactions that mediate biological processes. This is particularly important as the number of experimentally determined structures of isolated proteins exceeds the number of structures of complexes. A comprehensive docking procedure is described in which efficient sampling of conformations is achieved by matching surface normal vectors, fast filtering for shape complementarity, clustering by RMSD, and scoring the docked conformations using a supervised machine learning approach. Contacting residue pair frequencies, residue propensities, evolutionary conservation, and shape complementarity score for each docking conformation are used as input data to a Random Forest classifier. The performance of the Random Forest approach for selecting correctly docked conformations was assessed by cross-validation using a nonredundant benchmark set of X-ray structures for 93 heterodimer and 733 homodimer complexes. The single highest rank docking solution was the correct (near-native) structure for slightly more than one third of the complexes. Furthermore, the fraction of highly ranked correct structures was significantly higher than the overall fraction of correct structures, for almost all complexes. A detailed analysis of the difficult to predict complexes revealed that the majority of the homodimer cases were explained by incorrect oligomeric state annotation. Evolutionary conservation and shape complementarity score as well as both underrepresented and overrepresented residue types and residue pairs were found to make the largest contributions to the overall prediction accuracy. Finally, the method was also applied to docking unbound subunit structures from a previously published benchmark set.

Stabilization of a transition-state analogue at the active site of yeast cytosine deaminase: importance of proton transfers.

It is believed that the binding of pyrimidin-2-one to cytosine deaminase (CD) leads to the formation of 4-[R]-hydroxyl-3,4-dihydropyrimidine (DHP). Here the formation of transition-state analogue (TSA) at the active site of yeast cytosine deaminase (yCD) is investigated by quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) and free energy simulations. It is shown that DHP may in fact be unstable in the active site and a proton transfer from the Zn hydroxide group to Glu-64 may occur during the nucleophilic attack, leading to an alkoxide-like TSA complex instead. The free energy simulations for the nucleophilic attack process show that the proton transfer from the Zn hydroxide to Glu-64 may play an important role in stabilizing the TSA complex.

Optimal construction of theoretical spectra for MS/MS spectra identification.

We derive the optimal number of peaks (defined as the minimum number that provides the required efficiency of spectra identification) in the theoretical spectra as a function of (i) the experimental accuracy, sigma, of the measured ratio m/z; (ii) experimental spectrum density; (iii) size of the database; (iv) number of peaks in the theoretical spectra; and (v) types of ions that the peaks represent. We show that if theoretical spectra are constructed including b and y ions alone, then for sigma = 0.5, which is typical for high-throughput data, peptide chains of eight amino acids or longer can be identified based on the positions of peaks alone, at a rate of false identification below 1%. To discriminate between shorter peptides, additional (e.g., intensity-inferred) information is necessary. We derive the dependence of the probability of false identification on the number of peaks in the theoretical spectra and on the types of ions that the peaks represent. Our results suggest that the class of mass spectrum identification problems, for which more elaborate development of fragmentation rules (such as intensity model) is required, can be reduced to the problems that involve homologous peptides.

Towards structural genomics of RNA: rapid NMR resonance assignment and simultaneous RNA tertiary structure determination using residual dipolar couplings.

We report a new residual dipolar couplings (RDCs) based NMR procedure for rapidly determining RNA tertiary structure demonstrated on a uniformly (15)N/(13)C-labeled 27 nt variant of the trans-activation response element (TAR) RNA from HIV-I. In this procedure, the time-consuming nuclear Overhauser enhancement (NOE)-based sequential assignment step is replaced by a fully automated RDC-based assignment strategy. This approach involves examination of all allowed sequence-specific resonance assignment permutations for best-fit agreement between measured RDCs and coordinates for sub-structures in a target RNA. Using idealized A-form geometries to model Watson-Crick helices and coordinates from a previous X-ray structure to model a hairpin loop in TAR, the best-fit RDC assignment solutions are determined very rapidly (

Concerted motions in HIV-1 TAR RNA may allow access to bound state conformations: RNA dynamics from NMR residual dipolar couplings.

Ground-state dynamics in RNA is a critical precursor for structural adaptation observed ubiquitously in protein-RNA recognition. A tertiary conformational analysis of the stem-loop structural element in the transactivation response element (TAR) from human immunodeficiency virus type 1 (HIV-I) RNA is presented using recently introduced NMR methods that rely on the measurement of residual dipolar couplings (RDC) in partially oriented systems. Order matrix analysis of RDC data provides evidence for inter-helical motions that are of amplitude 46(+/-4) degrees, of random directional character, and that are executed about an average conformation with an inter-helical angle between 44 degrees and 54 degrees. The generated ensemble of TAR conformations have different organizations of functional groups responsible for interaction with the trans-activator protein Tat, including conformations similar to the previously characterized bound-state conformation. These results demonstrate the utility of RDC-NMR for simultaneously characterizing RNA tertiary dynamics and average conformation, and indicate an avenue for TAR complex formation involving tertiary structure capture.

PanFP: pangenome-based functional profiles for microbial communities.

BACKGROUND: For decades there has been increasing interest in understanding the relationships between microbial communities and ecosystem functions. Current DNA sequencing technologies allows for the exploration of microbial communities in two principle ways: targeted rRNA gene surveys and shotgun metagenomics. For large study designs, it is often still prohibitively expensive to sequence metagenomes at both the breadth and depth necessary to statistically capture the true functional diversity of a community. Although rRNA gene surveys provide no direct evidence of function, they do provide a reasonable estimation of microbial diversity, while being a very cost-effective way to screen samples of interest for later shotgun metagenomic analyses. However, there is a great deal of 16S rRNA gene survey data currently available from diverse environments, and thus a need for tools to infer functional composition of environmental samples based on 16S rRNA gene survey data. RESULTS: We present a computational method called pangenome-based functional profiles (PanFP), which infers functional profiles of microbial communities from 16S rRNA gene survey data for Bacteria and Archaea. PanFP is based on pangenome reconstruction of a 16S rRNA gene operational taxonomic unit (OTU) from known genes and genomes pooled from the OTU's taxonomic lineage. From this lineage, we derive an OTU functional profile by weighting a pangenome's functional profile with the OTUs abundance observed in a given sample. We validated our method by comparing PanFP to the functional profiles obtained from the direct shotgun metagenomic measurement of 65 diverse communities via Spearman correlation coefficients. These correlations improved with increasing sequencing depth, within the range of 0.8-0.9 for the most deeply sequenced Human Microbiome Project mock community samples. PanFP is very similar in performance to another recently released tool, PICRUSt, for almost all of survey data analysed here. But, our method is unique in that any OTU building method can be used, as opposed to being limited to closed-reference OTU picking strategies against specific reference sequence databases. CONCLUSIONS: We developed an automated computational method, which derives an inferred functional profile based on the 16S rRNA gene surveys of microbial communities. The inferred functional profile provides a cost effective way to study complex ecosystems through predicted comparative functional metagenomes and metadata analysis. All PanFP source code and additional documentation are freely available online at GitHub ( https://github.com/srjun/PanFP ).

Endogenous Sterol Metabolites Regulate Growth of EGFR/KRAS-Dependent Tumors via LXR.

Meiosis-activating sterols (MAS) are substrates of SC4MOL and NSDHL in the cholesterol pathway and are important for normal organismal development. Oncogenic transformation by epidermal growth factor receptor (EGFR) or RAS increases the demand for cholesterol, suggesting a possibility for metabolic interference. To test this idea in vivo, we ablated Nsdhl in adult keratinocytes expressing KRAS(G12D). Strikingly, Nsdhl inactivation antagonized the growth of skin tumors while having little effect on normal skin. Loss of Nsdhl induced the expression of ATP-binding cassette (ABC) transporters ABCA1 and ABCG1, reduced the expression of low-density lipoprotein receptor (LDLR), decreased intracellular cholesterol, and was dependent on the liver X receptor (LXR) α. Importantly, EGFR signaling opposed LXRα effects on cholesterol homeostasis, whereas an EGFR inhibitor synergized with LXRα agonists in killing cancer cells. Inhibition of SC4MOL or NSDHL, or activation of LXRα by sterol metabolites, can be an effective strategy against carcinomas with activated EGFR-KRAS signaling.

Carbon sequestration in Synechococcus Sp.: from molecular machines to hierarchical modeling.

The U.S. Department of Energy recently announced the first five grants for the Genomes to Life (GTL) Program. The goal of this program is to "achieve the most far-reaching of all biological goals: a fundamental, comprehensive, and systematic understanding of life." While more information about the program can be found at the GTL website (www.doegenomestolife.org), this paper provides an overview of one of the five GTL projects funded, "Carbon Sequestration in Synechococcus Sp.: From Molecular Machines to Hierarchical Modeling." This project is a combined experimental and computational effort emphasizing developing, prototyping, and applying new computational tools and methods to elucidate the biochemical mechanisms of the carbon sequestration of Synechococcus Sp., an abundant marine cyanobacteria known to play an important role in the global carbon cycle. Understanding, predicting, and perhaps manipulating carbon fixation in the oceans has long been a major focus of biological oceanography and has more recently been of interest to a broader audience of scientists and policy makers. It is clear that the oceanic sinks and sources of CO(2) are important terms in the global environmental response to anthropogenic atmospheric inputs of CO(2) and that oceanic microorganisms play a key role in this response. However, the relationship between this global phenomenon and the biochemical mechanisms of carbon fixation in these microorganisms is poorly understood. The project includes five subprojects: an experimental investigation, three computational biology efforts, and a fifth which deals with addressing computational infrastructure challenges of relevance to this project and the Genomes to Life program as a whole. Our experimental effort is designed to provide biology and data to drive the computational efforts and includes significant investment in developing new experimental methods for uncovering protein partners, characterizing protein complexes, identifying new binding domains. We will also develop and apply new data measurement and statistical methods for analyzing microarray experiments. Our computational efforts include coupling molecular simulation methods with knowledge discovery from diverse biological data sets for high-throughput discovery and characterization of protein-protein complexes and developing a set of novel capabilities for inference of regulatory pathways in microbial genomes across multiple sources of information through the integration of computational and experimental technologies. These capabilities will be applied to Synechococcus regulatory pathways to characterize their interaction map and identify component proteins in these pathways. We will also investigate methods for combining experimental and computational results with visualization and natural language tools to accelerate discovery of regulatory pathways. Furthermore, given that the ultimate goal of this effort is to develop a systems-level of understanding of how the Synechococcus genome affects carbon fixation at the global scale, we will develop and apply a set of tools for capturing the carbon fixation behavior of complex of Synechococcus at different levels of resolution. Finally, because the explosion of data being produced by high-throughput experiments requires data analysis and models which are more computationally complex, more heterogeneous, and require coupling to ever increasing amounts of experimentally obtained data in varying formats, we have also established a companion computational infrastructure to support this effort as well as the Genomes to Life program as a whole.

Molecular pathways: sterols and receptor signaling in cancer.

Accelerated cholesterol and lipid metabolism are the hallmarks of cancer and contribute to malignant transformation due to the obligatory requirement for cholesterol for the function of eukaryotic membranes. To build new membranes and maintain active signaling, cancer cells depend on high intensity of endogenous cholesterol biosynthesis and uptake of lipid particles. This metabolic dependency of cancer cells on cholesterol and other lipids is tightly regulated by the cholesterol homeostasis network, including (i) sterol response element-binding proteins (SREBP), master transcriptional regulators of cholesterol and fatty acid pathway genes; (ii) nuclear sterol receptors (liver X receptors, LXR), which coordinate growth with the availability of cholesterol; and (iii) lipid particle receptors, such as low-density lipid particle (LDL) receptor, providing exogenous sterol and lipids to cancer cells. In addition, activity of oncogenic receptors, such as MUC1 or EGFR, accelerates sterol uptake and biosynthesis. Therefore, a general strategy of reducing the cholesterol pool in cancer cells is challenged by the highly efficient feedback loops compensating for a blockade at a single point in the cholesterol homeostatic network. Besides the well-established structural role of cholesterol in membranes, recent studies have uncovered potent biologic activities of certain cholesterol metabolic precursors and its oxidized derivatives, oxysterols. The former, meiosis-activating sterols, exert effects on trafficking and signaling of oncogenic EGF receptor (EGFR). Cholesterol epoxides, the highly active products of cholesterol oxidation, are being neutralized by the distal sterol pathway enzymes, emopamyl-binding protein (EBP) and dehydrocholesterol-7 reductase (DHCR7). These recently discovered "moonlighting" activities of the cholesterol pathway genes and metabolites expand our understanding of the uniquely conserved roles these sterol molecules play in the regulation of cellular proliferation and in cancer.

Targeting C4-demethylating genes in the cholesterol pathway sensitizes cancer cells to EGF receptor inhibitors via increased EGF receptor degradation.

UNLABELLED: Persistent signaling by the oncogenic EGF receptor (EGFR) is a major source of cancer resistance to EGFR targeting. We established that inactivation of 2 sterol biosynthesis pathway genes, SC4MOL (sterol C4-methyl oxidase-like) and its partner, NSDHL (NADP-dependent steroid dehydrogenase-like), sensitized tumor cells to EGFR inhibitors. Bioinformatics modeling of interactions for the sterol pathway genes in eukaryotes allowed us to hypothesize and then extensively validate an unexpected role for SC4MOL and NSDHL in controlling the signaling, vesicular trafficking, and degradation of EGFR and its dimerization partners, ERBB2 and ERBB3. Metabolic block upstream of SC4MOL with ketoconazole or CYP51A1 siRNA rescued cancer cell viability and EGFR degradation. Inactivation of SC4MOL markedly sensitized A431 xenografts to cetuximab, a therapeutic anti-EGFR antibody. Analysis of Nsdhl-deficient Bpa(1H/+) mice confirmed dramatic and selective loss of internalized platelet-derived growth factor receptor in fibroblasts, and reduced activation of EGFR and its effectors in regions of skin lacking NSDHL. SIGNIFICANCE: This work identifies a critical role for SC4MOL and NSDHL in the regulation of EGFR signaling and endocytic trafficking and suggests novel strategies to increase the potency of EGFR antagonists in tumors.

Regulation of cholesterol biosynthesis and cancer signaling.

Cellular growth is highly dependent on sustained production of lipids. Sterol composition of cellular membranes determines multiple biochemical and biophysical properties of membrane-based processes including vesicle traffic, receptor signaling, and assembly of protein complexes. Lipid biogenesis has become an attractive biochemical target in cancer given the high level of dependency on sterols and lipids in a cancer cell. This review summarized the current knowledge of mechanisms of interaction between the metabolism of sterols and receptor signaling.

A peptide-linkage deletion procedure for estimate of energetic contributions of individual peptide groups in a complex environment: Application to parallel beta-sheets.

A peptide-linkage deletion procedure is introduced for extracting the quantum mechanical (QM) interaction energies of individual groups in a complex environment and applied for the determination of the energetic contributions of the individual hydrogen bond acceptors (C=O's) and donors (N-H's) in parallel beta-sheets. For the beta-sheets studied here, the results show that the contributions from the H-bond acceptors (C=O) can be significantly greater than the contributions from the donors (N-H). It is suggested that this imbalance may be induced, at least in part, by the inter-strand C(alpha)-Hcdots, three dots, centeredO=C interactions which may play an important role in stabilizing beta-sheets. The results demonstrate the usefulness of the approach proposed in this paper to study interactions in complex protein environments.

Extensive exploration of conformational space improves Rosetta results for short protein domains.

With some simplifications, computational protein folding can be understood as an optimization problem of a potential energy function on a variable space consisting of all conformation for a given protein molecule. It is well known that realistic energy potentials are very "rough" functions, when expressed in the standard variables, and the folding trajectories can be easily trapped in multiple local minima. We have integrated our variation of Parallel Tempering optimization into the protein folding program Rosetta in order to improve its capability to overcome energy barriers and estimate how such improvement will influence the quality of the folded protein domains. Here we report that (1) Parallel Tempering Rosetta (PTR) is significantly better in the exploration of protein structures than previous implementations of the program; (2) systematic improvements are observed across a large benchmark set in the parameters that are normally followed to estimate robustness of the folding; (3) these improvements are most dramatic in the subset of the shortest domains, where high-quality structures have been obtained for >75% of all tested sequences. Further analysis of the results will improve our understanding of protein conformational space and lead to new improvements in the protein folding methodology, while the current PTR implementation should be very efficient for short (up to approximately 80 a.a.) protein domains and therefore may find practical application in system biology studies.

Protein dynamics and enzymatic catalysis: investigating the peptidyl-prolyl cis-trans isomerization activity of cyclophilin A.

A growing body of evidence suggests a connection between protein dynamics and enzymatic catalysis. In this paper, we present a variety of computational studies designed to investigate the role of protein dynamics in the detailed mechanism of peptidyl-prolyl cis-trans isomerization catalyzed by human cyclophilin A. The results identify a network of protein vibrations, extending from surface regions of the enzyme to the active site and coupled to substrate turnover. Indications are that this network may have a role in promoting catalysis. Crucial parts of this network are found to be conserved in 10 cyclophilin structures from six different species. Experimental evidence for the existence of this network comes from previous NMR relaxation studies, where motions in several residues, forming parts of this network, were detected only during substrate turnover. The high temperature factors (from X-ray crystal structures) associated with the network residues provide further evidence of these vibrations. Along with the knowledge of enzyme structure, this type of network could provide new insights into enzymatic catalysis and the effect of distant ligand binding on protein function. The procedure outlined in this paper is general and can be applied to other enzymatic systems as well. This presents an interesting opportunity; collaborative experimental and theoretical investigations designed to characterize in detail the nature and function of this type of network could enhance the understanding of protein dynamics in enzymatic catalysis.

Bending of DNA by asymmetric charge neutralization: all-atom energy simulations.

DNA dodecamers of the alternating d(CG).d(CG) sequence with six phosphate groups either charge-neutralized or substituted by neutral methylphosphonates across the major or minor groove have been subjected to energy minimization to determine the conformational effect of the asymmetric elimination of phosphate charge. We report bending angles, directions of bending, and detailed structural characteristics such as groove widths and local base-pair parameters. Our principal results are that charge neutralization on one face of the DNA induces significant bending toward the neutralized face, in agreement with theoretical predictions on a simplified model and experimental data on a similar base-pair sequence, and that the DNA conformation averaged over all stereospecific methylphosphonate substitutions is nearly the same as the conformation produced by charge neutralization of the phosphates. Individual isomers, however, cover a wide range of structures, with the magnitude and direction of overall bending sensitive to the precise stereochemical pattern of neutralization. Our simulation does not explicitly contain counterions, and the results therefore suggest that counterions can influence DNA structure by neutralizing the phosphate charge. These data provide new hints into the molecular mechanisms which underlie the deformations of DNA structure induced by the binding of positively charged proteins and other tightly associated cationic species.