Using reporter gene assays to identify cis regulatory differences between humans and chimpanzees

Most phenotypic differences between human and chimpanzee are likely to result from differences in gene regulation, rather than changes to protein-coding regions. To date, however, only a handful of human-chimpanzee nucleotide differences leading to changes in gene regulation have been identified. To hone in on differences in regulatory elements between human and chimpanzee, we focused on 10 genes that were previously found to be differentially expressed between the two species. We then designed reporter gene assays for the putative human and chimpanzee promoters of the 10 genes. Of seven promoters that we found to be active in human liver cell lines, human and chimpanzee promoters had significantly different activity in four cases, three of which recapitulated the gene expression difference seen in the microarray experiment. For these three genes, we were therefore able to demonstrate that a change in cis influences expression differences between humans and chimpanzees. Moreover, using site-directed mutagenesis on one construct, the promoter for the DDA3 gene, we were able to identify three nucleotides that together lead to a cis regulatory difference between the species. High-throughput application of this approach can provide a map of regulatory element differences between humans and our close evolutionary relatives.     

Ontology integration to identify protein complex in protein interaction networks

Background

Protein complexes can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of protein complexes detection algorithms.

Methods

We have developed novel semantic similarity method, which use Gene Ontology (GO) annotations to measure the reliability of protein-protein interactions. The protein interaction networks can be converted into a weighted graph representation by assigning the reliability values to each interaction as a weight. Following the approach of that of the previously proposed clustering algorithm IPCA which expands clusters starting from seeded vertices, we present a clustering algorithm OIIP based on the new weighted Protein-Protein interaction networks for identifying protein complexes.

Results

The algorithm OIIP is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes. Experimental results show that the algorithm OIIP has higher F-measure and accuracy compared to other competing approaches.

     

Using ensemble classifier to identify membrane protein types

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.     

Soluble CD14 discriminates slight structural differences between lipid as that lead to distinct host cell activation

Soluble CD14 (sCD14) in serum is known to sensitize host cells to LPS. In the present study, the contributions of sCD14 and LPS-binding protein to a lipid A moiety from LPS preparations of periodontopathogenic Fusobacterium nucleatum sp. nucleatum were compared with that of Escherichia coli-type synthetic lipid A (compound 506). F. nucleatum lipid A was identified to be a hexa-acylated fatty acid composed of tetradecanoate (C(14)) and hexadecanoate (C(16)), similar to dodecanoate (C(12)) and C(14) in compound 506. The two lipid A specimens exhibited nearly the same reactivity in Limulus amoebocyte lysate assays, though F. nucleatum lipid A showed a weaker lethal toxicity. Both lipid A specimens showed nearly the same activities toward host cells in the absence of FBS, though compound 506 exhibited much stronger activity in the presence of FBS, sCD14, or sCD14 together with LPS-binding protein. Furthermore, native PAGE/Western immunoblot assays demonstrated that F. nucleatum lipid A had a weaker binding to sCD14 as compared with compound 506. These results suggest that sCD14 is able to discriminate the slight structural differences between these lipid As, which causes their distinct host cell activation activities.     

Using structural motif templates to identify proteins with DNA binding function

This work describes a method for predicting DNA binding function from structure using 3-dimensional templates. Proteins that bind DNA using small contiguous helix–turn–helix (HTH) motifs comprise a significant number of all DNA-binding proteins. A structural template library of seven HTH motifs has been created from non-homologous DNA-binding proteins in the Protein Data Bank. The templates were used to scan complete protein structures using an algorithm that calculated the root mean squared deviation (rmsd) for the optimal superposition of each template on each structure, based on C_α backbone coordinates. Distributions of rmsd values for known HTH-containing proteins (true hits) and non-HTH proteins (false hits) were calculated. A threshold value of 1.6 Å rmsd was selected that gave a true hit rate of 88.4% and a false positive rate of 0.7%. The false positive rate was further reduced to 0.5% by introducing an accessible surface area threshold value of 990 Ų per HTH motif. The template library and the validated thresholds were used to make predictions for target proteins from a structural genomics project.     

Semantic integration to identify overlapping functional modules in protein interaction networks

Background  

The systematic analysis of protein-protein interactions can enable a better understanding of cellular organization, processes and functions. Functional modules can be identified from the protein interaction networks derived from experimental data sets. However, these analyses are challenging because of the presence of unreliable interactions and the complex connectivity of the network. The integration of protein-protein interactions with the data from other sources can be leveraged for improving the effectiveness of functional module detection algorithms.     

Using UHF-dielectrometry to study protein structural transitions

UHF-dielectrometry method is based on the following facts: i) there is dispersion (i.e. dependence on frequency) of the dielectric permeability epsilon; ii) bound and free water have remarkable different epsilon, mobility and dispersion regions; iii) conformational changes in a macromolecule lead to redistribution of free and bound water and to change of the amount of free water molecules. Choosing the working frequency in the region of dispersion of free water molecules (9.2 GHz) we can detect conformational changes in proteins using free water as a marker. In this work the temperature dependencies of dielectric parameters of albumin and fibrinogen solutions were obtained in the temperature interval 5-40 degrees C. In contrast to dependencies for poor solvent, temperature dependencies of dielectric parameters for protein solutions are of non-monotonous character; they have a number of peculiarities in the temperature ranges of 8-10, 22-24 and 34-36 degrees C. At these temperatures redistribution of free and bound water in protein-water system occurs due to structural changes in protein molecules. In this work the mechanism of temperature changes of spatial organisation of protein molecules was proposed. Perhaps, this mechanism is responsible for maintenance of thermal stability of the functionally active conformation of native proteins.     

Ligand binding to distinct states diverts aggregation of an amyloid-forming protein

Although small molecules that modulate amyloid formation in vitro have been identified, significant challenges remain in determining precisely how these species act. Here we describe the identification of rifamycin SV as a potent inhibitor of β(2) microglobulin (β(2)m) fibrillogenesis when added during the lag time of assembly or early during fibril elongation. Biochemical experiments demonstrate that the small molecule does not act by a colloidal mechanism. Exploiting the ability of electrospray ionization-ion mobility spectrometry-mass spectrometry (ESI-IMS-MS) to resolve intermediates of amyloid assembly, we show instead that rifamycin SV inhibits β(2)m fibrillation by binding distinct monomeric conformers, disfavoring oligomer formation and diverting the course of assembly to the formation of spherical aggregates. The results demonstrate the power of ESI-IMS-MS to identify specific protein conformers as targets for intervention in fibrillogenesis using small molecules and reveal a mechanism of action in which ligand binding diverts unfolded protein monomers toward alternative assembly pathways.     

Three distinct actin-attached structural states of myosin in muscle fibers

We have used thiol cross-linking and electron paramagnetic resonance (EPR) to resolve structural transitions of myosin's light chain domain (LCD) and catalytic domain (CD) that are associated with force generation. Spin labels were incorporated into the LCD of muscle fibers by exchanging spin-labeled regulatory light chain for endogenous regulatory light chain, with full retention of function. To trap myosin in a structural state analogous to the elusive posthydrolysis ternary complex A.M'.D.P, we used pPDM to cross-link SH1 (Cys(707)) to SH2 (Cys(697)) on the CD. LCD orientation and dynamics were measured in three biochemical states: relaxation (A.M.T), SH1-SH2 cross-linked (A.M'.D.P analog), and rigor (A.M.D). EPR showed that the LCD of cross-linked fibers has an orientational distribution intermediate between relaxation and rigor, and saturation transfer EPR revealed slow rotational dynamics indistinguishable from that of rigor. Similar results were obtained for the CD using a bifunctional spin label to cross-link SH1-SH2, but the CD was more disordered than the LCD. We conclude that SH1-SH2 cross-linking traps a state in which both the CD and LCD are intermediate between relaxation (highly disordered and microsecond dynamics) and rigor (highly ordered and rigid), supporting the hypothesis that the cross-linked state is an A.M'D.P analog on the force generation pathway.     

Using multiple sequence correlation analysis to characterize functionally important protein regions

Protein co-evolution under structural and functional constraints necessitates the preservation of important interactions. Identifying functionally important regions poses many obstacles in protein engineering efforts. In this paper, we present a bioinformatics-inspired approach (residue correlation analysis, RCA) for predicting functionally important domains from protein family sequence data. RCA is comprised of two major steps: (i) identifying pairs of residue positions that mutate in a coordinated manner, and (ii) using these results to identify protein regions that interact with an uncommonly high number of other residues. We hypothesize that strongly correlated pairs result not only from contacting pairs, but also from residues that participate in conformational changes involved during catalysis or important interactions necessary for retaining functionality. The results show that highly mobile loops that assist in ligand association/dissociation tend to exhibit high correlation. RCA results exhibit good agreement with the findings of experimental and molecular dynamics studies for the three protein families that are analyzed: (i) DHFR (dihydrofolate reductase), (ii) cyclophilin, and (iii) formyl-transferase. Specifically, the specificity (percentage of correct predictions) in all three cases is substantially higher than those obtained by entropic measures or contacting residue pairs. In addition, we use our approach in a predictive fashion to identify important regions of a transmembrane amino acid transporter protein for which there is limited structural and functional information available.     

Using mechanistic Bayesian networks to identify downstream targets of the Sonic Hedgehog pathway

Background  

The topology of a biological pathway provides clues as to how a pathway operates, but rationally using this topology information with observed gene expression data remains a challenge.     

Using a residue clash map to functionally characterize protein recombination hybrids

In this article, we introduce a rapid, protein sequence database-driven approach to characterize all contacting residue pairs present in protein hybrids for inconsistency with protein family structural features. This approach is based on examining contacting residue pairs with different parental origins for different types of potentially unfavorable interactions (i.e. electrostatic repulsion, steric hindrance, cavity formation and hydrogen bond disruption). The identified clashing residue pairs between members of a protein family are then contrasted against functionally characterized hybrid libraries. Comparisons for five different protein recombination studies available in the literature: (i) glycinamide ribonucleotide transformylase (GART) from Escherichia coli (purN) and human (hGART), (ii) human Mu class glutathione S-transferase (GST) M1-1 and M2-2, (iii) beta-lactamase TEM-1 and PSE-4, (iv) catechol-2,3-oxygenase xylE and nahH, and (v) dioxygenases (toluene dioxygenase, tetrachlorobenzene dioxygenase and biphenyl dioxygenase) reveal that the patterns of identified clashing residue pairs are remarkably consistent with experimentally found patterns of functional crossover profiles. Specifically, we show that the proposed residue clash maps are on average 5.0 times more effective than randomly generated clashes and 1.6 times more effective than residue contact maps at explaining the observed crossover distributions among functional members of hybrid libraries. This suggests that residue clash maps can provide quantitative guidelines for the placement of crossovers in the design of protein recombination experiments.     

Using LogitBoost classifier to predict protein structural classes

Prediction of protein classification is an important topic in molecular biology. This is because it is able to not only provide useful information from the viewpoint of structure itself, but also greatly stimulate the characterization of many other features of proteins that may be closely correlated with their biological functions. In this paper, the LogitBoost, one of the boosting algorithms developed recently, is introduced for predicting protein structural classes. It performs classification using a regression scheme as the base learner, which can handle multi-class problems and is particularly superior in coping with noisy data. It was demonstrated that the LogitBoost outperformed the support vector machines in predicting the structural classes for a given dataset, indicating that the new classifier is very promising. It is anticipated that the power in predicting protein structural classes as well as many other bio-macromolecular attributes will be further strengthened if the LogitBoost and some other existing algorithms can be effectively complemented with each other.     

Free energy differences between enzyme bound states

A theory is presented that describes the free energy difference between the enzyme-substrate (ES) and enzyme-product (EP) complexes that is expected in enzymes optimized for catalytic efficiency. In such enzymes, the free energy drop between ES and EP complexes reflects a portion of the chemical potential difference between substrates and products outside the active site under physiological conditions. Qualitative and quantitative predictions of the model are discussed and compared with experimental data. The controversy over the kinetically optimal free energy profile for an enzymatic reaction operating under constraints set forward by Albery & Knowles (1976) is resolved.     

Crystal structures of the Synechocystis photoreceptor Slr1694 reveal distinct structural states related to signaling

Crystal structures of the Synechocystis BLUF phototaxis photoreceptor Slr1694 have been determined in two crystal forms, a monoclinic form at 1.8 A resolution and an orthorhombic form at 2.1 A resolution. In both forms, the photoreceptor is comprised of two pentamer rings stacked face to face. Twenty total subunits in the two asymmetric units of these crystal forms display three distinct tertiary structures that differ in the length of the fifth beta-strand and in the orientation of Trp91, a conserved Trp residue near the FMN chromophore. Fluorescence spectroscopic analysis on Slr1694 in solution is consistent with motion of Trp91 from a hydrophobic environment in the dark state to a more hydrophilic environment in the light-excited state. Mutational analysis indicates that movement of Trp91 is dependent on the occupancy of the hydrophobic Trp binding pocket with a nearby Met. These different tertiary structures may be associated with absorption changes in the blue region of the spectrum.     

TRPC1: the link between functionally distinct store-operated calcium channels

Although store-operated calcium entry (SOCE) was identified more that two decades ago, understanding the molecular mechanisms that regulate and mediate this process continue to pose a major challenge to investigators in this field. Thus, there has been major focus on determining which of the models proposed for this mechanism is valid and conclusively establishing the components of the store-operated calcium (SOC) channel(s). The transient receptor potential canonical (TRPC) proteins have been suggested as candidate components of the elusive store-operated Ca(2+) entry channel. While all TRPCs are activated in response to agonist-stimulated phosphatidylinositol 4,5, bisphosphate (PIP(2)) hydrolysis, only some display store-dependent regulation. TRPC1 is currently the strongest candidate component of SOC and is shown to contribute to SOCE in many cell types. Heteromeric interactions of TRPC1 with other TRPCs generate diverse SOC channels. Recent studies have revealed novel components of SOCE, namely the stromal interacting molecule (STIM) and Orai proteins. While STIM1 has been suggested to be the ER-Ca(2+) sensor protein relaying the signal to the plasma membrane for activation of SOCE, Orai1 is reported to be the pore-forming component of CRAC channel that mediates SOCE in T-lymphocytes and other hematopoetic cells. Several studies now demonstrate that TRPC1 also associates with STIM1 suggesting that SOC and CRAC channels are regulated by similar molecular components. Interestingly, TRPC1 is also associated with Orai1 and a TRPC1-Orai1-STIM1 ternary complex contributes to SOC channel function. This review will focus on the diverse SOC channels formed by TRPC1 and the suggestion that TRPC1 might serve as a molecular link that determines their regulation by store-depletion.     

A two-step two-hybrid system to identify functionally significant protein-protein interactions

The two-step two-hybrid approach described here is an adaptation of the classic two-hybrid system. Its purpose is to identify proteins that interact with a relatively small, defined, functionally significant domain of a protein of interest. In this method, a first round of screening is performed to identify proteins that interact with bait comprised of the wild type protein. Next, each of the prey identified in this first round is tested for its ability to interact with functionally impaired, mutant bait. Any proteins that interact with the wild type bait, but not the mutant bait, are candidate effectors or regulators of the protein of interest.     

Using supervised fuzzy clustering to predict protein structural classes

Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425-435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes.     

Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings

Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant's characteristic whole-brain neural activation patterns, excluding visual areas; (2) identify the category of the object with even higher accuracy, based on that participant's activation; and (3) identify, for the first time, both individual objects and the category of the object the participant was viewing, based only on other participants' activation patterns. The voxels important for category identification were located similarly across participants, and distributed throughout the cortex, focused in ventral temporal perceptual areas but also including more frontal association areas (and somewhat left-lateralized). These findings indicate the presence of stable, distributed, communal, and identifiable neural states corresponding to object concepts.