Predicting the coupling specificity of GPCRs to G-proteins by support vector machines

G-protein coupled receptors （GPCRs） represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction. Predicting the coupling specificity of GPCRs to G-proteins is vital for further understanding the mechanism of signal transduction and the function of the receptors within a cell, which can provide new clues for pharmaceutical research and development. In this study, the features of amino acid compositions and physiochemical properties of the full-length GPCR sequences have been analyzed and extracted. Based on these features, classifiers have been developed to predict the coupling specificity of GPCRs to G-protelns using support vector machines. The testing results show that this method could obtain better prediction accuracy.     

Membrane components can modulate the substrate specificity of protein kinase C

The cationic amphiphile, cholesteryl-3-carboxyamidoethylene-trimethylammonium iodide, can alter the substrate specificity of protein kinase C (PKC). The phosphorylation of histone catalyzed by PKC requires the binding of the enzyme to phospholipid vesicles. This cationic amphiphile reduces both the binding of PKC to lipid and as a consequence its rate of phosphorylation of histone. In contrast, PKC bound to large unilamellar vesicles (LUVs) composed of 50 mol % POPS, 20 mol % POPC, and 30 mol % of this amphiphile catalyzes protamine sulfate phosphorylation by an almost 4 fold greater rate. This activation requires phosphatidylserine (PS) and is inhibited by Ca²⁺. The extent of activation is affected by the time of incubation of PKC with LUVs. This data suggests a novel mechanism by which PKC-dependent signal transduction pathways may be altered by altering the protein targets of this enzyme.     

Prediction of functional specificity determinants from protein sequences using log-likelihood ratios

MOTIVATION: A number of methods have been developed to predict functional specificity determinants in protein families based on sequence information. Most of these methods rely on pre-defined functional subgroups. Manual subgroup definition is difficult because of the limited number of experimentally characterized subfamilies with differing specificity, while automatic subgroup partitioning using computational tools is a non-trivial task and does not always yield ideal results. RESULTS: We propose a new approach SPEL (specificity positions by evolutionary likelihood) to detect positions that are likely to be functional specificity determinants. SPEL, which does not require subgroup definition, takes a multiple sequence alignment of a protein family as the only input, and assigns a P-value to every position in the alignment. Positions with low P-values are likely to be important for functional specificity. An evolutionary tree is reconstructed during the calculation, and P-value estimation is based on a random model that involves evolutionary simulations. Evolutionary log-likelihood is chosen as a measure of amino acid distribution at a position. To illustrate the performance of the method, we carried out a detailed analysis of two protein families (LacI/PurR and G protein alpha subunit), and compared our method with two existing methods (evolutionary trace and mutual information based). All three methods were also compared on a set of protein families with known ligand-bound structures. AVAILABILITY: SPEL is freely available for non-commercial use. Its pre-compiled versions for several platforms and alignments used in this work are available at ftp://iole.swmed.edu/pub/SPEL/     

Computationally‐predicted CB1 cannabinoid receptor mutants show distinct patterns of salt‐bridges that correlate with their level of constitutive activity reflected in G protein coupling levels,thermal stability,and ligand binding

The cannabinoid receptor 1 (CB1), a member of the class A G‐protein‐coupled receptor (GPCR) family, possesses an observable level of constitutive activity. Its activation mechanism, however, has yet to be elucidated. Previously we discovered dramatic changes in CB1 activity due to single mutations; T3.46A, which made the receptor inactive, and T3.46I and L3.43A, which made it essentially fully constitutively active. Our subsequent prediction of the structures of these mutant receptors indicated that these changes in activity are explained in terms of the pattern of salt‐bridges in the receptor region involving transmembrane domains 2, 3, 5, and 6. Here we identified key salt‐bridges, R2.37 + D6.30 and D2.63 + K3.28, critical for CB1 inactive and active states, respectively, and generated new mutant receptors that we predicted would change CB1 activity by either precluding or promoting these interactions. We find that breaking the R2.37 + D6.30 salt‐bridge resulted in substantial increase in G‐protein coupling activity and reduced thermal stability relative to the wild‐type reflecting the changes in constitutive activity from inactive to active. In contrast, breaking the D2.63 + K3.28 salt‐bridge produced the opposite profile suggesting this interaction is critical for the receptor activation. Thus, we demonstrate an excellent correlation with the predicted pattern of key salt‐bridges and experimental levels of activity and conformational flexibility. These results are also consistent with the extended ternary complex model with respect to shifts in agonist and inverse agonist affinity and provide a powerful framework for understanding the molecular basis for the multiple stages of CB1 activation and that of other GPCRs in general. Proteins 2013; 81:1304–1317. © 2013 Wiley Periodicals, Inc.     

Predicting amino acid residues responsible for enzyme specificity solely from protein sequences

We describe a general, modular method for developing protocols to identify the amino acid residues that most likely define the division of a protein superfamily into two subsets. As one possibility, we use PROBE to gather superfamily members and perform an ungapped alignment. We then use a modified BLOSUM62 substitution matrix to determine the discriminating power of each column of aligned residues. The overall method is particularly useful for predicting amino acids responsible for substrate or binding specificity when no structures are available. We apply our method to three pairs of protein classes in three different superfamilies, and present our results, some of which have been experimentally verified. This approach may accelerate the elucidation of enzymic substrate specificity, which is critical for both mechanistic insights into biocatalysis and ultimate application.     

Prediction of reversibly oxidized protein cysteine thiols using protein structure properties

Protein cysteine thiols can be divided into four groups based on their reactivities: those that form permanent structural disulfide bonds, those that coordinate with metals, those that remain in the reduced state, and those that are susceptible to reversible oxidation. Physicochemical parameters of oxidation-susceptible protein thiols were organized into a database named the Balanced Oxidation Susceptible Cysteine Thiol Database (BALOSCTdb). BALOSCTdb contains 161 cysteine thiols that undergo reversible oxidation and 161 cysteine thiols that are not susceptible to oxidation. Each cysteine was represented by a set of 12 parameters, one of which was a label (1/0) to indicate whether its thiol moiety is susceptible to oxidation. A computer program (the C4.5 decision tree classifier re-implemented as the J48 classifier) segregated cysteines into oxidation-susceptible and oxidation-non-susceptible classes. The classifier selected three parameters critical for prediction of thiol oxidation susceptibility: (1) distance to the nearest cysteine sulfur atom, (2) solvent accessibility, and (3) pKa. The classifier was optimized to correctly predict 136 of the 161 cysteine thiols susceptible to oxidation. Leave-one-out cross-validation analysis showed that the percent of correctly classified cysteines was 80.1% and that 16.1% of the oxidation-susceptible cysteine thiols were incorrectly classified. The algorithm developed from these parameters, named the Cysteine Oxidation Prediction Algorithm (COPA), is presented here. COPA prediction of oxidation-susceptible sites can be utilized to locate protein cysteines susceptible to redox-mediated regulation and identify possible enzyme catalytic sites with reactive cysteine thiols.     

Delineating the conformational landscape of the adenosine A2A receptor during G protein coupling

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Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.     

Generation of an antibody with a designed specificity difference for protein C and activated protein C

Rabbit polyclonal antibodies to a synthetic peptide, NH₂-Asp-Thr-Asn-Gln-Val-Asp-Gln-Lys-Asp-Gln-Leu-Asp-Phe-Arg-CONH₂ (APep), have been produced. This sequence is identical to that contained in the tetradecapeptide released from bovine protein C (PC) as a result of its conversion to its activated form (APC), except that Phe₁₃ replaced the normal Pro₁₃, in order to discourage cross-reactivity of antibodies to the carboxylterminal portion of APep with PC. The antibody pool obtained reacted with PC and showed virtually no cross-reactivity toward either APC or several typical plasma proteins. This general approach should serve well as a means of production of antibodies with a designed specificity capable of distinguishing between forms of the same protein that arise by release of peptide material.     

Substrate specificity and ion coupling in the Na+/betaine symporter BetP

BetP is an Na(+)-coupled betaine-specific transporter of the betaine-choline-carnitine (BCC) transporter family involved in the response to hyperosmotic stress. The crystal structure of BetP revealed an overall fold of two inverted structurally related repeats (LeuT-fold) that BetP shares with other sequence-unrelated Na(+)-coupled symporters. Numerous structures of LeuT-fold transporters in distinct conformational states have contributed substantially to our understanding of the alternating access mechanism of transport. Nevertheless, coupling of substrate and co-transported ion fluxes has not been structurally corroborated to the same extent. We converted BetP by a single-point mutation--glycine to aspartate--into an H(+)-coupled choline-specific transporter and solved the crystal structure of this mutant in complex with choline. The structure of BetP-G153D demonstrates a new inward-facing open conformation for BetP. Choline binding to a location close to the second, low-affinity sodium-binding site (Na2) of LeuT-fold transporters is facilitated by the introduced aspartate. Our data confirm the importance of a cation-binding site in BetP, playing a key role in a proposed molecular mechanism of Na(+) and H(+) coupling in BCC transporters.     

Linking structural features of protein complexes and biological function

Protein–protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure–function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.     

Design of multispecific protein sequences using probabilistic graphical modeling

In nature, proteins partake in numerous protein– protein interactions that mediate their functions. Moreover, proteins have been shown to be physically stable in multiple structures, induced by cellular conditions, small ligands, or covalent modifications. Understanding how protein sequences achieve this structural promiscuity at the atomic level is a fundamental step in the drug design pipeline and a critical question in protein physics. One way to investigate this subject is to computationally predict protein sequences that are compatible with multiple states, i.e., multiple target structures or binding to distinct partners. The goal of engineering such proteins has been termed multispecific protein design. We develop a novel computational framework to efficiently and accurately perform multispecific protein design. This framework utilizes recent advances in probabilistic graphical modeling to predict sequences with low energies in multiple target states. Furthermore, it is also geared to specifically yield positional amino acid probability profiles compatible with these target states. Such profiles can be used as input to randomly bias high‐throughput experimental sequence screening techniques, such as phage display, thus providing an alternative avenue for elucidating the multispecificity of natural proteins and the synthesis of novel proteins with specific functionalities. We prove the utility of such multispecific design techniques in better recovering amino acid sequence diversities similar to those resulting from millions of years of evolution. We then compare the approaches of prediction of low energy ensembles and of amino acid profiles and demonstrate their complementarity in providing more robust predictions for protein design. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Prediction of kinase-specific phosphorylation sites with sequence features by a log-odds ratio approach

Protein phosphorylation plays important roles in a variety of cellular processes. Detecting possible phosphorylation sites and their corresponding protein kinases is crucial for studying the function of many proteins. This article presents a new prediction system, called PhoScan, to predict phosphorylation sites in a kinase-family-specific way. Common phosphorylation features and kinase-specific features are extracted from substrate sequences of different protein kinases based on the analysis of published experiments, and a scoring system is developed for evaluating the possibility that a peptide can be phosphorylated by the protein kinase at the specific site in its sequence context. PhoScan can achieve a specificity of above 90% with sensitivity around 90% at kinase-family level on the data experimented. The system is applied on a set of human proteins collected from Swiss-Prot and sets of putative phosphorylation sites are predicted for protein kinase A, cyclin-dependent kinase, and casein kinase 2 families. PhoScan is available at http://bioinfo.au.tsinghua.edu.cn/phoscan/.     

Substrate specificity of archaeon Sulfolobus tokodaii biotin protein ligase

Biotin protein ligase (BPL) is an enzyme mediating biotinylation of a specific lysine residue of the carboxyl carrier protein (BCCP) of biotin-dependent enzymes. We recently found that the substrate specificity of BPL from archaeon Sulfolobus tokodaii is totally different from those of many other organisms, in reflection of a difference in the local sequence of BCCP surrounding the canonical lysine residue. There is a conserved glycine residue in the biotin-binding site of Escherichia coli BPL, but this residue is replaced with alanine in S. tokodaii BPL. To test the notion that this substitution dictates the substrate specificity of the latter enzyme, this residue, Ala-43, was converted to glycine. The K(m) values of the resulting mutant, A43G, for substrates, were smaller than those of the wild type, suggesting that the residue in position 43 of BPL plays an important role in substrate binding.     

The substrate specificity of a neuronal glutamate transporter is determined by the nature of the coupling ion

Glutamate transporters are essential for terminating synaptic transmission. Glutamate is translocated together with three sodium ions. In the neuronal glutamate transporter EAAC1, lithium can replace sodium. To address the question of whether the coupling ion interacts with the 'driven' substrate during co-transport, the kinetic parameters of transport of the three substrates, L-glutamate and D- and L-aspartate by EAAC-1 in sodium- and lithium-containing media were compared. The major effect of the substitution of sodium by lithium was on Km. In the presence of sodium, the values for Km and Imax of these substrates were similar. In the presence of lithium, the Km for L-aspartate was increased around 13-fold. Remarkably, the corresponding increase for L-glutamate and D-aspartate was much larger, around 130-fold. In marked contrast, the Ki values for a non-transportable substrate analogue were similar in the presence of either sodium or lithium. The preference for L-aspartate in the presence of lithium was also observed when electrogenic transport of radioactive substrates was monitored in EAAC1-containing proteoliposomes. Our results indicate that, subsequent to substrate binding, the co-transported solutes interact functionally in the binding pocket of the transporter.     

Rotamer strain as a determinant of protein structural specificity

We present direct evidence for a change in protein structural specificity due to hydrophobic core packing. High resolution structural analysis of a designed core variant of ubiquitin reveals that the protein is in slow exchange between two conformations. Examination of side-chain rotamers indicates that this dynamic response and the lower stability of the protein are coupled to greater strain and mobility in the core. The results suggest that manipulating the level of side-chain strain may be one way of fine tuning the stability and specificity of proteins.     

Prediction of protein secondary structure content for the twilight zone sequences

Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure.     

乳酸杆菌S-层蛋白的特性及功能性研究进展

文章综述了S-层蛋白的性质和功能,重点介绍了S-层蛋白对乳酸杆菌表面性质和黏附性的影响以及调节肠道功能的作用,包括减少病原菌引起的细胞凋亡、调节免疫细胞的活性、与SIGNR3相互作用参与肠道免疫反应、通过TLRS-My D88-NF-κB途径发挥生物学功能以及调控肠道黏膜相关蛋白表达。由此证明了S-层蛋白对于乳酸杆菌发挥免疫调节功能具有重要的作用。