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PubMed2005

Analysis of sequence-reactivity space for protein-protein interactions.

Sequence-reactivity space is defined by the relationships between amino acid type choices at some residue positions in a protein and the reactivities of the resulting variants. We are studying Kazal superfamily serine proteinase inhibitors, under substitution of any combination of residue types at 10 binding-region positions. Reactivities are defined by the standard free energy of association for an inhibitor against an enzyme, and we are interested in both the strength (the free energy value) and specificity (relative free energy values for one inhibitor against different enzymes). Characterizing the structure of such a space poses several interesting questions: (1) How many sequences achieve particular strength and specificity characteristics? (2) What is the best such sequence? (3) What are some nearly-as-good alternatives? (4) What are their common residue type characteristics (e.g., conservation and correlation)? Although these problems are all highly combinatorial in nature, this article develops an efficient, integrated mechanism to address them under a data-driven model that predicts reactivity for given sequences. We employ sampling and a novel deterministic distribution propagation algorithm, in order to determine both the reactivity distribution and sequence composition statistics; integer programming and a novel branch-and-bound search algorithm, in order to optimize sequences and enumerate near-optimal sequences; and correlation-based sequence decomposition, in order to identify sequence motifs. We demonstrate the value of our mechanism in analyzing the Kazal superfamily sequence-reactivity space, providing insights into the underlying biochemistry and suggesting hypotheses for further experimental consideration. In general, our mechanism offers a valuable tool for investigating the available degrees of freedom in protein design within a combined computational-experimental framework.

Algorithms↗
PubMed2005

A "solvated rotamer" approach to modeling water-mediated hydrogen bonds at protein-protein interfaces.

Water-mediated hydrogen bonds play critical roles at protein-protein and protein-nucleic acid interfaces, and the interactions formed by discrete water molecules cannot be captured using continuum solvent models. We describe a simple model for the energetics of water-mediated hydrogen bonds, and show that, together with knowledge of the positions of buried water molecules observed in X-ray crystal structures, the model improves the prediction of free-energy changes upon mutation at protein-protein interfaces, and the recovery of native amino acid sequences in protein interface design calculations. We then describe a "solvated rotamer" approach to efficiently predict the positions of water molecules, at protein-protein interfaces and in monomeric proteins, that is compatible with widely used rotamer-based side-chain packing and protein design algorithms. Finally, we examine the extent to which the predicted water molecules can be used to improve prediction of amino acid identities and protein-protein interface stability, and discuss avenues for overcoming current limitations of the approach.

Algorithms↗
PubMed2005

Small-world network approach to identify key residues in protein-protein interaction.

We show that protein complexes can be represented as small-world networks, exhibiting a relatively small number of highly central amino-acid residues occurring frequently at protein-protein interfaces. We further base our analysis on a set of different biological examples of protein-protein interactions with experimentally validated hot spots, and show that 83% of these predicted highly central residues, which are conserved in sequence alignments and nonexposed to the solvent in the protein complex, correspond to or are in direct contact with an experimentally annotated hot spot. The remaining 17% show a general tendency to be close to an annotated hot spot. On the other hand, although there is no available experimental information on their contribution to the binding free energy, detailed analysis of their properties shows that they are good candidates for being hot spots. Thus, highly central residues have a clear tendency to be located in regions that include hot spots. We also show that some of the central residues in the protein complex interfaces are central in the monomeric structures before dimerization and that possible information relating to hot spots of binding free energy could be obtained from the unbound structures.

Amino Acids↗
PubMed2005

Classification of protein complexes based on docking difficulty.

Based on the results of several groups using different docking methods, the key properties that determine the expected success rate in protein-protein docking calculations are measures of conformational change, interface area, and hydrophobicity. A classification of protein complexes in terms of these measures provides a prediction of docking difficulty. This classification is used to study the targets of the CAPRI docking experiment. Results show that targets with a moderate expected difficulty were indeed predicted well by a number of groups, whereas the use of additional a priori information was necessary to obtain good results for some very difficult targets. The analysis indicates that CAPRI and other relatively large-scale docking studies represent very important steps toward understanding the capabilities and limitations of current protein-protein docking methods.

Algorithms↗
PubMed2006

The role of residue stability in transient protein-protein interactions involved in enzymatic phosphate hydrolysis. A computational study.

Finding why protein-protein interactions (PPIs) are so specific can provide a valuable tool in a variety of fields. Statistical surveys of so-called transient complexes (like those relevant for signal transduction mechanisms) have shown a tendency of polar residues to participate in the interaction region. Following this scheme, residues in the unbound partners have to compete between interacting with water or interacting with other residues of the protein. On the other hand, several works have shown that the notion of active site electrostatic preorganization can be used to interpret the high efficiency in enzyme reactions. This preorganization can be related to the instability of the residues important for catalysis. In some enzymes, in addition, conformational changes upon binding to other proteins lead to an increase in the activity of the enzymatic partner. In this article the linear response approximation version of the semimacroscopic protein dipoles Langevin dipoles (PDLD/S-LRA) model is used to evaluate the stability of several residues in two phosphate hydrolysis enzymes upon complexation with their activating partners. In particular, the residues relevant for PPI and for phosphate hydrolysis in the CDK2/Cyclin A and Ras/GAP complexes are analyzed. We find that the evaluation of the stability of residues in these systems can be used to identify not only active site regions but it can also be used as a guide to locate "hot spots" for PPIs. We also show that conformational changes play a major role in positioning interfacing residues in a proper "energetic" orientation, ready to interact with the residues in the partner protein surface. Thus, we extend the preorganization theory to PPIs, extrapolating the results we obtained from the above-mentioned complexes to a more general case. We conclude that the correlation between stability of a residue in the surface and the likelihood that it participates in the interaction can be a general fact for transient PPIs.

Adenosine Triphosphate↗
PubMed2006

Evolutionary and structural feedback on selection of sequences for comparative analysis of proteins.

It has been noted that slowly evolving protein residues have two properties: (a) they tend to cluster in the native fold, and (b) they delineate functional surfaces-parts of the surface through which the protein interacts with other proteins or small ligands. Herein, we demonstrate that the two are coupled sufficiently strongly that one effect, when observed, statistically implies the other. Detection of both can be accomplished in multiple sequence alignment related methods by the careful selection of relevant sequences. For the demonstration, we use two sets of protein families: a small set of diverse proteins with diverse functional surfaces, and a large set of homodimerizing enzymes. A practical outcome of our considerations is a simple prescriptive rule for the selection of homologous sequences for the comparative analysis of proteins: in order to optimize the detection of (potentially unknown) functional surfaces, it is sufficient to select sequences in such a way that the residues observed at any level of evolutionary divergence, as implied by the alignment, cluster on the folded protein.

Animals↗
PubMed2006

Towards computing with proteins.

Can proteins be used as computational devices to address difficult computational problems? In recent years there has been much interest in biological computing, that is, building a general purpose computer from biological molecules. Most of the current efforts are based on DNA because of its ability to self-hybridize. The exquisite selectivity and specificity of complex protein-based networks motivated us to suggest that similar principles can be used to devise biological systems that will be able to directly implement any logical circuit as a parallel asynchronous computation. Such devices, powered by ATP molecules, would be able to perform, for medical applications, digital computation with natural interface to biological input conditions. We discuss how to design protein molecules that would serve as the basic computational element by functioning as a NAND logical gate, utilizing DNA tags for recognition, and phosphorylation and exonuclease reactions for information processing. A solution of these elements could carry out effective computation. Finally, the model and its robustness to errors were tested in a computer simulation.

Adenosine Triphosphate↗
PubMed2005

Data mining for protein-protein interactions in invertebrate model organisms.

Well-annotated genome databases are available for many invertebrate species, notably the fruitfly, Drosophila melanogaster, and the nematode, Caenorhabditis elegans. An adequate interpretation of this information at the biological level requires the exploration of the interactions between the gene products. Knowledge of protein interactions and the components of cell signalling pathways in the fly and worm are particularly valuable as hypotheses can be rapidly tested using the powerful genetic toolkits available. Invertebrates offer additional experimental advantages when attempting to characterise protein-protein interactions (PPIs). Their relatively small genome size compared to mammals helps to reduce missed interactions due to redundancy, and their function can be addressed using forward (mutants) and reverse (RNA interference) genetics. However, the researcher looking for evidence of PPIs for a protein of interest is faced with the challenge of extracting interaction data from sources that are highly varied, such as the results of microarray experiments in the unstructured text of research papers. This challenge is greatly reduced by a range of public databases of curated information, as well as publicly available, enhanced search engines, which can provide either direct experimental evidence for a PPI, or valuable clues for generating new hypotheses.

Animals↗
PubMed2004

Catching and separating protein ligands by functional affinity electrophoresis.

A new kind of affinity electrophoresis called functional affinity electrophoresis (FAEP) is a technique used to separate and/or capture proteins according to their functions in a native polyacrylamide gel. Protein A:immunoglobulin G, avidin:biotin, antibody:antigen, and concanavalin A:glycoprotein interactions are used to demonstrate this technique. Protein A, avidin, monoclonal anti-bovine serum albumin (BSA) antibody, and concanavalin A are embedded in distinct regions of a 7.5% native polyacrylamide gel. Some of each of the embedded proteins get covalently and/or noncovalently incorporated into the gel matrix network. Under electrophoresis conditions, these proteins do not show significant electrophoretic mobility or they migrate in a direction opposite to the protein analytes, as in avidin. We clearly observe that polyclonal anti-human myoglobin antibody, biotinylated insulin, BSA, and ovalbumin (glycoprotein) are captured and separated in distinct regions of a FAEP gel by protein A, avidin, monoclonal anti-BSA antibody, and concanavalin A, respectively.

Antibodies, Monoclonal↗
PubMed2006

Protein expression changes in the nucleus accumbens and amygdala of inbred alcohol-preferring rats given either continuous or scheduled access to ethanol.

Chronic ethanol (EtOH) drinking produces neuronal alterations within the limbic system. To investigate changes in protein expression levels associated with EtOH drinking, inbred alcohol-preferring (iP) rats were given one of three EtOH access conditions in their home-cages: continuous ethanol (CE: 24h/day, 7days/week access to EtOH), multiple scheduled access (MSA: four 1-h sessions during the dark cycle/day, 5 days/week) to EtOH, or remained EtOH-naïve. Both MSA and CE groups consumed between 6 and 6.5g of EtOH/kg/day after the 3rd week of access. On the first day of EtOH access for the seventh week, access was terminated at the end of the fourth MSA session for MSA rats and the corresponding time point (2300h) for CE rats. Ten h later, the rats were decapitated, brains extracted, the nucleus accumbens (NAcc) and amygdala (AMYG) microdissected, and protein isolated for 2-dimensional gel electrophoretic analyses. In the NAcc, MSA altered expression levels for 12 of the 14 identified proteins, compared with controls, with six of these proteins altered by CE access, as well. In the AMYG, CE access changed expression levels for 22 of the 27 identified proteins, compared with controls, with 8 of these proteins altered by MSA, as well. The proteins could be grouped into functional categories of chaperones, cytoskeleton, intracellular communication, membrane transport, metabolism, energy production, or neurotransmission. Overall, it appears that EtOH drinking and the conditions under which EtOH is consumed, differentially affect protein expression levels between the NAcc and AMYG. This may reflect differences in neuroanatomical and/or functional characteristics associated with EtOH self-administration and possibly withdrawal, between these two brain structures.

Alcohol Drinking↗
PubMed2005

Chemical biology on PINs and NeeDLes.

Systematic studies of the organization of biochemical networks that make up the living cell can be defined by studying the organization and dynamics of protein interaction networks (PINs). Here, we describe recent conceptual and experimental advances that can achieve this aim and how chemical perturbations of interactions can be used to define the organization of biochemical networks. Resulting perturbation profiles and subcellular locations of interactions allow us to 'place' each gene product at its relevant point in a network. We discuss how experimental strategies can be used in conjunction with other genome-wide analyses of physical and genetic protein interactions and gene transcription profiles to determine network dynamic linkage (NDL) in the living cell. It is through such dynamic studies that the intricate networks that make up the chemical machinery of the cell will be revealed.

Computational Biology↗
PubMed2004

High-throughput phenomics: experimental methods for mapping fluxomes.

Many technologies have been developed to help explain the phenotypic consequences of genetic and/or environmental modifications in areas like functional genomics, pharmaceutical research and metabolic engineering. The missing link in contemporary functional analyses that focus on the analysis of cellular components is the capacity to directly observe functional units. By linking genes and proteins to higher level biological functions, the molecular fluxes through metabolic networks (the fluxome) determine the cellular phenotype. Quantitative monitoring of such whole network operations by methods of metabolic flux analysis, thus bridges the gap by providing a global perspective of the integrated regulation at the transcriptional, translational and metabolic level. This review highlights recent developments towards high-throughput flux analysis.

Animals↗
PubMed2004

Identification of a new autoantibody in patients with chronic hepatitis.

To comprehensively study autoantibodies in patients with chronic hepatitis (CH), especially those with hepatitis C virus (HCV) infection, proteins extracted from HepG2 cells were separated by two-dimensional electrophoresis. Spots reacting with sera from 15 patients with CH-C were detected by Western blotting. Proteins extracted from the spots were subjected to mass spectroscopy for identification by mass fingerprinting. Antigenicity of the proteins identified was confirmed by Western blotting and enzyme-linked immunoabsorbent assay. The localization of the autoantigens so detected was investigated by immunohistochemistry. Among 20 protein spots detected, four were identified as actin, heat shock protein (HSP) 70, HSP60, and a novel protein (hepalaminin). Hepalaminin consists of two domains of laminin beta-2 and a specific domain. Autoantibodies against the specific domain were detected in 60.8% of patients with CH-C, 37.7% of those with CH-B, 42.3% of those with autoimmune CH, 28.6% of nonalcoholic steatohepatitis, 10.0% of asymptomatic HCV carriers, but in no healthy volunteers. Antihepalaminin positivity in CH-C and CH-B was related to histologic grading. Immunohistochemical staining demonstrated that hepalaminin is present in the cytoplasm of hepatocytes and cholangiocytes but not of fibroblasts or the vascular epithelium. Hepalaminin is a novel protein expressed in hepatocytes and cholangiocytes. Autoimmunity to this protein may exacerbate inflammation in chronic viral hepatitis.

Amino Acid Sequence↗
PubMed2005

The Phanerochaete chrysosporium secretome: database predictions and initial mass spectrometry peptide identifications in cellulose-grown medium.

The white rot basidiomycete, Phanerochaete chrysosporium, employs an array of extracellular enzymes to completely degrade the major polymers of wood: cellulose, hemicellulose and lignin. Towards the identification of participating enzymes, 268 likely secreted proteins were predicted using SignalP and TargetP algorithms. To assess the reliability of secretome predictions and to evaluate the usefulness of the current database, we performed shotgun LC-MS/MS on cultures grown on standard cellulose-containing medium. A total of 182 unique peptide sequences were matched to 50 specific genes, of which 24 were among the secretome subset. Underscoring the rich genetic diversity of P. chrysosporium, identifications included 32 glycosyl hydrolases. Functionally interconnected enzyme groups were recognized. For example, the multiple endoglucanases and processive exocellobiohydrolases observed quite probably attack cellulose in a synergistic manner. In addition, a hemicellulolytic system included endoxylanases, alpha-galactosidase, acetyl xylan esterase, and alpha-l-arabinofuranosidase. Glucose and cellobiose metabolism likely involves cellobiose dehydrogenase, glucose oxidase, and various inverting glycoside hydrolases, all perhaps enhanced by an epimerase. To evaluate the completeness of the current database, mass spectroscopy analysis was performed on a larger and more inclusive dataset containing all possible ORFs. This allowed identification of a previously undetected hypothetical protein and a putative acid phosphatase. The expression of several genes was supported by RT-PCR amplification of their cDNAs.

Cellulose↗
PubMed2006

FTMS and TOF/TOF mass spectrometry in concert: identifying peptides with high reliability using matrix prespotted MALDI target plates.

In this paper we describe a combination of the mass spectrometric techniques MALDI-TOF/TOF and MALDI-FTMS to identify proteins in complex samples using prespotted MALDI target plates. By this procedure accurate FTMS mass measurements and TOF/TOF data are obtained from the same spot. We have found that this combination of techniques leads to more reliable identification of peptides.

Blood Proteins↗
PubMed2004

Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction.

The concept of 'protein function' is rather 'fuzzy' because it is often based on whimsical terms or contradictory nomenclature. This currently presents a challenge for functional genomics because precise definitions are essential for most computational approaches. Addressing this challenge, the notion of networks between biological entities (including molecular and genetic interaction networks as well as transcriptional regulatory relationships) potentially provides a unifying language suitable for the systematic description of protein function. Predicting the edges in protein networks requires reference sets of examples with known outcome (that is, 'gold standards'). Such reference sets should ideally include positive examples - as is now widely appreciated - but also, equally importantly, negative ones. Moreover, it is necessary to consider the expected relative occurrence of positives and negatives because this affects the misclassification rates of experiments and computational predictions. For instance, a reason why genome-wide, experimental protein-protein interaction networks have high inaccuracies is that the prior probability of finding interactions (positives) rather than non-interacting protein pairs (negatives) in unbiased screens is very small. These problems can be addressed by constructing well-defined sets of non-interacting proteins from subcellular localization data, which allows computing the probability of interactions based on evidence from multiple datasets.

Algorithms↗
PubMed2003

Novel approaches to map protein interactions.

Although we now have the sequence of the human genome at hand, we face the challenge of assigning function to the identified genes. Genes usually ascribe their function through proteins, and the role of proteins is to interact with other molecules. Therefore, if we could map the interactions of proteins we would be able to understand protein function. The challenge of mapping protein interactions is vast and many novel approaches have recently been developed for this task using molecular biology, mass spectrometry and chemiproteomic techniques.

Carbohydrate Metabolism↗