Immune responses to stress proteins: Applications to infectious disease and cancer

Heat shock proteins, or stress proteins have been identified as part of a highly conserved cellular defence mechanism mediated by multiple, distinct gene familes and corresponding gene products. As intracellular chaperones, stress proteins participate in many essential biochemical pathways of protein maturation and function active during times of stress and during normal cellular homeostasis. In addition to their well-characterized role as protein chaperones, stress proteins are now realized to possess another important biological property: immunogenicity. Stress proteins are now understood to play a fundamental role in immune surveillance of infection and malignancy and this body of basic research has provided a framework for their clinical application. As key targets of both humoral and cellular immunity during infection, stress proteins have accordingly received considerable research interest as prophylactic vaccines for infectious disease applications. The unique and potent immunostimulatory properties of stress proteins have similarly been applied to the development of new approaches to cancer therapy, including both protein and gene-based modalities.     

Methods for the purification of ubiquitinated proteins

Post-translational protein modification by the covalent conjugation of ubiquitin, originally implicated as a signal for proteolytic degradation by 26S proteasome, has now been realised to play important roles in the regulation of almost all biological processes in eukaryotes. In order to understand these processes in greater detail there is a requirement for techniques that can purify mixtures of ubiquitin-conjugated proteins, as a prerequisite to their identification and characterisation. Here we review the methods that have been applied to the bulk purification of ubiquitinated proteins and discuss their applications in proteomic analyses of the 'ubiquitome'.     

Great interactions: How binding incorrect partners can teach us about protein recognition and function

Protein–protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross‐docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross‐docking predictions using the area under the specificity‐sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding‐site predictions resulting from the cross‐docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408–1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

α-Helical transmembrane peptides: A “Divide and Conquer” approach to membrane proteins

α-Helical membrane proteins fulfill many vital roles in all living cells and constitute the majority of drug targets. However, their relevance is in no way paralleled by our current understanding of their structures and functions. This is because membrane proteins present a number of experimental obstacles that are difficult to surmount by classical methods developed for water-soluble proteins. Moreover, membrane proteins are not only challenging on their very own but, when embedded in a biological membrane, also reside in an outstandingly complex milieu. These difficulties have fostered a “divide and conquer” approach, in which a membrane protein is dissected into shorter and easier-to-handle transmembrane (TM) peptides. Under suitable conditions, such peptides fold independently and retain many of the properties displayed in the context of the full-length parent protein.This contribution reviews some of the most notable insights into α-helical membrane proteins gleaned from experiments on protein-derived TM peptides. We recapitulate some peculiar properties of lipid bilayers that render them such a complex and unique environment and discuss generic features pertaining to hydrophobic peptides derived from α-helical membrane proteins. The main part of the review is devoted to a critical discussion of particularly interesting examples of TM peptides studied in membrane-mimetic systems of increasing complexity: isotropic solvents, detergent micelles, lipid bilayers, and biological membranes. The unifying theme is to explore to what extent TM peptides in combination with different membrane-mimetic systems can aid in advancing our knowledge and comprehension of α-helical membrane proteins as well as in developing new pharmacological tools.     

An experimental strategy for the identification of AMPylation targets from complex protein samples

AMPylation is a posttranslational modification (PTM) that has recently caught much attention in the context of bacterial infections as pathogens were shown to secrete Fic proteins that AMPylate Rho GTPases and thus interfere with host cell signaling processes. Although Fic proteins are widespread and found in all kingdoms of life, only a small number of AMPylation targets are known to date. A major obstacle to target identification is the limited availability of generic strategies allowing sensitive and robust identification of AMPylation events. Here, we present an unbiased MS‐based approach utilizing stable isotope‐labeled ATP. The ATP isotopes are transferred onto target proteins in crude cell lysates by in vitro AMPylation introducing specific reporter ion clusters that allow detection of AMPylated peptides in complex biological samples by MS analysis. Applying this strategy on the secreted Fic protein Bep2 of Bartonella rochalimae, we identified the filamenting protein vimentin as an AMPylation target that was confirmed by independent assays. Vimentin represents a new class of target proteins and its identification emphasizes our method as a valuable tool to systematically uncover AMPylation targets. Furthermore, the approach can be generically adapted to study targets of other PTMs that allow incorporation of isotopically labeled substrates.     

The nuclear protein HMGB1, a new kind of chemokine?

The chromosomal protein HMGB1 is now regarded as a proinflammatory cytokine. Importantly, HMGB1 has chemotactic activity suggesting its involvement in the early and late events of the inflammatory reaction. Therefore, HMGB1 has all the hallmarks of a chemokine (chemotactic cytokine). We propose to classify HMGB1 into a new group of proteins unrelated structurally to chemokines but having chemokine-like functions, and to name this class CLF (chemokine-like functions). The CLF class should include other unrelated molecules such as urokinase and its receptor, cytokines macrophage migration inhibitory factor (MIF) and interleukin (IL)-6, anaphylatoxin C5a, ribosomal protein S19, and thioredoxin that have similar chemokine-like activities. This innovative concept may lead to the identification of new therapeutic targets.     

Far-Western based protein-protein interaction screening of high-density protein filter arrays

Even though a rough sketch of the human genome is now available and the number of newly discovered genes, which carry the potential of being biologically and medically relevant is currently greater than ever, only a small proportion has been assigned a biological function. Therefore, enormous attention is now increasingly being drawn towards functional genomics, i.e. the functional characterization of these newly identified sequences. In order to elucidate the role of a particular gene product within its cellular context, we have screened high-density protein filter arrays for protein-protein interactions on the basis of a 'Far-Western' based approach. The methodology described herein easily allows the identification and isolation of cDNAs of proteins, which interact with specific ligands (interacting proteins, antibodies and DNA/RNA sequences), and represents an alternative to tedious conventional protein interaction analyses. Far-Western screening in the context of a whole-genome expression analysis not only facilitates the assignment of biological functions to specific, newly identified protein and DNA sequences, but also is useful in studies that assess the binding capacity of mutant proteins to their interaction partner and in the identification of domains and amino acids involved in known protein-protein interactions. Taken together, we describe an approach that allows the easy and reproducible identification of protein ligands on the basis of a whole-genome expression analysis.     

Estimation of membrane proteins in the human proteome

Genomics and proteomics have added valuable information to our knowledgebase of the human biological system including the discovery of therapeutic targets and disease biomarkers. However, molecular profiling studies commonly result in the identification of novel proteins of unknown localization. A class of proteins of special interest is membrane proteins, in particular plasma membrane proteins. Despite their biological and medical significance, the 3-dimensional structures of less than 1% of plasma membrane proteins have been determined. In order to aid in identification of membrane proteins, a number of computational methods have been developed. These tools operate by predicting the presence of transmembrane segments. Here, we utilized five topology prediction methods (TMHMM, SOSUI, waveTM, HMMTOP, and TopPred II) in order to estimate the ratio of integral membrane proteins in the human proteome. These methods employ different algorithms and include a newly-developed method (waveTM) that has yet to be tested on a large proteome database. Since these tools are prone for error mainly as a result of falsely predicting signal peptides as transmembrane segments, we have utilized an additional method, SignalP. Based on our analyses, the ratio of human proteins with transmembrane segments is estimated to fall between 15% and 39% with a consensus of 13%. Agreement among the programs is reduced further when both a positive identification of a membrane protein and the number of transmembrane segments per protein are considered. Such a broad range of prediction depends on the selectivity of the individual method in predicting integral membrane proteins. These methods can play a critical role in determining protein structure and, hence, identifying suitable drug targets in humans.     

All D-amino acid hexapeptide inhibitors of melittin's cytolytic activity derived from synthetic combinatorial libraries

The identification of peptides that inhibit the biological functions of proteins was used as a means to explore protein/ligand interactions involved in molecular recognition processes. This approach is based on the use of synthetic combinatorial libraries (SCLs) for the rapid identification of individual peptides that block the interaction of proteins with their biological targets. Thus, each peptide mixture of an all-D -amino acid hexapeptide SCL in a positional scanning format was screened for its ability to inhibit the hemolytic activity of melittin, a model self-assembling protein. The potent inhibitory activity of the identified individual peptides suggests that protein-like complexes are able to specifically bind to peptides having an all-D configuration. These results also show that SCLs are useful for the identification of short, non-hydrolysable sequences having potential intracellular inhibitory activities.     

Predicting sRNAs and Their Targets in Bacteria

Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40-500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.     

Selection of genomic target RNAs by iterative screening

A growing number of proteins are known to exert their regulatory or biological functions via RNA binding. In some cases genetic interactions allow us to infer candidate targets for RNA directed regulation, but in many other cases identification of potential regulatory targets is problematic. We have developed an in vitro biochemical screen, SETIS (SElection of Target RNAs by Iterative Screening) that allows screening of a major portion of the genome for identification of potential targets for RNA binding proteins.     

Structural genomics target selection for the New York consortium on membrane protein structure

The New York Consortium on Membrane Protein Structure (NYCOMPS), a part of the Protein Structure Initiative (PSI) in the USA, has as its mission to establish a high-throughput pipeline for determination of novel integral membrane protein structures. Here we describe our current target selection protocol, which applies structural genomics approaches informed by the collective experience of our team of investigators. We first extract all annotated proteins from our reagent genomes, i.e. the 96 fully sequenced prokaryotic genomes from which we clone DNA. We filter this initial pool of sequences and obtain a list of valid targets. NYCOMPS defines valid targets as those that, among other features, have at least two predicted transmembrane helices, no predicted long disordered regions and, except for community nominated targets, no significant sequence similarity in the predicted transmembrane region to any known protein structure. Proteins that feed our experimental pipeline are selected by defining a protein seed and searching the set of all valid targets for proteins that are likely to have a transmembrane region structurally similar to that of the seed. We require sequence similarity aligning at least half of the predicted transmembrane region of seed and target. Seeds are selected according to their feasibility and/or biological interest, and they include both centrally selected targets and community nominated targets. As of December 2008, over 6,000 targets have been selected and are currently being processed by the experimental pipeline. We discuss how our target list may impact structural coverage of the membrane protein space.     

The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.     

Oceanic Metagenomics: The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.     

The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families

Metagenomics projects based on shotgun sequencing of populations of micro-organisms yield insight into protein families. We used sequence similarity clustering to explore proteins with a comprehensive dataset consisting of sequences from available databases together with 6.12 million proteins predicted from an assembly of 7.7 million Global Ocean Sampling (GOS) sequences. The GOS dataset covers nearly all known prokaryotic protein families. A total of 3,995 medium- and large-sized clusters consisting of only GOS sequences are identified, out of which 1,700 have no detectable homology to known families. The GOS-only clusters contain a higher than expected proportion of sequences of viral origin, thus reflecting a poor sampling of viral diversity until now. Protein domain distributions in the GOS dataset and current protein databases show distinct biases. Several protein domains that were previously categorized as kingdom specific are shown to have GOS examples in other kingdoms. About 6,000 sequences (ORFans) from the literature that heretofore lacked similarity to known proteins have matches in the GOS data. The GOS dataset is also used to improve remote homology detection. Overall, besides nearly doubling the number of current proteins, the predicted GOS proteins also add a great deal of diversity to known protein families and shed light on their evolution. These observations are illustrated using several protein families, including phosphatases, proteases, ultraviolet-irradiation DNA damage repair enzymes, glutamine synthetase, and RuBisCO. The diversity added by GOS data has implications for choosing targets for experimental structure characterization as part of structural genomics efforts. Our analysis indicates that new families are being discovered at a rate that is linear or almost linear with the addition of new sequences, implying that we are still far from discovering all protein families in nature.     

Challenges in mass spectrometry-based proteomics

During the last decade, protein analysis and proteomics have been established as new tools for understanding various biological problems. As the identification of proteins after classical separation techniques, such as two-dimensional gel electrophoresis, have become standard methods, new challenges arise in the field of proteomics. The development of "functional proteomics" combines functional characterization, like regulation, localization and modification, with the identification of proteins for deeper insight into cellular functions. Therefore, different mass spectrometric techniques for the analysis of post-translational modifications, such as phosphorylation and glycosylation, have been established as well as isolation and separation methods for the analysis of highly complex samples, e.g. protein complexes or cell organelles. Furthermore, quantification of protein levels within cells is becoming a focus of interest as mass spectrometric methods for relative or even absolute quantification have currently not been available. Protein or genome databases have been an essential part of protein identification up to now. Thus, de novo sequencing offers new possibilities in protein analytical studies of organisms not yet completely sequenced. The intention of this review is to provide a short overview about the current capabilities of protein analysis when addressing various biological problems.     

A superfamily of protein tags: ubiquitin,SUMO and related modifiers

The biological functions of many proteins are altered by their covalent attachment to polypeptide modifiers. The best-known example of this type of modification is ubiquitination. Ubiquitin has a well-documented role in targeting proteins for degradation by the proteasome, but additional effects of protein ubiquitination are now being uncovered. Furthermore, multiple polypeptides that are distinct from, but related to, ubiquitin are also enzymatically coupled to target macromolecules, and these ubiquitin-like proteins participate in diverse biological processes such as DNA repair, autophagy and signal transduction.