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Using RNA antisense purification and mass spectrometry, we identified more than 100 human proteins that directly and specifically bind SARS-CoV-2 RNA in infected cells. To gain insights into the functions of selected RNA interactors, we applied genetic perturbation and pharmacological inhibition experiments, and mapped the contact sites on the viral RNA. This led to the identification of host dependency factors and defense strategies, which can guide the design of novel therapeutics against SARS-CoV-2.

The DARPin® drug platform was established with a vision to expand the medical use of biologics beyond what was possible with monoclonal antibodies. It is based on naturally occurring ankyrin repeat domains that are typically building blocks of multifunctional human proteins. The platform allows for the generation of diverse, well-behaved, multifunctional drug candidates. Recent clinical data illustrate the favorable safety profile of the first DARPin® molecules tested in patients. With the positive phase III results of the most advanced DARPin® drug candidate, abicipar, the DARPin® drug platform is potentially about to achieve its first marketing approval. This review highlights some of the key milestones and decisions encountered when transforming the DARPin® platform from an academic concept to a biotech drug pipeline engine.

The functional and optimal expression of genes is crucial for survival of all living organisms. Numerous experiments and efforts have been performed to reveal the mechanisms required for the functional and optimal expression of human genes. The yeast Saccharomyces cerevisiae has evolved independently of humans for billions of years. Nevertheless, S. cerevisiae has many conserved genes and expression mechanisms that are similar to those in humans. Yeast is the most commonly used model organism for studying the function and expression mechanisms of human genes because it has a relatively simple genome structure, which is easy to manipulate. Many previous studies have focused on understanding the functions and mechanisms of human proteins using orthologous genes and biological systems of yeast. In this review, we mainly introduce two recent studies that replaced human genes and nucleosomes with those of yeast. Here, we suggest that, although yeast is a relatively small eukaryotic cell, its humanization is useful for the direct study of human proteins. In addition, yeast can be used as a model organism in a broader range of studies, including drug screening.

Background Identifying human protein-phenotype relationships has attracted researchers in bioinformatics and biomedical natural language processing due to its importance in uncovering rare and complex diseases. Since experimental validation of protein-phenotype associations is prohibitive, automated tools capable of accurately extracting these associations from the biomedical text are in high demand. However, while the manual annotation of protein-phenotype co-mentions required for training such models is highly resource-consuming, extracting millions of unlabeled co-mentions is straightforward. Results In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions with the help of unlabeled data. This framework allows the ability to incorporate an extensive collection of unlabeled sentence-level co-mentions of human proteins and phenotypes with a small labeled dataset to enhance overall performance. We develop PPPredSS, a prototype of our proposed semi-supervised framework that combines sophisticated language models, convolutional networks, and recurrent networks. Our experimental results demonstrate that the proposed approach provides a new state-of-the-art performance in classifying human protein-phenotype co-mentions by outperforming other supervised and semi-supervised counterparts. Furthermore, we highlight the utility of PPPredSS in powering a curation assistant system through case studies involving a group of biologists. Conclusions This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning. The insights and findings from this work have implications for biomedical researchers, biocurators, and the text mining community working on biomedical relationship extraction.

Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins. How drugs restore these functions, however, is often unknown as a drug’s therapeutic effects are not limited to the proteins that the drug directly targets. Here, we develop the multiscale interactome, a powerful approach to explain disease treatment. We integrate disease-perturbed proteins, drug targets, and biological functions into a multiscale interactome network. We then develop a random walk-based method that captures how drug effects propagate through a hierarchy of biological functions and physical protein-protein interactions. On three key pharmacological tasks, the multiscale interactome predicts drug-disease treatment, identifies proteins and biological functions related to treatment, and predicts genes that alter a treatment’s efficacy and adverse reactions. Our results indicate that physical interactions between proteins alone cannot explain treatment since many drugs treat diseases by affecting the biological functions disrupted by the disease rather than directly targeting disease proteins or their regulators. We provide a general framework for explaining treatment, even when drugs seem unrelated to the diseases they are recommended for., Most diseases disrupt multiple proteins, and drugs treat such diseases by restoring the functions of the disrupted proteins; how drugs restore these functions, however, is often unknown. Here, the authors develop the multiscale interactome, a powerful approach to explain disease treatment.

The full might of a world-leading artificial-intelligence laboratory has been brought to bear on protein-structure prediction. The resulting method, AlphaFold2, promises to transform our understanding of proteins. Accurate predictions of the structures of almost all human proteins.

Nucleic acids can adopt G-quadruplex folds whose cellular roles remain poorly defined. Synthesis of new probes has now enabled the identification of human proteins that interact with G-quadruplexes. This could provide new clues to decipher the function of these curious folds.

Polyphosphorylation is a newly described non-enzymatic post-translational modification wherein long chains of inorganic phosphates are attached to lysine residues. The first targets of polyphosphorylation identified were S. cerevisiae proteins Nsr1 and Top1. Building on this theme, we recently exploited functional genomics tools in yeast to identify 15 new targets, including a conserved network of nucleolar proteins implicated in ribosome biogenesis. We also described the polyphosphorylation of six human proteins, suggesting that this unique post-translational modification could be conserved throughout eukaryotes. The study of polyphosphorylation seems poised to uncover novel modes of protein regulation in pathways spanning diverse biological processes. In this review, we establish a framework for future work by outlining critical questions related to the biochemistry of polyphosphorylation, its therapeutic potential, and everything in between.

Collectively, cell penetrating peptide (CPP) vectors and intrinsically active bioportides possess tremendous potential for drug delivery applications and the discrete modulation of intracellular targets including the sites of protein–protein interactions (PPIs). Such sequences are usually relatively short (

Delivery into mammalian cells remains a significant challenge for many applications of proteins as research tools and therapeutics. We recently reported that the fusion of cargo proteins to a supernegatively charged (–30)GFP enhances encapsulation by cationic lipids and delivery into mammalian cells. To discover polyanionic proteins with optimal delivery properties, we evaluate negatively charged natural human proteins for their ability to deliver proteins into cultured mammalian cells and human primary fibroblasts. Here we discover that ProTα, a small, widely expressed, intrinsically disordered human protein, enables up to ~10-fold more efficient cationic lipid-mediated protein delivery compared to (–30)GFP. ProTα enables efficient delivery at low- to mid-nM concentrations of two unrelated genome editing proteins, Cre recombinase and zinc-finger nucleases, under conditions in which (–30)GFP fusion or cationic lipid alone does not result in substantial activity. ProTα may enable mammalian cell protein delivery applications when delivery potency is limiting., Inefficient delivery of proteins into mammalian cells limits their use in research and therapy. Here, the authors discover that ProTα, a small, intrinsically disordered human protein can facilitate efficient cationic lipid-mediated protein delivery of genome editing proteins into mammalian cells.

Microbe induced cardiovascular diseases (CVDs) are less studied at present. Host-pathogen interactions (HPIs) between human proteins and microbial proteins associated with CVD can be found dispersed in existing molecular interaction databases. MorCVD database is a curated resource that combines 23,377 protein interactions between human host and 432 unique pathogens involved in CVDs in a single intuitive web application. It covers endocarditis, myocarditis, pericarditis and 16 other microbe induced CVDs. The HPI information has been compiled, curated, and presented in a freely accessible web interface (http://morcvd.sblab-nsit.net/About). Apart from organization, enrichment of the HPI data was done by adding hyperlinked protein ID, PubMed, gene ontology records. For each protein in the database, drug target and interactors (same as well as different species) information has been provided. The database can be searched by disease, protein ID, pathogen name or interaction detection method. Interactions detected by more than one method can also be listed. The information can be presented in tabular form or downloaded. A comprehensive help file has been developed to explain the various options available. Hence, MorCVD acts as a unified resource for retrieval of HPI data for researchers in CVD and microbiology.

The composition of ancient oral microbiomes has recently become accessible owing to advanced biomolecular methods such as metagenomics and metaproteomics, but the utility of metaproteomics for such analyses is less explored. Here, we use quantitative metaproteomics to characterize the dental calculus associated with the remains of 21 humans retrieved during the archeological excavation of the medieval (ca. 1100–1450 CE) cemetery of Tjærby, Denmark. We identify 3671 protein groups, covering 220 bacterial species and 81 genera across all medieval samples. The metaproteome profiles of bacterial and human proteins suggest two distinct groups of archeological remains corresponding to health-predisposed and oral disease-susceptible individuals, which is supported by comparison to the calculus metaproteomes of healthy living individuals. Notably, the groupings identified by metaproteomics are not apparent from the bioarchaeological analysis, illustrating that quantitative metaproteomics has the potential to provide additional levels of molecular information about the oral health status of individuals from archeological contexts., Mineralized plaque, or dental calculus, is a valuable reservoir of the ancient oral microbiome. Here, the authors use quantitative metaproteomics to analyze the dental calculus of 21 individuals from a medieval cemetery, identifying human and microbial proteins that shed light on their oral health status.

In-cell NMR provides structural and functional information on proteins directly inside living cells. At present, the high costs of the labeled media for mammalian cells represent a limiting factor for the development of this methodology. Here we report a protocol to prepare a homemade growth medium from Spirulina platensis autolysate, suitable to express uniformly labeled proteins inside mammalian cells at a reduced cost-per-sample. The human proteins SOD1 and Mia40 were overexpressed in human cells grown in (15)N-enriched S. platensis algal-derived medium, and high quality in-cell NMR spectra were obtained.

To uncover the pharmacological mechanism of Astragalus Salvia compound (ASC) on pregnancy-induced hypertension syndrome (PIH), to provide useful information for clinical, as well as to connect the basic and clinical by a network pharmacological approach, we used network pharmacological approach. We collected ASC’s compounds by traditional Chinese Medicine databases, and input them into PharmMapper to got their targets. Then we acquired PIH targets from Genecards and OMIM, collected the interactions of all the targets and other human proteins via String and INACT. We also constructed the network by Cytoscape and analyze it by MCODE so as to get clusters. Finally, we put all the targets of clusters into DAVID to do GO enrichment analysis. After these, four networks are constructed by Cytoscape; they are PIH network, compound-compound target network of ASC, ASC-PIH network, and compound target-PIH target-other human proteins’ PPI network. According to the results, we think that ASC may directly regulate several biological processes and their genes in “endothelial cell activation and injury” and “placental or trophoblast cell ischemia” models to treat PIH. And it may indirectly act on the rest of the biological process to treat PIH or may not.

Protein-protein interactions (PPIs) are useful for understanding signaling cascades, predicting protein function, associating proteins with disease and fathoming drug mechanism of action. Currently, only ∼ 10% of human PPIs may be known, and about one-third of human proteins have no known interactions. We introduce FpClass, a data mining-based method for proteome-wide PPI prediction. At an estimated false discovery rate of 60%, we predicted 250,498 PPIs among 10,531 human proteins; 10,647 PPIs involved 1,089 proteins without known interactions. We experimentally tested 233 high- and medium-confidence predictions and validated 137 interactions, including seven novel putative interactors of the tumor suppressor p53. Compared to previous PPI prediction methods, FpClass achieved better agreement with experimentally detected PPIs. We provide an online database of annotated PPI predictions (http://ophid.utoronto.ca/fpclass/) and the prediction software (http://www.cs.utoronto.ca/~juris/data/fpclass/).

Mass spectrometry (MS) enables absolute quantification of endogenous proteins by the use of isotope-labeled standards as internal references. QPrEST standards, currently available for >13,000 human proteins, represent a novel class of recombinantly produced heavy isotope–labeled standards that are added early in the quantification workflow. These multipeptide standards contain 50–150 amino acids identical to a human target sequence, resulting in absolute quantification data based on multiple tryptic peptides.

Multiple reaction monitoring (MRM) mass spectrometry has been successfully applied to monitor targeted proteins in biological specimens, raising the possibility that assays could be configured to measure all human proteins. We report the results of a pilot study designed to test the feasibility of a large-scale, international effort for MRM assay generation. We have configured, validated across three laboratories and made publicly available as a resource to the community 645 novel MRM assays representing 319 proteins expressed in human breast cancer. Assays were multiplexed in groups of >150 peptides and deployed to quantify endogenous analytes in a panel of breast cancer-related cell lines. The median assay precision was 5.4%, with high interlaboratory correlation (R(2) > 0.96). Peptide measurements in breast cancer cell lines were able to discriminate among molecular subtypes and identify genome-driven changes in the cancer proteome. These results establish the feasibility of a large-scale effort to develop an MRM assay resource.

While plasma donation is still necessary as a unique source of human proteins and to treat coagulation disorders, FFP administration seems to have high rate of inappropriate indication. After all, FFP transfusion is not risk free, and is associated with lung injury, infectious disease and circulatory overload in recipients. On the other hand, elderly patients may have impaired physical status and increased demand for FFP transfusion.

We carried out a test sample study to try to identify errors leading to irreproducibility, including incompleteness of peptide sampling, in LC-MS-based proteomics. We distributed a test sample consisting of an equimolar mix of 20 highly purified recombinant human proteins, to 27 laboratories for identification. Each protein contained one or more unique tryptic peptides of 1250 Da to also test for ion selection and sampling in the mass spectrometer. Of the 27 labs, initially only 7 labs reported all 20 proteins correctly, and only 1 lab reported all the tryptic peptides of 1250 Da. Nevertheless, a subsequent centralized analysis of the raw data revealed that all 20 proteins and most of the 1250 Da peptides had in fact been detected by all 27 labs. The centralized analysis allowed us to determine sources of problems encountered in the study, which include missed identifications (false negatives), environmental contamination, database matching, and curation of protein identifications. Improved search engines and databases are likely to increase the fidelity of mass spectrometry-based proteomics.

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

Discoveries in molecular sciences have advanced research into therapeutics that target an array of newly identified ligands and receptors with the hope of meeting critical needs of patients with chronic or life-threatening diseases. Protein or biologic therapeutics include peptides, endogenous proteins, monoclonal antibodies, hybrids such as Fc construct molecules (including peptibodies and soluble receptors), and avimers. Proteins may be purified animal derivatives, fully human, or have portions of animal and human proteins engineered together into chimeric and humanized monoclonal antibodies. Although some reports distinguish therapeutic proteins from monoclonal antibodies, for the purposes of this article the terms biologics and proteins are used interchangeably. Clinical Pharmacology & Therapeutics (2008); 84, 5, 624–627 doi:10.1038/clpt.2008.158

Most circulating human proteins exist as heterogeneously glycosylated variants (glycoforms) of an otherwise homogeneous polypeptide. Though glycan heterogeneity is most likely important to glycoprotein function, the preparation of homogeneous glycoforms is important both for the study of the consequences of glycosylation and for therapeutic purposes. This review details selected approaches to the production of homogeneous human N- and O-linked glycoproteins with human-type glycans. Particular emphasis is placed on recent developments in the engineering of glycosylation pathways within yeast and bacteria for in vivo production, and on the in vitro remodeling of glycoproteins by enzymatic means. The future of this field is very exciting.

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.

As many of the structural genomics centers have ended their first phase of operation, it is a good point to evaluate the scientific impact of this endeavour. The Structural Genomics Consortium (SGC), operating from three centers across the Atlantic, investigates human proteins involved in disease processes and proteins from Plasmodium falciparum and related organisms. We present here some of the scientific output of the Oxford node of the SGC, where the target areas include protein kinases, phosphatases, oxidoreductases and other metabolic enzymes, as well as signal transduction proteins. The SGC has aimed to achieve extensive coverage of human gene families with a focus on protein-ligand interactions. The methods employed for effective protein expression, crystallization and structure determination by X-ray crystallography are summarized. In addition to the cumulative impact of accelerated delivery of protein structures, we demonstrate how family coverage, generic screening methodology, and the availability of abundant purified protein samples, allow a level of discovery that is difficult to achieve otherwise. The contribution of NMR to structure determination and protein characterization is discussed. To make this information available to a wide scientific audience, a new tool for disseminating annotated structural information was created that also represents an interactive platform allowing for a continuous update of the annotation by the scientific community.

A bioreactor was developed as an instrument to culture human or animal cells that require attachment in a large quantity or at a high density. The purpose for developing such a bioreactor is two-fold: to produce a large quantity of animal or human cells that have been modified by gene recombination technology to accommodate manufacture of physiologically-active substances or human proteins on an industrial scale; and for research to culture animal cells to form a high-density 3-dimensional structure as a morphological or functional tissue or organ entity. In the current report, the circulatory flow bioreactor and radial flow bioreactor (RFB) are introduced, in which the former can be scaled up. As a small bioreactor produced for the latter purpose, a rotary cell culture system and novel multicoaxial hollow-fiber bioreactor are introduced. Finally, a small RFB culture system that was scaled down by the present author and his collaborators for the study of a 3-dimensional high density culture system is described. The RFB can be readily scaled up for manufacturing or scaled down for research purposes. This is a cell culturing system that can induce the functions of human tissues by preparing a high density 3-dimensional organization of cells of human origin.

Proteins are the key components of the cellular machinery responsible for processing changes that are ordered by genomic information. Analysis of most human proteins and nucleic acids is important in order to decode the complex networks that are likely to underlie many common diseases. Significant improvements in current technology are also required to dissect the regulatory processes in high-throughtput and with low cost. Miniaturization of biological assays is an important prerequisite to achieve these goals in the near future.

We here describe a normalization method to combine quantitative proteomics data. By merging the output of two popular quantification software packages, we obtained a 20% increase (on average) in the number of quantified human proteins without suffering from a loss of quality. Our integrative workflow is freely available through our user-friendly, open-source Rover software (http://compomics-rover.googlecode.com/).

Biotechnology derived medicines will have an increasing impact not only upon medical practice but also upon the working lives of many pharmaceutical scientists. Whilst such medicines may be viewed as highly sophisticated to the clinician and scientist, the computer will still rightly demand that they are both efficacious and safe. Impacting as it does upon all phases of drug development and facilitating quantitative relationship between administered dose and systemic drug concentration, pharmacokinetics has an important role to play in the development of all medicines. Bioanalysis is an essential prelude to any pharmacokinetic investigation. For many biotechnology products the immunoassay and bioassay methodologies employed are often relatively non-specific and imprecise and yield assay dependent pharmacokinetic parameters. Other factors may also confound the pharmacokinetic evaluation of biotechnological products. In vivo binding proteins (including antibodies) may not only interfere with bioanalytical methodology but also have a significant effect on the pharmacokinetics and biological activity of certain macromolecules. Antibody formation is a particular problem in the preclinical evaluation of human proteins. Unlike most conventional pharmaceuticals, the rate and time of delivery into the systemic circulation is a fundamental component of the biological activity of many biological molecules.

SRMAtlas contains mass spectrometry assays allowing targeted analysis of nearly 100% of human proteins.

Structural genomics efforts at the Chinese Academy of Sciences and Peking University are reported in this article. The major targets for the structural genomics project are targeted proteins expressed in human hematopoietic stem/progenitor cells, proteins related to blood diseases and other human proteins. Up to now 328 target genes have been constructed in expression vectors. Among them, more than 50% genes have been expressed in Escherichia coli, approximately 25% of the resulting proteins are soluble, and 35 proteins have been purified. Crystallization, data collection and structure determination are continuing. Experiences accumulated during this initial stage are useful for designing and applying high-throughput approaches in structural genomics.

Gene Ontology (GO) provides a controlled vocabulary, which is used by several groups around the world to provide functional annotation to proteins across a wide range of species ("www.geneontology.org":http://www.geneontology.org). The BHF-UCL team is funded by the British Heath Foundation to supply GO annotation specifically for human proteins involved in cardiovascular (CV) processes. This is the first time that a physiological process-centered approach has been used for human protein GO annotation. Experienced GO curators from the BHF-UCL team work alongside the bench scientists from the CV genetics group at University College London. By working in a CV dedicated environment the GO curators are able to build up their expertise in the CV field. Over 4000 proteins have been identified as being cardiovascular related and over the last 14 months, the BHF-UCL team has added 6840 Gene Ontology annotations to 603 human CV related proteins, including CDKN2A, CDKN2B, EDN2, GH1 and TERF1. In order to provide detailed annotations the BHF-UCL GO curators also initiate the development of new GO terms related to cardiovascular processes. The development of these terms, such as lipoprotein-mediated signalling, regulation of cardiac muscle hypertrophy and cardiac epithelial to mesenchymal transition, are made in collaboration with the GO editorial office and CV scientists. We will present details of the progress made in the first year of the BHF-UCL GO annotation project and the impact on microarray analysis.

Background Although the UniProt KnowledgeBase is not a medical-oriented database, it contains information on more than 2,000 human proteins involved in pathologies. However, these annotations are not standardized, which impairs the interoperability between biological and clinical resources. In order to make these data easily accessible to clinical researchers, we have developed a procedure to link diseases described in the UniProtKB/Swiss-Prot entries to the MeSH disease terminology. Results We mapped disease names extracted either from the UniProtKB/Swiss-Prot entry comment lines or from the corresponding OMIM entry to the MeSH. Different methods were assessed on a benchmark set of 200 disease names manually mapped to MeSH terms. The performance of the retained procedure in term of precision and recall was 86% and 64% respectively. Using the same procedure, more than 3,000 disease names in Swiss-Prot were mapped to MeSH with comparable efficiency. Conclusions This study is a first attempt to link proteins in UniProtKB to the medical resources. The indexing we provided will help clinicians and researchers navigate from diseases to genes and from genes to diseases in an efficient way. The mapping is available at: http://research.isb-sib.ch/unimed.

While plasma donation is still necessary as a unique source of human proteins and to treat coagulation disorders, FFP administration seems to have high rate of inappropriate indication. After all, FFP transfusion is not risk free, and is associated with lung injury, infectious disease and circulatory overload in recipients. On the other hand, patients who die into the ICU have impaired physical status and may have increased demand for FFP transfusion.

This study describes the development of a system that calculates the fluxes of a metabolic network. It integrates several open source software tools in order to simulate the behaviour of specific metabolic pathways using Flux Balance Analysis (FBA). This tool receives as input the stoichiometry, the thermodynamic and capacity constraints of the metabolic network, and also an objective function.

Background The aggregation of over 100,000 non-redundant, experimentally-determined post-translational modification sites on human proteins in PhosphoSitePlus (PSP) has enabled system-wide analyses of modification sites on proteins that regulate gene expression. The location of modification sites within specific domains or motifs, their distribution on subsets of proteins and in specific locations within the cell presumably underlie the flow of information within signaling networks that regulate gene expression. We will present the results of analyses of the structure, location and distribution of phosphoryl, acetyl, ubiquitinyl, and methyl modification sites on proteins that regulate transcription. The goals of this work are to gain an overview of protein modifications and identify novel modification sites that may regulate gene expression.

Selective breeding has gradually altered the physical characteristics and genetic make-up of sheep, and transgenic technology has allowed us to add new genes (encoding, for example, useful human proteins) to the sheep genome. But it has not been possible to alter sheep gene sequences in specific ways - until now.

We developed Ulysses as a user-oriented system that uses a process called Interolog Analysis for the parallel analysis and display of protein interactions detected in various species. Ulysses was designed to perform such Interolog Analysis by the projection of model organism interaction data onto homologous human proteins, and thus serves as an accelerator for the analysis of uncharacterized human proteins. The relevance of projections was assessed and validated against published reference collections. All source code is freely available, and the Ulysses system can be accessed via a web interface http://www.cisreg.ca/ulysses.

A group of multinational companies, together with the Wellcome Trust, is attempting to form a charitable organization, the Structural Genomics Consortium. The goal will be to obtain X-ray structures for a broad representation across families of human proteins and to place the structural coordinates in the public database.

It seems that the methods described in 1971 have proved to be useful. Advances in other fields have greatly simplified their adoption. The availability of genetically engineered human proteins, together with monoclonal antibodies, suggests that the next few years will see a very large growth in the use of highly specific immunoradiometric assays for a wide variety of polypeptides, many of relevance to research in diabetes. Perhaps we may even finally describe what is really happening to plasma insulin and its related polypeptides during the different stages and in the different types of diabetes. Advances in our understanding of the basic mechanisms of the immune system, together with their role in diabetes, suggest that immunological methods and concepts will be at least as relevant to the subject in the next 25 years.