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PubMed2004

Chemical proteomics from a nuclear magnetic resonance spectroscopy perspective.

Proteomics is the study of the protein complement of a genome and employs a number of newly emerging tools. One such tool is chemical proteomics, which is a branch of proteomics devoted to the exploration of protein function using both in vitro and in vivo chemical probes. Chemical proteomics aims to define protein function and mechanism at the level of directly observed protein-ligand interactions, whereas chemical genomics aims to define the biological role of a protein using chemical knockouts and observing phenotypic changes. Chemical proteomics is therefore traditional mechanistic biochemistry performed in a systems-based manner, using either activity- or affinity-based probes that target proteins related by chemical reactivities or by binding site shape/properties, respectively. Systems are groups of proteins related by metabolic pathway, regulatory pathway or binding to the same ligand. Studies can be based on two main types of proteome samples: pooled proteins (1 mixture of N proteins) or isolated proteins in a given system and studied in parallel (N single protein samples). Although the field of chemical proteomics originated with the use of covalent labeling strategies such as isotope-coded affinity tagging, it is expanding to include chemical probes that bind proteins noncovalently, and to include more methods for observing protein-ligand interactions. This review presents an emerging role for nuclear magnetic resonance spectroscopy in chemical proteomics, both in vitro and in vivo. Applications include: functional proteomics using cofactor fingerprinting to assign proteins to gene families; gene family-based structural characterizations of protein-ligand complexes; gene family-focused design of drug leads; and chemical proteomic probes using nuclear magnetic resonance SOLVE and studies of protein-ligand interactions in vivo.

Magnetic Resonance Spectroscopy↗
PubMed2003

Proteomics of Staphylococcus aureus--current state and future challenges.

This paper presents a short review of the proteome of Staphylococcus aureus, a gram-positive human pathogen of increasing importance for human health as a result of the increasing antibiotic resistance. A proteome reference map is shown which can be used for future studies and is followed by a demonstration of how proteomics could be applied to obtain new information on S. aureus physiology. The proteomic approach can provide new data on the regulation of metabolism as well as of the stress or starvation responses. Proteomic signatures encompassing specific stress or starvation proteins are excellent tools to predict the physiological state of a cell population. Furthermore proteomics is very useful for analysing the size and function of known and unknown regulons and will open a new dimension in the comprehensive understanding of regulatory networks in pathogenicity. Finally, some fields of application of S. aureus proteomics are discussed, including proteomics and strain evaluation, the role of proteomics for analysis of antibiotic resistance or for discovering new targets and diagnostics tools. The review also shows that the post-genome era of S. aureus which began in 2001 with the publication of the genome sequence is still in a preliminary stage, however, the consequent application of proteomics in combination with DNA array techniques and supported by bioinformatics will provide a comprehensive picture on cell physiology and pathogenicity in the near future.

Bacterial Proteins↗
PubMed2005

COPASAAR--a database for proteomic analysis of single amino acid repeats.

BACKGROUND: Single amino acid repeats make up a significant proportion in all of the proteomes that have currently been determined. They have been shown to be functionally and medically significant, and are associated with cancers and neuro-degenerative diseases such as Huntington's Chorea, where a poly-glutamine repeat is responsible for causing the disease. The COPASAAR database is a new tool to facilitate the rapid analysis of single amino acid repeats at a proteome level. The database aims to simplify the comparison of repeat distributions between proteomes in order to provide a better understanding of their function and evolution. RESULTS: A comparative analysis of all proteomes in the database (currently 244) shows that single amino acid repeats account for about 12-14% of the proteome of any given species. They are more common in eukaryotes (14%) than in either archaea or bacteria (both 13%). Individual analyses of proteomes show that long single amino acid repeats (6+ residues) are much more common in the Eukaryotes and that longer repeats are usually made up of hydrophilic amino acids such as glutamine, glutamic acid, asparagine, aspartic acid and serine. CONCLUSION: COPASAAR is a useful tool for comparative proteomics that provides rapid access to amino acid repeat data that can be readily data-mined. The COPASAAR database can be queried at the kingdom, proteome or individual protein level. As the amount of available proteome data increases this will be increasingly important in order to automate proteome comparison. The insights gained from these studies will give a better insight into the evolution of protein sequence and function.

Algorithms↗
PubMed2004

PEDRo: a database for storing, searching and disseminating experimental proteomics data.

BACKGROUND: Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets. RESULTS: This paper describes the design, implementation and use of a Proteome Experimental Data Repository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available. CONCLUSIONS: The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.

Animals↗
PubMed2002

Market opportunity in computational proteomics.

The current exuberance on the potential of proteomics as a means to deploy the wealth of the human genome is expected to last into the coming years. Unlike the genome, a finite entity with a fixed number of base pairs of the genetic material, the proteome is "plastic", changing throughout growth and development and environmental stresses, as well as in pathological situations. Our proteomes change over time, and therefore there is no one proteome; the proteome is for practical purposes an infinite entity. It is therefore crucial to build systems that are capable of manipulating the information content that is the proteome, thence the need for computational proteomics as a discipline. In this Market View article, we present the industry landscape that is emerging in the computational proteomics space. This space is still in its infancy and for the most part undefined; therefore we seek to present the market opportunity in informatics in the drug discovery space and then extend that to an examination of industry trends in proteomics. Thus, the gestalt is a set of predictions as to the evolution of the landscape in computational proteomics over the coming years.

Biotechnology↗
PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗
PubMed2026

Quantitative Proteomic Analysis of APP/PS1 Transgenic Mice.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-β (Aβ) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD. OBJECTIVE: The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD. METHODS: To dissect the proteomic perturbations induced by Aβ and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts. RESULTS: Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks. CONCLUSIONS: This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.

Animals↗
PubMed2006

Clinical proteomics: present and future prospects.

Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.

Journal Article↗
PubMed2002

Design of proteome-based studies in combination with serology for the identification of biomarkers and novel targets.

Recently proteome analysis has rapidly developed in the post-genome era and is now widely accepted as a complementary technology to genetic profiling. The improvement in the technology of both two-dimensional electrophoresis (2-DE) analysis as well as protein identification has made proteomics a valuable and powerful tool to study human diseases. A combination of conventional proteome analysis with serology has been developed as a promising experimental approach for the discovery of serological markers in different malignancies. However, the design of proteome-based studies has to be carefully performed since there are a number of critical needs for systematic and reproducible proteome analysis. In particular, the selection of tissue and its preparation represent an important step in proteome analysis. Besides the preparation of protein samples, the 2-DE and protein identification is a further critical issue. So far proteome-based technologies have been successfully used in tumor immunnology for the identification of tumor-specific autoantigens. Similarly, this technology has been employed for the detection of virulence factors, antigens and vaccine candidates in infectious diseases, as well as for the identification of diagnostic and prognostic markers, suggesting that proteome-based analysis is a promising tool for the identification of prognostic, diagnostic markers as well as for novel therapeutic targets which could be used for treatment of diseases. The integration of proteome-based approaches with data from genomic or genetic profiling will lead to a better understanding of different diseases, which will then contribute to the direct translation of the research findings into clinical practice.

Antigens, Neoplasm↗
PubMed2025

Analytical Considerations for the Development of Plate-Based Proteomics Platforms Using Isobaric Labeling.

Mass spectrometer-based proteomics platforms have great potential to rapidly advance our systematic understanding of complex biological problems, enable drug discovery, decipher drug mechanisms of action, and discover novel biomarkers. As the demand for processing large sets of samples in an automatic manner is constantly increasing, the integration of automation platforms (nanoliter dispensers, liquid handlers, etc.) has become a routinary configuration paired with liquid chromatography-mass spectrometers. The functional integration of all of those instruments into a single unit is what we call a plate-based high-throughput proteomics platform (HT proteomics). The readout of the platform is the quantitative proteome data at the protein or peptide level. In this work, we developed a plate-based HT proteomics standard that we called the HT-sKO. The HT-sKO allows the evaluation of accuracy and the estimation of the relative limit of quantification when the target proteins vary up to 60-fold in abundance. The HT-sKO utilizes nonhuman recombinant proteins that can be spiked into the samples, allowing for sample acquisition and HT proteomics platform evaluation at the same time. We also showed the foundational role of a robust acquisition strategy for developing a stable HT proteomics platform and the value of using a tube-based method as an informant assay on data quality expectations for the platform. Using this new standard, we demonstrated that the intra- and inter-plate variance is around 4-6% for the protein level or around 10% for the peptide-level readout. We also showed that the HT-sKO standard is compatible with whole-proteome, phospho-proteome, and reactive cysteine profiling platforms.

Proteomics↗
PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58 years and ∼45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗
PubMed2004

New approaches towards integrated proteomic databases and depositories.

Since the publication of the human genome, two key points have emerged. First, it is still not certain which regions of the genome code for proteins. Second, the number of discrete protein-coding genes is far fewer than the number of different proteins. Proteomics has the potential to address some of these postgenomic issues if the obstacles that we face can be overcome in our efforts to combine proteomic and genomic data. There are many challenges associated with high-throughput and high-output proteomic technologies. Consequently, for proteomics to continue at its current growth rate, new approaches must be developed to ease data management and data mining. Initiatives have been launched to develop standard data formats for exchanging mass spectrometry proteomic data, including the Proteomics Standards Initiative formed by the Human Proteome Organization. Databases such as SwissProt and Uniprot are publicly available repositories for protein sequences annotated for function, subcellular location and known potential post-translational modifications. The availability of bioinformatics solutions is crucial for proteomics technologies to fulfil their promise of adding further definition to the functional output of the human genome. The aim of the Oxford Genome Anatomy Project is to provide a framework for integrating molecular, cellular, phenotypic and clinical information with experimental genetic and proteomics data. This perspective also discusses models to make the Oxford Genome Anatomy Project accessible and beneficial for academic and commercial research and development.

Databases, Protein↗
PubMed2024

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Proteomics↗
PubMed2007

Quantitative proteomic profiling of membrane proteins from the mouse brain cortex, hippocampus, and cerebellum using the HysTag reagent: mapping of neurotransmitter receptors and ion channels.

Analysis of the brain proteome and studying brain diseases through clinical biopsies and animal disease models require methods of quantitative proteomics that are sensitive and allow identification and quantification of low abundant membrane proteins from minute amount of tissue. Taking advantage of recently developed methods for isolation of membrane proteins from 10-20 mg brain tissue [Nielsen, P.Aa., Olsen, J.V., Podtelejnokov, A.V., Andersen, J.R., Mann, M., Wiśniewski, J.R., 2005. Proteomic mapping of brain plasma membrane proteins. Mol. Cell. Proteomics 4, 402--408] and the HysTag-quantification method [Olsen, J.V., Andersen, J.R., Nielsen, P.Aa., Nielsen, M.L., Figeys, D., Mann, M., Wiśniewski, J.R., 2004. HysTag---A novel proteomic qualification tool applied to differential analysis of membrane proteins from distinct areas of mouse brain. Mol. Cell. Proteomics 3, 82--92] we performed quantitative proteomic analysis of three functionally distinct compartments of mouse brain: cortex, hippocampus, and cerebellum. In total, 976 unique peptides corresponding to 555 unique proteins were quantified. Up to 20-fold differences in the levels of some proteins between brain areas were measured. For many quantified proteins--as for glutamate receptors, calcium channel subunits, and ATP-ases--an excellent correlation between our proteomic data and previously published mRNA expression levels or intensity of immunostaining was found. Our results clearly demonstrate differences in levels of membrane proteins mapped in distinct brain compartments and offer a technology that allows in depth study of brain membrane proteomes, such as mouse models of neurological diseases.

Animals↗
PubMed2007

Proteomics in cardiovascular surgery.

Proteomics describes, analogous to the term genomics, the study of the complete set of proteins present in a cell, organ, or organism at a given time. The genome tells us what could theoretically happen, whereas the proteome tells us what does happen. Therefore, a genomic-centered view of biologic processes is incomplete and does not describe what happens at the protein level. Proteomics is a relatively new methodology and is rapidly changing because of extensive advances in the underlying techniques. The core technologies of proteomics are 2-dimensional gel electrophoresis, liquid chromatography, and mass spectrometry. Proteomic approaches might help to close the gap between traditional pathophysiologic and more recent genomic studies, assisting our basic understanding of cardiovascular disease. The application of proteomics in cardiovascular medicine holds great promise. The analysis of tissue and plasma/serum specimens has the potential to provide unique information on the patient. Proteomics might therefore influence daily clinical practice, providing tools for diagnosis, defining the disease state, assessing of individual risk profiles, examining and/or screening of healthy relatives of patients, monitoring the course of the disease, determining the outcome, and setting up individual therapeutic strategies. Currently available clinical applications of proteomics are limited and focus mainly on cardiovascular biomarkers of chronic heart failure and myocardial ischemia. Larger clinical studies are required to test whether proteomics may have promising applications for clinical medicine. Cardiovascular surgeons should be aware of this increasingly pertinent and challenging field of science.

Animals↗
PubMed2002

Sub-proteome differential display: single gel comparison by 2D electrophoresis and mass spectrometry.

Two-dimensional (2D) gel electrophoresis and mass spectrometry (MS) have been used in comparative proteomics but inherent problems of the 2D electrophoresis technique lead to difficulties when comparing two samples. We describe a method (sub-proteome differential display) for comparing the proteins from two sources simultaneously. Proteins from one source are mixed with radiolabelled proteins from a second source in a ratio of 100:1. These combined proteomes are fractionated simultaneously using column chromatographic methods, followed by analysis of the pre-fractionated proteomes (designated sub-proteomes) using 2D gel electrophoresis. Silver staining and (35)S autoradiography of a single gel allows precise discrimination between members of each sub-proteome, using commonly available computer software. This is followed by MS identification of individual proteins. We have demonstrated the utility of the technology by identifying the product of a transfected gene and several proteins expressed differentially between two renal carcinoma proteomes. The procedure has the capacity to enrich proteins prior to 2D electrophoresis and provides a simple, inexpensive approach to compare proteomes. The single gel approach eliminates differences that might arise if separate proteome fractionations or 2D gels are employed.

Animals↗
PubMed2005

Transplantation proteomics.

The field of proteomics is developing at a rapid pace in the post-genome era. Translational proteomics investigations aim to apply a combination of established methods and new technologies to learn about protein expression profiles predictive of clinical events, therapeutic response, and underlying mechanisms. However, in contrast to genetic studies and in parallel with gene expression studies, the dynamic nature of the proteome in conjunction with the challenges of accounting for post-translational modifications requires the translational proteomics investigator to understand the strengths and limitations of proteomics approaches. In this review, we provide an overview of proteomics approaches and techniques, and proteomics informatics for clinical transplantation investigators. We also review recent publications pertaining to transplantation proteomics, and discuss the implications and utility of urine proteomics for non-invasive investigation of transplant outcomes.

Animals↗
PubMed2006

Tracing the origin of functional and conserved domains in the human proteome: implications for protein evolution at the modular level.

BACKGROUND: The functional repertoire of the human proteome is an incremental collection of functions accomplished by protein domains evolved along the Homo sapiens lineage. Therefore, knowledge on the origin of these functionalities provides a better understanding of the domain and protein evolution in human. The lack of proper comprehension about such origin has impelled us to study the evolutionary origin of human proteome in a unique way as detailed in this study. RESULTS: This study reports a unique approach for understanding the evolution of human proteome by tracing the origin of its constituting domains hierarchically, along the Homo sapiens lineage. The uniqueness of this method lies in subtractive searching of functional and conserved domains in the human proteome resulting in higher efficiency of detecting their origins. From these analyses the nature of protein evolution and trends in domain evolution can be observed in the context of the entire human proteome data. The method adopted here also helps delineate the degree of divergence of functional families occurred during the course of evolution. CONCLUSION: This approach to trace the evolutionary origin of functional domains in the human proteome facilitates better understanding of their functional versatility as well as provides insights into the functionality of hypothetical proteins present in the human proteome. This work elucidates the origin of functional and conserved domains in human proteins, their distribution along the Homo sapiens lineage, occurrence frequency of different domain combinations and proteome-wide patterns of their distribution, providing insights into the evolutionary solution to the increased complexity of the human proteome.

Animals↗