Structural proteomics: developments in structure-to-function predictions

Bi-SeqCNN: A Novel Light-Weight Bi-Directional CNN Architecture for Protein Function Prediction

Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50-0.70 times) fewer parameters than the SOTA methods.

Deep learning methods for 3D structural proteome and interactome modeling

Bolstered by recent methodological and hardware advances, deep learning has increasingly been applied to biological problems and structural proteomics. Such approaches have achieved remarkable improvements over traditional machine learning methods in tasks ranging from protein contact map prediction to protein folding, prediction of protein-protein interaction interfaces, and characterization of protein-drug binding pockets. In particular, emergence of ab initio protein structure prediction methods including AlphaFold2 has revolutionized protein structural modeling. From a protein function perspective, numerous deep learning methods have facilitated deconvolution of the exact amino acid residues and protein surface regions responsible for binding other proteins or small molecule drugs. In this review, we provide a comprehensive overview of recent deep learning methods applied in structural proteomics.

Omics Technologies for High-Throughput-Screening of Cell-Biomaterial Interactions

Recent research effort in biomaterial development has largely focused on engineering bio-instructive materials to stimulate specific cell signaling. Assessing the biological performance of these materials using time-consuming and trial-and-error traditional...

Theory and applications of differential scanning fluorimetry in early-stage drug discovery

Differential scanning fluorimetry (DSF) is an accessible, rapid, and economical biophysical technique that has seen many applications over the years, ranging from protein folding state detection to the identification of ligands that bind to the target protein. In this review, we discuss the theory, applications, and limitations of DSF, including the latest applications of DSF by ourselves and other researchers. We show that DSF is a powerful high-throughput tool in early drug discovery efforts. We place DSF in the context of other biophysical methods frequently used in drug discovery and highlight their benefits and downsides. We illustrate the uses of DSF in protein buffer optimization for stability, refolding, and crystallization purposes and provide several examples of each. We also show the use of DSF in a more downstream application, where it is used as an in vivo validation tool of ligand-target interaction in cell assays. Although DSF is a potent tool in buffer optimization and large chemical library screens when it comes to ligand-binding validation and optimization, orthogonal techniques are recommended as DSF is prone to false positives and negatives.

Terminology of bioanalytical methods (IUPAC Recommendations 2018)

Recommendations are given concerning the terminology of methods of bioanalytical chemistry. With respect to dynamic development particularly in the analysis and investigation of biomacromolecules, terms related to bioanalytical samples, enzymatic methods, immunoanalytical methods, methods used in genomics and nucleic acid analysis, proteomics, metabolomics, glycomics, lipidomics, and biomolecules interaction studies are introduced.

Understanding Leishmania parasites through proteomics and implications for the clinic

INTRODUCTION

Leishmania spp. are causative agents of leishmaniasis, a broad-spectrum neglected vector-borne disease. Genomic and transcriptional studies are not capable of solving intricate biological mysteries, leading to the emergence of proteomics, which can provide insights into the field of parasite biology and its interactions with the host. Areas covered: The combination of genomics and informatics with high throughput proteomics may improve our understanding of parasite biology and pathogenesis. This review analyses the roles of diverse proteomic technologies that facilitate our understanding of global protein profiles and definition of parasite development, survival, virulence and drug resistance mechanisms for disease intervention. Additionally, recent innovations in proteomics have provided insights concerning the drawbacks associated with conventional chemotherapeutic approaches and Leishmania biology, host-parasite interactions and the development of new therapeutic approaches. Expert commentary: With progressive breakthroughs in the foreseeable future, proteome profiles could provide target molecules for vaccine development and therapeutic intervention. Furthermore, proteomics, in combination with genomics and informatics, could facilitate the elimination of several diseases. Taken together, this review provides an outlook on developments in Leishmania proteomics and their clinical implications.

The role of structural proteomics in vaccine development: recent advances and future prospects

Vaccines are the most effective way to fight infectious diseases saving countless lives since their introduction. Their evolution during the last century made use of the best technologies available to continuously increase their efficacy and safety. Mass spectrometry (MS) and proteomics are already playing a central role in the identification and characterization of novel antigens. Over the last years, we have been witnessing the emergence of structural proteomics in vaccinology, as a major tool for vaccine candidate discovery, antigen design and life cycle management of existing products. In this review, we describe the MS techniques associated to structural proteomics and we illustrate the contribution of structural proteomics to vaccinology discussing potential applications.

Functional Diversity of Haloacid Dehalogenase Superfamily Phosphatases from Saccharomyces cerevisiae: BIOCHEMICAL, STRUCTURAL, AND EVOLUTIONARY INSIGHTS

The haloacid dehalogenase (HAD)-like enzymes comprise a large superfamily of phosphohydrolases present in all organisms. The Saccharomyces cerevisiae genome encodes at least 19 soluble HADs, including 10 uncharacterized proteins. Here, we biochemically characterized 13 yeast phosphatases from the HAD superfamily, which includes both specific and promiscuous enzymes active against various phosphorylated metabolites and peptides with several HADs implicated in detoxification of phosphorylated compounds and pseudouridine. The crystal structures of four yeast HADs provided insight into their active sites, whereas the structure of the YKR070W dimer in complex with substrate revealed a composite substrate-binding site. Although the S. cerevisiae and Escherichia coli HADs share low sequence similarities, the comparison of their substrate profiles revealed seven phosphatases with common preferred substrates. The cluster of secondary substrates supporting significant activity of both S. cerevisiae and E. coli HADs includes 28 common metabolites that appear to represent the pool of potential activities for the evolution of novel HAD phosphatases. Evolution of novel substrate specificities of HAD phosphatases shows no strict correlation with sequence divergence. Thus, evolution of the HAD superfamily combines the conservation of the overall substrate pool and the substrate profiles of some enzymes with remarkable biochemical and structural flexibility of other superfamily members.

Sugarcane proteomics: An update on current status, challenges, and future prospects

Sugarcane is one of the most important commercial crops cultivated worldwide for the production of crystal sugar, ethanol, and other related by-products. Unlike other comparable monocots like sorghum, maize, and rice, sugarcane genome by virtue of its polyploidy nature remains yet to be fully deciphered. Proteomics-an established complementary tool to genomics is at its infancy in sugarcane as compared to the other monocots. However, with the surge in genomics research accomplished by next-generation sequencing platforms, sugarcane proteomics has gained momentum. This review summarizes the available literature from 1970 to 2014, which ensures a comprehensive coverage on sugarcane proteomics-a topic first of its kind to be reviewed. We herewith compiled substantial contributions in different areas of sugarcane proteomics, which include abiotic and biotic stresses, cell wall, organelle, and structural proteomics. The past decade has witnessed a paradigm shift in the pace with which sugarcane proteomics is progressing, as evident by the number of research publications. In addition to extensively reviewing the progress made thus far, we intend to highlight the scope in sugarcane proteomics, with an aspiration to instigate focused research on sugarcane to harness its full potential for the human welfare.

Cardiovascular translational medicine (III). Genomics and proteomics in heart failure research

Heart failure is a complex syndrome and is one of the main causes of morbidity and mortality in developed countries. Despite considerable research effort in recent years, heart failure prevention and treatment strategies still suffer significant limitations. New theoretical and technical approaches are, therefore, required. It is in this context that the "omic" sciences have a role to play in heart failure. The incorporation of "omic" methodologies into the study of human disease has substantially changed biological approaches to disease and has given an enormous impetus to the search for new disease mechanisms, as well as for novel biomarkers and therapeutic targets. The application of genomics, proteomics and metabonomics to heart failure research could increase our understanding of the origin and development of the different processes contributing to this syndrome, thereby enabling the establishment of specific diagnostic profiles and therapeutic templates that could help improve the poor prognosis associated with heart failure. This brief review contains a short description of the fundamental principles of the "omic" sciences and an evaluation of how these new techniques are currently contributing to research into human heart failure. The focus is mainly on the analysis of gene expression microarrays in the field of genomics and on studies using two-dimensional electrophoresis with mass spectrometry in the area of proteomics.

An optimal protocol to analyze the rat spinal cord proteome

Since the function of the spinal cord depends on the proteins found there, better defing the normal Spinal Cord Proteome is an important and challenging task. Although brain and cerebrospinal fluid samples from patients with different central nervous system (CNS) disorders have been studied, a thorough examination of specific spinal cord proteins and the changes induced by injury or associated to conditions such as neurodegeneration, spasticity and neuropathies has yet to be performed. In the present study, we aimed to describe total protein content in the spinal cord of healthy rats, employing different proteomics tools. Accordingly, we have developed a fast, easy, and reproducible sequential protocol for protein extraction from rat spinal cords. We employed conventional two dimensional electrophoresis (2DE) in different pH ranges (eg. 4–7, 3–11 NL) combined with identification by mass spectrometry (MALDI-TOF/TOF), as well as first dimension protein separation combined with Liquid Chromatography Mass Spectrometry/Mass Spectrometry (LC-MS/MS) to maximise the benefits of this technology. The value of these techniques is demonstrated here by the identification of several proteins known to be associated with neuroglial structures, neurotransmission, cell survival and nerve growth in the central nervous system. Furthermore this study identified many spinal proteins that have not previously been described in the literature and which may play an important role as either sensitive biomarkers of dysfunction or of recovery after Spinal Cord Injury.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

Structure-based function prediction: approaches and applications

The ever increasing number of protein structures determined by structural genomic projects has spurred much interest in the development of methods for structure-based function prediction. Existing methods can be roughly classified in two groups: some use a comparative approach looking for the presence of structural motifs possibly associated with a known biochemical function. Other methods try to identify functional patches on the surface of a protein using only its physicochemical characteristics. This review will cover both kinds of approaches to structure-based function prediction as well as their use in real-world cases. The main issues and limitations in using protein structure to predict function will also be discussed. These are mainly: the assessment of the statistical significance of structural similarities and the extent to which these methods depend on the accuracy and availability of structural data.

A better understanding of molecular mechanisms underlying human disease

This review summarises and discusses the degree to which proteomics is contributing to medical care, providing examples and signspots for future directions. Why do genomic approaches provide a limited view of gene expression? Because of the multifactorial nature of many diseases, proteomics enables us to understand the molecular basis of disease, not only at the organism, whole-cell or tissue levels, but also in subcellular structures, protein complexes and biological fluids. The application of proteomics in medicine is expected to have a major impact by providing an integrated view of individual disease processes. This review describes several proteomic platforms and examines the role of proteomics as a tool for clinical biomarker discovery, the identification of prognostic and earlier diagnostic markers, their use in monitoring the effects of drug treatments and eventually find more efficient and safer therapeutics for a wide range of pathologies.

Predicting protein function by machine learning on amino acid sequences--a critical evaluation

Background

Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins.

Results

Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier.

Conclusion

We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function.

The role of mass spectrometry in plant systems biology

Large-scale analyses of proteins and metabolites are intimately bound to advancements in MS technologies. The aim of these non-targeted "omic" technologies is to extend our understanding beyond the analysis of only parts of the system. Here, metabolomics and proteomics emerged in parallel with the development of novel mass analyzers and hyphenated techniques such as gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and multidimensional liquid chromatography coupled to mass spectrometry (LC-MS). The analysis of (i) proteins (ii) phosphoproteins, and (iii) metabolites is discussed in the context of plant physiology and environment and with a focus on novel method developments. Recently published studies measuring dynamic (quantitative) behavior at these levels are summarized; for these works, the completely sequenced plants Arabidopsis thaliana and Oryza sativa (rice) have been the primary models of choice. Particular emphasis is given to key physiological processes such as metabolism, development, stress, and defense. Moreover, attempts to combine spatial, tissue-specific resolution with systematic profiling are described. Finally, we summarize the initial steps to characterize the molecular plant phenotype as a corollary of environment and genotype.

Automated Interpretation of Protein Subcellular Location Patterns

Proteomics is a major current focus of biomedical research, and location proteomics is the important branch of proteomics that systematically studies the subcellular distributions for all proteins expressed in a given cell type. Fluorescence microscopy of labeled proteins is currently the main methodology to obtain location information. Traditionally, microscope images are analyzed by visual inspection, which suffers from inefficiency and inconsistency. Automated and objective interpretation approaches are therefore needed for location proteomics. In this article, we briefly review recent advances in automated imaging interpretation tools, including supervised classification (which assigns location pattern labels to previously unseen images), unsupervised clustering (which groups proteins based on the similarity among their subcellular distributions), and additional statistical tools that can aid cell and molecular biologists who use microscopy in their work.

Probing Protein Tertiary Structure with Amidination

A chemical derivatization method, amidination, that has recently been effectively employed in peptide mass spectrometry experiments is used to covalently modify lysines in several standard proteins. Protein and peptide mass spectra identify sites at which the reaction does or does not occur. This is therefore a rapid approach to elucidate solvent-accessible regions of folded proteins.

Pattern similarity study of functional sites in protein sequences: lysozymes and cystatins

BACKGROUND

Although it is generally agreed that topography is more conserved than sequences, proteins sharing the same fold can have different functions, while there are protein families with low sequence similarity. An alternative method for profile analysis of characteristic conserved positions of the motifs within the 3D structures may be needed for functional annotation of protein sequences. Using the approach of quantitative structure-activity relationships (QSAR), we have proposed a new algorithm for postulating functional mechanisms on the basis of pattern similarity and average of property values of side-chains in segments within sequences. This approach was used to search for functional sites of proteins belonging to the lysozyme and cystatin families.

RESULTS

Hydrophobicity and beta-turn propensity of reference segments with 3-7 residues were used for the homology similarity search (HSS) for active sites. Hydrogen bonding was used as the side-chain property for searching the binding sites of lysozymes. The profiles of similarity constants and average values of these parameters as functions of their positions in the sequences could identify both active and substrate binding sites of the lysozyme of Streptomyces coelicolor, which has been reported as a new fold enzyme (Cellosyl). The same approach was successfully applied to cystatins, especially for postulating the mechanisms of amyloidosis of human cystatin C as well as human lysozyme.

CONCLUSION

Pattern similarity and average index values of structure-related properties of side chains in short segments of three residues or longer were, for the first time, successfully applied for predicting functional sites in sequences. This new approach may be applicable to studying functional sites in un-annotated proteins, for which complete 3D structures are not yet available.

Recent developments in structural proteomics for protein structure determination

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.

Use of high throughput genomic tools for the study of endothelial cell biology

The endothelium is an active, dynamic and heterogeneous organ. It lines the vessels in every organ system and regulates diverse and important biological functions. Over the past several years researchers have gained enormous insights into endothelial cell function in physiological processes such as coagulation and vascular reactivity, and pathophysiological disease states such as inflammation and atherosclerosis. Despite our expanding knowledge of endothelial cell biology, the molecular mechanisms underlying these functions remain largely unknown. The newly developed high throughput genomic tools and accompanying analytical methods provide powerful approaches for identifying new endothelial cell genes and characterizing their role in health and disease. Here, we review some of the recent genomics and proteomic advances that are providing new methodologies for endothelial cell and vascular biology research.

Study of the preferred modification sites of the quinone methide intermediate resulting from the latent trapping device of the activity probes for hydrolases

Use of activity probes has been demonstrated to be a powerful tool in modern chemical proteomic study. Previously we have designed and synthesized a series of mechanism-based activity probes that utilized quinone methide chemistry. Here, we characterized the trend of chemical reactivity for the reactive quinone methide intermediate 3 (QM-3) resulting from the latent trapping device. In a competition assay, the labeling of PTP1B by probe 1a was blocked by externally added cysteine without affecting the catalytic activity of the enzyme. Further sequencing analysis on the trypsin-digested peptides of probe 1a-labeled PTP1B using tandem mass spectrometry revealed that all six cysteine residues of PTP1B are capable of being modified by probe 1a. These results indicated that the sulfhydryl group of cysteine residue is the preferred nucleophile for the reactive QM-3. Our finding provides the first example in understanding the preferred amino acid residues modified by the reactive QM-3, which is also the key structural unit responsible for forming covalent bonds in many biochemical applications.

Automated prediction of protein function and detection of functional sites from structure

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present PHUNCTIONER, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (approximately 15%), our method assigns the correct GO annotation in 90% of the protein structures considered, approximately 20% higher than inheritance of function from the closest homologue.

Structural proteomics: a tool for genome annotation

In any newly sequenced genome, 30% to 50% of genes encode proteins with unknown molecular or cellular function. Fortunately, structural genomics is emerging as a powerful approach of functional annotation. Because of recent developments in high-throughput technologies, ongoing structural genomics projects are generating new structures at an unprecedented rate. In the past year, structural studies have identified many new structural motifs involved in enzymatic catalysis or in binding ligands or other macromolecules (DNA, RNA, protein). The efficiency by which function is deduced from structure can be further improved by the integration of structure with bioinformatics and other experimental approaches, such as screening for enzymatic activity or ligand binding.

Microbial genomes have over 72% structure assignment by the threading algorithm PROSPECTOR_Q

The genome scale threading of five complete microbial genomes is revisited using our state-of-the-art threading algorithm, PROSPECTOR_Q. Considering that structure assignment to an ORF could be useful for predicting biochemical function as well as for analyzing pathways, it is important to assess the current status of genome scale threading. The fraction of ORFs to which we could assign protein structures with a reasonably good confidence level to each genome sequences is over 72%, which is significantly higher than earlier studies. Using the assigned structures, we have predicted the function of several ORFs through "single-function" template structures, obtained from an analysis of the relationship between protein fold and function. The fold distribution of the genomes and the effect of the number of homologous sequences on structure assignment are also discussed.

From protein structure to biochemical function?

Here we describe various methods currently under development aimed at identifying a protein's function from its three-dimensional structure. We are combining a number of these methods to create a pipeline of applications, called ProFunc, which will take a given 3D structure, run all the applications on it and compile and summarise the results obtained. The aim is to provide a best guess as to the protein's function from the evidence provided by the different methods. Here we present three examples, using structures solved by the Midwest Center for Structural Genomics consortium, illustrating the strengths and weaknesses of current approaches.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

BACKGROUND

Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images.

RESULTS

We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images.

CONCLUSIONS

The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.

In vivo uniform 15 N-isotope labelling of plants: Using the greenhouse for structural proteomics

Isotope labelling of proteins is important for progress in the field of structural proteomics. It enables the utilisation of the power of nuclear magnetic resonance spectroscopy (NMR) for the characterisation of the three-dimensional structures and corresponding dynamical features of proteins. The usual approach to obtain isotopically labelled protein molecules is by expressing the corresponding gene in bacterial or yeast host organisms, which grow on isotope-enriched media. This method has several drawbacks. Here, we demonstrate that it is possible to fully label a plant with (15)N-isotopes. The advantage of in vivo labelling of higher organisms is that all constituting proteins are labelled and become available as functional, post-translationally modified, correctly folded proteins. A hydroponics set-up was used to create the first example of a uniformly (15)N-labelled (> 98%) plant species, the potato plant (Solanum tuberosum L., cv. Elkana). Two plants were grown at low costs using potassium-[(15)N]-nitrate as the sole nitrogen source. At harvest time, a total of 3.6 kg of potato tubers and 1.6 kg of foliage, stolons and roots were collected, all of which were fully (15)N-labelled. Gram quantities of soluble (15)N-labelled proteins (composed mainly of the glycoprotein patatin and Kunitz-type protease inhibitors) were isolated from the tubers. NMR results on the complete proteome of potato sap and on an isolated protease inhibitor illustrate the success of the labelling procedure. The presented method of isotope labelling is easily modified to label other plants. Its envisioned impact in the field of structural proteomics of plants is discussed.

Overview of structural genomics: from structure to function

The unprecedented increase in the number of new protein sequences arising from genomics and proteomics highlights directly the need for methods to rapidly and reliably determine the molecular and cellular functions of these proteins. One such approach, structural genomics, aims to delineate the total repertoire of protein folds, thereby providing three-dimensional portraits for all proteins in a living organism and to infer molecular functions of the proteins. The goal of obtaining protein structures on a genomic scale has motivated the development of high-throughput technologies for macromolecular structure determination, which have begun to produce structures at a greater rate than previously possible. These new structures have revealed many unexpected functional and evolution relationships that were hidden at the sequence level.

Chemical proteomics and its application to drug discovery

The completion of the human genome sequencing project has provided a flood of new information that is likely to change the way scientists approach the study of complex biological systems. A major challenge lies in translating this information into new and better ways to treat human disease. The multidisciplinary science of chemical proteomics can be used to distill this flood of new information. This approach makes use of synthetic small molecules that can be used to covalently modify a set of related enzymes and subsequently allow their purification and/or identification as valid drug targets. Furthermore, such methods enable rapid biochemical analysis and small-molecule screening of targets thereby accelerating the often difficult process of target validation and drug discovery.

The phenylpropanoid pathway and plant defence-a genomics perspective

Summary The functions of phenylpropanoid compounds in plant defence range from preformed or inducible physical and chemical barriers against infection to signal molecules involved in local and systemic signalling for defence gene induction. Defensive functions are not restricted to a particular class of phenylpropanoid compound, but are found in the simple hydroxycinnamic acids and monolignols through to the more complex flavonoids, isoflavonoids, and stilbenes. The enzymatic steps involved in the biosynthesis of the major classes of phenylpropanoid compounds are now well established, and many of the corresponding genes have been cloned. Less is understood about the regulatory genes that orchestrate rapid, coordinated induction of phenylpropanoid defences in response to microbial attack. Many of the biosynthetic pathway enzymes are encoded by gene families, but the specific functions of individual family members remain to be determined. The availability of the complete genome sequence of Arabidopsis thaliana, and the extensive expressed sequence tag (EST) resources in other species, such as rice, soybean, barrel medic, and tomato, allow, for the first time, a full appreciation of the comparative genetic complexity of the phenylpropanoid pathway across species. In addition, gene expression array analysis and metabolic profiling approaches make possible comparative parallel analyses of global changes at the genome and metabolome levels, facilitating an understanding of the relationships between changes in specific transcripts and subsequent alterations in metabolism in response to infection.

Function first: a powerful approach to post-genomic drug discovery

In the post-genomic era, pharmaceutical researchers must evaluate vast numbers of protein sequences and formulate novel, intelligent strategies for identifying valid targets and discovering leads against them. The identification of small molecules that selectively target proteins or protein families will be aided by knowing the function and/or the structure of the target(s). By identifying protein function first, efficiencies are gained that allow subsequent focus of resources on particular protein families of interest. This article reviews current proteomic-scale approaches to identifying function as a way of accelerating lead discovery.