Structural proteomics by NMR spectroscopy

Structural proteomics is one of the powerful research areas in the postgenomic era, elucidating structure-function relationships of uncharacterized gene products based on the 3D protein structure. It proposes biochemical and cellular functions of unannotated proteins and thereby identifies potential drug design and protein engineering targets. Recently, a number of pioneering groups in structural proteomics research have achieved proof of structural proteomic theory by predicting the 3D structures of hypothetical proteins that successfully identified the biological functions of those proteins. The pioneering groups made use of a number of techniques, including NMR spectroscopy, which has been applied successfully to structural proteomics studies over the past 10 years. In addition, advances in hardware design, data acquisition methods, sample preparation and automation of data analysis have been developed and successfully applied to high-throughput structure determination techniques. These efforts ensure that NMR spectroscopy will become an important methodology for performing structural proteomics research on a genomic scale. NMR-based structural proteomics together with x-ray crystallography will provide a comprehensive structural database to predict the basic biological functions of hypothetical proteins identified by the genome projects.     

Application of NMR in structural proteomics: screening for proteins amenable to structural analysis

In the time of structural proteomics when protein structures are targeted on a genome-wide scale, the detection of "well-behaved" proteins that would yield good quality NMR spectra or X-ray images is the key to high-throughput structure determination. Already, simple one-dimensional proton NMR spectra provide enough information for assessing the folding properties of proteins. Heteronuclear two-dimensional spectra are routinely used for screenings that reveal structural, as well as binding, properties of proteins. NMR can thus provide important information for optimizing conditions for protein constructs that are amenable to structural studies.     

Optimal region of average side-chain entropy for fast protein folding

Search and study the general principles that govern kinetics and thermodynamics of protein folding generates new insight into the factors that control this process. Here, we demonstrate based on the known experimental data and using theoretical modeling of protein folding that side-chain entropy is one of the general determinants of protein folding. We show for proteins belonging to the same structural family that there exists an optimal relationship between the average side-chain entropy and the average number of contacts per residue for fast folding kinetics. Analysis of side-chain entropy for proteins that fold without additional agents demonstrates that there exists an optimal region of average side-chain entropy for fast folding. Deviation of the average side-chain entropy from the optimal region results in an anomalous protein folding process (prions, alpha-lytic protease, subtilisin, some DNA-binding proteins). Proteins with high or low side-chain entropy would have extended unfolded regions and would require some additional agents for complete folding. Such proteins are common in nature, and their structure properties have biological importance.     

Predicting protein--protein interactions from primary structure

MOTIVATION: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. RESULTS: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.     

Protein folding observed by time-resolved synchrotron x-ray scattering. A feasibility study.

A solution to the "protein folding" problem, the successful prediction of tertiary and quaternary protein structure from amino acid or gene sequence, would be a major advance in biology and biotechnology. Knowledge of any intermediate structure between fully unwound and folded would aid folding calculations. The use of high intensity synchrotron x-rays from the SUNY X21 beamline at National Synchrotron Light Source has been investigated as a probe of structural changes during protein folding and unfolding in solution. A temperature jump apparatus was used to study thermally-induced folding and unfolding. Scattering of solutions of myoglobin in the angular range 20 = 1-50 mrad. was measured during temperature jumps between 26 and 76 degrees C. There are clear signs of time/temperature-dependent structural changes, in the small angle region, consistent with those from other equilibrium techniques. Analysis indicates that this experimental technique can be extended to the higher angle region where theoretical calculations indicate more detailed structural information, for example when alpha-helix formation, is present.     

Xylanase homology modeling using the inverse protein folding approach.

Xylanase has been used in wood pulp bleaching in an effort to reduce chlorine release into the environment and pollution associated with paper production. The three-dimensional structure of xylanase is important to enable better understanding of the enzyme mechanism and to help design a more thermostable xylanase mutant. At the time this work was begun, there was no sequence homologous protein available for traditional sequence-based homology modeling. In order to circumvent this problem, the inverse protein folding approach was undertaken to find a suitable template structure. Model structures of Bacillus circulans xylanase were built based on the data-base search results of related proteins. The model structures were refined and compared to the recently solved xylanase X-ray crystal structure. The overall structural similarity between the theoretical model and experimental structure demonstrate the usefulness of this approach. Disagreement in folding topology, however, warrants further research into the inverse protein folding approach.     

Adapting arrays and lab-on-a-chip technology for proteomics

The impact of proteomics as a discovery engine in life science and in drug discovery has increased tremendously over the last seven years. At the same time, proteomics has expanded from the initial trust as a two-dimensional gel based approach to cover more functional and structural properties of proteins. The development of lab-on-a-chip and protein arrays for proteomics will have to evolve with the changes in proteomics to stay relevant. Here, we review the changes in the field of proteomics and their impact on the development in protein arrays and lab-on-a-chip.     

Protein reactivity of natural product-derived γ-butyrolactones

The discovery of novel and unique target-drug pairs for the treatment of human diseases such as cancer and bacterial infections is an urgent goal of chemical and pharmaceutical sciences. Natural products represent an inspiring source of compounds for designing chemical biology methods with applications in target identification and characterization. Inspired by the huge structural diversity of γ-butyrolactones, which constitute up to 10% of all known compounds of natural origin, we extended the "activity-based protein profiling" (ABPP) target identification technology to this promising and so far unexplored natural compound class. We designed and synthesized a comprehensive set of natural product-derived γ-lactones and thiolactones that varied in protein reactivity. Several important bacterial enzymes that are involved in diverse cellular functions such as metabolism (dihydrolipoyl dehydrogenase and 6-phosphofructokinase), cell wall biosynthesis (MurA1 and MurA2), and protein folding (trigger factors) were obtained. Especially protein folding in bacteria could represent a novel strategy for antibiotic intervention and requires chemical tools for characterization and inhibition. Future studies that extend structural modifications to protein reactive α-methylene-γ-butyrolactone as well as to reversible binding γ-lactones and thiolactones will reveal if this premise holds true.     

NFkappaB selectivity of estrogen receptor ligands revealed by comparative crystallographic analyses

Our understanding of how steroid hormones regulate physiological functions has been significantly advanced by structural biology approaches. However, progress has been hampered by misfolding of the ligand binding domains in heterologous expression systems and by conformational flexibility that interferes with crystallization. Here, we show that protein folding problems that are common to steroid hormone receptors are circumvented by mutations that stabilize well-characterized conformations of the receptor. We use this approach to present the structure of an apo steroid receptor that reveals a ligand-accessible channel allowing soaking of preformed crystals. Furthermore, crystallization of different pharmacological classes of compounds allowed us to define the structural basis of NFkappaB-selective signaling through the estrogen receptor, thus revealing a unique conformation of the receptor that allows selective suppression of inflammatory gene expression. The ability to crystallize many receptor-ligand complexes with distinct pharmacophores allows one to define structural features of signaling specificity that would not be apparent in a single structure.     

Conformation biases of amino acids based on tripeptide microenvironment from PDB database

We have constructed a bank (FTTP) of tendentious factors of three states of three-peptide units from PDB database based on conformational dihedral angle library and demonstrated that amino acid biases toward protein secondary structure are present in natural protein sequences. Our research results reveal that 20 standard amino acids fall into three groups: nine residues inclined to alpha-helix with a common character (e.g. direct side chain aliphatic residues or positive/negative charged residues) arrange in three grades, viz EA, QKRLD, and MN, in turn; seven residues are apt to beta-strand with 2'-branched side chain aliphatic residues or benzyl-included residues, namely PV, IYTC, and F, in three ranks; and four residues SHWG show a double tendency to both alpha and beta. Noticeably, proline has the strongest ability to form extended conformation, especially the Re value up to 9.5298 at position 3 (Table 3). Thus, biases of codons show an evident tendency in protein folding, where GC-rich codons are mainly in charge of forming contracted conformation, especially the codon's first letter plays a dominant role in translating the genomic GC signature into protein sequences and structures. So, biases of amino acids will play an important role in protein folding, folding codons, refining domain, structure prediction, and structural genomics/proteomics.     

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.     

Protein structure modeling in the proteomics era

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects – to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.     

Protein structure modeling in the proteomics era

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.     

Are heat shock proteins therapeutic target for Parkinson's disease?

Heat shock proteins (HSPs), known as molecular chaperone to assist protein folding, have recently become a research focus in Parkinson's disease (PD) because the pathogenesis of this disease is highlighted by the intracellular protein misfolding and inclusion body formation. The present review will focus on the functions of different HSPs and their protective roles in PD. It is postulated that HSPs may serve as protein folding machinery and work together with ubiquitin-proteasome system (UPS) to assist in decomposing aberrant proteins. Failure of UPS is thought to play a key role in the pathogenesis of PD. In addition, HSPs may possess anti-apoptotic effects and keep the homeostasis of dopaminergic neurons against stress conditions. The critical role of HSPs and recent discovery of some novel HSPs inducers suggest that HSPs may be potential therapeutic targets for PD and other neurodegenerative disorders.     

Integrating forward and reverse proteomics to unravel protein function

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.