Proteomics: Recent Applications and New Technologies

Proteomics in systems toxicology

Proteins are the ultimate product of gene expression. As they hinge between gene transcription and phenotype, they offer a more realistic perspective of toxicopathic effects, responses and even susceptibility to insult than targeting genes and mRNAs while dodging some inter-individual variability that hinders measuring downstream endpoints like metabolites or enzyme activity. Toxicologists have long focused on proteins as biomarkers but the advent of proteomics shifted risk assessment from narrow single-endpoint analyses to whole-proteome screening, enabling deriving protein-centric adverse outcome pathways (AOPs), which are pivotal for the derivation of Systems Biology informally named Systems Toxicology. Especially if coupled pathology, the identification of molecular initiating events (MIEs) and AOPs allow predictive modeling of toxicological pathways, which now stands as the frontier for the next generation of toxicologists. Advances in mass spectrometry, bioinformatics, protein databases and top-down proteomics create new opportunities for mechanistic and effects-oriented research in all fields, from ecotoxicology to pharmacotoxicology.

Analytical platforms in vitreoretinal proteomics

Current proteomic technologies can effectively be used to study the proteins of the vitreous body and retina in health and disease. The use of appropriate samples, analytical platform and bioinformatic method are essential factors to consider when undertaking such studies. Certain proteins may hinder the detection and evaluation of more relevant proteins associated with pathological processes if not carefully considered, particularly in the sample preparation and data analysis stages. The utilization of more than one quantification technique and database search program to expand the level of proteome coverage and analysis will help to generate more robust and worthwhile results. This review discusses important aspects of sample processing and the use of label and label-free quantitative proteomics strategies applied to the vitreous and retina.

Heterogeneous nuclear ribonucleoprotein K (hnRNP K) is a tissue biomarker for detection of early hepatocellular carcinoma in patients with cirrhosis

Background

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors occurring mainly in patients with chronic liver disease. Detection of early HCC is critically important for treatment of these patients.

Methods

We employed a proteomic profiling approach to identify potential biomarker for early HCC detection. Based on Barcelona Clinic Liver Cancer (BCLC) staging classification, 15 early HCC and 25 late HCC tissue samples from post-operative HCC patients and their clinicopathological data were used for the discovery of biomarkers specific for the detection of early HCC. Differential proteins among cirrhotic, early, and late tissue samples were separated by two-dimensional gel electrophoresis (2-DE) and subsequently identified by mass spectrometry (MS). Receiver operating characteristic (ROC) curves analysis were performed to find potential biomarkers associated with early HCC. Diagnosis performance of the biomarker was obtained from diagnosis test.

Results

Protein spot SSP2215 was found to be significantly overexpressed in HCC, particularly in early HCC, and identified as heterogeneous nuclear ribonucleoprotein K (hnRNP K) by tandem mass spectrometry (MALDI TOF/TOF). The overexpression in HCC was subsequently validated by western blot and immunohistochemistry. ROC curve analysis showed that hnRNP K intensity could detect early HCC at 66.67 % sensitivity and 84 % specificity, which was superior to serum α-fetoprotein (AFP) in detection of early HCC. Furthermore, the diagnosis test demonstrated, when combined with hnRNP K and serum AFP as biomarker panel to detect early HCC at different cut-off value, the sensitivity and specificity could be enhanced to 93.33 % and 96 %, respectively.

Conclusions

hnRNP K is a potential tissue biomarker, either alone or in combination with serum AFP, for detection of early HCC. High expression of hnRNP K could be helpful to discriminate early HCC from a nonmalignant nodule, especially for patients with liver cirrhosis.

Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach

Noninvasive biomarkers are urgently needed for early detection of breast cancer since the risk of recurrence, morbidity and mortality are closely related to disease stage at the time of primary surgery. In the past decade, many proteomics-based approaches were developed that utilize the protein profiling of human body fluids or identification of putative biomarkers to obtain more knowledge on the effects of cancer emergence and progression. Herein, we report on an analysis of proteins in the tear fluid from breast carcinoma patients and healthy women using a de novo proteomic approach and 25 mixed samples from each group. This study included 25 patients with primary invasive breast carcinoma and 25 age-matched healthy controls. We performed a MALDI-TOF-TOF-driven semi-quantitative comparison of tear protein levels in cancer (CA) and control (CTRL) using a de novo approach in pooled samples. Over 150 proteins in the tear fluid of CTRL and CA were identified. Using an in-house-developed algorithm we found more than 20 proteins distinctly upregulated or downregulated in the CTRL and CA groups. We identified several proteins that had modified expression in breast cancer patients. These proteins are involved in host immune system pathways (e.g., C1Q1 or S100A8) and different metabolic cascades (ALDH3A or TPI). Further validation of the results in an independent population combined with individual protein profiling of participants is needed to confirm the specificity of our findings and may lead to a better understanding of the pathological mechanism of breast cancer.

Piezoelectric microelectromechanical resonant sensors for chemical and biological detection

Piezoelectric microelectromechanical systems (MEMS) resonant sensors, known for their excellent mass resolution, have been studied for many applications, including DNA hybridization, protein-ligand interactions, and immunosensor development. They have also been explored for detecting antigens, organic gas, toxic ions, and explosives. Most piezoelectric MEMS resonant sensors are acoustic sensors (with specific coating layers) that enable selective and label-free detection of biological events in real time. These label-free technologies have recently garnered significant attention for their sensitive and quantitative multi-parameter analysis of biological systems. Since piezoelectric MEMS resonant sensors do more than transform analyte mass or thickness into an electrical signal (e.g., frequency and impedance), special attention must be paid to their potential beyond microweighing, such as measuring elastic and viscous properties, and several types of sensors currently under development operate at different resonant modes (i.e., thickness extensional mode, thickness shear mode, lateral extensional mode, flexural mode, etc.). In this review, we provide an overview of recent developments in micromachined resonant sensors and activities relating to biochemical interfaces for acoustic sensors.

Hexapeptide library as a universal tool for sample preparation in protein glycosylation analysis

Recent analytical advancements allow for large-scale glycomics and glycan-biomarker research with N-glycans released from complex protein mixtures of e.g. plasma with a wide range of protein concentrations. Protein enrichment techniques to obtain samples with a better representation of low-abundance proteins are hardy applied. In this study, hexapeptide ligands previously described for enrichment of low-abundance proteins in proteomics are evaluated for glycan analysis. A repeatable on-bead glycan release strategy was developed, and glycans were analyzed using capillary sieving electrophoresis on a DNA analyzer. Binding of proteins to the hexapeptide library occurred via the protein backbone. At neutral pH no discrimination between protein glycoforms was observed. Interestingly, glycan profiles of plasma with and without hexapeptide library enrichment revealed very similar patterns, despite the vast changes in protein concentrations in the samples. The most significant differences in glycosylation profiles were ascribed to a reduction in immunoglobulin-derived glycans. These results suggest that specific and sensitive biomarkers will be hard to access on the full plasma level using protein enrichment in combination with glycan analysis. Instead, fractionation techniques or profiling strategies on the glycopeptide level after enrichment are proposed for in-depth glycoproteomics research.

Methyl-binding domain protein-based DNA isolation from human blood serum combines DNA analyses and serum-autoantibody testing

Background

Circulating cell free DNA in serum as well as serum-autoantibodies and the serum proteome have great potential to contribute to early cancer diagnostics via non invasive blood tests. However, most DNA preparation protocols destroy the protein fraction and therefore do not allow subsequent protein analyses. In this study a novel approach based on methyl binding domain protein (MBD) is described to overcome the technical difficulties of combining DNA and protein analysis out of one single serum sample.

Methods

Serum or plasma samples from 98 control individuals and 54 breast cancer patients were evaluated upon silica membrane- or MBD affinity-based DNA isolation via qPCR targeting potential DNA methylation markers as well as by protein-microarrays for tumor-autoantibody testing.

Results

In control individuals, an average DNA level of 22.8 ± 25.7 ng/ml was detected applying the silica membrane based protocol and 8.5 ± 7.5 ng/ml using the MBD-approach, both values strongly dependent on the serum sample preparation methods used. In contrast to malignant and benign tumor serum samples, cell free DNA concentrations were significantly elevated in sera of metastasizing breast cancer patients. Technical evaluation revealed that serum upon MBD-based DNA isolation is suitable for protein-array analyses when data are consistent to untreated serum samples.

Conclusion

MBD affinity purification allows DNA isolations under native conditions retaining the protein function, thus for example enabling combined analyses of DNA methylation and autoantigene-profiles from the same serum sample and thereby improving minimal invasive diagnostics.

Accelerating analysis for metabolomics, drugs and their metabolites in biological samples using multidimensional gas chromatography

Gas chromatography (GC) with mass spectrometry (MS) is one of the great enabling analytical tools available to the chemical and biochemical analyst for the measurement of volatile and semi-volatile compounds. From the analysis result, it is possible to assess progress in chemical reactions, to monitor environmental pollutants in a wide range of soil, water or air samples, to determine if an athlete or horse trainer has contravened doping laws, or if crude oil has migrated through subsurface rock to a reservoir. Each of these scenarios and samples has an associated implementation method for GC-MS. However, few samples and the associated interpretation of data is as complex or important as biochemical sample analysis for trace drugs or metabolites. Improving the analysis in both the GC and MS domains is a continual search for better separation, selectivity and sensitivity. Multidimensional methods are playing important roles in providing quality data to address the needs of analysts.

Comparative analysis of viperidae venoms antibacterial profile: a short communication for proteomics

Bacterial infections involving multidrug-resistant strains are one of the ten leading causes of death and an important health problem in need for new antibacterial sources and agents. Herein, we tested and compared four snake venoms (Agkistrodon rhodostoma, Bothrops jararaca, B. atrox and Lachesis muta) against 10 Gram-positive and Gram-negative drug-resistant clinical bacteria strains to identify them as new sources of potential antibacterial molecules. Our data revealed that, as efficient as some antibiotics currently on the market (minimal inhibitory concentration (MIC) = 1–32 μg mL⁻¹), A. rhodostoma and B. atrox venoms were active against Staphylococcus epidermidis and Enterococcus faecalis (MIC = 4.5 μg mL⁻¹), while B. jararaca inhibited S. aureus growth (MIC = 13 μg ml⁻¹). As genomic and proteomic technologies are improving and developing rapidly, our results suggested that A. rhodostoma, B. atrox and B. jararaca venoms and glands are feasible sources for searching antimicrobial prototypes for future design new antibiotics against drug-resistant clinical bacteria. They also point to an additional perspective to fully identify the pharmacological potential of these venoms by using different techniques.

Systems biology approaches and tools for analysis of interactomes and multi-target drugs

Systems biology is essentially a proteomic and epigenetic exercise because the relatively condensed information of genomes unfolds on the level of proteins. The flexibility of cellular architectures is not only mediated by a dazzling number of proteinaceous species but moreover by the kinetics of their molecular changes: The time scales of posttranslational modifications range from milliseconds to years. The genetic framework of an organism only provides the blue print of protein embodiments which are constantly shaped by external input. Indeed, posttranslational modifications of proteins represent the scope and velocity of these inputs and fulfil the requirements of integration of external spatiotemporal signal transduction inside an organism. The optimization of biochemical networks for this type of information processing and storage results in chemically extremely fine tuned molecular entities. The huge dynamic range of concentrations, the chemical diversity and the necessity of synchronisation of complex protein expression patterns pose the major challenge of systemic analysis of biological models. One further message is that many of the key reactions in living systems are essentially based on interactions of moderate affinities and moderate selectivities. This principle is responsible for the enormous flexibility and redundancy of cellular circuitries. In complex disorders such as cancer or neurodegenerative diseases, which initially appear to be rooted in relatively subtle dysfunctions of multimodal physiologic pathways, drug discovery programs based on the concept of high affinity/high specificity compounds ("one-target, one-disease"), which has been dominating the pharmaceutical industry for a long time, increasingly turn out to be unsuccessful. Despite improvements in rational drug design and high throughput screening methods, the number of novel, single-target drugs fell much behind expectations during the past decade, and the treatment of "complex diseases" remains a most pressing medical need. Currently, a change of paradigm can be observed with regard to a new interest in agents that modulate multiple targets simultaneously, essentially "dirty drugs." Targeting cellular function as a system rather than on the level of the single target, significantly increases the size of the drugable proteome and is expected to introduce novel classes of multi-target drugs with fewer adverse effects and toxicity. Multiple target approaches have recently been used to design medications against atherosclerosis, cancer, depression, psychosis and neurodegenerative diseases. A focussed approach towards "systemic" drugs will certainly require the development of novel computational and mathematical concepts for appropriate modelling of complex data. But the key is the extraction of relevant molecular information from biological systems by implementing rigid statistical procedures to differential proteomic analytics.

Proteomic analysis for developing new biomarkers of hepatocellular carcinoma

AIM

To identify new markers of hepatocellular carcinoma (HCC) using a proteomic analysis.

METHODS

Patients with liver cirrhosis of the three most frequent etiologies: hepatitis C virus, hepatitis B virus and alcoholic liver disease, were included in the study. The samples were analysed by 2D-electrophoresis in order to determine the differential protein expression. The proteins were separated according to the charge in immobilized pH 3-10 gradient strips and then by sodium dodecyl sulfate polyacrylamide gel electrophoresis. Proteins of interest were excised, digested with trypsin and the resulting peptides were separated and identified.

RESULTS

Three differentially expressed apolipoproteins (Apo) were identified based on the protein profile using proteomic techniques: Apo-A1, Apo-A4 and Apo-E. Apo-A4 levels were significantly lower in HCC than in non-HCC patients regardless of etiology (P < 0.01). Multivariate logistic regression showed that Apo-A4 and Apo-A1 were the only independent factors related to HCC diagnosis (P < 0.05). The receiver operating characteristic (ROC) curve including both Apo-A4 and Apo-A1 showed an area under the ROC of 0.944 (P < 0.001), a sensitivity of 0.89 and a specificity of 0.81 for diagnosis of HCC.

CONCLUSION

Apo-A4 and Apo-A1 may be used clinically as biomarkers of HCC with a high sensibility and specificity. These findings may provide additional insights into the mechanism of HCC development and progression.

The Sod2 mutant mouse as a model for oxidative stress: a functional proteomics perspective

Oxidative stress has been implicated in the pathogenesis of numerous human diseases and disorders, but the mechanistic basis often remains enigmatic. The Sod2 mutant mouse, which is sensitized to mitochondrial stress, is an ideal mutant model for studying the role of oxidative stress in a diverse range of complications arising from mitochondrial dysfunction and diminished antioxidant defense. To fully appreciate the widespread molecular consequences under increased oxidative stress, a systems approach utilizing proteomics is able to provide a global overview of the complex biological changes, which a targeted single biomolecular approach cannot address fully. This review focuses on the applications of mass spectrometry and functional proteomics in the Sod2 mouse. The combinatorial approach provides novel insights into the interplay of chemistry and biology, free radicals and proteins, thereby augmenting our understanding of how redox perturbations influence protein dynamics. Ultimately, this knowledge can lead to the development of free radical-targeted therapies.

The use of proteomics to identify novel therapeutic targets for the treatment of disease

The completion of the Human Genome Project has revealed a multitude of potential avenues for the identification of therapeutic targets. Extensive sequence information enables the identification of novel genes but does not facilitate a thorough understanding of how changes in gene expression control the molecular mechanisms underlying the development and regulation of a cell or the progression of disease. Proteomics encompasses the study of proteins expressed by a population of cells, and evaluates changes in protein expression, post-translational modifications, protein interactions, protein structure and splice variants, all of which are imperative for a complete understanding of protein function within the cell. From the outset, proteomics has been used to compare the protein profiles of cells in healthy and diseased states and as such can be used to identify proteins associated with disease development and progression. These candidate proteins might provide novel targets for new therapeutic agents or aid the development of assays for disease biomarkers. This review provides an overview of the current proteomic techniques available and focuses on their application in the search for novel therapeutic targets for the treatment of disease.

Rapid elevation of Inos and decreases in abundance of other proteins at pupal diapause termination in the flesh fly Sarcophaga crassipalpis

We analyzed changes in brain proteins 24 h after pupal diapause termination in Sarcophaga crassipalpis by a combination of 2-dimensional gel electrophoresis and mass spectrometry. The proteome analysis revealed significant changes in 20 proteins, 11 of which represented >or=2.5-fold changes. Three proteins were present only in the brains of diapausing pupae. Among the most abundant proteins that showed a change, 1 was more abundant, 7 were less abundant, and 2 were absent following diapause termination. The protein that increased in abundance following diapause termination showed highest identity to myo-inositol-1-phosphate synthase (Inos). Proteins that decreased at diapause termination included those showing highest identities to fatty acid binding protein, CG2331-PA, twinstar, catalase, and a histone. Proteins absent at diapause termination included ribosomal protein L17A and one unnamed protein. An increase of Inos protein level was confirmed using Western blot analysis. Attempts to terminate diapause by injection of several Inos-related metabolites failed, thus suggesting that the elevation of Inos at diapause termination is downstream of the physiological regulation that initiates development.

Protein identification and quantification by two-dimensional infrared spectroscopy: implications for an all-optical proteomic platform

Electron-vibration-vibration two-dimensional coherent spectroscopy, a variant of 2DIR, is shown to be a useful tool to differentiate a set of 10 proteins based on their amino acid content. Two-dimensional vibrational signatures of amino acid side chains are identified and the corresponding signal strengths used to quantify their levels by using a methyl vibrational feature as an internal reference. With the current apparatus, effective differentiation can be achieved in four to five minutes per protein, and our results suggest that this can be reduced to <1 min per protein by using the same technology. Finally, we show that absolute quantification of protein levels is relatively straightforward to achieve and discuss the potential of an all-optical high-throughput proteomic platform based on two-dimensional infrared spectroscopic measurements.

Key developments in endocrine disrupter research and human health

Environmental etiologies involving exposures to chemicals that mimic endogenous hormones are proposed for a number of adverse human health effects, including infertility, abnormal prenatal and childhood development, and reproductive cancers (National Research Council, 1999; World Health Organization, 2002). Endocrine disrupters represent a significant area of environmental research with important implications for human health. This article provides an overview of some of the key developments in this field that may enhance our ability to assess the human health risks posed by exposure to endocrine disrupters. Advances in methodologies of hazard identification (toxicogenomics, transcriptomics, proteomics, metabolomics, bioinformatics) are discussed, as well as epigenetics and emerging biological endpoints.

Novel technologies for the discovery and quantitation of biomarkers of toxicity

Reliable biomarkers of toxicity are necessary both for the safe conduct of pre-clinical and clinical trials, and are increasingly needed for accurate clinical evaluation of treatment regimens with the potential to cause tissue injury. Recent advances in technology have added several new tools to the biomarker screening toolkit and improved the throughput of existing quantitative assays. Genomics, proteomics, and metabolomics have provided a wealth of data in the search for predictive, specific biomarkers. Multiplexed ELISA-based assay systems, silicon nanowire arrays, and patterned paper present unique abilities for fast, efficient sample analysis over a broad dynamic range. Powerful integrative systems biology software and growing open-source data repositories offer new ways to share, reduce, and analyze data from multiple sources. Novel technologies reviewed here have the potential to significantly reduce assay time and cost and improve the sensitivity of screening methods for candidate biomarkers of toxicity.

American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer

PURPOSE

To update the recommendations for the use of tumor marker tests in the prevention, screening, treatment, and surveillance of breast cancer.

METHODS

For the 2007 update, an Update Committee composed of members from the full Panel was formed to complete the review and analysis of data published since 1999. Computerized literature searches of MEDLINE and the Cochrane Collaboration Library were performed. The Update Committee's literature review focused attention on available systematic reviews and meta-analyses of published tumor marker studies. In general, significant health outcomes (overall survival, disease-free survival, quality of life, lesser toxicity, and cost-effectiveness) were used for making recommendations. Recommendations and

CONCLUSIONS

Thirteen categories of breast tumor markers were considered, six of which were new for the guideline. The following categories showed evidence of clinical utility and were recommended for use in practice: CA 15-3, CA 27.29, carcinoembryonic antigen, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, urokinase plasminogen activator, plasminogen activator inhibitor 1, and certain multiparameter gene expression assays. Not all applications for these markers were supported, however. The following categories demonstrated insufficient evidence to support routine use in clinical practice: DNA/ploidy by flow cytometry, p53, cathepsin D, cyclin E, proteomics, certain multiparameter assays, detection of bone marrow micrometastases, and circulating tumor cells.

From vulnerable plaque to vulnerable patient: the search for biomarkers of plaque destabilization

There is a strong need for biomarkers to identify patients at risk for future cardiovascular events related with progressive atherosclerotic disease. Ideally, increasing knowledge of the mechanisms of atherosclerotic plaque destabilization should be translated in clinical practice. Currently, the following commonly followed strategies can be identified with the objective to detect either the local vulnerable plaque that is prone to rupture and gives rise to a thrombotic occlusion, or the systemic vulnerable patient, who has a high probability to suffer from an adverse clinical event. On the one hand, studies are ongoing to determine local atherosclerotic plaque characteristics to predict future local plaque rupture and subsequent vascular thrombosis. Newly developed imaging modalities are being developed and validated to detect these plaques in vivo. On the other hand, systemic approaches are pursued to discover serum biomarkers that are applicable to define patients at risk for future cardiovascular events. We propose a third original approach that is optional but yet unexplored, that is, to use local plaque characteristics as a biomarker not just for local plaque destabilization but for future cardiovascular events due to plaque progression in any vascular system. This review aims to provide an overview of the current standings of the identification of the vulnerable plaque and the vulnerable patient.

Micro- and nanomechanical sensors for environmental, chemical, and biological detection

Micro- and nanoelectromechanical systems, including cantilevers and other small scale structures, have been studied for sensor applications. Accurate sensing of gaseous or aqueous environments, chemical vapors, and biomolecules have been demonstrated using a variety of these devices that undergo static deflections or shifts in resonant frequency upon analyte binding. In particular, biological detection of viruses, antigens, DNA, and other proteins is of great interest. While the majority of currently used detection schemes are reliant on biomarkers, such as fluorescent labels, time, effort, and chemical activity could be saved by developing an ultrasensitive method of label-free mass detection. Micro- and nanoscale sensors have been effectively applied as label-free detectors. In the following, we review the technologies and recent developments in the field of micro- and nanoelectromechanical sensors with particular emphasis on their application as biological sensors and recent work towards integrating these sensors in microfluidic systems.

Proteomic profiling of animal models mimicking skeletal muscle disorders

Over the last few decades of biomedical research, animal models of neuromuscular diseases have been widely used for determining pathological mechanisms and for testing new therapeutic strategies. With the emergence of high-throughput proteomics technology, the identification of novel protein factors involved in disease processes has been decisively improved. This review outlines the usefulness of the proteomic profiling of animal disease models for the discovery of new reliable biomarkers, for the optimization of diagnostic procedures and the development of new treatment options for skeletal muscle disorders. Since inbred animal strains show genetically much less interindividual differences as compared to human patients, considerably lower experimental repeats are capable of producing meaningful proteomic data. Thus, animal model proteomics can be conveniently employed for both studying basic mechanisms of molecular pathogenesis and the effects of drugs, genetic modifications or cell-based therapies on disease progression. Based on the results from comparative animal proteomics, a more informed decision on the design of clinical proteomics studies could be reached. Since no one animal model represents a perfect pathobiochemical replica of all of the symptoms seen in complex human disorders, the proteomic screening of novel animal models can also be employed for swift and enhanced protein biochemical phenotyping.

Methods for samples preparation in proteomic research

Sample preparation is one of the most crucial processes in proteomics research. The results of the experiment depend on the condition of the starting material. Therefore, the proper experimental model and careful sample preparation is vital to obtain significant and trustworthy results, particularly in comparative proteomics, where we are usually looking for minor differences between experimental-, and control samples. In this review we discuss problems associated with general strategies of samples preparation, and experimental demands for these processes.

Global screening of protein chromatographic behavior on ion exchangers from a complex cell proteome. Towards in silico downstream processing of bioproducts

Protein separation during ion-exchange chromatography implies complex physicochemical events. This work has evaluated the chromatographic behaviour of a complex cell proteome on commercial agarose-based adsorbents. Various ligand types in the cation- and anion-exchange mode were studied. ANX-Sepharose, a weak anion exchanger, performed similarly to the strong anion exchanger-type materials. Proteomic tools were applied in order to understand protein separation. Experimental evidence showed a correlation between apparent isoelectric point distributions and the mobile phase conductivity. Molecular weight distributions were unaffected by the elution position. On the basis of two-dimensional electrophoresis, operational windows were described having typical minor contaminants. These could be annotated for future implementation of in silico downstream processing.