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At the intersection of biology, chemistry, and technology is the field of Bioinformatics. Defined by Luscombe, bioinformatics is “conceptualising biology in terms of molecules (in the sense of physical chemistry) and applying informatics techniques (derived from disciplines such as applied maths, computer science and statistics) to understand and organise the information associated with these molecules, on a large scale.”1
Many believe that the birth of bioinformatics occurred with the sequencing of the human genome in the 1990’s; however, the field of bioinformatics dates back to almost sixty years ago when early computational biologists developed methods for data analysis of protein sequencing. Fredrick Sangar and his group were the first to sequence a protein, insulin. This achievement proved the polypeptide theory and was a groundbreaking concept that displaced the previous scientific belief that proteins were without definite structure.2 Margaret Oakley Dayhoff, a mathematician and physical chemist who is widely known as the founder of bioinformatics, was the first to create a computational software to manage the abundance of data from the study of protein sequences. One such program, Comprotein, was developed for punch-card computers in the 1960’s. It was comprised of several combinatorial algorithms, which focused on the overlapping of protein fragments (see Figure 1).3 Though seemingly rudimentary compared to today’s technology, this was one of the first instances of computation being paired with biological discovery. Dayhoff created databases through her computational sequence alignment methods that were invaluable in the study of evolutionary biology and phylogeny, but also had expertise in cosmic matters and biophysics.2 In 1965, Margaret Dayhoff and her colleague Richard Eck first published the Atlas of Protein Sequence and Structure, a culmination of the computational sequencing work performed on seventy proteins.4,5 Once the bioinformatic groundwork had been laid, the drive to decipher molecular information extended to the study of the genome, resulting in full genome sequencing in the 1990’s.2
With the exploration of the genome and an increase in understanding of molecular biology, the theory behind the next phase of cellular and biomolecular understanding shifted from the “one gene-one protein-one function” model to a systems or network biology that conceptually allowed for a multidirectional relationship between biological pathways and variability based on molecular contexts (see Figure 2). Biomolecules are viewed not just in terms of their genetic origin, but also in the reality of dynamic modulation that is directed by external and internal changes that occur over the course of time.6 This developing knowledge is derived from the study of these systems in tandem and in isolation, but within the context of the system as a whole. The tremendous amount of data gathered through analysis of these biological systems requires sophisticated information processing to make the data biologically useful.1 Biological research disciplines that comprise the “omics” are extensive, with subsets including genomics, proteomics, lipidomics, metabolomics, pharmacogenomics, phenomics, secretomics, and interactomics. The utility of mass spectrometry (MS) and the literature on the subject are most robust for proteomics and metabolomics, though mass spectrometry has also been used in genomics as well as other omic subclasses. A variety of modes and analysis tools specific to mass spectrometry have been developed for these purposes.
- PROTEOMICS: TOP DOWN & BOTTOM UP
Of particular interest in the realm of Bioinformatics is the rising discipline of proteomics, a field in which the versatility and comprehensive nature of mass spectrometry is being fully realized. Proteomics can be defined as “The direct qualitative and quantitative analysis of the full complement or subset of the proteins present in an organism, tissue or cell under a given set of physiological or environmental conditions.”7 Prior to the development of mass spectrometry and proteomics, peptide sequencing was a long and tedious process, involving ion exchange column use, electrophoresis, and sequential degradation of large proteins from the amino terminus to the carbonyl terminus through Edman degradation.2,8 Currently, the primary instrument for proteomic analysis is mass spectrometry (MS).
There are two primary divisions of MS for proteomics, denoted by the terms “bottom up” proteomics and “top down” proteomics. Bottom up proteomics involves digestion of a protein into fragments and analysis of these fragments for potential amino acid sequencing and protein identification. A strategy of comparing the fragmentation pattern from MS/MS to known databases is known as “mass fingerprinting” and can help with positive identification of proteins.7 One of four approaches can be used in “bottom up” proteomics: i) Data Dependent Acquisition (DDA), ii) Directed Approach, iii) Targeted Approach, and iv) Data Independent Analysis (DIA) (see Figure 3). Data Dependent Acquisition utilizes an algorithm that selects the most abundant peptides in real time and delivers these ions, often via a radiofrequency-only quadrupole, to a collision chamber for fragmentation and mass analysis. This heuristic approach to protein analysis, with automatic selection of precursor ions is based on the on relative abundance of ions a survey scan. Most of the ion peaks found in data obtained from the initial scan in this method do not undergo further analysis, as the peptides found in lower abundance are less likely to be selected for fragmentation and therefore, for sequencing.6 DDA is commonly used on Fourier Transform-Collision Induced Dissociation (FT-CID) devices. This method is also known as “shotgun” or “discovery” proteomics.
A directed approach can also be taken by strategically selecting certain proteome segments by creating an inclusion list based on mass to charge ratio (m/z) ranges, for further analysis and quantification.9
Targeted approaches focus on a very small subset of the proteome and result in highly reproducible data. If the target is a known peptide, Selected Reaction Monitoring (SRM) can be utilized. Triple quadrupole (QQQ) instrumentation is used after Liquid Chromatography (LC) separation. The first portion of the experiment involves selection of a specific m/z for the initial quadrupole, followed by isolation in the second quadrupole, and subsequently, fragmentation is induced in the third quadrupole.10,11 Figure 4 shows the data for about 100 synthetic peptides that were analyzed in SRM-MS/MS mode. Image (a) demonstrates the SRM-XIC Chromatogram for all 100 peptides. Image (b) reveals the spectra of the two charged states of a specific, targeted peptide sequence (EAENANLELESK) and finally, (c) provides the MS/MS spectrum of the selected m/z. Specific precursor ion fragment masses, known as SRM transitions, are selected and quantified. This information is then used to deduce the presence and quantity of the digests of a particular protein of interest. This is a very sensitive technique with a broad dynamic range, but validation of the assays is a limiting factor for the use of SRM.11 In addition, because of the potential for oxidation of methionine and tryptophan, analysis using the SRM method are limited to peptides that do not contain these particular amino acids.10
Data Independent Analysis attempts to identify all proteins within a sample. There is no application of an ion transmission window for DIA and, therefore, all parent ions are delivered to the collision cell for fragmentation and analysis. This results in a near-100% duty cycle. Chromatic peaks and retention times are used in the extensive deconvolution process in order to tie the precursor ions to the product ions. 12 One of the primary advantages of DIA is the ability to detect and identify proteins in very low concentrations. It is estimated that differences in concentration up to ten orders of magnitude can be present in human serum between the proteins of greatest abundance and those in lowest concentration.13 DDA systematically ignores many of the proteins in lower concentration. The analysis of proteins in low abundance becomes a primary strength in DIA; however, reproducibility and scan rates often suffer with this approach.12
Top down proteomics, on the other hand, characterizes intact proteins and their fragments for confident identification. This approach is invaluable for increased peptide sequence coverage and mapping, protein quantification, and the characterization of post translational modifications (PTMS) and protein isoforms.14,15 Absolute and relative quantification is an important component of the systems biology perspective and both can be obtained through MS. Metabolic labeling, isotopic labeling, and chemical derivatization are used to aid in protein quantification.14 PTMS are covalent modifications that are enzymatically made to proteins after ribosomal protein synthesis. These include addition or removal of a molecular group or a change in the peptide sequence that is irreversible.6 Protein isoforms are a family of proteins with slight variability that perform similar biological roles and often arise from a single gene sequence. A top down proteomics experiment can be optimized for PTMS identification and localization. Limitations to top-down proteomics include difficulty with protein separation, ionization, and fragmentation due to the large, often globular, nature of undigested proteins.15
- SAMPLE PREPARATION
In both bottom up and top down proteomics, proper subcellular preparation is crucial. The extensive protein dynamic range within a biological system is one of the most challenging sample preparation issues. It is estimated that greater than ninety-nine percent of the protein composition in human serum is comprised of only fifty different proteins, though over 20,000 proteins are expressed.16 Dynamic range adjustment can be accomplished through depletion of known high-abundance proteins, such as albumin in the serum, via chemical or antibody precipitation.15 In addition, the use of solid phase bead-bound combinatorial ligand libraries can be used to not only reduce highly abundant proteins, but also enrich those with low abundance. The process of protein equalization through the ligand libraries can be more efficient and cost-effective than antibody precipitation.13
After cell lysis, protein separation and protein fractionation can be performed prior to top-down MS analysis by using two dimensional polyacrylamide gel electrophoresis (2D-PAGE), a method which harnesses both the mass and the charge of proteins for effective, though not necessarily efficient separation.17 There are also hybrid methods that pair LC and gel separation that have been shown to increase efficiency of separation and also substantially increase experiment reproducibility.18
Whole protein fragmentation is another crucial step in sample preparation for bottom up approaches. Proteolysis, or the protein digest process, involves hydrolysis of amide bonds. Many of the proteolytic agents have bond specificity, cleaving bonds through hydrolysis at certain amino acid sites. For example, trypsin, a protease commonly used in sample preparation for discovery proteomics cleaves arginine and lysine residues at the carboxyl side. Endoproteinases, chymotrypsin, subtilisin, and elastase are other examples of digestive enzymes used in sample preparation. In order for the primary amino acid sequence to be exposed to the enzymes for selective hydrolysis, detergents (SDS and CHAPS) and chaotropes (urea and thiourea) are used. Though it seems somewhat counterintuitive to increase the complexity of the sample through selective protease digestion, it in fact, encourages a more heterogeneous biochemical sample, particularly when considering the impact of PTMS and splice isoforms on protein diversity.15
Posttranslational modifications are another key area for proteomic analysis and often require additional sample processing steps to improve identification. PTMS occur as a result of chemical modifications to parent proteins. These increase the overall diversity of the proteome and are key for most biological cascades. Mass Spectrometry can be used detect these critical modifications through identification of mass shifts. A measured parent peptide ion undergoes fragmentation and these fragments are compared against the predicted protein in a database. Differences in mass of the protein and predicted mass from the DNA sequence can be used to infer the presence of PTMS. The mass to charge ratios of the fragment ions can confirm the presence of such modifications. A number of PTMS are possible, including biotinylation, caramylation, acetylation, ubiquination, phosphorylation, methylation, oxidation, and glycosylation. Potential locations of the modifications are based on database predictions of the amino acid sequence from the DNA sequence. Computational methods for structure elucidation can assist in proper identification and placement of the modifications.15 In addition, identification of PTMS often require the use of enrichment strategies. Phosphorylation is a critical post translational modification that is enzymatically controlled by protein phosphatases and kinases for the purpose of signal pathway regulation. The process of phosphorylation is substoichiometric in nature and requires enrichment for adequate detection in mass spectrometry. This particular PTM occurs only on threonine, tyrosine, and serine residues.19 Metal affinity chromatography with Ga(III), Zr(III), Al(III), and Fe(III) or the use of TiO2 can be used to enrich phosphopeptides and improve mass detection and relative quantification. Immunoprecipitation with kinase-specific antibodies or chemical derivatization methods, through beta-elimination and phosphoramidate chemistry, can also be used.20 Ubiquitination, a reversible, ATP-dependent process, is usually associated with a series of amino acids (arginine – lysine – arginine – glycine – glycine). The signature for this PTM is a mass shift of 114.043 Daltons. A trypsin digest will result in a missed cleavage at lysine. The combination of these three features in a protein or polypeptide can positively identify a ubiquitinated protein.15 Antiubiquitin antibodies, histadine-tagged ubiquitin, utilization of ubiquitin binding domains can be effective tools for enrichment.21 Lectin affinity is commonly used to enrich glycosylated peptides. Other approaches such as boronic acid affinity, hydrazide coupling, and hydrophilic interaction liquid chromatography have also been found to be effective in identifying the glycan modification of an amine group within the peptide.15 A number of other strategies to improve detection of PTMS have been developed and are well outlined in the literature.
- MASS SPECTROMETRY INSTRUMENTATION FOR PROTEOMICS
Mass Spectrometry is one of the key instruments and an unbiased tool that is commonly used for proteomic analysis. The three main components of mass spectrometers include the ion source with ion optics, the mass analyzer, and the electronics for data processing. Each mode of mass spectrometry has different utility, benefits, and limitations. A listing of different MS instrument types with their primary applications in proteomics is listed in Figure 6 and will be discussed in this section.
- IONIZATION SOURCES
The two primary ionization sources for bottom up proteomics include nanoelectrospray ionization (nESI) and Matrix-Assisted Laser Desorption Ionization (MALDI). These soft ionization techniques are optimal for proteomics because of the nonvolatile, polar, and thermally unstable nature of proteins and peptides.14 Electrospray ionization is a form of ionization that involves desorption and aerosolation of the analyte of interest from the liquid solvent. The result is often a multi-charged species which effectively extends the mass range for the experiment. Nanoelectrospray ionization has a similar mechanism, but a petite electrospray needle is used and the flow rate is therefore reduced. Liquid chromatography instrumentation is often used for protein separation prior to introduction of the sample by nanoelectrospray. With the development of nanoelectrospray ionization, there is improved sensitivity for MS proteomics, discriminating even to subpicomole concentrations. However, throughput is often decreased and extensive preparation of samples is required.22 The goal of nanoelectrospray ionization is to ionize, but not fragment the proteins as the subsequent stage of MS typically involves fragmentation.
Matrix Assisted Laser Desorption Ionization (MALDI) methods were first introduced in the late 1990’s and demonstrated a substantial mass range with the ability to ionize proteins with masses in excess of 50,000 Daltons.23 Prior to this, desorption of ions through secondary ion mass spectrometry (SIMS) was limited to proteins with a mass less than about 2,000 Daltons. In MALDI, an analyte is mixed with a substance that absorbs energy, known as a matrix. The substance is placed on a plate for analysis by automated means or manually. A laser beam is used to irradiate the mixture of analyte and matrix causing ionization with sublimation. Positive mode MALDI results in protonated molecular ions, denoted by [M+H]+, whereas negative mode results in deprotonated molecular ions, denoted by [M-H]–. MALDI is paired with a mass analyzer, often a time of flight (ToF), for measurement of the ionized molecule or protein of interest. Of note, trypsin digestions can also be performed on tissue samples in situ followed by application of the matrix. Protein analysis can then be performed resulting in the ability to map the relative molecular distributions.24 This has a direct clinical impact in the realm of oncology for both diagnosis and prognostication. In addition, drug discovery applications for MALDI have also developed as the technology provides insight about drug response.25 An example of a work flow for fresh frozen tissue samples using MALDI MS is shown in Figure 7. The tissue is obtained and mounted on a conductive target. Here, an in situ digestion is used followed by matrix application. Laser desorption and tandem MS and protein identification through database searches follow. Images that map the tissue are then created based on relative abundances of the proteins.
The ionization methods used in the collision cell result in a variety of ions, the tuning of which is based on the instrumentation used and the intensity of fragmentation. Low energy conditions facilitation fragmentation at the amide bonds which comprise the peptide backbone. The result of this fragmentation are b-type ions (containing the N-terminus) and y-type ions (containing the C-terminus). As an increase in fragmentation energy is provided, the fragmentation pattern also produces a-type, c-type, and z-type ions. The mass difference of the same type of fragments can be evaluated sequentially and then used to infer the amino acid residue lost in the fragmentation process. This allows the peptide sequence to be theoretically reconstructed. The massive amount of information that is produced by a MS proteomics experiment must be not only managed effectively, but also translated into meaningful data. Bioinformatic protein databases can be used to streamline the process of sequence elucidation by utilizing these fragmentation patterns. Tandem MS/MS and MSn are particularly useful for sequencing, particularly de novo sequencing, and for confirmation of sequences based on in silico experiments.26
Several instrumental modalities can be used to induce peptide fragmentation, including collision-induced dissociation (CID), beam-type, and electron transfer dissociation (ETD). In CID, ions are introduced to a reagent gas. The resulting collisions lead to a conversion of kinetic energy into internal energy relaxation and fragmentation, often through proton transfer. In ion traps, a specific frequency can be applied in order to cause excitation of ions with a specific mass to charge ratio. Further fragmentation is unlikely and the resulting pattern is comprised of predominately b-type and y-type ions. In beam-type fragmentation, both the fragments and the precursor ions undergo further collision, resulting in more fragmentation of the less stable b-type ions. Therefore, y-type ions are the predominant ions seen. Finally, ETD results in collisions between the peptide and ETD reagents, resulting in predominate fragments of the c-type and z-type ions.15 Figure 8 shows the sites of fragmentation, the correlating ion production, and the corresponding fragmentation sources.
- MASS ANALYZERS
Mass analyzers that are most useful for proteomics experiments include quadrupoles, Time of Flight (ToF), linear Ion traps (LIT), Orbitraps, and Fourier Transform-Ion Cyclotron Resonance (FT-ICR).15 A QToF device typically places a triple quadrupole in line with a time of flight mass analyzer. The first quadrupole is used for as a jet ion guide, the second for precursor ion selection via mass filtering, and the third for fragmentation in the collision cell. Ions then enter the mass analyzer through ion compression optics in a manner orthogonal to the flight tube. A reflectron can be used to further improve mass accuracy. A multichannel detector that converts time into a mass to charge ratio is used to create the spectra. The Orbitrap and Linear Ion Trap-type mass spectrometers have a different configuration (see Figure 9). Using electrospray, proteins are sprayed directly from the LC to the inlet source. As stated above, desolvation occurs through the addition of heat and the ions are guided, focused, filtered, and then transferred to the ion trap via a quadrupole or an octopole. Ions with the same m/z are delivered to a collision cell where energy is added through a high-pressure cell and fragmentation ensues. The remaining parent ions and correlating fragments are then detected in a low-pressure trap. If even more
mass accuracy and resolving power is needed, an Orbitrap can be used. The peptide ions would be passed to a higher-energy collisional dissociation (HCD) cell and then transferred, as an ion packet with the assistance of the C-trap to the spindle-shaped Orbitrap mass analyzer. In this device, the ions with the same mass to charge ratio oscillate with the same frequency. A Fourier transform is used to convert the frequency-based ion current to a m/z value for production of a spectra.15 The utility of mass spectrometry for proteomics has expanded over the last couple of decades, particularly with the development of Fourier Transform Ion Cyclotron Resonance (FT-ICR).7 This technology has improved accuracy and sensitivity of mass spectral acquisition, making protein elucidation even more achievable. FT-ICR instruments have superior mass accuracy, but at the expense of speed and sensitivity.
- DATABASES AND FUTURE DIRECTION
As big data analysis, public repositories, and data sharing continue to advance, it will be important to streamline data input in order to ensure that the plethora of information is utilized effectively. Big data produced from genomics is often placed in public repositories, whereas, proteomic databases are less cohesive and less utilized. This is in part a result of the highly-complicated nature of proteomics with data extending beyond peptide sequencing and into the realms of posttranslational modifications, splicing, degradation, and complex interactions. This creates a unique challenge to the field of Bioinformatics, particularly as the sheer volume of data increases with the rise of more effective, high-throughput analytical methods that help elucidate these biological languages of life. Lack of funding is another challenge that has threatened the field of Bioinformatics. Unfortunately, two highly-used proteomic databases were recently discontinued due to lack of funding. Data sharing through these resources was therefore terminated.27 Many researchers in the fields of computational biology and bioinformatics are advocating for a centralized “resourceome” that would classify data according to common algorithms and serve as a global and cohesive cross-linked ontology.28 An annual international consortium known as the “Semantic Web Applications and Tools for the Life Sciences” attempts to unite researchers to discuss the progression of the field.29 In addition, the goals of The National Center for Biomedical Ontology are similar, with a vision for dissemination of biomedical knowledge and data via principled ontologies that will then have relevance and application to biomedical science.30
The data from proteomic experiments can be divided into three main categories: raw data, processed data with peptide/ protein identification and quantification, and the resulting biological conclusions (see Figure 10). Correlating with the degree of processing and interpretation, a variety of databases have been formulated to improve data sharing and interpretation. Raw data searches can be facilitated by the use of programs such as MaxQuant, which is publicly available without fee, and Mascot by Matrix Science. These programs use a variety of algorithms to match, identify, and score protein and peptide fragments.27 After tandem mass spectral data is obtained for example, peptides and proteins can be identified via in silico experiments based on digestions on proteins in collective databases. Since homologous peptides can be found in multiple proteins, inferences based on additional fragments can increase the confidence of protein identification.15 The processed data can be stored in and accessed from proteome libraries such as UniprotKB in addition to the libraries listed in Figure 10. These libraries are updated frequently and are actively curated based on the most up-to-date literature. Bioinformatic applications using protein “knowledge-bases” allow for analysis of greater systems biology queries. Utilization of existing data form entire databases using programs such as Ingenuity Pathway Analysis or Cytoscape can augment predictions of upstream regulation and downstream effects.27
The future of proteomics in the field of Bioinformatics is promising as instrumentation and technology continue to advance. There are profound implications for bioinformatic innovation and application with the potential for discovery of novel molecular mechanisms, metabolic biomarkers, and therapeutic targets.
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