The isolation of peptides by high-performance liquid chromatography using predicted elution positions

Assessing the hydrophobicity of glycopeptides using reversed-phase liquid chromatography and tandem mass spectrometry

Retention time is one of the most important parameters that has been widely used to demonstrate the separation results obtained from liquid chromatography (LC) platforms. However, retention time can shift when samples are tested with different instruments and laboratories, which hinders the identification process of analytes when comparing data collected from different LC systems. To address this problem, hydrophobicity index was introduced for retention time normalization of the glycopeptides separated by reversed-phase LC (RPLC). Tandem MS was used for the detection and identification of glycopeptides. In addition, the influence of different types of glycans on the hydrophobicity of peptide backbones was studied by comparing the retention time of glycopeptides with their non-glycosylated counterparts. The hydrophobicity of tryptic digested glycopeptides derived from model glycoproteins, including bovine fetuin, α1-acid glycoprotein, and haptoglobin from human plasma, were evaluated based on the hydrophobicity index of the standard peptides from a peptide retention time calibration mixture. The reduction of hydrophobicity of multiple peptide backbones was observed due to the hydrophilic glycan structures. By comparing the hydrophobicity index of glycopeptides collected from different time and instruments, the day-to-day and lab-to-lab comparisons suggested high reliability and reproducibility of this approach. The RSD% of hydrophobicity index from inter-lab experiments was 1.2%, while the RSD% of retention time was 5.1%. Then, the applications of this method were demonstrated on complex glycopeptide samples extracted from human blood serum. The hydrophobicity index can be applied to address the retention time shift when using different instruments, thereby boosting confidence of the characterization of glycopeptides.

Exploring the variable space of shallow machine learning models for reversed-phase retention time prediction

Peptide retention time (RT) prediction algorithms are tools to study and identify the physicochemical properties that drive the peptide-sorbent interaction. Traditional RT algorithms use multiple linear regression with manually curated parameters to determine the degree of direct contribution for each parameter and improvements to RT prediction accuracies relied on superior feature engineering. Deep learning led to a significant increase in RT prediction accuracy and automated feature engineering via chaining multiple learning modules. However, the significance and the identity of these extracted variables are not well understood due to the inherent complexity when interpreting "relationships-of-relationships" found in deep learning variables. To achieve both accuracy and interpretability simultaneously, we isolated individual modules used in deep learning and the isolated modules are the shallow learners employed for RT prediction in this work. Using a shallow convolutional neural network (CNN) and gated recurrent unit (GRU), we find that the spatial features obtained via the CNN correlate with real-world physicochemical properties namely cross-collisional sections (CCS) and variations of assessable surface area (ASA). Furthermore, we determined that the discovered parameters are "micro-coefficients" that contribute to the "macro-coefficient" - hydrophobicity. Manually embedding CCS and the variations of ASA to the GRU model yielded an R2 = 0.981 using only 525 variables and can represent 88% of the ∼110,000 tryptic peptides used in our dataset. This work highlights the feature discovery process of our shallow learners can achieve beyond traditional RT models in performance and have better interpretability when compared with the deep learning RT algorithms found in the literature.

PON-Sol2: Prediction of Effects of Variants on Protein Solubility

Genetic variations have a multitude of effects on proteins. A substantial number of variations affect protein-solvent interactions, either aggregation or solubility. Aggregation is often related to structural alterations, whereas solubilizable proteins in the solid phase can be made again soluble by dilution. Solubility is a central protein property and when reduced can lead to diseases. We developed a prediction method, PON-Sol2, to identify amino acid substitutions that increase, decrease, or have no effect on the protein solubility. The method is a machine learning tool utilizing gradient boosting algorithm and was trained on a large dataset of variants with different outcomes after the selection of features among a large number of tested properties. The method is fast and has high performance. The normalized correct prediction rate for three states is 0.656, and the normalized GC2 score is 0.312 in 10-fold cross-validation. The corresponding numbers in the blind test were 0.545 and 0.157. The performance was superior in comparison to previous methods. The PON-Sol2 predictor is freely available. It can be used to predict the solubility effects of variants for any organism, even in large-scale projects.

iDPPIV-SCM: A Sequence-Based Predictor for Identifying and Analyzing Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides Using a Scoring Card Method

The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C.3.4.14.5) is well recognized as a new avenue for the treatment of Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize the blood glucose concentration in T2D subjects. To the best of our knowledge, there is yet no computational model for predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present for the first time a simple and easily interpretable sequence-based predictor using the scoring card method (SCM) for modeling the bioactivity of DPP-IV inhibitory peptides (iDPPIV-SCM). Particularly, the iDPPIV-SCM was developed by employing the SCM method together with the propensity scores of amino acids. Rigorous independent test results demonstrated that the proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers (e.g., k-nearest neighbor, logistic regression, and decision tree) with demonstrated improvements of 2-11, 4-22, and 7-10% for accuracy, MCC, and AUC, respectively, while also achieving comparable results to that of the support vector machine. Furthermore, the analysis of estimated propensity scores of amino acids as derived from the iDPPIV-SCM was performed so as to provide a more in-depth understanding on the molecular basis for enhancing the DPP-IV inhibitory potency. Taken together, these results revealed that iDPPIV-SCM was superior to those of other well-known ML classifiers owing to its simplicity, interpretability, and validity. For the convenience of biologists, the predictive model is deployed as a publicly accessible web server at http://camt.pythonanywhere.com/iDPPIV-SCM. It is anticipated that iDPPIV-SCM can serve as an important tool for the rapid screening of promising DPP-IV inhibitory peptides prior to their synthesis.

Retention Time Prediction and Protein Identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.

14-Helical β-Peptides Elicit Toxicity against C. albicans by Forming Pores in the Cell Membrane and Subsequently Disrupting Intracellular Organelles

Synthetic peptidomimetics of antimicrobial peptides (AMPs) are promising antimicrobial drug candidates because they promote membrane disruption and exhibit greater structural and proteolytic stability than natural AMPs. We previously reported selective antifungal 14-helical β-peptides, but the mechanism of antifungal toxicity of β-peptides remains unknown. To provide insight into the mechanism, we studied antifungal β-peptide binding to artificial membranes and living Candida albicans cells. We investigated the ability of β-peptides to interact with and permeate small unilamellar vesicle models of fungal membranes. The partition coefficient supported a pore-mediated mechanism characterized by the existence of a critical β-peptide concentration separating low- and high-partition coefficient regimes. Live cell intracellular tracking of β-peptides showed that β-peptides translocated into the cytoplasm, and then disrupted the nucleus and vacuole sequentially, leading to cell death. This understanding of the mechanisms of antifungal activity will facilitate design and development of peptidomimetic AMPs, including 14-helical β-peptides, for antifungal applications.

Predictive chromatography of peptides and proteins as a complementary tool for proteomics

In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.

Peptide retention prediction using hydrophilic interaction liquid chromatography coupled to mass spectrometry

A model that predicts retention for peptides using a HALO® penta-HILIC column and gradient elution was created. Coefficients for each amino acid were derived using linear regression analysis and these coefficients can be summed to predict the retention of peptides. This model has a high correlation between experimental and predicted retention times (0.946), which is on par with previous RP and HILIC models. External validation of the model was performed using a set of H. pylori samples on the same LC-MS system used to create the model, and the deviation from actual to predicted times was low. Apart from amino acid composition, length and location of amino acid residues on a peptide were examined and two site-specific corrections for hydrophobic residues at the N-terminus as well as hydrophobic residues one spot over from the N-terminus were created.

Incorporation of β-Amino Acids Enhances the Antifungal Activity and Selectivity of the Helical Antimicrobial Peptide Aurein 1.2

Antimicrobial peptides (AMPs) are attractive antifungal drug candidates because they kill microbes via membrane disruption and are thus unlikely to provoke development of resistance. Low selectivity for fungal vs human cells and instability in physiological environments have limited the development of AMPs as therapeutics, but peptidomimetic AMPs can overcome these obstacles and also provide useful insight into AMP structure-function relationships. Here, we describe antifungal peptidomimetic α/β-peptides templated on the natural α-peptidic AMP aurein 1.2. These α/β-aurein analogs fold into i → i + 4 H-bonded helices that present arrays of side chain functionality in a manner virtually identical to that of aurein 1.2. By varying charge, hydrophobicity, conformational stability, and α/β-amino acid organization, we designed active and selective α/β-peptide aurein analogs that exhibit minimum inhibitory concentrations (MIC) against the opportunistic pathogen Candida albicans that are 4-fold lower than that of aurein 1.2 and elicit less than 5% hemolysis at the MIC. These α/β-aurein analogs are promising candidates for development as antifungal therapeutics and as tools to elucidate mechanisms of AMP activity and specificity.

Detection and confirmation of α-cobratoxin in equine plasma by solid-phase extraction and liquid chromatography coupled to mass spectrometry

α-Cobratoxin (CTX) is a large peptide (71 amino acids) with strong analgesic effect and may be misused in sports such as horse racing. To prevent such misuse, a sensitive method is required for detection and confirmation of the toxin in equine samples. CTX was extracted from equine plasma using an optimized mixed-mode solid-phase extraction (SPE) procedure. Extracted CTX was reduced with dithiothreitol and alkylated with iodoacetamide, and then was digested by trypsin at 56°C for 30min. The digest was analysed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and tryptic peptides T2 (³CFITPDITSK¹²) and T4 (²⁴TWCDAFCSIR³³) were monitored for detection and confirmation of CTX. The limit of detection (LOD) was 0.05ng/mL for CTX in plasma, and the limit of confirmation (LOC) 0.2ng/mL. Unlike small peptides consisting of the 20 canonical amino acids, CTX was stable in equine plasma at ambient temperature for at least 24h. The developed analytical method was successfully applied to analysis of incurred plasma samples; CTX was detected in plasma collected 15min through 36h post subcutaneous administration of CTX (2.0mg dose) to a research horse, and confirmed 30min through 24h. Additionally, an approach named "reliable targeted SEQUEST search" has been proposed for assessing the specificity of T2 at product ion spectrum level for confirmation of CTX. T2 is uniquely specific for CTX, as evaluated with this approach and BLAST search. Furthermore, the effect of dimethyl sulfoxide (DMSO) as a mobile phase additive on electrospray (ESI) response of T2 and T4, background noise level and signal to noise ratio (S/N) was examined; DMSO increased signal intensity of T2 and T4 by a factor of less than 2. It is the first report that DMSO raised background noise level and did not improve S/N for the peptides, to the authors' knowledge. The developed analytical method may be applicable for analysis of CTX in plasma from other species such as greyhound dogs or even human beings.

Brain-specific HIV Nef identified in multiple patients with neurological disease

HIV-1 Nef is a flexible, multifunctional protein with several cellular targets that is required for pathogenicity of the virus. This protein maintains a high degree of genetic variation among intra- and inter-host isolates. HIV Nef is relevant to HIV-associated neurological diseases (HAND) in patients treated with combined antiretroviral therapy because of the protein's role in promoting survival and migration of infected brain macrophages. In this study, we analyzed 2020 HIV Nef sequences derived from 22 different tissues and 31 subjects using a novel computational approach. This approach combines statistical regression and evolved neural networks (ENNs) to classify brain sequences based on the physical and chemical characteristics of functional Nef domains. Based on training, testing, and validation data, the method successfully classified brain Nef sequences at 84.5% and provided informative features for further examination. These included physicochemical features associated with the Src-homology-3 binding domain, the Nef loop (including the AP-2 Binding region), and a cytokine-binding domain. Non-brain sequences from patients with HIV-associated neurological disease were frequently classified as brain, suggesting that the approach could indicate neurological risk using blood-derived virus or for the development of biomarkers for use in assay systems aimed at drug efficacy studies for the treatment of HIV-associated neurological diseases.

Peptide retention time prediction

Most methods for interpreting data from shotgun proteomics experiments are to large degree dependent on being able to predict properties of peptide-ions. Often such predicted properties are limited to molecular mass and fragment spectra, but here we put focus on a perhaps underutilized property, a peptide's chromatographic retention time. We review a couple of different principles of retention time prediction,and their applications within computational proteomics. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:615-623, 2017.

On the Physicochemical and Structural Modifications Associated with HIV-1 Subtype B Tropism Transition

HIV-1 enters immune cells via binding the viral envelope to a host cell CD4 receptor, and then a secondary co-receptor, usually CCR5 (R5) or CXCR4 (X4), and some HIV can utilize both co-receptors (R5X4). Although a small set of amino-acid properties such as charge and sequence length applied to HIV-1 envelope V3 loop sequence data can be used to predict co-receptor usage, we sought to expand the fundamental understanding of the physiochemical basis of tropism by analyzing many, perhaps less obvious, amino-acid properties over a diverse array of HIV sequences. We examined 74 amino-acid physicochemical scales over 1,559 V3 loop sequences with biologically tested tropisms downloaded from the Los Alamos HIV sequence database. Linear regressions were then calculated for each feature relative to three tropism transitions (R5→X4; R5→R5X4; R5X4→X4). Independent correlations were rank ordered to determine informative features. A structural analysis of the V3 loop was performed to better interpret these findings relative to HIV tropism states. Similar structural changes are required for R5 and R5X4 to transition to X4, thus suggesting that R5 and R5X4 types are more similar than either phenotype is to X4. Overall, the analysis suggests a continuum of viral tropism that is only partially related to charge; in fact, the analysis suggests that charge modification may be primarily attributed to decreased R5 usage, and further structural changes, particularly those associated with β-sheet structure, are likely required for full X4 usage.

Protein sub-cellular localization prediction for special compartments via optimized time series distances

Predicting the sub-cellular localization of proteins is an important task in bioinformatics, for which many standard prediction tools are available. While these tools are powerful in general and capable of predicting protein localization for the most common compartments, their performance strongly depends on the organism of interest. More importantly, there are special compartments, such as the apicoplast of apicomplexan parasites, for which these tools cannot provide a prediction at all. In the absence of a highly conserved targeting signal, even motif searches may not be able to provide a lead for the accurate prediction of protein localization for a compartment of interest. In order to approach difficult cases of that kind, we propose an alternative method that complements existing approaches by using a more targeted protein sequence model. Moreover, our method makes use of (weighted) measures for time series comparison. To demonstrate its performance, we use this method for predicting localization in special compartments of three different species, for which existing methods yield only sub-optimal results. As shown experimentally, our method is indeed capable of producing reliable predictions of sub-cellular localization for difficult cases, i.e. if training data is scarce and a potential protein targeting signal may not be well conserved.

Hydrophobicity and helicity regulate the antifungal activity of 14-helical β-peptides

Candida albicans is one of the most prevalent fungal pathogens, causing both mucosal candidiasis and invasive candidemia. Antimicrobial peptides (AMPs), part of the human innate immune system, have been shown to exhibit antifungal activity but have not been effective as pharmaceuticals because of low activity and selectivity in physiologically relevant environments. Nevertheless, studies on α-peptide AMPs have revealed key features that can be designed into more stable structures, such as the 14-helix of β-peptide-based oligomers. Here, we report on the ways in which two of those features, hydrophobicity and helicity, govern the activity and selectivity of 14-helical β-peptides against C. albicans and human red blood cells. Our results reveal both antifungal activity and hemolysis to correlate to hydrophobicity, with intermediate levels of hydrophobicity leading to high antifungal activity and high selectivity toward C. albicans. Helical structure-forming propensity further influenced this window of selective antifungal activity, with more stable helical structures eliciting specificity for C. albicans over a broader range of hydrophobicity. Our findings also reveal cooperativity between hydrophobicity and helicity in regulating antifungal activity and specificity. The results of this study provide critical insight into the ways in which hydrophobicity and helicity govern the activity and specificity of AMPs and identify criteria that may be useful for the design of potent and selective antifungal agents.

An improved sample preparation method for the sensitive detection of peptides by MALDI-MS

We describe here an optimization study of the sample preparation conditions for sensitive detection of peptides by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Among many factors in the conditions, we varied the percent acetonitrile in the peptide solution, the percent acetonitrile in the matrix solution and the α-cyano-4-hydroxycinnamic acid (CHCA) concentration in the matrix solution. CHCA was chosen because it is the most frequently used matrix for analyzing peptides. The well-established dried-droplet method was employed for sample deposition. The examined range of the concentration of CHCA was from 0.01 to 10 mg/ml, and the MeCN content of the solvent for matrix/analyte was 10% to 50%. The indicator for the detection sensitivity was the S/N ratio of the peaks of peptides used. Highly increased sensitivity (100- to 1000-fold) was observed for the optimal CHCA concentration of 0.1 mg/ml in 20% MeCN/0.1% aq. trifluoroacetic acid (TFA), as compared with the conventional concentration (10 mg/ml) in 50% MeCN/0.1% aq. TFA. For example, the limit of detection of human ACTH 18-39 was 10 amol/well for the optimal condition but 10 fmol/well for the conventional condition. The optimal condition (0.1 mg/ml CHCA in 20% MeCN/0.1% aq. TFA) was verified with five model peptides and provided significant improvement in sensitivity (by two to three orders of magnitude) compared with the conventional conditions. Optimizing the CHCA concentration and solvent composition significantly improved the detection sensitivity in the analysis of peptides by MALDI-MS.

Alkylated trihydroxyacetophenone as a MALDI matrix for hydrophobic peptides

Hydrophobic peptides are difficult to detect in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), because of the hydrophilic properties of conventional matrices and the low affinity for hydrophobic peptides. Recently, we reported on alkylated dihydroxybenzoic acid (ADHB) as a matrix additive for hydrophobic peptides; however, the peptides were detected in the rim of the matrix-analyte dried spot. Here, we report on a novel matrix, alkylated trihydroxyacetophenone (ATHAP), which is a 2,4,6-trihydroxyacetophenone derivative incorporating a hydrophobic alkyl chain on the acetyl group and thus is expected to have an affinity for hydrophobic peptides. ATHAP increased the sensitivity of hydrophobic peptides 10-fold compared with α-cyano-4-hydroxycinnamic acid (CHCA), in which the detection of hydrophilic peptides was suppressed. The peptides were detected throughout the entire matrix-analyte dried spot using ATHAP, overcoming the difficulty of finding a "sweet spot" when using ADHB. In addition, ATHAP functioned alone as a matrix, unlike ADHB as an additive. In phosphorylase b digests analysis, hydrophobic peptides, which were not detected with CHCA for 1 pmol, were detected with this matrix, confirming that ATHAP led to increased sequence coverage and may extend the range of target analytes in MALDI-MS.

Does the genetic code have a eukaryotic origin?

In the RNA world, RNA is assumed to be the dominant macromolecule performing most, if not all, core “house-keeping” functions. The ribo-cell hypothesis suggests that the genetic code and the translation machinery may both be born of the RNA world, and the introduction of DNA to ribo-cells may take over the informational role of RNA gradually, such as a mature set of genetic code and mechanism enabling stable inheritance of sequence and its variation. In this context, we modeled the genetic code in two content variables—GC and purine contents—of protein-coding sequences and measured the purine content sensitivities for each codon when the sensitivity (% usage) is plotted as a function of GC content variation. The analysis leads to a new pattern—the symmetric pattern—where the sensitivity of purine content variation shows diagonally symmetry in the codon table more significantly in the two GC content invariable quarters in addition to the two existing patterns where the table is divided into either four GC content sensitivity quarters or two amino acid diversity halves. The most insensitive codon sets are GUN (valine) and CAN (CAR for asparagine and CAY for aspartic acid) and the most biased amino acid is valine (always over-estimated) followed by alanine (always under-estimated). The unique position of valine and its codons suggests its key roles in the final recruitment of the complete codon set of the canonical table. The distinct choice may only be attributable to sequence signatures or signals of splice sites for spliceosomal introns shared by all extant eukaryotes.

Retention time prediction and protein identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in proteomics.

Using iRT, a normalized retention time for more targeted measurement of peptides

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.

Mutagenesis Objective Search and Selection Tool (MOSST): an algorithm to predict structure-function related mutations in proteins

Background

Functionally relevant artificial or natural mutations are difficult to assess or predict if no structure-function information is available for a protein. This is especially important to correctly identify functionally significant non-synonymous single nucleotide polymorphisms (nsSNPs) or to design a site-directed mutagenesis strategy for a target protein. A new and powerful methodology is proposed to guide these two decision strategies, based only on conservation rules of physicochemical properties of amino acids extracted from a multiple alignment of a protein family where the target protein belongs, with no need of explicit structure-function relationships.

Results

A statistical analysis is performed over each amino acid position in the multiple protein alignment, based on different amino acid physical or chemical characteristics, including hydrophobicity, side-chain volume, charge and protein conformational parameters. The variances of each of these properties at each position are combined to obtain a global statistical indicator of the conservation degree of each property. Different types of physicochemical conservation are defined to characterize relevant and irrelevant positions. The differences between statistical variances are taken together as the basis of hypothesis tests at each position to search for functionally significant mutable sites and to identify specific mutagenesis targets. The outcome is used to statistically predict physicochemical consensus sequences based on different properties and to calculate the amino acid propensities at each position in a given protein. Hence, amino acid positions are identified that are putatively responsible for function, specificity, stability or binding interactions in a family of proteins. Once these key functional positions are identified, position-specific statistical distributions are applied to divide the 20 common protein amino acids in each position of the protein's primary sequence into a group of functionally non-disruptive amino acids and a second group of functionally deleterious amino acids.

Conclusions

With this approach, not only conserved amino acid positions in a protein family can be labeled as functionally relevant, but also non-conserved amino acid positions can be identified to have a physicochemically meaningful functional effect. These results become a discriminative tool in the selection and elaboration of rational mutagenesis strategies for the protein. They can also be used to predict if a given nsSNP, identified, for instance, in a genomic-scale analysis, can have a functional implication for a particular protein and which nsSNPs are most likely to be functionally silent for a protein. This analytical tool could be used to rapidly and automatically discard any irrelevant nsSNP and guide the research focus toward functionally significant mutations. Based on preliminary results and applications, this technique shows promising performance as a valuable bioinformatics tool to aid in the development of new protein variants and in the understanding of function-structure relationships in proteins.

[Prediction of peptide retention time in reversed-phase liquid chromatography and its application in protein identification]

Liquid chromatography-mass spectrometry (LC-MS) is the mainstream of high throughput protein identification technology. Peptide retention time in reversed-phase liquid chromatography (RPLC) is mainly determined by the physicochemical properties of the peptide and the LC conditions (stationary phase and mobile phase). Retention time can be predicted by analyzing these properties and quantifying their effects on peptide chromatographic behavior. Prediction of peptide retention time in LC can be used to improve identification of peptides and post translational modifications (PTM). There are mainly two methods to predict retention time: i.e., retention coefficients and machine learning. The coefficient of determination between observed and predicted retention times can reach 0.93. With the development of LC-MS technology, retention time prediction will become an important tool to facilitate protein identification.

[Application of peptide retention time in proteome research]

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has been one of the most popular approaches in proteome analysis. As an independent parameter to mass spectrometry information, peptide retention time has been utilized to facilitate protein identification and quantification. In the field of peptide identification, the prediction of the retention time combined with routine tandem mass spectrometry database searching methods could help improve the confidence of identification. The sensitivity of identification could also be improved by matching peaks with both the accurate mass and retention time in multiple aligned LC-MS runs. Meanwhile, because small changes of liquid chromatography conditions lead to variability in retention times unavoidably, retention time alignment is crucial to label-free quantification. Additionally, post-translational modifications (PTM) could be identified by combining retention time shifts and mass deviation information.

Correctness of protein identifications of Bacillus subtilis proteome with the indication on potential false positive peptides supported by predictions of their retention times

The predictive capability of the retention time prediction model based on quantitative structure-retention relationships (QSRR) was tested. QSRR model was derived with the use of set of peptides identified with the highest scores and originated from 8 known proteins annotated as model ones. The predictive ability of the QSRR model was verified with the use of a Bacillus subtilis proteome digest after separation and identification of the peptides by LC-ESI-MS/MS. That ability was tested with three sets of testing peptides assigned to the proteins identified with different levels of confidence. First, the set of peptides identified with the highest scores achieved in the search were considered. Hence, proteins identified on the basis of more than one peptide were taken into account. Furthermore, proteins identified on the basis of just one peptide were also considered and, depending on the possessed scores, both above and below the assumed threshold, were analyzed in two separated sets. The QSRR approach was applied as the additional constraint in proteomic research verifying results of MS/MS ion search and confirming the correctness of the peptides identifications along with the indication of the potential false positives.

Proteomics analysis of perilymph and cerebrospinal fluid in mouse

OBJECTIVES

Proteins in perilymph may alter the delivery profile of implantable intracochlear drug delivery systems through biofouling. Knowledge of protein composition will help anticipate interactions with delivered agents.

STUDY DESIGN

Analysis of mouse perilymph.

METHODS

Protein composition of perilymph and cerebrospinal fluid (CSF) was analyzed using a capillary liquid chromatography-mass spectrometry-based iTRAQ quantitative proteomics approach. We searched against a mouse subset of the Uniprot FASTA protein database. We sampled perilymph from the apex of the mouse cochlea to minimize CSF contamination.

RESULTS

More than 50 explicit protein isoforms were identified with very high confidence. iTRAQ reporter ions allowed determination of relative molar amounts of proteins between perilymph and CSF. Protein in perilymph was almost three times more concentrated than in CSF. More than one-third of the proteins in perilymph comprised protease inhibitors, with serpins being the predominant group. Apolipoproteins constituted 16%. Fifteen percent of the proteins were enzymes. Albumin was the most abundant single protein (14%). Proteins with relatively high perilymph/CSF ratios included broad-spectrum protease inhibitors and apolipoproteins.

DISCUSSION

Some proteins found in perilymph, such as albumin and HMW kininogen, have been implicated in biofouling through adsorption to device materials. The relatively large quantities of apolipoprotein and albumin may serve as a reservoir for acidic and lipophilic drugs. Alpha-2-glycoprotein can bind basic drugs.

CONCLUSIONS

Perilymph is similar in protein composition to CSF, though amounts are 2.8 times higher. Protease inhibitors comprise the largest category of proteins.

Intrinsic amino acid side-chain hydrophilicity/hydrophobicity coefficients determined by reversed-phase high-performance liquid chromatography of model peptides: comparison with other hydrophilicity/hydrophobicity scales

An accurate determination of the intrinsic hydrophilicity/hydrophobicity of amino acid side-chains in peptides and proteins is fundamental in understanding many area of research, including protein folding and stability, peptide and protein function, protein-protein interactions and peptide/protein oligomerization, as well as the design of protocols for purification and characterization of peptides and proteins. Our definition of intrinsic hydrophilicity/hydrophobicity of side-chains is the maximum possible hydrophilicity/hydrophobicity of side-chains in the absence of any nearest-neighbor effects and/or any conformational effects of the polypeptide chain that prevent full expression of side-chain hydrophilicity/hydrophobicity. In this review, we have compared an experimentally derived intrinsic side-chain hydrophilicity/hydrophobicity scale generated from RP-HPLC retention behavior of de novo designed synthetic model peptides at pH 2 and pH 7 with other RP-HPLC-derived scales, as well as scales generated from classic experimental and calculation-based methods of octanol/water partitioning of Nalpha-acetyl-amino-acid amides or free energy of transfer of free amino acids. Generally poor correlation was found with previous RP-HPLC-derived scales, likely due to the random nature of the peptide mixtures in terms of varying peptide size, conformation and frequency of particular amino acids. In addition, generally poor correlation with the classical approaches served to underline the importance of the presence of a polypeptide backbone when generating intrinsic values. We have shown that the intrinsic scale determined here is in full agreement with the structural characteristics of amino acid side-chains.

Methods of calculating protein hydrophobicity and their application in developing correlations to predict hydrophobic interaction chromatography retention

Hydrophobic interaction chromatography (HIC) is a key technique for protein separation and purification. Different methodologies to estimate the hydrophobicity of a protein are reviewed, which have been related to the chromatographic behavior of proteins in HIC. These methodologies consider either knowledge of the three-dimensional structure or the amino acid composition of proteins. Despite some restrictions; they have proven to be useful in predicting protein retention time in HIC.

Targeted identification of phosphorylated peptides by off-line HPLC-MALDI-MS/MS using LC retention time prediction

Protein phosphorylation is a type of posttranslational modification which plays an important role in cell regulation and signal transduction. Because of its biological relevance, a considerable amount of interest has been paid to the development of efficient techniques for phosphopeptide analysis. Although advances in MS control have enabled the high-throughput discovery of proteins from limited amounts of sample, automated selection of MS/MS precursor ions based on intensity alone can significantly hamper the detection of low-abundance phosphopeptides. On the basis of the observation that the introduction of a phosphate moiety does not dramatically change peptide retention time in reverse-phase chromatography, phosphopeptide specific MS/MS fragmentation attempts based on LC retention time and m/z were evaluated using a standard protein mixture, then using in vitro phosphorylated myelin basic protein. Results indicated that the majority (98%) of phosphopeptides identified eluted within a +/- 4-min window of the predicted LC elution time. While studies presented here are primarily proof of concept in nature, data suggest that the use of LC retention time prediction could be a valuable constraint for the identification of phosphopeptides within a set of off-line LC deposited sample spots. It is expected that the development of these methods will not only permit the targeted identification of protein phosphorylation sites but also allow the in-depth analysis of the dynamic events linked to the posttranslational modification.

A robust linear regression based algorithm for automated evaluation of peptide identifications from shotgun proteomics by use of reversed-phase liquid chromatography retention time

BACKGROUND

Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches.

RESULTS

A robust linear regression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time.

CONCLUSION

Analysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.