Sequence-Specific Model for Peptide Retention Time Prediction in Strong Cation Exchange Chromatography

Retention time prediction for post-translationally modified peptides: Ser, Thr, Tyr-phosphorylation

The development of a peptide retention prediction model for reversed-phase chromatography applications in proteomics is reported for peptides carrying phosphorylated Ser, Thr and Tyr-residues. The major retention features have been assessed using a collection of over 10,000 phosphorylated/non-phosphorylated peptide pairs identified in a series 1D and 2D LC-MS/MS acquisitions using formic acid as ion pairing modifier. Single modification event on average results in increased peptide retention for phosphorylation of Ser (+ 1.46), Thr (+1.33), Tyr (+0.93% acetonitrile, ACN) on gradient elution scale for Luna C18(2) stationary phase. We established several composition and sequence specific features, which drive deviations from these average values. Thus, single phosphorylation of serine results in retention shifts ranging from -2.4 to 5.5% ACN depending on position of the residue, nature of nearest neighbour residues, peptide length, hydrophobicity and pI value, and its propensity to form amphipathic helical structures. We established that the altered ion-pairing environment upon phosphorylation is detrimental for this variability. Hydrophobicity of ion-pairing modifier directly informs the magnitude of expected shifts: (most hydrophilic) 0.5 % acetic acid (larger positive shift upon phosphorylation) > 0.1 % formic acid (positive) > 0.1 % trifluoroacetic (negative) > 0.1 % heptafluorobutyric acid (larger negative shift). The effect of phosphorylation has been also evaluated for several separation conditions used in the first dimension of 2D LC applications: high pH reversed-phase (RP), hydrophilic interaction liquid chromatography (HILIC), strong cation- and strong anion exchange separations.

Improved LC-MS identification of short homologous peptides using sequence-specific retention time predictors

Peptides are an important group of compounds contributing to the desired, as well as the undesired taste of a food product. Their taste impressions can include aspects of sweetness, bitterness, savoury, umami and many other impressions depending on the amino acids present as well as their sequence. Identification of short peptides in foods is challenging. We developed a method to assign identities to short peptides including homologous structures, i.e. peptides containing the same amino acids with a different sequence order, by accurate prediction of the retention times during reversed phase separation. To train the method, a large set of well-defined short peptides with systematic variations in the amino acid sequence was prepared by a novel synthesis strategy called 'swapped-sequence synthesis'. Additionally, several proteins were enzymatically digested to yield short peptides. Experimental retention times were determined after reversed phase separation and peptide MS2 data was acquired using a high-resolution mass spectrometer operated in data-dependent acquisition mode (DDA). A support vector regression model was trained using a combination of existing sequence-independent peptide descriptors and a newly derived set of selected amino acid index derived sequence-specific peptide (ASP) descriptors. The model was trained and validated using the experimental retention times of the 713 small food-relevant peptides prepared. Whilst selecting the most useful ASP descriptors for our model, special attention was given to predict the retention time differences between homologous peptide structures. Inclusion of ASP descriptors greatly improved the ability to accurately predict retention times, including retention time differences between 157 homologous peptide pairs. The final prediction model had a goodness-of-fit (Q2) of 0.94; moreover for 93% of the short peptides, the elution order was correctly predicted.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

Peptide retention time prediction for peptides with post-translational modifications: N-terminal (α-amine) and lysine (ε-amine) acetylation

Development of a peptide retention prediction model in reversed-phase chromatography is reported for acetylated peptides - both N-terminal (α-) and side chain of Lys (ε-amine) residues. Large-scale proteomic 2D LC-MS analyses of acetylated/non-acetylated tryptic digest of whole human cell lysate have been used to assemble representative retention data sets of 25,000+ modified/non-modified pairs. This allowed elucidating chromatographic behaviour of modified peptides in three different separation modes: high pH reversed-phase, HILIC separation on amide phase (first dimension of 2D) and reversed-phase separation with formic acid as ion-pairing modifier in the second dimension. On average, N-terminal acetylation increases peptide RP retention at acidic pH by 5 Hydrophobicity Index units (% acetonitrile). Acetylation of first lysine adds another 4.1%. The magnitude of the retention shift varies greatly depending on the number of modified amines, peptide length, and N-terminal peptide sequence. Large retention shifts have been observed for peptides with hydrophobic N-termini and specifically peptides carrying sequences characteristic for amphipathic helical structures - all in complete agreement with major sequence-specific features of RP retention mechanism. The utility of the modified Sequence Specific Retention Calculator model has been verified for the in-vivo N-terminally acetylated peptides detected by 2D LC-MS/MS analysis of a yeast tryptic digest. The effect of N-terminal acetylation was also evaluated for six different HILIC columns, strong cation- and strong anion exchange separations using previously acquired 2D LC-MS/MS data.

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into eight groups (four charges for both tryptic/nontryptic peptides). Position-dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide's ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in a preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position-dependent ISP behavior consistent with electrostatic interaction with the peptide macrodipole, which affects the peptide helicity. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository (http://jpostdb.org) with the dataset identifiers PXD021440/JPST000959, PXD022800/JPST001017, and PXD026087/ JPST001176.

Retention time prediction using neural networks increases identifications in crosslinking mass spectrometry

Crosslinking mass spectrometry has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information in the mass spectra of crosslinked peptides limits the numbers of protein-protein interactions that can be confidently identified. Here, we leverage chromatographic retention time information to aid the identification of crosslinked peptides from mass spectra. Our Siamese machine learning model xiRT achieves highly accurate retention time predictions of crosslinked peptides in a multi-dimensional separation of crosslinked E. coli lysate. Importantly, supplementing the search engine score with retention time features leads to a substantial increase in protein-protein interactions without affecting confidence. This approach is not limited to cell lysates and multi-dimensional separation but also improves considerably the analysis of crosslinked multiprotein complexes with a single chromatographic dimension. Retention times are a powerful complement to mass spectrometric information to increase the sensitivity of crosslinking mass spectrometry analyses.

Towards low false discovery rate estimation for protein-protein interactions detected by chemical cross-linking

Chemical cross-linking (CX) of proteins in vivo or in cell free extracts followed by mass spectrometric (MS) identification of linked peptide pairs (CXMS) can reveal protein-protein interactions (PPIs) both at a proteome wide scale and the level of cross-linked amino acid residues. However, error estimation at the level of PPI remains challenging in large scale datasets. Here we discuss recent advances in the recognition of spurious inter-protein peptide pairs and in diminishing the FDR for these PPI-signaling cross-links, such as the use of chromatographic retention time prediction, in order to come to a more reliable reporting of PPIs.

Confident Identification of Citrullination and Carbamylation Assisted by Peptide Retention Time Prediction

The chromatographic behavior of peptides carrying citrulline and homocitrulline residues in proteomic two-dimensional (2D) liquid chromatography-mass spectrometry (LC-MS) experiments has been investigated. The primary goal of this study was to determine the chromatographic conditions that allow differentiating between arginine citrullination and deamidation of asparagine based on retention data, improving the confidence of MS-based identifications. Carbamylation was used as a reference point due to a high degree of similarity between modification products and anticipated changes in chromatographic behavior. We applied 2D LC-MS/MS (a high-pH-low-pH reversed phase (RP), hydrophilic interaction liquid chromatography (HILIC)-low-pH RP, and strong cation exchange (SCX)-low-pH RP) to acquire retention data for modified-nonmodified peptide pairs in the four separation modes. Modifications of a standard protein mixture were induced enzymatically (PAD-2) or chemically (urea) for citrullination and carbamylation, respectively. Deamidation occurs spontaneously. Similar retention shifts were observed for all three modifications in a high-pH RP (decrease) and a low-pH RP (increase), thus limiting the applicability of this 2D LC combination. HILIC on bare silica and strong cation exchange separations have been probed to amplify the effect of charge loss upon citrullination, with SCX demonstrating the most differentiating power: the elimination of basic residues upon citrullination/carbamylation results in an ∼58 mM KCl retention decrease, while retention of deamidated products decreases slightly.

Isolation of Acetylated and Unmodified Protein N-Terminal Peptides by Strong Cation Exchange Chromatographic Separation of TrypN-Digested Peptides

We developed a simple and rapid method to enrich protein N-terminal peptides, in which the protease TrypN is first employed to generate protein N-terminal peptides without Lys or Arg and internal peptides with two positive charges at their N termini, and then, the N-terminal peptides with or without N-acetylation are separated from the internal peptides by strong cation exchange chromatography according to a retention model based on the charge/orientation of peptides. This approach was applied to 20 μg of human HEK293T cell lysate proteins to profile the N-terminal proteome. On average, 1550 acetylated and 200 unmodified protein N-terminal peptides were successfully identified in a single LC/MS/MS run with less than 3% contamination with internal peptides, even when we accepted only canonical protein N termini registered in the Swiss-Prot database. Because this method involves only two steps, protein digestion and chromatographic separation, without the need for tedious chemical reactions, it should be useful for comprehensive profiling of protein N termini, including proteoforms with neo-N termini.

Deep Learning in Proteomics

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.

Proteomic Profiling of Emiliania huxleyi Using a Three-Dimensional Separation Method Combined with Tandem Mass Spectrometry

Emiliania huxleyi is one of the most abundant marine planktons, and it has a crucial feature in the carbon cycle. However, proteomic analyses of Emiliania huxleyi have not been done extensively. In this study, a three-dimensional liquid chromatography (3D-LC) system consisting of strong cation exchange, high- and low-pH reversed-phase liquid chromatography was established for in-depth proteomic profiling of Emiliania huxleyi. From tryptic proteome digest, 70 fractions were generated and analyzed using liquid chromatography-tandem mass spectrometry. In total, more than 84,000 unique peptides and 10,000 proteins groups were identified with a false discovery rate of ≤0.01. The physicochemical properties of the identified peptides were evaluated. Using ClueGO, approximately 700 gene ontology terms and 15 pathways were defined from the identified protein groups with p-value ≤0.05, covering a wide range of biological processes, cellular components, and molecular functions. Many biological processes associated with CO₂ fixation, photosynthesis, biosynthesis, and metabolic process were identified. Various molecular functions relating to protein binding and enzyme activities were also found. The 3D-LC strategy is a powerful approach for comparative proteomic studies on Emiliania huxleyi to reveal changes in its protein level and related mechanism.

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. Here, the authors propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm for accurate retention time prediction.

Peptide retention time prediction in hydrophilic interaction liquid chromatography: Zwitter-ionic sulfoalkylbetaine and phosphorylcholine stationary phases

Peptide retention time prediction models have been developed for zwitter-ionic ZIC-HILIC and ZIC-cHILIC stationary phases (pH 4.5 eluents) using proteomics-derived retention datasets of ~30 thousand tryptic peptides each. Overall, hydrophilicity of these stationary phases was found to be similar to the previously studied Amide HILIC phase, but lower compared to bare silicas. Peptide retention is driven by interactions of all charged (hydrophilic) residues at pH 4.5 (Asp, Glu, Arg, Lys, His), but shows specificity according to orientation of functional groups in zwitter-ionic pair. Thus, ZIC-cHILIC exhibits an increased contribution of negatively charged Asp and Glu due to the distal positioning of positively charged quaternary amines on the stationary phase. These findings confirm that HILIC interactions are driven by both peptide distribution between water layer adsorbed on the stationary phase and by interactions specific to functional groups of the packing material. Sequence-Specific Retention Calculator HILIC models were optimized for these columns showing 0.967-0.976 R²-values between experimental and predicted retention values. ZIC-HILIC separations represent a good choice as a first dimension in 2D LC-MS of peptide mixtures with correlations between retention values of ZIC-HILIC against RPLC found at 0.197 (ZIC-HILIC) and 0.137 (ZIC-cHILIC) R²-values, confirming a good orthogonality.

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.

Retention Time Prediction for Glycopeptides in Reversed-Phase Chromatography for Glycoproteomic Applications

The sequence-specific retention calculator algorithm (SSRCalc) [ Krokhin , O. V. Anal. Chem. 2006 , 78 , 7785 ] was adapted for the prediction of retention times of N-glycopeptides separated by reversed-phase high performance liquid chromatography (RPLC). The retention time shifts (dHI = HI_glyco - HI_deglyco, where HI is the hydrophobicity index, measured in percent acetonitrile units) used for modeling were measured for 602 glycopeptides versus 123 of their deglycosylated analogues. Our method used a tryptic digest of 12 purified glycoproteins, glycopeptide enrichment, deglycosylation with PNGaseF, and RPLC-MS/MS analysis of combined (deglycosylated and intact) peptide mixtures. On average, glycosylation yields a 0.79% acetonitrile unit decrease in retention, compared with the hydrophobicity indices of their deglycosylated analogues. These values, however, are drastically different for asialo (-1.37% acetonitrile units), monosialylated (-0.47% acetonitrile units), disialylated (+0.61% acetonitrile units), and trisialylated (+1.94% acetonitrile units) glycans. Peptide retention time shifts upon glycosylation (dHI) vary depending on the number of monosaccharide units, the presence or absence of sialic acid, peptide hydrophobicity, and the number of position-dependent features. These features are mostly driven by competing effects of acidic residues (aspartic acid and sialic acid) on ion-pair formation and by nearest-neighbor effects of hydrophilic glycans. The accuracy of the modified prediction model for glycopeptides approaches that of the prediction for nonmodified species (R² = 0.97 vs 0.98). However, retention time prediction based on the experimental retention values of deglycosylated analogues (HI_glyco = HI_deglyco + dHI, R² = 0.995) is much more accurate, thus providing a solid support for glycopeptide identification in complex samples based on mass and retention time.

Universal retention standard for peptide separations using various modes of high-performance liquid chromatography

Peptide retention standards are widely used by chromatography specialists. They can be used for quality control of peptide separations (separation efficiency, selectivity, retention values) and for accurate concatenation of retention data from multiple acquisitions in proteomics. So far the repertoire of available retention standards is mostly limited to reversed-phase separations. We introduce a synthetic peptide mixture which can be used in conjunction with the most popular peptide separation techniques: reversed-phase (RPLC), strong-cation exchange (SCX), (strong-anion exchange) SAX and hydrophilic interaction liquid chromatography (HILIC). Target sequences were first designed in-silico using Sequence-Specific Retention Calculator models covering all major peptide separation mechanisms. Peptides were also designed while keeping in mind the simplicity of retention time assignment using MS detection: they all have nearly identical masses and identical intense y₃ fragment ions. This contribution demonstrates the application of this mixture for characterization of eight HILIC as well as SAX, SCX and C18 columns.

Isolation of high-purity peptide Val-Val-Tyr-Pro from Globin Peptide using MCI gel column combined with high-speed counter-current chromatography

Peptides have gained increased interest over the past several decades because of their therapeutics. In this research, a strategy combining MCI gel column chromatography and high-speed countercurrent chromatography was developed for the separation of high-purity peptide Val-Val-Tyr-Pro from Globin Peptide. First, the fraction of Val-Val-Tyr-Pro mixtures with a purity of 15.8% was obtained by using MCI gel column with a mixture of ethanol/water (20:80, v/v/v). Then, the high-purity Val-Val-Tyr-Pro was separated by high-speed countercurrent chromatography with a aqueous two phase systems of ethanol/acetonitrile/iso-propyl alcohol/(NH₄ )₂ SO_{4 Saturated solution} /H₂ O (0.5:0.5:0.25:1.5:0.7,v/v). The ammonium sulfate from high-speed countercurrent chromatography fractions was removed from target compound by MCI gel column chromatography using ethanol/water in stepwise elution mode. A 78 mg of Val-Val-Tyr-Pro was successfully purified with the purities of 98.80% from 30 g crude Globin Peptide. The amino acid sequence of the Val-Val-Tyr-Pro was determined by electrospray ionization high resolution tandem mass spectrometry. The method presents a practical strategy for the large-scale separation of pure peptide Val-Val-Tyr-Pro from Globin Peptide, and provides a reference method for obtaining high-purity peptide from other polypeptide mixtures.

Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning

The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this prediction. A new peptide RT prediction tool, DeepRT, was designed using a capsule network model, and the public data sets containing peptides separated by reverse-phase liquid chromatography were used to evaluate the DeepRT performance. Compared with other prevailing RT predictors, DeepRT attained overall improvement in the prediction of peptide RTs with an R² of ∼0.994. Moreover, DeepRT was able to accommodate to the peptides that were separated by different types of LC, such as strong cation exchange (SCX) and hydrophilic interaction liquid chromatography (HILIC) and to reach the RT prediction with R² values of ∼0.996 for SCX and ∼0.993 for HILIC, respectively. If a large peptide data set is available for one type of LC, DeepRT can be promoted to DeepRT(+) using transfer learning. Based on a large peptide data set gained from SWATH, DeepRT(+) further elevated the accuracy of RT prediction for peptides in a small data set and enabled a satisfactory prediction upon limited peptides approximating hundreds. Further, DeepRT automatically learns retention-related properties of amino acids under different separation mechanisms, which are well consistent with retention coefficients (Rc) of the amino acids. DeepRT was thus proven to be an improved RT predictor with high flexibility and efficiency. DeepRT is available at https://github.com/horsepurve/DeepRTplus .

Peptide Retention in Hydrophilic Strong Anion Exchange Chromatography Is Driven by Charged and Aromatic Residues

Hydrophilic strong anion exchange chromatography (hSAX) is becoming a popular method for the prefractionation of proteomic samples. However, the use and further development of this approach is affected by the limited understanding of its retention mechanism and the absence of elution time prediction. Using a set of 59 297 confidentially identified peptides, we performed an explorative analysis and built a predictive deep learning model. As expected, charged residues are the major contributors to the retention time through electrostatic interactions. Aspartic acid and glutamic acid have a strong retaining effect and lysine and arginine have a strong repulsion effect. In addition, we also find the involvement of aromatic amino acids. This suggests a substantial contribution of cation-π interactions to the retention mechanism. The deep learning approach was validated using 5-fold cross-validation (CV) yielding a mean prediction accuracy of 70% during CV and 68% on a hold-out validation set. The results of this study emphasize that not only electrostatic interactions but rather diverse types of interactions must be integrated to build a reliable hSAX retention time predictor.