Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3.

Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

pDeepXL: MS/MS Spectrum Prediction for Cross-Linked Peptide Pairs by Deep Learning.

In cross-linking mass spectrometry, the identification of cross-linked peptide pairs heavily relies on the ability of a database search engine to measure the similarities between experimental and theoretical MS/MS spectra. However, the lack of accurate ion intensities in theoretical spectra impairs the performance of search engines, in particular, on proteome scales. Here we introduce pDeepXL, a deep neural network to predict MS/MS spectra of cross-linked peptide pairs. To train pDeepXL, we used the transfer-learning technique because it facilitated the training with limited benchmark data of cross-linked peptide pairs. Test results on more than ten data sets showed that pDeepXL accurately predicted the spectra of both noncleavable DSS/BS3/Leiker cross-linked peptide pairs (>80% of predicted spectra have Pearson's r values higher than 0.9) and cleavable DSSO/DSBU cross-linked peptide pairs (>75% of predicted spectra have Pearson's r values higher than 0.9). pDeepXL also achieved the accurate prediction on unseen data sets using an online fine-tuning technique. Lastly, integrating pDeepXL into a database search engine increased the number of identified cross-link spectra by 18% on average.

Precision De Novo Peptide Sequencing Using Mirror Proteases of Ac-LysargiNase and Trypsin for Large-scale Proteomics.

De novo peptide sequencing for large-scale proteomics remains challenging because of the lack of full coverage of ion series in tandem mass spectra. We developed a mirror protease of trypsin, acetylated LysargiNase (Ac-LysargiNase), with superior activity and stability. The mirror spectrum pairs derived from the Ac-LysargiNase and trypsin treated samples can generate full b and y ion series, which provide mutual complementarity of each other, and allow us to develop a novel algorithm, pNovoM, for de novo sequencing. Using pNovoM to sequence peptides of purified proteins, the accuracy of the sequence was close to 100%. More importantly, from a large-scale yeast proteome sample digested with trypsin and Ac-LysargiNase individually, 48% of all tandem mass spectra formed mirror spectrum pairs, 97% of which contained full coverage of ion series, resulting in precision de novo sequencing of full-length peptides by pNovoM. This enabled pNovoM to successfully sequence 21,249 peptides from 3,753 proteins and interpreted 44-152% more spectra than pNovo+ and PEAKS at a 5% FDR at the spectrum level. Moreover, the mirror protease strategy had an obvious advantage in sequencing long peptides. We believe that the combination of mirror protease strategy and pNovoM will be an effective approach for precision de novo sequencing on both single proteins and proteome samples.

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework.

MOTIVATION: De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing. RESULTS: In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29-102% more correct spectra, and the precision was 11-89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21-50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences. AVAILABILITY AND IMPLEMENTATION: pNovo 3 can be freely downloaded from http://pfind.ict.ac.cn/software/pNovo/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

pValid: Validation Beyond the Target-Decoy Approach for Peptide Identification in Shotgun Proteomics.

As the de facto validation method in mass spectrometry-based proteomics, the target-decoy approach determines a threshold to estimate the false discovery rate and then filters those identifications beyond the threshold. However, the incorrect identifications within the threshold are still unknown and further validation methods are needed. In this study, we characterized a framework of validation and investigated a number of common and novel validation methods. We first defined the accuracy of a validation method by its false-positive rate (FPR) and false-negative rate (FNR) and, further, proved that a validation method with lower FPR and FNR led to identifications with higher sensitivity and precision. Then we proposed a validation method named pValid that incorporated an open database search and a theoretical spectrum prediction strategy via a machine-learning technology. pValid was compared with four common validation methods as well as a synthetic peptide validation method. Tests on three benchmark data sets indicated that pValid had an FPR of 0.03% and an FNR of 1.79% on average, both superior to the other four common validation methods. Tests on a synthetic peptide data set also indicated that the FPR and FNR of pValid were better than those of the synthetic peptide validation method. Tests on a large-scale human proteome data set indicated that pValid successfully flagged the highest number of incorrect identifications among all five methods. Further considering its cost-effectiveness, pValid has the potential to be a feasible validation tool for peptide identification.

MS/MS Spectrum Prediction for Modified Peptides Using pDeep2 Trained by Transfer Learning.

In the past decade, tandem mass spectrometry (MS/MS)-based bottom-up proteomics has become the method of choice for analyzing post-translational modifications (PTMs) in complex mixtures. The key to the identification of the PTM-containing peptides and localization of the PTM-modified residues is to measure the similarities between the theoretical spectra and the experimental ones. An accurate prediction of the theoretical MS/MS spectra of the modified peptides will improve the similarity measurement. Here, we proposed the deep-learning-based pDeep2 model for PTMs. We used the transfer learning technique to train pDeep2, facilitating the training with a limited scale of benchmark PTM data. Using the public synthetic PTM data sets, including the synthetic phosphopeptides and 21 synthetic PTMs from ProteomeTools, we showed that the model trained by transfer learning was accurate (>80% Pearson correlation coefficients were higher than 0.9), and was significantly better than the models trained without transfer learning. We also showed that accurate prediction of the fragment ion intensities of the PTM neutral loss, for example, the phosphoric acid loss (-98 Da) of the phosphopeptide, will improve the discriminating power to distinguish the true phosphorylated residue from its adjacent candidate sites. pDeep2 is available at https://github.com/pFindStudio/pDeep/tree/master/pDeep2 .

pSite: Amino Acid Confidence Evaluation for Quality Control of De Novo Peptide Sequencing and Modification Site Localization.

MS-based de novo peptide sequencing has been improved remarkably with significant development of mass-spectrometry and computational approaches but still lacks quality-control methods. Here we proposed a novel algorithm pSite to evaluate the confidence of each amino acid rather than the full-length peptides obtained by de novo peptide sequencing. A semi-supervised learning approach was used to discriminate correct amino acids from random one; then, an expectation-maximization algorithm was used to adaptively control the false amino-acid rate (FAR). On three test data sets, pSite recalled 86% more amino acids on average than PEAKS at the FAR of 5%. pSite also performed superiorly on the modification site localization problem, which is essentially a special case of amino acid confidence evaluation. On three phosphopeptide data sets, at the false localization rate of 1%, the average recall of pSite was 91% while those of Ascore and phosphoRS were 64 and 63%, respectively. pSite covered 98% of Ascore and phosphoRS results and contributed 21% more phosphorylation sites. Further analyses show that the use of distinct fragmentation features in high-resolution MS/MS spectra, such as neutral loss ions, played an important role in improving the precision of pSite. In summary, the effective and universal model together with the extensive use of spectral information makes pSite an excellent quality control tool for both de novo peptide sequencing and modification site localization.

Modeling Protein Excited-state Structures from "Over-length" Chemical Cross-links.

Chemical cross-linking coupled with mass spectroscopy (CXMS) provides proximity information for the cross-linked residues and is used increasingly for modeling protein structures. However, experimentally identified cross-links are sometimes incompatible with the known structure of a protein, as the distance calculated between the cross-linked residues far exceeds the maximum length of the cross-linker. The discrepancies may persist even after eliminating potentially false cross-links and excluding intermolecular ones. Thus the "over-length" cross-links may arise from alternative excited-state conformation of the protein. Here we present a method and associated software DynaXL for visualizing the ensemble structures of multidomain proteins based on intramolecular cross-links identified by mass spectrometry with high confidence. Representing the cross-linkers and cross-linking reactions explicitly, we show that the protein excited-state structure can be modeled with as few as two over-length cross-links. We demonstrate the generality of our method with three systems: calmodulin, enzyme I, and glutamine-binding protein, and we show that these proteins alternate between different conformations for interacting with other proteins and ligands. Taken together, the over-length chemical cross-links contain valuable information about protein dynamics, and our findings here illustrate the relationship between dynamic domain movement and protein function.

Mapping native disulfide bonds at a proteome scale.

We developed a high-throughput mass spectrometry method, pLink-SS (http://pfind.ict.ac.cn/software/pLink/2014/pLink-SS.html), for precise identification of disulfide-linked peptides. Using pLink-SS, we mapped all native disulfide bonds of a monoclonal antibody and ten standard proteins. We performed disulfide proteome analyses and identified 199 disulfide bonds in Escherichia coli and 568 in proteins secreted by human endothelial cells. We discovered many regulatory disulfide bonds involving catalytic or metal-binding cysteine residues.

Comparison of Percutaneous Kyphoplasty and Bone Cement-Augmented Short-Segment Pedicle Screw Fixation for Management of Kümmell Disease.

BACKGROUND The purpose of this study was to compare the efficacy of percutaneous kyphoplasty (PKP) and bone cement-augmented short segmental fixation (BCA+SSF) for treating Kümmell disease. MATERIAL AND METHODS Between June 2013 and December 2015, 60 patients were treated with PKP or BCA+SSF. All patients were followed up for 12-36 months. We retrospectively reviewed outcomes, including Oswestry Disability Index (ODI), visual analogue scale (VAS), and kyphotic Cobb angle. RESULTS VAS, ODI, and Cobb angle, measured postoperatively and at the final follow-up, were lower than those measured preoperatively in both groups (P<0.05). VAS, ODI, and Cobb angle measured postoperatively demonstrated no significant differences when compared with those measured at the final follow-up in the PKP group (P>0.05). In the BCA+SSF group, VAS and ODI at the final follow-up were lower than those measured postoperatively (P<0.05), but no significant difference was found in the Cobb angle (P>0.05). The PKP group had better VAS and ODI than the BCA+SSF group, postoperatively (P<0.05). No significant difference was found in VAS and ODI at the final follow-up (P>0.05) or the Cobb angle measured postoperatively and at the final follow-up (P>0.05) between the 2 groups. Operative time, blood loss, and hospital stay in the PKP group were lower than those in the BCA+SSF group (P<0.05). No significant difference was found in complications (P>0.05). CONCLUSIONS PKP patients had better early clinical outcomes, shorter operation times and hospital admission times, and decreased blood loss, but had similar complications, radiographic results, and long-term clinical outcomes compared with BCA+SSF patients.

Open-pNovo: De Novo Peptide Sequencing with Thousands of Protein Modifications.

De novo peptide sequencing has improved remarkably, but sequencing full-length peptides with unexpected modifications is still a challenging problem. Here we present an open de novo sequencing tool, Open-pNovo, for de novo sequencing of peptides with arbitrary types of modifications. Although the search space increases by ∼300 times, Open-pNovo is close to or even ∼10-times faster than the other three proposed algorithms. Furthermore, considering top-1 candidates on three MS/MS data sets, Open-pNovo can recall over 90% of the results obtained by any one traditional algorithm and report 5-87% more peptides, including 14-250% more modified peptides. On a high-quality simulated data set, ∼85% peptides with arbitrary modifications can be recalled by Open-pNovo, while hardly any results can be recalled by others. In summary, Open-pNovo is an excellent tool for open de novo sequencing and has great potential for discovering unexpected modifications in the real biological applications.

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

pTop 1.0: A High-Accuracy and High-Efficiency Search Engine for Intact Protein Identification.

There has been tremendous progress in top-down proteomics (TDP) in the past 5 years, particularly in intact protein separation and high-resolution mass spectrometry. However, bioinformatics to deal with large-scale mass spectra has lagged behind, in both algorithmic research and software development. In this study, we developed pTop 1.0, a novel software tool to significantly improve the accuracy and efficiency of mass spectral data analysis in TDP. The precursor mass offers crucial clues to infer the potential post-translational modifications co-occurring on the protein, the reliability of which relies heavily on its mass accuracy. Concentrating on detecting the precursors more accurately, a machine-learning model incorporating a variety of spectral features was trained online in pTop via a support vector machine (SVM). pTop employs the sequence tags extracted from the MS/MS spectra and a dynamic programming algorithm to accelerate the search speed, especially for those spectra with multiple post-translational modifications. We tested pTop on three publicly available data sets and compared it with ProSight and MS-Align+ in terms of its recall, precision, running time, and so on. The results showed that pTop can, in general, outperform ProSight and MS-Align+. pTop recalled 22% more correct precursors, although it exported 30% fewer precursors than Xtract (in ProSight) from a human histone data set. The running speed of pTop was about 1 to 2 orders of magnitude faster than that of MS-Align+. This algorithmic advancement in pTop, including both accuracy and speed, will inspire the development of other similar software to analyze the mass spectra from the entire proteins.

Increasing the Depth of Mass-Spectrometry-Based Structural Analysis of Protein Complexes through the Use of Multiple Cross-Linkers.

Chemical cross-linking of proteins coupled with mass spectrometry (CXMS) is a powerful tool to study protein folding and to map the interfaces between interacting proteins. The most commonly used cross-linkers in CXMS are BS(3) and DSS, which have similar structures and generate the same linkages between pairs of lysine residues in spatial proximity. However, there are cases where no cross-linkable lysine pairs are present at certain regions of a protein or at the interface of two interacting proteins. In order to find the cross-linkers that can best complement the performance of BS(3) and DSS, we tested seven additional cross-linkers that either have different spacer arm structures or that target different amino acids (BS(2)G, EGS, AMAS, GMBS, Sulfo-GMBS, EDC, and TFCS). Using BSA, aldolase, the yeast H/ACA protein complex, and E. coli 70S ribosomes, we showed that, in terms of providing structural information not obtained through the use of BS(3) and DSS, EGS and Sulfo-GMBS worked better than the other cross-linkers that we tested. EGS generated a large number of cross-links not seen with the other amine-specific cross-linkers, possibly due to its hydrophilic spacer arm. We demonstrate that incorporating the cross-links contributed by the EGS and amine-sulfhydryl cross-linkers greatly increased the accuracy of Rosetta in docking the structure of the yeast H/ACA protein complex. Given the improved depth of useful information it can provide, we suggest that the multilinker CXMS approach should be used routinely when the amount of a sample permits.

A note on the false discovery rate of novel peptides in proteogenomics.

MOTIVATION: Proteogenomics has been well accepted as a tool to discover novel genes. In most conventional proteogenomic studies, a global false discovery rate is used to filter out false positives for identifying credible novel peptides. However, it has been found that the actual level of false positives in novel peptides is often out of control and behaves differently for different genomes. RESULTS: To quantitatively model this problem, we theoretically analyze the subgroup false discovery rates of annotated and novel peptides. Our analysis shows that the annotation completeness ratio of a genome is the dominant factor influencing the subgroup FDR of novel peptides. Experimental results on two real datasets of Escherichia coli and Mycobacterium tuberculosis support our conjecture. CONTACT: yfu@amss.ac.cn or xupingghy@gmail.com or smhe@ict.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The impact of posterior temporary internal distraction on stepwise corrective surgery for extremely severe and rigid scoliosis greater than 130°.

AIM: To investigate the efficacy and safety of temporary internal distraction corrective surgery for extremely severe scoliosis. METHODS: Eleven scoliosis patients (3 males and 8 females) with curves ≥130° (mean 148.8°; range 130°-157°) who underwent a two-stage surgery, including a posterior temporary internal distraction correction and definitive posterior spinal correction with posterior pedicle screw instrumentation from 2008 to 2011 were retrospectively reviewed. Minimum follow-up was 2-years (mean 41.8 months; range 27.0-63.0 months). The analysis focused on the impact of temporary internal distraction on curve correction, pulmonary function tests (PFTs), complications and surgical outcomes. Neurosurveillance of sensory (somatosensory-evoked potentials) and motor (motor-evoked potentials) potential was performed in all cases. Posterior instrumentation was used in all patients. RESULTS: After the use of internal distraction, the preoperative major curve (mean 148.8°; range 130°-157°) was corrected to a mean of 79° (range 63°-87°), the T5-T12 kyphosis Cobb angle (mean 79°; range 30°-97°) was corrected to a mean of 59° (range 20°-75°), the coronal imbalance (mean 0.8 cm; range -3.6 to 2.8 cm) was improved to a mean of 0.6 cm (range -1.5 to 2.0 cm), the forced vital capacity percentage (FVC%) was improved from 59.3 ± 11.6 to 68.7 ± 13.7, and the forced expiratory volume in 1 s (FEV1%) was improved from 61.4 ± 13.6 to 71.3 ± 9.3. The average increase in body height was 6.7 cm, and the dorsum razor was corrected to 3-5 cm. During definitive surgery, the final major curves were corrected to a mean of 55° (range 32°-72°), the T5-T12 kyphosis Cobb was corrected to 35° (range 15°-68°), the coronal imbalance was improved to 0.5 cm (range -1.2 to 1.8 cm), the FVC% was improved to 71.2 ± 8.3, the FEV1% was improved to 76.3 ± 16.7, the increase in body height was 3.1 cm, and the dorsum razor was corrected to 1-3 cm. The mean interval time between the two surgeries was 3.5 months. None of the patients exhibited postoperative neurologic deficits or infections. No instrument complications were found during the final follow-up. CONCLUSIONS: Temporary internal distraction in a two-stage corrective surgery provided patients who had extremely severe and rigid scoliosis, an effective and safe solution for scoliosis without significant complications.

Identification of cross-linked peptides from complex samples.

We have developed pLink, software for data analysis of cross-linked proteins coupled with mass-spectrometry analysis. pLink reliably estimates false discovery rate in cross-link identification and is compatible with multiple homo- or hetero-bifunctional cross-linkers. We validated the program with proteins of known structures, and we further tested it on protein complexes, crude immunoprecipitates and whole-cell lysates. We show that it is a robust tool for protein-structure and protein-protein-interaction studies.

Nematode sperm maturation triggered by protease involves sperm-secreted serine protease inhibitor (Serpin).

Spermiogenesis is a series of poorly understood morphological, physiological and biochemical processes that occur during the transition of immotile spermatids into motile, fertilization-competent spermatozoa. Here, we identified a Serpin (serine protease inhibitor) family protein (As_SRP-1) that is secreted from spermatids during nematode Ascaris suum spermiogenesis (also called sperm activation) and we showed that As_SRP-1 has two major functions. First, As_SRP-1 functions in cis to support major sperm protein (MSP)-based cytoskeletal assembly in the spermatid that releases it, thereby facilitating sperm motility acquisition. Second, As_SRP-1 released from an activated sperm inhibits, in trans, the activation of surrounding spermatids by inhibiting vas deferens-derived As_TRY-5, a trypsin-like serine protease necessary for sperm activation. Because vesicular exocytosis is necessary to create fertilization-competent sperm in many animal species, components released during this process might be more important modulators of the physiology and behavior of surrounding sperm than was previously appreciated.

The beneficial effect of Batroxobin on blood loss reduction in spinal fusion surgery: a prospective, randomized, double-blind, placebo-controlled study.

OBJECTIVE: Our objective was to evaluate the efficacy and safety of Batroxobin on blood loss during spinal operations. METHODS: After obtaining approval from the ethics committee at the hospital along with informed written consent, we performed a double-blind, randomized, placebo-controlled study with 100 patients who were randomized equally into 2 groups (Batroxobin and placebo). Patients received either 2 ku IV 15 min before surgery and followed 1 ku IM of Batroxobin following surgery, or an equivalent volume of placebo (normal saline). Cost of Batroxobin treatment is amounted to 84.75 euros. The primary outcomes were intraoperative, 24 h postoperative, and total perioperative blood loss. Secondary outcomes were hemoglobin (Hb), red blood cell count (RBC), the volume of blood/fluid transfusion intraoperatively, and 24 h postoperatively. Safety evaluation parameters were the incidence of venous thrombosis in the lower extremities, active partial thromboplastin time, prothrombin time, thrombin time, and fibrinogen. The data were analyzed using the Statistical Package for the Social Science Version 12.0. The results were presented as mean ± SEM. The Mann-Whitney test and Independent Student t test, when appropriate, were used to compare the 2 groups, and differences were considered significant if the P value was <0.05. RESULTS: 88 patients were included in the analysis while 12 patients were withdrawn from the study due to extended surgical duration, change of surgical procedure, or after the patients' request. The total perioperative blood loss was approximately 31% lower in patients given Batroxobin versus placebo (700.5 ± 45.81 vs 485.7 ± 30.01 mL, P = 0.001). The Batroxobin group had significantly less intraoperative blood loss (326.1 ± 24.16) compared to the placebo group (556.0 ± 43.58), but there was no difference in the amount of blood/fluid transfused, postoperatively Hb, or RBC between the two groups. After the operation, coagulation parameters were not significantly different between the 2 groups at the days 1 or 3 postoperatively. No adverse events related to the use of Batroxobin were recorded. There were no cases of superficial wound infection. None of the subjects died during the study. CONCLUSIONS: In this study, prophylactic use of Batroxobin provided an effective and cheap method for reducing blood loss without coagulopathy during or after operations. The use of Batroxobin for patients undergoing one-level PLIF surgery safely and effectively reduced the total amount of perioperative blood loss.

pQuant improves quantitation by keeping out interfering signals and evaluating the accuracy of calculated ratios.

In relative protein abundance determination from peptide intensities recorded in full mass scans, a major complication that affects quantitation accuracy is signal interference from coeluting ions of similar m/z values. Here, we present pQuant, a quantitation software tool that solves this problem. pQuant detects interference signals, identifies for each peptide a pair of least interfered isotopic chromatograms: one for the light and one for the heavy isotope-labeled peptide. On the basis of these isotopic pairs, pQuant calculates the relative heavy/light peptide ratios along with their 99.75% confidence intervals (CIs). From the peptides ratios and their CIs, pQuant estimates the protein ratios and associated CIs by kernel density estimation. We tested pQuant, Census and MaxQuant on data sets obtained from mixtures (at varying mixing ratios from 10:1 to 1:10) of light- and heavy-SILAC labeled HeLa cells or (14)N- and (15)N-labeled Escherichia coli cells. pQuant quantitated more peptides with better accuracy than Census and MaxQuant in all 14 data sets. On the SILAC data sets, the nonquantified "NaN" (not a number) ratios generated by Census, MaxQuant, and pQuant accounted for 2.5-10.7%, 1.8-2.7%, and 0.01-0.5% of all ratios, respectively. On the (14)N/(15)N data sets, which cannot be quantified by MaxQuant, Census and pQuant produced 0.9-10.0% and 0.3-2.9% NaN ratios, respectively. Excluding these NaN results, the standard deviations of the numerical ratios calculated by Census or MaxQuant are 30-100% larger than those by pQuant. These results show that pQuant outperforms Census and MaxQuant in SILAC and (15)N-based quantitation.