Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups

Domain: neuroscience
Published: 2026-02-10
Editorial outcome: minor_revision

Abstract
While LC retention time prediction of peptides and their modifications has proven useful, widespread adoption and optimal performance are hindered by variations in experimental parameters. These variations can render retention time prediction models inaccurate and dramatically reduce the value of predictions for identification, validation, and DIA spectrallibrary generation. To date, mitigation of these issues has been attempted through calibration or by training bespoke models for specific experimental setups, with only partial success. We here demonstrate that transfer learning can successfully overcome these limitations by leveraging pre-trained model parameters. Remarkably, this approach can even fit highly performant models to substantially different peptide modifications and LC conditions than those on which the model was originally trained. This impressive adaptability of transfer learning makes it a highly robust solution for accurate peptide retention time prediction across a very wide variety of imaginable proteomics workflows.

Introduction
Liquid chromatography (LC) is indispensable in mass spectrometry (MS) analysis of bottom-up proteomics1. Through the separation of peptides based on their physicochemical properties, LC reduces the complexity of samples presented to the MS instrument, leading to less ionization competition, improved sensitivity for both data-dependent and data-independent acquisitions, and reduced chimericity in fragmentation spectra (MS2)2,3. Additionally, retention time measurements provide a crucial additional information dimension for the downstream analysis of acquired data4,5.

However, leveraging retention time information in downstream analysis requires the retention time of a peptide to be known, either experimentally from previous observations, or predicted by a model6,7. As a result, many such prediction models currently exist, trained with classical machine learning (ML) and deep learning (DL) algorithms8–13. These models learn the mapping between peptide sequences (or features derived from these sequences) and empirical LC retention times. Once trained, these models can also generate predictions for peptides not previously observed experimentally. Retention time prediction models have been successfully applied to various tasks in proteomics workflows, such as improving identification confidence4,5,14, designing more efficient experiments15,16, identifying chimeric fragmentation spectra17, and creating spectral libraries for data-independent acquisition (DIA) searches18–21.

Despite their success, the accuracy of peptide retention time predictions is heavily influenced by experimental parameters, including sample pH, stationary phase of the column, pressure, and temperature22–24. Even slight variations in these parameters can alter the retention mechanism, affecting elution times and the order in which peptides elute. Consequently, a model trained on a specific LC setup cannot be applied directly to different setups without proper calibration. This limitation is particularly problematic when predictions need to be applied across varied LC setups without the ability to retrain the model entirely for each setup. Moreover, current models can only predict retention times for peptides carrying modifications that closely resemble those in the training data9,12,13. For modifications with drastically different physicochemical properties, current models need to be trained on these specific peptide modifications, requiring large amounts of relevant data.

To apply trained models across different setups, a calibration step using indexed retention time (iRT) peptides or other internally identified peptides in the same run is therefore often used instead of model retraining. This calibration assumes that the predicted elution order of peptides is conserved23,24. However, even slightly different conditions can change the elution order, thus leading to poor performance with calibration25.

However, transfer learning (also called model fine-tuning) offers a generic solution to maintain high performance across diverse data sets26, by initializing a setup specific model with parameters from a previously trained model rather than at random. This strategy is commonly used to adapt models trained on large datasets to smaller datasets with related prediction tasks27. While transfer learning has been applied to retention time prediction previously10,21,28, we here demonstrate in-depth that transfer learning with DeepLC can improve upon both calibration and retraining in nearly every situation, thus proving the general applicability of the approach. Moreover, and importantly, we show that transfer learning allows DeepLC to adapt to newly presented, complex peptide modifications as well as to substantially different LC setups, showing the promise of transfer learning for accurate predictions in even the most challenging applications.

Results
The “results” section is divided into five evaluations. The first evaluation consists of evaluating the performance of DeepLC with calibration, training a new model, and with transfer learning. The second performs a large-scale evaluation of transfer learning on 474 datasets. The third evaluates transfer learning’s ability to adapt to substantially different peptide modifications. The fourth evaluates the ability of transfer learning to adapt to basic LC conditions when initially trained on acidic LC conditions. The last evaluates the generalization ability of transfer learning by predicting retention times of peptides with modifications that are unseen during training.

Performance evaluation transfer learning
In the first evaluation, four different datasets are highlighted. For each dataset, a DeepLC model was fitted using three methods: calibration (mapping predictions to observations), randomly initialized parameters (new model), and parameters learned from a different dataset (transfer learning).

To illustrate the performance of these methods, we used the data from PXD030003 (Fig. 1a–c), a medium-sized data set with 27,954 training peptides. This size means there are enough examples to train a sufficiently accurate model from scratch. Indeed, the model starting from randomly initialized model parameters performs better than calibration in terms of its MAE, 95th error percentile, and Pearson correlation between predictions and observations (Fig. 1a, b). This performance difference is expected as model parameters are not fitted to the data for calibration, and calibration is only expected to perform very well when the assumptions of a perfect conservation of elution order are met. This latter condition, however, is not likely to be met when experimental conditions change as, even under slightly different LC conditions, peptides that are close in retention time can switch places in their elution order.

When the initial model parameters were set using previously fitted values through transfer learning, the model performed better across all metrics compared to the model starting with random parameters (Fig. 1b, c). This improvement is likely due to the previously obtained model parameters being close to optimal. Indeed, the applicability of these previously fitted parameters is also illustrated by the reasonably accurate predictions with calibration, showing that these existing parameters only need slight fine-tuning to the new LC setup.

Even though transfer learning showed top performance in this example, there is a clear correlation between training data set size and the performance of the three methods for prediction, as shown by the learning curves for three different datasets (Fig. 1d–f). Models with random starting parameters underperformed compared to transfer learning, but ultimately achieve better performance than calibration when sufficient training examples are available (here more than 25,000). For a very limited number of training peptides (less than 100), calibration performed the same or better than transfer learning. However, calibration then quickly reached a plateau, while transfer learning continued to improve with more data, easily outperforming both calibration and the models with random starting parameters.

Comprehensive analysis of 474 datasets
We have so far examined only four datasets to highlight differences between the three methods for prediction. Here, evaluation includes a much larger set of 362 different PRIDE projects and 474 evidence files that were previously analyzed with MaxQuant. This comprehensive set represents a wide range of different proteomics experiments from various labs, with substantial differences in training set sizes.

Observations from the highlighted datasets in Fig. 1 were consistent with the larger analysis (Fig. 2). In all datasets except one, where performance was almost equal, transfer learning outperformed models with randomly initialized parameters (Fig. 2a). As expected, the number of training peptides correlates highly with the observed difference in performance between the two methods for prediction. For example, datasets with 1000 to 10,000 training peptides showed a 1–2.5% decrease in RMAE with transfer learning compared to training a new model, while larger data sets with more than 10,000 peptides showed a more modest improvement of between 0% and 1% decrease in RMAE.

Transfer learning also outperformed calibration in most cases, with an average RMAE decrease of 1.4%. Datasets where calibration was performed better typically had fewer than 1000 training peptides. Further analysis binned datasets by size, showing that transfer learning and calibration performed equally well for the smallest bin (0–1000 peptides). However, for larger bins, transfer learning showed substantial improvements in RMAE (Fig. 2c). For completeness all data sets with their observed and predicted retention times are separately plotted in Supplementary Figs. 2–18 and performance metrics for data sets are available in Supplementary Data 1.

TMPP evaluation transfer learning
Although the evaluation on a large number of public domain datasets was comprehensive, it did not test the ability to apply transfer learning in substantially different contexts with minimal training data. This section, therefore, evaluates transfer learning for peptides carrying a TMPP modification (Fig. 3a–c) with a 10-fold CV approach. Due to the size and added hydrophobicity of this modification (572.18 Da), there is a significant change in the retention time of modified peptides.

As expected, retention times predicted with calibration differed significantly from observed retention times for TMPP-modified peptides (Fig. 3a). Over 96% of datasets in Fig. 2 with calibrations showed higher correlation compared to the TMPP data set with calibration (R = 0.82).

On the other hand, a newly trained model is already able to fit the context of TMPP modifications quite effectively, as the Pearson correlation falls within the best 28% of the data sets presented in the comprehensive analysis presented in Fig. 2. However, transfer learning still outperformed random parameter initialization. The extent of this improvement depended on the number of training peptides, with the largest differences observed for training sets below 2000 peptides (Fig. 3d), but with the MAE continuing to improve for larger training set sizes. The MAE of 0.913 min achieved for transfer learning at 500 peptides was only matched by the newly trained model at 1500–2000 peptides. While there is slight variation in performance when selecting different subsets for training, the order between the three different methods remains mostly consistent across different training set sizes.

Basic LC evaluation transfer learning
In the previous analysis, the TMPP modification could still be accounted for by a newly trained model, as the underlying retention mechanism is still the same while the model must extrapolate to a novel modification; something DeepLC has been designed to do. In this section, however, the analysis is now performed with a 10-fold CV approach on an LC setup under basic pH conditions. As a result of this drastic change in pH, the retention mechanism changed. Models fitted for acidic LC conditions, which are all models trained up till now, are therefore unable to predict accurate retention times on this setup. Calibration underperformed markedly in this context, with a Pearson correlation (R = 0.601) worse than 99% of datasets analyzed (Fig. 4a). Calibration performs poorly because it is unable to optimize its model parameters to resemble the retention mechanism under basic pH conditions.

Even though the model with calibration is unable to accurately predict for this different retention mechanism, transfer learning is still able to utilize previously fitted parameters. This ability is shown by the improvement of transfer learning over fitting a model from scratch (Fig. 4b, c), which demonstrates that even for substantial differences in data sets, peptide modifications, and LC conditions, fine-tuning a previously fitted model optimally improves performance. Again, as previously observed, the number of training peptides had a significant effect on performance (Fig. 4d). Here, the largest improvements are seen up to around 750 peptides, and a newly trained model only reached similar performance with approximately three times as many training peptides (around 2500). While there is slight variation in performance for small training set sizes, this variation mostly disappears when more than 500 training peptides are used for training.

Modified peptides evaluation
Previous evaluations demonstrate that transfer learning can outperform both calibration and training a model from scratch. This section evaluates the performance of transfer learning when presented with peptides containing modifications not seen during training (Fig. 5). This dataset contains a total of 5693 peptides containing modifications. The count of peptides carrying each specific PTM is indicated below the corresponding PTM in Fig. 5. By subtracting the count of peptides for each PTM from the total number of peptides in the data set, the number of peptides used in training is calculated.

The scatterplot in Fig. 5 compares the mean absolute error (MAE) for transfer learning against a model trained from scratch. There is a modest, but overall, mostly consistent improvement in performance for transfer learning in thirteen out of the fourteen modifications. The only exception is trimethyl where the model trained from scratch shows a much lower MAE. A boxplot showing the residuals confirms these findings between transfer learning and the new model (Supplementary Fig. 19). Furthermore, the results show that calibration has the highest error for twelve out of fourteen modifications.

Methods
Architecture
This study uses the same model architecture as was previously presented for DeepLC: a convolutional deep learning framework with four distinct paths for a given encoded peptide. The first two paths consist of convolutional layers that process the peptide’s atomic composition (C, H, N, O, P, and S). The third path also uses convolutional layers but relies on a conventional one-hot encoding of amino acids. Finally, the fourth path utilizes densely connected layers to encode global structural features, such as peptide length and the total sum of atoms. All paths are then flattened and passed through six densely connected layers. A comprehensive description and evaluation of this architecture can be found in the original DeepLC publication9.

Datasets and evaluation
To assess the performance of transfer learning with DeepLC, we analyzed 474 peptide identification files from 362 PRIDE projects across a wide variety of organisms (Supplementary Data 1). These data sets were selected because they were previously analyzed with MaxQuant29 and the resulting evidence files could be downloaded and parsed in a uniform manner. Moreover, many projects were historically analyzed with MaxQuant and it contains information about the elution apex, which is preferred over the retention time of the identification as it is more reproducible. To ensure consistency in retention times across different runs within a single evidence file, an alignment procedure was necessary. This alignment method, detailed on ProteomicsML30, uses overlapping peptides between runs for calibration. This procedure was performed separately for each evidence file, resulting in 474 internally aligned datasets for downstream usage.

The alignment procedure begins with the run that has the most overlapping peptide sequences with all other datasets. After identifying this run, the run with the second highest overlap is aligned to it. This alignment involves a two-stage fitting process using a Generalized Additive Model (GAM) with splines. In the first stage, all overlapping peptides are used to fit a calibration curve that maps retention times between the second and first run. Next, any overlapping peptides are removed that have a relative absolute error greater than 0.5% between retention times of the two runs after calibration. These remaining peptides are used to fit a more robust calibration curve. This second-stage calibration curve is then used to align all peptides (overlapping and non-overlapping) of the second run against the first. The median retention times of overlapping peptides are taken as the new representative retention time for that peptide sequence. This process is repeated iteratively until all runs are aligned, with the median taken regarding all previously aligned retention time values. After the alignment, peptide sequences are filtered on at least five observations and a maximum standard deviation of 2.0 iRT or minutes, ensuring only confident peptide and retention time pairs remain.

In order to evaluate the calibration procedure, a standard deviation is calculated for each peptidoforms across different LC-MS runs before calibration and after calibration. Each standard deviation is normalized by the elution length (retention time between the first and last observed peptide after calibration). This evaluation in Supplementary Fig. 1 shows that there is no substantial difference in standard deviation for the majority of datasets, but when calibration is required between runs they are properly aligned and show a mean or median standard deviation below 1%, lowering the standard deviation on average by 0.35% across all datasets.

Unless noted otherwise, each dataset was randomly divided into a test set (10%), a validation set (5%), and a training set (85%). For each of the learning-curves, a single 75%/25% train-test split was used, then models were trained on training subsets that become gradually larger and tested on the same test set. This strategy for the learning curves was repeated for 10 different seeds. All models were trained for up to 300 epochs, with early stopping based on the validation set if available. Prediction performance was evaluated using three commonly used metrics: MAE, Pearson correlation (R), and Δt95%, which describes the error for a retention time window that contains 95% of the peptides in the error distribution. To ensure comparability between experiments, MAE and Δt95% were normalized by dividing them by the retention time difference between the first and last detected peptide in each data set. These metrics are referred to as relative MAE and relative Δt95%.

Each evaluation presented here compared three methods: Transfer learning, calibration with a GAM, and a model with randomly initialized parameters (a new model). In these evaluations the calibration method uses the training set to determine a mapping from predicted values from a pretrained model using a GAM with splines. In the case of training a new model from scratch, the model is initialized with random weights drawn from a normal distribution (μ=0.0, σ=1.0). Finally, for transfer learning the parameters before training are set based on a model fitted on the dataset from PXD005573. This data set was chosen as it is one of the largest datasets (n = 113,213) and most accurate models in DeepLC. It is important to note that the only difference between the models trained from scratch (with randomly initialized parameters) and those trained with transfer learning are the initial parameters. All model parameters remained trainable, as no layers were frozen, and hyperparameters remained the same.

General training procedure
For each data set and prediction method three distinct models in DeepLC must be fitted, which differ in the kernel sizes for their convolutional layers, and these are then used in an ensemble that is averaged to obtain a single prediction per peptidoform. For transfer learning, the MD5 hash of the architecture is used to match the corresponding hyperparameters.

Evaluation of a TMPP-labeling dataset
To evaluate the performance of transfer learning in predicting retention times for peptides carrying substantially different modifications, an experiment with (N-succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) labeling was conducted. This very large, and rather atypical peptide modification (768.54 Da) causes a significant shift towards a more hydrophobic retention time, making it an effective test for transfer learning for previously unseen peptide modifications.

Evaluation of a highly basic LC setup
To evaluate the performance of transfer learning on a significantly different LC setup, an LC run under basic conditions (pH = 9) was used. These basic conditions drastically change the retention mechanism, providing an ideal testing ground for transfer learning’s ability to adapt to new LC conditions. In this evaluation, similarly to the TMPP evaluation, a 10-fold CV approach was used to provide more reliable performance results due to the limited size of this dataset (n = 6363 peptidoforms).

HeLa S3 cells lysate was prepared and further digested by trypsin (Promega, Madison, WI, USA) and GluC (Roche, Mannheim, Germany) at 37 °C and 25 °C, respectively. Digested peptides were analyzed using an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific, San Jose, CA, USA) coupled to an UltiMate 3000 LC system (Thermo Fisher Scientific, Germering, Germany) operating in high-pH RP-HPLC mode (mobile phases: 2.5 mM imidazole and 3% IPA in water; 2.5 mM imidazole and 3% IPA in 95% acetonitrile). A self-packed analytical column (XBridge Premier Peptide BEH C18, 2.5 μm, 75 μm i.d., 18 cm length, 130 Å; Waters, Milford, MA, USA) was used for the analysis. The mass spectrometer was operated in positive ion DDA mode; both MS1 and MS2 spectra were acquired in Orbitrap. The exact details and methodology of the study are described in Penanes et al.33.

Mass spectrometric data were converted to mzML format by ThermoRawFileParser (1.3.4) and searched with MSGF+ (v2022.01.07) against Uniprot human proteome (20185 proteins) supplemented with reversed decoys. The results were rescored by Percolator (3.05). LC-MS elution profiles were produced by Biosaur 2, and confident peptide-spectrum matches (q < 0.01) were mapped to the features by accurate monoisotopic m/z (5 ppm tolerance), retention time, and charge. The apex retention time of the LC-MS feature was used as the elution time of the corresponding peptide. For the peptides detected more than once across the runs, the average elution time was used, and peptides with an elution time coefficient of variation above 10% were excluded. The data was deposited in PRIDE under accession number PXD064311 (Experiment 2, March).

Evaluation of unseen modified peptides
DeepLC has the ability to predict retention times of unseen modifications accurately. This ability of DeepLC was previously evaluated with random starting parameters on fourteen modifications from Zolg et al.34, this dataset contains a total of 5693 peptides. Here, the same analysis is performed, with the inclusion of transfer learning and calibration. Each modification is evaluated by excluding all peptides carrying that modification and using the remainder for training or calibration. The generalization performance of all three methods is then evaluated on the excluded peptides carrying the specific modification. For transfer learning and calibration, the model trained on PXD005573 is used.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study focuses on model fine-tuning (transfer learning) for retention time (RT) prediction in dataset- or run-specific\nanalyses. The authors demonstrate that transfer learning achieves better prediction performance compared to conventional\ncalibration or full re-training approaches. However, this concept has already been widely adopted in existing tools such as\nDeepRT, AutoRT, AlphaPeptDeep, and DiaNN. These tools are also able to fine-tune the model for PTMs. So the results\ndid not particularly surprise me. While the authors evaluated their method on a large number of datasets, the methodology\nand findings do not present substantial novelty to the field.\nCompared to other approaches, the distinguishing feature of DeepLC lies in its ability to predict RTs for peptides with\nunseen post-translational modifications (PTMs). As a natural continuation of this work, I believe DeepLC should further\nemphasize RT prediction for unseen PTMs following model fine-tuning based on “seen” PTMs. This would clearly set it\napart from other methods. If this can be successfully demonstrated, the novelty and impact would be sufficient to merit\npublication in Nature Communications.", "reviewer_2": "Transfer learning is a machine learning technique that enables a deep learning model trained on large-scale data to adapt to\na new data domain, allowing effective predictions even with limited new data. This paper builds upon the previously\ndeveloped DeepLC model and applies transfer learning to predict peptide retention times in bottom-up proteomics data\nobtained from different liquid chromatography (LC) environments. The authors provide experimental results demonstrating\nthat transfer learning yields superior performance compared to conventional calibration methods or training new models from\nscratch. Furthermore, by applying their approach to various public datasets, the authors show that the effectiveness of\ntransfer learning is correlated with the size of the dataset used for fine-tuning.\n1. (Lines 90–102) The manuscript describes an alignment method used to calibrate retention times across runs. However, it\nis unclear whether this method is a novel contribution of this paper or if it has been previously published and validated. If this\nmethod is based on prior work (e.g., from ProteomicsML), the relevant references should be cited. If it is newly proposed in\nthis manuscript, additional explanation regarding its appropriateness and an evaluation of its performance would strengthen\nthe paper.\n2. The manuscript evaluates transfer learning using two specific datasets: one involving TMPP-labelled peptides and\nanother obtained from a highly basic LC setup. However, the exact number of peptides in these datasets is not explicitly\nstated. While the approximate sizes can be inferred from Figures 3d and 4d, providing the exact dataset sizes in the main\ntext would improve transparency and reproducibility.\n3. (Line 198) The manuscript claims that transfer learning consistently shows a decrease in MAE as training data increases,\ncompared to calibration as the size of the training data increases (Figure 1d–f). However, this trend appears to be evident\nprimarily in Figure 1f, while Figures 1d and 1e show less consistent patterns. Additionally, the manuscript does not explain\nwhy PXD016632, PXD017478, and PXD002929 were selected from the 362 PRIDE projects for this comparison. Clarifying\nthe rationale for choosing these datasets would help readers better understand the observed performance differences.\n4. It is unclear whether the learning curves presented in Figures 1, 3, and 4 represent training error or test error. If the curves\nshow training error, the corresponding performance on the test set should also be reported.\n5. The manuscript states that all models were trained using a 10:5:85 split for test, validation, and training sets. However,\ncross-validation is a standard approach for robust performance evaluation, especially when datasets are small. For the\nrelatively limited TMPP-labelled and basic LC datasets, k-fold cross-validation (e.g., 10-fold) would provide a more reliable\nassessment of model performance. The authors should consider including cross-validation results or justify why they chose\nnot to use this approach.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "We would like to thank the reviewer for their constructive and thoughtful review. The\nrecommended analysis demonstrating possible unobserved modifications via transfer learning\nhas strengthened the manuscript's conclusions.\nWe fully agree with the reviewer that transfer learning (or fine-tuning) has been previously shown\nto be beneficial for retention time prediction and is widely adopted. However, to the current day\nthere has not been a study that comprehensively analyzed the impact of transfer learning on such\na wide scale (i.e., 362 PRIDE projects). Furthermore, we show that transfer learning is not only\nbeneficial for regular LC-MS proteomics workflows but extends to two highly relevant situations\nfor fine-tuning: (i) modifications (TMPP) that are very different from anything trained on and (ii)\nsubstantially different chromatographic setups (basic conditions). Importantly, there is currently\nno paper that shows the ability of models to transfer learn to these scenarios that are so\nsubstantially different.\nCompared to other approaches, the distinguishing feature of DeepLC lies in its ability to\npredict RTs for peptides with unseen post-translational modifications (PTMs). As a natural\ncontinuation of this work, I believe DeepLC should further emphasize RT prediction for\nunseen PTMs following model fine-tuning based on “seen” PTMs. This would clearly set it\napart from other methods. If this can be successfully demonstrated, the novelty and impact\nwould be sufficient to merit publication in Nature Communications.\nWe fully agree with the reviewer that adding the effect of transfer learning for (unobserved)\nmodifications would strengthen the manuscript. Therefore, we performed similar evaluations as\npreviously presented in DeepLC1. Specifically, the performance for unseen modifications with\nand without fine-tuning is compared on a dataset with synthetic modified peptides2. For training,\none of the modifications was removed from the data and is used as the test set. For each\nmodification a previously trained model (“Calibration”), a model with randomly initialized\nparameters (“New model”), and a model fine-tuned (“Transfer Learning”) on the synthetic\npeptide data are then compared.\nThe results show that there is a modest, but overall, mostly consistent improvement in\nperformance for “Transfer learning” (Figure 1 in this document; Figure 5 in the manuscript). The\nonly exception (out of the fourteen tested) is trimethyl where the model trained from scratch\nshows a much lower mean absolute error (MAE). The figure along with its text has been added to\nthe results section of the manuscript. As “Calibration” performed substantially worse, we opted\nto include “Calibration” only in the supplemental figure (Figure 2 in this document;\nSupplementary Figure 19 in the manuscript).\nWe would like to thank the reviewer for the insightful comment. We feel that the new analysis has\nsubstantially strengthened the conclusions of the manuscript.\nWe would like to thank the reviewer for their constructive and thoughtful comments. The\nadditional analysis regarding the calibration and cross-validation have strengthened the\nconclusions of the manuscript.\n1. (Lines 90–102) The manuscript describes an alignment method used to calibrate retention\ntimes across runs. However, it is unclear whether this method is a novel contribution of this\npaper or if it has been previously published and validated. If this method is based on prior\nwork (e.g., from ProteomicsML), the relevant references should be cited. If it is newly\nproposed in this manuscript, additional explanation regarding its appropriateness and an\nevaluation of its performance would strengthen the paper.\nThis method is described in ProteomicsML however, it was not evaluated on a large amount of\ndata. Therefore, we agree with the reviewer that this evaluation should still be performed.\nFurthermore, the amount of data tested in this manuscript allowed us to do a more thorough\nevaluation of the calibration approach.\nFor this analysis we are comparing the standard deviation across runs for the same peptidoforms\n(Figure 3 in this document; Supplementary Figure 1 in the manuscript). For each peptidoform we\nobtain a standard deviation across different LC-MS runs before calibration and after calibration.\nEach standard deviation is normalized by the elution length (retention time between the first and\nlast observed peptide after calibration). To summarize the normalized standard deviations, we\ntook either the mean or median value per dataset.\nTo our own surprise most of the mean or median standard deviations did not change before or\nafter calibration. This can be explained by MaxQuant already aligning the different runs. However,\nif runs are not grouped by the user in MaxQuant, calibration is still required. The evaluation of the\ncalibration shows that there is no difference for the majority of datasets, but when calibration is\nneeded they are properly aligned and show a mean or median standard deviation below 1%.\n2. The manuscript evaluates transfer learning using two specific datasets: one involving\nTMPP-labelled peptides and another obtained from a highly basic LC setup. However, the\nexact number of peptides in these datasets is not explicitly stated. While the approximate\nsizes can be inferred from Figures 3d and 4d, providing the exact dataset sizes in the main\ntext would improve transparency and reproducibility.\nIndeed, this would improve transparency and reproducibility. We also feel that the other Figures\ncould benefit from this information, so we included a table that included all dataset sizes and\nmetrics as a supplementary table 1.\n3. (Line 198) The manuscript claims that transfer learning consistently shows a decrease in\nMAE as training data increases, compared to calibration as the size of the training data\nincreases (Figure 1d–f). However, this trend appears to be evident\nprimarily in Figure 1f, while Figures 1d and 1e show less consistent patterns. Additionally, the manuscript\ndoes not explain why PXD016632, PXD017478, and PXD002929 were selected from the 362 PRIDE\nprojects for this comparison. Clarifying the rationale for choosing these datasets would help\nreaders better understand the observed performance differences.\nThese datasets were randomly chosen after first sorting on their respective number of datapoints.\nThe goal was to have a subset that contained many peptides while still showing some variety in\nterms of datapoints.\n4. It is unclear whether the learning curves presented in Figures 1, 3, and 4 represent training\nerror or test error. If the curves show training error, the corresponding performance on the\ntest set should also be reported.\nThe show performance is the test error, we have clarified the text at in the methods and the figure\ncaptions.\n5. The manuscript states that all models were trained using a 10:5:85 split for test,\nvalidation, and training sets. However, cross-validation is a standard approach for robust\nperformance evaluation, especially when datasets are small. For the relatively limited\nTMPP-labelled and basic LC datasets, k-fold cross-validation (e.g., 10-fold) would provide a\nmore reliable assessment of model performance. The authors should consider including\ncross-validation results or justify why they chose not to use this approach.\nWe agree with the reviewer that showing different splits for training and testing with cross-\nvalidation (CV) can help to get a more robust performance estimate for small datasets. We have\nre-run all analysis for TMPP and basic LC with a 10-fold CV (Figures 4 and 5 in this document;\nFigures 3 and 4 in the manuscript). The conclusions remained the same, but we are now much\nmore confident in concluding that transfer learning outperforms training a model from scratch or\ncalibration. Furthermore, TMPP shows folds with a visible deviation from the expected trend for\nthe learning curve. Indeed, this shows dependence on the exact selected subset and importance\nof CV."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The issue of novelty remains inadequately addressed. I acknowledge that the revised manuscript now includes Figure 5 and\nsupplementary figures on unseen post-translational modifications (PTMs). Nevertheless, the other figures primarily\nreanalyze existing data using the well-established methodology of transfer learning for retention time prediction.\nWhile the study leverages an impressively large dataset from 362 PRIDE projects, a large-scale reanalysis is not inherently\nnovel—particularly when the underlying approach echoes that of DeepRT (2018,\nhttps://pubs.acs.org/doi/10.1021/acs.analchem.8b02386). Crucially, this reanalysis does not appear to yield substantial new\ninsights that go beyond the contributions of DeepRT and subsequent related work.\nIn their response, the authors highlight two scenarios for fine-tuning: (i) unfamiliar modifications (TMPP) and (ii) substantially\ndifferent chromatographic setups. I am skeptical of both points. First, DeepRT already demonstrated the adaptation of\nmodels to different LC conditions via fine-tuning. Second, the handling of PTMs through transfer learning was also\npreviously explored, as illustrated in AlphaPeptDeep's Figure 3b.\nThe authors further argued that their work is the first to show transfer learning in \"so substantially different scenarios\". This,\nhowever, describes a suitable application for transfer learning, not a novel scientific result. The technique is explicitly\ndesigned for such generalization; proving its necessity in challenging cases does not, by itself, demonstrate an\nadvancement over the state of the art.\nFor this reason, I previously urged a focus on rigorously evaluating performance on genuinely \"unseen\" PTMs, where\nneither the specific modification nor chemical analogues were present in the training data of prior models. Unfortunately, the\nrevision does not center on this critical direction, and it still lacks evidence that the work substantively progresses beyond\nthe foundation laid by studies like DeepRT other AI-based RT prediction tools.\nWhile the authors have made efforts to address the concerns, my overall assessment is that the advance described is\nincremental rather than transformative. The work is solid and would be of interest to the proteomics community, but its scope\nand level of conceptual novelty are better suited for a specialized journal. I therefore cannot recommend publication in\nNature Communications.", "reviewer_2": "1. In reviewing the fine-tuning results on unseen PTMs (author’s response to reviewer 1), I have a question regarding the\nexperimental setup. PTMs such as phosphorylation, oxidation, and carbamidomethylation are typically among the most\nabundant modification types across datasets. Were these PTMs entirely excluded from the training set and only used for\ntesting in the unseen PTM evaluation? Additionally, could the authors provide the total number of instances for each PTM\ntype in the dataset?\n2. In the newly added Figure 5 in manuscript, in addition to the MAE scatter plot, it would be informative to assess whether\nthe distributions between the transfer learning and the new model differ significantly across individual PTM types. It would\nalso be helpful if the authors could include the corresponding p-values from statistical tests to support these comparisons.\n3. A more detailed rationale for selecting the specific datasets among the 362 PRIDE projects would strengthen the\nmanuscript. Notably, PXD002929 exhibits higher MAE despite an increased number of training peptides. Further analysis is\nrecommended to explain whether this limited improvement is due to discrepancies between the pretrained dataset and\nPXD002929 in sample composition or experimental conditions.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "We thank the reviewer for acknowledging that the work is solid. However, we wholeheartedly\ndisagree with the reviewer’s statement that the work is merely incremental. Below, we provide a\npoint-by-point reply to the reviewer's concerns.\nWhile the DeepRT paper presents a total of eight datasets —a number that is considerably\nsmaller, and likely far less diverse, than the 362 included in our study— it is also important to note\nthat only two datasets were evaluated in the DeepRT paper for transfer learning (the ones named\n“Hela” and “yeast”). We believe that this limited evaluation does not provide a sufficient basis for\napplying transfer learning in future research, given the substantial variability in LC conditions\nacross datasets. Instead, the breadth of datasets in our manuscript does in fact enable\nconclusions about transfer learning to be generalized, thanks to the large amount of data.\nMoreover, and importantly, we also consider three distinct scenarios: (i) unseen modifications,\n(ii) substantially different LC conditions (acidic vs basic), and (iii) substantially different\nmodifications (TMPP). Note that this covers essentially all possibilities: (i) variation in biology, (ii)\nvery strong variation in experimental protocol, and (iii) highly complex modifications. We thus\nshow that DeepLC can adapt to biology, can adapt to changes in workflow and protocol, and can\nadapt extremely flexibly to very large and complex peptide modifications. In summary, although\nDeepRT did apply transfer learning, its conclusions remain very restricted, which is\nunderstandable, as this was not the main point of the DeepRT paper. Rather, the emphasis was\nthere on illustrating the possibility. Our manuscript, in contrast, establishes a much better\nfoundation for the actual applicability and versatility that future research can refer to, and also\nadds a showcase for the specific ability of DeepLC to adapt to arbitrary modifications.\nAs such, we firmly believe that there is a considerable advance here, and that our manuscript\nestablishes an important baseline for transfer learning capability on a very wide data set that\ncovers all relevant aspects. Indeed, we are convinced that our manuscript, and its extensive and\nvaried datasets, will become the gold standard to which all other future efforts will be\nbenchmarked, something that is unlikely to happen for the DeepRT paper and dataset.\nWhile DeepRT applied transfer learning to two datasets, the base model and the data transfer\nlearned to, concern RP datasets run at the same pH, using the same solvents (A and B), and\nidentical column chemistry. In fact, this is essentially trivial in comparison to the wide variety of\nconditions in our manuscript. As explained above, we explore experiments where the column\nchemistry, solvents, and pH differ very substantially between experiments, actually taxing the\ntransfer learning capability of the algorithm. Notably, the drastic change in pH has a significant\nimpact on all peptide retention times (https://doi.org/10.1016/j.chroma.2005.03.121). Therefore,\nthe transfer learning performed in DeepRT on two datasets is simply not comparable to the\ntransfer learning applied in our ‘basic pH’ LC setup experiment, which demonstrates a distinctly\ndifferent and quite important aspect of transfer learning. As highlighted in our manuscript, it is\nnoteworthy that even with a complete change in the retention mechanism, transfer learning still\nmanages to substantially improve performance. This has never been shown in any previous\nmanuscript, and is highly interesting to experimentalists. Indeed, the message to proteomics\nresearchers is very clear: thanks to transfer learning in DeepLC, you can now drastically alter the\nexperimental conditions, and still be able to rely on accurate and precise retention time\nprediction. The importance of such experimental flexibility should not be underestimated, as the\nproteome is difficult to analyse precisely because of the enormous physico-chemical diversity of\nproteins, necessitating different protocols to analyse the full spectrum of proteins.\nThe reviewer also points out that modification analysis has already been demonstrated for\nAlphaPeptDeep. This is correct; however, for AlphaPeptDeep, this was performed on only a very\nlimited set of relatively standard modifications; in fact, the same as we show in our manuscript’s\nFigure 5. The TMPP evaluation is fundamentally different from this prior analysis, as TMPP is far\nmore complex, and causes a far greater shift in retention time, compared to the modifications in\nthe AlphaPeptDeep evaluation. Indeed, most modifications depicted in Figure 5 of our\nmanuscript (or Figure 3 in the AlphaPeptDeep paper) cause only a fairly small retention time shift.\nAs above, therefore, our evaluation goes very far beyond any previous work by truly challenging\nthe transfer learning with TMPP, which, unlike the previously used modifications in the\nAlphaPeptDeep paper, dramatically alters the physicochemical properties of the peptides. This\nis clear from its large size (the molecular weight of TMPP is 572.18 Da, larger than five times an\naverage amino acid), and from its many different functional hydrophobic groups such as aromatic\nphenyl rings and methoxy groups on the one hand, and hydrophilic groups such as carbonyl and\nphosphonium on the other hand. In short, such a molecule is truly outlandish to a retention time\nprediction algorithm, and it is a clear testament to the impressive capabilities of DeepLC that it\ncan adapt to this molecule with such ease using transfer learning. Therefore, just as with the\ndifferent experimental setups discussed above, the AlphaPeptDeep analyses are simply not\ncomparable to ours and this distinction is moreover clearly emphasized in our manuscript.\nThe distinction between what constitutes ‘a novel scientific result’ is often unclear, and may well\nbe in the eye of the beholder. We respectfully disagree with the reviewer that novelty in research\nis exclusively achieved through the introduction of entirely new methods. Indeed, this definition\nwould disqualify many highly cited papers that were extremely impactful to the field of\nproteomics. Here, the application of one of the most used retention time prediction algorithms\nfor peptides in completely novel and highly challenging scenarios establishes an important\nfoundation for the actual use of transfer learning as a means to drastically extend the applicability\nof a retention time algorithm through transfer learning. We are confident that our manuscript will\nserve as the reference work for the application of transfer learning to LC and future ML\napplications in proteomics, as we set a novel benchmark for this technology that goes very far\nbeyond the limited proof-of-concept efforts the reviewer highlights. It is our strong opinion that\ncomprehensive analysis and validation of methods in novel and thoroughly challenging\nconditions that really puts methods to the test, represent far more genuine scientific progress\nthan mere incremental improvement where an existing machine learning technique (transfer\nlearning did not originate with the DeepRT nor the AlphaPeptDeep papers) is applied in a non-\nchallenging context to provide basic proof-of-concept.\nThere appears to be a misunderstanding regarding our manuscript’s intent. Our aim is to evaluate\nthe performance and applicability of transfer learning across a wide range of scenarios. While\nincluding other modifications could be of biological interest, our focus is on using these\nmodifications—such as TMPP—as representative cases to evaluate transfer learning. In fact, we\nexplain in detail above that TMPP is essentially exactly what the reviewer demands: a genuinely\nunseen PTM, of a complexity, chemical character, and size that was never present in any of the\nother datasets or models. Indeed, as noted above, the research presented here is distinct from\nprevious evaluations in the literature, providing an important, and much-needed foundation for\nfuture applications of transfer learning.\nFurthermore, we would like to emphasise that the analysis presented in Figure 5 concerns\nmodifications that were not part of the training data of the algorithm. Whenever we show transfer\nlearning outcomes, these are tested on one or more modifications that the model being tested\nhas never seen before. This is a very basic aspect of AI-model evaluation in general, of course,\nbut, in light of the comment by the reviewer, we felt it important to point this out here. Thus, our\nresponse above assumes that the reviewer is up-to-date with this basic testing procedure, and\nwe therefore interpret their comment as meaning that the assessment should focus on\nmodifications not previously used for training of any other model or paper. In fact, this is true for\nTMPP, as this is the first instance where a model has been trained on this distinctly different\nmodification, providing a true outgroup example.\nAs we have documented in detail above, our manuscript is the first to truly test the capabilities of\ntransfer learning for peptide retention time modelling under seriously challenging conditions (as\nopposed to simple proof-of-principle results shown previously), and this in all relevant possible\nways (biological diversity, protocol diversity, modification diversity). Our manuscript therefore\nsets the first serious benchmark for transfer learning in predictive modelling in proteomics, and\nmoreover sets a quite high performance bar to clear for future such models, which will\nundoubtedly render it highly impactful for the field at large. Incidentally, the other reviewers seem\nto agree with this view. To deny our manuscript the exposure it will get in Nature Communications\ntherefore seems counterproductive, and not well substantiated.\nFirst, we would like to thank the reviewers with their thoughtful remarks and questions. In\nresponse to their remarks, we confirm that even the most abundant PTMs were entirely excluded\nfrom the training set and only used for testing evaluation, we clarified this part better in the\nrelative method section.\nAdditionally, we have included the number of peptides carrying each PTM beneath the respective\nPTM in figure 5. We also mention the total number of peptides in the dataset (5693 peptides),\nwhich provides the reader with the information on training and test set sizes for each PTM.\nWe have previously shown the distribution between transfer learning, training a model from\nscratch, and calibration in the supplementary figure 19. In the updated figure 19, we have also\nadded the p-values for each PTM beneath the corresponding PTM to include statistical\ncomparison.\nDatasets were selected based on the availability of MaxQuant result files. This selection was\napplied as there is a large number of datasets that have these identification files available.\nFurthermore, MaxQuant determines the elution apex, which is important for accurate retention\ntime prediction. We have described this selection procedure in the methods section of the\nmanuscript in more detail.\nWe have inspected PXD002929 and it seems there is a gap in the chromatogram where no\npeptides are identified (or quantified). This could be due to issues with either the LC or MS, and\nthus this results in lower performance in the region between 100 and 150 retention time.\nImportantly, the conclusion for this dataset is the same as all the other ones; transfer learning\nimproves over calibration and starting with random parameters."}]

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