The initial data was acquired from IEDB (Vita et al., 2019) and VDJdb (Bagaev et al., 2020) on the 23rd and 24th of August 2022, respectively, using a query to select only positive T-cell assays for MHC class I and Human cells. Additionally, only paired-chain (αβ) data was collected. This resulted in a dataset of 21,825 observations across 631 peptides for IEDB and 27,005 observations across 898 peptides for VDJdb.

This data was subsequently filtered to exclude data originating from 10 X sequencing, which was done by manually investigating references with at least 100 observations. Furthermore, filtering was conducted to include only observations with annotated V and J genes and fully specified MHC alleles. In cases where the V and J genes did not have a fully specified allele, the most common allele (*01) was assigned. Furthermore, CDR3 sequences which did not follow the nomenclature of beginning with a cysteine and ending with a phenylalanine (F) or tryptophan (W) were modified to follow this nomenclature by adding a cysteine to the start of the sequence if missing, and adding phenylalanine to the end of the sequence if phenylalanine or tryptophan was not present at the end of the sequence. This filtering resulted in 4439 observations across 405 peptides after merging the two datasets together and dropping duplicate entries.

Next, a dataset from a 10 x sequencing study (10x Genomics, 2020) which was denoised with iTRAP (Povlsen et al., 2023) was included, resulting in a combined dataset of 10,239 observations across 435 peptides.

To retrieve the full TCR sequences required for annotating all CDRs, Stitchr (Heather et al., 2022) was used. In brief, Stitchr looks up the sequences for the V and J genes in IMGT/GENE-DB and attempts to align these sequences with the specified CDR3 amino acid sequence. In case of mismatches in the alignment, the CDR3-proximal residues of the V and J gene products, respectively, are progressively removed until a match can be found. As the alignment failed on either one chain or both for some of the sequences, 9045 full TCR sequences were retrieved in this step.

In cases where Stitchr failed to reconstruct the TCR, a second run of Stitchr was performed where tryptophan was added instead of phenylalanine for the CDR3s with the wrong nomenclature. This resulted in the rescue of 20 additional TCR sequences, bringing the total number of full TCR sequences up to 9065 (88.5% of the inputs given to Stitchr).

Finally, the CDR1, CDR2, and CDR3 amino acid sequences were annotated by submitting the full TCR sequences to the ANARCI software (Dunbar and Deane, 2016), which is a tool that is used for annotating the sequences according to the IMGT naming scheme (Lefranc et al., 2003). Here, CDR1 was defined as position 27–38, CDR2 as position 56–65 and CDR3 as position 105–117.

The CDR-annotated data was redundancy reduced in two steps using the Hobohm 1 algorithm (Hobohm et al., 1992) based on a summed BLOSUM62 encoded kernel similarity (Shen et al., 2012) of CDR3α and CDR3β. In the first step, the dataset was split according to peptides, and a redundancy reduction was carried out separately for TCRs belonging to each unique peptide using a 0.95 kernel similarity threshold. Here, only peptides with at least 30 unique TCRs after the first redundancy reduction were kept. This redundancy reduction and filtering resulted in a dataset of 6415 observations across 26 peptides.

A second redundancy reduction was subsequently carried out also at a 0.95 kernel similarity threshold across all remaining observations and peptides, where the data was sorted by peptide according to TCR count (least abundant to most abundant) in order to limit the risk of removing observations from peptides with few observations. This resulted in the further removal of 68 observations, resulting in a final dataset of 6,353 positive observations across 26 peptides. The amount of redundant data removed by the redundancy reductions are summarized in Table 1, as well as information regarding source organism and MHC allele for each peptide, and number of observations originating from 10 X sequencing data. The vast majority of 10 X data comes from the iTRAP filtered dataset, with a few observations originating from other 10 X studies that managed to slip through the initial manual filtering.

To prepare the data for model training, this data was randomly split into five partitions, and negatives were generated by swapping the TCRs for a given peptide with TCRs binding to other peptides. Here, such TCRs were only samples from peptides which had a Levenshtein distance greater than 3, to reduce the risk of generating false negatives. For each positive observation, five negative observations were generated using this approach, except for the GILGFVFTL peptide, where all TCRs from the other peptides were used as negatives, since there was not enough data to allow for a 1:5 positive to negative ratio for this peptide (a 1:4.647 ratio was achieved here). The generation of swapped negatives was done separately within each partition, in order to reduce the risk of data leakage.

TCRbase (Montemurro et al., 2022), a distance-based model, was used as the baseline model. For a given peptide, TCRbase calculated the similarity between sets of CDRs found in the test partition to all positive CDR sets found in the remaining partitions. In short, the similarity is calculated per CDR as the mean kernel-similarity of BLOSUM62-encoded kmers ranging from size 1–30 between the two sets of CDRs that are compared (Shen et al., 2012). The weighting for the CDRs was set to 1,1,3,1,1,3 for CDR1α-, CDR2α-, CDR3α-, CDR1β-, CDR2β-, and CDR3β, respectively, in line with earlier recommendations (Montemurro et al., 2022).

The CNN architecture for NetTCR 2.1 (Montemurro et al., 2022) was reconstructed in Keras (Chollet, 2015), in preparation for further updates to the architecture. In brief, the original architecture consists of a set of convolutional 1D layers for each input feature, where each layer has 16 filters of kernel size of 1, 3, 5, 7, and 9, respectively, which are activated by a sigmoid activation function. Each layer is then max-pooled, concatenated, and fed to a dense layer of size 32 followed by a linear output layer of size 1, representing the final prediction score. The outputs of both linear layers are activated by a sigmoid activation function.

Except for the first models referred to as NetTCR 2.1 (which ran in PyTorch Paszke et al., 2019), the version 2.2 CNN models described in this paper used a slightly modified architecture compared to NetTCR 2.1. Here, the activation function for the max-pooling layer was replaced with a rectified linear unit (previously sigmoid), a dropout layer was introduced for the concatenated max-pooling output, and the size of the dense layer was doubled to 64 neurons. For the models utilizing dropout, a dropout rate of 0.6 was used. The models referred to here as NetTCR 2.1 uses the original pan-specific NetTCR 2.1 architecture (Montemurro et al., 2022), which also includes convolutional filters for the peptide-sequence.

The input features for the CNN models consisted of peptide-, CDR1α-, CDR2α-, CDR3α-, CDR1β-, CDR2β-, and CDR3β-amino acid sequence. These were each represented using a BLOSUM50-embedding (calculated using a normalization factor of 5) and right-padded to the maximum length observed for that feature in the dataset, by assigning a vector of 20 times –1 for each missing residue. For reference, the maximum length observed was 12, 7, 8, 22, 6, 7, and 23 residues for the peptide-, CDR1α-, CDR2α-, CDR3α-, CDR1β-, CDR2β-, and CDR3β-amino acid sequences, respectively.

All CNN models were trained in a nested cross-validation setup with four folds in the inner loop and fivefolds in the outer loop. Here, three partitions were used for training, one was used for validation, while the remaining partition was used as a test partition to evaluate the performance of the model. For all CNN models, Binary Cross Entropy was used as the loss function, and the Adam optimizer (Kingma and Ba, 2014) was used for updating the weights during training. A learning rate of 0.001 was used for training of all models.

A patience of 200 epochs was used for the early stopping for the peptide-specific CNNs, whereas for the pan-specific CNNs, a patience of 100 epochs was used. The increased patience for the peptide-specific models was introduced to allow the models to escape local minima imposed by small training set sizes. For the NetTCR 2.1 models (PyTorch) (Paszke et al., 2019), the validation loss was used as a stopping criterion for early stopping, and validation AUC 0.1 was used as the stopping criterion for the updated models in Keras.

For the pan-specific models, a batch size of 64 was used together with shuffling. For the peptide-specific models, an adaptive batch size was used, which ensured that no batch ended up having less than 32 observations. Here, it was first tested if it was possible to use a batch size of 64 while still having at least 32 observations for the final batch. If not, the default batch size of 64 was progressively increased by 1, until it was ensured that the final batch had at least 32 observations.

The cross-validation setup results in four models generated in the inner loop. The test set predictions were then calculated from the average over the four predictions for each entry. The performance was evaluated on the five concatenated test sets in terms of AUC and AUC 0.1 on a per-peptide basis, as well as the unweighted and weighted average performance across all peptides:

where M_unweighted and M_weighted is the unweighted and weighted average metric, respectively, M_peptide is the metric for a given peptide, N_peptide is the number of positive observations for a given peptide, N_{unique peptides} is the number of unique peptides, and N_total is the total number of positive observations across all peptides.

A summary of the per-peptide performance of all models is found in Supplementary file 1.

To assess the difference in performance between models, bootstraps were performed by sampling with replacement from the model predictions 10,000 times and calculating the weighted and unweighted performance metrics for each subsample as described above. The same seed for subsampling and order of predictions was used for all bootstraps, to ensure that performance within a given subsample could be compared between models. The p-value for the null hypothesis that two models had equal performance was then calculated as the number of times that the first model had a higher performance than the second model within the same subsample, normalized by the total number of subsamples.

A weighted loss was implemented for the pan-specific CNN model to allow the model to focus more on the observations from the less abundant peptides in the training dataset. Here, the binary cross entropy loss for observations from each peptide was weighted according to the formula:

where N_total is the total number of observations, N_peptide is the number of observations for the given peptide, and c is a constant that is used to scale the loss, so the overall loss becomes close to that of the unweighted approach. The value of c was set to 3.8 to ensure that the overall weighted loss was comparable to the training done without sample weighting. For the peptide-specific models, a weight of 1 was used for all samples.

A dataset was constructed based on the primary training dataset, where redundant data from the first redundancy reduction (see Table 1) was added back by assigning them to the partition of the data point that they were redundant to. Only positive data was added back in this way, and additional swapped negatives were not generated for this dataset to keep it as similar to the original as possible. Models trained on this dataset were evaluated on the original test datasets without redundant data.

Using the prediction scores for the validation partitions of the updated peptide-specific CNN model, additional datasets were constructed by removing observations that consistently received a poor prediction score in relation to their designated label. That is, positive observations were removed if they received a validation prediction score of less than the nth percentile of the negative prediction scores for the given peptide for all four models that were not trained on that partition, while negative observations were removed if they received a validation prediction score of more than the (1 – n)th percentile of the positive validation prediction scores for all four models that were not trained on that partition. Thresholds of n=50, 60, 70, 80, 85, 90, and 95 were tested in this way.

A modified version of the NetTCR 2.2 architecture was made to combine the properties of the pan- and peptide-specific models, as shown in Figure 1. This architecture consists of a pan-specific and a peptide-specific CNN block. The pan-specific CNN block consists of 32 1D convolutional filters of size 1, 3, 5, 7, and 9, respectively for each of the peptide-, CDR1α-, CDR2α-, CDR3α-, CDR1β-, CDR2β-, and CDR3β embeddings. The peptide-specific CNN block consists of 16 1D convolutional filters, also of size 1, 3, 5, 7, and 9, respectively, for the same feature embeddings, except the peptide embedding, as this information is redundant when trained on a single peptide. The outputs from each CNN block are max-pooled with a rectified linear unit activation function, concatenated, and fed to two dropout layers with a dropout rate of 0.6, one for each output of a CNN block.

Figure 1

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Each of the two resulting tensors are fed separately to dense layers with 64 units and sigmoid activation, both of which are connected to a second dense layer with 32 units and a sigmoid activation. The output of the second dense layer is finally connected to an output layer of size 1, which is also activated by a sigmoid activation function, to give a prediction score between 0 and 1.

These models are trained in two rounds. During the first round of training, a pan-specific training is performed. Here the weights in the peptide-specific CNN block are kept frozen, as shown in Figure 1. This pre-trained model is then used as the starting point for a second round of training performed in a peptide-specific setup, where the weights in the pan-specific CNN block are frozen, while those in the peptide-specific CNN block are unfrozen. During both training rounds, a patience of 100 is used and the maximum number of epochs is set to 200.

The pre-trained CNN model was combined with the sequence similarity based TCRbase model (Montemurro et al., 2022; Shen et al., 2012). The predictions for this new ensemble were calculated using the following formula:

where P_{TCRbase ensemble} is the prediction of the combined ensemble, P_CNN is the prediction of the CNN model, P_TCRbase is the prediction from TCRbase, and α is a scaling factor used to give TCRs with low similarity to known binders a harsher penalty. This ensemble was tested on the validation partitions of the full dataset, where α was varied from 0 to 40.

The Pearson correlation coefficients between the α resulting in the best performance in terms of AUC and AUC 0.1, respectively, and the corresponding performance metric for the TCRbase and pre-trained model without scaling, was calculated using the pearsonr function from scipy.stats (Virtanen et al., 2020). Five samples were used for each peptide, as there were five different validation partitions to consider, resulting in a total of 130 samples for calculating the Pearson correlation coefficients. p-Values for the null hypothesis that there was no correlation was also reported using this function.

Prediction scores were rescaled to a percentile rank by comparing the score to the score distribution obtained for 15,957 negative controls paired to the corresponding peptide. These negative controls were obtained from the IMMREP 2022 workshop dataset (Meysman et al., 2023). Here, the percentile rank score for a given TCRs was calculated as the percentage of negative controls which had a score above the score of that of the TCR.

To evaluate the models’ ability to correctly identify which peptide is most likely to bind a given TCR, all TCRs were paired with all peptides present within each partition, and predictions were performed by the models which had not seen the given partition during training. The specificity was then calculated per peptide as the number of times that the true peptide-TCR complex was given the highest prediction score, compared to the total number of positive observations in the original dataset for the given peptide. The test was performed on the limited dataset, where the peptides KLGGALQAK, AVFDRKSDAK, NLVPMVATV, CTELKLSDY, RLRAEAQVK, RLPGVLPRA, and SLFNTVATLY were discarded, due to low performance of the full model (AUC 0.1<0.65).

To test the models’ ability to learn from small data sets, models were re-trained on small subsets of the original data. For each of the peptides with at least 100 positive observations in the limited training dataset except for KLGGALQAK, AVFDRKSDAK, and NLVPMVATV (e.g. GILGFVFTL, RAKFKQLL, ELAGIGILTV, IVTDFSVIK, LLWNGPMAV, CINGVCWTV, GLCTLVAML and SPRWYFYYL were included), new training datasets were constructed by subsampling 5, 10, 15, 20, 25, 50, and 100 positive peptides, respectively, per partition, as well as five negative observations per positive. KLGGALQAK, AVFDRKSDAK, and NLVPMVATV were excluded from this analysis, due to low performance of the full model (AUC 0.1<0.65). All models trained here were evaluated on the full dataset (not limited).

As a baseline, TCRbase was used to perform predictions on the test partitions, using the positives from the four remaining partitions as the positive database for similarity inference.

In addition, a set of peptide-specific models were also trained on these datasets, using the same hyperparameters as the best (non-pre-trained) peptide-specific model, when evaluated on the full dataset.

A set of pre-trained models were also re-trained on these datasets, where the first training round of the pan-specific CNN was conducted on the leave one out dataset. For each peptide and each number of positives, the pan-specific CNN block was fine-tuned by training for 30 epochs in a pan-specific setup, where observation for the leave-most-out peptide was assigned a sample weight of 1, while the observations for the remaining peptides were assigned a weight of 0.1. Swapped negatives assigned to other peptides than the one the models were trained for were removed for this training, if they originated from an observation belonging to the peptide in question. Following this, the pan-specific CNN block was frozen, and the peptide-specific CNN block was trained on the observations for the peptide of interest.

Finally, an ensemble consisting of the pre-trained models scaled by the TCRbase prediction (α=10) were evaluated (see CNN - TCRbase ensemble).

Due to the low number of positives for some of the leave-most-out datasets, the default batch size was set to 32 for the peptide-specific training, while the criteria for early stopping and model saving was changed from validation AUC 0.1 to a custom metric taking both validation AUC 0.1 and binary cross entropy loss into account. This custom metric was calculated as:

and the model was saved when this value was maximized. A patience of 100 was used for early stopping during the peptide-specific training.

The labeled training and test data for the IMMREP 2022 workshop (Meysman et al., 2023) was collected from GitHub (GitHub - viragbioinfo/IMMREP_2022_TCRSpecificity; viragbioinfo et al., 2022) on the 5th of July 2023. The training data was randomly split into five partitions, and models were trained in the same cross-validation as described above, for example nested cross-validation for the neural network models and a fivefold cross-validation for TCRbase. To make the data compatible with our models, the labels for the negative observations were changed from –1 to 0. The performance of each model was then evaluated on the separate test dataset, using the average in prediction score given by all models resulting from the cross-validation.

A separate redundancy reduced dataset was created based on the IMMREP dataset following the strategy described above. An overview of the number of observations removed by this redundancy reduction is shown in Table 2.

Swapped negatives were generated within each partition, by randomly sampling TCRs binding to other peptides with a Levenshtein distance of at least three, until a 1:3 ratio of positives to negatives were achieved. Negative controls were first subjected to a redundancy reduction at a 95% similarity threshold, followed by random partitioning. Within each partition, negative controls were sampled in a 1:2 ratio of positive to negatives for each peptide, bringing the total positive to negative ratio up to 1:5.

Models were then trained on this training dataset using nested cross-validation (or fivefold cross-validation for TCRbase), while the performance was evaluated on the test-partitions, which were not seen during training. The average prediction score of the four cross-validation models per test partition was used as the final prediction score for this performance evaluation. A summary of the per-peptide performance of all models trained and tested on the IMMREP 2022 dataset is found in Supplementary file 2.

Having demonstrated a robust and high performance of the CNN-pan-specific model in the context of TCR specificity towards known peptides, i.e. peptides included in the training data, we next turned to the uttermost challenging question namely prediction of TCR specificity towards novel peptides.

To investigate this, we trained models in a pan-specific leave-one-out setup, where for a given peptide, both positives and negatives generated from that peptide were removed from the training data, thus preventing data leakage. This was done both for the NetTCR 2.1 and the updated NetTCR 2.2 architecture. For this experiment, the limited training dataset with outliers removed was used. This resulted in 26 different models, each of which was evaluated on the peptide dataset for the left-out peptide. As shown in Figure 11, a performance in terms of AUC slightly better than random was observed for most of the peptides. Furthermore, a noticeable improvement in performance was seen for the updated NetTCR 2.2 model. However, the performance was almost completely random when evaluated in terms of AUC 0.1, as can be seen in Figure 11—figure supplement 1. The only peptides with non-random AUC 0.1 performance were FEDLRLLSF and FEDLRVLSF, and this was only the case for the NetTCR-2.2 model architecture. These peptides differ by only a single amino acid, and the result thus indicates that the updated model in this case is able to transfer the knowledge gained from training on another similar peptide, which was not the case with the old architecture.

Figure 11 with 1 supplement see all

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We next extended the analysis to a leave-most-out setting to investigate how little data is required in order to train models with non-random performance. Here, a number of training datasets were generated by subsampling the limited dataset in order to achieve 5, 10, 15, 20, 25, 50, and 100 positive observations, respectively, per peptide. Swapped negatives were also subsample in this way, keeping a ratio of 1:5 between binders and non-binders. This was only done for the peptides GILGFVFTL, RAKFKQLL, ELAGIGILTV, IVTDFSVIK, LLWNGPMAV, CINGVCWTV, GLCTLVAML and SPRWYFYYL, since they all had substantial performance (AUC 0.1 ≥ 0.65) for the full model and more than 100 positive observations to begin with.

In the case of the pre-trained model, the leave-one-out model was used as the startpoint. Rather than having to re-train the full pan-specific CNN block (which may be impractical, if a user wants to re-train the model on a new peptide), we decided to instead fine-tune this CNN block by adding the subsampled data to the leave-one-out training data, while setting the sample weight to 1 for the new peptide observations, and 0.1 for the remaining observations. The pan-specific CNN block was then trained for 30 epochs in this way (for details refer to Materials and methods).

As shown in Figure 12 and Figure 12—figure supplement 1 all models demonstrated a non-random performance with as low as 5 positive observations. As expected, a general increase in performance was observed as more and more data was available for training. This was especially the case for the TCRbase ensemble model, which strongly outperformed all other models with an AUC close to 0.8, when the number of training points surpassed 15.

Figure 12 with 1 supplement see all

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Noticeably, the performance of the baseline TCRbase model did not improve nearly as much as the CNN-based models when the amount of training data was increased, suggesting that the CNN models are able to benefit much more from the increased amount of information present in larger datasets.

The scarness of paired TCR data means that it often could be tempting to include all available data to the fullest, and include all redundant data for training. However, as we show here, the addition of redundant data in the training does not lead to improved performance. In fact, we found that the addition of redundant data may cause pan-specific models to underperform if the peptide imbalance of data is not accounted for, since the inclusion of redundant data often results in a further increased peptide imbalance.

The observation that the predictive performance for some peptides was much lower than expected given the amount of available training data, led us to believe that outliers in the form of false positives might be a potential issue. Furthermore, many of these peptides had in common that the main source of data was 10 X sequencing (10x Genomics, 2020), a platform known to have a high proportion of false annotations (Zhang et al., 2021; Povlsen et al., 2023). To deal with this issue, we implemented a machine learning driven approach for outlier detection using the predictions of the peptide-specific NetTCR models to identify observations which repeatedly received very poor predictions. The removal of these potential outliers from the training led to significantly improved test performance. It should also be noted that the data applied in the study included denoising for most of the 10 X data in the form of ITRAP (Povlsen et al., 2023), which together with ICON Zhang et al., 2021 have earlier been shown to properly remove outliers (Montemurro et al., 2023). Nevertheless, our results suggest that some outliers had escaped these denoising steps, indicating that denoising methods should still be improved upon. While the use of our model predictions to remove outliers resulted in improved performance, we believe that this approach should only be considered a proof-of-concept, and that more elaborate ways to identify outliers merit further investigation.

Inspired by the observation that sequence similarity distance-based models often achieve very high performance for the prediction of TCR specificity (Meysman et al., 2023), we investigated if integrating TCRbase predictions could improve the performance of our models. We integrated TCRbase by scaling the CNN prediction with the TCRbase prediction to a power of α, and found that the performance of this ensemble (Pre-trained +TCRbase) achieved a significantly improved performance in terms of AUC and AUC 0.1. Interestingly, further inspections revealed that the increased performance mainly resulted from improved discrimination of binders and non-binders when the binding-threshold was set to result in a low FPR. Given how we are often interested in keeping the FPR very low for TCR specificity predictions, the simple integration of TCRbase can thus vastly benefit many real-world use-cases for TCR specificity predictions. While we decided to use a general α of 10 for the TCRbase scaling, it is possible that performance could be improved further, if α is allowed to be flexible depending on the peptide. For example, one could imagine that a peptide with a very high TCRbase predictive performance could benefit from a higher α, compared to another peptide with a lower TCRbase performance. Furthermore, the amount of positive data also influences which α is optimal. It would therefore be interesting to further investigate this, as this could potentially lead to further improved performance. Finally, investigating the relation between TPR and FPR at different values of α could also benefit many actual use-cases, considering that the optimal alpha value could be determined based on the desired maximum FPR rate.

It has repeatedly been shown that predicting TCR specificity for “unseen” peptides is extremely hard, especially for peptides that are very dissimilar to the peptides included in the training data (Moris et al., 2021; Grazioli et al., 2022). Investigating the performance of the pan-specific models in a leave-one-out setup revealed that the performance on unseen peptides overall remained very poor, also for the updated NetTCR-2.2 model. While the performance for NetTCR-2.2 in terms of AUC was generally better than random, the performance in terms of AUC 0.1 was very close to random, severely limiting its general potential use. Nevertheless, we observed that the performance of NetTCR 2.2 in the leave-one-out setup was improved when compared to NetTCR 2.1, especially for two peptides sharing a high mutual similarity. While the performance for these two peptides was still low compared to that observed in the full training setup, this result affirms that given a broad enough peptide coverage, pan-specific models have the potential to predict binding also for unseen peptides.

While high performance for unseen peptides so far remains very challenging, another important issue is to boost performance for peptides with relatively few observations. Performing a leave-most-data-out, a substantial increase in performance was observed compared to the leave-out-out experiment with as little as five training observations, and already with 15 observations, a satisfactory performance was observed. This is in great contrast to earlier work, where a number of ~150 was suggested to be required for modeling TCR specificity (Montemurro et al., 2021). These results thus suggest that the pre-trained models can beneficially be used as seeds for the development of peptide-specific models allowing for rapid fine-tuning to new data.

We also observed that the TCRbase ensemble based on the pre-trained model consistently outperformed any of the other models, both when data was very scarce, but also as the amount of training data was increased, highlighting the benefits of integrating distance-based methods for predictions. As a final note, we would also expect that the discrepancy between the performance of the peptide-specific- and pre-trained model will become larger as the number of peptides to train on increases in the future, as a pan-specific CNN block trained on a more diverse dataset should allow for better generalization.

To compare the updated models with other models for TCR specificity predictions, we applied the modeling framework to the dataset from the IMMREP 2022 benchmark (Meysman et al., 2023). Here, we observed that the updated peptide-specific model performed on par with the best models in the benchmark. We however also observed that the pre-trained model performed worse than expected. Careful inspection of the data revealed that swapped negatives had been generated across the test and training data, meaning that some TCRs were only seen as negatives in the training, whereas they could be positive in the test data, albeit for a different peptide. This problem strongly affected the pre-trained model, which had a pan-specific component. Furthermore, since redundancy was only dealt with by removing duplicate TCRs, redundancy in both training and test data was observed, resulting in a certain degree of performance overestimation. This was for instance reflected in an unusually high performance for the peptides which had higher degrees of redundancies between training and test data.

To deal with these problems, we performed redundancy reduction on the training data identical to what was done for our novel extended data set, and made sure to only generate swapped negatives from TCRs within a given partition. We then trained and evaluated our models using the nested cross-validation approach on this redundancy reduced data. Here, we recovered the earlier conclusion that the pre-trained models outperformed the peptide-specific models, and that the integration of TCRbase led to the highest overall performance. These results thus strongly underline a problematic issue with data redundancy and the leakage of swapped negative TCR between training and test datasets present in the IMMREP benchmark. This is of high concern, since these properties, as shown here, are in particular detrimental for pan-specific models. Considering this, we encourage the creation of a new benchmark which takes these issues into account, while ideally also expanding on the number of peptides present for predictions.

In this work, we have demonstrated how prediction of TCR specificity can be greatly improved by introducing minor but critical updates to the NetTCR training and modeling framework. While also improving on the peptide-specific models, these updates in particular boost the performance of pan-specific models. In addition, we show that pre-training models on pan-specific data, followed by training in a peptide-specific setup, leads to substantially improved performance, especially when the amount of data is low. Scaling the predictions from NetTCR with similarity to known binders is also shown to boost performance. Further, we have for the first time demonstrated how machine learning models can be designed and applied for rational data denoising in the context of TCR specificity data. The performance for ‘unseen’ peptides was found to be overall low. However, the results demonstrated an encouraging tendency of high predictive power in cases of ‘unseen’ peptides with high similarity to the training data.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

1. Preprint posted: October 16, 2023 (view preprint)
2. Sent for peer review: November 7, 2023
3. Preprint posted: December 20, 2023 (view preprint)
4. Preprint posted: February 2, 2024 (view preprint)
5. Version of Record published: March 4, 2024 (version 1)

You can cite all versions using the DOI https://doi.org/10.7554/eLife.93934. This DOI represents all versions, and will always resolve to the latest one.

© 2023, Jensen and Nielsen

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