Learning the language of protein-protein interactions

Domain: biology
Published: 2026-01-07
Editorial outcome: minor_revision

Abstract
Protein Language Models (PLMs) trained on large databases of protein sequences have proven effective in modeling protein biology across a wide range of applications. However, while PLMs excel at capturing individual protein properties, they face challenges in natively representing protein-protein interactions (PPIs), which are crucial to understanding cellular processes and disease mechanisms. Here, we introduce MINT, a PLM specifically designed to model sets of interacting proteins in a contextual and scalable manner. Using unsupervised training on a large curated PPI dataset derived from the STRING database, MINT outperforms existing PLMs in diverse tasks relating to protein-protein interactions, including binding affinity prediction and estimation of mutational effects. Beyond these core capabilities, it excels at modeling interactions in complex protein assemblies and surpasses specialized models in antibody-antigen modeling and T cell receptor-epitope binding prediction. MINT’s predictions of mutational impacts on oncogenic PPIs align with experimental studies, and it provides reliable estimates for the potential for cross-neutralization of antibodies against SARS-CoV-2 variants of concern. These findings position MINT as a powerful tool for elucidating complex protein interactions, with significant implications for biomedical research and therapeutic discovery.

Introduction
The success of large language models in natural language processing—where complex semantic and syntactic relationships are learned from sequences of words—has inspired their application to protein sequences. By treating amino acid sequences as “sentences,” protein language models (PLMs) can implicitly learn structural and functional patterns, enabling predictions of protein folding, mutational effects, and antibody optimization without explicit structural labels1–9. However, within cellular environments, proteins rarely act in isolation; instead, they form extensive interaction networks essential for processes such as signal transduction, metabolic pathways, and cellular structural stability. Hence, a comprehensive understanding of protein biology requires moving beyond isolated protein sequences to consider the complex interactions between multiple proteins. Despite this, even PLMs that predict high-resolution structures have thus far been limited to learning patterns from single protein sequences8,10.

Since almost every PLM is trained using a self-supervised objective on single chains, models often struggle to effectively capture protein-protein interactions (PPIs). Previous approaches that have used PLMs to predict PPIs11–16 utilized representations generated from each protein sequence without incorporating the contextual information from the interacting partners. This independence causes critical interaction-specific features to be overlooked, as the representations of each protein sequence are generated in isolation. Furthermore, this approach becomes increasingly impractical for PPIs that involve complex multi-sequence (2+) interactions, such as those seen in antibody-antigen or TCR-epitope-MHC complexes17,18. While concatenating input sequences has been proposed as a workaround, it risks degrading embedding quality by treating all sequences as a single unified entity, potentially masking distinct sequence-specific features19. To address these challenges, we propose Multimeric INteraction Transformer (MINT), an extension of the single-sequence paradigm that allows PLMs to learn distributions of sets of interacting protein sequences. We hypothesize that this approach will enable PLMs to produce context-aware representations that more accurately capture the nuances of PPIs.

MINT contains two new conceptual advances that enable it to model PPIs effectively. The first modification is of the popular Masked Language Modeling (MLM) objective, so that our model can learn from the interacting protein chains present in STRING-DB, a comprehensive and high-confidence database filtered to contain information on 96 million experimental and predicted PPIs20. By training a PLM on this vast corpus of interactions, we aim to uncover deeper representations of not only individual proteins but also how sets of proteins function together in concert. To the best of our knowledge, no existing PLM has been trained extensively on STRING-DB using a self-supervised pre-training objective. Our second innovation is the adaptation of the model architecture and the training of the popular PLM ESM-210 to handle multiple inputs of protein sequences at once. We present a strategy that allows us to fine-tune ESM-2 on STRING-DB by adding a cross-attention module that explicitly extracts inter-sequence information21.

We comprehensively benchmarked MINT against widely used PLMs across multiple PPI prediction tasks. MINT consistently outperformed baseline models in binary interaction classification, binding affinity prediction, and mutational impact assessment, achieving a new state-of-the-art AUPRC of 0.69 on the gold-standard dataset constructed by Bernett et al.22, and delivering a 30% improvement in predicting binding affinity changes upon mutation on the SKEMPI dataset23. Furthermore, MINT demonstrated superior performance in antibody modeling, exceeding antibody-specific baselines on property prediction tasks from the FitnessLandscapes for Antibodies (FLAB) benchmark24 by over 10%. It also outperformed AbMap19 in predicting binding affinity changes in SARS-CoV-2 antibody mutants, achieving a 14% performance gain in the setting where only 0.5% of the data was available for training. In TCR-epitope modeling, MINT surpassed state-of-the-art models like PISTE17 and AVIB-TCR25 with minimal fine-tuning. We show how MINT can model the important task of predicting variant effects in PPIs. In oncogenic PPI (oncoPPI) analysis, MINT effectively distinguished between pathogenic and non-pathogenic interactions, with its predictions matching 23 of 24 previously experimentally validated mutational effects. Similarly, in the prediction of antibody cross-neutralization against SARS-CoV-2 variants, MINT achieved high precision-recall performance, capturing shifts in neutralization profiles across Omicron sub-variants and demonstrating an 80% accuracy in identifying antibodies with consistent neutralization capabilities. By treating multimeric interactions as interdependent sets of sequences rather than isolated groups, MINT provides a unified approach to computational PPI modeling, offering a powerful framework for studying disease mechanisms, guiding therapeutic design, and advancing immunological research.

Results
Overview of the MINT model and training
PLMs have been successfully applied to predict the structural, functional, and evolutionary attributes of proteins1,3–7. However, encoding PPIs poses unique challenges, as PLMs are traditionally trained to model single protein sequences independently. To address these challenges, prior approaches have involved passing each sequence through the PLM separately and then concatenating the embeddings, or alternatively, embedding interacting proteins as a single continuous sequence (Fig. 1a)16. However, these methods introduce limitations, such as the loss of inter-residue context and an oversimplified treatment of multi-sequence interactions.

To address these limitations, we developed MINT, a PLM specifically designed to model PPIs by enabling the simultaneous input of multiple interacting protein sequences (Fig. 1b). Building on the 650-million-parameter ESM-2 architecture, MINT introduces a cross-chain attention mechanism that preserves inter-sequence relationships and scales effectively to interactions with more than two chains. Whereas the self-attention mechanism in ESM-2 utilizes rotary positional encoding to capture intra-sequence positional relationships, MINT applies self-attention solely within chains and incorporates additional attention blocks for cross-chain interactions without rotary encoding10. This modification ensures that each token representation in our model captures contextual information across all input sequences (Fig. 1c). Further architectural details and pseudocode are provided in the “Methods” Section “MINT architecture”.

To train MINT, we curated a dataset from the STRING database, which includes 2.4 billion physical PPIs and 59.3 million unique protein sequences. By applying clustering and diversity measures, we refined this dataset to 96 million high-quality PPIs involving 16.4 million unique sequences (“Methods” Section “STRING dataset construction”). This dataset serves as the foundation for training MINT using a MLM objective augmented with cross-attention26. Unlike traditional MLM, where token prediction is conditioned solely on intra-sequence context, MINT leverages cross-chain representations to capture co-evolutionary constraints imposed by interacting residues (Fig. 1c, “Methods” Section “MINT architecture”). Model training followed a multiphase approach, beginning with the initialization of attention and embedding weights from ESM-2. This warm-start approach allowed us to preserve foundational sequence-level knowledge while optimizing cross-chain interactions. The model was trained with a masking scheme and hyperparameters closely aligned with ESM-2, with the objective function incorporating both sequence-specific and interaction-specific signals (“Methods” Section “MINT training”). Performance during training was assessed using the perplexity metric (“Methods” Section “MINT training”) on a validation set obtained from a random split of the curated dataset, and compared against the base ESM-2 models (Supplementary Note 2). MINT exhibited lower perplexity than the sequence-concatenated ESM-2 baseline, indicating improved modeling of PPIs. To further examine MINT’s generalization capacity, we performed supervised PPI prediction on a held-out STRING validation set that was excluded from pretraining, achieving superior performance over the ESM-2-650M baseline (Supplementary Note 3).

Together, these architectural and training innovations enable MINT to overcome the inherent limitations of existing PLMs in learning PPI rules. We demonstrate versatility in handling diverse protein sequence inputs, including general complexes, antibody heavy and light chains, T-Cell Receptor (TCR) regions, and peptides, without restrictions on the number of sequences processed concurrently (Fig. 1d). This capability allows MINT to capture intricate features relevant to disease-specific interactions and mechanisms, such as mutational impacts in cancer and the cross-neutralization potential of SARS-CoV-2 antibodies.

Comprehensive benchmarking of MINT on PPI prediction
PPI prediction is fundamental to understanding cellular processes, offering insights into disease mechanisms and therapeutic targets. Although structure-based models such as AlphaFold7,27 provide detailed predictions, they face challenges with scalability and accuracy for non-interacting pairs15. Sequence-based methods, in contrast, offer efficiency and flexibility but fall short in explicitly encoding PPIs. To address these gaps, we benchmarked MINT against widely used PLMs on supervised tasks including binary interaction classification, binding affinity prediction, and mutational impact assessment. We use standard dataset splits and provide detailed descriptions of dataset construction, splitting protocols, and measures to mitigate potential information leakage in “Methods” Section “Benchmarking tasks”.

We initially assessed the performance of MINT in predicting the presence or absence of interactions between protein chains, as well as in binary interaction prediction. This evaluation was conducted using three distinct datasets, including a comprehensive dataset curated by Bernett et al.22 that employs rigorous sequence similarity thresholds to mitigate data leakage, alongside two additional datasets derived from the PEER benchmark, which specifically focus on binary predictions of PPIs present in human and yeast systems12. Next, we evaluated how well MINT can predict absolute binding affinity by leveraging binding data for protein-protein complexes from PDB-Bind, where each complex can have a variable number of protein chains28. Both the binary interaction prediction and binding affinity prediction tasks involve a single interacting group of protein sequences, and an overview of the prediction process is illustrated in Fig. 2a.

We also evaluated the ability of MINT to resolve mutational effects on binding on two tasks. The first contained data from SKEMPI—a database documenting changes in binding affinity due to mutations in one of the interacting proteins—for which we evaluated the models with previously established cross-validation splits by protein complex23. The second task in this context involved predicting whether two human proteins remain bound after a mutation occurs in one of them, thereby testing the model’s sensitivity to sequence alterations that impact binding outcomes29. These tasks involve encoding the wild-type protein pairs and the mutant protein pairs, as described in Fig. 2a.

In each task, MINT’s embeddings were utilized to train lightweight predictor models, facilitating equitable comparisons with baseline pretrained language models (PLMs) such as ESM-1b30, ESM-2 (comprising both 650M and 3B parameters)10, ProGen (3B parameters)31, and ProtT5 (3B parameters, specifically trained on the UniRef and BFD databases)32 (“Methods” Section “Benchmarking tasks”). We compared two baseline embedding strategies: (1) concatenating independently computed embeddings from the PLM and (2) concatenating interacting sequences as a single input to the PLM (Fig. 1). For all tasks except PDB-Bind, the first strategy of concatenating embeddings worked better for all baseline models. For the PDB-Bind task, we evaluated only the second method of concatenation since each input can contain a variable number of interacting sequences, making it impractical to perform a variable number of PLM calls. We show the results for the best performing embedding strategy in Fig. 2c. Results for both embedding strategies are available in Supplementary Table 1.

MINT consistently outperformed all baseline PLMs across all tasks (Fig. 2c). On the gold-standard dataset constructed by Bernett et al., MINT achieved a new state-of-the-art performance with an area under the precision-recall curve (AUPRC) of 0.69, demonstrating its ability to excel under challenging conditions and to capture non-trivial interaction signals missed by traditional PLMs. In the other binary PPI prediction tasks, MINT achieved an improvement of 11% over the second-best baseline in yeast interactions and 3% over the average baseline for human interactions. On the SKEMPI dataset for predicting binding affinity changes upon mutation, our model achieved a 32% improvement, representing the highest performance for any model using only sequence information. Notably, MINT consistently outperformed the base ESM-2 model, indicating that the architectural enhancements and fine-tuning on STRING-DB enable the learning of essential PPI signatures absent in ESM-2. Furthermore, its performance advantages over larger general-purpose PLMs like ProGen and ProtT5 underscore the importance of task-specific adaptations over mere increases in model size. Finally, inference time benchmarking (Supplementary Note 4) showed that MINT also achieves competitive runtime efficiency compared to the baselines while outperforming them in predictive accuracy.

Performance on antibody modeling tasks
Building on its superior performance in general PPI tasks, we extended MINT to domain-specific challenges in antibody and immune modeling to test its ability to generalize beyond traditional protein interaction datasets. Antibodies are essential for the adaptive immune system’s ability to recognize and neutralize pathogens. They are symmetrical, Y-shaped molecules composed of two heavy chains and two light chains33. Antibody-antigen interactions are also uniquely difficult to model because CDR regions are highly variable and are not as constrained by evolution34. Here, we show how MINT can be used to predict the binding characteristics of antibodies to antigens by leveraging its ability to jointly model the heavy and light chains, thereby enabling richer representations suitable for various downstream tasks such as binary interaction and binding affinity prediction. We compare MINT with three deep learning-based approaches specific to antibody modeling, evaluating our model on the same datasets and employing the same evaluation strategies used in each approach.

We compared MINT against IgBert and IgT5—two models fine-tuned on extensive datasets comprising two billion unpaired and two million paired sequences of antibody light and heavy chains18. These models explicitly “learn the language” of antibodies, encoding heavy and light chains either jointly as pairs or separately in an unpaired manner. We assessed their performance on four supervised property prediction tasks using the FLAB dataset, which includes experimentally measured binding energy data from Shanehsazzadeh et al.35, Warszawski et al.36, Koenig et al.37, and expression data from the latter study. To ensure a faithful comparison, we followed the tenfold cross-validation procedure outlined by Kenlay et al.18, fitting a ridge regression model on embeddings from MINT (Fig. 3a, “Methods” Section “Benchmarking tasks”). Performance was evaluated by measuring the coefficient of determination (R2) between the predicted and actual values. MINT achieves a performance boost over the antibody-specific baselines on all four datasets, with an increase of over 10% on three of them (Fig. 3b). Moreover, it is able to perform equally well in predicting antibody binding affinity and expression tasks. This performance gain demonstrates that pretraining on a diverse corpus of PPI data enhances generalizability to antibody-specific tasks.

We further evaluated the performance of MINT in comparison with AbMap, a transfer learning framework fine-tuned specifically for antibodies by focusing on their hypervariable regions and trained on antibody-specific structural and binding specificity datasets19. Both models were assessed on a mutational variation prediction task involving the prediction of changes in binding affinity (ddG) for 396 antibody mutants against the SARS-CoV-2 virus, using experimental data from Desautels et al.38 (Fig. 3c). The m396 antibody originally targets the receptor-binding domain (RBD) of the SARS-CoV-1 spike protein39; mutants were generated to evaluate potential binding to the SARS-CoV-2 RBD. Approximately 90,000 mutants were generated in silico, with binding efficacy estimated through ddG scores computed using five energy functions: FoldX Whole, FoldX Interface Only40, Statium41, Rosetta Flex, and Rosetta Total Energy42. Adhering to the dataset splitting strategy employed by the AbMap method, we trained on designated subsets of the data and evaluated performance on the remaining portions using the Spearman rank correlation (“Methods” Section “Benchmarking tasks”). Again, MINT achieves comparable or better performance compared to AbMap across all data-splitting instances (Fig. 3d). Crucially, our model achieves a 14% increase in performance when trained on just 0.5% of the samples, suggesting that it can be used for in-silico antibody design even with a small number of training data.

Learning the language of TCR-Epitope-MHC interactions with minimal fine tuning
The interactions between T cell receptors (TCRs), epitopes, and major histocompatibility complex (MHC) molecules are central to the human immune response, allowing the immune system to identify and respond to pathogens, cancer cells, and other threats. TCRs on the surface of T cells recognize specific antigenic peptides (epitopes) that are presented by MHC molecules on antigen-presenting cells. This recognition is the first step in activating T cells, which is critical for orchestrating an immune response43. Several deep learning-based approaches have been developed to model the interactions between TCR, epitopes, and MHC molecules, primarily focusing on binary predictions of TCR-epitope or MHC-epitope binding (first-order interactions)44,45. More recently, methods have been designed to consider all three entities simultaneously, enabling the prediction of TCR-epitope-MHC binding (second-order interactions)17. Additionally, some techniques aim to enhance specificity by predicting the interface residues between interacting TCR-epitope pairs46. MINT offers a flexible framework to model these tasks by accommodating the various input sequences. However, the short length of epitopes and the common practice of considering only the Complementarity-determining Region 3 (CDR3) sequences of MHC and TCRs in most datasets present challenges for its direct application, since MINT is not trained on peptides. To address these limitations, we minimally fine-tuned the final layer of MINT to learn TCR-epitope-MHC interactions across the three task types mentioned above (Supplementary Note 5).

For the first-order tasks, we use the TCR-epitope binding prediction benchmark from Therapeutics Data Commons (TDC), which focuses on predicting whether a TCR binds to an epitope44. The interaction data is originally sourced from the IEDB47, VDJdb48, and McPAS-TCR49 databases. We compare popular TCR-Epitope prediction models, namely AVIB-TCR25, MIX-TPI50, Net-TCR251, PanPep52, TEINet53, and TITAN54. We only use TCR CDR3-beta and epitope sequences as input to MINT to maintain a fair comparison with all baseline models (Fig. 4a). MINT obtains an average AUROC score of 0.581, surpassing the best baseline, which has an AUROC of 0.576 (Fig. 4b). These results demonstrate that minimal fine-tuning of MINT can effectively model TCR-epitope interactions, even for protein types not encountered during its pre-training.

For the second-order interaction prediction, we train our model on the dataset of human TCR, Epitope, and Human leukocyte antigen (HLA) interactions, where HLA is the human version of MHCs (Fig. 4c). This binding data was originally procured from the McPAS-TCR, VDJdb, and pMTnet55 databases and curated in the PISTE model work17. The evaluation set consists of TCR-Epitope-HLA triplets obtained from studies of different cancer types. We used all three dataset splitting schemes as described in the same work and show the performance of the fine tuned MINT with PISTE, pMTnet, and other first-order models. MINT achieves the highest performance across all dataset splits by leveraging its ability to process multiple protein chains (Fig. 4d). These results highlight MINT’s ability to process multiple protein chains and achieve state-of-the-art performance on complex multi-protein interaction tasks. The scalability of MINT to handle an increasing number of protein chains further emphasizes its utility for modeling intricate biological systems.

For the interface prediction task, we used the dataset constructed in the TEIM work46, with the TCR-Epitope structure data originally sourced from STCRDab56 (Fig. 4e). We restrict ourselves to the splitting strategies where the test set contains novel epitopes or novel TCRs that are not seen during training, and each split results in a total of 122 data points. MINT matches the performance of TEIM on the unseen TCR-CDR3 split and performs better on the unseen epitope split (Fig. 4f). These results underscore MINT’s capacity to infer structural relationships between TCRs and epitopes without extensive pretraining and its robust performance with small datasets, suggesting that its internal protein sequence representations are readily transferable to specialized tasks.

Leveraging MINT for predicting mutation-induced perturbations in oncogenic PPIs
After establishing the superior performance of MINT against popular PLMs on general PPI prediction tasks and against domain-specific models on antibody and TCR-Epitope-MHC tasks, we highlight the types of analyses that can be achieved with MINT. We first applied it to estimate the pathogenicity of missense mutations affecting PPIs in cancer, focusing on cancer-associated mutations that disrupt PPIs, which we refer to as oncoPPIs. Disease-associated germline and somatic mutations are enriched at PPI interfaces, indicating their potential role in disrupting PPI networks. Cheng et al.57 identified 470 potential oncoPPIs linked to patient survival and drug responses and experimentally validated the perturbation effects of 24 somatic missense mutations across 13 oncoPPIs using Yeast Two-Hybrid (Y2H) assays, hypothesizing their functional impact on cancer progression.

Existing computational approaches distinguishing pathogenic from non-pathogenic interactions typically focus on single protein sequences, employing biophysical and evolutionary constraints or leveraging protein embeddings (e.g., EVE58, AlphaMissense59, ESM-1b3). However, in oncoPPIs or any other pathogenic PPIs, mutations rarely significantly affect protein structure but alter protein-partner binding60,61. Therefore, we took advantage of MINT’s ability to effectively model multiple interacting proteins and trained it on a dataset containing information on whether a mutation in a human protein sequence disrupts its binding to another human protein or not29. We then predicted the mutational effects on the experimentally validated oncoPPIs from Cheng et al., as outlined in Fig. 5a. Since the training dataset is quite small, we performed multiple repetitions of the training runs, or model ensembling, and computed a binding score that is representative of the proportion of times that the model predicts binding overall repetitions (“Methods” Section “Case studies”).

We show the real mutational effects and model predicted scores for all of the experimentally validated oncoPPIs from Cheng et al. in Fig. 5c. Using a threshold value computed in an unsupervised manner (“Methods” Section “Case studies”), MINT’s predictions match the mutational effects for 23 out of 24 PPIs. Our model is able to correctly assign the ALOX5-MAD1L1 interaction as being lost after the M146K mutation in ALOX5, and both of these proteins have been implicated in tumor progression in several types of cancer62. Interestingly, our analysis correctly predicts a destabilizing mutation for the HOMEZ-EBF1 interaction, and previous ZDOCK analysis has shown that the mutation occurs at the binding interface, potentially disrupting the salt bridge and hydrogen bond at that position13. Out of all 24 mutations, only one oncoPPI, the ARHGDIA-RHOA pair, was incorrectly assigned. Our results indicate that MINT has the ability to effectively model mutational effects in oncoPPIs and can be used to provide insights into how mutations disrupt PPIs in cancer.

MINT predicts antibody cross-neutralization against SARS-CoV-2 variants
Next, we consider the task of estimating the cross-neutralization of antibodies against different SARS-CoV-2 variants. SARS-CoV-2 undergoes a very high rate of mutation, leading to significant changes in transmissibility, immune evasion, and severity between strains. One highly mutated version of the virus is the Omicron variant, which was able to evade immunity from several neutralizing antibodies produced by natural infection and vaccination against previous variants63. Using experimental methods like deep mutational scans to predict immune escape for each newly emerging variant, such as Omicron, is time-consuming, expensive, and intractable. Computational methods such as EVEScape, which use pre-pandemic data, do not directly model antibody-spike protein interactions and thus fall short in predicting how existing antibody titers might target emerging or potential variants5,6. To address these limitations, we utilized MINT in conjunction with the CoV-AbDab database64 to encode antibodies—both naturally produced and vaccine-induced—and the SARS-CoV-2 spike proteins. The resulting embeddings were used to train an MLP model to predict whether an antibody-spike protein pair results in neutralization. Training was conducted using data from early pandemic variants (wild-type, Alpha, Beta, and others). For evaluation, we predicted the cross-neutralization capabilities of antibodies targeting Omicron sub-variants using a normalized score that reflects the likelihood of neutralization (Fig. 6a, “Methods” Section “Case studies”). This approach mirrors a realistic pandemic scenario in which existing antibody titers or designed antibodies need to be evaluated for their cross-neutralization capabilities against newly emerging variants. Figure 6b highlights the composition of the evaluation dataset, showing the distribution of data points for each Omicron sub-variant and their origins, categorized as vaccine-induced or infection-derived.

MINT demonstrated robust predictive performance, achieving high area under the precision-recall curve (AUPRC) values for the first three Omicron sub-variants (Fig. 6c). Notably, predictions for vaccine-induced antibodies outperformed those for infection-derived antibodies, with higher AUPRC scores observed across all variants (Fig. 6d). This enhanced performance likely reflects the greater homogeneity of vaccine-induced antibody repertoires compared to those generated through natural infection65. To further validate the model, we compared MINT’s normalized scores against IC50 (Half maximal inhibitory concentration) binding affinity values reported in experimental studies. Using BBIBP-CorV vaccine-induced antibodies evaluated in Liu et al.66, we observed an 80% hit rate for correctly identifying antibodies with consistent neutralization across Omicron sub-variants (Fig. 6e). Importantly, the model captured transitions in neutralization capacity along the evolutionary trajectory of Omicron, from BA.1 to BA.5, for antibodies such as Liu 14-5G, Liu 16-1C, and Liu 14-1C. Hence, MINT is able to discern nuanced shifts in neutralization profiles associated with variant evolution.

Methods
Here we describe the following methodological details: (1) curation and processing of training datasets, (2) architectural design and implementation of MINT, (3) datasets and workflows for downstream benchmarking tasks, and (4) datasets and workflows used in case studies.

STRING dataset construction
We start with 2.4 billion PPIs comprising 59.3 million unique protein sequences classified as physical links from the STRING database. We use mmseqs69 to cluster the protein sequences at a 50% sequence similarity threshold, resulting in 15.6 million unique clusters. We then apply a filtering scheme that keeps only one PPI between any two clusters. This is done to ensure that PPIs between proteins belonging to the same two clusters are not repeated, so that our model can learn interactions between diverse protein types. This results in 382 million PPIs comprising 29 million unique sequences. Next, we set aside 250,000 PPIs for validation. For the training set, we apply further filtering to ensure that the training and validation sets are disjoint. Finally, we end up with 95.8 million training PPIs comprising 16.4 million unique protein sequences. We provide an illustration of the dataset construction in Supplementary Fig. S1 along with alternative splitting strategies we considered in Supplementary Note 1.

MINT architecture
We use ESM-2 as our backbone PLM10. This is due to its strong empirical performance, architectural flexibility, and broad adoption within the protein modeling community70. Its modular architecture enables straightforward modifications, which we leverage to integrate cross-chain attention mechanisms for modeling multi-chain proteins in MINT.

Each layer of MINT receives as input an embedding of the sequences x 2 RL×F, a self-attention mask matt 2 RL×L, and a padding mask mpad 2 RL×L, where L is the total length of the input comprising multiple sequences, and F is the feature dimension (F = 1280 in MINT). The main attention mechanism in MINT is the MultiHeadAttention module, as used in ESM-210. We adapt this module to output both the pre-softmax attention matrix and the value tensor, since our architecture requires combining attention weights from both intra-sequence (self-attention) and inter-sequence (cross-attention) computations prior to the softmax operation. A key design choice in our implementation is that both self-attention and cross-attention are realized via the same MultiHeadAttention module, but with different attention masks. Intra-sequence (self-attention) restricts attention to within each individual sequence, and inter-sequence (cross-attention) allows attention across different sequences (chains). Although both steps use the same MultiHeadAttention module, the different masking patterns effectively control whether attention is intra- or inter-sequence. This approach allows us to avoid introducing a separate MultiHeadCrossAttention module, while still enabling cross-chain interactions when required. We present further details for the transformer block as pseudocode in Algorithm 1.

Algorithm 1. Single functional transformer block of MINT
1: function TransformerLayer(x, mpad, matt)
2: x <- LayerNorm(x)
3: s, vself = MultiHeadAttention(x, mpad, beforesoftmax=True)
4: c, vcross = MultiHeadAttention(x, mpad, beforesoftmax=True)
5: Ah ij = s ij, if matt = 1; c ij, otherwise
6: Ah ij = Softmax(Ah ij)
7: d = Dropout(A)
8: pself = d ij, if matt = 1; 0, otherwise
9: pcross = d ij, if matt = 0; 0, otherwise
10: z = pself vself + pcross vcross
11: xout = Mean h (Linear(zh))
12: xout = x + xout
13: return xout
14: end function

MINT training
We train MINT using the MLM objective as defined in the main text. Formally, for a given set of input amino acids in a sequence tokenized to be x = x1 x2 ... xn, where x 2 RL×F, the tokens that are masked M 2 RL, and the output vector h 2 RL×F (corresponding to xout from the last layer of the model), the MLM loss is described as:

L MLM = - 1/|M| sum i2M log P(xi | hi) (1)

During model training, we consider 15% of tokens randomly sampled from the sequence, similar to ESM-210. For those 15% of tokens, we change the input token to a special masking token with 80% probability, a randomly chosen alternate amino acid token with 10% probability, and the original input token (i.e., no change) with 10% probability. We take the loss to be the whole batch average cross-entropy loss between the model’s predictions and the true token for these 15% of amino acid tokens.

We set the Adam optimizer with parameters b1 = 0.9, b2 = 0.98, and e = 10-8, along with an L2 weight decay of 0.01. The learning rate was increased over the first 2000 steps to a peak of 4e-4, then gradually reduced to one-tenth of the peak value over 90% of the training. To process large proteins efficiently, we cropped long sequences to random 512-token lengths and used special “BOS” and “EOS” tokens to mark the beginning and end of proteins. We use a batch size of 2, and we accumulate gradients every 32 batches, resulting in an effective batch size of 64. The models were trained on NVIDIA A100 80GB and NVIDIA RTX A6000 GPUs in a distributed data parallel manner. We use Pytorch Lightning (https://lightning.ai/docs/pytorch/stable/) to handle the complete training and validation loops.

We trained MINT for 4 million iteration steps.

We use the perplexity metric (eLMLM) on the validation set to measure how well the model is learning across the training run (Supplementary Fig. S3). We compare this with the perplexity of ESM-2 models as a baseline on the validation set in two settings: the first where the inputs are treated as separate protein sequences and the second where the two protein sequences are concatenated. ESM-2 with single protein chains as input achieves a perplexity of 5.41 on the validation set, while concatenating achieves a perplexity of 5.16. Our MINT model achieves a validation perplexity of 4.84 at the end of its training run. We also provide perplexity values across training runs for alternative configurations of MINT initialization and training in Supplementary Note 2 and Supplementary Fig. S2.

Benchmarking tasks
We compile a list of all datasets used across all tasks (general PPI, antibody, and TCR-epitope), along with information about task type, evaluation metric used, and references in Table 1. For the antibody and TCR-epitope tasks, we use the evaluation metric used by baseline methods for consistency. For all the tasks, we use sequence-level embeddings from MINT (or a baseline PLM) to train a downstream model (MLP, ridge regression, or CNN). Specifically, we extract the residue-level embedding h from the final layer for each group of interacting sequences, h 2 RL×F, where L is the total length of the input comprising multiple sequences and F is the feature dimension. We average over the sequence length L to get a sequence-level embedding of shape R F for each sequence. For tasks involving mutational effect prediction, we extract the sequence-level embedding for both the wild-type and mutant sequence, and use the difference between these embeddings as the input to the downstream model. The subsections describe task-specific methodologies.

General PPI tasks. We briefly discuss the construction of training-test splits in each benchmarking dataset. Unless specified, we use the final train-test splits as provided by the authors of each work.
1. Gold-Standard PPI: This dataset was designed explicitly to prevent models from leveraging sequence similarity as a shortcut for PPI prediction. Using SIMAP2 bitscores, the human proteome was first partitioned into training, validation, and test sets to minimize pairwise sequence similarity across splits. CD-HIT was then applied with a 40% sequence identity threshold both within and across splits to ensure non-redundancy. As a result, no similar protein sequences exist in both training and test data, making this benchmark particularly stringent22.
2. Yeast-PPI: The dataset was initially constructed by Guo et al.71 using a protocol where negative PPI pairs are constructed using proteins from different subcellular locations. Protein redundancy was then removed in two stages: first, a 90% sequence identity threshold was used to filter similar sequences across the entire dataset; then, a 40% identity threshold was applied across train/validation/test splits to prevent redundancy between them12.
3. Human-PPI: We use Pan et al.’s72 dataset with positive interactions from HPRD and negative examples derived from different subcellular compartments. The data splitting mirrors the yeast PPI protocol, with initial redundancy removal at 90% identity and a further 40% cutoff applied between the resulting train/validation/test splits (using an 8:1:1 ratio). This setup supports evaluation of generalization to dissimilar protein sequences12.
4. SKEMPI: To mitigate the risk of information leakage from structurally similar complexes, we split the dataset by protein complex into three mutually exclusive folds. Each fold contains unique complexes, ensuring that mutations from the same protein complex do not appear in both training and test sets. We follow Luo et al.73 and report results via threefold cross-validation so that each data point is tested exactly once.
5. PDB-Bind and Mutational PPI: For these tasks, we apply tenfold cross-validation, with folds split by protein complex rather than by individual sequences. This strategy ensures that all chains involved in a complex are assigned to the same fold, preventing any component of a test complex from appearing in training data.

We evaluate two embedding strategies for the baseline PLMs. The first strategy involves making two calls to the PLM, generating the sequence-level embeddings separately, and concatenating these embeddings for all input sequences. The other involves concatenating the input sequences and generating a sequence-level embedding from the PLM. For all experiments except SKEMPI, we perform 3 repetitions of the training and evaluation and report the mean and standard deviation values. For SKEMPI, we calculate the mean and standard deviation for each fold and aggregate according to the following heuristic:

mu total = (sum i=1 3 n i mu i) / (sum i=1 3 n i) (2)

sigma total 2 = (sum i=1 3 n i (sigma i 2 + mu i 2)) / (sum i=1 3 n i) - mu total 2 (3)

Where n, mu, and sigma are the number of entries, the mean, and the standard deviation for each fold i.

The objective function used for training the regression tasks is the Mean Squared Error loss, and for the binary classification tasks, we use the Binary Cross-Entropy (BCE) loss. Training was conducted using a 2-layer MLP with a hidden size of 640 for all experiments. We chose this number because it is half of the smallest embedding dimension across all models. We train all models for 100 epochs and choose the epoch with the best validation metric for test set evaluation. For datasets with validation splits, we use fivefold inner cross-validation to choose the best model.

Antibody tasks
FLAB. This dataset includes the binding energy datasets from Shanehsazzadeh et al. (2023)35, Warszawski et al. (2019)36, and from Koenig et al. (2017)37, as well as the corresponding expression data from this most recent study. We use the sequence-level embedding over both antibody chains from MINT. We then follow 18 to perform the training experiments. This involves running a linear least squares fit with L2 regularization on the mean embedding representation features, evaluated using tenfold cross-validation. To choose the regularization hyperparameter lambda from the set 1, 10-1, ..., 10-6, 0, we perform an additional fivefold inner cross-validation. We use the best-performing model across each fold on the held-out testing fold and average the computed R2 across all folds.

SARS-CoV2 binding. We use sequences from ref. 38 and split the data according to ref. 19. We use the wild-type m396 sequence from PDB (PDB ID: 2G75) and feed that into MINT separately from the mutant sequences. We extract the sequence-level embedding for both wild-type and mutant sequences and take the difference between the two as the final embedding. We then train a ridge regression model using an alpha value of 0.01 on these embeddings.

TCR-Epitope-MHC tasks. We train the last layer of MINT and the downstream projector module (MLP or CNN) for the TCR-Epitope-MHC tasks.

TDC-Tchard. We use the “TDC.tchard” dataset from TDC-244, which contains 5 different folds of train-test splits. We train the model using a batch size of 48 and an initial learning rate of 1e-4 for 15 epochs. We choose the best epoch as the one that has the highest validation AUROC value and report those metrics. Then, we take the weighted average of the AUROC across all splits (the product of the AUROC and the test set size) to report as the final AUROC value, as suggested by TDC-2 benchmarking.

TCR-Epitope-HLA. For this task, we use the evaluation strategy as described in ref. 17. Specifically, we generate embeddings from MINT for the TCR-CDR3, HLA-CDR3, and epitope sequences and train an MLP model for binary interaction prediction using the BCE loss. We perform three repetitions and report the mean and standard deviation values in our results.

TCR-epitope interface prediction. We procure and use the dataset splits as constructed in ref. 46. For the sake of simplicity, we skip the pre-training task of TCR-epitope interaction prediction employed in the baseline model, TEIM. We also do not use the epitope autoencoder features as in TEIM. We do, however, use the same training setup after generating the embeddings for the TCR-CDR3 and epitope sequences using MINT. This entails training a three-layer CNN model to predict the contact map. We then use a BCE loss over the predicted and true contact maps to train the model.

Case studies
oncoPPI prediction. We used the dataset from ref. 29 that contains labels indicating whether two human proteins continue to bind after a missense mutation as our main training set. For the evaluation of mutations on oncoPPIs, we extracted the experimentally validated mutational effect data from Cheng et al.57 and retrieved the sequence data from UniProt. Following Cheng et al.57, we converted the Y2H score (range of 0–4) to a 0 (non-binding) or 1 (binding) based on a cutoff of 2. For example, a Y2H score of 4 (yeast colony grows) for a mutated oncoPPI would map to a 1 (binding conserved).

The MLP is trained on the difference between wild-type and mutated PPI embeddings from MINT (in a manner similar to 2b). Since the training dataset is very small, we performed 100 repetitions of a training and evaluation run. We then calculated a binding score S binding that is defined as:

S binding = (sum i=1 N I(yi = 1)) / N (4)

where yi is the output of the model for entry i in the evaluation set, and N = 100 repetitions.

To determine an optimal threshold for classification using only the predicted binding score, we fitted the data using a Gaussian Mixture Model. This method assumes that the scores belong to two distinct groups (binding vs non-binding) and models them as a mixture of two Gaussian distributions. The threshold for classification was identified as the point where the two Gaussian distributions overlap most significantly. In our case, where the two distributions have similar variances, the threshold is approximated as the midpoint between their means, resulting in a value of 0.68. Hence, binding scores below 0.68 would be classified as non-binding, while those above would be classified as binding. We provide the AUROC curves for this analysis in Supplementary Fig. S4.

SARS-CoV-2 Cross-neutralization prediction. We first download the entire CoV-AbDab database64. We then filter out non-antibody entries and entries that do not have a sequence associated with them. We then keep antibodies that fall into one of these origins: “B-cells SARS-CoV2 WT”, “Convalescent Patient (Unvaccinated)”, “B-cells; SARS-CoV1 Human Patient; SARS-CoV2 Vaccinee”, “B-cells; SARS-CoV2 WT Convalescent Patients”, “B-cells; SARS-CoV2 WT Vaccinee (BBIBP-CoV)”, “B-cells; SARS-CoV2 WT Vaccinee”, “B-cells; SARS-CoV2 WT Human Patient”, “B-cells; Unvaccinated SARS-CoV2 WT Human Patient”, “B-cells; SARS-CoV2 Gamma Human Patient”, “B-cells; SARS-CoV1 Human Patient”, “B-cells (SARS-CoV2 Beta Human Patient)”. Next, we only keep antibodies that bind to the RBD of the SARS-CoV-2 spike protein. Finally, we split the data to ensure that only neutralization values against early variants (Wild-type, Alpha, Beta, Delta, Epsilon, Gamma, Eta, Iota, Lambda, and Kappa) are included in the training set. The evaluation set contains only Omicron sub-variants, namely BA.1, BA.2, BA.4, and BA.5. This results in 3365 data points in the training set and 1052 entries for evaluation. We downloaded the spike protein sequences for all included variants and sub-variants from Expasy (https://viralzone.expasy.org/9556).

During training, we generate sequence-level embeddings from MINT for the antibody heavy-chain, light-chain, and the RBD domain of the spike protein. We train an MLP on these embeddings to predict whether the antibody is able to neutralize that spike protein using the BCE loss. In the training set, antibodies may be repeated since they have neutralization data against multiple variants. After training, we test the model on the evaluation set and record the raw outputs. Since the raw outputs across the different sub-variants may have different scales, we use quantile normalization to normalize the scores across the sub-variants to calculate the “Normalized score”. We retrieve the IC50 values from ref. 66.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript “Learning the language of protein-protein interactions” by V Ullanat et al describes an interesting\nmodification of protein language model (PLM) to accommodate joint embedding of features for several protein chains. The\nidea proposed by the authors (adding cross attention module into standard PLM architecture) is original and worth\npublishing. The manuscript is written in clear and concise language, figures are well drawn, and code is available on the\ngithub. However, the main weakness of the study is that improvement in the model performance seems to be marginal\ncompared to other PLM architectures considered in the manuscript, which may diminish general impact to the field and\ninterest of the broad community.\nSome other comments/suggestions which, in my opinion, would help to improve the quality of the manuscript:\n• Name MINT should be replaced by something else, as name MINT originally belongs to the widely known database of\ninteracting proteins (Molecular INTetractions). Of course, presently, MINT is part of the INTACT database, but Google search\nby “MINT” keyword still points to legacy website of the MINT database.\n• Comparison to the present gold-standard in the modeling of protein-protein interactions, AlphaFold3, is completely missing\nin the manuscript.\n• Comparison in the performance should be made not with respect to the somewhat obscure baseline, but rather with respect\nto the performance of the second-best performing method.\n• It would be interesting to have more in-depth discussion of what difference adding cross-attention module makes with\ncomparison to the concatenated input as it seems that the performance of the both approaches is very similar.\n• References 19 and 25 are identical, just one refers to the bioRxiv preprint and another to the same preprint published in the\njournal.\n(Remarks on code availability)\nThe code is present and instructions how to run it seems to be adequate.", "reviewer_2": "This paper introduces MINT, a pre-trained protein language model designed to model protein-protein interactions (PPIs)\nbased on amino acid sequences. The model builds upon the ESM framework but introduces a distinction between self-\nattention within the same chain and cross-attention between chains. It is trained using interaction protein sequences from the\nSTRING database and employs a masked language modeling (MLM) loss function. The authors evaluate MINT's\nperformance on several downstream tasks related to PPI prediction, comparing it against multiple protein language models\nas baselines. They demonstrate that MINT achieves improvements across all benchmarks.\nWhile the algorithmic innovation over ESM is limited (primarily distinguishing self-attention from cross-attention), protein-\nprotein interactions are a critical area of research, and any genuine progress in their prediction holds significant value.\nHowever,there are several areas where further clarification and improvement are needed.\nMajor issues:\n(1) The authors primarily rely on existing benchmarks from the literature to demonstrate MINT's superiority over baseline\nmodels. However, since these benchmarks involve supervised learning for specific downstream tasks, ensuring no\ninformation leakage from the training set to the test set is crucial. Although the authors use the same dataset splits as\nreported in the literature, they merely cite these sources. They should providing detailed explanations of how the training-test\nsplits were conducted for each benchmark and discuss/analyze concerns of potential information leakage. Additionally, the\ndegree of improvement over baseline models varies significantly across benchmarks (ranging from 30% to only 2–3%). The\nauthors should discuss this discrepancy and analyze whether it might be influenced by differences in dataset splits or other\nfactors.\n(2)Beyond benchmark comparisons, evaluating MINT's true performance on datasets with guaranteed independence from\nboth pre-training and supervised training sets is essential. The authors have already divided the STRING dataset into\ntraining and validation sets but have only used the validation set to monitor training progress (i.e., perplexity). They could\nfurther split the validation set to construct downstream supervised learning models and directly assess MINT's predictive\npower on truly independent interaction data.Such an analysis would help clarify MINT's practical applicability in real-world\nscenarios and identify the contexts where it performs best.\nMinor issues:\n(1) Algorithm 1 Pseudocode:\n-In Line 4, should \"MultiHeadSelfAttention\" be \"MultiHeadCrossAttention\" ? Currently, it appears identical to Line 3.\n-What is the difference between \"d_ij\" (Line 8) and \"d_ij^h\" (Line 9)? A clearer explanation of their respective roles would\nimprove readability.\n-In Line 13, what does \"t\" represent?\n(2) Formula 1 on P21:\n-Does the variable \"h\" correspond to \"x^out\" from the last layer of Algorithm 1? If so, this connection should be explicitly\nstated.\n-The notation \"h_i^L\" is ambiguous. Does it imply predicting \"x_i\" based on the entire tensor \"h,\" or only based on the\ncomponent of \"h\" at position \"i\"?\nData availability:\nThe training and valiation datasets derived from STRING could also be made available.\n(Remarks on code availability)", "reviewer_3": "The authors have done a commendable job in creating a pretrained model for PPI. The work is comprehensive and very well\nexplained. However, there are certain points that need to be clarified before the final decision is made.\n1)Instead of going through such heavy pretraining, why have the authors not chosen a methodology akin to \" Interpretable\nPrediction of SARS-CoV-2 Epitope-Specific TCR Recognition Using a Pre-Trained Protein Language Model\"? It will be\nreally helpful for the readers if the authors can provide a comparative analysis with this work.\n2) Why have the authors chosen ESM-2 and not any other model for their pretraining skeleton? Please justify.\n3) The model is quite resource intensive. Can the authors please provide a comparative analysis for the time taken as well?\nIs there a trade-off between the other models and MINT?\n(Remarks on code availability)\nYes, the code is quite easy to navigate."}, "author_response": "Response to Reviewers\nReviewer #1 (Remarks to the Author):\nThe manuscript “Learning the language of protein-protein interactions” by V Ullanat et al\ndescribes an interesting modification of protein language model (PLM) to accommodate\njoint embedding of features for several protein chains. The idea proposed by the authors\n(adding cross attention module into standard PLM architecture) is original and worth\npublishing. The manuscript is written in clear and concise language, figures are well\ndrawn, and code is available on the github. However, the main weakness of the study is\nthat improvement in the model performance seems to be marginal compared to other\nPLM architectures considered in the manuscript, which may diminish general impact to\nthe field and interest of the broad community.\nWe thank the reviewer for their positive assessment of our work. We also appreciate the\nreviewer’s concern regarding the marginal performance improvements on some tasks. While\nMINT demonstrates strong overall performance across multiple PPI-related tasks, we\nacknowledge that the degree of improvement over baseline PLMs varies. This variability,\nranging from modest (2–3%) to substantial (>30%), is consistent with trends seen in prior work\ninvolving PLMs. For example, SaProt, a PLM that integrates explicit structural information,\nachieves only 1–5% gains over ESM models on supervised tasks such as HumanPPI and Metal\nIon Binding, despite its multimodal design [1]. Similarly, AVIB-TCR, a specialized model for\nTCR–epitope binding, improved the AUROC by an increment of just 2% over baselines [2]. We\nbelieve these small gains reflect a saturation point in standard benchmarks.\nIn our paper, we observe that benchmarks with lower sequence similarity between train and test\nsets, such as the gold-standard dataset from Bernett et al. [3], yield good performance gains\n(e.g., a 4-5% AUPRC improvement), reflecting MINT’s strength in capturing inter-chain context\nthat generalizes across protein families. Tasks with higher redundancy or simpler classification\nobjectives, like HumanPPI, show smaller margins, indicating that such datasets may already\nsaturate the capacity of current PLMs. In these cases, even large-scale or structurally-aware\nmodels like SaProt often yield only incremental improvements. We now have included this\nlimitation of our work and others in the Discussion section.\nNotably, on more complex or data-limited tasks, such as mutation-induced binding changes in\nSKEMPI and antibody-antigen modeling in FLAB, MINT consistently achieves substantial gains\n(up to 29% and 30%, respectively). These improvements demonstrate the value of MINT’s\ncross-chain attention mechanism in tasks where inter-sequence context is critical but\nunderutilized by traditional PLMs. We believe that the modest improvements in some\nbenchmarks should be weighed alongside the broader generalizability and versatility of MINT,\nparticularly in novel applications like TCR–epitope–MHC modeling and oncogenic PPI\nperturbation prediction. We have already made this point in the Discussion.\nSome other comments/suggestions which, in my opinion, would help to improve the\nquality of the manuscript:\n• Name MINT should be replaced by something else, as name MINT originally belongs to\nthe widely known database of interacting proteins (Molecular INTetractions). Of course,\npresently, MINT is part of the INTACT database, but Google search by “MINT” keyword\nstill points to legacy website of the MINT database.\nThank you for your comment regarding the name “MINT.” We are aware that the acronym MINT\nhas traditionally referred to the Molecular INTeractions database. However, the original MINT\ndatabase has been merged into the IntAct resource and is no longer maintained as an\nindependent project. As you know, our use of “MINT” refers to the “Multimeric INteraction\nTransformer,” a protein language model with a distinct purpose and scope that is clearly\ndifferentiated from the original MINT database, both in function and context.\nWe have taken care throughout the manuscript to define “MINT” at first mention as “Multimeric\nINteraction Transformer,” and to consistently use this explicit definition. In our view, this clear\ndefinition at the outset, together with the substantial differences in scientific domain (deep\nlearning models vs. database resources), sufficiently minimizes the risk of confusion.\nMoreover, as evidence of community adoption and visibility under our usage, the GitHub\nrepository for our model has already attracted 88 stars since its release and 1,000+ views this\nmonth alone. Furthermore, “MINT” has also been benchmarked and cited in several preprints\nalready [4,5,6,7,8]. Overall, this suggests that the current usage of “MINT” is becoming\nincreasingly associated with the Multimeric INteraction Transformer rather than the historical\ndatabase.\n• Comparison to the present gold-standard in the modeling of protein-protein\ninteractions, AlphaFold3, is completely missing in the manuscript.\nWe thank the reviewer for this important suggestion. We would like to clarify that our work is\nfocused specifically on functional prediction tasks, such as binary PPI classification, binding\naffinity prediction, and estimation of mutational effects, using protein sequence-based models.\nWhile AlphaFold3 is currently the gold standard for structural modeling of protein complexes,\nits primary output is high-resolution 3D structural predictions, and it does not natively provide direct\nlabels or scores for PPI classification, affinity prediction, or mutational impact [9,10]. In contrast,\nsequence-based approaches like MINT allow for rapid, scalable, and direct prediction of\nfunctional interaction properties from sequence alone, without requiring structural information or\nresource-intensive computations (AlphaFold3 can take up to 30 minutes for a single data point\nwhile MINT requires <1 second). Recent studies like AbRank, which benchmarks MINT, have\nshown that sequence-based methods outperform AlphFold3 + FoldX on antibody–antigen\naffinity ranking [7].\nFor these reasons, we have limited our benchmarking to sequence-based models, which are\nmore broadly applicable and simpler to use for large-scale PPI prediction tasks. While we agree\nthat comparing sequence-based and structure-based approaches is a valuable direction, such a\ncomparison is beyond the scope and focus of this work. We now make this point clear in the\nDiscussion.\n• Comparison in the performance should be made not with respect to the somewhat\nobscure baseline, but rather with respect to the performance of the second-best\nperforming method.\nWe thank the reviewer for this insightful suggestion. In our original manuscript, we reported\nperformance improvements over the average of all baseline models because certain models\nperformed better on specific tasks while underperforming on others.\nHowever, we understand that this may obscure the magnitude of improvement over the\nstrongest competing method. In response, we have revised the manuscript to additionally report\nimprovements over the second-best performing model for each task. In cases where MINT is\ncomparable to the second-best model (less than 1% increase in performance), we also provide\nthe comparison to the average performance for additional clarity. These changes are reflected in\nthe Introduction, Methods (Comprehensive benchmarking of MINT on PPI prediction) and\nFigure 2.\nFor example, on the Gold-standard PPI task (Fig. 2e), MINT improves over the second-best\nmodel, ESM-2-650M, by 4%, rather than the previously reported 5% over the average. Similar\nadjustments are now reflected across all benchmark comparisons in Figure 2. The raw\nperformance data for baselines and MINT is detailed in Supplementary Table 1.\n• It would be interesting to have more in-depth discussion of what difference adding\ncross-attention module makes with comparison to the concatenated input as it seems\nthat the performance of the both approaches is very similar.\nWe thank the reviewer for raising the astute question about the practical differences between\nemploying a cross-attention module and simply concatenating input sequences as concatenated\ntokens into a PLM. We would like to first state that the method of concatenating input sequences\ninto the base ESM-2 models actually results in significantly inferior performance across most\ntasks when compared to MINT, as shown in Supplementary Table 1. For example, on the\nGold-standard PPI prediction task, where the train-test splits are designed to have very low\nsequence similarity, MINT achieves a 11% performance gain over concatenated inputs into the\nESM-2 650M model. On the HumanPPI and YeastPPI prediction tasks, MINT achieves a 17%\nperformance gain. On SKEMPI, we see close to double the performance. Hence, MINT\nrepresents an advancement over simply concatenating the input sequences by demonstrating\nstrong performance gains over currently used PLMs.\nThe performance between MINT and baseline PLMs are closer on all tasks only when we\nconcatenate the output embeddings from independently processed sequences using two PLM\ncalls, namely the “concatenate embeddings” as shown in Supplementary Table 1. Even here,\nMINT consistently outperforms baselines or matches (less than 1% improvement) the best\nperforming baseline in all tasks. However, using two PLM calls increases inference times of\nbaselines as shown in Supplementary Table 3. So, even though current PLMs come close to\nMINT’s performance in some tasks while concatenating embeddings, their latency is higher due\nto the fact that you have to make multiple calls to the PLM.\nWe would also like to describe the architectural distinction between the cross-attention\narchitecture of MINT and concatenating input sequences. Adding a cross-attention module\nincreases the number of trainable parameters in the model, thereby enhancing its\nexpressiveness and capacity to learn more complex inter-sequence relationships. Specifically,\ncross-attention enables each residue in one protein to directly attend to all residues in its\ninteracting partner(s) at every layer, allowing the model to capture context-dependent features\nthat would otherwise be inaccessible if the proteins are simply concatenated and treated as a\nsingle sequence. The concatenation approach also does not explicitly encode the directionality\nor identity of individual chains, which may be important for modeling complexes with more than\ntwo interacting proteins.\nFinally, we would also like to refer the reviewer to our updated experiment in the Supplementary\n(Supplementary Note 3) that shows the perplexity achieved by the baseline model, ESM-2,\nand MINT through its first 2.5 million training steps. We calculate the average perplexity attained\nby ESM-2 while encoding PPIs from our STRING validation set by concatenating its tokens and\ntreating them as a single sequence. MINT was able to achieve a lower perplexity after 2.5\nmillion steps (~4.9) when compared to the single sequence (5.41) and the concatenated (5.16)\nESM-2 versions. After its full training run of 4 million steps, MINT ends up with a perplexity of\n4.84. The lower perplexity indicates that MINT is more effective at capturing the joint distribution\nover interacting sequences, reflecting its ability to model context-aware dependencies between\nresidues across chains.\nHence, MINT’s cross-attention design not only delivers consistent performance gains across\ndiverse PPI tasks but also achieves lower perplexity during pretraining, indicating a better\nunderstanding of protein–protein interactions than methods relying on simple concatenation.\n• References 19 and 25 are identical, just one refers to the bioRxiv preprint and another\nto the same preprint published in the journal.\nWe thank the reviewer for the good catch. We have updated this reference to reflect the final\njournal publication.\nReviewer #1 (Remarks on code availability):\nThe code is present and instructions how to run it seems to be adequate.\nWe appreciate the reviewer’s confirmation that our code and usage instructions are clear.\nReviewer #2 (Remarks to the Author):\nThis paper introduces MINT, a pre-trained protein language model designed to model\nprotein-protein interactions (PPIs) based on amino acid sequences. The model builds\nupon the ESM framework but introduces a distinction between self-attention within the\nsame chain and cross-attention between chains. It is trained using interaction protein\nsequences from the STRING database and employs a masked language modeling (MLM)\nloss function. The authors evaluate MINT's performance on several downstream tasks\nrelated to PPI prediction, comparing it against multiple protein language models as\nbaselines. They demonstrate that MINT achieves improvements across all benchmarks.\nWhile the algorithmic innovation over ESM is limited (primarily distinguishing\nself-attention from cross-attention), protein-protein interactions are a critical area of\nresearch, and any genuine progress in their prediction holds significant value.\nHowever,there are several areas where further clarification and improvement are needed.\nWe appreciate the reviewer’s positive feedback and the opportunity to clarify the broader scope\nof our contributions. While MINT indeed introduces a key architectural distinction by decoupling\nself-attention within chains from cross-attention between chains, we emphasize that our\ncontributions are not limited to model architecture. A major novelty of MINT lies in the\nconstruction and scale of its training dataset, as well as the design of its training objective. Both\nof these factors are critical for enabling PLMs to learn the language of protein–protein\ninteractions from sequence alone.\nTo pretrain MINT, we curated a large, high-confidence dataset from the STRING database,\nstarting from 2.4 billion candidate PPIs and systematically filtering down to 96 million\nhigh-quality, non-redundant PPIs involving 16.4 million unique protein sequences. This scale\nand curation process are unprecedented for sequence-based PPI modeling. To our knowledge,\nno previous PLM has been pretrained on a PPI corpus of this size or quality.\nBeyond data curation, we introduced a novel training setup that adapts the traditional masked\nlanguage modeling objective to learn from sets of interacting sequences. Specifically, we retain\nthe intra-sequence context via self-attention while incorporating inter-sequence signals through\ncross-attention modules that do not rely on positional encoding. This ensures that predictions for\nmasked tokens are informed not just by the local sequence context but also by their interaction\npartners, thereby capturing co-evolutionary constraints between them. MINT’s training leverages\nthis cross-chain-aware MLM to build a model that generalizes not only to binary interaction\nprediction but also to more complex tasks like affinity estimation, mutation impact assessment,\nand immune interaction modeling without requiring explicit structural supervision. We have\nemphasized these contributions in the Introduction.\nEmpirically, we demonstrate that these training innovations lead to significant improvements\nover prior models. MINT achieves new state-of-the-art results across multiple PPI prediction\nbenchmarks, and outperforms even larger general-purpose PLMs such as ESM-2-3B and\nProGen on downstream tasks (Methods (Comprehensive benchmarking of MINT on PPI\nprediction) and Figure 2).\nMajor issues:\n(1) The authors primarily rely on existing benchmarks from the literature to demonstrate\nMINT's superiority over baseline models. However, since these benchmarks involve\nsupervised learning for specific downstream tasks, ensuring no information leakage from\nthe training set to the test set is crucial. Although the authors use the same dataset splits\nas reported in the literature, they merely cite these sources. They should providing\ndetailed explanations of how the training-test splits were conducted for each benchmark\nand discuss/analyze concerns of potential information leakage.\nWe appreciate the reviewer’s concern about data leakage. While we followed standard dataset\nsplits as reported in prior work for each benchmark, we agree that transparency regarding the\nsplit methodology is critical for assessing the risk of information leakage. In response, we now\nprovide detailed descriptions of the dataset construction and splitting protocols used in each\nbenchmark task in Methods (Benchmarking tasks). Specifically, we now clarify how training\nand test sets were derived, and where applicable, emphasize the steps taken to ensure that\nsequences in the test set are not overly similar to those in the training set. We provide this\ninformation here.\nGold-Standard PPI: This dataset was designed explicitly to prevent models from leveraging\nsequence similarity as a shortcut for PPI prediction. Using SIMAP2 bitscores, the human\nproteome was first partitioned into training, validation, and test sets to minimize pairwise\nsequence similarity across splits. CD-HIT was then applied with a 40% sequence identity\nthreshold both within and across splits to ensure non-redundancy. As a result, no similar protein\nsequences exist in both training and test data, making this benchmark particularly stringent.\nPrior work has shown that models relying on sequence similarity fail on this benchmark,\nreinforcing its value in evaluating generalizable representations.\nYeast PPI: We follow the protocol established by Guo et al., where negative pairs are\nconstructed using proteins from different subcellular locations. Protein redundancy was removed\nin two stages: first, a 90% sequence identity threshold was used to filter similar sequences\nacross the entire dataset; then, a 40% identity threshold was applied across train/validation/test\nsplits to prevent redundancy between them. This ensures that the model's generalization ability\nis evaluated on sequence-disjoint protein pairs.\nHuman PPI: We use Pan et al.’s dataset with positive interactions from HPRD and negative\nexamples derived from different subcellular compartments. The data splitting mirrors the yeast\nPPI protocol, with initial redundancy removal at 90% identity and a further 40% cutoff applied\nbetween the resulting train/validation/test splits (using an 8:1:1 ratio). This setup supports\nevaluation of generalization to dissimilar protein sequences.\nSKEMPI: To mitigate the risk of information leakage from structurally similar complexes, we split\nthe dataset by protein complex into three mutually exclusive folds. Each fold contains unique\ncomplexes, ensuring that mutations from the same protein complex do not appear in both\ntraining and test sets. We follow Luo et al. (2023) and report results via 3-fold cross-validation\nso that each data point is tested exactly once.\nPDB-Bind and MutationalPPI: For these tasks, we apply 10-fold cross-validation, with folds split\nby protein complex rather than by individual sequences. This strategy ensures that all chains\ninvolved in a complex are assigned to the same fold, preventing any component of a test\ncomplex from appearing in training data.\nAdditionally, the degree of improvement over baseline models varies significantly across\nbenchmarks (ranging from 30% to only 2–3%). The authors should discuss this\ndiscrepancy and analyze whether it might be influenced by differences in dataset splits\nor other factors.\nSee Reviewer 1, response #1.\n(2) Beyond benchmark comparisons, evaluating MINT's true performance on datasets\nwith guaranteed independence from both pre-training and supervised training sets is\nessential. The authors have already divided the STRING dataset into training and\nvalidation sets but have only used the validation set to monitor training progress (i.e.,\nperplexity). They could further split the validation set to construct downstream supervised learning models and directly assess MINT's predictive power on truly independent interaction data. Such an analysis would help clarify MINT's practical applicability in real-world scenarios and identify the contexts where it performs best.\nWe thank the reviewer for the insightful suggestion to evaluate MINT on data independent from\nthe pre-training stage. We would like to first point out that the pre-training for MINT is done in a\nself-supervised fashion, and so there is no explicit label leakage between the pre-training and\ndownstream supervised tasks. For the supervised downstream tasks themselves, we ensure\nthat all datasets follow realistic and previously adopted data splitting strategies into training and\nevaluation sets as described in the updated Methods (Benchmarking tasks) section.\nIn accordance with the reviewer’s suggestion, to further assess MINT’s predictive power in a\nsupervised setting using fully independent data, we have conducted a new experiment using the\n250,000 held-out validation PPIs from STRING, none of which were used during pre-training. As\ndescribed in the manuscript, the validation set was constructed by clustering proteins at 50%\nsequence similarity and ensuring no clusters overlapped with the training set, thereby\nminimizing potential data leakage and ensuring sequence-level independence. We treated these\nas putative positive examples and generated negative pairs by randomly shuffling protein\npartners. We then trained binary classifiers using embeddings from MINT and ESM-2 on 80% of\nthis dataset and evaluated performance on the remaining 20% over three experimental repeats.\nESM-2 Baselines were evaluated under two common strategies: (1) concatenating the input\nsequences as a single tokenized input to the PLM (“concatenate tokens”) , and (2)\nconcatenating the output embeddings from independently processed sequences using two PLM\ncalls (“concatenate embeddings”). Results are provided below.\nModel Concatenation Type Accuracy AUPRC F1 score\nESM-2-650M Concatenate tokens 0.72 0.78 0.73\nESM-2-650M Concatenate embeddings 0.79 0.85 0.80\nMINT Concatenate embeddings 0.82 0.88 0.83\nWe emphasize that this setup constitutes a weakly supervised signal: the positive labels are\nputative interactions from STRING and not experimentally validated, while the negative\nexamples are artificial, created by shuffling protein partners. Despite this, MINT demonstrates\nclear improvements over ESM-2, indicating that it captures interaction-relevant features even\nunder noisy supervision. We have added this analysis to the Supplementary (new\nSupplementary Note 3 and Supplementary Table 2), and refer to this in the Results\n(Overview of the MINT model and training) in the main manuscript.\nMinor issues:\n(1) Algorithm 1 Pseudocode:\n-In Line 4, should \"MultiHeadSelfAttention\" be \"MultiHeadCrossAttention\" ? Currently, it\nappears identical to Line 3.\nThank you for pointing this out. We acknowledge the confusion caused by the naming. The\nfunction call in Line 4 is renamed to MultiHeadAttention. Both lines use the same attention\ncomputation, but they operate over different attention masks. Specifically, Line 3 performs\nintra-sequence attention (i.e., within each individual protein chain), while Line 4 uses a\ncomplementary attention mask to capture inter-sequence relationships (i.e., across different\nchains). Although the same function is used, the change in the attention mask effectively turns\nthe second instance into a cross-attention mechanism in behavior, without requiring a separate\nMultiHeadCrossAttention module. We have clarified this distinction in the Methods (MINT\narchitecture) section of the revised manuscript.\n-What is the difference between \"d_ij\" (Line 8) and \"d_ij^h\" (Line 9)? A clearer\nexplanation of their respective roles would improve readability.\nThank you for pointing this out. To clarify:\n● 𝑑 refers to the final combined attention weights between token i and token j after\n𝑖𝑗\napplying either self-attention or cross-attention, depending on whether i and j are from\nthe same sequence or different sequences. It is computed by combining the raw\nℎ ℎ\nattention logits from self-attention (𝑠 ) and cross-attention (𝑐 ) before the softmax step,\n𝑖𝑗 𝑖𝑗\nas shown in Algorithm 1 (Line 5).\nℎ\n● 𝑑 represents the raw (pre-softmax) attention logits for head h from either the\n𝑖𝑗\nself-attention or cross-attention mechanism, used internally to compute 𝑑 .\n𝑖𝑗\nHowever, we have changed Line 9 to reflect only 𝑑 since this step is computed across all\n𝑖𝑗\nattention heads h in the Methods (MINT architecture) section.\n-In Line 13, what does \"t\" represent?\nThank you for catching this. “t” should refer to input “x” and Line 13 is a straightforward residual\nconnection. We have made these changes in the manuscript in the Methods (MINT\narchitecture) section.\n(2) Formula 1 on P21:\n-Does the variable \"h\" correspond to \"x^out\" from the last layer of Algorithm 1? If so,\nthis connection should be explicitly stated.\n𝑜𝑢𝑡\nThanks for this comment. On page 21, “h” corresponds to 𝑥 from the last layer of the model.\nWe have made changes in the manuscript to make this more clear in the Methods (MINT\narchitecture) section.\n-The notation \"h_i^L\" is ambiguous. Does it imply predicting \"x_i\" based on the entire\ntensor \"h,\" or only based on the component of \"h\" at position \"i\"?\nYes, it implies predicting 𝑥 based on the output vector ℎ since this output will be influenced by\n𝑖 𝑖\nall other positions during the self and cross-attention layers. We have clarified this in the\nmanuscript in the Methods (MINT architecture) section.\nData availability:\nThe training and valiation datasets derived from STRING could also be made available.\nWe appreciate the reviewer’s suggestion regarding the availability of the training and validation\ndatasets derived from STRING. Due to the size of these datasets (spanning tens of gigabytes),\ndirect hosting and distribution are not feasible. However, to ensure full reproducibility, we\nprovide detailed and step-by-step instructions in our GitHub repository\n(https://github.com/VarunUllanat/mint) on how to reconstruct the dataset from the publicly\navailable STRING database.\nReviewer #3 (Remarks to the Author):\nThe authors have done a commendable job in creating a pretrained model for PPI. The\nwork is comprehensive and very well explained. However, there are certain points that\nneed to be clarified before the final decision is made.\nWe thank the reviewer for appreciating our work! We attempt to clarify the points raised below\nand in the manuscript.\n1)Instead of going through such heavy pretraining, why have the authors not chosen a\nmethodology akin to \" Interpretable Prediction of SARS-CoV-2 Epitope-Specific TCR\nRecognition Using a Pre-Trained Protein Language Model\"? It will be really helpful for the\nreaders if the authors can provide a comparative analysis with this work.\nWe thank the reviewer for highlighting this point. The referenced work utilizes a pre-trained PLM\ncalled TAPE (built on BERT) and builds task-specific classifiers on top of frozen or lightly\nfine-tuned embeddings for SARS-CoV-2 epitope-specific TCR recognition. They show this\napproach is effective for PPI tasks like supervised classification over small datasets.\nIn contrast, our motivation with MINT is to go beyond such static representations and enable\ncontext-aware modeling of interacting proteins directly at the representation level. Existing\nPLMs, including the TAPE model used in the referenced work, encode each protein sequence\nindependently in their pretraining. This inherently limits their capacity to capture\ninteraction-specific dependencies, especially in complex, multimeric systems like\nantibody-antigen or TCR-epitope-MHC complexes.\nMINT addresses this limitation by modifying the pretraining regime itself by introducing\ncross-chain attention and training directly on 96M interacting protein pairs from STRING-DB. As\nshown in our results (Figures 2–4), this architectural and training strategy enables MINT to\ngeneralize not only to binary PPI classification but also to regression tasks like binding affinity\nprediction and mutational impact assessment, across a wide range of domains including\nantibodies and immune interactions.\nTo further address reviewer comments, we now directly compare to the methodology in the cited\nwork by evaluating MINT on the more general TCR–epitope binding benchmarks (new Fig.\n4a-b) and find that a minimally fine-tuned MINT outperformed state-of-the-art models including\nAVIB-TCR, MIX-TPI, and TITAN. This shows that despite its heavier pretraining cost, MINT\nretains strong downstream performance without requiring extensive supervision, and\ngeneralizes across domains with minimal fine tuning.\nTherefore, we view MINT as a complementary approach: while methods like the referenced\npaper are efficient within specific tasks like SARS-CoV-2 epitope-TCR recognition, MINT is\ndesigned to provide a unified representation that enables broad generalization across PPI tasks.\nNevertheless, we have added a citation to this work in the Results (Learning the language of\nTCR-Epitope-MHC interactions with minimal finetuning) section due to its relevance and\nhave explained how we differ.\n2) Why have the authors chosen ESM-2 and not any other model for their pretraining\nskeleton? Please justify.\nWe thank the reviewer for asking this question. We chose ESM-2 as the pretraining skeleton for\nthe following reasons:\n1. State-of-the-Art Baseline: ESM-2 is one of the most, if not most, widely used PLMs,\ndemonstrating strong performance across a broad range of protein sequence-specific\ntasks, including structural and functional prediction [12]. Its transformer-based\narchitecture, along with its open-source availability and established benchmarks, made it\nan ideal and transparent starting point for MINT.\n2. Flexibility: The ESM-2 architecture is highly modular and amenable to modifications. Its\nself-attention mechanism and input tokenization pipeline can be adapted to incorporate\ncross-chain attention modules, as we have done in the MINT model.\n3. Community Adoption: By basing our approach on ESM-2, we facilitate comparison with a\nlarge body of recent work in protein representation learning [12]. This also lowers the\nbarrier for the community to adopt and build upon MINT.\nWe have added an introductory paragraph in Methods (MINT architecture) to justify why we\nchose ESM-2 as our backbone architecture.\n3) The model is quite resource intensive. Can the authors please provide a comparative\nanalysis for the time taken as well?\nIs there a trade-off between the other models and MINT?\nWe appreciate the reviewer’s request to benchmark MINT’s resource usage. To address this, we\nhave conducted a comparative inference time analysis across baseline models and MINT. We\nmeasure average inference time (in milliseconds) for a single protein pair input (batch size = 1),\naveraged over 100 runs, at different sequence lengths: L = 50, 100, 200, and 500 on a single\nNVIDIA A100 80Gb GPU. Baselines are evaluated under two common strategies: (1)\nconcatenating the input sequences as a single tokenized input to the PLM (“concatenate\ntokens”) , and (2) concatenating the output embeddings from independently processed\nsequences using two PLM calls (“concatenate embeddings”). Results are provided below.\nModel Parameters Concatenation Type L=50 time L=100 time L=200 time L=500 time\n(millions) (ms) (ms) (ms) (ms)\nProtT5-BFD 1208 Concatenate tokens 4.58 6.16 9.39 11.41\nProtT5-Uniref 1208 Concatenate tokens 4.58 6.08 9.25 11.43\nProgen2-Large 2779 Concatenate tokens 8.69 12.42 20.00 22.22\nESM-2-3B 2841 Concatenate tokens 8.41 13.02 21.48 22.87\nESM-1b-650M 652 Concatenate tokens 3.39 4.59 6.88 7.18\nESM-2-650M 652 Concatenate tokens 4.15 4.77 7.12 7.41\nESM-2-150M 148 Concatenate tokens 3.40 3.41 3.44 3.49\nProtT5-BFD 1208 Concatenate 7.49 9.11 12.20 22.66\nembeddings\nProtT5-Uniref 1208 Concatenate 7.23 8.34 12.40 23.03\nembeddings\nProgen2-Large 2779 Concatenate 14.23 17.83 24.91 43.98\nembeddings\nESM-2-3B 2841 Concatenate 11.64 16.82 25.92 45.69\nembeddings\nESM-1b-650M 652 Concatenate 7.02 7.73 9.19 14.30\nembeddings\nESM-2-650M 652 Concatenate 7.63 8.39 9.68 14.70\nembeddings\nESM-2-150M 148 Concatenate 6.12 6.82 6.23 6.86\nembeddings\nMINT 813 Concatenate 6.23 6.91 7.36 18.19\nembeddings\nDespite introducing cross-chain attention, MINT exhibits competitive inference times compared\nto similarly sized models. MINT exhibits competitive inference times, particularly when\ncompared with larger models like ESM-2-3B and ProGen2-Large. For example, at L = 500,\nMINT (18.19 ms) is faster than both ESM-2-3B (45.69 ms) and ProGen2-Large (43.98 ms) when\nembeddings are concatenated. At shorter sequence lengths, it is equivalent to or sometimes\nfaster than models of smaller sizes like ESM-650M using the “concatenate embeddings”\nstrategy. Even though MINT is slower than similarly sized baseline models using the\n“concatenate tokens” strategy, we note that this leads to substantially lower performance in the\nbaseline models and so the inference time tradeoffs are justified.\nWe acknowledge that MINT introduces architectural modifications, such as cross-chain\nattention, which can marginally increase computational overhead relative to simpler PLMs like\nESM-2-150M. However, this trade-off is offset by substantial gains in prediction accuracy (e.g.,\n+29% improvement on SKEMPI and +14% on YeastPPI tasks) and versatility across domains\n(e.g., antibody modeling and TCR-epitope interactions). Thus, MINT balances efficiency with\ntask-specific performance, offering a favorable trade-off for applications requiring accurate\nmodeling of protein-protein interactions.We have added this analysis to the Supplementary\n(Supplementary Note 4 and Supplementary Table 3), and referred to these in Results\n(Comprehensive benchmarking of MINT on PPI prediction) of the main manuscript.\nReviewer #3 (Remarks on code availability):\nYes, the code is quite easy to navigate.\nWe appreciate the reviewer’s confirmation that our code and usage instructions are clear.\nReferences\n[1] Su, J., Han, C., Zhou, Y., Shan, J., Zhou, X. and Yuan, F., 2023. Saprot: Protein\nlanguage modeling with structure-aware vocabulary. BioRxiv, pp.2023-10.\n[2] Grazioli, F., Machart, P., Mösch, A., Li, K., Castorina, L.V., Pfeifer, N. and Min, M.R.,\n2023. Attentive variational information bottleneck for tcr–peptide interaction prediction.\nBioinformatics, 39(1), p.btac820.\n[3] Bernett, J., Blumenthal, D.B. and List, M., 2024. Cracking the black box of deep\nsequence-based protein–protein interaction prediction. Briefings in Bioinformatics, 25(2).\n[4] Zheng, X., Du, H., Xu, F., Li, J., Liu, Z., Wang, W., Chen, T., Ouyang, W., Li, S.Z., Lu, Y.\nand Dong, N., 2025. PRING: Rethinking Protein-Protein Interaction Prediction from Pairs to\nGraphs. arXiv preprint arXiv:2507.05101.\n[5] Zhu, Y., Chen, J., Li, Y., Fang, X., Ye, X., He, J., Zhang, X., Ge, J., Shen, C., Zhang, X.\nand Hou, T., 2025. BioScore: A Foundational Scoring Function For Diverse Biomolecular\nComplexes. arXiv preprint arXiv:2507.10877.\n[6] Lála, J., Al-Saffar, A. and Angioletti-Uberti, S., 2025. BAGEL: Protein Engineering via\nExploration of an Energy Landscape. bioRxiv, pp.2025-07.\n[7] Liu, C., Pelissier, A., Shao, Y., Denzler, L., Martin, A.C., Paige, B. and Martinez, M.R.,\n2025. AbRank: A Benchmark Dataset and Metric-Learning Framework for Antibody-Antigen\nAffinity Ranking. arXiv preprint arXiv:2506.17857.\n[8] Gupta, A., Li, R., Leal, C., Sevvana, M., Mackness, B.C., Casner, R.G., Batchelor, J.D.,\nPark, A., Bailey, M., Anele, L.K. and Jager, S., NextGenPLM: A Novel Structure-Infused\nFoundational Protein Language Model for Antibody Discovery and Optimization.\n[9] Wee, J. and Wei, G.W., 2024. Benchmarking AlphaFold3’s protein-protein complex\naccuracy and machine learning prediction reliability for binding free energy changes upon\nmutation. ArXiv, pp.arXiv-2406.\n[10] Zheng, H., Lin, H., Alade, A.A., Chen, J., Monroy, E.Y., Zhang, M. and Wang, J., 2025.\nAlphaFold3 in Drug Discovery: A Comprehensive Assessment of Capabilities, Limitations, and\nApplications. bioRxiv, pp.2025-04.\n[12] Chen, J.Y., Wang, J.F., Hu, Y., Li, X.H., Qian, Y.R. and Song, C.L., 2025. Evaluating the\nadvancements in protein language models for encoding strategies in protein function prediction:\na comprehensive review. Frontiers in bioengineering and biotechnology, 13, p.1506508.\n13"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors responded satisfactory to the technical aspects of my first report. However, main concern remains, namely that\nimprovement in the model performance seems to be marginal compared to other PLM architectures, thus greatly diminishing\ninterest of the broad community. Also, authors did not want to change the name MINT which may cause confusion for many\nresearchers in the field (as it was pointed out in the previous report, Google search by “MINT” keyword still points to legacy\nwebsite of the MINT database, not the authors github).\n(Remarks on code availability)", "reviewer_2": "My concerns have been adequately addressed in the revised manuscript.\n(Remarks on code availability)", "reviewer_3": "The authors have provided satisfactory responses to all the comments. The manuscript can now be accepted.\n(Remarks on code availability)"}, "author_response": ""}]

Decision letter