Interpretable and generative deep learning models explicate phase separating intrinsically disordered motifs

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

Abstract
Intrinsically disordered regions (IDRs) in proteins drive phase separation (PS) to form biomolecular condensates, which organize cellular matter. While IDRs are recognized as critical drivers of PS, the systematic identification of sequence motifs governing this phenomenon and their compositional determinants remain a key challenge. Here we develop PhaSeMotif, a deep learning framework for interpretable and precise predictions of essential phase-separating motifs within IDRs. We experimentally validate PhaSeMotif, demonstrating that mutations of predicted motifs significantly reduce or eliminate the PS capabilities of IDRs. The identified motifs possess diverse amino acid compositional features that are critical for determining PS propensities and condensate partitioning. Furthermore, PhaSeMotif integrates generative models to create validation-ready motifs that preserve these critical compositional features, empowering direct experimental verification and deeper mechanistic investigation of PS-driving IDR motifs. Overall, by combining motifs prediction, generation, and validation, PhaSeMotif provides an open-access toolkit to facilitate more efficient IDR motifs investigation and provides insights into the molecular determinants of PS.

Introduction
Phase separation (PS) of proteins and nucleic acids commonly underlies the formation of biomolecular condensates, supporting the spatiotemporal regulation of cellular activities such as gene regulation and signal transduction1,2. Besides the identification of PS proteins, there have been major efforts to delineate the molecular mechanisms and functions under normal and pathological conditions. Recent studies have also illuminated the link between aberrant PS and diseases including developmental deficiency, cancers, and infectious diseases3–6. However, investigating the molecular mechanisms and functions of individual PS proteins often requires extensive manipulation of protein sequences and numerous experiments7,8. While the fundamental physicochemical principles driving PS by IDRs have been extensively studied7,9,10, systematically identifying the full diversity of these functional sequence motifs using computational tools remains a key challenge. This gap is particularly challenging for understudied novel proteins, where the absence of prior information makes it difficult to formulate feasible hypotheses and hinders systematic analysis.

Over the past decade, numerous computational tools have emerged to predict proteins with high PS propensities. Methods like PhaSePred11, FuzDrop12, DeePhase13, PSPire14, PSPhunter15, and others16–18 have significantly advanced the field by providing large-scale PS predictions, which aid in the initial screening of potential PS proteins. However, these existing tools primarily rely on protein-level features such as IDR content and conservation, offering only general PS propensity scores. Even residue-level scoring methods often identify broad IDRs (>100 residues) as potentially important12,17, but they lack the interpretability needed to reveal the underlying molecular determinants of PS. This gap in mechanistic insight hinders deeper investigation and experimental design, as biologists are left without clear guidance on why an IDR is predicted to promote PS or how to experimentally validate these predictions. As the need to decipher the molecular grammar of PS in IDRs grows, there is a pressing demand for computational approaches that provide interpretable insights and actionable hypotheses.

Most IDRs are biased in amino acids composition and possess sequence patterns such as short linear interaction motifs (SLiMs) or repeated sequences19. These IDR motifs are recognized for driving interactions that promote PS and modulate PS properties, including condensate material states20 and partitioning behavior21. For instance, the N-terminal IDR of Ddx4 arranges charged residues into clustered blocks of net positive and negative charge, driving PS through electrostatic interactions22. RNA-binding proteins such as FUS contain multiple RG/RGG motifs, whose cation-pi interactions with aromatic tyrosine residues govern PS properties8. Additionally, replacing negatively charged IDR motifs in HMGB1 with arginine-rich motifs enhances mis-partitioning into the nucleolus and causes nucleolar dysfunction3. Compared to structural domains, investigating important motifs in IDRs is more challenging due to the lack of sequence conservation and clear defining features. Typically, in-depth investigation of IDR motifs require extensive mutational studies, such as complete or partial IDR truncation and/or multiple substitutions8,23. These approaches are labor-intensive and difficult to conduct without guidance from effective computational tools.

Here, we introduce PhaSeMotif, an interpretable deep learning framework designed to address these critical limitations by enabling in-depth investigation of IDR motifs essential for PS and their molecular principles. PhaSeMotif predicts PS propensity of IDRs and, crucially, reverse infers sequence regions critical for positive predictions, pinpointing IDR motifs through attention mechanisms and guided backpropagation24,25. PhaSeMotif integrates generative models to design artificial motifs that preserve key features of identified motifs, providing validation-ready motifs for direct experimental substitution and verification. We experimentally validated PhaSeMotif, demonstrating that mutation of ~83% (14/17) of predicted motifs impaired or abolished PS. Furthermore, by clustering motifs based on compositional features and using generative models to create artificial motifs, we experimentally confirmed the importance of these features in driving PS, with ~86% (18/21) of generated motifs restoring PS capability. Systematic analysis of human IDR motifs revealed motif combinations linked to condensate partitioning and biological functions. Overall, PhaSeMotif provides a framework for efficient and mechanism-driven IDR motif investigation, integrating interpretable prediction, validation-ready motif generation, and experimental validation to accelerate the discovery of molecular determinants of phase separation.

Results
Framework for investigating IDR motifs essential for PS
To facilitate the investigation of PS IDRs and motifs within, we proposed a framework that utilizes computational predictions for hypothesis formulation and PS experiments for hypothesis validation (Fig. 1a). Specifically, for an IDR of interest, the PS propensity is predicted based on its sequence. For high PS propensity IDR, reverse inference provides the relative importance of each region and thus predicts key regions or motifs. The motifs can be validated through mutation experiments, in which the disruption of motifs would attenuate or even eliminate PS of IDRs while the negative control resulted in insignificant effects. The motifs are clustered according to their compositional features, which represent the underlying principles. If the attenuation of PS is rescued by substituting mutated motifs with generated motifs with similar features, the validity of motif features would be confirmed. Through the prediction, generation and validation framework, researchers would be able to capture the molecular determinants of PS IDRs.

Establishment of sequence-based high PS propensity IDR deep learning model
We organized the dataset by leveraging both IDRs prediction and PS propensity predictions. Comprehensive proteome-wide IDRs were identified using several predictors and the consecutive residues were kept. We used PhaSePred, a comprehensive multimodal PS protein predictor, to predict the PS propensities of these IDR-containing proteins11. Based on the predictions, we established our dataset: IDRs from proteins with high predicted PS propensity were denoted as positive set (“high propensity” set), while IDRs from proteins with low predicted propensity were used as negative set (“background propensity” set) (Supplementary Fig. 1a). We selected an optimal threshold for positive set IDRs in order to balance the true positive rate and precision, minimizing potential noise from predicted labels (Supplementary Fig. 1b) (Methods). As a result, a total of 3,131 IDRs with high PS propensity and 11,763 IDRs with background propensity in human proteome were collected (Supplementary Data 1). This dataset was randomly split into training, validation, and test subsets (3:1:1), which were confirmed to be balanced, diverse, and compositionally similar (Supplementary Fig. 1c-f). A similar approach was conducted to collect datasets in other species, which were used for model validation afterwards (Supplementary Fig. 1g).

Explaining the internal behavior of deep learning models has been extensively studied and attention mechanism are dominating the explainability of deep models26. We presumed that by predicting the PS propensity of an IDR using attention-based models, the relative importance of each region within the input sequence is determined. The most important regions identified in this way can represent potential motifs essential for PS to some extent. Hence, we built PhaSeMotif, an interpretable attention-based deep learning model for high PS propensity IDR prediction. Due to the variability in IDR lengths, the model accepts IDR sequences of arbitrary length to enhance ease of use. The input sequences are processed using one-hot encoding based on the amino acids and the amino acid alphabet (Supplementary Table 1, Methods). The model consists primarily of convolutional layers27, multi-head attention layer24, pyramid pooling layers, fully connected layers, and an output layer (Fig. 1b). The convolutional layers are responsible for extracting local features (15 residues in this context), while the attention mechanism layers capture both regional and global relationships within the sequence. The pyramid pooling layers convert inputs of arbitrary length into fixed-length representations, facilitating subsequent fully connected layers for output probability28. The model was trained with training and validation set and the performance was then evaluated with the independent test set (Methods). The final area under the receiver operating characteristic curve (AUROC) of PhaSeMotif in test set achieved 0.86 (Fig. 1c, Supplementary Fig. 1h) and area under the precision-recall curve (AUPRC) achieved 0.69 (Supplementary Fig. 1i), demonstrating its strong performance. PhaSeMotif also displayed similar performance in datasets of other four species (Fig. 1c). The distribution of PhaSeMotif prediction scores between validation and test datasets were similar and around 30% of human IDRs were scored above 0.5 (Supplementary Fig. 1j).

Due to the scarcity of experimentally verified short specific regions that are essential for PS, we compared the performance of PhaSeMotif with other methods in identifying high PS propensity IDRs. PhaSeMotif achieved the highest AUROC of 0.86 compared to 0.76-0.80 for other methods (Fig. 1d), and the highest AUPRC of 0.69 compared to 0.48-0.60 for other methods (Fig. 1e). To reduce potential bias from full-length sequence predictions, we also selected proteins with IDR fractions of no less than 80% as an additional test set (Supplementary Data 1). PhaSeMotif consistently outperformed other methods in these comparisons with the highest AUROC of 0.96 versus 0.85-0.92 for others and the highest AUPRC of 0.90 versus 0.69-0.81 for others (Supplementary Fig. 1k-l), demonstrating its strong performance.

Multiple IDRs of the same protein may contribute differently to PS behavior. Even though we assigned the same PS labels for all IDRs within the same protein, we found that the predicted probabilities reflect the actual PS capability differences. hnRNPDL, an RNA binding protein, bears arginine-rich and tyrosine-rich IDRs at the N- and C-terminus. The C-terminus IDR underwent PS, possibly through pi-pi interactions, and had a stronger self-aggregation tendency. In contrast, the N-terminus IDR could not undergo PS itself29. In PhaSeMotif prediction, the PS score of C terminus IDR is 0.97 while that of N terminus IDR is 0.33, reflecting real-world difference (Supplementary Fig. 1m). Similarly, the N terminus IDR of ARID1A was predicted as PS IDR (score 0.99), consistent with a previous report that the N terminus PLD underwent PS30. While the other IDR closer to C terminus was predicted less likely to be PS IDR (score 0.38) (Supplementary Fig. 1m).

PhaSeMotif extracted IDR motifs using guided backpropagation and attention scores
As mentioned above, convolution and attention layers captured regional and global features during training. We utilized guided backpropagation (GBP), a gradient-based method to interpret deep learning models25, and attention scores24 to extract the attribution of input IDR’s motifs that lead to the final positive prediction. Specifically, during GBP, only activated neurons with positive forward and backward gradients were retained (denoted as positive regions) (Supplementary Fig. 2a). As each positive region was the weighted combination of the input regions, larger attention scores reflect more important original input regions. Subsequently, we determined a cut-off to identify the lists of most important input regions and defined the attribution score of each input region as the relative frequency that it had been included in the positive regions set (Fig. 2a). Notably, as longer IDR sequence often harbors more IDR motifs, the cut off setting was relevant to the length of individual IDR (Methods). Consequently, the regions with top attribution scores were retained and merged, resulting in the final determination of motifs. Overall, we identified a total of 17594 motifs derived from 2418 PS IDRs through this approach (Supplementary Data 2). More than 59% of motifs were shorter than 20 amino acids (Fig. 2b), which represent a small fraction of IDRs (Fig. 2b).

Take the C terminus IDR of hnRNPDL as an example, 4 important regions were identified and merged into 2 motifs (Fig. 2d). Previous research highlighted the importance of tyrosine residues in mediating homotypic pi-pi interactions within IDRs and heterotypic cation-pi interactions with positive charged residues8,29. Here we found that tyrosine residues were enriched in these two motifs, consistent with previous findings. The other example is G3BP1, which modulates stress granule assembly31,32. PhaSeMotif predicted that its largest PS motif resided near C terminus while its N terminus was less important (Fig. 2e). Indeed, the C terminus RG-rich are crucial for PS and the acidic region near N terminus negatively regulate PS, which is highly aligned with PhaSeMotif prediction. In the case of G3BP1, relative importance also reflects the ability of attention-based mechanism to capture the global relationships between regions. Ubiquilin-2 (UBQLN2), a protein involved in protein degradation, forms stress-induced biomolecular condensates. Proline-rich region and ST1-II domain (UBQLN2379-624) are necessary for UBQLN2 PS and where most amyotrophic lateral sclerosis (ALS) related mutations reside33,34. The PhaSeMotif identified motifs were in line with these important regions (Fig. 2f). hnRNPA1 is known as a Prion-like domain (PLD) containing protein20, whose PS behavior is determined largely by the aromatic residues. PhaSeMotif identifies three motifs within the important PLD, each containing several aromatic residues (Fig. 2g). TIA1 is an RNA-binding protein and a component of stress granule. PhaSeMotif predicts two motifs which TIA1 C-terminal IDR, which contains proline residues for regulating its PS35 (Fig. 2h). NPM1 is abundant in the nucleolus and undergoes PS through electrostatic interactions. Within the NPM1 IDR enriched with charged residues, PhaSeMotif identifies one motif within the basic residue-enriched tract that mediates homotypic interactions and PS36,37 (Fig. 2i).

As PhaSeMotif provides finer-grained measurements of relative importance within IDRs, we examined whether the motifs recognized mutations that affect PS. We extracted point mutations from PhaSepDB and evaluated their effects of mutations on PS based on the original literature (Supplementary Data 3). The mutations were classified into two categories: those that severely eliminate PS (major effect mutations) and those with only a slight influence (minor effect mutations). For example, UBQLN2 P506T significantly impaired PS and condensate formation, while mutants such as A282V, M446R only showed little difference compared to wild type (WT)34. By comparing the mutation sites to predicted motifs by PhaSeMotif (Supplementary Fig. 2b), we found that major effect mutations are more likely to be located within motifs compared to minor effect mutations. Specifically, 12 out of 21 (57%) major effect mutations are covered by PS motifs, compared to only 21% (9/43) of minor effect mutations. When considering only self-assembly PS proteins, 11 out of 17 (65%) mutations fall within motifs (Fig. 2j). We normalized the PhaSeMotif attribution scores as z-scores across the IDRs and compared the distribution of major effect mutation sites to background controls (Supplementary Fig. 2c). The results show that major effect mutations have significantly higher scores than background controls (Fig. 2k). Overall, these results demonstrate that while PhaSeMotif may not capture all mutations related to PS, it is effective at identifying motifs that contain mutations with major effects on PS.

The accuracy and specificity of PhaSeMotif predictions were experimentally validated
In order to further validate the effectiveness of PhaSeMotif, we performed in-cell PS experiments with wild type (WT) and motif mutated IDRs (Mut) (Fig. 3a). The optogenetic optoDroplets system were constructed as previously described38,39. Specifically, the IDR was fused to the fluorescent protein mCherry and the blue light-sensitive protein Cry2, which form Cry2 oligomers and induce the formation of IDR droplets under blue light stimulation (Methods). First, we have selected 12 IDRs from top-ranking PS IDRs predicted by PhaSeMotif, derived from proteins namely hnRNPH1, CPEB2, TAF15, MLLT1, TIA1, TDP43, FMR1, EP300, FUS, and DDX4. More than 90% of these IDRs are scored above 0.90, while 33% of them are over 0.99 (Supplementary Data 4). Upon blue light inducement, all of the IDRs displayed fast droplet formation in cells within 1–2 min, which indicated successful construction of optoDroplets and the PS capability of selected IDRs (Fig. 3b–m, Supplementary Fig. 3b–d). The progression of droplet formation was assessed semi-quantitatively through the number of fluorescent puncta in cells over time.

PhaSeMotif identified motifs with high attribution scores, and we speculated that disrupting these regions would lead to the attenuated PS of IDRs (Fig. 3a). Hence, we engineered motifs within IDRs by replacing them with (GGS)n, a flexible and hydrophilic linker, maintaining the original length40. This modification resulted in a total of 17 distinct mutated IDRs (Supplementary Fig. 3e, Supplementary Data 4). The PS propensities of motif-mutated IDRs were examined in the same approach. As a result, the droplet formation was significantly attenuated in 82% (14/17) mutated IDRs. The condensate formation was almost eliminated in CPEB2, TDP43, TIA1, EP300, hnRNPH1, TAF15, FMR1 and MLLT1 Mut1/2/4 IDRs compared to corresponding WT (Fig. 3b–m), except for DDX4, ASCL1 and FUS IDRs (Supplementary Fig. 3b–d). The formation rate and intracellular puncta number of MLLT1 Mut3 largely decreased (Fig. 3i). Considering the relatively small length of mutated motifs (Fig. 2c), it’s likely that the affected PS activities were due to disruption of important interactions rather than overall sequence change. Besides, most of the tested motifs expand current knowledge and do not overlap with known mutations that weaken PS in PhaSepDB. (Supplementary Data 4). Together, these results highlight PhaSeMotif’s potential to identify important sequence motifs related to PS.

Meanwhile, we investigated the specificity of PhaSeMotif’s predictions. While several existing PS protein prediction methods provide residue-level scores, their putatively important regions often span a large fraction. To demonstrate the enhanced specificity of PhaSeMotif, we conducted negative control (NC) experiments in addition to the positive predictions. In these NC experiments, regions outside the predicted motifs of PhaSeMotif were substituted with (GGS)n repeats. The NC1/2/3 regions of TDP43 are located within the important regions predicted by catGRANULE, PLAAC and FuzDrop (Fig. 3d), while the NC1/2 regions of CPEB2 (Fig. 3b) and NC1 region of TIA1 (Fig. 3f) are located within important regions predicted by PLAAC and FuzDrop. Compared with WT IDR, the NC mutations resulted in insignificant changes in the PS capabilities (Fig. 3c, e, g). In addition, PhaSeMotif predicted important motifs of MLLT1 (Mut2/3) that are missed by other methods, which were verified to be essential for MLLT1 PS (Fig. 3h). Taken together, these results indicate that PhaSeMotif captures more precise motifs compared to existing methods.

Analysis of IDR motifs composition features revealed diverse clusters
After predicting motifs within high PS propensity IDRs, decoding the sequence features would advance our understanding of underlying principles. We constructed the IDR motifs dataset consisting of 17,594 predicted IDR motifs derived from 2418 IDRs (Supplementary Data 2, Methods). Both IDRs and motifs displayed high portion of disordered prompting residues such as proline, glutamic acid, serine and lysine (Fig. 4a)41. Furthermore, proline, serine and glycine were more significantly enriched in motifs, consistent with the previous finding that self-assembling PS proteins contain a higher portion of these residues11.

We next sought to cluster motifs based on sequence compositional features. The ratio of individual or two consecutive amino acids and each amino acid alphabet were calculated as basic sequence features (Supplementary Table 2). We also defined ‘abundant’ and ‘poly’ features which describe whether a particular amino acid is highly enriched and whether it appears consecutively multiple times. The distribution of each feature was then compared between high PS propensity IDRs, background IDRs, and IDR motifs, resulting in 207 significant sequence features (Supplementary Table 2, Supplementary Data 5). Considering the possible redundancy and linear/nonlinear relations between features, we applied autoencoder to perform dimensionality reduction. UMAP (Uniform Manifold Approximation and Projection)42 and DBSCAN (Density-Based Spatial Clustering of Applications with Noise)43 were utilized to visualize and cluster the motifs (Fig. 4b, Methods). Consequently, nine clusters with diverse representative sequence features were identified. We clustered the motif using the sequence features and named them after the most representative residue types, namely Cluster 0, Polar, Positive, negative (Pos&Neg), Proline, Glycine, Positive, Aliphatic, Negative and Glutamine (Fig. 4c, Supplementary Fig. 4a).

To further dissect compositional features, we analyzed the relative amino acid abundance across motif clusters to further dissect their compositional features (Fig. 4d). The result is consistent with previous findings that different types of interactions between IDRs drive PS, including pi-pi, pi-cation, electrostatic, hydrophobic interactions and so on (Fig. 4d, Supplementary Fig. 4a)10. For example, aromatic residues (tyrosine and phenylalanine), along with glycine are relatively enriched in Cluster 0 and Cluster Glycine. Aromatic residues are important to mediate the pi-pi and pi-cation multivalent interactions driving the PS of Prion-like domain (PLD) containing proteins8,20. Cluster Pos&Neg, Positive and Negative motifs possess basic and/or acid amino acids, contributing to electrostatic interactions. In addition, the negatively and positive charged residues are separately distributed in Pos&Neg motifs, which are important for selective partitioning into condensates3,22,44. In addition, the abundance of proline and glycine in Cluster Proline and Glycine motif clusters may modulate IDRs disordered state, as these residues were highly enriched in PLDs and low complexity domains (LCDs)8. The result is consistent with a previous report that proline and glycine are enriched in putatively important residues in PS proteins15. Glutamine motif cluster encompassed repeated glutamine residues. Polyglutamine was found to mediated condensation through abundant hydrogen bonding and lead protein aggregation45, examples are HTT protein and Ataxin46,47. Among the clusters, Cluster 0 and Polar motifs are the most common, indicating they may act as fundamental units in PS IDRs. More than 90% of IDRs contain at least two types of motifs (Supplementary Fig. 4b). The Cluster 0 and Polar motifs are usually partnered with other types of motifs. IDRs containing both Cluster 0 motifs and other motifs displayed higher PhaSeMotif scores, suggesting their collaborative functions in promoting PS (Supplementary Fig. 4c). IDRs with only Cluster Glycine, Glutamine motifs are also presented with higher PhaSeMotif scores, consistent with their strong self-assembling capability. Interestingly, Pos&Neg, Positive and Negative motifs are usually presented together in IDRs, suggesting that opposite charged residues within one IDRs but located separately are important to electrostatic interactions (Supplementary Fig. 4c). Also, motifs with enriched charged residues frequently partner with cluster 0 and Polar motifs.

As motifs’ properties are related to interaction forces and thus condensates partitioning, we also looked into the motifs preference of proteins within known biomolecular condensates components extracted from PhaSepDB48,49. Most condensates were enriched in the most common Cluster 0 and Polar motifs (Fig. 4e). Proteins with Glycine motifs are enriched in paraspeckles, while proteins with Positive motifs are enriched in nuclear speckles and spliceosomes. (Fig. 4e). This is consistent with their functions closely associated with nucleic acids, interactions with which would involve charged motifs. There is emerging evidence that PS is involved in various molecular functions; we conducted functional enrichment of proteins with different motifs. Not surprisingly, non-membrane bounded organelles assembly is enriched in Cluster 0, Polar and charged motifs (Pos&Neg, Positive, and Negative), suggesting their essential roles in condensate formation (Fig. 4f). RNA-related functions are associated with Cluster 0, charged, and Glycine motifs (Fig. 4f). For example, previous studies have shown that the IDR of FMR1 is essential for its condensation with RNA, which regulates the translation process50. Together, these results indicate that PhaSeMotif identified IDR motifs whose combination provides hints for understanding the underlying molecular determinants of PS. In addition, the preferences of motifs for different condensates suggested that alteration in motifs might represent a possible way to modulate partitioning.

Generated motifs substitution validated the importance of compositional features
Thus far, we decoded the sequence features underlying IDR motifs and revealed diverse clusters. Theoretically, if the PS of motif-mutated IDRs can be restored via substituting motifs with similar features, the importance of underlying principles is validated. Hence, we built generative models to generate artificial motifs with features similar to the original ones but not identical in sequence. Variational Autoencoders (VAEs) are commonly used stable generative models51. Specifically, the motifs within each cluster were first encoded, sampled, and then decoded by re-integrating multiple convolution channels to learn the features of each category (Fig. 5a). We specifically focused on designing generative models to reflect the prominent sequence compositional features within the same clusters. This focused approach would allow us to reveal fundamental principles for PS motifs independent of specific IDRs. We developed a total of 9 VAE models corresponding to the 9 motif clusters. We used the trained autoencoder to project both the original and the generated motifs into the same latent space. The result indicates that the generated sequences are inherently similar to corresponding original clusters, the generated sequences are proximal to their corresponding original motifs (Supplementary Fig. 5a).

The accuracy of the generative models was further assessed by testing whether the generated sequences could be correctly re-clustered into the original one. The median re-clustering accuracy was above 0.85, suggesting the motif features were well captured by VAEs (Fig. 5b). Moreover, except for motifs with strong amino acid preferences such as Proline and Glycine motifs, the generated sequences are diverse in sequence and the median Needleman-Wunsch sequence similarity score was lower than 0.5 (Fig. 5b). This demonstrated that the models balanced both accuracy and diversity. To verify whether generated artificial motifs can substitute the original one, we first compared the overall PhaSeMotif prediction scores. For each PS IDR, we randomly selected 50 generated motifs of similar length within the same cluster, which were then used for ΔPhaSeMotif score calculation. The results showed the motif substitution led to subtle changes in most cases (Fig. 5b), suggesting that the generated sequences are computationally close to the native ones.

Next, the rescue experiments with a total of 21 generated artificial motifs covering 6 clusters were conducted (Table 1, Supplementary Data 4). Specifically, the (GGS)n linker in mutated IDRs aforementioned were replaced with generated motif sequences. For each motif cluster, we conducted rescue experiments for at least 2 generated sequences. In cluster 0, the generated sequences for TDP43 Mut2 and TIA1 Mut restore the PS capabilities (Fig. 5c). Similarly, the PS capabilities were generally restored for motifs in cluster Polar (Fig. 5d), Glycine (Fig. 5e), Pos&Neg (Fig. 5f), Proline (Fig. 5g) and Glutamine (Fig. 5h) compared to IDRs with mutated motifs, except for two motifs in cluster Proline and one motif in cluster Polar (Supplementary Fig. 5b-c). Notably, the PS capabilities of IDRs with generated motifs were comparable to those of wild-type IDRs for most sequences. Overall, 86% (18/21) generated motifs by PhaSeMotif were able to rescue the PS propensities caused by motif mutations, which strongly demonstrates the reliability of the generative models.

However, the varying degrees of restoration across different IDRs are observed. For instance, the generated motifs for TDP43 Mut showed stronger PS capabilities than WT (Fig. 5c). For TIA1, while the generated motifs ultimately displayed a similar number of condensates, its formation rate was slower than that of the WT (Fig. 5c). Additionally, the number of condensates for hnRNPH1 generated motifs and one of two generated motifs of MLLT1 Mut2 was smaller than that of WT (Fig. 5d). The generated motifs for TAF15 IDR1 Mut can restore the PS when tyrosine residues are added manually (Supplementary Fig. 5b). These results indicate that despite occasional non-identical PS restoration, our generative models can effectively capture the major principles of PS motifs. Overall, these results in mutations, generation and substitution of motifs demonstrated the feasibility of our proposed framework in investigating PS IDRs. With the help of predicting and generative models provided by PhaSeMotif, the underlying principles can be efficiently decoded.

Methods
Construction of PhaSeMotif
A comprehensive summary of the machine learning dataset preparation, model construction, optimization, and evaluation, following the checklist recommended by Walsh et al.58., is provided in Supplementary Data 7.

Data preprocessing. To represent the input sequence, we employed a dual-channel encoding strategy for each amino acid. We first grouped the standard amino acids into a reduced alphabet of 9 classes based on their physicochemical side-chain properties (Supplementary Table 1). Each amino acid in the sequence was then converted into a 30-dimensional feature vector by concatenating two one-hot vectors: a 21-dimensional vector representing the specific amino acid type and a 9-dimensional vector representing its corresponding class in the reduced alphabet. The dual representation provides the model with both the specific residue identity and its general biochemical context.

Model architecture. The one-hot embeddings were passed to multiple two-dimensional convolution kernels with size of (21, 15) and (9, 15). The step size and padding were set as 1 and (0, 1). The two resulting feature maps were added and then transposed, with shape of (1, len, 8). A total of 8 multi-headed attention layers were set up to fully capture the local and global relationship between regions. The attention was calculated as

Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) V (1)

Here, Q is the query vector matrix. K is the key vector matrix and d_k is the dimension of the key vector. V is the value vector matrix. We set the shape of matrix of each head transformed from the input to V as (8, 1) to ensure that the resulted value obtained by each head is one-dimensional.

Spatial pyramid pooling. The length of the input amino acid sequence is arbitrary, so we generate a fixed-length feature vector through the pyramid pooling layer28. The length of the pooled data is determined by the division scale of the pyramid pooling layer. We set the division scale of the pyramid pooling layer to (1, 12) and allow overlaps between sub-regions.

Convolution and fully connected layers. We used a convolutional layer with 16 kernels with a shape of (5, 5) to further integrate the features. The stride and padding were 1. The feature map was transformed into a one-dimensional fully connected layer, which outputted the final probability.

Activation function, regularization, and model training. The ReLU activation function was adopted in each layer of the model. To prevent overfitting during training, we implemented a combination of regularization techniques, including dropout (p = 0.2), L2 regularization (λ = 0.001), batch normalization, and early stopping. The batch size of the model was 512. The training process adopted the cross-entropy loss function and the Adam optimization algorithm. By setting the pos_weight parameter to 3.5, the loss function would put more emphasis on correctly predicting the positive class, thus effectively mitigating the imbalance of the two classes during the training process. The step size of the Adam optimizer is set to 0.003. The model was developed using the Pytorch framework.

Model performance assessment
We use the ROC curve, the PR curve, and their corresponding AUC values to describe the model results and evaluate the performance of the classification model. The recall and precision were calculated as follows:

TPR (Recall) = TP / (TP + FN) (2)

FPR = FP / (FP + TN) (3)

where TP is True Positive, FN is False Negative, FP is False Positive, and TN is True Negative.

Model interpretation
First, the GBP algorithm was used to reveal the important regions that have a positive impact on prediction after the attention mechanism reorganizes the information. The GBP filtered out the regions with negative gradients that do not contribute or contribute little to the activation (Figs. 1B, 2A). We then selected the top 20 original regions with the highest attention scores as the positive sets for each positive region with positive forward and backward gradients. We defined the attribution score of each input region as the frequency of being included in the set. The top n regions with highest attribution scores were assigned as motifs. The cut off n can be calculated as 1 + int(length/30), which was designed to be related to the length of IDR. Overlapping regions were merged to final determined motifs.

Composition features screening
We defined sequence features for clustering, namely the basic single amino acid proportion, double amino acid proportion, alphabet proportion, abundant, and poly. When the proportion a type of amino acid in a 15-residue window exceeds 0.5, it was considered as “abundant”. When a type of amino acid appears continuously more than 6 times, it is considered “poly” and the number of times is calculated. (Supplementary Table 2). Next, the features in IDRs and motifs were compared and were retained the ones that were either significant between PS and non-PS IDRs or between motifs and PS IDRs. A total of 207 features were used for clustering (Supplementary Data 3). The statistical examination was from python scipy (v1.10.1).

Dimensionality reduction and clustering
First, we used autoencoder for dimensionality reduction to integrate linear and nonlinear relationships. The encoder and decoder were composed of fully connected layers with the ReLU activation function and 20-dimensional latent space. Next, UMAP was used for visualization (umap-learn v0.5.5). DBSCAN was used for preliminary clustering (scikit-learn v1.3.1). To evaluate the clustering results, we performed an independent t-test to reveal the significant features of each cluster. The clusters with fewer than 10 IDRs were removed and some clusters with similar features were grouped together.

Generative models and artificial sequences assessment
We adopted VAE as a generative framework. Specifically, we encoded the input sequence through convolution layers to generate multiple channels, then sampled in the latent space, and finally fuse the multiple channels back through a decoder to generate a new sequence. Considering the differences between clusters, we train a VAE model for each cluster respectively. The identified motifs were re-segmented into 15-residue sequences. The input one-hot encoding was the same as described above. The generated result is a matrix with a shape of (15, 21) and maximum normalized to be converted as one-hot embeddings, which can be translated into sequences. The resulting sequences were merged based on at least 3 head or tail amino acids to get sequences of various lengths.

The generated sequences were measured from two aspects: accuracy and diversity. Accuracy means whether the sequences can be correctly re-clustered using the same clustering parameters. Diversity means Needleman-Wunsch similarity between sequences within each cluster. The Needleman-Wunsch algorithm used was from the global mode of the PairwiseAligner in the Biopython (v1.81).

Extraction of important regions using alternative methods
We obtained the catGRANULES scores for each amino acid in the sequence through the catGRANULES web server [http://s.tartaglialab.com/new_submission/catGRANULES]. Regions with scores greater than 0 were considered significant. Due to the sliding window limitations of the catGRANULES method, the first 25 and last 25 amino acids in the sequence were not scored. Using the PLACC web server [http://plaac.wi.mit.edu/] with the default Core Length and the Homo sapiens background, we acquired scores where regions with values greater than 0.5 were deemed significant. We obtained scores using the PsPhunter web server [http://psphunter.stemcellding.org/index.php]. PsPhunter scores sites on a scale from 0 to 1, with smaller values indicating more significant regions; however, no explicit cutoff is defined. Due to the sliding window limitations of the method, the first 15 and last 15 amino acids in the sequence were not scored. The FuzDrop web server [https://fuzdrop.bio.unipd.it/predictor] directly provides the Droplet-promoting regions.

Cell culture
HeLa cells (ATCC CCL-2) were cultured in Dulbecco’s modified Eagle’s medium (HyClone, Inc.) supplemented with 10% fetal bovine serum (Invitrogen, Inc.) at 37 °C with 5% CO2.

Plasmid construction and transfection
The pcDNA3.1-IDRs-mCherry-Cry2 plasmids encoding WT IDRs were provided by Dr. Pilong Li’s Lab and Dr. Xin Zhang’s Lab38,39. The DNA fragments encoding IDR mutants and IDR-generated sequences were synthesized (Supplementary Data 4) (GENEWIZ, INC SUZHOU) and cloned into pcDNA3.1-mCherry-Cry2 plasmid, respectively. HeLa cells were plated on 35 mm glass-bottom dishes (MatTek) and were transiently transfected with 1 μg of the plasmid. Transfections were performed using Lipofectamine 3000 (Thermo Fisher) according to the manufacturer’s instructions. Then the cells were subjected to imaging and analysis 24- or 48-hours following transfection.

Live cell imaging and image analysis
All live cell imaging was performed using 60X oil immersion objective (NA 1.4) on a Nikon A1 laser scanning confocal microscope. Cells were imaged by use of laser wavelengths 488 nm for Cry2 activation and 561 nm for mCherry imaging. The duration of blue light stimulation varied from 1 to 12 seconds, while the imaging duration lasted between 1 to 3 minutes. Localized activation experiments were performed using the stimulation setting where the blue laser scans only a designated region of interest. Cells in one group are at similar expression levels and activated under identical conditions.

We employed CellProfiler version 4.2.6 to analyze the fluorescent images. The pipeline consisted of several steps: First, we converted raw RGB images to grayscale using the ColorToGray module. Next, we identified primary objects with a minimum diameter of 150 pixels using a global threshold strategy with the Minimum Cross-Entropy method. Then, we used the MaskObjects module to mask the identified primary objects and output them as ‘Cell’. Subsequently, we enhanced the speckle features of size 15 using the EnhanceOrSuppressFeatures module. We identified droplets as primary objects from the enhanced image with diameters between 3 to 40 pixels, employing an adaptive threshold strategy with the Robust Background method and a lower threshold of 0.2. Finally, we used the RelateObjects module to relate droplets (child) to cells (parent). Other parameters were set to their default values. For each sample, puncta were quantified in at least 10 cells (n ≥ 10).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors clearly demonstrate the need for interpretability in machine learning tools to support biologists in experimental design, particularly in phase separation (PS) studies. The rationale for the study is well described and includes a practical example of how the developed method can be applied in PS experiments.\nThe authors outline the key steps in data preparation, model construction, and evaluation. I recommend taking into account this checklist (PubMed:34316068) to ensure that all critical methodological details are reported, including the exact version of the protein sequences used for IDR and PS prediction during data preparation.\nCould the authors please briefly explain why predicted IDRs shorter than 50 residues were excluded?\nWhen describing the annotation of PS and non-PS IDRs, please elaborate on how the 3:1:1 split was determined and how the authors ensured that each subset was diverse and balanced. This is especially important in light of the sequence similarity considerations discussed elsewhere in the manuscript. Additionally, please clarify what techniques were used to prevent overfitting.\nThe authors mention the use of an amino acid alphabet. How were the categories chosen? How does this classification compare to others, such as those indexed in AAindex (PubMed:17998252) or the more recent AAontology (PubMed:39053689)?\nIn the section titled “The extraction of PS-affecting mutations and z-scores calculation.” please elaborate on how the PhaSeMotif attribution scores were normalized and how they relate to PhaSeDB, particularly in the context of the reported threshold of 0.5 for predicted scores.\nFinally, some technical comments on the website.\n1) User input validation (Predict Online Page):\nPlease ensure that the user input is validated correctly. On the \"Predict Online\" page, it appears that only a single continuous string is accepted as input. If the input is not in this format, the system displays an infinite loading spinner, insead of provideing an error message. If the server returning an error (error code 500 or similar), please show it to the user.\n1.1) As I understand, the tool supports searching by UniProt/SwissProt accession numbers. In that case, it would be helpful to also support input in FASTA format (e.g., directly copied from the UniProt website), where the first line starts with a > character and includes newline characters.\n1.2) Please display a warning if the user includes non-standard one-letter amino acid codes (such as B, Z, or X), or other invalid characters (e.g., Á instead of A).\n1.3) Indicate the maximum allowed sequence length. For example, clarify whether very long proteins like Titin (UniProt:Q8WZ42) are supported.\n2) Protein sequence feature visualization:\nUsing the Plotly library for the \"Picture of the Result\" section is a good choice. However, the authors may also consider dedicated libraries such as Nightingale (PubMed:37359723) or Feature-Viewer (PubMed:31985787).\nIf Plotly is used, please ensure the responsive attribute in the config object is enabled so the plot resizes automatically with the browser window. Currently, for long sequences, the amino acid characters on the first row overlap.\n3) Cluster legend:\nPlease provide a legend or explanation for the cluster labels. For example, does cluster G refer to a glycine-rich motif?\n4) Are you considering to provide public API (application programmaing interface) support, to provide integrartion of bioinformatics pipelines?\n5) Security and compliance:\nPlease use the HTTPS protocol. Modern browsers display security warnings for sites which support HTTP-only, which negatively impacts user experience and limits outreach to a broader research audience.\n5.1) If cookies are used, include a GDPR (EU 2016/679) notice. Please also add GDPR-compliant privacy policy on the website.\n6) Dockerization:\nThank you for open-sourcing the code. I recommend also providing containerization options (e.g., Docker, Podman, or Singularity) to simplify installation and support deployment on HPC systems.\n7) Guide, tutorial\nThank you for provideing a ‘Guide’ page as help/tutorial. I would like to recommend to extend the annotated screenshopts with some supporting text, and inlcude definition of the predicted clusters. I more extented introduction on a separate page of the the aims of the tool to help users how are arrived to the web-site before reading the article previuosly.\n(Remarks on code availability)\nDockerization:\nThank you for open-sourcing the code. I recommend also providing containerization options (e.g., Docker, Podman, or Singularity) to simplify installation and support deployment on HPC systems.\nwhen i tried to use the instruction at REDME, i got this error messgage (Apple M1 Pro chip, macOS 15.5):\n% pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu121\nLooking in indexes: https://download.pytorch.org/whl/cu121\nERROR: Could not find a version that satisfies the requirement torch==2.3.1 (from versions: none)\nERROR: No matching distribution found for torch==2.3.1\nI could install the necessary dependencies following https://pytorch.org/get-started/previous-versions/", "reviewer_2": "The authors provide a conceptually interesting approach to the challenge of predicting phase separation from sequence. The paper is well written and the approach not unreasonable, but there are unfortunately some major issues.\nOn the positive side of things, the paper is well written, the DL architecture is reasonable, and the authors have taken, I think, a conceptually reasonable approach to training /test /validation. Further, the authors have done a good job of experimentally testing predictions, although they make it extremely difficult to determine exactly what has been done (discussed below).\nThat said, I appreciate their conceptual approach here, in terms of using (GGS)n replacement (instead of just deletion) and NC controls based on regions that other predictors predict to be important (instead of cherry-picking obviously unimportant regions).\nHowever, I do have some major concerns.\nAt a high level, I have philosophical concerns regarding whether deep learning predictors for “phase separation” are sensible. Absent any constraints on concentration, binding partners, or solution conditions, the question of ‘will my protein phase separate’ is simply “yes”. There will, of course, be solution conditions in which any polypeptide can be coaxed into forming two existing phases, perhaps far from physiological relevance but nevertheless possible. Therefore, the approach the authors are using here implicitly is not “a predictor of phase separation” but “a predictor of phase separation under physiological conditions”. Do the training/test/validation data support such a model?\nThe authors use predictions based on PhaSePred, which is trained on heterogeneous data from various sources, crucially, with no constraints on the underlying conditions. A protein may be annotated as “phase separating” even if this requires impossibly high concentrations of PEG. Similarly, the negative dataset used in PhaSePred does not include proteins that are not known to phase separate, but rather all other proteins from a set of proteomes that are distinct (as defined by similarity through CD-HIT) from those that have been “shown” to phase separate. From an epistemological standpoint, the assumption that “things that have not been reported in the literature to phase separate are non-phase separating” is a challenging position to support or defend.\nAs such, we’re left with two problems:\n1. The authors presume it is reasonable to divide proteins into two classes: those that phase separate and those that do not. I find this worldview particularly hard to subscribe to, given that we know all proteins that phase separate do so under certain conditions, yet not under others, which immediately renders such a classification invalid.\n2. Second, having made peace with that first assumption, we presume PhaSePred is able to distinguish between these two classes. Again, I am fundamentally unconvinced that methodologically, the approach used there makes any sense.\nThese two issues undermine the applicability of the work presented here. Now, these rather foundational issues notwithstanding, the work presented here is very good. But my major concern is that it’s built on a set of premises that do not make sense. If this is the case, we might expect evidence that the methodology has limited predictive power. Unfortunately, such evidence does emerge when testing the approach on sequences examined in this manuscript and beyond.\nFor example, the authors use the (GGS)n sequence to replace the motifs identified and find those sequences show reduced phase separation (Fig. 3). However, if you take the sequences (based on the information in the supplementary tables) and reconstruct a GGS-replaced version of the IDR used in the correlet system, all those GGS-containing sequences are strongly predicted to phase separate (despite the fact experimentally they do not). As such, my enthusiasm is somewhat dampened by the fact that it is not entirely clear whether the approach has really learned the residues that underlie phase separation, vs. those residues that co-occur with residues that drive phase separation.\nFor context, extensive work from many groups (Mittal, Banerjee, Mittal, Pappu, Hyman, Alberti, Forman-Kay etc.) has clearly shown how certain residues have an outside contribution to phase separation, yet in all studies to date glycine, proline, and serine have consistently shown up as “spacer” residues, those that are co-incident with residues that drive attractive intermolecular interactions but lack the requisite chemistry to drive attractive interactions to mediate phase separation. These spacer residues are typically far more abundant and provide a flexible but relatively inert scaffold within which other residues can sit. However, the compositional analysis in Fig. 4 suggests that the authors imply these residues underlie phase separation, when in fact the residues that have been demonstrably shown to drive phase separation (notably aromatic residues and arginine) are actually depleted from the motifs compared to general IDRs. Further, taking examples of sequences characterized previously (e.g., FUS LDC vs FUS Y2S variant) and generating a predicted score yields identical values, despite the fact that FUS Y2S is effectively infinitely soluble (no phase separation). Similar conclusions are arrived if we test other sequences where specific mutations have been made to suppress or enhance phase separation.\nIn short, my concerns here are essentially three-fold:\n1. Do binary predictors of phase separation make sense? I think is a question the authors need to discuss extensively in a revised version.\n2. Does the underlying approach for delineating phase separation vs. non-phase-separating proteins via PhaSePred work? The PhaSePred suffers from the same issues described in this review, so I think to convince readers of this the authors need to take as many examples of sequences where we have wildtype vs. variant data (of which there are many in the literature) where variant either enhances or suppresses phase separation, and ask if PhaSePred can correctly delineate between these two.\n3. Does PhaSeMotif actually identify relevant subregions in proteins where specific mutations have been made to suppress or enhance phase separation?\nComments:\nPoint 1\nThe set up for the work states “While it is known IDRs often mediate homotypic and/or heterotypic multivalent interactions that promote PS detailed information about the specific sequence motifs and compositional determinants within IDRs that drive PS remains limited.” but I would strongly contest this – there are an overwhelming number of studies that have – for IDRs at least – systematically explored the precise determinants of condensate formation in vitro and more recently in vivo. Over the last decade, literally hundreds of papers have explored this question, consistently arriving at the same conclusions. As such, to claim we have “limited information” about the “specific sequence motifs and compositional determinants within IDRs that drive PS” is a ludicrous statement. How those motifs and compositional determinants relate to function is indeed an open question, but to claim that the common physiochemistry associated with them is poorly explored is incorrect.\nPoint 2\nThe authors identify various residues enriched for PS motifs and PS IDRs; however, it’s probably important to explicitly note that the enrichment of residues does not necessarily mean that those residues drive phase separation. Case in point, extensive prior work has shown that serine, proline nor glycine appear to offer substantial attractive interactions in driving phase separation, yet these residues often co-occur in regions enriched for those residues that do underlie phase separation (e.g. arginine in RGG domains, aromatic residues in prion-like domains). The authors propose that glycine is important… yet their negative control involves inserting a GGS sequence, which reduces phase separation in most cases. As such, how meaningful are the predictions being made here?\nPoint 3\nThe generative aspect of the work is conceptually great; however, having played with the tool, the sequences being generated for a motif seem to straddle a very broad set of sequence space. I think it would be essential to provide readers with some reason to believe these generated sequences actually are “similar” to the input sequence in some way; i.e. statistical analysis to convince us (as readers) that these sequences are similar and not just totally randomly generated (perhaps using sequence analysis, embeddings in the VAE latent space, comparison of embedded sequences from say ESM2 etc).\nPoint 4\nOne major issue is that it is almost impossible to determine which sequences were used here. The authors provide numerous tables, but make it surprisingly difficult to follow the main text (and indeed, I could never determine what the sequences used in Fig. 5 were from the SI tables). Can I request that for any sequence where mutations are made (e.g., a motif is replaced by (GGS)n or a generated fake IDR motif is used), the authors actually include the sequence before/after comparison in the figure to make it easy for the reader to understand what they are looking at.\nPoint 5\nIn Fig. 2, it’s very unclear what the attribution score and “density” profiles are actually reporting on. What is density here? Density of what? Similarly, it’s hard to relate these figures to the output of the webserver? What is a “predict score”? What is the density of residues vs. the density of regions? Much greater clarity, both in the figure and in the web server, is needed here.\nFigure comments\nFig. 1\n• It seems like comparison with PhaSePred here would be important to include, under the expectation PhaSeMotif should certainly not be better\nFig 2.\n• It would be useful to show more examples of well studied proteins where we know of specific subregions that are particularly important for phase separation.\no IDRs and folded domains clearly annotated\no Experimentally-known regions highlighted\no Motifs identified by PhaSeMotif clearly highlighted\n• It seems the bottom of panels G and H may be chopped off? At least its impossible to understand what either of these are showing as presented in this PDF.\nFig 3\n• A discussed please show what the sequence changes are in line so the reader understands whats being done here.\n• Please ensure residue numbers are included in these figures\nFig 5\n• As described, we need more quantification /description of what the generated sequences are and whether they are similar (or not) to the wildtype sequence.\nFig. S3\n• A - Please include residue numbering on X-axis; otherwise these figures are not interpretable\n• Fig S3 lacks captions explicitly for panels C and D\n(Remarks on code availability)\nI have some minor comments in terms of interpretablity on the server in my review.\nThe install instructions WORKED PERFECTLY (this is not the case +90% of the time) and were simple to do.\nI did notice running the second example on GitHub that I get a\nTraceback (most recent call last):\nFile \"test.py\", line 7, in <module>\npm.predict_main(idr_list)\nFile \"/home/${USER}/miniconda3/envs/phasemotif/lib/python3.8/site-packages/PhaseMotif-0.1-py3.8.egg/PhaseMotif/predict.py\", line 188, in predict_main\nraise ValueError(f\"Error: The length of IDR '{idr}' is less than 50.\")\nValueError: Error: The length of IDR 'MSVAKTPKTAENAEKPHVNVGTIGPHEDTYYSEF' is less than 50.\nThis is obviously fixable but I just wanted to mention, however, from the perspective of getting the code running this is great and far easier than many other packages, so big congrats to the authors for this!"}, "author_response": "Point-by-Point Response to the Reviewers’ Comments\nWe have attached our point-by-point response (in blue) to each comment (pasted in italics) below and highlighted the place where the issue is addressed in the manuscript in red.\nReviewer #1\n1. General comments: The authors clearly demonstrate the need for interpretability in machine learning tools to support biologists in experimental design, particularly in phase separation (PS) studies. The rationale for the study is well described and includes a practical example of how the developed method can be applied in PS experiments.\nResponse: We thank the reviewer for the comprehensive summary of our work. We agree with the reviewer’s perspective on the importance of interpretability in machine learning and especially value the insightful feedback and suggestions for improving our manuscript.\n2. The authors outline the key steps in data preparation, model construction, and evaluation. I recommend taking into account this checklist (PubMed:34316068) to ensure that all critical methodological details are reported, including the exact version of the protein sequences used for IDR and PS prediction during data preparation.\nResponse: We thank the reviewer for the valuable suggestion on improving methodological documentation. In response, we have gone through the recommended checklist(R1) (PMID: 34316068) and summarized the key details for the machine learning dataset preparation, model construction, optimization, and evaluation in a dedicated supplementary table (revised Supplementary Data File 7). Furthermore, as the reviewer noted, we have attached the intrinsically disordered regions (IDRs) sequences in Supplementary Data File 1 and retained the UniProt IDs for all proteins, ensuring that the exact sequence versions used can be unambiguously identified.\nWe have revised the Materials and Methods section as follows:\nThe final dataset is consisting of 14,894 human, 11,348 mouse, 4,847 Rat, 2,343 Drome and 1,748 Danre IDRs. IDR sequences used in this study are detailed in Supplementary Data File 1 with corresponding UniProt IDs. (lines 462-465)\nA comprehensive summary of the machine learning dataset preparation, model construction, optimization, and evaluation, following the checklist recommended by Walsh et al. 51, is provided in Supplementary Data File 7. (lines 478-480)\n3. Could the authors please briefly explain why predicted IDRs shorter than 50 residues were excluded?\nResponse: We thank the reviewer for the suggestion on clarifying the data preprocessing. We excluded predicted IDRs shorter than 50 residues for consistency across different IDR predictions and filtering out possible noise. First, our dataset integrates predictions from multiple tools (PLAAC, SEG, Espritz Disprot, AlphaFold2), each with different minimum lengths. For instance, PLAAC’s default minimum is 60 residues. Second, there has been a common practice in IDR analysis to filter out short (often 30-50 residues), potentially noisy IDR predictions that may not be biologically significant (R2). Setting a threshold of 50 ensures a more consistent and high-quality dataset by removing variable and potentially noisy short segments. In response, we have revised the Materials and Methods section as follows:\nPredicted IDRs shorter than 50 residues were excluded for consistency across different IDR predictions and filtering out possible noise51. (lines 452-454)\n4. When describing the annotation of PS and non-PS IDRs, please elaborate on how the 3:1:1 split was determined and how the authors ensured that each subset was diverse and balanced.\nResponse: We appreciate the reviewer’s request for clarification on dataset diversity and balance during training. The 3:1:1 ration for the training, validation and testing subsets was achieved through random splitting. To ensure that the process did not introduce bias and that each subset remained diverse and balanced, we performed several validation checks.\n1） Class balance: The ratio of positive to negative samples was maintained at approximately 21%:79% across three subsets (revised Supplementary Fig. 1c).\n2） Diversity: We analyzed the sequence similarity using BLAST-P for each subset. For each sequence, we identified its highest similarity score against all other sequences. We found that for approximately 80% of the sequences, the maximum similarity score was zero, confirming high diversity and low redundancy (revised Supplementary Fig. 1f).\n3） Similarity across subsets: We confirmed that there were no significant differences in the distribution of IDR length and amino acid composition across subsets (revised Supplementary Fig. 1d-e).\nWe also summarized this information in the revised supplementary table (revised Supplementary Data File 7) and revised the manuscript accordingly:\nAs a result, a total of 3,131 PS IDRs and 11,763 non-PS IDRs in human proteome were collected (Supplementary Data File 1). This dataset was randomly split into training, validation, and test subsets (3:1:1), which were confirmed to be balanced, diverse, and compositionally similar (Supplementary Fig. 1c-f). A similar approach was conducted to collect PS and non-PS IDRs in other species, which were used for model validation afterwards (Supplementary Fig. 1g). (lines 133-139)\nSupplementary Figure 1. c, Ratio of positive to negative samples across the training (Train), validation (Val), and Test datasets. d, Length distribution of IDR samples in the training, validation, and test datasets. e, Comparison of amino acid composition across the training, validation, and test datasets. f, Intra-set IDR sequence similarity, calculated using BLAST-p, for the training, validation, and test datasets.\n5. Additionally, please clarify what techniques were used to prevent overfitting.\nResponse: We thank the reviewer for highlighting this important point. To prevent overfitting during training, we implemented a combination of standard regularization techniques, including dropout (p=0.2), L2 regularization (λ=0.001), batch normalization, and early stopping based on validation loss. These details have now been explicitly added to the revised PhaSeMotif model summary table (revised Supplementary Data File 7) and Materials and Methods section.\nTo prevent overfitting during training, we implemented a combination of regularization techniques, including dropout (p=0.2), L2 regularization (λ=0.001), batch normalization, and early stopping. (lines 506-508)\n6. The authors mention the use of an amino acid alphabet. How were the categories chosen? How does this classification compare to others, such as those indexed in AAindex (PMID:17998252) or the more recent AAontology (PMID:39053689)?\nResponse: We thank the reviewer for this question, which allows us to clarify the context and rationale for using an amino acid alphabet. Our amino acid alphabet is a feature engineering step designed to group amino acids by their core physicochemical properties (such as polar amino acids, charged amino acids, etc.), allowing us to incorporate essential physicochemical information without introducing excessive model complexity. This strategy has proven effective in tasks like protein folding prediction (R3) (PMID: 33937764). Our approach differs from comprehensive index such as AAindex (R4) and AAontology (R5) in granularity: they offer over 500 distinct quantitative scales and a hierarchical classification of those scales, whereas our alphabet provides a discrete classification. This categorical feature is more suited to our model architecture. We have added a sentence to the Materials and Methods section to clarify this rationale as follows:\nData preprocessing. To represent the input sequence, we employed a dual-channel encoding strategy for each amino acid. We first grouped the standard amino acids into a reduced alphabet of 9 classes based on their physicochemical side-chain properties (Supplementary Table 1). Each amino acid in the sequence was then converted into a 30-dimensional feature vector by concatenating two one-hot vectors: a 21-dimensional vector representing the specific amino acid type and a 9-dimensional vector representing its corresponding class in the reduced alphabet. The dual representation provides the model with both the specific residue identity and its general biochemical context. (lines 481-488)\n7. In the section titled “The extraction of PS-affecting mutations and z-scores calculation.” please elaborate on how the PhaSeMotif attribution scores were normalized and how they relate to PhaSeDB.\nResponse: We thank the reviewer for the important question about the analysis of PS-affecting mutations. The PS-affecting mutations were extracted from PhaSeDB and classified into two categories: those that severely eliminate PS (major effect mutations) and those with only a slight influence (minor effect mutations). The mutations located within PS IDR (PhaSeMotif predicted score above 0.5) were included for downstream analysis. The attribution scores generated by PhaSeMotif reflect the relative importance each residue for phase separation propensity within IDRs. To allow comparison across different IDRs, the attribution scores of each mutation site and 1000 randomly sampled sites within the same IDR were normalized into z-scores. This normalization provides a statistical measure of whether mutations occur preferentially at positions of high predicted importance for PS. The results show that major effect mutations have significantly higher PhaSeMotif attribution scores than background controls (revised Fig. 2k). We have revised the Materials and Methods section as follows:\nTo allow comparison across different IDRs, the attribution scores of each mutation site and 1000 randomly sampled sites within the same IDR were normalized into z-scores. This normalization provides a statistical measure of whether mutations occur preferentially at positions of high predicted importance for PS. (lines 472-476)\nSupplementary Fig. 2. b, Schematic of the z-score normalization process for mutation and background sites. Attribution scores for mutation sites and 1,000 randomly sampled background sites are normalized into z-scores to allow comparison across different proteins.\n8. Finally, some technical comments on the website.\nResponse: We sincerely thank the reviewer for these constructive and practical suggestions.\nWe have carefully considered all of them and implemented the requested improvements as follows:\nUser input validation (Predict Online Page):\nPlease ensure that the user input is validated correctly. On the \"Predict Online\" page, it appears that only a single continuous string is accepted as input. If the input is not in this format, the system displays an infinite loading spinner, insead of provideing an error message. If the server returning an error (error code 500 or similar), please show it to the user.\n1.1) As I understand, the tool supports searching by UniProt/SwissProt accession numbers. In that case, it would be helpful to also support input in FASTA format (e.g., directly copied from the UniProt website), where the first line starts with a > character and includes newline characters.\nResponse: We have added support for FASTA format input, including proper parsing of multi-line sequences. Clear error messages are now displayed for invalid inputs, and server errors (e.g., HTTP 500) are returned to the user instead of causing infinite loading.\n1.2) Please display a warning if the user includes non-standard one-letter amino acid codes (such as B, Z, or X), or other invalid characters (e.g., Á instead of A).\nResponse: Non-standard amino acid codes (such as B, Z, X) or other invalid characters are now flagged with real-time warnings.\n1.3) Indicate the maximum allowed sequence length. For example, clarify whether very long proteins like Titin (UniProt:Q8WZ42) are supported.\nResponse: While there is no strict limit on sequence length, we recommend an upper limit of 2000 amino acids due to device constraints. This information is now clearly indicated to the user on the prediction page.\n2) Protein sequence feature visualization:\nUsing the Plotly library for the \"Picture of the Result\" section is a good choice. However, the authors may also consider dedicated libraries such as Nightingale (PubMed:37359723) or Feature-Viewer (PubMed:31985787).\nIf Plotly is used, please ensure the responsive attribute in the config object is enabled so the plot resizes automatically with the browser window. Currently, for long sequences, the amino acid characters on the first row overlap.\nResponse: We have enabled the ‘responsive’ attribute in the Plotly configuration to ensure that plots automatically resize with the browser window. We optimized the interactive menu bar to facilitate both global (full sequence) and local (zoomed-in) inspection. Users can now easily use the zoom and pan tools to examine specific regions in high detail, which effectively resolves the issue of overlapping amino acid characters in long sequences.\n3) Cluster legend:\nPlease provide a legend or explanation for the cluster labels. For example, does cluster G refer to a glycine-rich motif?\nResponse: We have added clear legends and detailed explanations for the cluster labels in both the results table and as hover tooltips to help users interpret the output more easily. The cluster names have also been revised for clarity (e.g., Glycine-rich motif).\n4) Are you considering to provide public API (application programmaing interface) support, to provide integrartion of bioinformatics pipelines?\nResponse: We have now released a public API for integration into bioinformatics pipelines. The API usage is described on the website (https://predict.phasemotif.pro/Interpretation ).\nFor example: curl -X POST https://predict.phasemotif.pro/api-run -F \"file=@your_fasta.fasta\" -o your_result.csv\n5) Security and compliance:\nPlease use the HTTPS protocol. Modern browsers display security warnings for sites which support HTTP-only, which negatively impacts user experience and limits outreach to a broader research audience.\n5.1) If cookies are used, include a GDPR (EU 2016/679) notice. Please also add GDPR-compliant privacy policy on the website.\nResponse: We thank the reviewer’s suggestion. The website now uses the HTTPS protocol by default, resolving browser security warnings. Our web server does not use cookies, so a GDPR notice regarding cookies would not be applicable.\n6) Dockerization:\nThank you for open-sourcing the code. I recommend also providing containerization options (e.g., Docker, Podman, or Singularity) to simplify installation and support deployment on HPC systems.\nResponse: A Docker image has been released, and installation/deployment instructions are now included in the updated GitHub README file. This will simplify installation and enable deployment on HPC systems.\n7) Guide, tutorial\nThank you for provideing a ‘Guide’ page as help/tutorial. I would like to recommend to extend the annotated screenshopts with some supporting text, and inlcude definition of the predicted clusters. I more extented introduction on a separate page of the the aims of the tool to help users how are arrived to the web-site before reading the article previuosly.\nResponse: We have expanded the user guide with an additional “Interpretation” page. This page contains extended explanations, annotated screenshots, and definitions of the predicted clusters. A broader introduction to the aims and applications of the tool has also been added to assist users who may arrive at the website without prior knowledge of the article.\n9. The reviewer reported an installation error on an Apple M1 Pro chip when following the PyTorch installation command.\nResponse: We sincerely thank the reviewer for reporting the installation issue on an Apple M1 Pro chip. This issue is not caused by PhaSeMotif itself but is related to platform-specific PyTorch binaries. To address this, we have updated the README with corrected installation instructions:\nconda install pytorch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 pytorch-cuda=12.1 -c pytorch -c nvidia\nIn addition, we have added a note directing users to the official PyTorch installation guide to obtain the appropriate binaries for their specific hardware (e.g., Apple Silicon, CUDA versions).\nWe greatly appreciate the reviewer for these insightful and practical comments, which have significantly improved both the usability and accessibility of our tool.\nReviewer #2\n1. General comments: The authors provide a conceptually interesting approach to the challenge of predicting phase separation from sequence. The paper is well written and the approach not unreasonable, but there are unfortunately some major issues.\nOn the positive side of things, the paper is well written, the DL architecture is reasonable, and the authors have taken, I think, a conceptually reasonable approach to training /test /validation. Further, the authors have done a good job of experimentally testing predictions, although they make it extremely difficult to determine exactly what has been done (discussed below). That said, I appreciate their conceptual approach here, in terms of using (GGS)n replacement (instead of just deletion) and NC controls based on regions that other predictors predict to be important (instead of cherry-picking obviously unimportant regions).\nResponse: We thank the reviewer for their positive feedback on the conceptual approach of our study. We appreciate the reviewer's comments on the quality of our writing, the model design, and the experimental validation strategy. We have carefully considered the major issues raised and addressed each of them in the following responses.\n2. However, I do have some major concerns.\nAt a high level, I have philosophical concerns regarding whether deep learning predictors for “phase separation” are sensible. Absent any constraints on concentration, binding partners, or solution conditions, the question of ‘will my protein phase separate’ is simply “yes”. There will, of course, be solution conditions in which any polypeptide can be coaxed into forming two existing phases, perhaps far from physiological relevance but nevertheless possible. Therefore, the approach the authors are using here implicitly is not “a predictor of phase separation” but “a predictor of phase separation under physiological conditions”. Do the training/test/validation data support such a model? The authors use predictions based on PhaSePred, which is trained on heterogeneous data from various sources, crucially, with no constraints on the underlying conditions. A protein may be annotated as “phase separating” even if this requires impossibly high concentrations of PEG. Similarly, the negative dataset used in PhaSePred does not include proteins that are not known to phase separate, but rather all other proteins from a set of proteomes that are distinct (as defined by similarity through CD-HIT) from those that have been “shown” to phase separate. From an epistemological standpoint, the assumption that “things that have not been reported in the literature to phase separate are non-phase separating” is a challenging position to support or defend. As such, we’re left with two problems:\n1. The authors presume it is reasonable to divide proteins into two classes: those that phase separate and those that do not. I find this worldview particularly hard to subscribe to, given that we know all proteins that phase separate do so under certain conditions, yet not under others, which immediately renders such a classification invalid.\n2. Second, having made peace with that first assumption, we presume PhaSePred is able to distinguish between these two classes. Again, I am fundamentally unconvinced that methodologically, the approach used there makes any sense.\nResponse: We thank the reviewer for the insightful and fundamental critique, which provides us with an opportunity to clarify the conceptual framework and practical purpose of our work. Our study frames the PS prediction as an effort to distinguish between two protein populations with differential propensity for phase separation, rather than a definitive, binary classification of proteins into phase-separating and non-phase-separating classes.\n1) The model is designed to predict propensity for phase separation rather than an absolute binary state. We fully agree with the reviewer that phase separation is a condition-dependent process, making a definitive binary classification scientifically challenging. Our work, as well as phase separation prediction tools such as PhaSePred (R6, 7), does not operate under the assumption that proteins exist in two immutable classes. Instead, we employed a binary classification as a principled computational abstraction to model a protein’s intrinsic, sequence-based propensity to phase separate.\n2) The dataset construction is aligned with the goal of modeling differential phase separation propensity. The positive set is composed of proteins shown to phase separate either in cells or in vitro at moderate (often micromolar or lower) concentrations, providing a reasonable basis for a “high propensity” class. In contrast, by sampling from broader proteome, the negative set serves as a “background propensity” baseline, against which the enriched signals of the “high propensity” population can be identified through computational model training.\nThe significant difference in propensity makes the model robust to the inclusion of some yet-undiscovered phase-separating proteins. The usefulness of such strategy to sample negative set from background population has been examined in other phase separation predicting methods (R7–11) and in other areas such as protein-protein interaction prediction (R12), where the validated negative datasets are not readily available.\nGiven the framework, the utility of PhaSeMotif is to leverage the propensity differences to pinpoint the specific key regions within IDRs that are potentially to be functionally important for phase separation. The accuracy of our approach is supported by extensive mutagenesis experiments: mutations of 87% (20/23) predicted motifs markedly reduced or abolished PS.\nIn response to the reviewer’s concerns and improving the clarity of the manuscript, we have revised our manuscript as follows:\nIntroduction: Over the past decade, numerous computational tools have emerged to predict proteins with high PS propensities. (lines 63-66)\nResults: We organized the dataset by leveraging both IDRs prediction and PS propensity predictions. Comprehensive proteome-wide IDRs were identified using several predictors and the consecutive residues were kept. We used PhaSePred, a comprehensive multimodal PS protein predictor, to predict the PS propensities of these IDR-containing proteins 11. Based on the predictions, we established our dataset: IDRs from proteins with high predicted PS propensity were denoted as positive set (“high propensity” set), while IDRs from proteins with low predicted propensity were used as negative set (“background propensity” set) (Supplementary Fig. 1a). (lines 125-131)\nDiscussion: Finally, it should be noted that PhaSeMotif, predicting PS motifs that can facilitate hypothesis generation, is built upon qualitative PS propensity prediction. A key future direction will be to move beyond binary abstraction by incorporating quantitative, condition-dependent experimental data, which could enable a quantitative predictive model. (lines 433-436)\nIn addition, the “Non-PS” notation has now been revised as “background PS propensity” throughout the manuscript to improve clarity.\n3. These two issues undermine the applicability of the work presented here. Now, these rather foundational issues notwithstanding, the work presented here is very good. But my major concern is that it’s built on a set of premises that do not make sense. If this is the case, we might expect evidence that the methodology has limited predictive power. Unfortunately, such evidence does emerge when testing the approach on sequences examined in this manuscript and beyond.\nResponse: We appreciate the reviewer for constructive assessment and positive evaluation of our work. As aforementioned, the utility of PhaSeMotif built upon the differences of phase separation propensity between the positive and negative datasets. The overall predictive power of PhaSeMotif is supported by the experimental validation results that 87% (20/23) of the predicted motifs were essential for phase separation and that 86% (18/21) of engineered motifs restored PS capabilities. We thank the reviewer for highlighting several cases crucial for understanding the model’s strengths and current limitations, and we will address each of them in the subsequent responses.\n4. For example, the authors use the (GGS)n sequence to replace the motifs identified and find those sequences show reduced phase separation (Fig. 3). However, if you take the sequences (based on the information in the supplementary tables) and reconstruct a GGS replaced version of the IDR used in the correlet system, all those GGS-containing sequences are strongly predicted to phase separate (despite the fact experimentally they do not). As such, my enthusiasm is somewhat dampened by the fact that it is not entirely clear whether the approach has really learned the residues that underlie phase separation, vs. those residues that co-occur with residues that drive phase separation.\nResponse: We thank the reviewer for the critical and insightful analysis. This observation touches on a key aspect of our model: PhaSeMotif identifies functional regions that include both the potential driver residues (or 'stickers') and the 'co-occurring context' (or 'spacers'). The model is not designed to explicitly distinguish between them; rather, it successfully captures both components because they are collectively predictive of high phase separation propensity.\nAs our relative abundance analysis shows (revised Fig. 4d), residues often considered stickers, such as aromatic residues (tyrosine and phenylalanine), are co-enriched with 'spacer' residues like glycine in clusters such as Cluster 0 and Cluster Glycine. Similarly, Cluster Polar shows a co-enrichment of serine, threonine, and asparagine. This suggests the functional importance lies in the specific compositional pattern of the motif. We hypothesize that these high-frequency 'spacers' are also functional, providing a necessary flexible scaffold, even if their disruption has a less dramatic effect than disrupting the 'stickers'.\nFig. 4. d, Relative abundance (calculated as the z-score of the mean abundance across clusters) of each amino acid within each motif cluster.\nThis also explains the discrepancy the reviewer noted. When a functional motif is replaced in silico with a (GGS) linker, the model still captures an enrichment in glycine and serine. Because these n G/S residues are strongly associated with high-propensity IDRs in the positive training set, the model interprets their composition as a positive signal. The model lacks sufficient counterexamples in the training data—such as (GGS) linkers explicitly labeled as non-functional—to learn that this simple repeat, when stripped of its co-occurring sticker context, is insufficient for PS. To investigate this further, we conducted the same in-silico substitution test on a range of widely used sequence-based predictors (Figure R1). We found they exhibit the exact same limitation: the prediction scores do not significantly decrease. This consistency supports the hypothesis that this issue is rooted in the inherent biases of current PS training datasets.\nFigure R1. The predictions of original and GGS-substituted IDRs across different phase separation predictors.\nTherefore, direct quantitative prediction of in-silico substitutions with simple repeats or point mutations is beyond the scope of current predictors. We have revised our Discussion to explicitly state this limitation. We also note that with more comprehensive training data in the future, it may be possible to further distinguish the precise, independent contributions of stickers and their functional contexts.\nTo further dissect compositional features, we analyzed the relative amino acid abundance across motif clusters to further dissect their compositional features (Fig. 4d). The result is consistent with previous finding that different types of interactions between IDRs drive PS, including pi-pi, pi-cation, electrostatic, hydrophobic interactions and so on (Fig. 4d, Supplementary Fig. 4a)10. For example, aromatic residues (tyrosine and phenylalanine), along with glycine are relatively enriched in Cluster 0 and Cluster Glycine. Aromatic residues are important to mediate the pi-pi and pi-cation multivalent interactions driving the PS of Prion-like domain (PLD) containing proteins8,20. (lines 296-302)\nDiscussion: However, due to the limited sample size, the performance of PhaSeMotif in point mutations should be further examined. According to a benchmark evaluation, the performance of current sequence-based PS predictors is still limited in predicting the influence of mutations on PS behavior, constrained by the bias in current available data54. Therefore, built upon a PS predictor, direct quantitative predictions by PhaSeMotif of in-silico substitution with simple repeat or point mutations would be beyond the scope. (lines 421-426)\n5. For context, extensive work from many groups (Mittal, Banerjee, Mittal, Pappu, Hyman, Alberti, Forman-Kay etc.) has clearly shown how certain residues have an outside contribution to phase separation, yet in all studies to date glycine, proline, and serine have consistently shown up as “spacer” residues, those that are co-incident with residues that drive attractive intermolecular interactions but lack the requisite chemistry to drive attractive interactions to mediate phase separation. These spacer residues are typically far more abundant and provide a flexible but relatively inert scaffold within which other residues can sit. However, the compositional analysis in Fig. 4 suggests that the authors imply these residues underlie phase separation, when in fact the residues that have been demonstrably shown to drive phase separation (notably aromatic residues and arginine) are actually depleted from the motifs compared to general IDRs. Further, taking examples of sequences characterized previously (e.g., FUS LDC vs FUS Y2S variant) and generating a predicted score yields identical values, despite the fact that FUS Y2S is effectively infinitely soluble (no phase separation). Similar conclusions are arrived if we test other sequences where specific mutations have been made to suppress or enhance phase separation.\nResponse: We thank the reviewer for the detailed and important observations regarding the sticker-spacer model and specific test cases. In response, we have performed additional compositional analysis (revised Fig. 4d) which confirms that 'sticker' residues are in fact co-enriched with 'spacers' within predicted motifs, and we have revised the text to clarify that PhaSeMotif's scope is to identify these holistic functional regions.\n1) PhaSeMotif identifies functional regions comprising both stickers and spacers. PhaSeMotif is not designed to explicitly distinguish between “sticker” and “spacer” residues as defined by the sticker-spacer model. Instead, it identifies holistic sequence regions that are collectively predictive of high phase separation propensity. These functional regions inherently contain both the potential drivers (stickers) and the essential context provided by other residues (spacers). As shown in Fig 4a, residues that are often considered as stickers such as F/Y/W/R are comparably frequent, rather than depleted, in predicted motifs compared to PS IDRs. Furthermore, aromatic residues (tyrosine and phenylalanine), along with glycine are actually relatively enriched in Cluster 0 and Cluster Glycine (revised Fig. 4d). This is also supported by specific examples of our experimentally validated motifs, which contain well-established sticker residues:\n The TAF15 IDR1 motif (SYGQNYSGYSSYGQSQSGYSQSYGGYE) is rich in tyrosine(Y).\n The TDP43 Mut1 motif (GGNPGGFGNQGGFGNSRGGGA) contains multiple phenylalanine (F).\n The MLLT1 Mut1 motif (SAKKQKKSSSKGSRSAPGTSPRTSS) is enriched with charged residues lysine (K) and arginine (R).\n2) PhaSeMotif is able to identify functional regions without obvious canonical stickers. For example, CPEB2 Mut2 motif (GGPFSATAVPPPPPPAMNIPQQQPPPPAAPQQPQSRRSPV), without canonical sticker residues, is essential for its phase separation (revised Fig. 3b-c). The fact that artificial motif designed by PhaSeMotif (PGPGQGPPQPPPPPPPPPPPPAMQ) could rescue phase separation suggests a potentially important role for residues like Glutamine (Q) in this specific context, providing new avenues for experimental exploration (revised Fig. 5g).\nFig. 3. b, Analysis of the CPEB2 IDR. Left: Sequence of the CPEB2 IDR, indicating the PhaSeMotif-predicted motif (Mut) and a negative control (NC) region used for (GGS)n substitution. Right: Comparison of putative important regions predicted by PhaSeMotif, CatGRANULE, PLAAC, PsPhunter, and FuzDrop.\nc, Light-induced assembly of CPEB2 optoIDR variants in living cells. Left: Representative fluorescence images showing clustering upon blue light activation. Right: Quantification of the average number of puncta per cell over time for each sequence variant.\nFig. 5. Light-induced assembly of optoIDR variants in live cells, where native motifs were substituted with generated artificial motifs (Gen) from different clusters. Each panel shows representative time-lapse fluorescence images, the sequences of the original (WT/Mut) and generated (Gen) motifs, and quantification of puncta per cell over time. g, Cluster Proline (CPEB2 Mut2).\n6. In short, my concerns here are essentially three-fold:\nResponse: We thank the reviewer for this clear summary of the core concerns. We briefly recapitulate our responses and the corresponding actions taken below.\n1. Do binary predictors of phase separation make sense? I think is a question the authors need to discuss extensively in a revised version.\nResponse: As discussed in detail previously, we agree that strict binary classification is an oversimplification. We have therefore revised the Introduction and Discussion sections extensively to frame our approach as modeling the differential propensity for phase separation, which is a pragmatic and necessary computational abstraction.\n2. Does the underlying approach for delineating phase separation vs. non-phase separating proteins via PhaSePred work? The PhaSePred suffers from the same issues described in this review, so I think to convince readers of this the authors need to take as many examples of sequences where we have wildtype vs. variant data (of which there are many in the literature) where variant either enhances or suppresses phase separation, and ask if PhaSePred can correctly delineate between these two.\nResponse: We thank the reviewer for this suggestion. According to a comprehensive assessment of PhaSePred and a wide range of similar methods (R7), these methods achieve high accuracy on discriminating proteins with high propensity of phase separation. However, they are insufficient in predicting the effects of phase-separate-enhancing or -suppressing point mutations, which represents a distinct and arguably more challenging scientific question. Therefore, instead of predicting critical functional residues directly, PhaSeMotif captures relative importance from attention-based mechanisms and identify potential important subregions or motifs.\n3. Does PhaSeMotif actually identify relevant subregions in proteins where specific mutations have been made to suppress or enhance phase separation?\nResponse: Yes. The functional relevance of the subregions identified by PhaSeMotif is strongly supported by two lines of evidence:\n1) Extensive experimental validation presented in the manuscript, where targeted mutagenesis of 87% (20/23) of the regions identified by PhaSeMotif resulted in abolished phase separation.\n2) Mutation sites reported to have a major functional effect on phase separation possess significantly higher attribution scores from PhaSeMotif compared to mutations with minor effects (revised Fig. 2j-k).\n7. Point 1 The set up for the work states “While it is known IDRs often mediate homotypic and/or heterotypic multivalent interactions that promote PS detailed information about the specific sequence motifs and compositional determinants within IDRs that drive PS remains limited.” but I would strongly contest this – there are an overwhelming number of studies that have – for IDRs at least – systematically explored the precise determinants of condensate formation in vitro and more recently in vivo. Over the last decade, literally hundreds of papers have explored this question, consistently arriving at the same conclusions. As such, to claim we have “limited information” about the “specific sequence motifs and compositional determinants within IDRs that drive PS” is a ludicrous statement. How those motifs and compositional determinants relate to function is indeed an open question, but to claim that the common physiochemistry associated with them is poorly explored is incorrect.\nResponse: We thank the reviewer for this important correction. We agree that our original statement was imprecise and did not accurately reflect the existing literature. Our intention was to highlight the remaining challenge in computationally identifying the full diversity of specific functional motifs on a wider scale. In response, we have revised the sentence as follows:\nWhile the fundamental physicochemical principles driving PS by IDRs have been extensively studied7,9,10, systematically identifying the full diversity of these functional sequence motifs using computational tools remains a key challenge. (lines 63-66)\nThe abstract is also revised as:\nWhile IDRs are recognized as critical drivers of PS, the systematic identification of sequence motifs governing this phenomenon and their compositional determinants remain a key challenge. (lines 40-42)\n8. Point 2 The authors identify various residues enriched for PS motifs and PS IDRs; however, it’s probably important to explicitly note that the enrichment of residues does not necessarily mean that those residues drive phase separation. Case in point, extensive prior work has shown that serine, proline nor glycine appear to offer substantial attractive interactions in driving phase separation, yet these residues often co-occur in regions enriched for those residues that do underlie phase separation (e.g. arginine in RGG domains, aromatic residues in prion-like domains). The authors propose that glycine is important… yet their negative control involves inserting a GGS sequence, which reduces phase separation in most cases. As such, how meaningful are the predictions being made here?\nResponse: We thank the reviewer for this insightful comment. We agree that the enrichment of a single residue type does not necessarily mean it directly drives the interactions. In response to this point, we compared the relative abundance of each residue across our different motif clusters, rather than focusing on absolute abundance. As shown in revised Fig. 4d, this revealed a significant enrichment of aromatic residues within Cluster Glycine. This key finding demonstrates that the motifs identified by PhaSeMotif are not merely \"spacer\" regions but are in fact complex sequences that contain both the co-occurring spacer-like context and the embedded \"sticker\" residues known to drive interactions.\nFurthermore, each cluster possesses a specific combinational compositional preference rather than a random assortment of amino acids, suggesting that the functional importance of a predicted motif lies in the contribution of its specific compositional pattern, beyond merely “sticker” residues. This also clarifies why the (GGS) negative control, which is a simple, repetitive spacer, cannot functionally replace our predicted motifs: it lacks the specific and complex compositional patterns required for function.\n9. Point 3 The generative aspect of the work is conceptually great; however, having played with the tool, the sequences being generated for a motif seem to straddle a very broad set of sequence space. I think it would be essential to provide readers with some reason to believe these generated sequences actually are “similar” to the input sequence in some way; i.e. statistical analysis to convince us (as readers) that these sequences are similar and not just totally randomly generated (perhaps using sequence analysis, embeddings in the VAE latent space, comparison of embedded sequences from say ESM2 etc).\nResponse: We thank the reviewer for the constructive suggestion to validate our generative model. Following the reviewer's advice, we used the trained autoencoder to project both the original and the generated motifs into the same latent space. As shown in the revised Supplementary Fig. 5a, the generated sequences are proximal to their corresponding original motifs. This result provides statistical evidence that our generative model is not producing random sequences. We have incorporated this new analysis in the manuscript:\nSupplementary Fig. 5. a, UMAP (Uniform Manifold Approximation and Projection) visualization of original and generated motifs. The distribution of original motifs is represented by contours, and generated motifs are represented by triangular markers. Colors indicate cluster assignments.\nWe used the trained autoencoder to project both the original and the generated motifs into the same latent space. The result indicates that the generated sequences are inherently similar to corresponding original clusters, the generated sequences are proximal to their corresponding original motifs(Supplementary Fig. 5a). (lines 351-354)\n10. Point 4 One major issue is that it is almost impossible to determine which sequences were used here. The authors provide numerous tables, but make it surprisingly difficult to follow the main text (and indeed, I could never determine what the sequences used in Fig. 5 were from the SI tables). Can I request that for any sequence where mutations are made (e.g., a motif is replaced by (GGS)n or a generated fake IDR motif is used), the authors actually include the sequence before/after comparison in the figure to make it easy for the reader to understand what they are looking at.\nResponse: We thank the reviewer for this crucial feedback. In response, we have thoroughly reorganized the figure presentation as suggested. For all experiments involving sequence modifications, we now directly display the sequences, along with clear residue numbering (revised Fig. 3&5, Supplementary Fig. 3&5). We are confident these changes have significantly improved the readability of our manuscript.\n11. Point 5 In Fig. 2, it’s very unclear what the attribution score and “density” profiles are actually reporting on. What is density here? Density of what? Similarly, it’s hard to relate these figures to the output of the webserver? What is a “predict score”? What is the density of residues vs. the density of regions? Much greater clarity, both in the figure and in the web server, is needed here.\nResponse: We thank the reviewer for highlighting the need for clarity. In response, we have revised the manuscript to include detailed definitions and explanations.\n1) Attribution score: Figure 2A illustrates the calculation of the attribution score. The input IDR sequence is represented by a set of overlapping 15-residue regions. A combination of Guided Backpropagation and attention scores is used to generate lists of regions that contribute positively to the model's prediction. A 15-residue region's final attribution score is its frequency of appearance across all of these generated lists. Consequently, a higher score signifies that a region was consistently identified as critical for the positive prediction.\n2) The attribution score density: As the IDRs are split into 15-residue overlapping regions, we stack each region and calculated the frequency of each residue as the attribution score density. To ensure consistency and address the reviewer's point about the web server, we have updated all notations on the server to match the terms used in the revised manuscript. Furthermore, we have added a dedicated \"Interpretation\" page (https://predict.phasemotif.pro/Interpretation ) that explains each term.\nFig. 2. a, A schematic of motif prediction. Guided Backpropagation (GBP) and attention scores are used to identify 15-residue regions that contribute positively to the model's prediction. An attribution score for a 15-residue region is defined as its frequency of appearance across the generated positive-contribution lists. The attribution score density is calculated by stacking all overlapping 15-residue regions and computing the frequency of each residue.\n12. Figure comments\nFig. 1\n• It seems like comparison with PhaSePred here would be important to include, under the expectation PhaSeMotif should certainly not be better\nResponse: We thank the reviewer for this suggestion, which allows us to clarify the comparison in Figure 1. The comparison in Figure 1 is between PhaSeMotif and other sequence-based classifiers at the IDR-level. A direct comparison with PhaSePred is not included because the tools have different predictive objectives. PhaSePred is a protein-level classifier that outputs a single prediction for an entire protein. It is not designed to, and does not provide, a score for a specific sub-region such as an IDR. In contrast, PhaSeMotif is trained specifically to predict the phase separation propensity of IDR sequences. The other tools in Figure 1 are included because they can, like PhaSeMotif, produce a score for a specific input sequence, even if they are not specialized for IDRs. Therefore, including PhaSePred in this IDR-level comparison is not technically feasible. The primary purpose of our model, which Figure 1 serves to introduce, is not only to classify IDRs but also to provide the motif-level interpretability that protein-level predictors like PhaSePred lack.\n13. Fig 2.\n• It would be useful to show more examples of well studied proteins where we know of specific subregions that are particularly important for phase separation.\no IDRs and folded domains clearly annotated\no Experimentally-known regions highlighted\no Motifs identified by PhaSeMotif clearly highlighted\n• It seems the bottom of panels G and H may be chopped off? At least its impossible to understand what either of these are showing as presented in this PDF.\nResponse: We thank the reviewer for helpful suggestion to improve Figure 2. In addition to the three examples already in the figure, we have now added three new examples (hnRNPA1, TIA1, and NPM1, revised Fig. 2d-i). Following the reviewer’s suggestion, we have clearly annotated the IDRs, domains, experimentally-known functional regions and motifs identified by PhaSeMotif. A corresponding discussion of the new examples has been added to the main text.\nFig. 2. d-i, Examples of known phase-separating (PS) IDRs, annotating the full-length protein domain structure, the IDR location, known PS-promoting regions, and the PhaSeMotif predictions for hnRNPDL (d), G3BP1 (e), UBQLN2 (f), hnRNPA1 (g), TIA1 (h), and NPM1 (i).\nhnRNPA1 is known as a Prion-like domain (PLD) containing protein20, whose PS behavior is determined largely by the aromatic residues. PhaSeMotif identifies three motifs within the important PLD, each containing several aromatic residues (Fig. 2g). TIA1 is an RNA-binding protein and a component of stress granule. PhaSeMotif predicts two motifs which TIA1 C-terminal IDR, which contains proline residues for regulating its PS35 (Fig. 2h). NPM1 is abundant in the nucleolus and undergoes PS through electrostatic interactions. Within the NPM1 IDR enriched with charged residues, PhaSeMotif identifies one motif within the basic residue-enriched tract that mediates homotypic interactions and PS36,37 (Fig. 2i). (lines 209-216)\nRegarding panels G and H, we apologize for the formatting error. We have corrected this and ensured the complete figure is properly rendered in the revised manuscript.\nFig. 2. j, Percentage of previously reported major- or minor-effect mutations that are mapped within predicted motifs. 'Self' denotes self-assembly PS proteins. The asterisk (*) indicates a significant P-value (P < 0.05, chi-square test). k, Distribution of PhaSeMotif z-scores for sites containing major-effect mutations compared to random background sites. The P-value from an independent t-test is indicated.\n14. Fig 3\n• A discussed please show what the sequence changes are in line so the reader understands whats being done here.\n• Please ensure residue numbers are included in these figures\nResponse: We thank the reviewer for this suggestion. The revised figure 3 and 5 now explicitly show the sequences in line and includes residue numbering for clarity.\n15. Fig 5\n• As described, we need more quantification /description of what the generated sequences are and whether they are similar (or not) to the wildtype sequence.\nResponse: As in our response to Comment 9 (Point 3), we have demonstrated that generated sequences are inherently similar to corresponding original clusters, as evidenced in the UMAP visualization (revised Supplementary Fig. 5a).\n16. Fig. S3\n• A - Please include residue numbering on X-axis; otherwise these figures are not interpretable\n• Fig S3 lacks captions explicitly for panels C and D\nResponse: We thank the reviewer for pointing out these oversights. We've added residue numbering to the X-axis and the captions in Figure S3.\nReference:\nR1. Walsh,I., Fishman,D., Garcia-Gasulla,D., Titma,T., Pollastri,G., ELIXIR Machine Learning Focus Group, Harrow,J., Psomopoulos,F.E. and Tosatto,S.C.E. (2021) DOME: recommendations for supervised machine learning validation in biology. Nat Methods, 18, 1122–1127.\nR2. Tesei,G., Trolle,A.I., Jonsson,N., Betz,J., Knudsen,F.E., Pesce,F., Johansson,K.E. and Lindorff-Larsen,K. (2024) Conformational ensembles of the human intrinsically disordered proteome. Nature, 626, 897–904.\nR3. Anton,B., Besalú,M., Fornes,O., Bonet,J., Molina,A., Molina-Fernandez,R., De las Cuevas,G., Fernandez-Fuentes,N. and Oliva,B. (2021) On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction. NAR Genom Bioinform, 3, lqab027.\nR4. Kawashima,S., Pokarowski,P., Pokarowska,M., Kolinski,A., Katayama,T. and Kanehisa,M. (2008) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res, 36, D202–D205.\nR5. Breimann,S., Kamp,F., Steiner,H. and Frishman,D. (2024) AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning. Journal of Molecular Biology, 436, 168717.\nR6. Chen,Z., Hou,C., Wang,L., Yu,C., Chen,T., Shen,B., Hou,Y., Li,P. and Li,T. (2022) Screening membraneless organelle participants with machine-learning models that integrate multimodal features. Proceedings of the National Academy of Sciences, 119, e2115369119.\nR7. Kuechler,E.R., Huang,A., Bui,J.M., Mayor,T. and Gsponer,J. (2023) Comparison of Biomolecular Condensate Localization and Protein Phase Separation Predictors. Biomolecules, 13, 527.\nR8. Monti,M., Fiorentino,J., Miltiadis-Vrachnos,D., Bini,G., Cotrufo,T., Sanchez de Groot,N., Armaos,A. and Tartaglia,G.G. (2025) catGRANULE 2.0: accurate predictions of liquid-liquid phase separating proteins at single amino acid resolution. Genome Biology, 26, 33.\nR9. Hou,S., Hu,J., Yu,Z., Li,D., Liu,C. and Zhang,Y. (2024) Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions. Nat Commun, 15, 2147.\nR10. Sun,J., Qu,J., Zhao,C., Zhang,X., Liu,X., Wang,J., Wei,C., Liu,X., Wang,M., Zeng,P., et al. (2024) Precise prediction of phase-separation key residues by machine learning. Nat Commun, 15, 2662.\nR11. Hardenberg,M., Horvath,A., Ambrus,V., Fuxreiter,M. and Vendruscolo,M. (2020) Widespread occurrence of the droplet state of proteins in the human proteome. Proc Natl Acad Sci U S A, 117, 33254–33262.\nR12. Zhang,J., Humphreys,I.R., Pei,J., Kim,J., Choi,C., Yuan,R., Durham,J., Liu,S., Choi,H.-J., Baek,M., et al. (2025) Predicting protein-protein interactions in the human proteome. Science, 0, eadt1630."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I would like to thank the authors for their revision. They have addressed all the comments I raised.\nHere, I would like to add one minor comment: Thank you for adding the UniProt accession numbers to Supplementary Data File 1. To ensure that exact sequence versions are unambiguously identified, please indicate next to each UniProt accession number whether you used the canonical sequence or a specific isoform, and specify the UniProt release (sequence version). Alternatively, you could simply include the UniParc (UniProt Archive) identifier.\n(Remarks on code availability)\nI recommend adding a license file, such as the MIT License, to the GitHub repository to avoid confusion and encourage usage of this bioinformatics tool.", "reviewer_2": "I like to thank the authors for the detailed, careful, and extensive response to my original review.\nI will say, I think I remain somewhat skeptical that these types of tools are the right direction for the field to be pursuing - HOWEVER I also fully appreciate that's a bias I have, and the authors have gone above and beyond to I think very reasonably strengthen what was already probably the best paper in this category I have seen for a long time.\nAs such, I'd like to congratulate the authors on really doing a very comprehensive job here, and also thank them for taking my concerns in the original review in the spirit intended. I don't believe there are further changes I can suggest that would either strengthen the work or – honestly – be fair to ask at this stage :).\n(Remarks on code availability)\nI tried the docker container - on linux this ran fine\ndocker pull ghcr.io/tingtingligroup/phasemotif:latest\nand I was able to boot into the Python interpreter\ndocker run --rm -it --gpus all ghcr.io/tingtingligroup/phasemotif:latest\nHOWEVER the actual example provided gave this error:\nError: The length of MSVAKTPKTAENAEGGGGGGGGPPPPPPPKPHVNVGTIGPHEDTYYSEF is less than 50.\nError: The length of GPTLSEDNLSYYKSQPGFQKMSADK is less than 50.\nAlthough using an example that was longer than 50 it ran fine.\non MacOS (15.6.1 - MBP 2023) this however yielded:\ndocker pull ghcr.io/tingtingligroup/phasemotif:latest\nlatest: Pulling from tingtingligroup/phasemotif\nno matching manifest for linux/arm64/v8 in the manifest list entries"}, "author_response": ""}]

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