DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Pang, Yihe; Liu, Bin

doi:10.1186/s12915-023-01803-y

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Methodology article
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Published: 02 January 2024

Volume 22, article number 3, (2024)
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DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

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Abstract

Intrinsically disordered proteins and regions (IDPs/IDRs) are functionally important proteins and regions that exist as highly dynamic conformations under natural physiological conditions. IDPs/IDRs exhibit a broad range of molecular functions, and their functions involve binding interactions with partners and remaining native structural flexibility. The rapid increase in the number of proteins in sequence databases and the diversity of disordered functions challenge existing computational methods for predicting protein intrinsic disorder and disordered functions. A disordered region interacts with different partners to perform multiple functions, and these disordered functions exhibit different dependencies and correlations. In this study, we introduce DisoFLAG, a computational method that leverages a graph-based interaction protein language model (GiPLM) for jointly predicting disorder and its multiple potential functions. GiPLM integrates protein semantic information based on pre-trained protein language models into graph-based interaction units to enhance the correlation of the semantic representation of multiple disordered functions. The DisoFLAG predictor takes amino acid sequences as the only inputs and provides predictions of intrinsic disorder and six disordered functions for proteins, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linker. We evaluated the predictive performance of DisoFLAG following the Critical Assessment of protein Intrinsic Disorder (CAID) experiments, and the results demonstrated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. The standalone package and web server of DisoFLAG have been established to provide accurate prediction tools for intrinsic disorders and their associated functions.

Bioinformatics Approaches for Predicting Disordered Protein Motifs

IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins

Article Open access 12 July 2018

Sequence Patterns Mediating Functions of Disordered Proteins

Background

Proteins are essential macromolecules in living organisms, and the majority of proteins fold into specific and ordered three-dimensional conformations to perform their functions. Intrinsically disordered proteins and regions (IDPs/IDRs) are a special class of proteins or regions that exist without stable fold structures under native physiologic conditions. Despite lacking well-defined tertiary structures, IDPs/IDRs play essential roles in a wide range of biological processes, such as cell signaling [1], DNA regulation [2], and post-translational modification [3]. IDP/IDRs are also associated with many human diseases [4], including neurodegenerative disease [5, 6], diabetes [7], cancer [1, 8], and cardiovascular disease [9, 10]. The flexibility of IDRs in their structures enables them to bind many molecular ligands, thus making them effective drug targets [Full size image

Methods

Benchmark dataset of disorder functions

The DisProt [56,57,58] database provided the functional annotations of intrinsically disordered protein/region (IDP/IDR) following the Intrinsically Disordered Proteins Ontology (IDPO) and the Gene Ontology (GO) schemas. We investigated all the ontology terms in DisProt and obtained functional annotation term collections for protein-, DNA-, RNA-, ion-, and lipid-binding and flexible linker (Additional file 1: Table S1). Following previous studies [21, 29, 59], we annotated each functional class by collecting all the sub-terms. We extracted all the functionally annotated proteins from the DisProt9.3 database. To obtain high-quality data, we removed sequences whose functional regions lacked annotations for disordered structure. We also excluded the DP00072 sequence that was too long (> 30,000 residues) to be processed by the protein language model. Subsequently, a total of 925 sequences were obtained and used for splitting the training, validation, and independent test datasets. Following the same protocols as previous studies [26, 32], we clustered the 925 sequences using the CD-HIT algorithm [60] with 25% sequence similarity. Then, we randomly divided the clusters into five subsets, where three subsets (including 589 sequences) were used as the training dataset, and one subset (including 148 sequences) was used as the validation dataset. The remaining subset with 188 sequences was used as the independent test dataset, namely DP93. To further evaluate the performance of the proposed predictor, we collected an additional independent test dataset (DP94) containing 98 sequences using the same protocol as aforementioned. The sequences of DP94 are collected from the newly updated proteins in versions 9.3 to 9.4 of the DisProt database. The statistical information of these datasets is shown in Additional file 1: Table S2.

Graph-based interaction protein language model

Motivated by the language models (LMs) in natural language processing (NLP) [61, 62], the protein language models (PLMs) pre-trained with large numbers of amino acid sequences are able to discover the basic principles contained in the sequences [63]. Studies [55, 64, 65] have demonstrated that applying protein semantic information extracted from PLMs can facilitate the performance improvement of various prediction tasks. In DisoFLAG, we employed a graph-based interaction protein language model (GiPLM) to provide six functional predictions for intrinsically disordered regions (see Fig. 1b). The GiPLM integrates protein semantic information extracted from the ProtT5 [64] protein language model into graph-based interaction units to enhance the semantic correlation of multiple disordered functions. Specifically, a bidirectional gated recurrent neural network (Bi-GRU) [66] layer is employed to capture the protein contextual semantic encodings $\mathbf{P}$ based on the embeddings extracted from the ProtT5:

$$\mathbf{P}={\text{BiGRU}}({\mathbf{r}}_{1},{\mathbf{r}}_{2},\cdots ,{\mathbf{r}}_{L}]$$

(1)

where ${\mathbf{r}}_{i}$ is the PLM embedding vector for the ith residue, and L represents the length of the input sequence. Subsequently, the gate recurrent unit (GRU) layer with an attention mechanism [19, 67] was utilized to capture the global correlations among sequences and output the hidden representation ${\mathbf{h}}_{i}$ for each residue:

$${\mathbf{h}}_{i}={\text{GRU}}({\mathbf{h}}_{i-1},{\mathbf{c}}_{i})$$

(2)

$${\mathbf{c}}_{i}={\sum }_{j=1}^{l}{\alpha }_{ij}{\mathbf{p}}_{{\text{j}}}$$

(3)

$${\alpha }_{ij}=\frac{{\text{exp}}({s}_{ij})}{{\sum }_{j=1}^{l}{{\text{exp}}(s}_{ij})}$$

(4)

$${s}_{ij}={\mathbf{h}}_{i-1}^{T}{\mathbf{W}}_{a}{\mathbf{p}}_{{\text{j}}}$$

(5)

where ${\mathbf{p}}_{{\text{j}}}\in \mathbf{P}$ indicates the semantic encodings of the jth residue, ${\mathbf{W}}_{a}$ is the trainable weights of the attention mechanism, ${s}_{ij}$ the attention score between the ith and the jth residues, ${\alpha }_{ij}$ represents the attention weight between the ith and the jth residues, and ${\mathbf{c}}_{i}$ indicates the attention-based contextual representations. Then, the feature map** layers were used to generate the functional semantic representations (X) for each residue. Specifically, six fully connected layers were employed for map** the hidden global correlation representation ${\mathbf{h}}_{i}$ as functional semantic representations:

$${\mathbf{X}}_{i}^{(n)}={\text{ReLU}}\left({\mathbf{h}}_{i}{\mathbf{W}}^{\left(n\right)}+{\mathbf{b}}^{\left(n\right)}\right)$$

(6)

where ${\mathbf{X}}_{i}^{(n)}$ is the nth functional semantic representation for the ith residue and ${\mathbf{W}}^{\left(n\right)}$ and ${\mathbf{b}}^{(n)}$ are weights and bias variables, respectively; ReLU is the nonlinear activation function.

A single disordered region can bind to different ligands to perform multiple functions, and the multiple functions of IDRs are dependent and interrelated. In this study, we used the Shannon information entropy (IE) [68] and information gain (IG) [69] to describe the correlations among different functions:

$${IG}_{XY}={H}_{X\cup Y}- {H}_{XY }(0\le IG<1)$$

(7)

where ${H}_{X\cup Y}$ and ${H}_{XY}$ represent the information required for individual prediction and joint prediction of X and Y functions, respectively [68]:

$${H}_{X\cup Y}=-{\sum }_{i\in X\cup Y}p\left(i\right){{\text{log}}}_{2}p(i)$$

(8)

$${H}_{XY}=-{\sum }_{i\in X}{\sum }_{j\in Y}p\left(ij\right){{\text{log}}}_{2}p\left(ij\right)$$

(9)

A higher IG value indicates more reductions of IE in the joint prediction of two functions and a stronger correlation between the two functions. We pre-calculated the IG values on the training dataset and obtained the IG matrix of six disordered functions, which is visualized in Additional file 1: Fig. S1.

Then, each graph-based interaction unit in GiPLM models six disordered functions and their correlations as a functional graph G = (V, E), where V and E represent nodes and edges, respectively. The functional graph is fully connected (see Fig. 1b). Each node represents a function and is represented by functional semantic representation ${\mathbf{X}}^{\left(i\right)}$. Edges represent the correlations between functions and are represented by the adjacency matrix. In GiPLM, we employed a trainable weighted adjacency matrix to represent the degree of correlation between different functions and used the IG matrix pre-calculated on the training dataset by formula (7) as the initialization value:

$${A}_{ij}={IG}_{ij}$$

(10)

Then, the graph convolutional network (GCN) layer was used to propagate and aggregate the semantic correlation features for each node on the functional graph [70]:

$${\mathbf{Y}}_{i}^{(n)}={\text{ReLU}}({\sum }_{j\in {N}_{i}}{\mathbf{A}}_{ij}{\mathbf{W}}^{\left(n\right){\prime}}{\mathbf{X}}_{i}+{\mathbf{b}}^{\left(n\right){\prime}})$$

(11)

where ${\mathbf{Y}}_{i}^{(n)}$ is the aggregated semantic feature of the nth functional node, $\mathbf{A}$ is the trainable weighted adjacency matrix of the edges, ${\mathbf{X}}_{i}=[{\mathbf{X}}_{i}^{1},{\mathbf{X}}_{i}^{2},\cdots ,{\mathbf{X}}_{i}^{6}]$ is the concatenation of the six functional semantic representations, ${\mathbf{W}}^{\left(n\right){\prime}}$ is the convolution kernel, and ReLU is the nonlinear activation function. The semantic feature of the disorder ${\mathbf{Y}}_{i}^{IDR}$ was obtained by performing global max pooling over the functional graph (F represents the dimension of node features) [71]:

$${\mathbf{Y}}_{i}^{{\text{IDR}}}={{\text{max}}}_{k\in F}([{\mathbf{Y}}_{i}^{\left(1\right)},\cdots {\mathbf{Y}}_{i}^{\left(n\right)}\cdots ,{\mathbf{Y}}_{i}^{\left(6\right)}])$$

(12)

Finally, the propensity scores for disorder and six disordered functions were calculated based on the functional node features ${\mathbf{Y}}_{i}^{\left(1\right)\sim (6)}$ and disordered features ${\mathbf{Y}}_{i}^{{\text{IDR}}}$ by seven fully connected layers with the Sigmoid activation functions [32, 59].

Model training and evaluation

To train the GiPLM model of DisoFLAG to predict disorder and disordered functions for proteins, we employed the binary cross-entropy loss function to calculate the loss value of each prediction, and their combination was used as the final loss L($\theta$) [72]:

$$L(\theta )=-\frac{1}{n+1}{\sum }_{i=1}^{n+1}[{y}_{i}{\text{log}}\left(\widehat{{y}_{i}}\right)+(1-{y}_{i}){\text{log}}(1-\widehat{{y}_{i}})]$$

(13)

where ${y}_{i}$ (1 or 0) and $\widehat{{y}_{i}}$ represent the trues and predicted propensity score of the ith function, respectively. All the model variables and hyper-parameters were optimized according to the minimum loss function values on the validation dataset. A detailed description of all the trainable parameters and hyper-parameters of DisoFLAG was given in Additional file 1: Table S3.

The DisoFLAG outputs the real-valued propensity score results for disorder and disordered functions. We evaluated the predictive performances of DisoFLAG and other comparative methods with threshold-independent metrics [73,74,75,76,77]: AUC (area under the true-positive rates and false-positive rates curve across all thresholds), AUPR (area under the precisions and recalls curve across all thresholds), APS (average precision score along the precision-recall curve), and ${F}_{{\text{max}}}$ (the maximum harmonic mean between precision and recall rate across all thresholds). In addition, given a threshold, the binary results can be converted from the real-valued results (residue is predicted to be disordered/functional if its propensity score is higher than the threshold; otherwise, it is predicted as ordered/non-functional). We used the Matthews correlation coefficient (MCC) and balanced accuracy (BACC) to evaluate the binary prediction results. The definitions of the evaluation metrics are given in Additional file 1: Table S4.

Results and discussion

Protein semantic information facilitates the prediction of intrinsic disorder and disordered function

Protein feature representation is an essential step in DisoFLAG. We evaluated the performance of DisoFLAG using different protein representations, including protein language model-based (PLM) features (ProtT5 and ProtBERT), the position-specific scoring matrix (PSSM), and amino acid one-hot encodings (One-hot). Models taking different feature inputs were trained and optimized following the same protocol as described in the “Methods” section. The evaluation results on the DP93 independent test dataset and corresponding ranking results are shown in Fig. 2a and Additional file 1: Table S5, respectively. From these results, we can see that the model using PLM-based features outperformed PSSM and One-hot, and the model using ProtT5 consistently achieved the highest performance in predicting disorder and disordered functions. To further investigate the model performance improvement by the PLM-based features, we calculated the AUC values of DisoFLAG on the sequences with different multiple sequence alignment (MSA) [78, 79] depths (see Fig. 2b–e). Specifically, for each sequence in the DP93 dataset, we employed the HHblits [80] tools to conduct homology searches against the UniProt database and grouped the sequences according to the number of rows in the MSA search results. The results on disorder (Fig. 2b) and disordered functions (Fig. 2c–e) demonstrated that the performance of the model using the protein language model encodings improved the most as the number of sequence homologous alignments (i.e., MSA depth) increased. When the MSA depth is relatively small, the PSSM encoding method has better results than the protein language model coding method. The possible reasons for these results were attributed to the following: (1) The PLM as a data-driven deep learning method can accurately capture sequence features only when there is a sufficient number of homologous sequences available. (2) In contrast, PSSM encoding based on a probabilistic statistical model is more effective in capturing sequence features under a lower MSA depth condition. (3) The features captured by PSSM encoding and PLM are different. PSSM is designed to encode sequence conservation information, while PLM learns the contextual semantic information of protein sequences. Therefore, the conservation information is more accurate than the semantic information in predicting disordered functions when there are fewer homologous sequences.

Graph-based interaction unit enhances the semantic correlations of multiple disordered functions

The graph-based interaction unit (GiU) in DisoFLAG was employed to establish the correlations among multiple disordered functions. To investigate the critical role of GiU in DisoFLAG, we compared the performance of DisoFLAG using GiU with a simple sequence layer (Seq) (see Table 1). From this table, we found that DisoFLAG using GiU consistently outperformed the Seq, indicating that the semantic correlation features captured by GiU significantly boosted the predictive performance of DisoFLAG. In addition, the correlation among different disordered functions leads to one disordered residue being able to perform two or more different functions, which is referred to as the multifunctional (MF) residue. We compared DisoFLAG with other methods for predicting MF residues on the DP93 test dataset. A multifunctional residue is considered correctly predicted only if all its functions are accurately predicted. The F_max evaluation results of different methods are shown in Fig. 3a, from which we found that there are six types of MF residues in the DP93 dataset. DisoFLAG is the only predictor that can predict all types of MF residues. Additionally, compared to other predictors, DisoFLAG considered correlations among different functions and achieved the highest F_max values, which again indicated the importance of functional correlations captured by GiU for the accurate prediction of disordered functions.

Table 1 Performance comparisons of DisoFLAG using graph-based interaction unit (GiU) and sequence layer (Seq) for predicting different disordered functions on the DP93 independent test dataset

Full size table

Furthermore, we used layer-wise relevance propagation (LRP) [81, 82] to investigate the contributions of functional correlations to the prediction results. The LRP score was calculated as follows:

$${{\text{R}}}_{j}^{(l)}=(\alpha \frac{{w}_{jk}^{+}{h}_{j}}{{\sum }_{j}{w}_{jk}^{+}{h}_{j}+{b}_{k}^{+}}-\beta \frac{{w}_{jk}^{-}{h}_{j}}{{\sum }_{j}{w}_{jk}^{-}{h}_{j}+{b}_{k}^{-}}){{\text{R}}}_{k}^{(l+1)}$$

(14)

where ${{\text{R}}}_{j}^{(l)}$ and ${{\text{R}}}_{k}^{(l+1)}$ are the relevance scores of the current and previous layers, respectively. $\alpha$ and $\beta$ are the constraint parameters of the $\alpha \beta$ rule in LRP; ${w}_{jk}$, ${b}_{k}$, and ${h}_{j}$ represent the weights, bias, and hidden vector, respectively. We performed the LRP on the graph-based interaction units to obtain the importance of functional correlations. For each function, the importance scores of functional correlations to the propensity score were calculated by summing the relevance scores of all true-positive propensity predictions on the DP93 test dataset, which were described in Eq. (14). Figure 3b shows the IG values calculated on the DP93 dataset, which reflected the correlation among different functions. These correlations consistently made a positive contribution to the prediction of six disordered functions (see Fig. 3c).

Comparison of DisoFLAG to other methods in the prediction of disordered functions

We evaluated the performance of DisoFLAG for predicting disordered functions and compared it with methods specifically designed for disordered functions and performed well on CAID2. These methods include DisoRDPbind [29] and DeepDISOBind [30] for predicting protein-, DNA-, and RNA-binding disordered regions (IDRs); fIDPnn [26] method for predicting protein-, DNA-, and RNA-binding IDRs and disordered linkers; ANCHOR-2 [27] for predicting protein-binding IDRs; MoRFchibi (Light and Web) [34] and SPOT-MoRF [33] are methods for identifying molecular recognition features (MoRFs), which are protein-binding IDRs that undergo a disorder-to-order conformational transition; DisoLipPred [32] is the only available method for predicting lipid-binding IDRs; and TransDFL [39] and DFLpred [38] are methods for identification of disordered linkers. DisoFLAG is currently the only predictor providing predictions of ion-binding IDRs and covering the broadest range of disordered functional categories. The evaluation results on the DP93 test dataset suggested that the performance of DisoFLAG in predicting disordered protein-binding, DNA-binding, RNA-binding, lipid-binding, and linkers is better than the current tools quantified by AUC, MCC, and BACC metrics (Table 2). Moreover, DisoFLAG offered statistically significant improvement in AUC compared to other methods (see Additional file 1: Table S6). To further investigate the stability of the prediction performance of different methods, we performed the performance comparison on the DP94 test dataset, whose proteins were collected from the latest version 9.4 DisProt database. The results show that the performance quantified by the AUC metric of DisoFLAG is still significantly better than current tools in predicting disordered protein-binding, DNA-binding, and linkers; however, its performance decreased in predicting RNA-binding and lipid-bindings (see Additional file 1: Tables S7 and S8). We also reported the performance metrics at the protein level, as described in Additional file 1: Tables S9 and S10.

Table 2 Performance comparisons of DisoFLAG and other predictors on the DP93 independent test dataset

Full size table

We compared DisoFLAG with a broad range of predictors that participated in the Critical Assessment of protein Intrinsic Disorder (CAID2) challenge. Specifically, we assessed the performance of DisoFLAG on two CAID2 test datasets: disorder-binding and disorder-linker [22, 23]. The disorder-binding dataset contains 78 proteins annotated with interaction interfaces in disordered regions, and the disorder-linker dataset contains 40 proteins with disordered flexible linkers. We comprehensively aligned the sequences in CAID2 with all the benchmark datasets used in this study and found that CAID2 sequences were completely unseen with the training and validation of DisoFLAG. This is fully consistent with the assessment process of CAID2. Therefore, it is appropriate to directly compare the results of DisoFLAG with those reported in CAID2. We assessed the performance of DisoFLAG for predicting protein-binding, DNA-binding, RNA-binding, ion-binding, and lipid-binding on the disorder-binding dataset and predicting linkers on the disorder-linker dataset. The evaluation results and comparison with the 10 top-ranking methods reported in CAID2 [22, 23] are shown in Fig. 4. In Fig. 4a, b, we can see that the DisoFLAG’s protein-binding predictor generates the highest quality predictions with AUC = 0.879 and APS = 0.563 on the disorder-binding dataset. The DisoFLAG’s linker predictor achieves AUC = 0.8 and APS = 0.197 for the prediction of disordered linkers on the disorder-linker dataset (see Fig. 4c, d). The complete metrics are listed in Additional file 1: Tables S11 and S12.

Comparison of DisoFLAG to other methods in the prediction of intrinsic disorder

We assessed the performance of DisoFLAG in predicting the intrinsic disorder of proteins on two disordered test datasets provided in CAID2: DisProt-NOX and DisProt-PDB. The DisProt-NOX dataset is composed of IDRs from the DisProt database, excluding X-ray missing residues. In contrast, the DisProt-PDB dataset is more conservative by strictly limiting negative samples to structured residues observed in the PDB database. For more detailed information about the datasets, please refer to the CAID2 [22, 23]. We performed a thorough sequence comparison of two CAID2 datasets against the benchmark dataset used in this study to ensure that all sequences were independent and unseen by the training and validation of DisoFLAG. Subsequently, we compared the performance of DisoFLAG with the top 10 ranked methods reported in CAID2 (see Fig. 5). From these results, we observed that DisoFLAG achieved a second rank with an AUC of 0.836 and a fourth rank with an APS of 0.560 on the Disorder-NOX dataset. DisoFLAG showed lower performance on the Disorder-PDB dataset, but it achieved comparable performance to the CAID2 top 10 results in terms of AUC and APS metrics. The complete metrics are in Additional file 1: Tables S13 and S14.

Case study

We investigated the prediction results of DisoFLAG for one protein from the independent test data: the human immunodeficiency virus infectivity factor (HIV-1 Vif, DisProt: DP00875). Vif is a crucial accessory protein in HIV replication, and its role is to disrupt the antiviral activity of the human host defense factor APOBEC-3G (A3G) [83]. The functional implementation of Vif involves interactions with A3G, protein chaperones, ubiquitination machinery factors, and so on [84, 85]. Thus, elucidating the functional mechanism of Vif is of significant importance for discovering novel drugs to block its activity [85,86,87]. Nuclear magnetic resonance (NMR) revealed that the C-terminal domain (141–192) of Vif is unstructured under physiological conditions. Figure 6a shows a protein complex structure (PDB ID: 8E40) [88] composed of Vif from the PDB database [89]. Experimental evidence suggests that the disordered region of Vif is involved in binding with proteins and lipids [90]. The propensity scores for the Vif protein produced by DisoFLAG are visualized in Fig. 6b. To investigate the contribution of functional correlations to the DisoFLAG’s predictions, we mapped the highest protein-binding propensity score located at the T170 residue onto the functional graph in DisoFLAG. The map** process achieved by LRP is shown in Fig. 6c, from which we observed that protein-binding, RNA-binding, and lipid-binding nodes made a positive contribution to the prediction, and the edge between the protein-binding node and lipid-binding node contributed the most. We further compared the binary results of protein-binding and lipid-binding predicted by DisoFLAG and other methods for the Vif protein. From the comparison results shown in Fig. 6d, e, it can be seen that DisoFLAG is the only method that can simultaneously identify the complete disordered protein-binding and lipid-binding regions of the Vif protein and has the lowest number of false-positive predictions. These results highlighted again the semantic correlations captured through the graph-based interaction protein language model (GiPLM) enabling DisoFLAG to provide accurate and comprehensive predictions of multiple disordered functions.

Conclusions

Inspired by the similarities between biological sequences and natural language across three hierarchical levels, we designed the DisoFLAG predictor based on a graph-based interaction protein language model. DisoFLAG provides predictions of intrinsic disorder and its six types of functions, including protein-binding, DNA-binding, RNA-binding, ion-binding, lipid-binding, and flexible linkers. The performance assessments performed on two independent test datasets and CIAD2 benchmark test datasets indicated that DisoFLAG offers accurate and comprehensive predictions of disordered functions, extending the current coverage of computationally predicted disordered function categories. Our experimental analysis of the prediction results of DisoFLAG demonstrated that the use of protein semantic knowledge extracted from the pre-trained protein language model facilitated the accurate predictions of multiple disordered functions. The graph-based interaction unit used in DisoFLAG enhanced the semantic relevance of multiple disordered functions leading to a significant improvement in the identification of multifunctional disordered residues. We provide the standalone package and a convenient web server for DisoFLAG, which will be helpful tools to researchers in related fields.

Availability of data and materials

All data generated or analyzed during this study are included in this published article, its supplementary information files, and publicly available repositories. All the benchmark datasets used in this study and prediction results of the methods involved in the evaluation conducted of this study are available in the Zenodo repository (https://doi.org/https://doi.org/10.5281/zenodo.10361856). The source code and its descriptions of DisoFLAG are reproducible in the GitHub repository (https://github.com/YihePang/DisoFLAG), which is archived on Zenodo at https://doi.org/https://doi.org/10.5281/zenodo.10360345. The data values for the figures are provided in the Additional file 2. The web server of DisoFLAG can be accessed from http://bliulab.net/DisoFLAG/.

Abbreviations

IDP/IDR:: Intrinsically disordered protein and region
GiPLM:: Graph-based interaction protein language model
CAID:: Critical Assessment of protein Intrinsic Disorder
MoRF:: Molecular recognition feature
DFL:: Disordered flexible linker
NLP:: Natural language processing
LM:: Language model
PLM:: Protein language model
GCN:: Graph convolutional network
IG:: Information gain
GRU:: Gated recurrent unit
Bi-GRU:: Bi-directional gated recurrent unit
IDPO:: Intrinsically Disordered Proteins Ontology
GO:: Gene Ontology
ROC:: Receiver operating characteristic curve
AUC:: Area under the ROC curve
PR:: Precision-recall curve
AUPR:: Area under the PR curve
APS:: Average precision score
MCC:: Matthews correlation coefficient
BACC:: Balanced accuracy
PSSM:: Position-specific scoring matrix
MSA:: Multiple sequence alignment
GiU:: Graph-based interaction unit
LRP:: Layer-wise relevance propagation
HIV-1 Vif:: Human immunodeficiency virus infectivity factor
A3G:: APOBEC-3G protein
CBF-beta:: Core-binding factor subunit beta protein
NMR:: Nuclear magnetic resonance

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Acknowledgements

We are very much indebted to the three anonymous reviewers, whose constructive comments are very helpful for strengthening the presentation of this paper.

Funding

This work was supported by the National Natural Science Foundation of China (No. 62325202, 62271049, U22A2039, and 62250028).

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School of Computer Science and Technology, Bei**g Institute of Technology, No. 5, South Zhongguancun Street, Bei**g, Haidian District, 100081, China
Yihe Pang & Bin Liu
Advanced Research Institute of Multidisciplinary Science, Bei**g Institute of Technology, No. 5, South Zhongguancun Street, Bei**g, Haidian District, 100081, China
Bin Liu

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Yihe Pang
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YP: methodology, software, validation, formal analysis, data curation, writing of original draft, and writing review and editing. BL: conceptualization, resources, writing review and editing, supervision, project administration, and funding acquisition. All authors read and approve the final manuscript.

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Additional file 1: Fig. S1.

Visualization of the IG matrix. Table S1. The ontology term and its sub-terms for each disordered functional class. Table S2. The statistical information of the datasets. Table S3. The number of trainable variables and hyper-parameters of DisoFLAG. Table S4. The definition of evaluation metrics. Table S5. The performance ranking of DisoFLAG using different features. Table S6. The statistical significance of differences (p-value) in predictive performance by different methods on the DP93 test dataset. Table S7. Performance comparisons of DisoFLAG and other predictors on the DP94 independent test dataset. Table S8. The statistical significance of differences (p-value) in predictive performance by different methods on the DP94 test dataset. Table S9. Per-protein performance of different disordered function predictors on the DP93 test dataset. Table S10. Per-protein performance of different disordered function predictors on the DP94 test dataset. Table S11. Performance metrics for Disorder-Binding prediction on the CAID2 test dataset. Table S12. Performance metrics for Disorder-Linker prediction on the CAID2 test dataset. Table S13. Performance metrics for disorder prediction on the CAID2 Disorder-NOX and Disorder-PDB test datasets. Table S14. Per-protein performance of different disorder predictors on the CAID2 Disorder-NOX and Disorder-PDB test datasets.

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Pang, Y., Liu, B. DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model. BMC Biol 22, 3 (2024). https://doi.org/10.1186/s12915-023-01803-y

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Received: 25 June 2023
Accepted: 15 December 2023
Published: 02 January 2024
DOI: https://doi.org/10.1186/s12915-023-01803-y

DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model

Abstract

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Bioinformatics Approaches for Predicting Disordered Protein Motifs

IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins

Sequence Patterns Mediating Functions of Disordered Proteins

Background

Methods

Benchmark dataset of disorder functions

Graph-based interaction protein language model

Model training and evaluation

Results and discussion

Protein semantic information facilitates the prediction of intrinsic disorder and disordered function

Graph-based interaction unit enhances the semantic correlations of multiple disordered functions

Comparison of DisoFLAG to other methods in the prediction of disordered functions

Comparison of DisoFLAG to other methods in the prediction of intrinsic disorder

Case study

Conclusions

Availability of data and materials

Abbreviations

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Acknowledgements

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Contributions

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Supplementary Information

Additional file 1: Fig. S1.

Additional file 2.

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