Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. Firstly, we design a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates. Secondly, we improve the performance of AlphaFold2 using the templates identified by PAthreader. Thirdly, we explore protein folding pathways based on our conjecture that dynamic folding information of protein is implicitly contained in its remote homologs. The results show that the average accuracy of PAthreader templates is 11.6% higher than that of HHsearch. In terms of structure modelling, PAthreader outperform AlphaFold2 and ranks first on the CAMEO blind test for the latest three months. Furthermore, we predict protein folding pathways for 37 proteins, in which the results of 7 proteins are almost consistent with those of biological experiments, and the other 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures. PAthreader generates templates of remote homologous structures to improve protein structure prediction by AlphaFold2 and predict folding pathways under the hypothesis that folding information is evolutionarily conserved in remote homologs. It is well known that templates play a critical role in the protein structure modelling4. Meanwhile, the evolutionary relationship implicitly contained in templates are probably favorable for study of protein folding6.AlphaFold2 developed by the DeepMind team has achieved a major breakthrough in machine learning-based protein structure modelling. However, the physics of how proteins dynamically fold into their equilibrium structures is not explored in AlphaFold27. Knowledge-based modelling approaches usually perform template-based large-scale conformational sampling guided by the energy function, such as RosettaCM8, D-I-TASSER9, SWISS-MODEL10 and MODELLER11. In end-to-end deep learning methods, such as AlphaFold23, RoseTTAFold12 and RGN213, template information is used explicitly or implicitly for deep learning models. Some recent work have shown that almost all of the popular protein structure prediction methods strongly depend on the quality of the template2. For example, it has been reported that the AlphaFold2 structure accuracy in the AlphaFold Protein Structure Database (AlphaFold DB) is largely affected by template availability14. The distribution of AlphaFold2 models of human proteins with Protein Data Bank (PDB) structures available is heavily skewed to higher average confidence scores than those without PDB structures available16. There is evidence that the structural space of the PDB is complete and can be used to solve most single-domain proteins17. Therefore, developing a new method to recognize high-quality remote homologous templates (PDB structures with a sequence identity <30%)19 is extremely important for protein structure prediction.Templates are essential for improving the accuracy of protein structure prediction. In general, protein structure modelling methods are divided into three categories: physics-based methods, knowledge-based methods, and end-to-end deep learning methods. Physics-based structure modelling methods use the template as the initial structure for folding simulations, such as metropolis monte carlo and molecular dynamics simulation20. Protein folding is a very fast process , which results in intermediates that only transiently populate during kinetic folding21. Experimentalists usually use spectrometry methods such as circular dichroism chromatography, fluorescence spectroscopy, etc. to study protein folding pathways. Circular dichroism spectroscopy uses the different absorption of left and right circularly polarized light by chiral molecules to analyze protein interactions and determine protein folding paths22. Fluorescence spectroscopy is based on the fluorescence emission characteristics of aromatic amino acids in proteins to monitor the folding-unfolding pattern and protein denaturation23. These methods can follow kinetic folding but provide very limited information to define the structure of folding intermediates24. In some related works, attempts have been made to avoid these difficulties by simulating the folding process. Kresten Lindorff-Larsen et al. studied the folding process of proteins through equilibrium MD simulations by improving the CHARMM force field to make it easier to transfer between different protein classes21. Charlotte M Deane et al. use DMPfold to predict the distribution of distances for each residue pair to determine rigid and flexible behavior of proteins25. However, inferring the folding path from the mass of simulated data is still a challenge. As a result of gene fusion in the organism, protein folding intermediates may be found in the final state of other proteins24. Is it possible to explore folding pathways from a large number of remote homologous structures? For billions of years, organisms in nature have produced stable, functionally safe three-dimensional protein shapes that are reused repeatedly, ranging from short structure to oligomeric complexes26. Therefore, all known PDB structures can be grouped into a very limited number of hierarchical families28. The evolutionary relationships of these protein families may implicitly contain folding information of individual protein. Recently, the DeepMind and the EMBL-European Bioinformatics Institute collaborated to create a new data resource, AlphaFold DB, which has attracted the extensive attention of biological scientists14. It greatly expands the structural coverage of the known protein sequence space. This makes it possible for AlphaFold DB to complement PDB with respect to the recognition of remote homologous structures and the discovery of template-inspired protein folding pathways. With more than 188,502 publicly available PDB structures (as of August 2020) and 564,449 AlphaFold DB structures (as of March 2022), developing an efficient method is essential for recognizing remote templates and exploring folding pathways.Templates are also crucial for the research on protein folding pathways. AlphaFold2 has been successfully used for protein structure prediction using statistical knowledge of the crystal structure. However, it is not clear whether AlphaFold2 can learn the physics of how proteins dynamically fold into equilibrium structure18, SPARKS-X29 and MUSTER30. Binary contact/distance-based threading methods have been developed as great progress has been made by deep learning in inter-residue contacts or distances prediction. For example, in EigenTHREADER31 and CEthreader19, eigenvector decomposition is used to resolve the principal eigenvectors, and then an alignment search is performed on the eigenvectors using standard dynamic programming algorithms. DeepThreader32 aligns the query protein to the template and uses the predicted distance potential to further improve the sequence-template alignment. In addition, there are methods to integrate sequence profile information and contact maps into scoring functions, such as LOMETS33 and CATHER34. These methods improve the accuracy of template recognition to a certain extent, but they can be further improved, as shown in many studies33.In the current literature, the common template recognition approaches can be roughly divided into two categories: profile-based alignment methods and binary contact/distance-based threading methods. Most profile-based alignment methods use sequential information, including sequence profile, secondary structure, solvent accessibility and torsion angles to build scoring functions, such as HHsearchIn this work, we proposed a method, PAthreader, to recognize the remote template and explore the folding pathway by learning from PDB and AlphaFold DB. The results on a large-scale benchmark dataset indicate that PAthreader is significantly better than state-of-the-art methods for remote template recognition. To the best of our knowledge, AlphaFold DB is applied to remote template recognition and folding pathway exploration for the first time. We also analyzed the relationship between the template and the model of AlphaFold2, which shows that the quality of model mainly depends on the template availability. When feeding the better template of PAthreader into AlphaFold2, the accuracy of its model was improved. Furthermore, we propose a hypothesis that the evolutionary relationship of protein families implicitly contains the folding information of individual proteins. That is, the folding pattern that occur frequently in protein structures are more evolutionarily conserved, and these conserved regions are structurally stable and preferentially formed in protein folding. Based on the above, we identified folding intermediates from homologous templates and obtained protein folding pathways of 7 widely studied cases and 30 human proteins.35, a global structure scoring metric that is complementary to alignScore and linearly weighted with the alignScore for template ranking. Finally, the top templates are fed into state-of-the-art protein structure prediction methods for structure modelling. Meanwhile, the folding pathway is explored based on the folding intermediates, which are deduced according to secondary structures and frequency distributions of residues calculated on the basis of different distance deviation thresholds after template alignment by TM-align36.The pipeline of PAthreader is illustrated in Fig. 37 database. The resolutions of these proteins are less than 2 Å, and their lengths range from 120 to 700 residues. We compared our method with state-of-the-art threading protocols, including HHsearch18 and LOMETS333, where homologous templates with a sequence identity ≥30% to the query were excluded. In order to objectively evaluate the performance of these methods at different difficulty levels, we divided the test set into 4 subsets based on the TM-score of the best template in PDB, i.e., 0–0.5, 0.5–0.7, 0.7–0.9 and 0.9–1 , proteins with 2 domains (2dom), and proteins consisting of ≥3 domains (≥3dom). The results are reported in Fig. 40. Supplementary Fig. Until now, achieving accurate predictions for multi-domain protein structures has been more challenging than for single-domain proteins15 with a predicted local distance difference test (pLDDT) score ≥90 to complement the master structure database from PDB. We investigate the effect of AlphaFold DB on template recognition through ablation experiments, and the results are shown in Fig. There may be three reasons for the better performance of PAthreader over HHsearch and LOMETS3. Firstly, AlphaFold DB helps to improve the accuracy of template recognition by expanding the family coverage of model organism proteomes. From the structures of 48 species provided by AlphaFold DB, which provides novel folding architectures and motifs not found in PDB, we selected 100,912 high-confidence structures# and HHsearch# represent the results obtained by comparing the identified structure with the native structure through TM-align, which is denoted as the native alignment. The comparison of PAthreader# and HHsearch# shows that the template structures identified by our method are better than those of HHsearch. It also highlights that there is a gap between HHsearch and its native alignment HHsearch#, while PAthreader is almost close to its native alignment PAthreader#. These results indicate that more accurate structures are identified when using our method, and more accurate sequence-template alignments than HHsearch are presented. The performance is further confirmed by the comparison of the results shown in Fig. Secondly, the three-track alignment takes full advantage of the structural information and predicted multiple promising distances for residue alignment, residue pair alignment, and distance-structure profile alignment. In order to analyze the performance of the three-track alignment, we perform sequence-structure threading for the identified structures in different ways on the benchmark set. Figure Thirdly, we combine the pDMScore predicted by deep learning and the alignScore obtained from the three-track alignment to select better templates. Figure AlphaFold2 has made a great advance in protein structure prediction based on the current sequence library and structure library. Here, we performed two experiments to analyze the relationship between the template and the accuracy of the AlphaFold2 model. Firstly, we examined the quality of templates for high-scoring models (pLDDT ≥ 90) of AlphaFold DB by structural comparison. As of March 2022, AlphaFold DB provides 564,449 structures for 48 organisms. We clustered 100,912 high-scoring models selected from AlphaFold DB using TM-align with 80% structural similarity. We found that 55.7% of the structures could be classified into the 34,701 PDB clusters determined during the construction of the master structure database, and the remaining structures could form 22,105 new clusters. This indicates that more than half of the high-scoring models in AlphaFold DB have templates with TM-score ≥ 0.8 in PDB.4. We also used the CAMEO test set (2022/04/01-2022/06/18) to analyze the relationship between the template and the accuracy of the AlphaFold2 model. The results are presented in Supplementary Fig. Secondly, we further analyzed the relationship between the model accuracy and the first template used for AlphaFold2 modelling on 551 test proteins, where all templates with sequence identity ≥30% were removed. The results are presented in Supplementary Fig. The above results suggest that the performance of AlphaFold2 depends on the quality of the template to some extent. Therefore, it is possible to improve AlphaFold2 by providing better templates.https://www.cameo3d.org/modelling/). Figure We participated in the CAMEO blind test by replacing the template recognition component (HHsearch) of AlphaFold2 with PAthreader. In a continuous three-month test, our method (PAthreader) achieved better results than the state-of-the-art methods, and ranked first among the public servers  were then calculated at different distance deviation thresholds, where a higher value indicates a higher frequency of residue alignment at corresponding positions of template structures. Second, the target was divided into multiple segments based on the secondary structure, and the average ResFscore for each segment was calculated by dividing the corresponding area of the segment by its length. Folding intermediates were finally determined by adaptively selecting fragments with an average ResFscore greater than a given threshold (see details in Methods section). We explored protein folding pathways on 7 widely studied cases and 30 human proteins  pulse labeling and nuclear magnetic resonance (NMR). Figure 45. Figure 48. It contains 4 folding intermediates, Horse heart cytochrome c is a member of the cytochrome c superfamily. It is an electron-transfer protein with a heme c group, and it binds to protein via thioether bonds to complete the transfer of electrons. The structure consists of five α-helices connected by random coil segments, which fold around the heme moiety with histidine residue (H18) and methionine residue (M80) as axial ligandsFigure 49, which are characterized by heme crevice undergoing closed-open transitions concomitant with shifts of residues 77–85 and residues 35–5646. These flexible local structures have a high probability of mutation in biological evolution, while the pocket region formed by the three helices is stable. Therefore, the order of protein folding is related to its function, and relatively stable regions of structure are preferentially formed during protein folding. These results suggest that the final state structures of protein families may be the folding intermediates of individual protein, which implicitly contain folding information. Interestingly, we also found an intermediate-like structure of cytochrome c in the complex, which is composed of the N-terminal of chain 1 and the C-terminal of chain 2 . However, we found that directly providing better templates can only improve AlphaFold2 to a certain extent, which may be the reason for the over-engineering of AlphaFold2. Embedding PAthreader into AlphaFold2 and retraining it may obtain a more suitable model, but it would be a challenge because it requires an extremely large number of computational resources.We demonstrated that the state-of-the-art protein modelling method AlphaFold2 depends on the quality of templates to some extent. Especially for multi-domain proteins, the model predicted by AlphaFold2 still has a large deviation due to inaccurate domain orientationsBased on the recognized homologous templates, we further explored protein folding pathways by identifying folding intermediates. In this study, we proposed that the evolutionary relationships of protein families implicitly contain folding information of individual protein, and protein folding intermediates can be inferred from collections of remote homologous structures. We explored the protein folding pathways of 37 proteins. The results of 7 proteins are almost consistent with biological experiments, and the remaining 30 human proteins have yet to be verified by biological experiments. The ability of PAthreader to explore folding pathways is mainly attributed to two aspects. First, AlphaFold DB complements a large number of remote templates, which provide sufficient evolutionary information between protein families. The second important contribution to PAthreader is that DeepMDisPre predicts multiple promising distances for flexible protein regions . We selected 100,912 structures with pLDDT ≥ 90 from these structures as available templates and clustered them using the 34,701 PDB cluster seeds according to 80% structural similarity, resulting in 55.7% of the structures being classified into 34,701 PDB clusters and the remaining structures forming 22,105 new clusters. Finally, PAcluster80 was constructed by the 56,805 clusters, which consists of 106,275 PDB and 100,912 AlphaFold DB structures.We constructed a master structure database, named PAcluster80, based on PDB and AlphaFold DB, and the flowchart is shown in Supplementary Fig. 59 with HHblits60. Importantly, DeepMDisPre predicted a multi-peak distance distribution within 20 Å for flexible protein region, which provides more information for template recognition and folding pathway exploration.The structure profiles are histogram distributions of pairwise residue distances, which were derived from structure classes of PAcluster80 by statistical consistency analysis. The member structures of the cluster were globally aligned with the centroid by TM-align, and the distance distribution of the residue pairs of centroid structures was extracted. As shown in Supplementary Fig. Nclu structural clusters are identified by the three-track alignment between the query sequence and the cluster seeds. In the second stage, the optimal templates are identified from the structures within the clusters determined in the first stage by repeating the three-track alignment.To take full advantage of the deposited structure information to identify templates, we developed a three-track alignment  for two stages. In the first stage, the optimal The purpose of the three-track alignment is to find an optimal alignment between the query sequence and the template sequence by maximizing the alignScore defined by Eq. , as showa and b are the query sequence and template, respectively; L is the length of the alignment; i-th and j-th maxima of probability between the n-th and m-th residues of distance profiles and structure profiles, respectively; and n-th and m-th residues of the distance profiles. n-th and m-th residues within The alignment scores of the query sequence and template are defined as follows:entclass1pt{minimad by Eq. , respectNpDM templates to predict pDMScore. The physical and geometric features of the template structure were extracted as the input of the network. Physical features include Rosetta intra-residue energy terms, secondary structure and amino acid properties61. Geometric features include multiple distances of inter-residue, orientations, voxelization and ultrafast shape recognition62. More detailed descriptions of the features are listed in Supplementary Table According to alignScore, we selected the top We used a convolutional network with self-attention to predict the quality score of the template. The first step is feature concatenation. The voxelization is input to a 3D convolutional layer and flattened into a 1D vector. It is concatenated with 1D features and fed into a 1D convolutional layer. The output 1D vectors are horizontally and vertically striped into 2D vectors and concatenated with 2D features. It is then turned into a 128-channel feature matrix through 2D convolution, instance normalization, and ELU activation. The second step is the self-attention operation, which contains 3 axial-attention blocks. The feature matrix is processed by an axial multi-head attention method, which alternates attention on the rows and columns of all features. The number of attention heads is 8. The encoded representation is a set of query-key-value triples in each head of the self-attention layer. The output from masked multi-head attention is further processed by a pointwise feedforward layer. The third step is the convolution operation. The convolution operation consists of 15 residual blocks, each of which is composed of three ELU activation layers, three 2D convolutional layers, and three instance normalization layers. The output layer is processed through sigmoid and softmax functions to predict the Cα distance deviations for all residual pairs.35, is used to evaluate the model scores by computing the Euclidean distance difference of the residue pairs, taking values . DMScore not only evaluates the global topology but also considers the local structure differences, which are defined as follows:L is the length of the protein structure; i-th and j-th residues; DMScore, a global structure scoring metric proposed in our previous studyThe loss function includes the distance deviation of the residual pair and pDMScore of the residue, which are evaluated by the multivariate cross-entropy loss function and the mean square loss function, respectively. The loss function is defined as follows:We ranked the templates by rankScore calculated with linear weighting of alignScore and pDMScore, which is defined as follows:Nt templates for global structural alignment with the target protein structure using TM-align. The frequency scores of each residue were then calculated by Eq. , and loop segments ≤4 AAs were merged into neighboring helix or sheet segments. The average ResFscore for each segment was calculated by dividing the corresponding area of the segment by its length, which is defined as follows:p-values are described.All data were carefully collected and analyzed using standard statistical methods. Comprehensive information on the statistical analyses used is included in various places, including the figures, figure legends and results, where the methods, significance and Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Condensed states of proteins, including liquid-like membraneless organelles and solid-like aggregates, contribute in fundamental ways to the organisation and function of the cell. Perturbations of these states can lead to a variety of diseases through mechanisms that we are now beginning to understand. We define protein condensation diseases as conditions caused by the disruption of the normal behaviour of the condensed states of proteins. We analyze the problem of the identification of targets for pharmacological interventions for these diseases and explore opportunities for the regulation of the formation and organisation of aberrant condensed states of proteins. In this review, the authors define protein condensation diseases as conditions caused by aberrant liquid-like or solid-like states of proteins, and describe opportunities for therapeutic interventions to restore the normal phase behaviour of proteins. The review accompanies the related collection of articles published in Nature Communications focusing on possible therapeutic approaches involving liquid-liquid phase separation. The phenomenon of protein folding is a prominent example of the ability of biological systems to self-assemble by bringing together reactive groups in complex arrangements that enable sophisticated biochemical functions.By folding into their native states, proteins perform myriad molecular functions that are essential for the maintenance of cellular homoeostasis3, also referred to as the droplet state4. Furthermore, many proteins can also form a highly ordered solid-like state, known as the amyloid state5, which in certain cases can be functional7. Since in the cell most proteins are typically expressed at concentrations at which they can form condensed states9, the droplet and amyloid states could be considered as fundamental states of proteins along with the native state4  in melanin synthesis12 and of cyclic GMP-AMP synthase (cGAS) in innate immune signalling13. Liquid–liquid phase separation can amplify signals by low-affinity effectors and ligands by facilitating the formation of signalling clusters, such as in T cell receptors14 or Wnt signalling15. Droplets can serve as non-membrane bound cellular compartments, such as the nucleolus16 or facilitate their formation through nucleation of polymerisation reactions, such as microtubulin for centrosome formation17. The assembly and disassembly of condensates promote morphological changes in developmental processes, such as the pattern specification process3. Condensates may orchestrate components of cellular pathways, such as in the cases of p53-binding protein 1 (53BP1) droplets, which concentrate components for DNA repair18 or of heterochromatin protein 1 (HP1) droplets, which induce gene silencing19. Furthermore, an increasing number of cellular processes have been associated with solid-like scaffolds7. In particular, signalling complexes in the innate immune system, such as inflammasomes, faddosomes, myddosomes often form solid-like condensates21 to recruit downstream signalling components.Proteins in condensed states can perform a wide range of biological functions by increasing the efficiency of cellular processes and by reducing biological noiseIn this work, we first characterise protein condensation diseases as disorders caused by aberrant liquid- or solid-like states of proteins. We then address the problem of identifying possible targets for drug discovery in order to restore the normal phase behaviour or proteins.22  and the RNA-binding protein senataxin (SETX) in spinal cord motor neurons34.Stress granule clearance in mammalian cells can be also reduced by inhibition of autophagy, or by impairment of valosin-containing protein , which plays a critical role in protein quality control36.As a counterpart to the wide range of the cellular processes described above, it is becoming increasingly clear that failures in the regulation of condensed states may lead to dysfunctional protein assemblies that could be involved in a range of pathological processes38 and solid-like39 condensates  being critical for heterochromatin assembly, it has been reported that mutations that disrupt this process lead to transcriptional dysregulation in Rett syndrome40 Table . Mutatio43  may cause tumorigenesis by compromising splicing functions48.More generally, shifting the phase boundary either towards the formation of condensates or towards their disassembly can have pathological consequences. Cancer-causing mutations in the speckle-type POZ protein (SPOP), by reducing its tendency to phase separate, lead to a failure in its co-localisation with DAXX, thus dysregulating ubiquitin-dependent protein homoeostasis46 Table . In cont49. The irreversible maturation of granules of RNA-binding proteins, including TDP-4350, heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1)51, fused in sarcoma (FUS)52 and T-cell intracellular antigen 1 (TIA)53, can results in loss of function, as for example in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The conversion of the droplet state into the amyloid state may lead to loss of function by amyloid fibril formation, as well as the formation of promiscuous intermediates causing cytotoxicity53. Protein aggregation may be induced by a deficiency of an interaction partner, such as in the case of progranulin, whose down-regulation contributes to microglial toxicity of TDP-4354. Droplet maturation, however, may also be required for physiological functions. For example, the innate immune mechanism involving the virus-induced inflammasome formed by NOD-like receptor family pyrin domain containing 6 (NLRP6) undergoes solidification after the recruitment of apoptosis-associated speck-like protein (ASC) for downstream activation55. Likewise, an acquisition of solid-like behaviour of the condensates of the active-zone scaffold proteins SYD-2 and ELKS-1 is required for synaptic functions56.A wide range of disorders is caused by the shifting of the droplet state towards the amyloid state59. Ribonucleoprotein granule biogenesis is modulated by the contact sites with the endoplasmic reticulum (ER), which regulate the fusion and fission of processing bodies (P-bodies) and stress granules60. This process couples the ER translational capacity with the generation of membraneless organelles. In a similar vein, the ER forms a compartment with TIS granules, which through interactions between 3’ untranslated RNA regions modulates the expression of membrane proteins61. In addition, interactions with ER membranes affect the size of Whi3 membraneless organelles, thereby limiting the local concentration increase on the ER surface62. Via modulating protein concentrations, ER-linked STING protein condensates influence innate immunity responses63. Although growing evidence demonstrates the biological importance of condensate-organelle communications, only a few disease-associated mutations have been directly linked to this process. As annexin A11 enhances RNA transport in neurons by tethering RNP granule cargos to lysosomes64, ALS-associated mutations of annexin A11 disrupt its interactions with lysosomes and impair its adaptor function64.Perturbing the interplay between membrane-bound organelles and condensates may lead to additional disease mechanismsTo identify links between condensate-forming proteins  and viral infections (influenza) associated with genes encoding PYD and CARD domain-containing proteins, the condensation of which is required for innate immune signalling55  and found that over 2168 orphan diseases (i.e. over two-thirds) have a considerable contribution from genes encoding droplet-forming proteins  and hydrophobic89 interactions. Organisation of such non-canonical motifs into patterns, such as those of aromatic and charged residues, was observed to enable phase separation90 Perturbing interaction patterns modulates the conformational propensity of a protein sequence91, which can shift the droplet state to the native state88. Along these lines, linker regions contribute to phase separation by influencing the number of accessing binding, such as in the case of the adaptor protein Nck92.According to our current understanding of the protein condensation process, liquid-like condensates are stabilised and regulated by disordered interactions93, including the pH94 and salt concentration78. Cellular localisation and concentration of interaction partners, including RNA, are critical for promoting the formation and controlling the properties of condensates96. Together with hydrophobic interactions, aromatic interactions are important under high salt conditions, while electrostatic interactions dominate the process under low salt conditions78. Post-translational modifications and allosteric effects of the flanking regions can provide a further layer of regulation to switch the motifs on and off68. For example, phosphorylation regulates the formation of FMRP and caprin-1 condensates to control mRNA deadenylation97, dual-specificity kinases are important regulators of condensate homoeostasis24, and histone H1 acetylation antagonises chromatin phase separation98.The multivalent interactions driving liquid–liquid phase separation exhibit strong dependence on the cellular context99  by hardening the interactions within the condensates of the host proteins that drive viral RNA synthesis45. An appealing aspect of this strategy is that protein condensates can selectively partition small molecules. Mitoxantrone, for example, was observed to be selectively concentrated in nuclear condensates of the transcriptional coactivator MED1 and of nucleophosmin, driven by interactions of aromatic groups105. Similarly, small molecules can be used to shift the phase boundaries between the native and condensed states. The flavonoid compound myricetin was shown to inhibit droplet formation of the protein tau, resulting in decreased aggregation and toxicity106. The phase boundary of TDP-43 was modulated by multivalent interactions of an aromatic compound, bis-ANS107. Small molecules can be further used to specifically destabilise conformations that drive droplet formation, as in the case of allosteric inhibitors of the protein tyrosine phosphatase SHP2, which restored its normal MAPK activity108.Small molecules could be developed to modulate the interactions required for the stability of the droplet state. This is a mechanism of action that may for example be applicable to regulate cancer-driving super-enhancers109. Small molecules may also stabilise the native conformations of aggregation-prone proteins, thus inhibiting the conversion between the native and amyloid states111. In addition, the inhibition of the formation of toxic oligomers and the removal of amyloid aggregates by degradation pathways and can be promoted by conformation-specific antibodies113.Small molecules can also be exploited to interfere with protein aggregation. The nucleation and elongation rates in the aggregation process of the Aβ peptide were inhibited by compounds that can be potentially developed as drugs for Alzheimer’s disease115. Valosin is known to activate autophagy, and its familial mutations lead to delayed droplet clearance29.Alternatively, activation of degradation pathways can be exploited for the removal of aberrant liquid-like condensates. The ubiquitination of Ras GTPase-activating protein-binding protein 1 (G3BP1) was shown to induce stress-granule disassembly via its interactions with the ubiquitin-dependent segregase valosin116. Cellular relocalisation may also prevent droplet aggregation, as shown by karyopherin-β2, which dissolves aberrant fibrillar hydrogels formed by FUS and hnRNPA1, and importin-α with karyopherin-β1 can revert TDP-43 aggregation117. Furthermore, as condensate assembly and biophysical properties are also regulated by the concentration of interaction partners118, modifying the expression level of these partners may offer a strategy to regulate the condensed states. For example, stress-granule hyper-assembly induced by medulloblastoma-associated DDX3 mutants can be reverted by depletion of other assembly factors119. In addition, one could activate or inhibit post-translational modifications that regulate the condensed states, such as those that stabilise the droplet state120, or promote formation of prion-like states121. Inhibitors of the dual-specificity kinase DYRK3 for example can prevent stress-granule dissolution23.More generally, the modulation of the protein homoeostasis system can be explored for therapeutic purposes in protein condensation diseases. Molecular chaperone activation may stabilise aggregation-prone domains within droplets, as shown by the chaperoning the folded RNA-binding domain of FUS by the small heat shock protein HspB8, which inhibited the formation of aberrant condensatesAn increasing body of experimental observations suggests that protein condensation diseases may be ubiquitous. The strategies for drug discovery Table  and the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary data 4


Identifying protein complexes from protein-protein interaction network is one of significant tasks in the postgenome era. Protein complexes, none of which exceeds 10 in size play an irreplaceable role in life activities and are also a hotspot of scientific research, such as PSD-95, CD44, PKM2 and BRD4. And in MIPS, CYC2008, SGD, Aloy and TAP06 datasets, the proportion of small protein complexes is over 75%. But up to now, protein complex identification methods do not perform well in the field of small protein complexes.In this paper, we propose a novel method, called BOPS. It is a three-step procedure. Firstly, it calculates the balanced weights to replace the original weights. Secondly, it divides the graphs larger than MAXP until the original PPIN is divided into small PPINs. Thirdly, it enumerates the connected subset of each small PPINs, identifies potential protein complexes based on cohesion and removes those that are similar.In four yeast PPINs, experimental results have shown that BOPS has an improvement of about 5% compared with the SOTA model. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and BOPS gets the best result in it. These results give new insights into the identification of small protein complexes, and the weighted Homo sapiens PPIN provides more data for related research. Since the launch of the Human Genome Project in 1990, massive amounts of genomic data have emerged, and bioinformatics appeared. With the advent of the post-genomic era, the focus of life science research has shifted from genomics to proteomics . With thProtein complexes are groups of proteins that interact with each other at the same time and place, forming a single multimolecular machine . Due to A protein-protein interaction network can be modeled as an undirected graph. The vertices in the graph represent proteins, and the edges represent the interactions between proteins. Therefore, the problem of protein complex prediction can be approximated as a graph theory problem. The predecessors proposed some computational algorithms to predict protein complexes in PPI networks. Most of these protein complexes identification methods are based on the principle that densely linked regions in the PPI network correspond to actual protein complexes . TherefoSubject to biological technology, researchers usually conduct in-depth research on smaller proteins. At the same time, small protein complexes also play an irreplaceable role in life activities. For example, PSD-95 consists of 6 proteins and plays an important role in synaptic plasticity and the stabilization of synaptic changes during long-term potentiation . CD44 coTherefore, we designed the BOPS algorithm to specially predict smaller protein complexes. The BOPS algorithm means “Based On PPIN Segmentation”. The basic idea of BOPS is to divide the PPIN according to the reliability of the interaction. The BOPS algorithm divides the original graph into some small networks and enumerates connected subsets of small PPINs to check if subsets are protein complexes. Finally, BOPS successfully transforms the problem of predicting protein complexes into a problem of judging whether a subgraph is a protein complex, thereby greatly improving the prediction effect. We evaluate BOPS compared to the state-of-the-art methods. The experimental results show that the BOPS algorithm has achieved very great results for complexes with not exceeding ten.Generally, the computational methods for protein complex prediction can be divided into three main categories: network-based, biological-context-aware, and specialized methods , 25. NetAmong the three main categories, there are many studies in this field of network-based algorithms. These algorithms are based solely on PPIN. The network-based algorithms can be further divided into agglomerative methods and divisive methods. For example, CFinder , PEWCC  and ClusSome methods are based on PPIN and some additional biological insights . The numThe smaller protein complex contains fewer proteins, so the topology in the PPI network is not obvious. All of the aforementioned methods try to predict protein complexes of various sizes and densities. Those general algorithms cannot efficiently find specific types of complexes, particularly sparse and small ones . These cV| proteins which are indicated by vertices. And the PPIN has |E| interactions which are indicated by edges. Additionally, W reflects the weights of the interactions. The protein complex can be represented as a subset of proteins with high cohesion in the graph. As a result, the protein complex prediction problem can be regarded as a graph theory problem. Therefore, in "A PPIN can be represented as a graph e, and e. The v, which is the degree of vertex v without weight. The v, which is the degree of vertex v with weight.An undirected edge can be represented as The BOPS algorithm for protein complex prediction is a three-step procedure. First, the BOPS algorithm calculates the balanced weights, and replaces the original weights with balanced weights (3.3). Second, the BOPS algorithm divides the graphs larger than MAXP until the original PPIN is divided into small networks 3.4.1) and the details is described in (3.4.2). Third, the BOPS algorithm enumerates every connected subset of each small network (3.5.1), calculates the cohesion of each connected subset (3.5.2), identifies potential protein complexes based on cohesion and removes those that are similar (3.5.3). Figure  and the PPIN is obtained by many biological experiment methods. The weight reflects the reliability of interactions. Based on previous studies in yeast, each complex is composed of a core and attachments . Proteine, we define its balanced weight The proteins in the core usually have a high level of interaction with a large number of proteins in the same core. But the attachment usually only interacts with a small number of proteins in the core, and the level of interaction is low. Because the BOPS algorithm only uses cohesion to determine whether a set of proteins is a complex, the BOPS algorithm adjusts the weights of edges according to the core-attachment biological structure. Indeed, the BOPS algorithm calculates balanced weights to balance the importance of the original edge weight and core-attachment structure. For an edge e is between the core and an attachment, one of e becomes larger than the original weight. As a result, the BOPS algorithm indirectly considers the core-attachment structure in the balanced weights.When the interaction The BOPS algorithm constructs an empty queue, and pushes the original graph into it. As long as the queue is not empty, the algorithm pops the head element, a graph every time, and then checks whether its size is greater than MAXP. If the size of the graph does not exceed MAXP, the BOPS will no longer split this graph. If the size of the graph is greater than MAXP, the BOPS algorithm will split it and push the subgraphs into the back of queue. When the queue is empty, the size of all graphs are all not greater than MAXP.The MAXP represents the maximum number of proteins in each graph after splitting the original graph into small graphs. The larger MAXP is, the better result is. The time complexity is The details of the splitting process is described below. The algorithm deletes some edges to make the graph disconnected, and simultaneously makes sure the highest weight of the deleted edges are minimized. The cost is defined as follow:Indeed, the BOPS algorithm firstly removes all the edges from the graph into the array and sorts them from largest to smallest by edge weight. Then, BOPS adds the edges back to the graph, one at a time. The BOPS maintains a classic data structure called disjoint set union to support queries of the connectivity of the graph. If the edge that is currently being added causes the graph to become connect, It is proved that this edge must be removed if the graph is to be divided at minimum cost. In this case, BOPS removes this edge completely.The connected subsets of each graph may be protein complexes. After splitting the original graph into small graphs, all graphs do not exceed MAXP. Therefore, the BOPS algorithm can enumerate all connected subsets of each graph to calculate their cohesion, which is used for filtering the potential complexes. In this way, we convert a generative problem into a decision problem.The BOPS algorithm uses breadth-first search to obtain all connected subsets in graph x. x in the current complex. The BOPS adds 1 to the numerator because the protein is always interacting with itself.In a protein complex, a protein should interact with most of other proteins. As a result, the BOPS algorithm uses x.At the same time, a protein should have high level of interactions with other proteins. Therefore, the BOPS algorithm uses x in the complex.As a result, the algorithm uses V, |V|, Finally, the algorithm averages the possibility of each protein in the set to reflect the cohesion of the entire set.The BOPS algorithm calculates the cohesion of every complexes in each graph and ranks the complexes from most cohesive to least cohesive. Then the BOPS iterates through all the complexes, if one complex is similar to complexes which have larger cohesion, it will be deleted. Finally, the algorithm takes the most cohesive half of the candidates as the final result.The bottleneck of the algorithm is enumerating the connected subset of all graphs and calculating the cohesion. The graph As a result, the time complexity of calculating cohesion is O(MAXP), the time complexity of calculating cohesion of all connected subsets of one graph is O. The method groups proteins of similar functions and then uses the Markov clustering algorithm to discover clusters in each group and merge some of them. The merged clusters, as well as the remaining clusters, constitute the set of detected complexes. BOPS predicts that the number of proteins in the small complex does not exceed ten. So BOPS is more universal than CPredictor.GANE GANE is a method to predict protein complexes based on Gene Ontology. First, it learns the vector representation for each protein from a GO attributed PPI network. Then, it uses the clique mining method to generate candidate cores. Similar to BOPS, it selects the proteins with more significant correlation scores as predicted proteins.P denotes the set of predicted protein complexes from one method, and B denotes the set of gold standard protein complexes. And NA is defined as:p and b. Following the previous studies when NA is not less than 0.25, we consider the p and b are matched [To formally evaluate the performance of our method, we use the same evaluation metrics as other methods , 35. In  matched .Based on the neighborhood affinity score, }F-score . They cap in predicted cluster P and a known complex b in gold reference cluster B. The other three measures used in experiments are clustering-wise sensitivity (Sn), clustering-wise positive predictive value (PPV) and geometric accuracy (ACC) , 39. Givio (MMR) , which ietection .We conduct experiments on four PPI networks: Krogan-core , Krogan-To evaluate the effectiveness of our proposed mothed, we compare it with other seven protein identification methods: GANE , WCOACH entclass1pt{minimaAccording to the data in Table But BOPS do not achieve the highest Precision and PPV. For these datasets, WCOACH is the best for Precision. WCOACH is a semantic similarity measure between proteins, based on Gene Ontology structure. The complexes detected by WCOACH generally had more proteins, so the number of small complexes is very rare, which leads to the high “hitting accuracy” relatively.As for PPV, MCL algorithm utilizes random walk theory and Markov chains rule. It divides PPI network into many dense subgraphs; thus, every protein only belongs to one specific complex. So, the PPV is higher than our method. But the Precision and Recall of ours are all over than MCL.BOPS achieved the highest MMR, indicating that the predictions matched the gold standard quite naturally. This shows that BOPS does not rely on increasing similar prediction results to improve the values of entclass1pt{minimaWe recommend the applicability of each algorithm. If you need protein complex prediction to guide biological experiments, BOPS will be the best choice. But if you are short on funds and can only detect fewer protein complexes, then WCOACH will be the best choice. WCOACH has few predictions, but a high hit rate. When funding is replenished, the BOPS predictions can be used to detect more complexes.B and b is recalled. Based of regression degree score, we define expected regression ratio (ERR) in Eps. b is a reference complex in the reference complex set B. n is a segmented PPI subgraph in the PPI subgraph set N. M is the original PPI network. We set the According to "As shown in Table As described before, there is only one parameter in BOPS: mentclass2pt{minimfication . We try As the Fig. In the previous section, we evaluate the performance of BOPS with small protein complexes identification. In order to make the experimental data more comprehensive, we compare it with other methods in the field of total protein complexes. Considering ACC is used to evaluate the overall performance in the field of quality, when one small identified complex is matched to a large reference complex, although all of the predicted proteins can be found in the reference complex, the Sn will be very low. This situation is inconsistent with our original intention. Thus, ACC is not a fair reference standard. Therefore, we evaluate The STRING database  aims to BOPS is compared to PEWCC and ClusterONE which are the second best in the Yeast experiment. The parameter is Corum . The resP-value by the tool GOTermFinder [P-value is defined as follows:C contains k proteins in the functional group F and the whole PPI network contains |V| protein. The functional homogeneity of a predicted complex is the Min P-value overall of the possible functional groups. A predicted complex with a low functional homogeneity indicates it is enriched by proteins from the same function group [To assess the biological sense of the predicted protein complexes generated by BOPS, we calculate the Min rmFinder . P-valueon group . So, theon group .P-value of the unmatched protein complexes predicted and plotted it into Fig. P-values of BOPS are less than 1e-5, which indicates that these unmatched complexes in krogan-extended also have high biological significance. In addition, the P-values of GANE and WCOACH are small compared to BOPS, which may be because these methods consider GO information in the process of the complex prediction. In a word, the predicted results of BOPS showed a higher biological function.We counted the distribution of the negative logarithm of the In this paper, a protein complex prediction algorithm based on graph segmentation, BOPS is proposed. Firstly, the BOPS algorithm calculates the balanced weight. Secondly, the BOPS algorithm divides the original PPIN into small networks. Thirdly, the BOPS algorithm enumerates the connected subset of each small network and determines whether it is a protein complex based on the cohesion of the subset.The experimental performance proves that the BOPS algorithm can obtain the best results when identifying small protein complexes. And the performance of BOPS is better than most algorithms for the whole protein complexes. In addition, we constructed a weighted Homo sapiens PPIN based on STRINGdb and BioGRID, and provided more data for related research.At the same time, we convert a generative problem into a decision problem by splitting PPIN into small PPINs and enumerating connected subsets. We have succeeded in segmenting PPIN, retaining most of the protein complexes. We believe that graph segmentation can be combined with many other algorithms to make better results in the future. And the way to solve problems by converting a generative problem into a decision problem is firstly introduced into protein complex prediction. We believe that this method will have greater application prospects in the future.In the future, we will attempt to improve the performance of BOPS, develop a better graph segmentation algorithm, apply the convert way to more problems, and focus on combining the identified proteins’ structural information with BOPS to assess the structural compatibility of predicted protein complexes.


Exploring the functional effect of a non-synonymous coding variant at the protein level requires multiple pieces of information to be interpreted appropriately. This is particularly important when embarking on the study of a potentially pathogenic variant linked to a rare or monogenic disease. Whereas accurate protein stability predictions alone are generally informative, other effects, such as disruption of post-translational modifications or weakened ligand binding, may also contribute to the disease phenotype. Furthermore, consideration of nearby variants that are found in the healthy population may strengthen or refute a given mechanistic hypothesis. Whilst there are several bioinformatics tools available that score a genetic variant in terms of deleteriousness, there is no single tool that assembles multiple effects of a variant on the encoded protein, beyond structural stability, and presents them on the structure for inspection.nglo and the results can be exported to it, allowing them to be annotated and shared with other researchers.Venus is a web application which, given a protein substitution, rapidly estimates the predicted effect on protein stability of the variant, flags if the variant affects a post-translational modification site, a predicted linear motif or known annotation, and determines the effect on protein stability of variants which affect nearby residues and have been identified in healthy populations. Venus is built upon Michelahttps://venus.cmd.ox.ac.uk and its source code is openly available at https://github.com/CMD-Oxford/Michelanglo-and-Venus.Venus is freely accessible at  Whole genome sequencing (WGS) is increasingly being used in a clinical setting to provide genetic diagnoses for patients with rare disease.in silico approaches can be used to assess the likely deleteriousness of a variant at the genetic level and are routinely incorporated into bioinformatics pipelines for WGS data analysis. These include CADD, PolyPhen-2, SIFT, MutationTaster and subRVIS (reviewed in A range of POLR2A gene) and the severity of the associated neurodevelopmental phenotype. Variants expected to retain the ability to form stable subunit complexes were found to be more deleterious than truncations,POLR2H), which is required by all three polymerases.The aforementioned tools assign a predicted severity score but do not suggest what the effect is at the protein level. Furthermore, some cases have been reported where the CADD scores do not correlate with disease severity.It is therefore important to assess the effect of an amino acid substitution at the structural level to understand its effect on protein function and the associated phenotype. A potential first step in assisting in the formulation of a hypothesis of the mechanism of any associated functional effect is to visualise the structural location of the target variant. Whilst 20% of the residues in the human proteome are covered by an experimentally determined structure, a further 30% are accessible via homologues.The destabilisation of protein tertiary and quaternary structures is the main contributor to variants' functional effects in around 50–70% of known pathogenic cases.Although the resulting change in protein function cannot be precisely predicted, it is possible to estimate the difference in relative Gibbs folding potential (ΔΔG) between the mutant and wild-type proteins using force-field–based molecular mechanics or statistically derived models. Several web and software applications exist that employ molecular mechanics to varying degrees. These are computationally expensive and provide only estimates of the effect due to complex technical limitations and assumptions, such as their use of a static structural snapshot and implicit solvent or force-fields that are imperfectly calibrated or too simplistic. Full force-field single-state calculations can be performed with the Rosetta suiteA wide range of machine-learning–derived statistical models have been developed to address the issues around ΔΔG estimate calculation speed and accuracy. These include CUPSAT, SDM, DUET, mCSM, SNPMuSiC, MAESTROweb, pPerturb, MutaFrame and INPS-MDMissense3D avoids the need for ΔΔG calculations by flagging whether the variant matches any one or more of multiple criteria known to be destabilising,A recent article comparing these various methods with the aim of classifying pathogenic variantse.g. protein localisation signals) instead provide improved insights. Whilst a variant may result in decreased functionality  equivalent to a decreased protein concentration, it may also result in an increase in effective protein concentration by means of decreased degradation, altered localisation, diminished interactions or loss of regulation.As discussed, it is known that the equivalence between destabilisation and altered protein functionality is only partialDifferent variants of the same protein can result in different pathogenic phenotypes. For example, dominantly inherited variants of LZTR1 result in a severe form of the developmental disorder, Noonan syndrome. These variants are predominantly located in the binding interface between LZTR1 and HRAS. In contrast, recessive destabilising variants result in a milder phenotype.23de novo variants may result in gain-of-function, such as loss of regulation from a post-translational modification site (PTM). Over 1,950 known cases of pathogenic variants that affect a PTM are knownStructurally destabilising variants may have a dominant effect if the protein is affected by haploinsufficiency or by imbalanced inhibition as seen with G-protein β2,UniProte.g. Phyre2,A further limitation of available online tools to investigate the effect of a variant is the requirement for the researcher to possess significant structural biology expertise, including knowledge of how to obtain the most appropriate experimental model from the PDBVenus (https://venus.cmd.ox.ac.uk), a web application that, for a given species, protein name and protein substitution of interest, retrieves a suitable protein structural model and estimates the ΔΔG for that variant as well as any nearby known variants, and provides annotations for these neighbours which may impact the function of the protein. All of these annotations can be clicked upon within the interface resulting in their focus in the protein view . Meanwhile, the ΔΔG is estimated for the variant. Finally, the ΔΔG is also estimated for any nearby gnomAD and ClinVar variants. Additionally, on request, a more precise ΔΔG estimation for a specified variant can be calculated or a post-translationally modified model can be generated.Venus is a web-based tool providing rapid access to information concerning a protein substitution in terms of the impact on predicted stability and protein features. Venus proceeds via several guided steps and displays the results to the user as these steps are completed, allowing initial inspection to immediately occur pending further analyses . Firstlynglo,nglo has been shown to be of great utility by virtue of being able to clearly convey information in a more intuitive and interactive manner than an information-heavy and flat representation of a 3D structure. Several diverse uses of Michelanglo have been described, ranging from demonstrating the location of rare mutations to providing the active site configuration for biocatalysis and drug design applications.45Because Venus utilises Michelawiss-Model homology models,wiss-Model (Qmean < –2. or identity < 20%) and AlphaFold2 models (pLDDT < 70%). Where multiple protein chains are to be considered, Swiss-Model is used rather than AlphaFold2 because AlphaFold2 does not by default generate quaternary structures. This approach enables Venus to present the location of binding partners to the user. This was found to be a beneficial approach with MEF2C , therefore subsets are generally taken which yield different scores on benchmarks depending on the subset adopted. Three benchmark subsets were used that are filtered to be less biased and possess a structure from the PDB. These were ProTherm* ,mutant − ΔGwildtype: mean 1.0 kcal/mol)To determine the optimal balance of speed and accuracy using different settings, predicted ΔΔG values were compared with empirically determined ΔΔG values. Public databases exist that have significant quantities of thermodynamic data, most notably ProTherm, ProThermDB and ThermoMutDB.Venus does not correct substantial backbone alterations that might be induced by protein substitutions relative to the wild type and as a result may overestimate the deleterious effect of certain variants. In these circumstances the values are shown to the user as “>10 kcal/mol”, an arbitrary cut-off close to the upper outlier cut-offs (Tukey upper fence) of the distribution of experimental values, which varies between 8 and 13 kcal/mol depending on the dataset and settings adopted.Based on the benchmarking tests, the chosen default settings were two minimisation cycles under the standard Rosetta scorefunction (ref2015),wiss-Model and AlphaFold2 models. The ΔΔG for variants in the O2567 dataset was scored using either a Swiss-Model or an AlphaFold2 model instead of the available PDB structure. This resulted in similar errors, but slower calculation times  which may be considered to be acceptable in terms of user experience. The ProTherm*, O2567 and S1342 datasets contain high-quality single chain crystal structures, whilst the structure or model chosen within Venus may not meet these quality criteria . To explore whether these may fail or cause an increase in calculation time, 300 randomly generated protein substitutions in different human proteins were tested (SI Table 2). The ΔΔG calculations were completed for all substitutions, with 85% being completed in under one minute whilst for five proteins, all components of large complexes, the calculations took over 5 minutes.Venus aims to be able to analyse any given proteins, hence its use of Swiss-Model-sourced structures, this is expressed as normalised score from ConsurfDB), (ii) entries in gnomAD or ClinVar databases, (iii) post-translational modifications and (iv) overlapping features reported in UniProt. These residues, along with other regions mentioned in the results, can be clearly displayed in 3D by clicking on their green links.An important feature of Venus is its ability to provide the user with information concerning the neighbourhood surrounding the target variant. Detailed annotations are provided for residues within 10 Å of the variant of interest. This includes (i) conservation information (in the case of structures from the PDB and Sde novo variant identified in the OxClinWGS WGS dataset.An example of the utility of this approach is furnished by α-tubulin 1A (TUBA1A) E423G  were with 10 Å of a predicted deleterious gnomAD variant with high frequency. It is important to note that the ClinVar dataset is biased towards recurrent variants, and de novo variants may be under-represented, therefore the distributions are indicative only. Nevertheless, this demonstrates the utility of nearby variants to either lend support or disprove a destabilisation hypothesis for the cause of pathogenicity of a variant.To quantify, from a global viewpoint, the frequency of pathogenic or benign variants in large datasets, the ClinVar dataset and the nearby gnomAD variants were scored with Venus (SI Table 2). The analysed subset of ClinVar variants with a pathogenic consequence  contained 3.5 times more variants with a ΔΔG greater than 2 kcal/mol than the subset with a benign consequence , but this accounted for only 19% of the subset. However, only 3 of these destabilising pathogenic variants  were within 10 Å of a predicted destabilising gnomAD variant that was found in the population in a homozygous state or with a frequency greater than 5x10Enrichment of other features provide possible explanation for the cause of pathogenicity. Relative to benign variants, pathogenic variants were 4.5-fold more abundant within 10 Å of a ligand or cofactor (11% of pathogenic variants) or an interface (16% of pathogenic variants). The most abundant features observed were post-translational modifications, which were within 10 Å for 54% of the pathogenic variants and 37% of the benign variants. This difference is modest and reflects the fact that most post-translational modification may have little to no role in protein function, whilst a small minority may be critical for conformational switching or enabling the binding of other proteins. By presenting possible contributors to destabilisation, Venus provides opportunities to explore these and support further hypothesis generation.Our investigations of potential pathogenic variants from large genome sequencing projects aimed at providing genetic diagnoses for patients with rare diseases, such as WGS500,Venus provides an interactive visualisation of a structural model of the variant for inspection, in context with other interacting proteins where known, along with location of residues that have non-structural functional roles . It provA forcefield method was adopted for the estimation of the ΔΔG of a given protein substitution in order to be able to display a plausible structural model. Nearby sidechains and backbones may be shifted with this approach as opposed to a simple selection of a rotamer of the target residue, which may result in artifactual clashes. On average, the ΔΔG estimation is complete within 30 seconds. But since Venus presents results sequentially, rather than all at once, the wild type structure visualisation is quickly displayed in an interactive form for inspection pending the ΔΔG estimation being completed.Whilst the error of the ΔΔG estimations for the highly destabilising variants is relatively high, the overall error is comparable to other methods when removing outliers or using median based metrics. The median absolute error is 1.1 kcal/mol for the S1342 dataset. In context, 1 kcal/mol is approximately the strength of a hydrogen bond and the cut-off for a destabilising variant is generally taken to be 2 kcal/mol. Many machine-learning–derived models possess intrinsic cut-offs for the maximum calculated ΔΔG value. For example, the SIMBA-I second degree equationThe goal of Venus is to provide the user with multiple pieces of information about the neighbourhood which can be explored interactively and interpreted. The estimated ΔΔG of the protein substitution is not the sole possible determinant of pathogenicity. Our global survey of pathogenic and benign ClinVar variants found only 19% of pathogenic variants to have a ΔΔG greater than 2 kcal/mol . When the estimated ΔΔG values of nearby variants from gnomAD were considered, the difference between pathogenic and benign ClinVar variants becomes more apparent. Additionally, the details of the system become important when considering variants case-by-case, as demonstrated in the examples presented.Our investigations of the rare variants emerging from the OxClinWGS WGS datasetwiss-Model threaded models and post-translational modification detected solely in high-throughput screens can be used. The examples of MEF2C R3G and S36FVenus supports the exploration of proteins where information may be limited, as is often the case with WGS datasets which lend themselves to novel gene discoveries where the encoded proteins have been poorly characterised. Protein partners may be included from the template structure in SSubstitutions of surface residues involved in protein–protein interactions are a very important class of pathogenic variant. However, Venus is currently unable to provide information on protein-binding sites without empirical evidence for the site of interaction. For some protein–protein interactions there are experimental complex structures available, but in most cases the precise structural detail of an interaction is not known. Enhanced evolutionary conservation of the residues may provide some indication of an interaction. MutPred2, a deep learning algorithm, is able to assign the probability of a residue being involved in an inter-molecular interaction from the primary sequence context.Whilst for post-translational modifications high throughput data is used in Venus to complement the curated data in Uniprot, there is presently no mature dataset for protein–protein interaction sites. The most applicable high-throughput technique to identify the precise location of a protein–protein interaction are untargeted cross-linking mass-spectrometry (XL-MS) techniques,One future feature that would be useful for Venus is the consideration of alternative conformations. AlphaFold2 has prompted a flurry of research in a variety of directions, including modelling of alternative states of proteins and protein complexes, including conformers that may be transient.nglo application, Venus extends the analyses possible with existing tools. We anticipate that this will be a valuable resource for helping geneticists and other scientists investigate the potential effects a variant of interest is having on protein function and hence its likely pathogenicity when studied in the context of patients with rare diseases.Venus integrates multiple sources of information to aid in the interpretation of the effect of a genetic variant on the function of its encoded protein. By presenting information concerning protein structure, energies of destabilisation, effects on post-translational modifications and protein interaction sites, and displaying these in the interactive Michelanglo and the codebase is openly available in GitHub (https://github.com/CMD-Oxford/Michelanglo-and-Venus). Michelanglo is a Python 3 webapp running the Pyramid framework with a PostgreSQL database for user data.Venus is built into MichelaVenus aggregates information from UniProt entries with data derived from various sources. UniProt is parsed for sequence and feature information,wiss-Modelwiss-Model structures, any other chains present in the template are copied unless steric clashes are present. For PDB and Swiss-Model structures, ConsurfDB is queried for the conservation data and then applied as B-factors to these.During structure model selection, Venus takes experimental crystal structures with the best resolution deposited in the RCSB PDB,In the web interface, the protein structure is visualised using the NGL JavaScript libraryIn addition to browser-based access, Venus can also be queried computationally with a client-side Python API (pypi: michelanglo-api). To assess the frequency that Venus successfully completes a requested analysis, 300 random protein substitutions were requested via the API (summary results in SI Table 2).https://github.com/CMD-Oxford/validation_of_venus_ddG). Specifically, the protein analysis module of Venus was used in isolation on a computing cluster with different Rosetta forcefields , within cartesian or dihedral space, different number of FastRelax descent cycles (1–5), different neighbourhood radii (6–12 Å) and with or without minor correction artifices. These corrections were tested because the model structures are only energy minimised within a sphere of neighbours around the mutated residue. The primary focus of these was on the interactions between the outer neighbourhood shell to the residues beyond the shell, which were not energy minimised, but may have been energetically strained. These corrections included scoring only the minimised neighbourhood, constraining the residues at the neighbourhood interface, and preventing the acceptance of a poorer overall score caused by an improvement of a locally bad conformation. The median absolute error was calculated by taking the median of the absolute difference between the predicted and experimental ΔΔG values. The Tukey fences were calculated with a scaling factor of 1.5 . These were used to eliminate the outliers prior to the calculations of metrics thrown off by few spuriously large values, such as mean absolute error, root mean square deviation and Pearson correlation coefficient. The confusion matrices were cross-tabulated by rounding to one decimal digit the predicted ΔΔG values  and by classifying the values for greater or equal to 0 kcal/mol or 2 kcal/mol.To determine the optimal settings for energy minimisation for ΔΔG calculations, mutations from the ProTherm*,ClinVar and gnomAD variants were scored using the protein analysis module of Venus (summary results in SI Table 3). All human protein were filtered for the presence of a ClinVar variant and further filtered against protein with submitted variants whose mutations were inconsistent with the canonical sequence (222). The ClinVar and gnomAD variants in the resulting protein list  were scored and the output parsed to extract key details that would normally be shown by the front-end.Venus is free to use without requiring user registration. Due to the licences associated with the datasets and modules used, the protein data is not disseminated in the repositories and commercial users must obtain licences from PyRosetta, ELM and PhosphoSitePlus prior to usage. Venus is intended for research and not diagnostic purposes.Matteo P. Ferla: Conceptualization, Methodology, Software, Investigation, Writing – original draft, Visualization. Alistair T. Pagnamenta: Conceptualization. Leonidas Koukouflis: Resources. Jenny C. Taylor: Funding acquisition, Supervision, Writing – review & editing. Brian D. Marsden: Supervision, Writing – review & editing.


Intrinsically disordered proteins (IDPs) participate in many biological processes by interacting with other proteins, including the regulation of transcription, translation, and the cell cycle. With the increasing amount of disorder sequence data available, it is thus crucial to identify the IDP binding sites for functional annotation of these proteins. Over the decades, many computational approaches have been developed to predict protein-protein binding sites of IDP (IDP-PPIS) based on protein sequence information. Moreover, there are new IDP-PPIS predictors developed every year with the rapid development of artificial intelligence. It is thus necessary to provide an up-to-date overview of these methods in this field. In this paper, we collected 30 representative predictors published recently and summarized the databases, features and algorithms. We described the procedure how the features were generated based on public data and used for the prediction of IDP-PPIS, along with the methods to generate the feature representations. All the predictors were divided into three categories: scoring functions, machine learning-based prediction, and consensus approaches. For each category, we described the details of algorithms and their performances. Hopefully, our manuscript will not only provide a full picture of the status quo of IDP binding prediction, but also a guide for selecting different methods. More importantly, it will shed light on the inspirations for future development trends and principles. With the rapid development in the protein field, there are increasingly number of intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDRs) identified in viruses, bacteria, archaea, and eukaryotes .IDPs lack stable tertiary structure under physiological conditions and are highly flexible compared with globular proteins . TherefoSpike), and the “disorder-to-order transition” of Spike-Furin complex has been found  and non-equilibrium transient kinetic techniques . NMR ideBesides the experiment methods, there are a series of computational approaches developed for IDP protein interaction sites (IDP-PPIS) prediction such as MoRFpred , SLiMPreThere are several review articles  on the pFrom https://www.disprot.org/.The Database of Protein Disorder (DisProt)  is the fhttps://www.rcsb.org/.Disordered protein regions are often characterized by the missing electron density found by X-ray . Severalhttps://www.uniprot.org/.The missing annotations are related to the inherently disordered protein regions. Several methods implemented UniProt  for disohttp://www.ideal.force.cs.is.nagoya-u.ac.jp/IDEAL/.Intrinsically Disordered proteins with Extensive Annotations and Literature (IDEAL)  is anothhttp://dibs.enzim.ttk.mta.hu/.The Disordered Binding Site (DIBS)  is a larhttp://elm.eu.org.The Eukaryotic Linear Motif (ELM)  is the fhttp://mobidb.bio.unipd.it/.MobiDB  is a dathttp://scop.mrc-lmb.cam.ac.uk.The Structural Classification of Proteins (SCOP)  is a dathttp://d2p2.pro.There are some relevant and commonly used databases such as Database of Disordered Protein Prediction (D2P2) and Mutual Folding Induced by Binding (MFIB). D2P2  is a dathttp://mfib.enzim.ttk.mta.hu/.MFIB  is the dThe second critical part of constructing predictors is to obtain representative features of disordered protein sequences. Features integrated by the predictor determine the accuracy of distinguishing between IDP protein sites and general amino acid residues. In IDP-PPIS prediction, the most widely used features include amino acid compositions, predicted structural features, disorder scores, physicochemical properties, evolutionary information, and other features. We summarize these features and tools to generate these features in Amino acid composition (AAC) is the proportion of a particular amino acid in the whole protein sequence. The amino acid compositions are distinct among IDR, MoRF, flanking regions and other protein regions. It is reported that there are more Ile, Leu, Phe, Tyr, Lys, Arg and Asp enriched and less Ala and Gly comparing with the normal regions. Meanwhile, the flanking region includes more Ala, Gly, Glu, Ser and Thr which turns to promote disorder .AAC could be calculated by the formula : composThe rapid structural transformation of disordered proteins during binding  suggestsMolecular recognition feature (MoRF) and short linear motif (SLim) are involved in protein-protein interaction as a secondary structure element , includiThe biophysical properties of disordered protein change during protein interaction which could be used for IDP-PPIS prediction. For instance, protein-protein interactions are mainly achieved through surface contact, and when the protein state is transformed, the Accessible surface area (ASA) of contact area changes as well, which helps us to identify the binding site . ContactDisorder propensity is an important feature for predicting disordered protein binding sites , and couThe structure and function of proteins are affected by the physicochemical properties of amino acids. Disordered proteins are more hydrophobic than ordered proteins, and disordered binding sites can be predicted by an increase in the hydrophobicity . MoRF anThe physicochemical properties of amino acids can be obtained from the AAindex database . The phySome studies show that the evolution speed of disordered regions is often faster than other parts of proteins . Davey eProtein evolutionary information is often obtained through position-specific scoring matrix (PSSM), hidden Markov model (HMM) profiles and Information entropy. To improve prediction performance, certain studies use modified PSSM to enhance the sequence conservation signal, such as MFSPSSMpred  by maskiIt is shown that using bigram to extract evolutionary features in natural language processing can reduce redundant features , and alsProtein conservation is closely related to its structure and function, and the average Shannon entropy is used as a characteristic of general conservatism in protein. Some studies have used relative entropy to improve the prediction of protein functional sites , and ShaThe length and location of IDRs correlate with general protein functional classes. Compared with the ordered protein, the sequence complexity of the disordered protein is lower , so the Algorithm is essential to take the features as input and predict the IDP-PPIS, which is the core element for each predictor. We summarize main information about 30 predictors for IDP-PPIS prediction in The scoring function method is widely used in the evaluation of protein interaction . Its priSequence alignment is a commonly used tool to predict the structural and functional properties of proteins . Retro-MANCHOR  is a benThe algorithm of the ANCHOR can be expressed as:On the basis of ANCHOR , ANCHOR2The new function is defined as follows:Here, From Support vector machine (SVM)  is a supMoRFpred  is a preDue to the slow running speed of MoRFpred for large-scale prediction , developMFSPSSMpred  improvedIn 2016 , developDISOPRED3  used amiS and SVMT models of Sigmoid and Radial Basis Function (RBF) Gaussian kernels using the physicochemical properties of amino acids. MoRFCHiBi predicts MoRF by integrating the results of both models with the help of Bayesian rules. It employed SVMS to predict MoRF propensity based on component comparison information and SVMT to predict MoRF propensity based on similarity information. MoRFCHiBi performs better than other predictors with the highest AUC of 0.770 but slower than ANCHOR.MoRFCHiBi  trained MoRFCHiBiLight  used BayDC predictors  model  is widely flDPnn  to prediFlDPnn  used mulNeural network is widely used in the study of protein-protein interaction. In this paper, we introduce five types of neural network models: Feedforward neural network , BidirecFeedforward neural networks  are artiAlpha-MoRFpred  used conBidirectional recurrent neural network (BRNN) was applied to predict IDP-PPIS by SLiMPred  and PepBThe architecture of BRNN :oj=N(O).,where SLiMPred  applied SPINE-D  used preTwo-hidden layer neural network architecture used by SPINE-D :Calculation formula of output result of the hidden layers:Where The training process of a neural network is to compare the output results, Error, Both SPOT-Disorder  and SPOTSPOT-Disorder  used deeThe neural network structure of SPOT-Disorder2  consistsBased on the information derived from protein sequences, DeepDISOBind  applied Consensus-based predictor  refers tOPAL  is a conAnother consensus-based predictor was also constructed by DEPICTER  designedThe most recent consensus predictor HybridPBRpred  combinedDisorder proteins lack stable structures but have many important functions through protein-protein interactions. There are a large number of studies have focused on identifying the protein binding sites of disordered proteins which will provide better functional annotations of disorder proteins. We scanned the literature since the publication of alpha-MoRFpred in 2007 and found that there has been a sharp increase in protein-binding IDR predictors, with 6–10 new predictors published every 3 years. These predictors continuously improve the prediction performance by applying different algorithms, screening more representative features or combining multiple models, and so on. The AUC is increased from 0.6 to 0.9. However, there might be still some limitations for current methods which can be improved from several directions.We found that the existing predictors mainly curated the datasets from two large databases Disprot and PDB, especially there are many predictors on MoRF using the high-quality dataset from Disfani et al. Since this dataset contains a large number of immune-related proteins , the traIn addition, most of the existing predictors are trained only for disordered protein data, but HybridPBRpred expands the benchmark dataset by combining predictors trained from structured protein datasets, allowing the predictors to improve the prediction performance in terms of protein binding sites for disordered proteins. There might be some common properties shared by different types of protein interfaces . TherefoThe existing predictors mainly focus on short binding regions such as MoRF  and SLiMSpike and the anions of Furin form dynamically interchangeable and durable salt bridge networks at the Spike-Furin binding interface, which triggers the transition from disorder to order   exist inIn this paper, we collected 30 predictors related to IDP-PPIS published up to January 2022, and described them in terms of three key aspects of the predictor construction process: databases, features, and algorithms. By summarizing the advantages and disadvantages of the existing predictors, we believe that the development of more comprehensive protein-binding site predictors by expanding the data sources, applying the features related to structural changes and binding to ordered protein-binding site predictors may further improve the performance of IDP-PPIS in the near future. In addition, since disordered proteins are involved in a variety of important physiological and biochemical processes using protein-protein interactions in various organisms, we also hope our review will help researchers to gain new ideas for solving various disease problems mediated by disordered proteins.