Knowing which proteins and RNAs directly interact is essential for understanding cellular mechanisms. Unfortunately, discovering such interactions is costly and often unreliable. To overcome these limitations, we developed rec-YnH, a new yeast two and three-hybrid-based screening pipeline capable of detecting interactions within protein libraries or between protein libraries and RNA fragment pools. rec-YnH combines batch cloning and transformation with intracellular homologous recombination to generate bait–prey fusion libraries. By developing interaction selection in liquid–gels and using an ORF sequence-based readout of interactions via next-generation sequencing, we eliminate laborious plating and barcoding steps required by existing methods. We use rec-Y2H to simultaneously map interactions of protein domains and reveal novel putative interactors of PAR proteins. We further employ rec-Y2H to predict the architecture of published coprecipitated complexes. Finally, we use rec-Y3H to map interactions between multiple RNA-binding proteins and RNAs—the first time interactions between protein and RNA pools are simultaneously detected. Current methods to unveil protein–protein and protein–RNA interactions often involve long, costly procedures. Here the authors present a rapid yeast 2- and 3-hybrid screening pipeline to detect interactions within protein libraries or between protein libraries and RNA fragment pools. Yeast two-hybrid and three-hybrid  screens3 enable the detection of putative, direct protein–protein and protein–RNA interactions under physiological conditions, respectively. In the past, the Y2H assay has been significantly enhanced. Y2H-Seq4 coupled Y2H screening to a next-generation sequencing (NGS) readout to reduce workload and costs while increasing sensitivity. To this end, selected baits and thousands of preys are grown in an array format, individually mated and pooled. Prey pools are narrowed down by NGS and finally interactions are detected by massive spotting assays on selective media. BFG-Y2H5 first requires the creation of barcoded, arrayed libraries of baits, and preys. After pooled mating of barcoded prey and bait libraries and selection, intracellular Cre-recombination is induced to generate fused barcode tags that can be read by paired-end NGS in order to identify interacting pairs. Recently, CrY2H-Seq6 used individual Gateway cloning of bait and prey libraries and subsequent individual transformations in yeast strains to generate screening libraries. After pooled-mating and intracellular fusion of interacting pairs by Y2H-reporter-induced Cre-recombination on plates, interactions are read out by NGS with a low number of reads containing information about interacting protein pairs. Compared to BFG-Y2H, CrY2H-Seq uses protein coding sequences to identify interacting protein pairs, avoiding the need for laborious library barcoding. While the discussed techniques offer significant advances, costly arraying6, barcoding steps5, or one-by-one subcloning and transformation of screening library constituents6 are still frequently required. Furthermore, all existing techniques require library plating steps during interaction screening. Improvements of Y3H screens, however, have mostly focussed on optimising hybrid-RNA binding and presentation8. To date, no method allows the many-by-many detection of direct interactions between full-length RNA-binding proteins (RBPs) and RNA fragments.Discovering which proteins, protein domains, and RNAs directly interact is an essential prerequisite for understanding the principles by which cellular machines operate. Assays capable of detecting such interactions are in high demand as they generate both system-level overviews of cellular coordination and hypotheses that can be tested in subsequent mechanistic studies6. Furthermore, rec-YnH produces 40–50% reads containing interaction information, thereby using sequencing capacity comparably efficient. We demonstrate that the same assay pipeline can be used for rec-Y2H and rec-Y3H screening, creating for the first time an assay applicable to both, many-by-many protein–protein and protein–RNA interaction detection. We aimed to develop a tool especially useful for research labs focussing on mechanistic studies. Hence, we focused on molecular level questions involving 10s–100s of factors while aiming for maximal specificity to avoid false positives.By advancing on and integrating past developments of the Y2H and Y3H technologies, we developed a novel assay termed rec-YnH (recombination-YnH) that is capable of screening protein libraries against protein libraries or RNA fragment libraries. We focussed on keeping the workflow as simple as possible to make it both affordable and feasible. We combined batch transfer of DNA into screening vectors with batch library transformation, highly efficient DNA assembly by homologous recombination in yeast, liquid–gel culturing, and NGS-based readout of interactions using ORF sequences as identifiers to avoid any arraying, plating, or barcoding steps. As the endogenous yeast recombination machinery is used to fuse bait and prey, readily available yeast strains can be used for the screen, eliminating the need to create or obtain new strains expressing Cre-recombinase9 and uncover novel putative interactions between PAR proteins, kinesin-cargo adaptors, and a dynein regulator. Furthermore, we discover new putative interactions between specific microtubule end-binding proteins (EBs) and plus-end-tracking proteins (+TIPs)10, and show that quantitative measures of protein abundance obtained via mass spectrometry (MS)11 depend on the number of nodes between baits and preys. Finally, we use rec-Y3H to precisely and reproducibly detect sparse true positive protein–RNA interactions among hundreds of possibilities. This is the first time that high-throughput screening is adapted and used for simultaneous multi-domain interaction mapping and multi-protein–RNA interaction mapping.Using a well-characterised test matrix of proteins, we first benchmark rec-Y2H performance. Then, we demonstrate rec-YnH by focussing on problems typically faced by biomedical research laboratories: (i) understanding the interactions between hundreds of proteins in a given pathway, (ii) mapping the interactions of multiple protein domains, (iii) predicting the putative architecture of a coprecipitated complex, and (iv) detecting the RNA targets of a number of RBPs at once. rec-YnH enables us to validate interactions between PAR (partitioning defective) protein domains2 and Y3H systems7. pAWH (prey vector) is used in combination with either pBWH (protein bait vector) or pMS22H (RNA bait vector) for rec-Y2H or rec-Y3H screens, respectively. While pBWH and pMS22H carry the TRP1 auxotrophic marker, pAWH carries the 2 μ yeast origin of replication (ORI). All three vectors also carry homology regions, which become exposed after digestion with homing enzymes. Both pAWH and pBWH are Gateway compatible and recombine efficiently in yeast  Fig.  based on8, respectively  or pAWH and pMS22H libraries  with well-characterised interactions5. To remove experimental noise, we applied a Gaussian mixture-based noise filter14 to the RIS matrix with the assumption that most of the protein pairs are not interacting with each other. Normalising the noise-filtered RIS matrix to the reconstructed null matrix yields a matrix of interaction scores (IS). Finally, matrices obtained from screen replicates are averaged to filter-out stochastic false positives and the row-wise upper quartile score is subtracted to compensate for basal auto-activation activity. Through the removal of false positives generated from experimental noise, our method improves correlation between experiments by 17.6%  compared to using an existing processing method5. All scripts needed for rec-YnH data analysis are provided on GitHub (see “Data availability” for details).A pipeline we developed to process the NGS data generated by rec-YnH screening Fig.  normalis15. Using direct protein interactions found in the BioGRID and HIPPIE protein–protein interaction (PPI) databases as true positive control values and defining all interactions not present as true negative values, we calculated an optimal, normalised read cut-off value of 1.2 based on the F1-Score and 2.8 based on the Matthews Correlation Coefficient (MCC)  Fig. . Using tCC) Fig. . RetestiCC) Fig.  yielded CC) Fig. . This coCC) Fig. . Four ouCC) Fig. . We hypoCC) Fig. . Upon coCC) Fig. . By analCC) Fig. . TherefoCC) Fig. 5,6,15.Fi6  which were not detected by rec-Y2H were detected in spot tests. We noticed that rec-Y2H detects homodimers less well due less efficient PCR amplification of homodimers that presumably form long hairpin structures , hampering library preparation and NGS9 whose localisation, for example, control axon determination during neuronal differentiation16. However, little is known about how PAR proteins are localised in neurons or how they affect microtubule regulation16. Furthermore, we were interested in understanding if and how different EB homologues recruit different +TIP networks10. Expression and localisation of EB homologues differs during neuronal differentiation18, and it is speculated that they carry out distinct functions by recruiting different regulatory +TIP networks to microtubule ends19. Nonetheless, the extent to which direct EB interactomes differ is not understood. To shed light on these questions, we assembled an ORF library that, after removing auto-activators, contained 163 mouse and human proteins  in an affinity capture MS study11. This latter group contains several microtubule-associated proteins (MAPs) that control microtubule organisation.Next, we wanted to apply rec-Y2H to confirm and extend our knowledge of interactions between PAR proteins, their domains, motor proteins, and microtubule cytoskeleton-organising proteins. PAR proteins are conserved polarity regulators20 or due to the lack of a PAR3-fragment containing the full PAR6 interaction domain21, (3) on the other hand, was detected but fell below the F1-Score-derived cut-off value  -based interaction assay that can detect interactions between reporter-fused proteins in the cytoplasm of mammalian cells. Limitations of NanoBRET are that reporters must be in close proximity (<10 nm) for signal induction and proteins need to localise to the cytoplasm. Still, with NanoBRET major possible causes for false positives in Y2H-based screens (BD-fused proteins recruiting RNA polymerase or AD-fused proteins binding to DNA) can be ruled out. Furthermore, screening in a different background reduces the chance of confirming false positives caused by “bridging proteins”. Overall, we could validate 60.0% of interactions (21/35)  Fig. , which a11. We found that of the 98 EB-coprecipitated proteins we tested within the X163 set, only 7 directly bind to EBs  of the indirect interactors carry one . Nonetheless, as proteins not containing any known EB interaction motif were found to bind directly to EBs , it is possible that unknown EB-binding motifs exist. Compared to EB2, for which three out of four direct connections are distinct interactors, EB1 and EB3 share two out of three direct connections. This is in line with published evidence, showing that EB1 and EB3 have similar deletion phenotypes and more readily hetero-dimerise compared to EB219. The published emPAI scores, which are a measure of protein abundances in MS samples agree with the results from our rec-Y2H assay. For example, RBM6 and RBM10, which were found to directly interact only with EB2, are not found or had a low emPAI score when EB1 or EB3 were used as baits. These proteins had high emPAI scores when EB2 was used as bait. Of note, we were able to confirm the EB2–RBM6 interaction by NanoBRET, while neither EB1 nor EB3 were interacting with RBM6 , suggesting that the interaction hierarchy predicted by our screen reflects the organisation of the pulled-down complex to some extent. Finally, we note that putative binary interactions found using rec-Y2H could account for 32.7% of EB-coprecipitated proteins we tested, a value significantly higher than the ones obtained using current PPI databases  have affinities in the >10 μM range33  consisting of 17 RNA targets and 19 proteins  can be combined with existing techniques35. At the same time, the flexibility of the Y3H method in being able to screen RNA targets between ~20 and 200 nucleotides in length opens up the possibility to investigate sequence-context effects of RBP–RNA interactions on a systematic level. Lastly, the combination of rec-Y2H and rec-Y3H screening provides a tool to predict specific RNP structures from binary protein–protein and protein–RNA interaction data. This type of information promises to provide valuable insights into the regulation of mRNA localisation and expression.We envision that rec-Y2H will not only be used to understand the wiring of protein networks, but also to map interaction domains in a high-throughput manner. Using rec-Y2H as a complementary assay to MS analysis of coprecipitated complexes will significantly increase our level of knowledge by predicting which proteins in an isolated complex directly interact. The wide availability of ORFeome collections makes such a complementary use of rec-Y2H feasible. By combining multi-domain mapping with rec-Y3H screening, the RNA targets of RBP domains can be analysed in a high-throughput fashion. The same approach can be used for protein–RNA interaction perturbation mapping13. The two fragments were then fused together in a Gibson assembly reaction to produce pAWH. On the other hand, two different pBWH  vectors were constructed. (1) pBKWH, which contains a kanamycin resistance gene and is compatible with pENTR223. (2) pBSWHhc, which has a spectinomycin resistance gene and is compatible with pENTR221. First, a BglII fragment containing the attR1-ccdB-attR2 cassette  was cloned into the BamHI site of pGBKT7 (Clontech), to give pBW. Second, to introduce the homology regions 1 and 2, pBW was used as PCR template and combined with either a kanamycin or a spectinomycin resistance cassette via Gibson assembly. This generated pBKWH and pBSWHhc, respectively.See Supplementary Table 600. Each pDEST-ORF pool was diluted to 5 nM. pDEST X163_a and pDEST_163_b were mixed at an equimolar ratio to produce the final pDEST-X163 library. Each library is sufficient for performing 5–10 screen replicates, each with two selection media (RS and RIS).See Supplementary Fig. See Supplementary Data See Supplementary Fig. See Supplementary Data Y2HGold was purchased from Clontech. YBZ-1 was kindly provided by Dr. Marvin Wickens, University of Wisconsin-Madison. See Supplementary Table All yeast media was prepared using Minimal SD base (Clontech) and Amino Acid Dropout mixes (Clontech), as indicated by Clontech. Agar plates were prepared by adding 20 g/l of Agar (Conda). Competent Y2HGold (Clontech) was prepared following Clontech's recommendations. To maximise the number of transformants, competent yeast was transformed the same day it was prepared. To minimise bias, 12 small-scale transformations were set up for each type of selection media used (RS and RIS). Each pDEST-ORF pool (pAWH-ORF pool and pBWH-ORF pool) was diluted to 5 nM. The day before yeast transformation, each pDEST-ORF pool was linearised with I-SceI and I-CeuI homing enzymes (New England Biolabs) at 37 °C for 16 h, followed by 20 min at 65 °C. For each transformation, 20 fmol of linear pAWH-ORF pool (approximately 100 ng) and 20 fmol of linear pBWH-ORF pool were co-transformed into competent Y2HGold cells following Clontech's protocol. Each transformation was resuspended in 200 μl of 0.9% NaCl, and all transformation reactions were pooled together. A small aliquot (100 μl) was saved to make serial dilutions for colony counting on 10-cm selection media agar plates. For each selection media (RS and RIS), four square BioAssay dishes, 245 × 245 mm (Corning) were used. For each plate, 600 μl of transformed yeast cells from the transformation pool were spread and grown at 30 °C for 60 h. Yeast colonies were independently harvested from the RS plates and RIS plates by scraping them off with a cell spreader and 2 × 20 ml of PBS. Each pool of cells was spun down at 700 g for 5 min, and resuspended in PBS. Cell concentration was determined from the OD at 660 nm.Unless stated otherwise, screening steps are identical to the ones described in the previous section. On the same day of transformation, 250 ml liquid–gel media was prepared for each selection media (RS and RIS) by adding Seaprep Agarose (Cultek) to a final concentration of 0.5% (w/v). After autoclaving, liquid–gel media was allowed to cool down at room temperature with constant stirring. For each selection media, 2.4 ml of the co-transformed Y2HGold pool was added to 250 ml of liquid–gel media, mixed by stirring, transferred to a 5 l flask, and incubated on ice for 1 h. Subsequently, flasks were carefully transferred to a 30 °C incubator, taking care that the gel matrix was not perturbed. Growth and Y2H screening was done for 60 h. Then, yeast colonies suspended in the liquid–gel were gently stirred and harvested by centrifugation at 1600 g for 10 min. The yeast cell pellet was washed with PBS to remove remaining agarose, spun down at 700 g for 5 min, and resuspended in PBS. Cell concentration was determined from the OD at 660 nm.The rec-Y3H screen follows exactly the same protocol as the rec-Y2H screen with only three modifications. (1) Instead of the pBWH-ORF pool, the pMS22H-RNA library is used. (2) The YBZ-1 yeast strain (kindly provided by Marvin Wickens) is used instead of the Y2HGold strain. (3) Different dropout RIS media are used (see Supplementary Table ® End Repair Module (New England Biolabs). Then, DNA was purified using the MinElute PCR Purification Kit (Qiagen). Finally, DNA was circularised by an intramolecular ligation reaction using the Quick Ligation™ Kit (New England Biolabs). To minimise PCR bias, circular DNA was split into 10 independent PCR reactions, with Q5 High-Fidelity 2X Master Mix (New England Biolabs), and primers Pr4seq_F_TS_R1 (5′-CCCTACACGACGCTCTTCCGATCTCGCTGCAGGTCGACGGATC-3′) and Pr4seq_R_TS_R2 (5′-TTCAGACGTGTGCTCTTCCGATCTGCAGCTCGAGCTCGATGGATC-3′). Primers were custom synthesised by Integrated DNA Technologies. These primers specifically amplify from a pool of random fragments, those circular fragments that contain 3′ ends of both a bait-ORF and a prey-ORF, and at the same time add R1 and R2 Illumina adaptors. The R1/R2 PCR product was purified with Agencourt AMPure XP beads (Beckman Coulter) at a ratio of 35 μl beads for every 50 μl of PCR product. To introduce P5/P7 Illumina adaptors and a multiplexing index sequence, the R1/R2 PCR product was used as a template in a PCR reaction with a final volume of 150 μl split into six reactions with Q5 High-Fidelity 2X Master Mix (New England Biolabs), and NEBNext® Multiplex Oligos for Illumina®, Index Primers Set 1 (New England Biolabs). P5/P7 PCR was purified with Agencourt AMPure XP beads (Beckman Coulter) at a ratio of 30 μl of beads for every 50 μl of PCR product. To prepare rec-Y3H library DNA for paired-end sequencing, the same steps as for rec-Y2H were followed. Recombined yeast plasmid DNA is referred as pFAM protein–RNA pool (plasmid Fusion of Activation Domain and MS2 stem-loop). To specifically amplify those fragments containing the 3′ end of a protein domain and an RNA site, and to add R1/R2 adaptors, Pr4seq_MS22_R1_rev (5′-CCCTACACGACGCTCTTCCGATCTGCAGGCATGCAAGCTGCC-3′) was used instead of primer Pr4seq_F_TS_R1, in combination with Pr4seq_R_TS_R2 .Y2H and R2Y2H sequences for read 1 and read 2, respectively (see below). Trimmed reads that are shorter than 15 nucleotides in length were discarded. The underlined nucleotides indicate differences between the R1Y2H and R2Y2H sequences. Bold characters represent the common attB2 sequence (ACCACTTTGTACAAGA AAGCTGGGT).See Supplementary Fig. Y2H: CGCTGCAGGTCGACGGATCTTAGTTACTTACCACTTTGTACAAGAAAGCT GGGTR1Y2H: GCAGCTCGAGCTCGATGGATCTTAGTTACTTACCACTTTGTACAAG AAAGCTGGGTR2–8. We picked the best aligned target protein and further filtered out the reads that were reverse-oriented aligned or aligned far from the 3′ end (>10 nts). When both read 1 and read 2 sequences were properly mapped to the input library sequences, we counted them as usable reads for interaction mapping. Since our library consists of recombined baits and preys, and we are reading from constant plasmid parts into the 3′ ends of fused ORFs, we can reliably distinguish baits (as read 1) and preys (as read 2). For rec-Y3H, similarly, raw paired-end reads (read 1 and read 2) from RS and RIS conditions were filtered for both the R1Y3H and R2Y3H sequences for read 1 and read 2, respectively (see below), and trimmed reads shorter than 15 nucleotides in length were discarded. The underlined nucleotides indicate any differences. Bold characters represent the attB2 sequence (ACCACTTTGTACAAGAAAGCTGGGT) for the protein part (read 2).Then, the trimmed read sequences were aligned to the 3′ reverse complement coding sequence (for the last 100 nts) of the target library using the blastn programme with the blastn-short option and an E-value cut-off of 1eY3H: GCAGGCATGCAAGCTGCCR1Y3H: GCAGCTCGAGCTCGATGGATCTTAGTTACTTACCACTTTGTACAAGAA AGCTGGGTR2For the RNA part (read 1), we only used exactly matching cases given the short length of some target RNAs. The protein part (read 2) was mapped as for rec-Y2H.FRIS) and RS conditions ), and normalised the number of mapped PPI reads to the total PPI reads counts  represents the number of PPI read counts mapped for bait, x and prey, y proteins in a specific conditions. FRS stands for –W (RS condition) and FRIS stands for nutrient dropout (RIS condition). Then, we generated a null matrix from the marginal distributions to reduce the random selection bias (such as highly or lowly selected pairs) because of library complexity and low sequencing depth5.FRS(x) and FRS(y) indicate the marginal probability of bait, x and prey, y proteins , respectively).To calculate IS for each pair, we first generated frequency matrixes for RIS (nts (Eq. ). Then, nts (Eq. ). These FRIS) were divided into two classes based on the probability of Gaussian fitting14. With non-zero frequency values, we used the R package “mixtools” to fit a bimodal  mixture model  to identify the probability of each component. If the frequencies had more probability of belonging to noise than to signal, we set them to 0; otherwise, we used the real frequency values. In this way we could reduce the noise of the selection matrix ), assuming that the majority of the bait–prey combinations were not interacting.Frequency values for RIS ) is further normalised by the null matrix ) to generate IS. This corrects for library amplification and clone overrepresentation. To remove stochastic false positives and increase sensitivity, several experiments are averaged. This generates the final average-IS matrix. Finally, the basal auto-activation signals are removed by subtraction of the upper quartile IS.The noise-filtered signal (n/4)th value from n total values. IS represents all the values belonging to a bait, x. In other words, the top 25th percentile IS for each bait protein. Usually, Q3) is 0 for non-auto-activator proteins. The same pipeline is applied to process the data from both rec-Y2H and rec-Y3H screens.Q3 represents the upper quartile function that gives  and a full (pBWH X76 pool/pAWH X76 pool) screen. Read count ratio was calculated as the number of reads in RIS conditions divided by the number of reads in RS conditions. We used an arbitrary cut-off which is a 2.5 fold higher read count ratio compared with mean read count ratio of all the proteins.r, using the stats.pearsonr function of the “scipy” python package. It converts a Pearson’s correlation coefficient to an appropriate t-value. The statistical significance  was then calculated using the approximation of Student’s t-distribution with degrees of freedom n−2. For the t-test, we used the two-tailed P-value from the stats. ttest_ind function of the same package. As for the Hypergeometric test, we used the stats.hypergeom.cdf function in “scipy” package. Area under the curve (AUC) of receiver operating characteristic curves was calculated using the metrics.auc function of the “sklearn” python package. F1-Score and MCC were calculated by python script with the following formulas:Unless specified otherwise, we tested the statistical significance (with Fisher Z-transformation) of the correlation coefficient TP, FP, TN, and FN indicate true positive, false positive, true negative, and false negative, respectively.36. All network visualisations were generated with Cytoscape37. Heatmaps such as in Fig. 38. RNA–protein complex structures were generated with pymol. To generate figures, we first plotted them with R software in PDF format, and then merged them with Illustrator v15.1.The circular plot in Fig. Selected pairs from the rec-Y2H screen were re-tested by individual pair transformations. Selected baits (pBWH-ORF-X) and preys (pAWH-ORF-Y) from the X76 screen (see Supplementary Data Selected RNA motifs (pMS22H-RNA-x) and protein domains (pAWH-Prey-Y) from RNAlib were individually linearised by I-SceI and I-CeuI (New England Biolabs) digestion for 16 h at 37 °C followed by 20 min at 65 °C. Indicated pairs (10 fmol linear RNA and 10 fmol linear prey vector) were co-transformed in freshly prepared YBZ-1 competent yeast and spotted on –Trp agar OmniTray plates. After 3 days, spots were replicated on the indicated RIS media.® CMV-neo (Promega) by Gibson assembly to generate pHTNW. An attR1-ccdB-attR2 cassette (from pAWH) was generated by PCR and cloned into the NotI site of pNLF1-N [CMV/Hygro] (Promega) by gibson, to give pNLF1W. Indicated ORFs were cloned into either pHTNW or pNLF1W by Gateway LR reaction (Thermo Fisher Scientific) following the manufacturer’s indications. HEK293T cells were plated in 24-well plates at a density of 1.4 × 105 cells per well. Cells were transfected with 500 ng of pHTNW-ORF, 5 ng of pNLF1W-ORF, 0.75 μl of Lipofectamine 3000 and 1 μl of P3000 Reagent (Thermo Fisher Scientific). After 20 h, each transformation was re-plated in four wells of 96-well plates at a density of 1 × 104 cells per well for duplicate control and experimental samples , and PPIs were analysed with NanoBRET™ Nano-Glo® Detection System kit (Promega) following manufacturer’s instructions. Each transformation experiment was performed at least twice. The corrected NanoBRET ratio was calculated according to the manufacturer’s instructions.An attR1-ccdB-attR2 cassette (from pBWH) was generated by PCR and cloned into the NotI site of pHTN HaloTaghttps://github.com/lionking0000/recYnH/.Computer code is available at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7


Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency. To this end, computational approaches that utilize machine learning classifiers and rely on available PPI data predict uncovered segments of the interactome at an accuracy similar to those of high-throughput techniques and appear to be particularly useful for their comprehensiveness3. These methods operate on protein sequences converted into feature vectors, so they are universal and easily applicable, which is an advantage over computationally-intensive structure-based molecular docking methods. However, supervised machine learning algorithms for PPI predictions suffer from inherent limitations in foreseeing interactions of components that were unseen by the model during the training process. Several papers have reflected on sustainable estimates of PPI prediction performances and their ability to generalize to physiological environments5.Protein–protein interactions (PPIs) underlie many processes essential to living organisms and hence, are fundamental to the functional annotation of proteins. So far, we have been acquiring genomic information much faster than knowledge and understanding of protein interactions. Low-throughput experimental studies that reveal PPIs are not only expensive and labour-intensive, but also suffer from bias in favour of well-studied proteins. Under the circumstances, 8. This conformational plasticity is associated with sequence compositional characteristics including low proportions of bulky hydrophobic amino acids and a high content of charged and hydrophilic residues9. The ability of a single IDP to bind to several structurally dissimilar partners, complemented with the ability of many different IDPs to bind to a single partner, results in exceptional binding promiscuity10. Coupled folding and binding, one inherent property of IDPs, is associated with their propensity to form low-affinity complexes which govern or maintain signalling processes11. Thus, many IDPs act as hubs in interaction networks and have central roles in the regulation of signalling pathways12. On the other hand, in fuzzy complexes, bound IDPs might remain fully or partially disordered13.Intrinsically disordered proteins (IDPs) represent a structural class of proteins that do not have well-defined tertiary structures in several regions or throughout the entire sequence17. IDPs are the prevailing protein class associated with noncommunicable diseases19 and therefore, mapping the interactome of IDPs will lead to improved understanding of disease mechanisms and provide the platform for novel therapeutic approaches21. Thus, it is an important task of computational biology to provide model PPI networks of IDPs and to enable reliable predictions of candidate interactors. The flexible structure of IDPs imposes restrictions on the application of precise computational methods for modelling interactions based on docking. This is because they rely on putative binding modes according to favourable interaction energies and surface complementarities, thus requiring candidates with stable, well-defined, globular three-dimensional structures23. On the other hand, data-driven methods have no inherent barriers to respond to this task, but require methods to prevent bias in the training while carrying out a realistic estimation of performance.IDPs habitually have multiple functions and underpin key cellular processes, including the regulation of transcription, translation and the cell cycle. In addition to regulatory roles, IDPs are key players in many other biological processes such as molecular recognition, binding of small molecules, and the organization of chromatinIn this study, we used compositional features to decode amino acid strings and implemented machine learning algorithms to predict binary interactions that involve IDPs. With a strict evaluation procedure and attention to potential setbacks intrinsic to data-based methods, we demonstrate that our classifier grasps key characteristics of PPIs and is capable of predicting interactions of an IDP of interest with significant efficiency.24. Some of these proteins are entirely disordered, whereas others contain both disordered regions and globular domains. In this study, we utilized this dataset as a representative of human disordered proteins and the term IDP is used as a universal expression to denote an intrinsically disordered human protein. The interactome made of binary PPIs that involve at least one IDP from the DisProt was extracted from the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database25, a source specialized in human interactions, which combines information on PPIs with experimental annotation from ten primary repositories. The HIPPIE data are manually curated and associated with a quality scoring scheme that we used to eliminate less reliable data. As mentioned earlier, the complexity of the disorder-based interactomes is increased through the capacity of a single IDP to bind to multiple partners. Several studies have shown a correlation between the disorder of a protein and the number of its partners28. Indeed, according to the statistics of the version of HIPPIE that we used, the number of interactions per IDP is approximately 3.5 times higher than that of PPIs per ordered protein. Specifically, the average number of interactions per IDP is 112.77, whereas the average number of interactions per ordered protein is 31.58  collects data on intrinsically disordered proteins and its current version holds information on 237 human entities30.In such an environment, the concern that hub proteins will dominate the PPI prediction algorithm is realistic and reasonable. In order to preclude representational bias in the learning process, we performed a balanced sampling of negative PPIs. Thus, all sequences, components of protein pairs appeared evenly in the positive and negative parts of the training set. A common approach towards building a negative dataset is to sample protein pairs that are not known to interact. The number of false negatives in such collections is considered low because number of protein pairs significantly exceeds the number of recognized interactions. For the test sets, negatives were subsampled randomly as required for evaluation of the PPI prediction method at a population-level31. The remaining dataset encompassed 20,216 interactions involving 5,805 unique ordered sequences. Next, we removed interactions that were less reliable in experimental annotation. The remaining items are partitioned randomly into training and test sets for holdout validation. We repeated this 5 times ensuring that the test pairs share only one IDP component with the training set. The entire list of 5 training sets can be found as Supplementary Tables As a first step in generating datasets we retrieved 24,994 interactions with at least one IDP included  and incorporated five amino acid propensity scales: the TOP-IDP scale which ranks residues by the their propensity to endorse order or disorder36, B-values which quantitates the flexibility parameters for each residue surrounded by two inflexible neighbours37, the FoldUnfold scale which represents the capacity of amino acid residues to form a sufficient number of contacts in a globular state38, the DisProt scale which is based on the statistical difference in the residue composition of ordered proteins and IDPs as in36 and the net charge scale39. The PAAC composition contains both a conventional amino acid composition and factors that incorporate sequence-order information via feature correlation functions40. Protein pairs were vectorized by a single sequence vectors concatenation and represented as 940-dimensional vectors.IDPs exhibit highly specific sequence composition4 explored the consequences of the component-level overlap between the training and the test sets by distinguishing three types of test-pairs. They did this according to whether they share both, one or no sequence/s with the protein pairs in the training set. This approach demonstrated that each method achieved the best performances for the test sets that shared both components with the training and the worst for those that shared none. In the present study, which aimed to assess how our method executes on proteome-scale predictions, we performed evaluation experiments on test pairs which shared only the IDP component with a training dataset. Firstly, we used 10-fold cross-validation to select the best performing machine learning algorithm among Random Forests (RF), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Generalized Linear Model (GLM). According to averaged results on 10 test folds 51, Nucleophosmin 1 (NPM)51 and caspase 3 (CASP3)52. We further experimentally evaluated and confirmed one interesting novel candidate interaction between BASP1 and the progesterone receptor, PRGR 51. Our finding that BASP1 associates with PRGR and that interactions are predicted with several other DNA-binding transcription factors suggests that BASP1 is likely to be a widely deployed transcription cofactor. Previous studies have reported that BASP1 binds to chromatin remodelling factors 50 suggesting that BASP1 acts as an interface between the DNA-bound transcription factors and proteins that modulate the transcription process. Indeed, the analysis presented here found that BASP1 potentially also interacts with several chromatin remodelling factors.Transcription and transcriptional regulation are among the most notable molecular functions that require proteins with flexible structures to underpin precisely timed and context specific processes. The brain acid-soluble protein-1 (BASP1) is a transcriptional cofactorRGR Fig. . Previou55 and the terms with scores above the predefined cut-off are presented by the visualisation tool, Revigo56. In these scatter plots . Enrichment of two sets  which links human genes with diseases and phenotypespts Fig. . Accordipts Fig. . We comppts Fig. . For eacThus, we anticipate that other over-represented terms in Fig. IDPs are distinct due to their compositional bias which influences their binding propensities and selection of partners. This motivated us to develop IDPpi, a method for PPI predictions which utilizes supervised machine learning algorithms and compositional content together with the distribution of features associated with the promotion of structural disorder along the sequence string. A subset of human IDPs that has been used for training is functionally important and well-explored and thus, its interactions provided a basis solid enough to develop a method fine-tuned to predict PPIs of this particular IDP dataset. The evaluation presented proves that IDPpi efficiently predicts interactions with proteins that were not used in the training process, which implies that this model can produce reliable predictions even on a proteome-scale. Further improvements of IDPpi will primarily encompass inclusion of new IDPs as prediction targets after an upgrade of the DisProt repository. In the long run, a computationally efficient and reliable approach implemented in IDPpi will serve as a solid foundation for a method that will reveal the full complexity of the IDP interactome by considering the effects of alternative splicing and PTM on protein-binding abilities. By introducing additional layers of information that will encompass a significant number of isoform- and PTM-specific interactions to the current training and testing datasets we will be able in the future to overcome limitations imposed by present data coverage. We anticipate that IDPpi may contribute not only to uncovering the human interactome but also to a significantly enhanced understanding of the diverse roles of IDPs and therefore we offer it as a simple and convenient web tool.24 and UniProt/Swiss-Prot. The PPI dataset was obtained by searching the content of the HIPPIE v2.025 for interaction partners of IDPs. Given that the HIPPIE database confers interacting partners only through gene name identifiers the isoform involved in the interaction is not known and therefore the sets were constructed from reference isoforms. As a result, the tau protein and calcyclin-binding protein that are present in DisProt only as alternative isoforms were not considered in this study. This list was further refined by the following steps: (1) proteins containing less than 50 amino acid were removed to filter out small proteins and protein fragments. The PPI dataset was obtained by searching the content of the HIPPIE v2.025 for interaction partners of IDPs. The dataset of interaction partners was cleared of sequences that correspond to protein names containing the words ‘putative’, ‘potential’ or ‘uncharacterized’. The non-redundant subset of interaction partners was generated at the sequence identity level of 40% by clustering analysis using the CD-Hit31. Negative PPI data were formed from all human protein pairs containing exactly one IDP component. No negative PPI was listed as a positive in the full Hippie database.The set of human disordered proteins and human proteins with disordered regions sequences were retrieved from the DisProt40 and (ii) dipeptide composition. PAAC combines conventional amino acid composition and sequence order correlates of various amino acid features. Amino acid scales that we utilized to encode sequences are provided in the Supplementary Table Sequences and protein pairs were represented by features derived from: (i) pseudo amino acid composition (PAAC)Sequences were presented with dipeptide composition which takes every two consecutive amino acids as a single unit and counts the frequency of all of the dipeptide patterns. It then represents each sequence with a fixed length vector of 400.Protein interactions were vectorized by concatenation of the vector representations for both proteins from the interaction pair. Therefore, the total number of dimensions of vector representation for single protein interaction is 940.63 and gradient boosting machine64; (ii) linear classifier, generalized linear model65 and (iii) kernel based support vector machine66. The process of modelling was carried out using RStudio integrated development environment67, while the software was implemented using R and JAVA programming languages. In order to determine whether the classifiers were able to learn general concepts or failed to generalize well on unseen data (overfit), we performed a ten-fold cross-validation over general protein datasets  tree based classifiers: random forests methodAs a performance metrics we used the area under receiver operating characteristic curve (AUC) or recall-fallout plot, area under precision-recall plots (AUPRC), and accuracy (ACC), F score, precision, recall (sensitivity or true positive rate), fall-out  and Matthews correlation coefficient (MCC), calculated with the 0.5 threshold value.68 were used for hyperparameter optimization for each of the machine learning methods. For Random Forest following hyperparameters were tuned: number of trees, maximum tree depth, minimum number of observations for a leaf, histogram type, number of bins for the histogram, row sampling rate and number of columns to randomly select at each level; for Gradient Boosting Machine: number of trees, maximum tree depth, minimum number of observations for a leaf, histogram type, number of bins for the histogram, learning rate, row sampling rate and loss function; for Generalized Linear Model: model type, solver, penalized model, regularization penalties, beta epsilon value and maximum number of active predictors; for Support Vector Machine: C and gamma parameters and kernel function.Cartesian grid search and random searchet al.41 and relies on a signature product that is implemented within a SVM classifier as a kernel function. Next, M2, the method developed by Guo and coworkers42 utilizes sequences represented by a feature vector based on autocorrelation values of 7 physicochemical scales. The feature vectors are then concatenated for a protein pair and classified by an SVM. Finally, M3 method43 represented a protein sequence vectorized by a tri-peptide composition.The method denoted M1 was developed by Martin 51. Immunoprecipitation was performed as described in53. PGR antibody was from Santa Cruz Biotechnology (Sc-538). BASP1 antibodies were described in Carpenter et al.53.MCF7 and T47D cells were maintained as described in55 was imported into ReviGO56 to depict the list of significant GO terms by selecting representative subsets using a simple clustering algorithm that relies on measures of semantic similarity between terms.Biological process (BP) gene ontology (GO) term over-representation was calculated using BiNGO v3.0.3in Cytoscape v3.4.0, employing the hypergeometric test and applying a significance cutoff of FDR-adjusted P-value ≤ 0.05. The 17,531 human genes were used as the reference set, and the GO ontology and annotation files used were downloaded on Oct. 25, 2017. The output from BiNGO59 to identify mentions of disease terms from the HPO58 in the text as described in69. We configured the service to find the whole-word, direct terms matches for term labels and synonyms from the HPO. The HPO is indicated by the BioPortal version 5.3.1. The obtained word lists were clarified from non-informative terms: abnormality, disease, disorder, system, morphology, physiology, function and involving. Term lists were submitted to the online cloud generating software Wordle (http://www.wordle.net) and visualized using the “Horizontal” layout. Non-specific terms  are not used in tag-cloud representations.For each PubMed article which contains the word BASP1 in the title and/or abstract, we used the NCBO Annotator Web-serviceSupplementary informationSupplementary Datasets 1-28


The influenza A virus (IAV) is a constant threat for humans worldwide. The understanding of motif-domain protein participation is essential to combat the pathogen.In this study, a data mining approach was employed to extract influenza-human Protein-Protein interactions (PPI) from VirusMentha,Virus MINT, IntAct, and Pfam databases, to mine motif-domain interactions (MDIs) stored as Regular Expressions (RegExp) in 3DID database. A total of 107 RegExp related to human MDIs were searched on 51,242 protein fragments from H1N1, H1N2, H2N2, H3N2 and H5N1 strains obtained from Virus Variation database. A total 46 MDIs were frequently mapped on the IAV proteins and shared between the different strains. IAV kept host-like MDIs that were associated with the virus survival, which could be related to essential biological process such as microtubule-based processes, regulation of cell cycle check point, regulation of replication and transcription of DNA, etc. in human cells. The amino acid motifs were searched for matches in the immune epitope database and it was found that some motifs are part of experimentally determined epitopes on IAV, implying that such interactions exist.The directed data-mining method employed could be used to identify functional motifs in other viruses for envisioning new therapies.The online version of this article (10.1186/s12859-018-2237-8) contains supplementary material, which is available to authorized users. Influenza viruses are negative-sense single-stranded RNA viruses that belong to the Orthomyxoviridae family. The influenza viruses are grouped in three types: A, B, and C. Among them, the Influenza A virus (IAV) is considered an infectious threat worldwide for humans . The IAVThe viral cycle of IAV is developed within host cells where the virus produces gene information that is translated into ten functional proteins which interact with host proteins during the infectious process . StudiesThe protein-protein interactions (PPI) can be classified into four classes: domain-domain interactions (DDIs), mutual fit interactions, induced fit interactions and linear motif-domain interactions (MDIs) . Among tIn the present study, human motif-domain interactions, reported for protein crystal structures, were searched on viral protein sequences from IAV strains. The obtained linear motif-sequences were analyzed for their conservation on a proteome-wide comparative analysis of five IAV strains. The MDIs functionality was further analyzed thorough gen ontology (GO) analysis. The results provide valuable information on host-viral interactions in the biology of the virus and could be used for devising rational therapy that targets IAV-human functional motifs.n = 26,421) and H1N1  strains are available than the other strains, and more information for HA  and NA  than for other proteins. Sequences for H1N2 and H2N2 were < 100 for each protein .The workflow for the MDIs extraction is depicted in Fig. http://www.bioinformatics.org/sms2/shuffle_protein.html) [First, it was carried out a descriptive analysis based on the most frequent counts of human MDIs RegExp motifs that matched an amino acid sequence in protein of at least one strain. As some motifs are very short (3 amino acids) they could occur in a protein sequence by chance leading to a high false-positive rate. Hence in order to reduce the false-positive rate, we shuffled each of all our IAV protein datasets Table  with thein.html) . The shuin.html) . The Regin.html) . The perin.html)  was usedThe domains associated to the mapped human MDIs were annotated for their Gen Ontology (GO) related terms with the help of the 3DID search engine and the Pfam database. The obtained GO annotation for domains were summarize with the REVIGO online tool  to deterFrom 1093 interactions of the mined IAV-human network  strains to some extent. For example, the widely conserved Pkinase_Tyr_LIG_1–1 motif, was found in the five strains  to identify potential functional motifs present on the IAV proteins Additional file 2:The MySQL database. (TAR 71770 kb)


RNA-protein (RNP) interactions play essential roles in many biological processes, such as regulation of co-transcriptional and post-transcriptional gene expression, RNA splicing, transport, storage and stabilization, as well as protein synthesis. An increasing number of RNP structures would aid in a better understanding of these processes. However, due to the technical difficulties associated with experimental determination of macromolecular structures by high-resolution methods, studies on RNP recognition and complex formation present significant challenges. As an alternative, computational prediction of RNP interactions can be carried out. Structural models obtained by theoretical predictive methods are, in general, less reliable compared to models based on experimental measurements but they can be sufficiently accurate to be used as a basis for to formulating functional hypotheses. In this article, we present an overview of computational methods for 3D structure prediction of RNP complexes. We discuss currently available methods for macromolecular docking and for scoring 3D structural models of RNP complexes in particular. Additionally, we also review benchmarks that have been developed to assess the accuracy of these methods. Ribonucleic acid (RNA) plays major roles in various biological processes including protein synthesis and gene regulation at the transcriptional and post-transcriptional level. RNA molecules are involved in catalysing biological reactions, controlling gene expression, or sensing and communicating responses to cellular signals ,2,3. TheRNA-binding proteins (RBPs) often contain structurally and functionally distinct modules. For instance, in all enzymes that act on RNA, RNA-binding is a common feature of catalytic domains that assume various well-defined three-dimensional (3D) structural folds. Some of these domains can bind RNA on their own, while others require dedicated RNA-binding domains (RBDs), which enable the recognition of substrate RNAs ,17. ExamThree dimensional structures of RNP interactions have provided important insights into the molecular intricacies governing these interactions, including the specificity of mutual recognition by protein and RNA components and assist in studying the physicochemical principles of RNP interactions. Although 3D structure-derived information is important for understanding biological roles of RNP interactions, experimental determination of RNP complex structures is a slow and laborious process ,28. FirsUntil the end of the 20th century there were only a handful of high-resolution structures of RNP complexes available but the number of such structures has exponentially increased over the past decade due to significant improvements in established techniques such as X-ray crystallography , as wellOver the past decade, various research groups have utilized different tools and techniques to estimate the total number of RBPs in the human proteome. The database of RNA-binding protein specificities (RBPDB) database documents 397 manually curated examples of RBPs from the literature . In 2012Given the scarcity of experimentally-determined structures of RNP complexes, computational techniques can complement existing data to help elucidation of RNP complex 3D structures. However, while the methodology for prediction and modelling of 3D structures for individual proteins and protein-protein complexes is very well established ,47,48,49The prediction of 3D structures of macromolecular complexes is usually done by the computational docking approach, which requires detailed knowledge of structures for all individual components of the complex. Ideally, the docking should be based on the knowledge of high-resolution atomic structures of the components, for example, determined by X-ray crystallography or NMR. However, in many cases, experimentally determined structures of components of the complex are not available. For many applications, these can be substituted by computationally-modelled structures. To this end, a large number of computer programs have been developed, which now allow for reasonably accurate and practically useful predictions of protein 3D structures and reviewing them is beyond the scope of this article. The state-of-the-art in protein 3D structure prediction has been systematically assessed by the Critical Assessment of protein Structure Prediction (CASP) experiment . More reIn computational biomolecular docking, the receptor refers to the larger molecule, while the ligand refers to the smaller one. The docking protocol comprises two steps: (i) conformational sampling, that is, searching for possible conformations and mutual orientations of the docking components that leads to the generation of docked models  and (ii) scoring of docked poses: assessing them by a mathematical function that aims to distinguish between models with different degrees of similarity to the unknown “true” structure . Some ofA major challenge in computational docking is related to the observation that structures of binding partners often undergo conformational changes during association, in a process known as induced fit. Despite the recent advancement of methods that take macromolecular flexibility into account , dealingMany protein-protein docking methods have been assessed in the Critical Assessment of PRediction of Interactions (CAPRI) experiment, analogous to CASP . In compExisting docking methods can be divided into two general classes: (i) rigid and (ii) flexible . Upon RNRigid body docking methods are usually the first method of choice, especially if little is known beyond the structures of the components. By virtue of compromising on computing cost for conformational flexibility, such methods are capable of exploring a bigger search space to identify potential binding sites on the protein and RNA molecules. For example, FTDock developed by the Katchalski-Katzir group generates orthogonal grids to represent the components for docking and then performs a global scan of the search space  to generate a quick approximate solution of the prediction problem . The resFlexible docking can simulate conformational changes; however, its utility is also correlated with the degree of the structural change between the unbound and bound forms. It is a method of choice for cases in which the conformational search space is relatively small or for the purpose of local structure refinement. In fact, none of the typical docking methods can reliably predict the structures of complexes that undergo large and complex conformational changes during binding—for this, multi-scale methods are a recommended solution (see below). An example of a popular flexible docking method is HADDOCK, developed by the Bonvin group. It can take the information from user-defined restraints  to drive the docking , as wellThe 3D structures for RNP complexes can be modelled by methods that do not involve explicit docking, or in which the docking is only a minor component of calculations.Template-based modelling of complexes (in which the structure of one complex is modelled based on the structure of a related complex used as a template) is widely used for protein-protein interactions  but lessAnother strategy in the prediction of RNP complex structures is MD simulations . The prohttp://genesilico.pl/pyry3d/), a multiscale modelling method that enables the construction of models for very large macromolecular complexes with components of known or unknown 3D structure. PyRy3D aims at cases of data-driven modelling of complexes where the users have only limited knowledge about the system analysed and wish to test alternative hypotheses. PyRy3D applies a MC search to sample the space of solutions restricted by various spatial restraints that determine 3D shapes or interactions between subunits of complexes. It has been used to model RNPs such as the complex between the 2′-5′-oligoadenylate synthetase OAS1 and the 3′-terminal region of the West Nile Virus (WNV) RNA genome [Another class of methods have been developed to model multi-scale resolution models of large macromolecular complexes. These methods often represent components as rigid bodies or as flexible forms, depending on the availability of information about their structure and dynamics. They usually rely on additional information encoded in the form of spatial restraints to define interactions between the components. The M3 framework from the Carlomagno group uses sparse and hybrid experimental data to model structures of macromolecular complexes, starting from 3D structures of individual domains, monomers and subcomplexes. These components can undergo rearrangements and yet retain their overall 3D structure upon complex formation . The expA genome .For the modelling of RNP complex structures, it is often useful to consider predictions from different modelling methods, followed by a ranking of the alternative poses using external scores. Scoring functions are essential for distinguishing between models of various accuracy, in particular, to discriminate between models that are close enough to the “true” structure to provide useful functional information and models that are inaccurate and could lead to wrong conclusions. Standalone scoring methods are invaluable for comparison of models obtained with different modelling approaches that rely on different internal scoring functions. The scoring of 3D structural models may be based on the following considerations: (i) verification to what extent the given decoys agree with independently obtained information  and (ii) evaluation of the overall quality of fitting between the protein and RNA structures.For scoring of models with an independent dataset, a large number of approaches exist. A variety of experimental methods can be applied to study RNA, protein and RNA-protein complex structures, which can be translated into spatial restraints. Furthermore, numerous computational methods are available for predicting RNA-binding residues in proteins ,102. OncIn the absence of independent data, structural models of RNP complexes can be evaluated with generic scoring functions, which can be broadly categorized into three types: force field, empirical and knowledge-based. Force fields are the functional form and parameter sets used to calculate the potential energy of a system of atoms or coarse-grained points. Empirical scoring functions are based on counting the number of various types of interactions at the interface of RNP complexes. Knowledge-based scoring functions, also known as statistical potentials are mathematical functions derived based on statistical observations of interactions at the interface of known RNP complexes. A list of methods available for computational scoring of RNP complexes is presented in The Varani group was one of the first groups to development scoring functions, for RNA-protein interactions. Initially, they developed a statistical potential based on hydrogen bonding (H-bond) at the RNP interfaces . HoweverThe Zacharias group developed a distance-dependent, coarse-grained force field for RNP interactions . During The Xiao group developed a scoring function, Distance- and Environment-dependent Coarse-grained and Knowledge-based potential for RNP complexes (DECK-RP), to evaluate the docked poses generated by RPDOCK . DECK-RPThe approximate utility of various potentials, with respect to the accuracy of docking decoys analysed, is indicated as a decoy discrimination threshold in The availability of different docking methods and scoring functions allows for various combinations to be applied. Matching the docking procedure with a scoring function of a similar resolution is recommended. For example, we developed NPDock, a web server for low-resolution RNP structure prediction , which fAn important consideration in the selection of decoys is not only scoring individual models but also finding ensembles of similar models . In geneCα < 1.5 Å) with 34 cases, (S) semi flexible (1.5 Å ≤ i-rmsdCα ≤ 3.0 Å) with eight cases and (X) full flexible (i-rmsdCα ≥ 3.0 Å) with three cases. Here, i-rmsdCα is defined as the RMSD of the interface Cα atoms after superposing the bound and unbound structures. The authors have calculated the interface residues using the PRince webserver [Evaluating the performance of existing docking and scoring methods requires datasets of 3D atomic coordinates of RNP complexes that serve as references (“true” structures) for validating the predictions. In this section, we discuss the various benchmark datasets curated by different groups . The difebserver . The benebserver . This inCα+P ≤ 2.5 Å) with 64 cases, intermediate (2.5 Å ≤ i-rmsdCα+P ≤ 5.0 Å) with 24 cases and difficult (i-rmsdCα+P > 5 Å) with 18 cases.An extended benchmark including both experimental structures and homology models was curated by the Fernandez-Recio group with 106 test cases. In this benchmark dataset, 71 out of 106 entries were taken from crystallography or NMR experiments, while 35 entries were built using homology modelling . Of the Cα+C4′ ≤ 1.5 Å or fnat ≥ 0.8) with 49 cases, medium (1.5 Å < i-rmsdCα+C4′ ≤ 4.0 Å and 0.4 ≤ fnat < 0.8) with 16 cases and difficult (i-rmsdCα+C4′ > 4.0 Å or fnat < 0.4) with seven cases. The Zou group developed another docking benchmark for RNP complexes with 72 cases, of which, 52 are unbound-unbound cases, 17 are unbound-bound cases and three are bound-unbound cases . In bounThe docking benchmarks discussed in this section were widely used, i.e., to test docking methods ,83, to dA number of studies covering the general properties of RNP complex structures used the benchmark dataset developed by the Bahadur group, i.e., due to the stringent criteria on the resolution (better than 3.0 Å) and redundancy (sequence identity ≤ 35%). Studies of RNA-protein binding sites of complexes in this dataset included the analysis of the role of water molecules in RNP complex formation , the stuRNA-Protein interactions are often affinity-driven processes, where the specificity of binding is determined by the conformation adopted by the molecule to bind with its partner. The free energy required for RNA and/or protein molecules, to adopt the particular conformation required for binding, is a determining factor in such binding events.The first RNP affinity benchmark was developed in 2013 by the Liu group with 73 cases . A comprIt is important to draw our readers’ attention to the fact that the bioinformatics methods discussed in this review are for predicting the possibilities in which a given protein and an RNA interact with each other and they assume that their interaction does happen. These methods are not appropriate for ascertaining if the protein and the RNA interact or not, or for predicting which RNA binds to a given protein or vice versa. Predicting the binding affinity would help us determine whether the molecules bind to each other. Methods for computational prediction of binding affinities are however still in their infancy. They require high-quality datasets of experimentally determined affinity values, which are currently sparse. The affinity dataset curated by the Bahadur group was used in the development of an algorithm to predict hot-spot residues within RNA-binding sites and is available as a web server HotSPRing . The samOver the past decade, there has been a growing interest in investigating RNP interactions. This is apparent from the increasing number of structures of RNP complexes that have been deposited in the PDB database per year (197 in 2017 vs. 87 in 2007), as well as a greater number of publications that appear each year in the PubMed database, with the keyword “RNA-binding proteins” (1405 in 2017 vs. 1278 in 2007). However, computational methods for prediction of RNP complex structures using information from structures of the individual components or directly from sequences, have been sought after due to the difficulties associated with the experimental determination of their structures.Computational prediction of RNP 3D structures can provide important information in cases where standard approaches for experimental structure determination fail. In our own work, we encountered numerous RNP systems, for which we or our collaborators attempted to crystallize the complex but failed to obtain diffraction-quality crystals. Consequently, the available data  were used to guide macromolecular docking and modelling of RNA-protein interactions, providing functional insight that could not be obtained from structures of the components in isolation from each other. One example includes the BsMiniIII endonuclease, which crystallized only in the apo form but not in complex with its dsRNA substrate and for which an RNP complex structure was modelled  and usedThe approaches for modelling RNP complexes discussed in this paper, though capable of providing practically useful predictions, suffer from various limitations. One of the biggest drawbacks concerning RNA modelling is the relative scarcity of experimentally determined RNA and RNP complex structures that can be used for training and testing the methods as well as templates in comparative modelling approaches. The value of RNP structures for the community of computational biologists can be illustrated by the repeated calls from organizers of the CAPRI and RNA Puzzles initiatives to experimental researchers for proposing the newly determined structures as prediction targets. One can hope that the significance of this problem will wane with the growing interest in RNA structural biology, leading to an increase in the availability of solved structures solved each year.In order to obtain biologically, chemically and physically relevant predictions, it may be advantageous to combine various existing methods for docking and scoring. Such meta-prediction was successfully applied in structural bioinformatics for modelling protein structures  and for One of the biggest challenges in docking (and in particular in RNA-protein docking) is the molecular flexibility  and the The conceptual similarity of successful algorithms for structural modelling of protein and RNA 3D structures  suggests


Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at  Long non-coding RNAs (lncRNAs) are non-protein-coding transcripts with a length of more than 200 nucleotides, which can regulate gene expression at different levels . LncRNAsSimilar to protein-coding genes and microRNAs, lncRNAs have also been found in the regulation of many human complex diseases, including various types of cancer. At present, there are many databases of lncRNA associated with diseases, such as LncRNADisease database  and Lnc2It is of great importance to identify lncRNA–protein interactions to gain a comprehensive and profound understanding of the potential functions encompassed in their molecular mechanisms. At present, the main methods for identifying lncRNA–protein interactions are based on experimental approaches and computational approaches. Several large-scale experimental approaches for lncRNA- protein interaction prediction include RNAcompete , RNP immThe methods of predicting lncRNA–protein interactions based on computational approaches are mainly divided into machine-based learning methods and network-based methods. The methods based on machine learning construct a classifier by fusing the features of sequence, structure and physicochemical properties, so as to form an interactive or non-interactive classification model. At present, the existing methods are RPISeq , de novoNowadays, network science is being extensively used in biological and related fields. It provides many practical descriptions to characterize various biological systems  and the https://github.com/HAN-Siyu/APINet/), that is, lncRNA–disease associations, lncRNA–lncRNA interactions, lncRNA–microRNA interactions, lncRNA–gene interactions, lncRNA–Gene Ontology (GO) interactions, microRNA–microRNA interactions, microRNA–disease associations, microRNA–gene interactions, microRNA–target interactions, gene–gene interactions, gene–metabolite interactions, metabolite–metabolite interactions, gene–GO interactions, gene–disease associations, gene–drug associations, metabolite–disease associations, drug–disease associations, drug–drug interactions, drug–side-effect interactions and and disease–disease interactions. The details of the data information are shown in The various databases discussed in this article incorporate lncRNAs from different tissues and focus on lncRNAs as well as lncRNA-related interactions. Some of these databases are available at RNAcentral . AlthougSeveral large-scale experimental approaches for lncRNA–protein interaction prediction include RNA immunoprecipitation (RIP) followed by mass spectrometry analysis, RNAcompete , RIP-ChiThere are many effective methods for the analysis of experimental data, such as several methods for finding RNA motifs from CLIP-Seq or other high-throughput experiments, such as BEAM  and SMARComputational approaches for lncRNA–protein interaction prediction can be grouped into the following two ways of expressions. The first category is based on sequence and structural information and physicochemical properties, including RPISeq , de novoFrom the point of view of characteristics such as sequence information, various classical methods have been proposed. RPISeq  is propoWith the development of computational approaches, experimental methods are now suffering the great disadvantage to predict lncRNA–protein interactions, such as high cost and long time. Intrinsic features of lncRNA and protein have increasingly interested the researchers. The advantage of intrinsic features has been demonstrated in the research of lncRNA identification. The methods of lncRNA–protein interaction prediction focus on intrinsic features of lncRNA and protein, such as sequence information, structure information, and physicochemical properties, including hydrogen-bond and van der Waals propensities. We analyzed the dataset of methods based on what kind of information they use, such as sequence, structure and physicochemical information. We also analyzed what machine learning algorithms are employed in the different methods. The comparison of each method is shown in Whereas the machine learning-based methods only consider the properties of the RNAs or proteins and neglect interactions between lncRNAs and proteins, the network-based methods pay more attention to this kind of interactions, which are implicated in the topologies between nodes in the heterogeneous networks of lncRNAs. When the sequence is too long or the randomness of structural information is predicted, the computational models based on machine learning will be affected to some extent.The previously described methods for predicting the interactions between lncRNAs and proteins more focus on the intrinsic features of lncRNAs and proteins but do not take the topological structures of biological networks associated with the lncRNAs into consideration. A biological network can apply to biological systems. Nowadays, network science is being used extensively in the biological and related fields. Network science provides many practical descriptions of biological systems and relationships between diseases and other biomolecules as biological factors . MoreoveG can be used to represent the lncRNA–protein interaction network. The structure of the bipartite network of lncRNA–protein is simply shown in graphic language, as shown in W, where P, where Inspired by resource methods in dynamically allocated networks, Zhou et al.  proposedIn the formula above, L propagates back to P. In the next step, all the information in After calculations, the protein sorted by the final score A to 0. The performance of LPBNI could be estimated by the ratio of its predicted interactions to the originally known lncRNA-protein. A receiver operating characteristic (ROC) curve was selected as a criterion to evaluate the LPBNI and random walk with restart methods. The propagation matrix W proposed in the LPBNI method is dependent on the adjacency matrix A of the bipartite network. When applying LOOCV, multiple values of W were obtained, owing to the change of A values during each step of the cross-validation. Consequently, the value of W was recalculated for each lncRNA–protein pair that was left out as a test sample. In addition, nodes that do not propagate information are not considered when evaluating the performance of the method, where nodes with fewer than two links are defined as nodes that do not propagate information in the process of cross-validation.LOOCV was performed on the heterogenous network containing lncRNA–protein interactions, leaving only one sample for the test set at a time, and the other samples were used as the training set. Although the calculation was more complicated than other verification methods, the sample utilization rate was the highest. LOOCV aws used to evaluate the performance of the proposed method. In the course of the calculation, each lncRNA–protein pair was omitted as a test sample by changing the value in the adjacency matrix To improve the performance of lncRNA–protein interaction prediction, Zheng et al.  fused muE represents a set of edges, each of which has a similarity weight. The authors denoted the corresponding similarity matrix as W, where D is a diagonal matrix and P involves self-similarities on the diagonal entries of W, a better form for avoiding numerical instability is as follows [The advantage of SNF algorithm is that it can obtain valuable information from a relatively small number of samples, and it has strong robustness in dealing with noise and data heterogeneity. It is a nonlinear method based on the typical nature of the complexity of the natural world based on message-passing. The nonlinear method is closer to the nature of the objective thing itself. It is one of the important methods to quantitatively study and understand complex knowledge. This method iteratively updates each network and makes it more and more similar to other networks. A protein similarity network can be represented as a graph  follows :(5)P to measure the local part as follows:Protein P provides all the information of the PPSN, whereas S provides the local similarity information of the network. Then, iterative computation can occur as follows:ith similarity matrix after kNN matrix of the similarity matrix or network. Following that, m is the number of PPSNs used. As S is the kNN matrix of P, it contains the most important information of P and also alleviates the noise effect of P. In each iteration, each similarity matrix can get more reliable information from other similarity matrices, at the same time, it will update its own matrix based on other similarity matrices. After t iterations, the fusion network can be replaced by a fusion matrix, which is defined as follows:A protein has much better similarities to its neighbors than it has to remote proteins. Similarity based on graph diffusion principle can be propagated to remote proteins. Matrix Note that the authors normalized matrix For this method, 15 settings made up of different combinations of the similarity matrices  were implemented. The path selection is very important since HeteSim is a path-constrained relevance measure. In the fusion work, the relevance path was chose as lncRNA-protein-protein, which was the same as that used in the work of Yang et al. . With thYang et al.  tried toP, denoted as x and y , where that for the 0.3 network was 0.968, which was higher than the value calculated by LPIHN. To verify that PLPIHS has better performance, the authors select the same number of positive and negative samples from different cutoff values of the network, respectively, and use this random selection to construct independent test datasets. Compared with the values generated by LPIHN, the AUC value of PLPIHS was 0.879. The accuracy, sensitivity, precision, Matthew’s correlation coefficient, and F1-Score were also chosen as measurements to evaluate performance.Fusing multiple PPSNs to effectively predict lncRNA–protein interactions was from the perspective of a fusion protein. The best relevance path was lncRNA–protein–protein according to HeteSim. The fusion matrix is an effective means for users to get more reliable and richer information matrix or network. The best AUC value was 0.9068 with Go+Pfam, Go+String, and Pfam+String. The AUC values of the 15 settings implemented in the paper by Zheng et al.  are showIn the LPIHN model, the determination of test datasets is consistent with other interaction prediction methods, leaving a cross-validation method. This model used not only LOOCV but also precision versus recall curves and fold enrichment to measure the performance, whereas the average fold enrichment of all test data was used for assessment. The LOOCV results showed that LPIHN obtained an AUC of 0.96. When more attention was paid to the predicted first 4870 lncRNA–protein interactions, 802 of the predicted LPIHN interactions were within this ranking.To better understand the performance of network-based computational models to predict lncRNA–protein interactions, we divided the heterogeneous network into three cases according to the source of components: (1) only the lncRNA–protein interaction network; (2) the network combining the interactions of lncRNA–protein and protein–protein; and (3) the network with the integration of the interactions of lncRNA–protein, protein–protein and lncRNA–lncRNA. For each case, different methods were validated with the same set of test datasets, and the performances are compared by AUC in Prediction of the interactions between lncRNAs and proteins is a very important step for research about lncRNAs. Based on the results of lncRNA–protein interactions, the functions as well as the associated diseases of lncRNAs can be inferred. The lncRNA–protein interaction is a very significant molecular mechanism. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on intrinsic features of the lncRNAs and proteins, including the sequence, structural information, and physicochemical property. The second category is based on the fusion of heterogeneous data to construct a network.Whereas the sequence-based methods only consider the properties of the RNA and neglect the internal relationship between the lncRNAs and proteins, the network-based methods have paid more attention to this kind of internal relationship. The main advantage of a network-based computational model is that it can predict lncRNA–protein interactions with heterogeneous data. Predictions using the intrinsic features of sequences alone may lead to more false-positive interaction pairs than that obtained using a network-based method. Unavoidably, the network-based computational model can have some disadvantages. The prediction of the network-based computational model can be affected when it is carried out in the case of finite interactions. When there are no interaction data, the network-based computational model cannot be used to predict interactions.New lncRNA–protein interactions are predicted more effectively by using several kinds of heterogeneous data sources. As the study of proteins becomes ever more comprehensive, the proposed effective computational models for predicting lncRNA–protein interactions from heterogeneous biological data can benefit our understanding of more comprehensive annotations for lncRNAs.Currently, there is very limited information on the interaction between lncRNAs and proteins, but computational methods can provide us with a large number of interaction pairs that can be further regarded as valuable material for the inference of lncRNA functions. First, a great deal of lncRNA–protein interactions can be provided by computational models based on intrinsic features. Second, since predictions using the intrinsic features of sequences alone may lead to some false-positive interaction pairs, computational models based on biological networks can be chosen to obtain more reliable predictions. In the future, a deep-learning-based framework can be considered to optimize the sequence-based and network-based computational models. Hopefully, long-short-term memory models can be employed to build a more advanced framework to build classifiers and achieve a more reliable classification model. We also can integrate machine learning with ab initio computing and network representation learning methods, and apply them to the prediction model of relationships between biological macromolecules. The interactions between lncRNAs and other molecules may enrich the functional annotations of lncRNAs. First, researchers can extract the characteristics of the molecules themselves by machine learning algorithm, and then they can use the appropriate algorithm in network representation learning to represent the feature vectors of relationships between nodes in heterogeneous networks. In this way, researchers can not only understand the internal features of molecules, but also not ignore the hidden topological information between molecules. This will overcome the weakness of most current research methods which only consider ab initio prediction or network-based methods.


Protein complexes are one of the keys to deciphering the behavior of a cell system. During the past decade, most computational approaches used to identify protein complexes have been based on discovering densely connected subgraphs in protein-protein interaction (PPI) networks. However, many true complexes are not dense subgraphs and these approaches show limited performances for detecting protein complexes from PPI networks.To solve these problems, in this paper we propose a supervised learning method based on network node embeddings which utilizes the informative properties of known complexes to guide the search process for new protein complexes. First, node embeddings are obtained from human protein interaction network. Then the protein interactions are weighted through the similarities between node embeddings. After that, the supervised learning method is used to detect protein complexes. Then the random forest model is used to filter the candidate complexes in order to obtain the final predicted complexes. Experimental results on real human and yeast protein interaction networks show that our method effectively improves the performance for protein complex detection.We provided a new method for identifying protein complexes from human and yeast protein interaction networks, which has great potential to benefit the field of protein complex detection. In recent years, with the development of human genomics and the development of high-throughput techniques, massive protein-protein interaction (PPI) data have been generated. These PPI data have enable to automatically detect protein complexes from PPI networks. During the past decade, most computational approaches used to identify protein complexes have been based on discovering densely connected subgraphs in protein-protein interaction (PPI) networks , 2. HoweRecently, a number of methods have been developed for protein complex identification. Dongen et al.  proposedIn recent years, some supervised learning methods have been proposed to detect complexes from PPI network by using informative properties of known complexes, including SCI-BN , NN 16]16] and Cd-dimensional feature in two phases: d/2 breadth-first search simulations and another d/2 2 hop distant nodes. In 2016, Grover et al. ) We detail our NodeEmbed-SLPC-RF method in this section. Specifically, Node embeddings used in the algorithm are presented, and then SLPC and RF are briefly described, finally NodeEmbed-SLPC-RF algorithm is introduced.At present, there are many approaches to generate network node embeddings. Node embeddings are distributed representations for the network nodes, which can be automatically learned based on the network adjacency information and topology structure obtained from the network. Compared with the traditional network structural features, node embedding methods can learn different vector representations for different networks according to their own structures, and thus can quickly mine the characteristics of different networks. And this kind of features are often not expressed by single values, but by dense vectors.In order to obtain high quality node embeddings, we use node2vec method  to automπ based on return parameter p and in-out parameter q; step 2: generate node sequences for each node based on G and π and, walk denotes all the node sequences. Specifically, r node sequences are generated for each node vi by using alias sampling strategy and the length of each node sequence is l; step 3: use stochastic gradient descent (SGD) strategy to train the model according to walk and obtain vectors for each node. Here, the sliding window size for training process is k, and the dimension of each vector is d. In the algorithm, a graph G is searched according to a certain strategy. Particularly, a number of node sequences are generated for each node, and the length of each node sequence is fixed to l. The number of sequences is determined by the hyperparameter r. And in the algorithm, k is the size of the sliding window and p determines the probability of traversal from the original path. The larger the p, the less likely to return to the same path. Parameter q decides the traversal strategy, the larger the q, the more likely to use breadth-first search strategy. Node2vec firstly generates the node sequences and all the generated node sequences are used as the contexts of the corresponding nodes. Then the skip-gram architecture 26]. The Random forest  is a modRandom forest model is tolerant to missing data and unbalanced data as well as it can handle high-dimensional data. During the training process of the random forest model, the number of trees is randomly selected in order to avoid the over-fitting problem. What’s more, it can process the high-dimensional data directly without feature selection process. On the other hand, the importances of each feature can be obtained after training and it can maintain good accuracy even with the missing data and unbalanced data. For protein complex detection task, it is well known that there exist false negative relations in the PPI networks , 5, and In this paper, we propose a method named NodeEmbed-SLPC-RF method to detect protein complexes from PPI networks. Figure We conducted the experiments on two different types of PPI networks: Human and Yeast. For human, protein and protein relations were downloaded from the human protein reference database (HPRD) , and thep=1,q=8,r=10,l=10,k=10. Besides, 1000 trees were used to make decision in the Random forest model.We evaluated the performance of NodeEmbed-SLPC-RF against SLPC, ClusterONE, MCODE, MCL, CMC, Coach, ProRank+ and PEWCC. We referred to the previous studies , 12–14 aprecision, recall and F−score are used as evaluation metrics. Precision is the fraction of the number of the predicted complexes which match at least one golden complex among all predicted complexes. Additionally, recall is the fraction of the golden complexes which match at least one predicted complex over the total number of all golden complexes. The F-score which shows the overall performance is the harmonic mean of precision and recall. For the purpose of evaluating the predicted protein complexes, three statistic measures which are widely used in related studies: NA, which is defined as follows, was used to measure the similarity between predicted complex (p) with golden standard complex (q). Here, the neighborhood affinity score V| denotes the set of proteins belong to the corresponding complex. Similar to many previous studies, a predicted complex p is regarded to be matched with a golden complex q if the NA score is not lower than 0.25.where |In the experiment, SLPC was used to detect candidate protein complexes from the original network and then RF model was trained to further classify the candidate complexes. Both SLPC and RF are supervised learning methods and the training set for them including the samples of three categories: positive, intermediate and negative samples. Similar to the construction of training set in SLPC , the staWe also compared our methods with some supervised methods, namely SCI-BN , NN 16]16] and CIn order to measure the effectiveness of RF model, Support Vector Machine (SVM) and Logistic Regression (LR) which have been proved to be prevalent in classification task –37 were In order to construct more reliable network, the relations in the network were assigned with weights which were calculated by the node embedding cosine similarities, and then some relations with lower weights in the original network were filtered out. In order to find the appropriate similarity threshold (semi-thres) for filtering the edges, we analyzed how many edges could be removed from the original network according to their weights from the original network as shown in Fig. 33Since the feature vector representations for each node in the network were obtained by node2vec and the similarities between vector representations might reflect the connectivity between two protein nodes, for each target node, a new relation was generated by determining which one had the highest similarity with the target node. Then some of the new relations were integrated into the original network if the similarity between two nodes was larger than a certain threshold. Finally, the NodeEmbed-SLPC-RF algorithm was used to identify candidate complexes from the integrated network.In order to find the appropriate simi-thres to add new relations, the similarities of all the new relations were analyzed and Fig. 44u and v, the AdjustCD score is calculated as: The node2vec algorithm is used to obtain the node embeddings in our method, since it can learn rich feature representations for nodes in a network. We conducted link prediction experiments in order to validate the effectiveness of node2vec algorithm. Link prediction problem aims to predict whether a link exists between two nodes in a network. It is well known that nodes with common neighbors tend to form future links , so we cNu and Nv are the numbers of the neighbors of each node, and u and v. Suppose that matrix P(k) is the score matrix in k iteration, then the score between u and v is the element p(k)uv of matrix P(k) which can be calculated as: where l: ∈E, ∈E. In the experiment, the number of iterations k was set to 2 as suggested by [where it takes the product by all ested by .T percentage of edges randomly sampled from the network, while ensuring that the remaining network remains connected. These "hidden" edges are considered as the ground truth, then we would like to predict these edges. In this test, mean ranking and Hits@N are adopted to evaluate the effectiveness of link prediction, and for each pair of nodes u and v, another 100 nodes that are not connected to u are selected as candidate nodes. Considering the fact that the predicted top-ranked results are more important in practice, we measure the performance of different methods in terms of the top-ranked results, i.e, the mean ranking of true edges, and the proportion of true edges ranked in the top N results. Usually, it is regarded as more effective if the method can rank more true edges in the top portions. In the test, 10% percentage of edge were removed from the network. We summarize our results for link prediction in Table For node2vec, cosine similarity is used to calculate the score of two nodes based on their obtained embeddings. In the test, we first hide a Z, which is composed by the corresponding node embeddings in the complex, to generate complex vector obtains better performance than others on both HPRD and DIP, the reason may be that max operation gathers the global important features from all the node embeddings of proteins in the specific protein complex.As described in the method section, the complex vector is generated based on its corresponding node embeddings of proteins in the complex. In order to evaluate how the generation strategy of complex vector affects the performance of NodeEmbed-SLPC-RF, we conducted experiments with three different complex vector generation strategies on both HPRD and DIP networks. The Table In the previous section, complex vector is generated by its corresponding node embeddings and the complex vectors are considered as features for RF model to further classify the candidate complexes. From the Table As mentioned in section of filtering edges, the original PPI network was reconstructed by filtering lower reliable edges based on the node embedding similarities between nodes, then SLPC was used to identify candidate complexes from the modified PPI network, and finally RF model was utilized to classify the candidate complexes based on their complex feature vectors in order to obtain the final predicted complexes. It can be seen from Fig. As mentioned in the section of augmenting networks, we calculated the similarities between all the node pairs using their node embeddings and then added new relations whose similarities were greater than a threshold to the original PPI networks. Then the candidate complexes were predicted by the SLPC algorithm, and finally the candidate complexes were further classified by RF model to obtain the final predicted complexes. It can be seen from Fig. In addition, we also conducted a experiment which was designed by filtering edges in accordance with the idea of the section of filtering lower reliable edges and then adding new relations in accordance with the idea of the section of augment networks. In order to find the appropriate threshold, for HRPD we first fixed the filtering simi-thres to be 0.86 as using this semi-thres our model can reach best F-score as shown in Table In this paper, we propose a protein complex detection method which is based on node embeddings, and the results demonstrate that our method can effectively improve the performance for detecting protein complexes from PPI network. Specifically, compared with using SLPC alone, when using RF model to classify the candidate complexes generated by SLPC based on their complex feature vectors and the candidate complexes labeled as positive by RF model were considered as the final predicted complexes, the performance in terms of F-score can be improved up to 8.93% and 2.33% on HPRD and DIP, receptively. In addition, When the original relations were filtered based on the similarity scores of node embeddings and the candidate complexes were further classified according to their complex vectors, the performance in terms of F-score can be increased up to 10.37% and 3.53% on HPRD and DIP respectively compared with using SLPC alone. The results indicate that the performance of protein complex detection methods could be improved by using node embeddings obtained by node2vec to measure the reliability of exiting relations in the PPI networks. What’s more, when adding new relations according to their similarity scores and using complex vectors to filter the candidate complexes, the performance in terms of F-score can be increased by up to 9.17% and 2.88% on HPRD and DIP respectively compared with using SLPC algorithm alone. To sum up, the experiment results demonstrate the effectiveness of using node embeddings and complex vectors for detecting protein complexes from PPI networks. In future work, we will further explore how to combine node embeddings with biological resources for predicting complexes from PPI network.