Precise prediction of phase-separation key residues by machine learning

Precise prediction of phase-separation key residues by machine learning

Datasets of phase-separating proteinsGiven the shortage of assets on phase-separating (PS) proteins and the complexity in defining part separation, we aimed to assemble knowledge comprehensively. Initially, we collected phase-separating proteins from numerous sources, together with PhaSepDB37, LLPSDB38, DrLLPS39 and PhaSePro40 (Supplementary Fig. 1a, Supplementary Data File 4). We manually retrieved scaffold proteins pivotal in part separation, serving as main parts present process liquid-liquid part separation both independently or at the side of co-scaffolds. This aggregation yielded 488 phase-separating proteins throughout 47 species, known as MixPS488. Our secondary goal was twofold: to conduct methodological comparisons and to gauge the influence of dataset dimension. From PhaSepDB. we collected 237 PS-Self proteins (proteins that may bear self-assembling PS in vitro) spanning 42 species, termed MixPS237. Additionally, we particularly curated a dataset targeted on human-specific phase-separating proteins. Integrating the most recent literature findings, we constructed hPS167, comprising 167 human part separation scaffold proteins.Datasets of non-phase-separating proteinsGiven our assortment of PS proteins from 47 organisms, we assembled corresponding proteomes from the Swiss-Prot database to function background proteins. To decrease knowledge redundancy, we particularly chosen 7 organisms with not less than 10 recorded situations of PS proteins in our datasets (Homo sapiens, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, Mus musculus, Xenopus laevis, Schizosaccharomyces pombe). Subsequently, protein sequences documented in MixPS488, MixPS237, and hPS167 datasets have been excluded. As part separation is deemed to be pushed by multivalent interactions between a number of folded domains or disordered domains, we extracted single-domain proteins from background proteins as destructive candidates, using PfamScan79 towards the Pfam-A database. The remaining proteins underwent blastclust algorithm80 evaluation with a sequence id threshold set at 0.3 to decrease sequence similarity. Ultimately, 16851 proteins, assembly the standard management standards, constituted the non-phase-separating (non-PS16851) protein set.Additionally, we developed a human-specific dataset of non-phase-separating proteins by utilizing UniProt81 database key phrases ‘human’ and filter criterion ‘Reviewed’, excluding hPS167. Further refining these single-domain proteins based mostly on a pairwise sequence id of lower than 30%, yielded a subset of 5754 proteins (non-hPS5754).Construction of coaching and testing datasetsThe phase-separating proteins throughout the Mix-PS488, Mix-PS237, and hPS167 datasets underwent additional refinement by limiting sequence similarity between any two proteins to under 30%, leading to 379, 172, and 135 optimistic samples, respectively. To generate destructive samples, an equal quantity of proteins have been randomly chosen from non-PS16851 and non-hPS5754 to make sure species specificity. This iterative course of was repeated 100 occasions. Subsequently, the paired optimistic and destructive samples have been divided into coaching (70%) and unbiased testing (30%) datasets, leading to 100 coaching datasets and 100 testing datasets. This process led to the creation of Training datasets for Training_I_MixPS488, Training_II_MixPS237, and Training_III_hPS167. Correspondingly, the unbiased take a look at datasets comprised Independent_Test_I_MixPS488, Independent_Test_II_MixPS237, and Independent_Test_III_hPS167. Additionally, human proteins have been excluded from the MixPS488 and MixPS237 datasets to assemble a non-human take a look at set, particularly Independent_Test_IV_NonHuman_MixPS237 and Independent_Test_V_NonHuman_MixPS488. The closing analysis on the unbiased take a look at dataset represents the common efficiency throughout these 100 units of knowledge.Overview of the PSPHunter algorithmIn our earlier works, we developed a sequence of algorithms to foretell functionally vital residues by combining machine learning- and template-based strategies82,83. Here, this framework was prolonged to the prediction of phase-separating proteins and key residues. To assemble PSPHunter (Fig. 1a), we characterised every protein utilizing 4 teams of sequence-based descriptors together with amino acid composition, evolutionary conservation, predicted useful web site annotations, and phrase embedding vectors. Additionally, we carried out 2 teams of useful options together with protein annotation data and community properties. Based on the complementarity of sequence attributes and useful attributes, we constructed a machine learning-based mannequin for phase-separating protein prediction. In addition, by elaborating on the sensitivity of word2vec characteristic to sequence modifications in response to part separation variations, we purpose to convey that the characteristic of word2vec can seize delicate alterations within the protein sequence, which can correspond to key residues chargeable for key the part separation.Amino acid compositionAccording to Quiroz’s analysis about how intrinsically disordered protein encoded part behavior84,85,86, we categorised every amino acid into classes of polar amino acids (N, Q, S, T), charged amino acids (R, Ok, D, E), hydrophobic amino acids (L, A, V, I, F, Y, M, H, W, C). G and P are positioned right into a separate class given their uncommon construction in triggering part separation. In this research, we calculated two options to symbolize residue composition. The first characteristic is the share of all residues in every class to the whole quantity of amino acids. The second characteristic is the share of the consecutive two amino acids. Specifically, each two consecutive amino acids are thought to be a unit and the class data was additional assigned to the unit. Then the odds of doublet classes resembling polar-polar, polar-charge have been calculated to symbolize the second residue composition characteristic. Collectively, we generated a 20-dimensional vector to symbolize the residue composition characteristic.Evolutionary conservationEvolutionary conservation options consist of three components: PSSM, distribution of residue conservation rating, and HMM profile. PSSM, also called Position-Specific Scoring Matrices, is a sequence profile comprising the evolutionary data of a protein sequence. In order to assemble this profile, a search towards the NR (Non-Redundant) database from NCBI was carried out for every question, using the PSI-BLAST program87 (v 2.2.31+) with the parameters (j = 3 and e = 0.001). By following this process, a substitution frequency matrix was obtained, representing the chance of amino acid substitutions at every place among the many 20 completely different amino acid sorts.To characterize every protein, a Z-score transformation was initially utilized to every row of the matrix. Subsequently, for every column, the common rating for every amino acid was computed. As a end result, all the matrix was condensed right into a 20-dimensional vector that encapsulates the evolutionary properties of every residue kind. This vector was then utilized to summarize the common scores based mostly on the classes of polar, charged, hydrophobic, and GP residues. Ultimately, to symbolize every protein, a concise four-dimensional vector was generated, which encapsulates the summarized data from the aforementioned classes.In addition to PSSM, we carried out relative entropy88 generated based mostly on the output by PSI-BLAST. Each residue of the question sequence obtained a conservation rating that would attribute its evolutionary signature. To symbolize the distribution of conservation rating for every protein, we calculated 5 statistical traits together with most, minimal, first quartile, second quartile and third quartile. The 5-dimensional vector was due to this fact used to symbolize the residue conservation rating.The HMM profile, particularly known as a profile hidden Markov mannequin (HMM), is a robust instrument for elucidating the distant homologous relationships that exist amongst proteins. In our research, we adopted the HHblits89 program (v 2.0.16) with default parameters to generate the HMM profile. This program facilitated a complete search of the question protein towards the UniProt20 database. Similar to the era of the PSSM profile, the HMM profile of every protein was first Z-score normalized by row after which averaged by column, which might end in a 20-dimensional vector in response to every residue. This 20-dimensional vector was additional condensed to 4 by summing the common scores in accordance with residue classes of polar, cost, hydrophobic and GP.Predicted useful web site annotationsSequence-derived useful options can be utilized to replicate the useful desire of a protein, and the intrinsically dysfunction area and protein post-modification had been authorized in relation to part separation. In the present research, the secondary construction states of residues and the accessible residues with relative solvent accessibility higher than 20% have been assigned by the SPIDER2 program90. The disordered residues have been recognized utilizing the SPINE-D program91 (v 2.0.0). RNA and DNA binding residues have been predicted by SNBRfinder92. For every sequence, we computed the odds of helix residues, sheet residues, coil residues, accessible residues, disordered residues, RNA binding residues and DNA binding residues. We adopted GPS93,94,95,96, a complete PTM predictor, to extract the all of the potential phosphorylation web site, methylation web site, S-nitrosylation web site and palmitoylation web site of every protein. Regarding to the anticipated mutation data, we utilized Rhapsody97(v 1.0) to conduct a saturation mutagenesis of every residue. According to the output of Rhapsody, we computed the odds of deleterious and impartial mutations. In addition, we carried out the protein size to symbolize the scale of protein. Finally, every protein was represented by a 14-dimensional vector.Word embedding vectorsWord embedding is a way broadly utilized in pure language processing and has been utilized to computational biology98,99,100. In this research, we leveraged phrase embedding to research protein sequences. To facilitate this evaluation, we adopted the word2vec method101, which is a well-liked strategy for developing the distributed representations. To apply phrase embedding to protein sequences, we handled every protein sequence as a sentence, and its subsequences have been thought-about as particular person phrases. By doing so, we aimed to seize the inherent construction and context throughout the sequences. The distributed representations have been then constructed utilizing the word2vec methodology, which enabled us to generate numerical vectors that encode the semantic properties of every phrase (subsequence) within the protein sequences.Generally, coaching a phrase embedding mannequin sometimes requires a corpus, which serves as the premise for capturing the relationships between phrases. In our analysis, we chosen the corpus by testing two datasets, particularly PS135 and SwissProt, which supplied a various vary of protein sequences for evaluation. Following the advice of earlier studies102, every protein sequence was remodeled into three sequences that have been composed of nonoverlapping 3-grams. To implement the word2vec methodology, we adopted the Gensim bundle (https://radimrehurek.com/gensim/) and employed a bag-of-words mannequin. Several parameters have been set to make sure optimum efficiency. The most distance between the present and predicted phrases was set to 70, whereas the dimensionality of phrase vectors was set to 60 based mostly on the parameter choice utilizing the PS135 dataset (Supplementary Fig. 1f). Consequently, every protein was represented by a 60-dimensional vector.Protein annotation dataIn this part, we utilized the experimental validated PTM and mutation data to annotate every protein. We downloaded 4 sorts of post-modification data together with phosphorylation, acetylation, ubiquitination and methylation from PhosphoSitePlus103 database. The PTM frequency of every modification was outlined because the quantity of annotated websites divided by the protein sequence size. We extracted the mutation data from the HuVarBase69 database which is a complete database integrating assets of 1000 Genomes, ClinVar, COSMIC, Humsavar and SwissVar. By looking with uniport ID of every protein, we lastly obtained 774,863 variants from 18,318 proteins. Among the variants 702,048 are illness inflicting and 72,815 are impartial variants. The protein expression abundance data was extracted from PAXdb104 (2017, organ: WHOLE_ORGANISM). The protein age of every question was inferred utilizing phylogenetic evaluation and extracted from ProteinHistorian, by which we chosen ‘PPODv4_Jaccard_families’ because the protein household database and ‘Wagner parsimony’ because the ancestral reconstruction algorithm105. The important gene listing together with 1,216 genes is the consensus outcomes generated by the genome-wide single-guide RNA screening106 and the haploid gene-trap screening107. The housekeeping gene listing consists of 8,874 genes expressed in all tissues108. Collectively, every protein was denoted by a 11-dimensional vector from an evolutionary perspective.Network propertiesBecause phase-separating proteins typically have related organic capabilities, these proteins can be anticipated to be densely situated in protein-protein interplay (PPI) networks. Based on this assumption, we established networks by utilizing the PPIs from the HIPPIE database109. Based on the entire PPI networks, we calculated 4 generic properties, which comprised diploma, betweenness, clustering coefficient, common neighbor diploma.Classification mannequinAfter extracting the above options, we developed fashions to foretell phase-separating proteins. In this work, we evaluated six sorts of ma Rhapsodychine learning algorithms, together with assist vector machine (SVM), naïve Bayesian classifier (NB), neural community (NN), random forest (RF), mild gradient boosting machine (LightGBM), and excessive gradient boosting (XGBoost). All these algorithms have been carried out utilizing the scikit-learn package110 (v 1.2.0). The parameters c and g of the radial foundation operate have been set to 2 and 0.125, respectively, in SVM, and the quantity of timber was set to 500 in RF. Other parameters have been set to the default within the implementation course of. To set up the ultimate mannequin, we built-in the multifaceted options by testing three sorts of ensemble methods, together with the common of the chance scores from the primary layer, the direct integration of completely different options right into a single mannequin, and the two-layer stacking mannequin. By evaluating the efficiency of completely different ensemble methods (Supplementary Fig. 2a–c), the direct integration mannequin based mostly on random forest was lastly chosen.To consider the predictive efficiency, 5-fold cross-validation was carried out on the first dataset. The goal dataset was initially divided into 5 subsets, every containing an equal quantity of proteins. During every cross-validation iteration, one subset was employed because the take a look at set, whereas the remaining subsets served because the coaching set. This course of was repeated 5 occasions, with every subset serving because the take a look at set as soon as, enabling the calculation of the common efficiency.Evaluation indicatorsThe main measure of prediction efficiency was assessed utilizing the realm beneath the receiver working attribute curve (AUC). The AUC quantifies the classifier’s capacity to differentiate between true positives and false positives at numerous classification thresholds. Additionally, different well-established metrics, together with recall, precision, F1-score, accuracy (ACC), and Matthews correlation coefficient (MCC), have been computed as follows: TP, TN, FP, and FN symbolize the numbers of true positives, true negatives, false positives, and false negatives, respectively.$${Recall} ,=frac{{{{{{rm{TP}}}}}}}{{{{{{rm{TP}}}}}}+{{{{{rm{FN}}}}}}}$$
(1)
$${Precision} ,=frac{{{{{{rm{TP}}}}}}}{{{{{{rm{TP}}}}}}+{{{{{rm{FP}}}}}}}$$
(2)
$$F1-{rating}=frac{2times {{{{{rm{Recall}}}}}}occasions {{{{{rm{Precision}}}}}}}{{{{{{rm{Recall}}}}}}+{{{{{rm{Precision}}}}}}}$$
(3)
$${Accuracy}=frac{{{{{{rm{TP}}}}}}+{{{{{rm{TN}}}}}}}{{{{{{rm{TP}}}}}}+{{{{{rm{FN}}}}}}+{{{{{rm{TN}}}}}}+{{{{{rm{FP}}}}}}}$$
(4)
$${MCC}=frac{{{{{{rm{TP}}}}}}occasions {{{{{rm{TN}}}}}}-{{{{{rm{FP}}}}}}occasions {{{{{rm{FN}}}}}}}{sqrt{({{{{{rm{TP}}}}}}+{{{{{rm{FN}}}}}})({{{{{rm{TP}}}}}}+{{{{{rm{FP}}}}}})({{{{{rm{TN}}}}}}+{{{{{rm{FP}}}}}})({{{{{rm{TN}}}}}}+{{{{{rm{FN}}}}}})}}$$
(5)
Feature significance and have choiceFeature importances have been computed utilizing the fitted ‘feature_importances_‘ attribute from the scikit-learn package110. We carried out characteristic choice on all 123 sequence and useful options. These options have been then ranked based mostly on their significance worth, and we assessed their contribution by progressively growing the quantity of options. We noticed that the mannequin’s efficiency reached its greatest when the quantity of options reached 60, with no important enhancements upon additional improve. Consequently, we settled on 60 options for the ultimate mannequin.Key residue detectionIn this research, we employed three distinct methods for screening these key residues. Specifically, the primary technique is designing options to make the PSPHunter delicate to amino acid variations within the sequence. Initially, we incorporate word2vec options, which encode the mixture patterns of brief sequence fragments. Furthermore, we compress and combine residue-level options resembling PSSM and HMM, enabling the transmission of sequence variations on the amino acid degree to the protein scale. These residue-level descriptions improve the sensitivity of PSPHunter in detecting sequence variations which are related to the protein’s part separation capability. The second technique aimed to review the affect of every amino acid on the potential of the protein to bear part separation. We achieved this by treating 20 consecutive amino acids as a unit to symbolize a given residue at its center place (Fig. 3a, Supplementary Fig. 4a). We chosen a truncated-unit worth of 20 amino acids based mostly on the common size of phase-separation proteins (~600 amino acids). This bin dimension is 1/30, which we think about comparatively cheap. It is price noting that theoretically, a bigger worth for this parameter results in a higher variation within the delta PSPHunter rating. Users have the pliability to regulate this parameter in our standalone model.Subsequently, we employed PSPHunter to calculate the phase-separation chance for every unit-deleted protein. After evaluating the impact of all truncation prospects, we will get hold of a curve of the impact of every residue (excluding 10 residues at every finish) in response to every truncation. The residues with the higher deviation from the common part separation capability are thought-about because the key residues.Key area detectionThe consecutive key residues are handled as key area (Supplementary Fig. 4a). To stability PSPHunter’s sensitivity in figuring out key residues, we purpose to seize consecutive amino acids with the best influence on part separation. To obtain this, we’ve set an empirical parameter, choosing 20-40 top-ranked key residues as candidates, aligning with 1-2 occasions the truncated unit. This balances consumer comfort and prioritizes essential areas. We subsequent join consecutive amino acids in candidates to kind key areas. For occasion, we saved the highest 1% of the whole quantity of residues when the sequence size is larger than 2000, 2% when the sequence size is between 1000-2000, 4% when the sequence size is between 500-1000, and 5% when the sequence size is lower than 500. After that, the retained residues are connected in accordance with the sequence place which might additional kind to key areas.Other part separation predictorsPLAAC111, LARKS112, R + Y26, DDX4-like28, catGRANULE113, PScore114 and CRAPome115 have been first-generation phase-separating protein predictors summarized by a lately review72. Youn et al.42 additional utilized these predictors to evaluation the properties of stress granule and P-body proteomes. We obtained all the anticipated chances of every protein from its authentic supplemental data. Additionally, the anticipated outcomes of PhaSePred44 (http://predict.phasep.pro/), MAGS116 (https://github.com/ekuec/2019_StressGranuleFeatures/) and PSPredictor45 (http://www.pkumdl.cn/PSPredictor/) have been obtained from the corresponding net server and GitHub repositories.Cell traces and tradition situationsThe MCF7 cell line was a present from Dr. Hai Hu (Sun Yat-sen Memorial Hospital). The HEK293T cell line was kindly gifted by Dr. Jianlong Wang from lcahn School of Medicine at Mount Sinai. HEK293T have been grown in DMEM medium (Hyclone, SH30022.01) containing 10% FBS (LONSERA, S711-001S), and MCF7 have been grown in RPMI 1640 (Gibco, C11875500BT) containing 10% FBS (LONSERA, S711-001S). All cells have been cultured at 37 °C with 5% CO2.To set up exogenous expression of GATA3, Mut-Control, Mut-Key, and Mut+IDR within the MCF7 cell line, reaching ranges of expression much like endogenous GATA3 in MCF7, we first established an MCF7-shGATA3 cell line. Endogenous GATA3 expression on this cell line was silenced utilizing brief hairpin RNA (shRNA) delivered through doxycycline (DOX) remedy. The shRNA oligonucleotides have been designed utilizing the instrument obtainable at http://www.broadinstitute.org/rnai/public/gene/search. The ahead primer sequence for shGATA3 is CTAGGCCAAGAAGTTTAAGGAATATCTCGAGATATTCCTTAAACTTCTTGGCTTTTTG, and the reverse primer sequence is AATTCAAAAAGCCAAGAAGTTTAAGGAATATCTCGAGATATTCCTTAAACTTCTTGGC.Motif evaluationMotifs of phase-separating proteins and key residues have been found by the MEME 5.4.1 server117 with motif dimension = 6–8 and different parameters at default.Functional enrichment evaluationTo discover the useful roles of proteins concerned in part separation, we recognized the related GO phrases utilizing Metascape118, by which ‘Homo sapiens’ have been chosen because the background and all of the 898 part separation proteome have been chosen because the enter. Then ‘Custom Analysis’ was adopted to additional evaluation. The over-represented GO organic processes, mobile parts, molecular capabilities, and illnesses sorts have been reserved with default parameters.Protein expression and purificationcDNA encoding the proteins have been cloned into pET28a expression vector. The base vector was engineered to incorporate His-tag adopted by EGFP. All expression constructs have been sequenced to make sure sequence id. For protein expression, plasmids have been remodeled into BL21 E.coli (TransGen Biotech, CD601-02) and grown as follows. A recent bacterial colony was inoculated into LB media containing kanamycin and grown in a single day at 37 °C. Then the cells have been diluted 1:30 in 300 mL LB with freshly added kanamycin and grown at 37 °C for roughly 5 h to ensure OD600 as much as 0.6–0.8. Then IPTG (Solarbio, I1020-5) was added to 0.3 mM and progress continued in a single day at 16 °C for 18 h. Protein purification was carried out in accordance with the instruction of Protein Purification Kit (Cwbio, CW0894S). The recombinant EGFP fusion proteins have been concentrated in Amicon Ultra 30KDa centrifugal filters (Millipore, UFC803024) to be used.In vitro droplet formationThe recombinant EGFP fusion proteins have been concentrated and desalted to an applicable focus utilizing Amicon Ultra centrifugal filters (Millipore, UFC803024). And then the proteins have been added to droplet formation buffer, which consists of 50 mM Tris-HCl pH 7.5 (Thermo Fisher, 15567-027), 10% glycerol (Sigma, G5516), 1 mM DTT (Sigma, D9163), 10% PEG8000 (Sigma, H-209Z-0T968) and 125 mM NaCl (Sigma, S5150). The protein resolution was instantly loaded onto a do-it-yourself chamber, after which imaged with microscopy (Nikon Eclipse Ts2R-FL).Fluorescence restoration after photobleaching (FRAP)HEK293T stably expressing recombinant EGFP fusion proteins have been cultured in glass backside dish for twenty-four h. Stable cell traces have been obtained after drug choice. Fluorescence pictures of EGFP have been acquired on a Nikon A1+ confocal microscope with 488 nm laser utilizing a 100x oil-immersion goal lens (HP Apo TIRF 100xH, 1.49 NA, Nikon). The fluorescence depth of bleached cell at every time level was normalized by fluorescence depth at background area and the fluorescence depth of the adjoining unbleached puncta. The pictures have been analyzed utilizing NIS-Elements software program. The inside fluidity of in vitro droplets was additionally evaluated by FRAP.Puncta evaluationTo quantify the puncta of in cells, cells have been imaged by confocal microscopy utilizing the identical parameters throughout completely different teams (GFP-fused GATA3, management area truncated GFP-fused GATA3 and key area truncated GFP-fused GATA3). Using Imaris software program (Bitplane), the quantity of puncta in cells was calculated with the spot module, respectively.Protein extraction and western blotsProteins have been extracted in Cytobuster (Merck) at room temperature for 10 min. The proteins have been blended in 5×SDS buffer (Bio-Rad) and have been separated on 12% Bis-Tris gel at 100 V for 90 min, after which wet-transferred to a 0.45 um PVDF membrane (Millipore) in ice-cold switch buffer in 300 mA for two h or in 40 V for 12 h. After blocked with 5% BSA in TBS for 1 h at room temperature with shaking, the membrane was incubated with the first antibody in a single day at 4 °C (anti-GATA3, Rabbit, Monoclonal, ABclonal, Cat. quantity A19636, Lot quantity 4000000115, Dilutions/quantities 1:1000; anti-β-Tubulin, Rabbit, Polvclonal, ABclonal, Cat. quantity AC008, Lot quantity 3523022349, Dilutions/quantities 1:200). After washed 3 times with TBST for five min at room temperature, the membrane was incubated with 1:1000 secondary antibodies for 1 h at room temperature. After washed 3 times with TBST for five min at room temperature, the membrane was developed with ECL substrate (Thermo Fisher) and imaged utilizing a CCD digital camera.Reporting abstractFurther data on analysis design is obtainable within the Nature Portfolio Reporting Summary linked to this text.

https://www.nature.com/articles/s41467-024-46901-9

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