For the results yielded by the leave-one-out cross-validation, although the similarity-based approach alone achieved an accuracy of 0.8756, it was unable to predict the functions of proteins with no homologues. Comparatively, the pseudo amino acid composition-based approach alone reached an accuracy of 0.6786. Although the accuracy was lower than that of the previous approach, it could predict the functions of almost all proteins, even proteins with no homologues. Therefore, the combined method balanced the advantages and disadvantages of both approaches to achieve efficient performance. Furthermore, the results yielded by the ten-fold cross-validation indicate that the combined method is still effective and stable when there are no close homologs are available. However, the accuracy of the predicted functions can only be determined according to known protein functions based on current knowledge. Many protein functions remain unknown. By exploring the functions of proteins for which the 1st-order predicted functions are wrong but the 2nd-order predicted functions are correct, the 1st-order wrongly predicted functions were shown to be closely associated with the genes encoding the proteins. The so-called wrongly predicted functions could also potentially be correct upon future experimental verification. Therefore, the accuracy of the presented method may be much higher in reality.Although the number of available protein sequences is growing exponentially, functional protein annotations lag far behind. Therefore, accurate identification of protein functions remains one of the major challenges in molecular biology. In this study, we presented a novel approach to predict mouse protein functions. The approach was a sequential combination of a similarity-based approach, an interaction-based approach and a pseudo amino acid composition-based approach. The method achieved an accuracy of about 0.8450 for the 1 Recent advances in sequencing technology have identified a large number of proteins that perform a wide variety of functions in cellular activities. Knowledge of protein function is crucial to understanding the mechanisms behind cellular processes and preventing and treating disease. However, most of the proteins identified to date have unknown functions. Approximately 1% of the more than 13 million protein sequences available have been experimentally annotated with essential functions; the remaining proteins have been marked with putative, uncharacterized, hypothetical, unknown or inferred functions .et al. [Computational approaches can make up for the deficiencies of experiments. Following the success of the computational approach in sequence alignment and comparison, many computational techniques have been presented to determine protein functions during the last decade . The moset al.  construcet al.  uses seqet al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [et al. [i.e., 1 indicates interaction and 0 indicates no interaction). Additionally, the interaction between proteins can be strong or weak. The STRING database [et al. [Recently, a large body of protein-protein interaction networks has become available to explore the functional relationships between interacting proteins. There are many computational models for predicting protein-protein interactions –24. The et al.  pioneere [et al.  presente [et al.  used an  [et al.  extended [et al. , Letovsk [et al.  and Kour [et al.  used the [et al.  modeled  [et al.  and Arna [et al.  used dif [et al.  presentedatabase  is a pro [et al.  used a wGreat progress has been made in the computational protein function prediction field, where state-of-the-art prediction algorithms substantially outperform first-generation methods and contribute to subsequent experimental studies. However, there still remains considerable need for the improvement of the current tools . To thishttp://mips.gsf.de/genre/proj/mfungd/) [A total of 14,732 mouse protein sequences with their functional annotations were downloaded from the Mouse Functional Genome Database  , which imfungd/)  to removmfungd/) . CompareTable 1, there are a total of 24 functional categories. The balance between the specificity of the categories, human usability and requirements for subsequent bioinformatic applications is a general consideration in the design of an annotation scheme [S1 Table.As shown in n scheme . In linehttp://string-db.org/) [Protein-protein interaction pairs in mice were retrieved from STRING  , which iBecause the manner in which the entries in the MfunGD are numbered differs from the method in STRING, comparison requires the mapping of associations between them. The mapping was performed using the BioMart database . A totalP based on n known-function proteins P1, P2, …, Pn, assuming that the function categories are f1, f2, …, f24. One protein may belong to several function categories . Thus, we used a 24-dimensional vector Fi=  to indicate the function categories of a protein Pi, where dj,i isThe aim of this study is to predict the function of a given protein P) against the known-function proteins  in our dataset. The alignment score between P and Pi represents their similarity. This score is denoted as si. The predicted protein function scores of protein P are given by a 24-dimensional vector W and are calculated bywj denotes the score of a protein having function fj. Elements in vector W are sorted from highest to lowest to obtain the predicted functions of protein P. A function receiving a high score is more likely to be an actual function of a given protein according to GBA [W. We provide an example to elaborate this point. If we obtain w23 ≥ w2 ≥…≥ w5, protein P is most likely to have function f23, followed by function f2 and so forth. The least likely function is f5. For convenience, we call function f23 the 1st-order prediction, function f2 the 2nd-order prediction and function f5 the 24th-order prediction. This scheme to define the predicted results for multi-label classification problems has been used in previous studies [Proteins with similar sequences likely share the same or similar functions. Therefore, it is possible to predict protein functions based on sequence similarities. Herein, we used the PSI-BLAST program  to align the given unknown-function protein  is ti. The predictive functional value is given by Y, a 24-dimensional vector computed byyj denotes the score of a given protein with function fj. Similar to the sequence similarity-based approach, each element yj in vector Y is sorted from highest to lowest to obtain the function sequence of protein P. For example, if we obtain y12 ≥ y21 ≥…≥ y2, protein P is most likely to have function f12, followed by f21, and the least likely function is f2. In this study, we call function f12 the 1st-order prediction, function f21 the 2nd-order prediction, and function f2 the 24th-order prediction.Proteins in a cell interact with each other to perform particular functions. Following the GBA rule , interacX1X2⋯XN, where Xi is an amino acid residue, then L(X) is the property value of amino acid X in the physicochemical and biochemical respects. The normalized property value is computed byfi is the occurrence frequency of amino acids. In this article, we set λ and ϖ to 50 and 0.15, respectively. Five physicochemical and biochemical properties of amino acids, i.e., codon diversity, electrostatic charge, molecular volume, polarity and secondary structure, are used to compute the PseAAC of protein sequences. These properties are retrieved from references [Table 2. For each category of property, we used the last 50 digits in the formula (6). In addition to the frequencies of 20 amino acids, we used a 270 (20+5*50)-dimensional vector to represent a protein sequence. The cosine distance between the query protein P and the known-function proteins Pi is given byVP and P and Pi, respectively. The predicted function value of the query protein was computed byR are sorted from high to low, such as r3 > r12 > ⋯ > r1, where protein P is most likely to have function f3, second most likely to have function f12, and least likely to have function f1.Protein sequences can be characterized by pseudo amino acid composition, which was proposed by Chou to predict protein subcellular localization  and has ferences –55, as ljth-order prediction is given byUj,i = 1 if the function category of the jth-order prediction is actually the function category of protein P according to current knowledge. Otherwise, Uj,i = 0.We used two cross-validation methods: leave-one-out cross-validation and ten-fold cross-validation to examine the performance of the presented methods. In the ten-fold cross-validation method, the original dataset are randomly and equally divided into ten parts. Samples in each part are singled out as testing samples, while samples in other nine parts are used as training samples. For the leave-one-out cross-validation approach, each sample in the original dataset is taken as a testing sample in turn and the remaining samples are used as training samples. To assess the experimental results, the prediction accuracy for the Table 3. The similarity-based approach yielded the best accuracy of 0.8756 in the 1st-order prediction but could not predict functions of 2,226 proteins because they have no homologues with annotated proteins in the dataset. The interaction-based approach produced a lower prediction accuracy of 0.7535 than the similarity-based approach and could not predict the functions of 1,939 proteins that have no interactions with annotated proteins. The PseAAC-based nearest-neighbor approach performed worst in terms of the prediction accuracy, but it was able to predict the functions of all the test proteins. The results indicated that each approach has its strengths and limitations. Table 3also shows that the 1st-order prediction performed best, followed by the 2nd-order prediction and the 3rd-order prediction, indicating the predicted function sequence for each test protein is quite reasonable.The performance of the three approaches for a dataset consisting of 12,478 proteins evaluated by the leave-one-out method is listed in Fig 1. The similarity-based approach performed best, followed by the interaction-based approach and the PseAAC-based nearest-neighbor approach. The similarity-based approach was much more accurate (by 0.11) than the interaction-based approach in the 1st-order prediction and more accurate (by 0.07) in the 2nd-order prediction, while the latter was much more accurate (by 0.09) than the PseAAC-based approach in the 1st-order prediction and more accurate (by 0.02) in the 2nd-order prediction. The results confirmed the advantage of the similarity-based approach over the other two approaches in terms of the prediction accuracy. As mentioned previously, the similarity-based approach cannot address non-homologous proteins, and the PseAAC-based approach can predict the functions off all proteins despite the lower prediction accuracy. Therefore, it is wise to jointly utilize the three methods to predict the protein functions.The three approaches were compared on different testing datasets in the above paragraph. For a fair comparison, we generated a common dataset where each protein could be tested by the leave-one-out method. The common dataset consisted of 8,481 proteins. The accuracies of the three approaches versus order are plotted in Table 3. The accuracy of the combined method was much higher than the interaction-based and PseAAC-based approaches and slightly lower than the similarity-based approach. However, the combined approach could predict all proteins, whereas the similarity-based approach could not. Therefore, the combined method has wide application at the cost of reduced prediction accuracy. For proteins with no homologues or interactions with annotated proteins, the best alternative is to use the combined approach. The contributions of the three approaches to the final predictive performance are shown in Table 4. The similarity-based approach contributed most, predicting more than 80% of all proteins and yielding an Acc1 of 0.8756, followed by the interaction-based approach and the PseAAC-based approach.We combined the three approaches to predict the functions of proteins to make use of their respective advantages and disadvantages. For a given protein, we first employed the similarity-based approach. If the protein had no homologues, we applied the interaction-based approach. If the protein could not be predicted by the interaction-based approach, we used the PseAAC-based nearest-neighbor approach. The performance of the combined approach based on leave-one-out validation on the 12,478 proteins is shown in the fifth row of st-order, 2nd-order and 3rd-order predictions in each time are listed in Table 5. Compared to the prediction accuracies yielded by the leave-one-out cross-validation that are listed in Table 3, the performances of these two cross-validation methods are almost at the same level, which indicates that the combined method is still quite effective when there are no close homologs are available. Furthermore, it can be observed from Table 5that the standard deviations for the 1st-order, 2nd-order and 3rd-order predictions are quite low, indicating the stability of the combined method.To fully indicate the effectiveness of the combined method, we also used ten-fold cross-validation to examine this method. Because the predicted results yielded by this cross-validation method may influenced by the division of the dataset, the combined method was executed five times with different divisions. The prediction accuracies for the 1FA and the predicted function was FB, the prediction was not correct. It is conceivable that with the development of our knowledge, the protein could be found to possess FB; thus, the prediction could be correct in the future. The currently annotated functions of the proteins are a subset of their actual functions. In this respect, some "wrong" predictions by our method in the current dataset may be correct. Next, we explore these wrong predictions.In this study, the assessment of the predicted results was based on currently annotated proteins. Therefore, "right" and "wrong" predictions were relatively defined. For example, if the studied protein had function st-order prediction is the most important, we investigated proteins with "wrong" 1st-order prediction but with "right" 2nd-order prediction. Because these proteins might possess the predicted 1st-order functions, we called them "false-wrong" 1st-order predicted proteins. As mentioned above, the combined method was evaluated by both the leave-one-out and ten-fold cross-validations. Because the predicted results yielded by the ten-fold cross-validations are not unique, we selected the predicted results yielded by the leave-one-out cross-validation to further analyze wrongly predicted proteins. In the leave-one-out test on the 12,478 proteins, we identified 966 such proteins: 658 proteins from the similarity-based approach, 258 proteins from the interaction-based approach and 50 proteins from the PseAAC-based approach. All these proteins are listed in S2 Table.It is worth performing further analysis on the wrongly predicted proteins. Because the 1The goal of this process was to further validate our method. If we found evidence indicating that any of these proteins possessed the "wrong-predicted" functions, the actual prediction accuracy of our method would be much higher than presented above. This would allow the method to be applied to new protein function discoveries, but further experimental validations may be required for these proteins.st-order functions were wrong and whose predicted 2nd-order functions were correct. There were 966 such proteins. Forty protein genes were closely related to "false-wrong" 1st-order predicted functions, of which sixteen were predicted by the similarity-based approach, twenty-two were predicted by the interaction-based approach, and two were predicted by the PseAAC-based approach, as listed in Table 6, Table 7and Table 8, respectively.We explored the functions of proteins whose predicted 1Table 6, sixteen significant proteins were predicted by the similarity-based approach. The proteins MYO1G, NEO1 and SDK1 were predicted to have the 1st-order function ‘subcellular localization’, suggesting that these gene products have specific cellular localizations. MYO1G has been reported as a hematopoietic-specific myosin that localizes to the plasma membrane [st-order function ‘protein fate (folding/modification/destination)’. MAPKs, BMP, KSR1, PRKD2, STRADA, NTRK3 and EPHB6 are responsible for protein phosphorylation and signal transduction [st-order function ‘protein with binding function or cofactor requirement’. Protein tyrosine kinase 7 (PTK7) was reported to interact with the Wnt family proteins [As shown in membrane . Moreovesduction –63. KLK9sduction , 65. Plasduction . KRT2, Pproteins  and playproteins . The intproteins , trichopproteins  and Albaproteins . SPEG coproteins .Table 7, twenty-two significant proteins were predicted by the interaction-based approach. ADRM1, ATP6V1F, AURKAIP1, BYSL, DHFR, DTYMK, GNE, HPD, HPS3, MAGOH, MED17, NUDC, PNO1, RGN, RPS25, SHCBP1 and SHFM1 were predicted to have the 1st-order function ‘subcellular localization’. ATPase, H+ transporting, lysosomal 14 kDa, V1 subunit F (ATP6V1F) and adhesion regulating molecule 1 (ADRM1) are likely to localize on the plasma membrane based on their biological functions. Several gene products are specifically localized in the nucleus, including AURKAIP1, DHFR, MAGOH, MED17, NUDC, PNO1 and RGN. Among them, NUDC is a nuclear movement protein that interacts with dynein [st-order function ‘protein with binding function or cofactor requirement’. NCAPH  binds to the chromosome and regulates the cell cycle [st-order function ‘cellular communication’. NPFF (neuropeptide FF) is an FMRFamide-like peptide with antiopiate properties that is involved in cellular communication as a part of the neurotransmitter system [As shown in h dynein . Mediatoh dynein , 75. Theh dynein . The ribh dynein . The proh dynein . SHFM1  has the known function of ‘protein fate ’ as it regulates cyclic AMP-dependent protein kinase (PKA) signaling in both a spatial and temporal manner. The specific subcellular localization of AKAP2 is closely related to its function [As shown in function . AKAP2 hfunction . As anotfunction  and indufunction , 92. Prefunction , 94. TheThe accurate identification of protein functions remains challenging in the post-genomic era. In this article, we employed protein sequence homology, weighted interactions and pseudo amino acid composition to explore protein functions. The experimental results indicate that homologous proteins are more likely to share functions than interacting proteins, which in turn share more functions than proteins with similar physicochemical and biochemical properties. Weighted interactions can be used to annotate the functions of proteins with no known homologues. The PseAAC-based approach was used for the functional annotation of proteins. These three approaches are complementary and represent an optimal combination for predicting protein functions. Further analyses of wrongly predicted functions will validate the effectiveness of the proposed method.S1 TableThe first column of the file is the protein entry ID in MfunGD. The other columns are the functional categories to which the protein belongs.(CSV)Click here for additional data file.S2 Tablest-order function predictions but "right" 2nd-order function predictions in our dataset were called "false-wrong" 1st-order predicted proteins. There were 658 such proteins based on the similarity-based approach, 258 based on the interaction-based approach and 50 based on the PseAAC-based approach. These proteins are listed on three separate sheets. The proteins may possess the function indicated by the 1st-order prediction and are worthwhile subjects for future analysis.Proteins with "wrong" 1(XLSX)Click here for additional data file.


MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 ( Transcription factors belonging to the SCAN zinc finger family have been implicated in a number of cellular malignancies , including a number of cancer-related genes. In addition, a subset of SCAN domain only factors (SCAND), which lack the DNA binding domains, has been discovered and suggested to function as regulators of the intact SCAN zinc finger factors . There are three known isoforms of The existence of highly conserved SCAN domains in the SCAN zinc finger family suggests that interactions between its family members may occur through hetero-dimerization  entry: in silico high-throughput approach based on the PRISM energy function . Gene ontology enrichment was performed using the gprofiler  were downloaded and pre-processed using the R-package TCGAbiolinks  for all the cancer studies deposited at TCGAAnalyse_Preprocessing function from TCGAbiolinks, which is a function that estimates the Pearson correlation coefficient among all pairs of samples (38). Samples with a correlation lower than 0.6 within the same cancer study were discarded (38). Next, we normalized the expression data using the TCGAnalyze_Normalization function from TCGAbiolinks. Normalization procedures included adjusting for GC-content, gene-length effects on read counts and full quantile filtering of datasets using a cutoff value of 0.25 , to evaluate how this would potentially affect results. The normalized counts were log2 transformed (pseudo count = 1) to overcome the problem of extreme values due to differences in sequencing depth , CanProVar ] from different cancer genomics databases including Provean , >2 (Mutation Assessor), >0.5 (Polyphen2 and PON-P2), <0 (SNAP2), and >0.75 (MutPred) to discriminate between the deleterious/partially deleterious and the neutral mutations. We considered a mutation as “damaging” if it was classified as being within C45, C55, and C65 classes of AlignGVGD.Prediction of the functional effects of mutations was carried out using different sequence-based methods. Specifically, we integrated FoldX energy function from the newest release of the FoldX suite (27) to carry out in silico saturation mutagenesis using a Python wrapper that we recently developed (to be published and available on request). The wrapper allows for the introduction of all possible 19 point mutations at each position of the protein using multithread calculations. Calculations with the wrapper resulted in an average ΔΔG (differences in ΔG between mutant and wild type variant) for each mutation over the whole NMR ensemble of 20 conformers for both the MZF1 monomer  and the dimer . The ensemble was used to account for flexibility in the protein since FoldX energy function only allows for local conformational changes. We calculated the ΔΔG between mutants and wild type variants associated with protein stability and in relation to the formation of the dimeric MZF1 complex. We applied the BuildModel module from the FoldX suite and five independent runs for mutations in our scan. The typical prediction error of FoldX is about 0.8 kcal/mol . We estimated the ΔG of unfolding for each mutant variant (ΔGumutant) from the equation:Moreover, we implemented a correction to the mutant is the estimated unfolding ΔG of the mutant variant, ΔGuWT_exp is the experimental unfolding ΔG of the wild type variant, ΔΔGFoldx is the ΔΔG estimated by FoldX, m and b are correction terms. In particular, m and b where selected according to the work by Tawfik et al.  derived either from linear regression or Principal Component Analysis (PCA). The latter correction led to essentially identical distribution between the experimental and computational data, therefore all the FoldX values were corrected using the PCA equation ΔΔGFoldX = −0.078 + 1.14 ΔΔGExperimental .Where ProTherm. We thus here used as an arbitrary reference a value of 5 kcal/mol as ΔGuWT_exp in the calculation of ΔGumutant.In our calculations, we cannot use an experimental value of unfolding ΔG for the wild-type variant of MZF1 SCAN domain since there are no experimental data available in the literature at the best of our knowledge. Nevertheless, values in the range of 5–15 kcal/mol are generally obtained for the net free energy of unfolding of proteins  which is <122 ppm , 2422 short-range, and 623 intermolecular.PPM One  approach was used as implemented in the pcrit = 20%), as previously applied in other cases of study . To evaluate the convergence of the PSN properties over the simulation time, we used the Jack-Knife resampling method . Moreover, we used the interaction partners, from I2D, for which at least one experimental structure was available in the PDB. We also included two additional SCAN domain proteins, the Zfp206 and Peg3 [PDB entries 4E6S . Residues known to contribute to MZF1 homo-dimerization are also conserved at the interface of other SCAN-SCAN complexes predicted by PRISM, such as F47, R48, Y52, P58, A61, L65, R66, W72, L73, P75, K82, L84, V88, Q93, P97  and different types of cancer, we analyzed genomic profiling data from 10173 samples stratifying into 24 different cancer studies—deposited in GA Table .MZF1 is expressed at an overall higher or lower level in tumor vs. normal samples in 19 out of the 24 cancer studies that we analyzed, strongly supporting its crucial but diverse role in cancer  along with 21 mutations which have not been associated with cancer so far Table .We subsequently employed both sequence- and structure-based methods to predict the impact of these mutations on function, stability and protein-protein interactions.MutPred, Provean PON-P2, Polyphen 2, and Mutation Assessor predictors yielded similar results, as illustrated by the hierarchical clustering reported in Figure MutPred also predicted with significant p-values (p < 0.05) a gain or a loss of Post-Translational Modifications (PTMs) upon certain mutations, such as gain of glycosylation in E41K (assuming that the lysine is hydroxylated), gain of phosphorylation at F47S and loss of methylation at R48 and R51.We integrated seven sequence-based methods mainly based on evolutionary information to assess the pathogenic potential of the mutations in the MZF1 SCAN domain. The methods employed were in agreement for most of the mutations even if a complete consensus could only be identified for nine mutations Figure . MutPredin silico saturation mutagenesis scan based on the FoldX energy function using the whole NMR conformational ensemble of the MZF1 SCAN deposited in the PDB  with respect to the wild type. The saturation scan allowed us to assess the impact of the mutations on protein structural stability (the scan is performed on the MZF1 monomers) and local effects influencing the interface for monomer-monomer interaction (when the scan is performed on the MZF1 dimer). Negative ΔΔG values indicate variants that are more stable than the wild type, whereas positive values indicate that the mutant variants are less stable than the wild type MZF1 protein. Thus, mutant variants with ΔΔG >0 upon the monomer scan have a higher population of  unfolded structures that are prone to aggregation, misfolding or degradation. Mutant variants with ΔΔG >0 upon the protein-protein binding scan have a decreased monomer-monomer binding affinity. The high-throughput mutation scan allowed us not only to assess the impact of MZF1 mutations collected from the mutation databases, but also to predict the effect of any other possible amino-acid substitution  of the mutant variants reported in Table Gumutant values are also reported in Table To better appreciate the effects of these mutations on protein stability, we also estimated a ΔG of unfolding  for the MD ensemble Figure  allowed A protein structure network (PSN) approach  and arginine sites  along with C69. C69 is central in the MZF1 network and has a key role in the path communications over long distances since it allows the SCAN dimerization region to mediate effects over long distances to solvent exposed sites, which might work as recruitment point for other cofactors  are likely candidates for cancer-related mutations and structural analyses. The ones presented here are a valuable addition to the molecular characterization of the mutational landscape of oncogenes or tumor suppressors. We could also identify a subset of cancer passenger mutations, i.e., mutations found in cancer patients but unlikely to have any major impact on cancer pathways since these have neutral effects on structure, interactions and protein dynamics.However, caution has to be taken since the energy function employed here can only predict local effects and will neglect all the long-range contributions that can perturb in a cascade of conformational changes to very distal sites. To overcome this issue, the integration of experimentally-validated molecular dynamics, to better account for conformational heterogeneity of the protein, and graph analyses, to identify paths of communication between distal sites, can help to achieve a more complete description of the intricate structural mutational landscape of cancer-related proteins.In particular, our study pointed out a major role of a cysteine residue (C69) at the biological interface for protein-protein interactions mediated by the SCAN domain of MZF1. Alterations at this site have only been reported in cancer cell lines so far, but our predictions suggest that it can be a critical disease-related hotspot. Indeed, it is at the cross-road between multiple key paths of communication from the biological interface for protein-protein interactions to other hotspots on the MZF1 surface which can act as recruitment sites for other biological partners.Conceived and designed the experiments: EP. Performed the experiments: MN, TT, VS, FR, MD, MV, ML. Analyzed the data: MN, TT, ML, EP. Discussion of the data: EP, MN, TT. Contributed reagents/materials/analysis tools: MN, TT, AV, VS, JS, SB, MT, EP. Wrote the paper: MN, TT, ML, MT, MJ, TK, EP.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


All of these results indicate the importance of structures in RNA-protein interactions, and suggest that the RPI-Bind model is a powerful theoretical framework for studying RNA-protein interactions.RNA and protein interactions play crucial roles in multiple biological processes, while these interactions are significantly influenced by the structures and sequences of protein and RNA molecules. In this study, we first performed an analysis of RNA-protein interacting complexes, and identified interface properties of sequences and structures, which reveal the diverse nature of the binding sites. With the observations, we built a three-step prediction model, namely RPI-Bind, for the identification of RNA-protein binding regions using the sequences and structures of both proteins and RNAs. The three steps include 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously, with the results from steps 1) and 2). Compared with existing methods, most of which employ only sequences, our model significantly improves the prediction accuracy at each of the three steps. Especially, our model outperforms the catRAPID by >20% at the 3 Their interactions may vary according to sequences and structures, and consequently perform distinct functions. For example, tRNAs are bound to aminoacyl-tRNA synthetases for the translation during protein synthesis6, and nascent RNA coordinates the transition of RNA polymerase (RNAP) II to regulate their own transcription7. A large class of long noncoding RNAs (lncRNAs) can bind and modulate the activity of chromatin proteins, and play roles in chromatin modifications13. In this process, lncRNAs, e.g. the Xist, with specific structures can localize chromatin-remodeling complex, such as DNMT3a and possibly also EZH2, to specific target regions whereby stable epigenetic gene silencing can be initiated16, or act as a scaffold, e.g. the Hotair, to bind more than two proteins with their modules and direct them to target loci18. It is now apparently observed that many lncRNAs are the key regulators of transcriptional and translational output21, in addition to other genetic and epigenetic regulators29.RNA-protein interactions are critical at many regulatory steps of gene expression and stages of organismal development31. Multiple works have been reported to map the full spectrum of RNA interactions of individual RNAs and proteins. For instance, the genome-wide binding of Xist and its silencing partners have been profiled34. Very recently, Hendrickson et al. performed experiments with fRIP-Seq to detect widespread binding of mRNA and lncRNA with 24 proteins35. However, these experimental methods are always expensive, time-consuming and labor-intensive.The development of high-throughput experimental methods, such as CLIP-seq and RIP-seq, has greatly advanced the genome-wide studies of RNA-protein interactions36, RPIseq37, and lncPro38; 2) the prediction of binding sites on either RNAs or proteins, such as the sequenced-based methods: BindN39, RNABindR40, RNAProB41, PPRint42, RNApin43, PRINTR44, RISP45, PiRaNhA46, BindN+47, NAPS48, PRBR49, SRCPred50, Predict_RBP51, RNABindRPlus52 and RBRIdent53; the structure-based methods: KYG54, RsiteDB55, PRIP56, OPRA57, DRNA58, PRNA59, aaRNA60, RBRDetector61 and RBscore62; and 3) the residue-nucleotide contacts prediction, such as catRAPID63. The catRAPID method is the only available method and different from those are specially designed to determine residue-nucleotide interactions of different known DNA-binding domain families65. The latter cannot be applied to study RNA-protein binding interactions, since RNA is more flexible than DNA and has more complicated structures.It is necessary to develop computational approaches to efficiently investigate RNA-protein interactions. A few methods have been reported for three purposes: 1) the investigation of associations between proteins and RNAs, such as RPI-Pred72. As an example, the RPI-Pred, using high-order three-dimensional protein and RNA structures, significantly improves the accuracy in contrast to others using only sequences.Most of the existing methods only employ the sequences of proteins or RNAs, although some, such as catRAPID, implement other information like van der Waals contacts, hydrogen bonds, electrostatic interactions and stacking interactions across the protein-RNA interfaces. However, it is well known that, in many cases, the structures of molecules, including both proteins and coding/non-coding RNAs, dictates their functions73, and 12 classes of RNA local conformations (RLCs) from ‘BEAR’ encoding74, respectively. Both of them can give more detailed descriptions of RNA and protein structures than other representations74. We firstly illustrated the preferring properties of PLCs/RLCs for RNA-protein interactions. For instance, the α-helix and β-sheet PLCs, and the stem RLCs are64 preferred to exist at protein-RNA interfaces. We then developed a three-step RPI-Bind method to identify the binding sites on a given pair of protein and RNA. The three steps include 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously 63. We also applied the RPI-Bind method to identify the interacting regions of lncRNA Xist and transcription factor YY1, as well as other 20 proteins35. The results show great agreements between our predictions and experimental measurements, indicating the RPI-Bind is a powerful theoretical framework for the study of RNA-protein interactions.In this work, we implemented both sequences and structures of RNAs and proteins for the study of RNA-protein binding sites. To represent structures, we used the protein local conformations (PLCs), named protein blocks (PBs)75 and the Protein Data Bank (PDB)76. We then constructed a database consisting of 28,780 nucleotide-residue contacts, consisting of 9,077 RNA binding sites (on proteins) and 5,692 protein binding sites (on RNAs), respectively. Meanwhile, 9,801 RNA non-binding sites and 3,078 protein non-binding sites were also collected for further analyses. The protein and RNA structures were analyzed with the PDB-2-PB database77 and the ‘BEAR’ approach74 for the PLC and RLC representations, respectively  matrices of PLCs and RLCs were analyzed for quantitative evaluation of protein/RNA structure preferences. According to the total number of occurrences, the pair of α-helix and stem (m-b pair) appears the most at the interface for the enzymes, structural and ‘others’ classes Fig. . While i79. RNA-binding proteins achieve RNA-binding affinity through favorable charge-charge interactions between positively charged Arg and Lys residues and the negatively charged RNA phosphate. The observed high propensity of single aromatic residues reflects the frequent stacking interactions between aromatic side chains and nucleic acid bases in a number of protein-RNA complexes. In contrast to the observation of enormous variances in individual residue at the interface, few nucleotide variations were observed among protein binding sites. This could be the reason why very few methods are currently available to predict the binding sites of proteins on RNAs.In addition to the PLCs and RLCs, we also analyzed the compositions, preferences and interaction propensities of amino acids and nucleotides from the four classes of protein-RNA complexes Fig. . The posst step) to predict RNA binding regions on proteins for the three-step RPI-Bind (RNA-protein binding region predictor) method , Specificity (SP), Accuracy (ACC), and Matthew’s Correlation Coefficient (MCC) were 66.8%, 74.8%, 71.3% and 0.425, respectively.We developed a Random Forest (RF)-based machine learning method for the prediction of RNA binding sites on proteins, by firstly using the features of sequence mutual interaction propensities, physicochemical characteristics, hydrophobic index, relative accessible surface area, conservation score and side-chain environment, respectively. These features were chosen because they had been shown to outperform other RNA binding residue prediction methodsWe then combined the structure local conformations features of protein and RNA structures (PLCs and RLCs), because it could be better to predict binding residues. A five-residue sliding window method was used, as it outperforms other  size windows. If the center residue or nucleotide in each window was detected to physically interact with each other, then the window was treated as a positive sample, otherwise negative. The performance could be improved to 71.1%, 77.7%, 74.8% and 0.489, respectively. The ROC curves were also adopted to show the prediction accuracy Fig. .Figure 4We also employed other machine learning methods for the model development, including Support Vector Machine (SVM) and Neural Network (NN). The result shows that the RF-based model achieves the best prediction Fig. . Therefo59. It should be noted that the independent dataset and our non-redundant dataset have partially overlaps. There are 26 common protein chains in the two datasets. So the redundant protein chains were removed from independent dataset and the final dataset contains 3,584 (10.3%) interacting residues and 31,284 (89.7%) non-interacting residues. Our model can achieve predictions with ACC of 81.3%, PPV of 31.9%, NPV of 97.5% and the Area Under ROC curves (AUC) of 0.88 . The CCA-adding enzymes are nucleotidyltransferases that catalyze the posttranscriptional addition of the nucleotide sequence CCA onto the 3′ terminus of immature tRNA without using a nucleic acid template, and tRNA Pseudouridine Synthase B catalyzes the isomerization of specific uridines in cellular RNAs to pseudouridines and may function as RNA chaperones. For the former, our method correctly identified 89 of 97 actual interface residues, and for the latter, we predicted 63 out of 83 actual interface residues. The prediction accuracy for each RNA-protein complex is shown in the Supplemental Table The Fig. nd step of our approach, Fig. 43. Similarly, within the constructed database of 5,692 protein binding sites and 3,078 protein non-binding sites, we used a standard five-fold cross validation procedure to estimate the performance, that is, SN, SP, ACC and MCC were 72.6%, 54.1%, 63.5% and 0.271, respectively.We also built a RF-based machine learning method for the prediction of protein binding sites on RNAs, firstly using three compositional features  with ACC of 81.9%, PPV of 59.5%, NPV of 95.0% and AUC of 0.88 on the new dataset  is a ribonucleoprotein, which associates with ribosomes to mediate co-translational targeting of membrane and secretory proteins to biological membranes. The S domain of SRP interacts with 7S.S RNA for SRP assembly in Eukarya and Archaea. Our model also successfully predicted 18 of 25 actual interface nucleotides for T-RNA, 20 of 27 for fragment of 23S rRNA and 12 of 20 for dsRNA, respectively. These results indicate that our model is reliable for identifying protein binding sites on RNAs , if the lowest score of actual interactions was set as a cutoff  also indicates that our model is reliable for residue and nucleotide interaction prediction Fig. . Our 1stoff Fig. . In cont84, and are implicated in serial steps of cancer development20. LncRNAs mainly perform the biological functions by interacting with different proteins13. Our previous RPI-Pred method36 can accurately predict protein-lncRNA interaction pairs or identify the binding partners of a given protein or RNA. On the basis of RPI-Pred, our three-step RPI-Bind model can further predict the interaction region between protein and lncRNA, and therefore can be useful for the study of lncRNA functions.LncRNAs play essential roles in a variety of biological processXist and a transcription factor YY1. Their specific contacts are required to load Xist onto X chromosome85. Due to the lack of solved structures, we applied PB-kPRED86 and RNAfold87 for the local conformation prediction for YY1 and Xist, respectively. As well, the Xist is very large, so in order to obtain better lncRNA structures, functionally important segments, A, F, B, C, and E, were selected, and analyzed separately. Our 1st step model predicted three regions of YY1  are interacted with Xist, while the 2nd step model predicted the B, C and E segments of Xist are more likely to associate with YY1. With our 3rd step model, we successfully identified that the sequence from 60–80, 183–210 and 303–320 of YY1, and the B and C segments of the Xist are really contacted  binds strongly to EZH2, CHD4, SUZ12 and HNRNPU, in agreement with experimental evidence. EZH2 also shows high interaction propensity to segment D (5550–5730). Neither segment A nor F is predicted to be in contact with any of the 20 chromatin-associated proteins except SUZ12. On the other hand, no or low interactions were found between four proteins  and any functional segments of Xist. For those Xist non-binding proteins , our method correctly predicted that they do not bind to any functional segment of Xist. Our model also predicts some unknown regions of Xist, such as Xist 3′terminus, showing high propensity to contact most of proteins, which implies that these regions might be new functional segments. In summary, our model provides highly accurate predictions of RNA-protein interactions, in agreement with experimental evidence  most of them are only able to identify the interacting regions of either RNA on protein or protein on RNA88. The model for residue-nucleotide contacts prediction is catRAPID, which unfortunately cannot provide highly accurate identifications. 2) Most of them only use the sequences of proteins or RNAs, but ignore their structures, which, however, are much known to affect their functions and interactions72. In this work, we extracted the general characteristics of protein and RNA binding. To do so, the best approach is to observe the difference between binding and non-binding sites from known structures. However, observation from one single pair of protein-RNA binding structures is always bias, and may not represent the general characteristics of binding/non-binding sites. In contrast, the comparison between the collection of residues including both binding and non-binding ones leads to the detection of these features. Therefore, we first illustrated the structure preferences of binding and non-binding sites at the protein-RNA complexes, and then developed a three-step RPI-Bind model for the prediction of RNA-protein interface by employing the sequence and structure information of RNAs and proteins. Tests show that our RPI-Bind model outperforms other existing models at each of the three levels, including 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously. Of note, at the third step, the inclusion of the predicted results from the first two steps can further improve the prediction accuracy, which is significantly better than the catRAPID (86.9% vs 62%). More tests on individual RNA and protein interactions, especially lncRNA-protein interactions, further illustrate the prediction ability of our RPI-Bind model, which, on the other hand, depict the importance of structures in RNA-protein interactions. Our model is freely available at http://ctsb.is.wfubmc.edu/publications/RPI-Bind-Pred.php.RNA-protein interactions are of importance for the function of RNAs, especially lncRNAs75 and their corresponding 3D structures in the Protein Data Bank (PDB)76. After a series of data processing steps , we obtained 172 non-redundant protein-RNA pairs The structures of proteins and RNAs were represented by protein local conformations (PLCs) as 16 types of the protein blocks (PBs), and 12 types of RNA local conformations (RLCs)  . We employed the sliding window approach to decode the amino acid residues of proteins. Whether a residue belongs to the interactions or not is determined by the middle residue. The feature vector representing the residue in the window is encoded by the properties of the included residues. We compared the performance of different window sizes , and the best prediction performance was obtained with a window of 5 residues. We should note that the mutation of protein leads to two different protein sequences, and maybe two different protein structures, at least for local structure. Therefore, the input to the model will be different, which will result in different binding site identification.The prediction of protein binding regions on RNA. The involved features include mono-, di- and tri-nucleotide sequence compositions, and RLCs descriptors  . We also compared the performance of four window sizes . The five, seven and nine-window size have similar performance, but better than three-window size. We then set the window size as 5.st step and 2nd step. We presented two models. One uses the combinational features to develop model, and the original dataset consisting of 28,780 residue-nucleotide contacts for training and testing the model. Another also uses the combinational features, but the dataset was constructed with the positive dataset, obtained from the successfully predicted interacting residues and interacting nucleotides at the 1st and 2nd steps, respectively. The original negative dataset consisting of 180,000 pairs was formed by pairing every binding site and neighbor non-binding sites around its interacting partners. We used this strategy to construct negative dataset, because these false contacts are more similar to true contacts. Distinguishing true protein-RNA contacts from these false contacts is more practical.The prediction of interacting regions on both RNA and protein simultaneously. Our goal is to predict all interacting information in a framework. So the involved dataset and features include all used in the 1With these observations, we presented a three-step RPI-Bind (RNA-protein binding region predictor) method for the prediction of binding sites on both RNAs and proteins Fig.  as folloMachine learning methods, such as the Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), were employed for model building at the three steps, and the performance was evaluated with Sensitivity (SN), Specificity (SP), Accuracy (ACC), the Area Under ROC curves (AUC), and Matthew’s Correlation Coefficient (MCC) .Supplemental MaterialsDataset


However, the currently available PPI data is not enough to describe all known protein complexes. In this paper, we express the problem of determining the minimum number of  required protein-protein interactions as a graph theoretic problem under the constraint that each complex constitutes a connected component in a PPI network. For this problem, we develop two computational methods: one is based on integer linear programming (ILPMinPPI) and the other one is based on an existing greedy-type approximation algorithm (GreedyMinPPI) originally developed in the context of communication and social networks. Since the former method is only applicable to datasets of small size, we apply the latter method to a combination of the CYC2008 protein complex dataset and each of eight PPI datasets . The results show that the minimum number of additional required PPIs ranges from 51 (STRING) to 964 (BIND), and that even the four best PPI databases, STRING (51), BioGRID (67), WI-PHI (93) and iRefIndex (85), do not include enough PPIs to form all CYC2008 protein complexes. We also demonstrate that the proposed problem framework and our solutions can enhance the prediction accuracy of existing PPI prediction methods. ILPMinPPI can be freely downloaded from  Identification of protein complexes is important for understanding cellular mechanisms because many proteins express their functions by forming complexes. Since it is difficult to experimentally determine protein complexes, extensive studies have been done on the prediction of protein complexes. Among them, many studies focused on the effective use of protein-protein interaction (PPI) data because proteins in a complex physically interact and a large amount of PPI data has been available due to developments of high-throughput experimental techniques , 2. In mWhen studying and analyzing these protein complex prediction methods, we encounter a fundamental question. Is the current PPI data enough to explain all known protein complexes? If not, how many additional PPIs are required? The main purpose of this paper is to tackle this fundamental question. In order to answer the question, we impose as a minimum requirement that the subgraph induced by the nodes  in each complex must be connected. The answer would enable us to deduce that many interactions to detect the associations between proteins forming all known protein complexes are missing from the current PPI data and if the number of the additional PPIs is large, more additional experiments might be necessary. Then, we define the problem of determining the minimum number of additional PPIs required to support known complexes (MinPPI) as: given a set of protein complexes  and a set of PPIs , find a minimum number of additional PPIs such that the connectivity requirement is satisfied for all given protein complexes. We also define MinPPI0 as the special case of MinPPI in which the set of given PPIs is empty.Interestingly, the same problem has been studied in the analysis of communication and social networks under the name of the Network Construction problem and a greedy, polynomial-time approximation algorithm for it has been proposed . This faet al.’s set Minimum Tr et al.  proved tn et al.  recentlyWe propose an exact method called ILPMinPPI, for the problem of predicting PPIs beginning with the set of known PPIs is empty (corresponding MinPPI0).Cp, we add the following constraints using different 0-1 variables for different Cp,i, j, k ∈ Cp, Tp = ⌈log|Cp|⌉. eij is a variable which indicates whether an edge exists between proteins i and j. If eij = 1, an interaction exists between proteins i and j. i and j are connected after t (0≤t≤Tp) steps and i and j are connected through protein k after t steps. The third constraint means that if i and k are connected and k and j are connected, i and j are also connected. Hence, after enough steps Tp, proteins i and j should be connected, which means that O(log|Cp|) steps. Hence, these constraints state that the subgraph of G induced by nodes (proteins) in each Cp must be connected, which guarantees that each protein complex is internally connected under the condition that edges with eij = 1 can only be used. Then, the required ILP is formulated asCps.MinPPI0 can be formulated using the following integer linear programming (ILP). For each complex The exact method ILPMinPPI requires exponential time and can thus only be applied to small datasets in practice. To deal with large-scale datasets, we now extend the approximation algorithm of  to MinPPet al.  were calculated to reflect the reliability where the firstly added interaction has the highest confidence which equals to 1344. Four existing methods also provided individual confidence scores, Struct2Net score ranging [0.25-0.98], ENTS score ranging [0.50-0.97], PIP score ranging [300.07-146673.28] and iWRAP score ranging [0.90-1.00].Since the four databases provide confidence scores, each of which was computed by evidences from their own sources and all five scored PPIs were sorted in descending order in advance –44, we eIn particular, the distribution with STRING exhibits that GreedyMinPPI detected the PPIs with uniformly high confidence score which suggests that the prediction is performed with a high reliability as shown by the high average score . Since iTherefore, the results suggest that the top 100 interactions predicted by GreedyMinPPI have higher confidence scores than those by existing methods but not providing the uniformly high scores except in the case of STRING. However, since it enables us to detect the interactions among proteins with most highly overlapping, most of these interactions would be expected to be more reliable.S. cerevisiae which consists of 6304 protein sequences. SPPS server is developed by combining Support Vector Machines (SVM) with the posterior probability which is derived from a sigmoid function, to search the possible interacting partners with the query protein sequence. InteroPORC provides an automated prediction based on the interolog concept, which reconstructs the orthology relationships across multiple species.In this subsection, we validated the predictive performance of GreedyMinPPI with three unweighted PPI prediction methods, PIPE , SPPS 4 and InteF-measure and matthew’s correlation coefficient (MCC) , MCC) see . Recall Needless to say, since the objective of MinPPI0 or MinPPI is to detect the minimum number of interactions or additional interactions so that all proteins belonging to each complex are connected, the number of predicted PPIs from GreedyMinPPI should mostly be lower than those from existing methods. This leads to the lower AUC performance of GreedyMinPPI compared to those of existing methods, as shown in SPE reflects the specific weight of scores predicted from GreedyMinPPI when combining the scores. Sc is the combined score and Sg and Se are the individual scores that are calculated from GreedyMinPPI and existing methods, respectively.To validate the performance enhancement of GreedyMinPPI with the weighted PPIs predicted from Struct2Net, ENTS, PIP and iWRAP in terms of AUC, we examined MinPPI using the dataset, combining all interactions and the corresponding confidence scores from GreedyMinPPI with those from the four existing methods. The combined scores were computed by,SPE, the AUC scores on the combined datasets increased than those on their own datasets from GreedyMinPPI. Because although GreedyMinPPI is based only on the connected components from graph theory, MinPPI tends to output pairs of proteins that are contained in many protein complexes. In other words, in this paper, we assume that the proteins consisting of highly overlapping probably interact with each other and these interactions might be missing interactions. In particular, the AUC on iWRAP was greatly improved when combining all interactions because fewer shared interactions exist in case of each database. On the other hand, the AUC performance was not improved using the combined datasets when combining top 200 and 500 interactions from GreedyMinPPI. These results suggest that GreedyMinPPI is also suitable for enhancement of the predictive performance of existing methods when all outputted interactions are utilized.Comparison results with the four databases are summarized in Tables A18–A21 in In this paper, we have introduced MinPPI, which is a problem of determining the minimum number of PPIs required to support known protein complexes. For solving this problem, we have developed a novel integer linear programming-based method (ILPMinPPI) and a greedy-type method (GreedyMinPPI) based on an existing greedy-type approximation algorithm. The comparison of these two methods using moderate size synthetic data suggests that GreedyMinPPI outputs optimal or near-optimal solutions for practical instances. Since ILPMinPPI cannot be applied to large-scale data, we have applied GreedyMinPPI to pairs of a protein complex dataset and eight PPI datasets. Our findings show that the minimum number of additional required PPIs ranges from 51 (STRING) to 964 (BIND). Significantly, this suggests that even the four best PPI databases, STRING (51), BioGRID (67), WI-PHI (93) and iRefIndex (85) do not have enough PPIs to form all CYC2008 protein complexes. We have also applied GreedyMinPPI to enhance the existing PPI prediction methods. The results suggest that it is also useful for that purpose.O(log m) approximation ratio is not enough to estimate the minimum number.Although ILPMinPPI and GreedyMinPPI output PPIs with scores, they output PPIs only based on the minimum requirement that each complex must constitute a connected subgraph in a PPI network. Therefore, these methods are not optimized for prediction of PPIs and thus should be used only as auxiliary methods to enhance existing PPI prediction methods. However it might be possible to modify the formalization of MinPPI as a kind of machine-learning problem to infer PPIs from protein complexes. Although such a variant would still not be enough to be used as a stand-alone prediction method, it would be useful to further enhance the prediction accuracy of existing PPI prediction methods. Another important future work is to improve ILPMinPPI so that it can be applied to real protein complex datasets, because it is unclear whether GreedyMinPPI outputs near-optimal solutions for large-scale protein complex datasets and the theoretically guaranteed S1 Fileall which is the estimated score by PSOPIA. Table A17:Comparison of prediction performance with the four databases using unweighted datasets. Table A18:Comparison of prediction performance using AUC with STRING. Table A19:Comparison of prediction performance using AUC with MINT. Table A20:Comparison of prediction performance using AUC with WI-PHI. Table A21:Comparison of prediction performance using AUC with IntAct.Fig A1:Distribution of PPI confidence score using STRING. Fig A2:Distribution of PPI confidence score using MINT. Fig A3:Distribution of PPI confidence score using WI-PHI. Fig A4:Distribution of PPI confidence score using IntAct. Table A1:Performance evaluation of ILPMinPPI and GreedyMinPPI using synthetic data. Table A2:Prediction results on GreedyMinPPI with different data configuration. Table A3:Results with three protein complex datasets. Table A4:The additional protein pairs for supporting CYC2008 protein complex with STRING. Table A5:The additional protein pairs and the included complexes on STRING. Table A6, A9, A12, A15:Frequency distribution of the assigned categories of additional proteins on STRING from PANTHER analysis. Table A7:The additional protein pairs for supporting CYC2008 protein complex with BioGRID. Table A8:The additional protein pairs and the included complexes on BioGRID. Table A10:The additional protein pairs for supporting CYC2008 protein complex with WI-PHI. Table A11:The additional protein pairs and the included complexes on WI-PHI. Table A13:The additional protein pairs for supporting CYC2008 protein complex with iRefIndex. Table A14:The additional protein pairs and the included complexes on iRefIndex. Table A16:67 additional protein pairs on BioGRID and S(PDF)Click here for additional data file.


Percolator’s processing speed has been sufficient for typical data sets consisting of hundreds of thousands of PSMs. With our new scalable approach, we can now also analyze millions of PSMs in a matter of minutes on a commodity computer. Furthermore, with the increasing awareness for the need for reliable statistics on the protein level, we compared several easy-to-understand protein inference methods and implemented the best-performing method—grouping proteins by their corresponding sets of theoretical peptides and then considering only the best-scoring peptide for each protein—in the Percolator package. We used Percolator 3.0 to analyze the data from a recent study of the draft human proteome containing 25 million spectra (PM:24870542). The source code and Ubuntu, Windows, MacOS, and Fedora binary packages are available from http://percolator.ms/ under an Apache 2.0 license.Percolator is a widely used software tool that increases yield in shotgun proteomics experiments and assigns reliable statistical confidence measures, such as  Percolator  has playFirst, as advances in technology continue to reduce the cost and effort needed to carry out shotgun proteomics experiments, the amount of data per study will keep rising steadily. Although previous versions of Percolator are able to process the data from the vast majority of current studies in a reasonable time frame, the algorithm has some limitations for laboratories without access to a high-performance computing facility. When processing millions of PSMs, the majority of Percolator’s processing time is spent on training support vector machine (SVM) classifiers. In some settings, however, the performance of the SVM as a function of the size of its training set plateaus at a relatively low number of input PSMs . Here, wp values.Second, we have investigated efficient ways to obtain protein-level accuracy estimates. One of the major obstacles was the question of how to deal with shared peptides and protein grouping. An implementation of Fido , which hi.e., cases where a single, potentially spurious PSM yields a spurious protein detection). An alternative that takes into account even more evidence is to compute the product of peptide-level PEPs. This procedure takes into account all peptides within a protein and provides some protection against one-hit wonders [p values [p values being combined and hence normalizes for protein length.Although each of these methods is efficient to compute, they each offer specific pros and cons. Savitski et al.  showed t wonders . The metp values . Like thp values –15. ThisWe downloaded three sets of spectra, two from large-scale studies on human samples with several millions of spectra and one smaller-scale study on yeast samples.The first large-scale set comprises 2212 runs on 17 adult tissues, seven fetal tissues, and six hematopoietic cell types with a total of ∼25 million spectra . The samThe second large-scale set was taken from a study aimed at studying variation of protein abundance in humans . It consFor verification of the accuracy of protein-level false discovery rate (FDR) estimates we additionally downloaded 92, 974 spectra from three injections of yeast cells grown to mid-log phase, collected on an LTQ Orbitrap Velos (Thermo Scientific), as described in Moruz et al. . We willConverting the RAW files to two separate files in the MS1 and MS2 formats , respecthttp://www.uniprot.org/, accessed: November 12, 2015) concatenated with a database of common contaminants  using the Tide search engine, again through the Crux interface. We used semi-tryptic searches and Tide’s default fragment tolerance. The other search parameters were kept the same as in [For the Kim data set, the data was searched against the human Swiss-Prot and Swiss-Prot+TrEMBL databases  using the Tide search engine through the Crux interface. We used Tide’s default fragment tolerance, and the other search parameters were kept the same as in [For the Wu data set, we searched the spectra against the IPI Human database ver. 3.74  and trains three separate SVM classifiers, each trained on two of the three subsets and tested on the remaining subset. Each SVM classifier produces a scoring vector, which can then be used to calculate a new score for each PSM based on its feature set. The final scores are thus calculated using the classifier for which the PSM was in the test set.By default, Percolator’s semi-supervised learning algorithm randomly splits the list of PSMs into three subsets  is actually true. For example, using an entrapment database nine times the size of the sample database means that we will underestimate the true amount of false positives in the entrapment FDR by ∼11% on the PSM level. However, under the assumption that many of the proteins in the sample database are in fact present, this underestimation is far lower on the protein level because correct proteins in the sample database will conceal incorrect PSMs matching to it.The assumption that any match to the sample database is a true positive is not necessarily true because the peptide or protein could have been inferred through an incorrect PSM. The purpose of the entrapment database is to attract the majority of these incorrect target PSMs, thereby ensuring that the majority of PSMs, peptides and proteins matching to the sample database are correct. The larger the entrapment database, the higher the probability that an assumed true positive  was taken as the sample database, and the entrapment database was nine times the size of the sample database. Furthermore, we artificially added shared peptides between the sample and entrapment database by keeping 4% of the sample peptides unshuffled in the entrapment database. This corresponded to the shared peptide rate in the original Swiss-Prot yeast sample database.Here, the yeast Swiss-Prot database  with no miscleavages, specifically chosen to prevent unintended shared peptides between the sample and entrapment databases. The minimum and maximum peptide lengths were, respectively, set to 7 and 50 amino acids. All other parameters were left at their default values. The procedure for the decoy model was identical to the one mentioned above for the Kim and Wu data sets.The Kim and Wu data sets were used as an indication of the performance on large-scale data by comparing the number of identified protein groups for different protein-level FDR thresholds for each of the protein inference methods. In addition, to assess the performance for differently sized data sets, we looked at the number of inferred protein groups for random subsets of the Kim data set. Unlike in our resource-saving downsampling described above, we this time reduced the size of the evaluated sets, as our interest this time was to test the performance of the inference procedures on smaller sets of peptides.q value threshold of 0.01. To characterize the performance of the SVM learning procedure when training on subsets of the PSMs, we evaluated performance using training subsets of 100,000, 500,000, 1,000,000 and 5,000,000 PSMs from the Kim data set, using the subset training procedure outlined in the Methods section. For each training subset size, we calculated the mean and standard deviation over 10 randomized runs of the number of PSMs and peptides with q value below 0.01.We used the Kim data set to evaluate the robustness of Percolator’s SVM classifier to reductions in the size of the training set. The Tide searches of the complete Kim data set against the human Swiss-Prot database resulted in 73 million target and decoy PSMs. Post-processing the full data set with Percolator resulted in 7,928,454 significant PSMs and 298,301 unique target peptides at a This experiment showed that using subsets as small as 100,000 PSMs (0.14%) for SVM training did not significantly reduce the number of inferred peptides and PSMs  FDR estimates Fig. . Fisher’We also investigated the accuracy of the FDR estimates calculated by Fido, as implemented in the Percolator package, which would still be a viable alternative for smaller data sets. Fido makes use of shared peptides and protein grouping, but contrary to the protein grouping procedure outlined above, only groups proteins with exactly the same inferred peptides. It relies on its Bayesian inference engine to solve the issue of proteins whose peptides form a strict subset of the set of inferred peptides of another protein. Furthermore, Fido estimates FDRs by its Bayesian inference engine rather than using a decoy FDR, although the decoy FDR curve is in fact used to calibrate its parameters. Fido’s FDR estimates proved to be better calibrated in the region below 1% protein-level FDR than the other four methods, but showed divergence from accurate estimates for higher protein-level FDRs, both in conservative and anti-conservative direction.A and B, are merged into a group if protein A’s peptides are a superset of protein B’s peptides or vice versa. We then retain the peptides that are unique to a protein group rather than to a single protein. A side effect of this change is that in contrast to the procedure used in Fig. We hypothesized that these calibration problems arise from the peptides that are shared between different proteins in the database. Accordingly, we next considered an approach that discards shared peptides, retaining only those that are unique to a single protein. To reduce the effect of fictitious shared peptides that correspond to protein fragments or other truncated protein forms, we used the approach to handling shared peptides from Nesvizhskii et al. . Here, pi.e., single proteins or protein groups) with a peptide uniquely identifying it. The experiment involved performing a fully tryptic digestion, with no missed cleavages, of three human protein databases—Swiss-Prot, Swiss-Prot+TrEMBL, and Ensembl—considering peptides with lengths of 6–50 amino acids. TrEMBL and Ensembl contain many proteoforms and therefore benefit significantly from this particular protein grouping approach in terms of number of identifiable protein entities . The Kim data set might not be as representative for other types of studies that have greater heterogeneity, but it seems likely that the downsamping strategy will work well as long as the selected subset of PSMs contains a sufficient number of positive training examples.The downsampling approach for SVM training on a subset of the PSMs shows great stability even when only sampling a tiny fraction, as small as 100,000 PSMs . However, this modification does not produce significantly more protein inferences, while introducing an extra parameter that needs to be set correctly.The most successful protein inference method turned out to be the one where proteins were grouped by their theoretical peptide sets and only the best-scoring peptide was considered. In this approach, the score assigned to a protein essentially ignores the majority of the PSMs, something that may feel quite unsatisfactory. On the other hand, including evidence from other, lower-scoring peptide inferences is difficult because it often involves lumping incorrect peptide inferences into correct protein inferences. This problem can clearly be seen in the poor performance of Fisher’s method for combining With regards to the discarded shared peptides, large-scale studies give us the luxury of deep coverage, therefore inferring many peptides that are unique to a protein. This mitigates the problem of ignoring shared peptides and makes the task of protein inference much simpler and intuitive.i.e., an inferred protein group means that we expect at least one of the proteins to be correct) but we are agnostic about which one. Compared with the conventional protein grouping approach, which is based on inferred peptides instead of the full set of theoretical peptides, the groups produced by our method are much smaller. For the Swiss-Prot database, virtually all protein groups contained only one protein and for the Swiss-Prot + TrEMBL database they typically contained just one or two proteins. Consequently, ambiguity about which specific proteins are present is much rarer. Furthermore, if genes, rather than proteoforms, are the entities of interest, using databases with few proteoforms such as Swiss-Prot should do the job.The protein grouping method employed here still suffers from the problem that an inference of a protein group leaves open the question of which proteins in the group are actually correct. Here, we interpreted it using the null hypothesis that all proteins in the group are incorrect  was 10 and 15 min for the Swiss-Prot and Swiss-Prot+TrEMBL database, respectively. We realize that other parts of the shotgun proteomics analysis pipeline might still have significantly higher computing requirements than Percolator, but fortunately these can often readily be parallelized as the runs can be analyzed independently. However, as we have pointed out elsewhere, obtaining significance measures per run or data set and combining them afterwards is not at all straightforward and should be handled with great caution [For our largest evaluated data set, the 73 million PSMs of the Kim data set, the combined runtime for Percolator 3.0 ( caution . This ne


Twenty-five years ago, we obtained our first mass spectra of molecular chaperones in complex with protein ligands and entered a new field of gas-phase structural biology. It is perhaps now time to pause and reflect, and to ask how many of our initial structure predictions and models derived from mass spectrometry (MS) datasets were correct. With recent advances in structure determination, many of the most challenging complexes that we studied over the years have become tractable by other structural biology approaches enabling such comparisons to be made. Moreover, in the light of powerful new electron microscopy methods, what role is there now for MS? In considering these questions, I will give my personal view on progress and problems as well as my predictions for future directions. In the early days of applying mass spectrometry (MS) to structural biology, our development of methods to study the folding of proteins was viewed with much cautious scepticism. ‘Life, as we know it, goes on in water’, how then could we study proteins in the vacuum of a mass spectrometer?  This opiThat the folded core of a protein could be maintained in the gas phase of a mass spectrometer, as evidenced by hydrogen–deuterium exchange protection, was for me the starting point of my interests in using MS in structural biology . These eIt was during these protein-folding experiments that I first noticed that the appearance of folded protein coincided with the time point at which binding of the cofactor took place. This implied that cofactor binding was not the result of non-specific interactions, but required the correctly folded protein . This waThis research field was more crowded than the protein-folding arena since it has obvious links to the pharmaceutical industry , and is 14-mer, the next obvious step for me was to try to retain the large GroEL complex intact in the gas phase of the mass spectrometer. Using a time-of-flight mass spectrometer, we recorded our first mass spectra of GroEL by employing nanoflow capillaries and elevated pressures [Having started my research studying the influence of molecular chaperones on folding by releasing protein substrates from the GroELressures . Our firressures ,13. UsinStudying molecular chaperone assemblies, comprising one protein subunit, prompted us to extend our approach to study heteromeric complexes. Introducing mixtures of dodecameric small heat shock proteins (sHSP) from pea (PsHSP18.1) and from wheat (TaHSP16.9), allowing them to equilibrate, and following the kinetics of this reaction by MS, we could show that exchange proceeds via sequential incorporation of subunits, with dimeric species being the principal units of exchange. We concluded that the sHSP complexes are in a rapidly exchanging dissociation/association equilibrium with sub-oligomeric forms and were able to obtain rate constants for the exchange of subunits between species .in situ [Along a similar line, we demonstrated that we could monitor assembly pathways of heteromeric chaperones from component subunits by incubating two different subunits (α and β) of the MtGimC complex . This wain situ . This ofSo began an interesting decade of uncovering the polydispersity of molecular chaperones , of measEscherichia coli [Thermus thermophilus yielded much greater mass spectral resolution, perhaps correlated to their greater stability and homogeneity. Mass spectra also highlighted the unexpected heptameric stoichiometry of the stalk complex [Ribosomes, which are protein–RNA complexes, represented a considerable challenge not least because of the heterogeneity of initial preparations from hia coli . Ribosom complex , shown f complex .in situ on the ribosome. Specifically, we targeted ribosomes from organisms with different optimal growth temperatures. Our results showed that, for the mesophilic bacterial ribosomes, the stalk complexes are exclusively pentameric, and entirely heptameric in the case of thermophilic bacteria. Only pentameric stalk complexes were observed in eukaryotic species. We also were surprised to find that, for mesophilic archaea, both pentameric and heptameric stoichiometries are present simultaneously within a population of ribosomes. Moreover, the ratio of pentameric-to-heptameric stalk complexes changed during the course of cell growth [Exploiting the ability of MS to probe dynamic exchange reactions, we were also able to link acetylated forms of the L12 protein (L7) with enhanced interactions with the ribosome  and usedl growth . These rl growth .The importance of dimeric interactions in the stalk complex was demonstrated by the exchange of subunits within the stalk complexes, while they were attached to ribosomes in different functional states . By incuThe yeast RNA exosome stands out as an early success in our quest to define the structural organisation of subunits using MS restraints alone. Through solution disruption measurements, it became possible to generate overlapping sub-complexes and to obtain pairwise interactions that form the ring structure . The modWhile in all cases the stoichiometry has been found to be correct, in some cases our models have not turned out to be entirely accurate predictions. It is interesting to consider how this arises. In the decameric structure of eIF2B , the dimIt was not until 2005 that we began to look at the shape of protein complexes in the gas phase using IM-MS, a technique that had previously been pioneered for the study of small molecules and individual proteins. Applying these methods to protein complexes was fascinating and led to new insights into the shape and multiple conformations exhibited by proteins in solution and preserved in the gas phase. With this extra dimension, we explored packing of a range of protein complexes in different topological states  and compWhile, for most of our experiments, we were keen to maintain proteins in their compact folded states, we also become interested in unfolding proteins as a means of investigating their stability. We began these experiments by carrying out protein unfolding of transthyretin (TTR). Many different states were populated along the unfolding pathway and it was unclear how to assign these intermediates . We did We used IM to distinguish different locations within the EM density for eIF3 f,h,m, which was a close packed trimer with all subunits in contact, from eIF3 e,l,k, which has a linear arrangement with subunit l in the centre . The oveEver since the first spectra of GroEL, it became a personal goal to attempt to soft-land protein complexes within the mass spectrometer and to use EM to visualise these molecules. By building a retractable probe incorporating a versatile target holder, and modifying the ion optics of a commercial mass spectrometer, we were able to show that we can steer the macromolecular ion beam onto a target for imaging by means of transmission electron microscopy and atomic force microscopy. Our data for the tetradecameric chaperonin GroEL showed that not only are the molecular volumes of the landed particles consistent with the overall dimensions of the complex, but also that their gross topological features can be maintained . We extePerhaps for me my ultimate goal has been to study membrane proteins from an environment that is as close to the natural membrane as possible. Interestingly, the commentary back in 1995 . In 2008T. thermophilus with those of its membrane and soluble sub-complexes. We defined regions with enhanced flexibility and assigned these to distinct subunits within the overall assembly. By isolating complexes at different phases of cell growth and manipulating nucleotides, metal ions, and pH during isolation, we were able to show differences that could be related to conformational changes in the Vo complex triggered by ATP binding. Subsequently, we went on to show how changes in the phosphorylation status in the soluble F1 subunit of the chloroplast ATPase affect nucleotide-binding sites [One of the first membrane protein complexes I was keen to tackle was that of the rotary ATPases. These fascinating proteins were at that time recalcitrant to most structural biology approaches in their intact state. They were therefore of considerable interest as we reasoned that if we could maintain them intact in the mass spectrometer, we would be able to learn something about the communication between the soluble head and the membrane-embedded base. From our first mass spectra, we were able to define the precise subunit stoichiometry and the lipid cohort, and even an unexpected nucleotide-binding site . Using Ing sites . To do tng sites  to diffeng sites .T. thermophilus, revealed lipids that were critical for structure and function [Returning to the theme of ABC transporters, given that our first success was with an ABC transporter BtuCD, we turned our attention to P-glycoprotein (P-gp) due to its importance in drug resistance mechanisms. We probed the synergy between lipid and drug binding by monitoring conformational changes within the pump . This wafunction  and werefunction .Figure Combining our membrane protein MS with the unfolding experiments described above, we were able to identify lipids that affect the structure of the ammonia channel (AmtB) and for the function of aquaporin Z. That led to the first lipid-bound structure of AmtB . SimilarDespite early scepticism, and initial surprise that these complexes did survive the phase transition, MS is now firmly established in structural biology. That it is now widely accepted is evidenced by applications from other groups using the approach to complement existing structural biology techniques , specifiPart of the future that I envisage for MS in structural biology is to move from detergent micelles to membrane mimetics . To thisA quarter of a century after my first GroEL mass spectra perhaps the aspect that I still find most exciting is the ability to form and test hypotheses based on the results of MS experiments. In this way, we have been able to propose and establish links between the changes in stoichiometry of the ribosomal stalk proteins from different species and their position on the phylogenetic tree, to extend the paradigm that intrinsic dynamics is crucial to the function of small heat shock proteins  and to eAnalogous to the role of PTMs in fine-tuning the function of soluble complexes is the influence of lipid binding on the properties of membrane proteins. The ability to rapidly test hypotheses based around questions on membrane protein lipid binding has been particularly fruitful. Specifically, we have shown that changes in lipid-binding patterns in rotary motors lead to conformational changes in the membrane ring . Similar


A number of genetic diseases are a result of missense mutations in protein structure. These mutations can lead to severe protein destabilization and misfolding. The unfolding mutation screen (UMS) is a computational method that calculates unfolding propensities for every possible missense mutation in a protein structure. The UMS validation demonstrated a good agreement with experimental and phenotypical data. 15 protein structures  were analyzed using UMS. The standard and clustered heat maps, and patterned protein structure from the analysis were stored in a UMS library. The library is currently composed of 15 protein structures from 14 inherited eye diseases including retina degenerations, glaucoma, and cataracts, and contains data for 181,110 mutations. The UMS protein library introduces 13 new human models of eye disease related proteins and is the first collection of the consistently calculated unfolding propensities, which could be used as a tool for the express analysis of novel mutations in clinical practice, next generation sequencing, and genotype-to-phenotype relationships in inherited eye disease. Genetic mutations lead to protein misfolding and in many cases disease3. Protein secondary structure is stabilized by hydrogen bonds from the amide and carboxyl groups of amino acids4. The side chains of the amino acids interact in a variety of ways to create the protein tertiary structure (hydrophobic and disulfide interactions). In folding, the protein structure goes through a series of trails and errors to identify the most thermodynamically stable conformation4. Therefore, correctly folded proteins have long term stability in biological systems.For globular proteins, primary protein structure dictates the folds and interactions that occur between amino acids in the structure3–7.The role of missense mutations in inherited disease is not well understood. Disease-causing missense mutations occur when a change at the DNA level causes an amino acid in the protein sequence to be substituted with another, changing the interactions between amino acids and occasionally lead to protein misfolding. Currently, many inherited diseases are caused by missense mutations leading to misfolding of proteins in the cellin-silico scan to evaluate the destabilizing effects of multiple point mutations derived from the protein atomic model. It may be used as a tool to analyze the complicated relationship between missense mutations, protein folding, and disease5. UMS reads a protein structure file (PDB file) and predicts the unfolding effect for a list of every possible missense mutation that may occur in the protein structure including an identity mutation. For each mutation, UMS calculates an unfolding propensity, derived from the Gibbs free energy equation, to describe whether the mutation will lead to protein misfolding. The output of UMS is a mutational matrix, standard heat map, clustered heat map, and patterned structure. UMS has the ability identify critical residues in the protein structure, which give insight into the most significant residues to protein stability and function. UMS also may explain mutations that can lead to both increased and decreased enzymatic activity, identify trends of residues relating to stability, and predict the severity of missense mutations in disease and their relation to disease phenotype.UMS is an 6–11. There are also programs that work to predict the functional consequences of missense mutations12–16. UMS provides various benefits and advances over current mutant screening techniques. Given that UMS is derived from the atomic structure level and thermodynamics rather than sequence conservation, it has the ability to predict the effect of de novo missense mutations17. The unfolding propensity is determined using the linear extrapolation model from the normalized sigmoidal unfolding curve obtained experimentally18. This data classifies the effects of the missense mutations and uses a universal value so that unfolding propensities from different protein structures may be compared.Currently there exist a number of protein stability predictorsThe 3 maps and mutation matrix are designed to make this large dataset readable for investigators with different backgrounds such as geneticists, clinicians, biochemists, pharmacologists or protein engineers, and those who may not have any preliminary experience in homology modeling and calculations of protein stability.19–21. It has been hypothesized that the conservation of ‘deep’ residues is related to folding requirements and function20. Relationships exist between highly conserved residues in structural neighbors of the same fold type, and their mean residue depth in the reference structure21. There are programs that use residue depth as a parameter to predict protein structural models using fold recognition19.Residue depth has been used to describe the protein interior and predict fold types5. We present 10 new homology models for human proteins related to retinal diseases that have been verified using the internal control. In addition, we provide new maps for each of the 15 proteins for prediction and express analysis of missense mutations. Finally, this study targets a number of new diseases that have not yet been studied using UMS.Here, we are reporting the library of 181,110 mutations from 15 proteins from inherited eye disease analyzed with UMS. This analysis includes the preparation of 13 homology models of human proteins. The UMS program has been subjected to intensive validation using the Protherm database and 3 proteins from retinal disease 22 or prepared using homology modeling. CYP1B1, IRBP, LRAT, NYX, RDH5, RDH8, RDH12, REP-1, RHO, RPE65, TIMP3, WDR36, and domains 4, 5, 14 and 17 from CFH are homology models. While the remaining crystal structures of CFH domains, CRYAB, and CRYBB1 were obtained from the protein data bank22. The pdb files used for the protein analysis are available on the server.A library of 15 different inherited eye disease related proteins was created for analysis. 5. In this work the internal control was adjusted from UMS to calculate the difference in the free energy of the side chain rotamers for the same amino acid. The output of the internal control program was used to either select the best protein models or determine if more refinement of the structures was required. In selecting the best structure, we looked for models with statistically significant data  and then looked for the smallest confidence interval with average close to 0. For those that required further refinement, structures were tested until they fit this requirement.After the homology models were created they were run through the internal control program. The internal control program for the analysis of unfolding propensities is explained in depth in McCafferty & Sergeev5. In summary, the program is written in Python, R, and Bash programing languages. The architecture of the program is designed to perform a full mutagenesis analysis as efficiently as possible by implementing a quick and space sensitive procedure. 5. These foldability values can then be used to identify critical residues in the protein structure. The critical residues are considered to be essential to proper protein folding.A full description of the UMS program can be found in the methods section of McCafferty & Sergeev23. The Biopython package was used to calculate the residue depth for each of the wild type residues in the protein structure24. The Biopython package uses the MSMS program for the surface calculation25, the residue depth is then presented as the average of the atom depth for each wild type residue in the native protein sequence. The other descriptor, informational entropy, also known as Shannon entropy, quantifies the uncertainty of the source of the information. Therefore, greater informational entropy relates to a greater degree of randomness amongst the mutations for a certain location26. For example, if the average unfolding propensity for two locations on the structure were both 0.5, the location where all unfolding propensities were 0.5 would have a lower informational entropy that those that were split between 1.0 and 0. A script was created using Python to calculate the informational entropy of the unfolding propensities. The equation of informational entropy used was In addition, two new descriptors, residue depth and informational entropy, were included as described below. First descriptor, the residue depth of an amino acid in the protein structure, is described as the distance of an atom from the solvent accessible surfaceFigshare (Data citation 1).The code is available on the Figshare (Data citation 1). The UMS library for 15 proteins from inherited eye disease is available on the The mutation matrix is available in the protein_matrix.txt format. This can be opened using a standard text editor as well as in Microsoft Excel. The mutation matrix is ideal for an investigator who wants access to the raw data. Since the file can be opened in a number of programs the user has the ability to analyze and manipulate the data however he/she pleases.The standard and cluster heat maps are available in the protein_standard.html protein_cluster.html format, respectively. The size of each of these maps for the proteins is shown in http://www.cgl.ucsf.edu/chimera/). Once opened in Chimera, residues may be identified by placing the mouse over the area of interest. The patterned structure is available as a python file, protein.py. This file is to be opened using UCSF Chimera . The descriptor file provides the user some innovation to analyze the data as he/she pleases. http://www.abren.net/protherm/)27 based on available experimental thermodynamic data. Proteins with single mutations whose ΔΔG values were determined using fluorescence from denaturants and CD were selected. Specifically, tryptophan fluorescence data for chemical unfolding/refolding in the presence of urea or Gdm-HCl. Proteins with a large number of mutations with thermodynamic data were ideal for the validation. Finally, the proteins needed to have an available PDB file on the Protein Data Bank (http://www.rcsb.org/pdb/)22. Based on this criteria the 16 proteins selected for the validation set were: T4 Lysozome (PDB id: 2LZM), Staphylococcal Nuclease (1STN), Protein L (1HZ6), Barnase (1BNI), Ribonuclease T1 Isozyme (1RN1), Gene V Protein (1VBQ), Chymotrypsin Inhibitor 2 (2CI2), Acyl-Coenzyme A (2ABD), Tyrosine-Protein Kinase (1FMK), Acylphosphatase (1APS), Alpha Spectrin (1AJ3), Dihydrofolate Reductase (1RK4), Ribosomal Protein (1RIS), Tryptophan synthase (1WQ5), ARC Repressor (1ARR), and Azurin (5AZU). From the ΔΔG values for each of the experimental mutants from the Protherm database the unfolding propensity was calculated. The percent matching and a Fit-Score were used to evaluate the quality of the output from UMS.The validation set for the UMS program was composed of 16 proteins. The proteins were selected from the ProTherm database .Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.