Rational design of specific inhibitors of protein-protein interactions is desirable for drug design to control cellular signal transduction but also for studying protein-protein interaction networks. We have developed a rapid computational approach to rationally design cyclic peptides that potentially bind at desired regions of the interface of protein-protein complexes. The methodology is based on comparing the protein backbone structure of short peptide segments (epitopes) at the protein-protein interface with a collection of cyclic peptide backbone structures. A cyclic peptide that matches the backbone structure of the segment is used as a template for a binder by adapting the amino acid side chains to the side chains found in the target complex. For a small library of cyclic peptides with known high resolution structures we found for the majority (~82%) of 154 protein-protein complexes at least one very well fitting match for a cyclic peptide template to a protein-protein interface segment. The majority of the constructed protein-cyclic peptide complexes was very stable during Molecular Dynamics simulations and showed an interaction energy score that was typically more favorable compared to interaction scores of typical peptide-protein complexes. Our cPEPmatch approach could be a promising approach for rapid suggestion of cyclic peptide binders that could be tested experimentally and further improved by chemical modification. Protein-protein interactions (PPIs) serve as the basis for nearly all biological processes, they play key roles in intercellular communication, cell-to-cell signaling, metabolic and developmental control, and programmed cell death . In a third step a cyclic peptide is superimposed on the matching backbone segment of a partner protein to form an appropriate binding placement. This step returns a coordinate file of the cyclic peptide in complex with the receptor partner protein. The side chains of the four consecutive (matching) residues at the interface between cyclic peptide and protein are then replaced by the side chains found in the original protein-receptor complex. Exceptions for residue substitutions are made when the matched residue of the cyclic peptide is one involved in a disulfide bond that is part of the peptide cyclization (not changed). The resulting complex was further refined and evaluated by energy minimization and Molecular Dynamics (MD) simulation (see below).Many protein–protein interactions are at least in part mediated by short linear motifs that can be part of α-helices, β-strands or loops . Each stage was simulated for 100 ps and included positional restraints on all non-hydrogen atoms with respect to the starting conformation. Subsequently, positional restraints were gradually reduced from 25 to 0.5 kcal·mol−1·A−2 in five consecutive simulations of 100 ps at 310 K and at constant pressure of 1 bar. The equilibrated structures served as input for the production runs for each system, with no restraints. Data gathering simulations were carried out for 5 ns. Coordinates were written out every 500 steps. A time step of 2 fs was used and all bonds involving hydrogens were constrained to the optimal length using Shake  binding free energy calculations were carried out to estimate the strength of cyclopeptide-receptor interactions  but the sunflower peptide is also significantly larger and forming a more extended interface compared to the generated potential small cyclic peptide binder. The calculated MMGBSA interaction energy for this test case was significantly more favorable than the MMGBSA interaction energy obtained for a typical peptide-protein complex with binding affinities in the micromolar regime (We first tested the approach on a complex formed by bovine trypsin inhibitor (BPTI) protein and the trypsin protease (pdb4dg4). Our matching approach identified a cyclic peptide backbone that closely resembled the protein backbone of the BPTI protein at the interface  with a r−1 . This isr regime .−1, respectively.Next we applied our cPEPmatch approach systematically to 154 protein-protein complexes with known structures to identify putative matches that could potentially bind at the PP interface and interfere with PP interaction. According to our threshold criteria (see Methods) we were able to identify at least one putative cyclic peptide match for 82% of all the cases studied . The put−1) which might be due to different sizes but may also be due to the targeted interface region in each complex. However, the MMGBSA interaction energies are in most cases more favorable compared to small linear peptides that binding with micromolar binding constants to target domains  for each of the highlighted systems in our study, 1cgi, 2hle, 2nz8, and 4dg4 . For each case and each simulation MMGBSA binding free energy calculations were calculated using 250 snapshots retrieved from the three time intervals corresponding to 2.5–5.0, 5.0–7.0, and 7.5–10 ns of every simulation . OverallIn addition, we evaluated the importance of the generated interface side chains on the ligand-fitted cyclic peptides constructed by our cPEPmatch approach that are identical to the side chains of the native complex. Our evaluation procedure was applied to protein bound cyclic peptides containing the original sequence of the corresponding cyclic peptide from the data base or containing just alanine substitutions at the interface . The samOne possibility to create cyclic peptide variants based on known protein-protein interface structures is to identify a linear interface motif with an appropriate distance of terminal side chains or backbone atoms such that cyclization is possible. Ideally, such motifs give stable cyclic peptide binders. However, it can be difficult to predict the backbone structure of a cyclized peptide if one introduces a chemical backbone or side chain crosslink. The crosslink can destabilize a desired geometry such that the cyclic peptide does not resemble the structure of the interface motif. An advantage of our strategy is that we base the construction of a desired cyclic peptide on known stable (high resolution) cyclic template structures. Hence, the approach avoids the uncertainty on how well a select cyclization of a given motif resembles a desired backbone structure. It is applicable if the structure of a protein-protein complex is known and even potentially useful if the receptor is flexible and adopts a different structure in the unbound form (as long as it can adopt the structure in the bound form). Most related to our method is an approach by Duffy et al.  to identThe initial screen of our approach (without explicit solvent refinement) is extremely rapid, hundreds of PPI can be screened within a few seconds for cyclic peptides that match to backbone structures at the PPI interface. Interestingly, with a relatively small set of cyclic peptide templates it is possible to identify cyclic peptide-protein complexes that are stable during explicit solvent MD simulations and indicate very favorable interaction energy scores comparable or better than for known stable peptide-protein complexes. We also found that substitution by alanine in the evaluation could be a useful strategy in order to check the contribution of the side chains copied from the native interface to the cyclic peptide-protein interface to the stability of the complexes. It should be noted that a short MD simulation as used in our approach is useful to relax the complex structure but not long enough to exhaustively sample the complex conformations. The putative cyclic peptide modulators could be further improved by individual post-modifications and by studying specific cases experimentally. It could also be combined with approaches to identify peptide epitopes specific for mediating protein-protein interactions as well as allosteric sites our unstable loop segments in protein interaction regions (Paladino et al., MZ designed and supervised research. BS performed research and analyzed data. All authors contributed to writing of the manuscript.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.


Escherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli. The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less.E. coli, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli, ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes.In this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli, and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.In this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of  Escherichia coli (E. coli) is the primary model organism of microorganisms, and perhaps it is the most intensively studied species of bacteria [E. coli K-12 currently have experimental evidence for their biological roles, and others remain unannotated (orphans) [E. coli. However, it is well-known that experimental analysis has always been dominant because of the lack of large-scale experimental data and the incompleteness of the E. coli dataset [E. coli have been released, so the protein complexes and functional modules of E. coli and their relationships can be predicted from a global perspective [bacteria –4. Even orphans) . Experim dataset –9. Experspective –19. AlthE. coli PPI network is an important research topic to understand the essential biological functions of proteins. Although some studies used complex network models to make predictions, due to the lack of large-scale PPI datasets and the existence of a large number of orphan proteins, the prediction results were difficult to achieve the expected ones. However, with the release of some high-throughput E. coli PPI datasets in recent years, it has become possible to predict protein complexes and functional modules of E. coli based on complex network models. With the development of high-throughput sequencing technologies, such as two-hybrid systems and mass spectrometry technology for pairwise protein interactions, large-scale PPI networks of E. coli can be constructed at genome level [E. coli PPI networks [E. coli. Even so, studies of predicting protein complexes and functional modules at the same time, especially the comparative analysis of them is relatively less. In this paper, based on the E. coli datasets of Hu et al. [A protein complex is formed by the interaction of more than two functional related peptide chains through disulfide bonds or other proteins, so it performs some given biological functions. A functional module is the basic functional unit of proteins, which implies complex relationships involving multiple biological interaction types . Revealime level . Some stnetworks , 10, 11,networks –16 and mnetworks –19 of E.u et al.  and Congu et al. , two comE. coli protein complexes and functional modules were predicted by a link clustering method  in two od  matched 91 predicted protein modules (75.8%), and in netC, 200 benchmark complexes (67.8%) matched 144 predicted protein modules (70.9%). Since most benchmark complexes consist of no more than ten proteins, larger protein modules may contain multiple complexes, which is consistent with the composition of real complexes.Those protein modules of netH and netC identified by ELPA were matched with 295 real benchmark protein complexes of potF, potH, and potI were the three proteins in the putrescine ABC transporter complex; potA, potB, potC, and potD covered all four proteins of the putrescine/spermidine ABC transporter complex  were significant functional modules, of which about 30% exactly matched a certain functional term . For example, as shown in Fig. mfd, nusA, pyrG, rpoB, rpsE, rpsU, and uvrA, which were completly annotated by GO:0005829. Similarly, ten proteins of module 26: dppA, dppB, dppC, dppD, dppF, nikA, oppA, oppB, oppD, and sapA were all annotated with GO:0006810. The analysis above indicated that ELPA is an effective method to predict E. coli functional modules in PPI network.Those protein modules of netH and netC identified by ELPA were matched with benchmark functional annotations of modules 8.5% were modules 8.5% were modules 8.5% were A protein complex is a physical aggregation of several proteins that interact at the same time and location through molecular interactions. A functional module also consists of multiple proteins that interact with each other to control or perform a particular cellular function. However, unlike protein complexes, these proteins do not necessarily interact at the same time and location. Therefore, the comparative analysis of protein complexes and corresponding functional modules is of great scientific significance.hycE, hycF and hycG were the components of the formate hydrogenlyase complex, and their functional annotations were hydrogenase 3, formate hydrogenlyase complex iron-sulfur protein, and hydrogenase 3 and formate hydrogenlyase complex-HycG subunit, respectively. This meant that the functions of these three proteins were consistent with the formate hydrogenlyase complex, and it also implied that the hydrogenase 3 complex was related to the formate hydrogenlyase complex. The Hydrogenase 4 complex proteins: hyfB, hyfD, hyfF, hyfG and hyfI were annotated with hydrogenase 4-component B, D, F, and large, small subunit respectively. This indicated that the functions of these five proteins and the hydrogenase 4 complex were highly coherent. The ten remaining proteins of this module: nuoB, nuoC, nuoE, nuoF, nuoG, nuoH, nuoI, nuoL, nuoM, and nuoN were all related to NADH: ubiquinone oxidoreductase complex. Therefore, we predicted that the above three protein complexes might significant functional correlations.For example, as shown in Fig. Sec Translocation Complex and SecD-SecF-YajC-YidC Secretion Complex. All four proteins of SecD-SecF-YajC-YidC Secretion Complex were covered by this module and Sec Translocation Complex, and they were all matched with GO:0016021, among them yidC were annotated with inner-membrane protein insertion factor. 7 out 9 proteins of Sec Translocation Complex matched this module, and the seven proteins: secA, secD, secF, secG, secY, yajC and yidC were enriched with the term “protein transport” of GO: 0015031, which meant their functions were consistent with the complex. Besides one uncharacterized protein ydiE, the two remaining proteins rplN and rpsE were enriched with the term “translation” of GO: 0006412, and the term “rRNA binding” of GO:0019843, respectively. The results above indicated that those predicted protein modules are significant related to corresponding protein complexes and functional modules. Usually, a functional module might cover more than one protein complex.Figure Most biological network module identification methods were based on node clustering, and among them, MCL has been proven to be superior to other methods in identifying the protein modules in most cases , 26, 27.Precision, Recall, F-measure were utilized to compare the performance of MCL and ELPA in predicting protein complexes. In netH, the performance comparison of two methods for predicting protein complexes was shown in Fig. Precision, Recall and F-measure of ELPA were 72.5, 61.5, and 66.5%, respectively, while the corresponding results of MCL were 55.1, 65.5, and 59.9%, respectively. Similar results were obtained in netC. As shown in Fig. Precision, Recall and F-measure of ELPA were 67, 62.3 and 64.6%, while the corresponding results of MCL were 67.9, 60 and 63.7%, respectively. From the Fig. SecD-SecF-YajC-YidC Secretion complex matched with module 69 of ELPA and module 149 of MCL, and the corresponding matching scores were 56.3 and 45%, respectively.Three metrics: Precision and AMS are two effective metrics to evaluate the predicted quality of functional modules. The performance comparison of two methods for predicting functional modules was shown in Fig. Precision and AMS of ELPA were 82.5 and 70.9%, respectively, and that of MCL were 85.9 and 70%, respectively. Analogous results were obtained in netC. As shown in Fig. Precision and AMS of ELPA were 91.6 and 77.5%, while the corresponding results of MCL were 93 and 77.8%, respectively. We concluded that the performance of both methods is comparable in the prediction of functional modules. For example, in netC, both module 190 of ELPA and module 66 of MCL were enriched in the GO: 0000018, and corresponding matching scores were 100 and 66.7%, respectively. Both module 29 of ELPA and module 101 of MCL were enriched in the GO: 0006281, and corresponding matching scores were 100 and 75%, respectively. Both module 83 of ELPA and module 168 of MCL were enriched in the GO: 0009060, and the corresponding matching scores were 80 and 75%, respectively. The results above showed that ELPA is an effective method to predict E. coli protein complexes and functional modules.In general, pheS, pheM, thrS, argS and erfK. ThrS is also a translational repressor protein, and it controls binds its own mRNA in the operator region upstream of the start codon. ThrRS represses translation by preventing the ribosome from to mRNA, and tRNA acts as an antirepressor allowing fine level control of enzyme synthesis. ThrS, argS, pheS and pheM are all involved in aminoacyl-tRNA ligase activity, and they are the members of arginine-tRNA ligase, phenylalanine-tRNA ligase alpha chain, phenylalanyl-tRNA synthetase operon leader peptide and threonine-tRNA ligase superfamily, respectively. In addition, pheS, thrS and argS are in Aminoacyl-tRNA biosynthesis pathway, and pheM is significant related to Aminoacyl-tRNA biosynthesis. We knew little about erfK except that it was involved in the peptidoglycan biosynthesis pathway. These annotations indicated that these proteins might constitute a protein complex involved in aminoacyl-tRNA biosynthesis.Besides well-characterized protein complexes and functional modules, we also identified modules that had not been matched to an EcoCyc protein complex or functional category in both networks. In netH and netC, we discovered 36 and 67 novel protein complexes, and 21and 17 novel functional modules, respectively  in a highly possessive manner, and also functions as a DNA deoxyribophosphodiesterase that releases deoxyribose-phosphate moieties following the cleavage of DNA at an apurinic/apyrimidinic (AP) site by either an AP endonuclease or AP lyase. HflC controls the lysogenization frequency of phage lambda. Together, these annotations suggested that these proteins form part of a translation module.An example for the novel functional module was module 10 in netC, and it consisted of seven proteins: E. coli PPI network. Through comparison with the EcoCyc database, we have discovered some novel and interesting complexes and functional modules. In addition, we compared and analyzed protein complexes and functional modules derived from the same predicted protein modules. It was found that protein complexes are highly overlapped with the corresponding functional modules, many of which contain more than one protein complex, which helps to understand the dynamic relationship between protein complexes and functional modules. Finally, the results of ELPA were compared with that of MCL, and we found that their performance is comparable in most cases. Therefore, we concluded that ELPA can be used as an effective cluster analysis tool for different types of biological networks. In further work, we will explore the key regulatory proteins and pathways in the transcriptional regulatory network of E. coli, based on the corresponding protein complexes and functional modules.In this paper, a link clustering algorithm (ELPA) was used to identify protein complexes and functional modules in the E. coli were retrieved from the original paper of Hu et al. [E. coli, which contained 1618 interactions among1, 476 proteins. To predict protein complexes more efficiently, only those binary interactions associated with known complex proteins were considered. As a result, 3280 interactions among 1298 proteins were retrieved from the dataset of Hu et al., and 1299 interactions among 785 proteins were retrieved from the dataset of Cong et al.Two high-throughput experiments of protein-protein interactions (PPIs) datasets of u et al.  and Congu et al. , respectE. coli downloaded from EcoCyc database [E. coli contain only two proteins, thus those complexes containing at least two proteins in the E. coli K-12 dataset of EcoCyc were kept as benchmark complexes. In this way, we obtained 295 benchmark protein complexes containing 732 proteins. Furthermore, the EcoCyc Gene Ontology (GO) database was taken as the benchmark functional classes of E. coli. In total, we obtained three protein datasets: EcoCyc, Hu et al., and Cong et al., and we found the relationships among them by Venn diagrams  , was utial paper  have shoal paper , 31. In Two criteria were employed to evaluate the performance of ELPA. One was matching the identified protein modules with known protein complex of EcoCyc benchmark dataset. The other was the functional enrichment of the identified protein modules.p and corresponding real complex b in the benchmark complex set, the Matching Score (MSpb) between them was calculated as [To determine the matching efficiency of a predicted complex lated as :1\documnpb is the number of proteins shared by the predicted complex p and the real complex b, np is the number of proteins in complex p, and nb is the number of proteins in complex b. A predicted complex and a real complex were considered to be a match if their matching score was no less than a specific threshold .Precision, Recall and F-measure [P and B denote the set of predicted and actual complexes, respectively. Let NP denote the number of protein complexes, and let NB denote the number of actual complexes in the benchmark dataset. Let NPC denote the number of predicted complexes that matched at least one real complex, and let NBC be the number of actual complexes that matched at least one predicted complex. Precision and Recall were then defined as follows:To evaluate the predicted protein complexes, we checked how well the predicted complexes matched the actual complexes. Three types of popular evaluation criteria: -measure , were us(AMS) was another metric to evaluate the predicted protein complexes.The arithmetic mean of matching score Precision and AMS were used to evaluate predicted functional modules.To evaluate the functional enrichment of a predicted protein module, the matching score between a predicted protein module and a given GO term was used to estimate whether the proteins in the predicted module were enriched for the GO term. Then, Additional file 1. S1-clustering resultsAdditional file 2. S2-novel complexes and functional modules


Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied.In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations.Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers. The knowledge of subcellular location of proteins is fundamental for understanding their functions in biological processes . In geneiLocator and applied it to location biomarker detection [In the past decades, lots of protein subcellular location prediction tools were developed, and some of them have been used in location biomarker analysis. Protein amino acid sequence, although ultimately determines the protein properties –11 and wetection ; Kumar eetection ; Yang etetection . HoweverIn recent years, the rise of deep learning provides another solution to study the protein spatial distributions. Some works based on convolutional neural networks (CNN) have been published, but most of these studies focus on the fluorescence images of cell lines –23, and In this work, we built an automated classifier of protein subcellular localization based on IHC images of colon tissue, and tested its ability of detecting protein translocation based on two constructed colon cancer biomarker datasets. The classifier used small image patches with high protein expression as model input, and combined both feature engineering and CNN models. Our results indicated that use of patches can improve the classification performance, and concatenating deep features and predefined features can be quite competitive in classifying subcellular localization of new samples. Proteins in the two biomarker datasets are collected from published literatures and the human protein atlas (HPA) database, respectively, and our classifier showed promising performance in identification of protein location changes.Flow chart of our experiments is shown in Fig. As a baseline, we firstly trained classification models based on the whole IHC images, where the patch extraction step was skipped. Each IHC image were unmixed using two methods, i.e., linear spectral unmixing (LIN) and blind spectral unmixing by non-negative matrix factorization (NMF), and then extracted global SLFs and local binary pattern (LBP) features. As SLFs extraction processed images by discrete wavelet transform using 10 Daubechies filters, we used db1 to db10 to represent different sets of features.Figure An original IHC image commonly consists of stained glands, unstained stroma, and unspecific background, but only the protein stained tissue section contains location pattern information. Therefore, we selected interest patches with high protein expression to represent subcellular patterns. It is assumed that two parameters, the number of patches in each image and the patch size, highly affect whether the patches contain enough and useful pattern information. Here, we set ranges for the two parameters and used grid search to determine the optimal ones. The number of patches was set from 25 to 385 in increments of 20, while the side length of the square patch was set from 45 to 225 pixels in increments of 30. Figure It can be seen from the heat maps that when the size of patches gets small and the number of regions increases, the classification accuracy gradually increases and then tends to be flat. Overall, the results below the main diagonal are better than the above. These results indicated that the patches should be sufficient to show protein patterns and be small to highlight the micropatterns in cells. Based on these heat maps, we selected optimal parameters for this classification task: the optimal size for the patches is 75 pixels and the optimal number of patches is 205. Some example patches with the optimal parameters are shown in Fig. Figure In addition, we can see prediction accuracies in per protein way are lower than the per image way. This is because per protein way is a rigorous method for dataset partition, and it can objectively illustrate the generalization ability of trained models for new protein samples.As deep learning methods perform well in image classification, we tried to use convolutional neural networks to predict the subcellular location of proteins from IHC images. Seven pre-trained networks were used in the study, i.e., GoogLeNet , ResNet1We investigated the features extracted from the seven pre-trained networks. For each image, 35 patches of 224*224 pixels . Proteins in the two datasets have images of both normal and cancerous colons, and are likely to have different subcellular patterns between the two situations. Our model was expected to detect the differences. An independent sample t-test. In the literature biomarker dataset, 18 of the 22 proteins were detected as having significant location changes  database, which is a public online database storing millions of IHC images of approximately 17,000 human proteins across various healthy and cancerous tissues [In this study, our image datasets were selected from the HPA  with radial basis function kernel to train classifier models [g and c were determined by grid search. 10-fold cross validation was employed here to evaluate the model performance.We used SVM from LIBSVM-3.23 toolbox (r models , and theSeven pre-trained networks were used in the study. The first six networks are GoogLeNet , ResNet1We operated two methods of transfer learning, i.e., extracting feature map from pre-trained networks and fine-tuning method. Firstly, the penultimate layers of the seven networks were extracted as patch features, where the GapNet-PL outputs 256 features, while the other six networks output 1000 features. Besides, fine-tuning was used on the pre-trained networks to adapt the classification models to our task. For each network, we replaced the last layer with our classification outputs, and fine-tuned the parameters of all the layers.t-test was used to evaluate the significance of location changes. In particular, suppose one protein has m images of normal tissue and n images of cancer tissue. First, we used the averaged classification score vectors to determine the predictions of subcellular location in normal and cancer tissues, respectively. Then for each protein, an independent sample t-test was conducted under a null hypothesis that the mean vectors are the same between the m weight vectors of the normal tissue images and the n weight vectors of the cancer tissue images. The t-test would output a P value vector, where each value indicated the significance of change of subcellular location from normal to cancer status. Protein was considered to be identified as location biomarkers only if the P value is less than 0.05.In this work, we used two biomarker datasets, i.e., literature biomarker dataset and HPA biomarker dataset, to verify whether the machine learning models can detect protein translocations in colon cancer. Independent sample Additional file 1: Table S1. Results of using different image separation methods and features. Table S2. Comparison of using whole images and using patches. Table S3. Results of combining conventional and deep learning features. Table S4. Training time of fine tuning pre-trained deep networks. Table S6. Subcellular location changes of proteins in the literature biomarker dataset.Additional file 2: Table S5. Predicted subcellular locations and P values of proteins in the literature biomarker dataset and the human protein atlas biomarker dataset.


An amendment to this paper has been published and can be accessed via the original article. Proteins are long sequences of amino acids that form the basis of life and plays vital role in all living organism through out the entire life-cycle. Proteins perform various functions in our body that needs to be understood to understand life, disease processes and guiding drug discovery efforts to combat the diseases. Due to the tremendous advancement in amino-acid sequencing technologies, it is now possible to sequence bulk amount of proteins in a rapid and affordable manner. Therefore, the number of protein sequences accumulating in public databases is rising at an unprecedented rate. This huge quantity of data calls for further exploitation and enrichment and it presents many challenges for biologists as well as computer scientists in annotating the functional properties of protein sequences. The UniProt knowledge base (UniProtKB)  is one oThe UniProt curators annotate UniProtKB/TrEMBL sequences using two complementary systems. The first, called UniRule, uses a large list of “if-then” rules. These rules have been generated manually, which is both a laborious and time consuming process. The rules in UniRule are generally very reliable but their coverage is low . The secRecently, the notion of network science  has attrThe following methods are widely used for predicting Enzyme Commission (EC) numbers using a variety of approaches based on machine learning, sequence encoding, functional domain similarity, and structural similarity. A deep learning based approach called DEEPre  predictsEzyPred  is a k-nA machine learning based approach called SVM-Prot that uses support vector machine (SVM) for classification is proposed in –33. And COFACTOR is described in [COFACTOR [A structure-based protein function annotation approach called ribed in , 34. TheCOFACTOR  combinesEFICAz , 36, 37 In ECPred , the autIn this paper, we give a complete description of our novel graph based annotation approach(GrAPFI) , and we Although, similar to EZYPred  and SVM-We compare the performance of GrAPFI with the recently published ECPred approach. Along with ECPred, we also present the accuracy for DEEPre and SVMProt as representative examples of other state of the art EC number prediction approaches. Our analysis shows that GrAPFI gives better annotation performance than these earlier approaches.We have collected 262,564 proteins from the March 2018 release of Uniprot-KB/SwissProt [The training network covers 25 level-2, 31 level-3 and 408 level-4 EC classes from 41,618 oxidoreductases, 70,530 transferases, 100,027 hydrolases, 14,677 lyases, 25,551 isomerases, and 29,735 ligases which are linked using 10,866 InterPro signatures.In the training network, there are 1) 4.3% of the proteins are single-domain proteins i.e. proteins having only one domain in their domain composition  containing 1,953 proteins, (ii) Mus musculus (UP000000589) containing 3,682 proteins, (ii) Saccharomyces cerevisiae (UP000002311) containing 1,581 proteins, (iv) Homo sapiens (UP000005640) containing 3,843 proteins, and (v) Arabidopsis thaliana (UP000006548) containing 5,352 proteins. (vi) E. Coli (UP000000625) containing 1465 proteins. For each of the reference proteomes, we collected the InterPro domains and EC labels from Uniprot-KB/Swissprot. We kept only the proteins which have at least one InterPro domain and are annotated with a single EC number.To valdiate GrAPFI, we used six popular reference proteomes from Uniprot-KB/SwissProt to as test set. The reference proteomes are the following: (i) COFACTOR benchmark dataset, we used the 318 protein sequences published in [To prepare the ished in , and we ished in  on thesey′ are the list of ground truths and predicted annotations. The accuracy is computed for each level of EC annotation. As the problem is a multi-class classification problem, we computed class-wise Macro-precision, Macro-recall, and Macro-F1 score as follows:yl is the part of y with the label l and y′ with label l. And M is the set of classes. In general the precision, recall, and F1-Measure are computed as follows when two sets A and P are given:To validate the annotation performance of GrAPFI, we computed the accuracy, Macro-precision, Macro-recall, and Macro-F1 score at different levels of EC number. For each query sequence, we picked the top ranked annotation only. The validation method we have used is similar to leave one out cross validation. For each proteomes, when annotating a protein, we have removed that protein from the training set so that a direct mapping is not present. we also present a 10-fold cross validation for enzyme vs. non-enzyme classification Fig. a and b. Coverage=M/T, where T is the total number of proteins in the test set and M is the number of proteins for which at least one EC is predicted. For each query sequence, we consider the top-most annotation. For evaluation purposes, we split the 4-digit EC annotation into its constituent parts. Then, for level-1 we consider first digit, for level-2 we take first 2 digits, for level-3 we take first 3-digits and finally for level-4 we take all four digits together.Here, A is the set of ground truths and P is the set of predictions. As EC numbers are hierarchical with 4 levels, we report level-wise precision, recall and F1-measure. Level-1 denotes main class, level-2 denotes sub-class, level-3 denotes sub-sub-class and level-4 denotes sub-sub-sub class. We also report coverage which is calculated according to For the validation dataset, GrAPFI was run by setting different minimum jaccard similarity index ranging from 0.05 to 0.5, and setting an upper limit of the similarity to 1 or less than 1.A. thaliana for various min. Jaccard similarity indices. Similarly, Figs. Homo sapiens, S. cerevisiae, Mus musculus, Rattus norvegicus and E. Coli, respectively. In Fig. COFACTOR benchmark dataset for various Jaccard domain similarity index ranging from 0.05 to 0.5, and setting an upper limit of the similarity to 1 and less than 1.Figure In these figures, we show the annotation accuracy (Y axis) against different Jaccard similarity thresholds (X axis) for the respective proteomes. We have considered similarity thresholds ranging from 0.05 to 0.5 as the annotation coverage falls significantly after 0.5. For each of the thresholds, we present the accuracy for EC-1, EC-2, EC-3 and EC-4 digit prediction shown in green, blue, orange and black color respectively. Along with accuracy, we also present the coverage of annotation (red curve). For each of the figures, we have two parts. The first part shows the accuracy and the coverage considering only the neighbors who have a Jaccard similarity of less than 1. The second part considers the Jaccard similarity of less than 1. It can be seen from these figures that GrAPFI performs very well for all of the cases with a good coverage.COFACTOR [To compare GrAPFI with other state of the art methods, we considered three machine learning based methods, namely ECPred , DEEPre COFACTOR  benchmarBecause not all of the methods can make predictions for all four EC levels, we compared GrAPFI only with ECPred and DEEPre for 4-digit EC numbers as shown in Fig. The SWISS-PROT  database was separated into enzymes and non-enzymes based on their annotation.To guarantee uniqueness and correctness, enzyme sequences with more than one set of EC numbers or incomplete EC number annotation were excluded.To avoid fragment data, enzyme sequences annotated with ’fragment’ or with less than 50 amino acids were excluded. Enzyme sequences with more than 5000 amino acids were also excluded.Redundancy bias is removed using CD-HIT  with 40%To construct the non-enzyme part, 22,168 non-enzyme protein sequences were randomly collected from the SWISS-PROT  non-enzyme part, which were also subject to the above (ii–iv) steps. Thus the original dataset contains 22,168 enzymes and an equal number of non-enzymes.GrAPFI can be used in Enzyme vs. Non-enzyme classification task in a similar fashion as described in above section. However, the training graph must include non-enzyme proteins. To experiment with enzyme vs. non-enzyme classification, To evaluate the method, we have used a well defined dataset of enzyme and non-enzyme proteins curated from UniprotKB . This daThe dataset contains the protein sequences along with their respective EC annotations. We have run InterProScan5  to identTo evaluate the annotation performance, we present 10-fold cross validation on the training graph and average macro-precision, macro-recall, macro-F1 scores are computed for various jaccard similarity indices. The result shows performance of enzyme vs. non-enzyme classification only. The experimental outcomes are shown in Fig. It is evident from the experimental outcome that GrAPFI can distinguish enzyme and non-enzyme proteins with a a good score in all evaluation metrics. However, the coverage goes down as we move towards higher similarity thresholds. One of the things to be noted that considering exact similarity match does not change the performance significantly as can be seen in Fig. Here, we explore new ways of connecting proteins. The proteins are connected based on domains that are potentially linked to the protein functions. This eventually means that GrAPFI is biologically meaningful approach. One of the major advantages of using GrAPFI to annotate proteins is that it produces explainable high quality annotations with a relatively simple annotation pipeline. The potential is evident from the experimental results. Although GrAPFI performs well, there are few drawbacks of using GrAPFI. For example, GrAPFI works on domain composition that can be achieved using another tool. GrAPFI can not be used with proteins without domain information. And also for the proteins with single domain, in most cases, GrAPFI fails to find appropriate annotation. The reason for this failure is that for a single domain protein, it is highly unlikely that there will be any high quality neighbors that can share annotations that eventually left the protein without any labels or wrong one. In any case, if GrAPFI fails to find an annotation, it is possible to identify the reason behind the failure and it restricts itself from predicting any annotation. This attitude reduces the false positives. However, from the experiment, it is evident that GrAPFI performs with high annotation coverage. Unlike other hierarchical classification models like ECPred  and DEEPIn this paper, We have extended and validated GrAPFI , a novelCOFACTOR dataset. This comparison shows that GrAPFI achieves better accuracy and comparable or better coverage with respect to these earlier approaches.Our neighborhood based label propagation algorithm was applied to the network in order to propagate annotations from reviewed proteins to non-reviewed query proteins. This approach was validated using six popular reference proteomes from UniProtKB/SwissProt. We also compared GrAPFI results with those of ECPred, DEEPre, and SVMProt as examples of state of the art EC prediction approaches using the GrAPFI combines the notion of protein domain similarity with a graph neighborhood inference technique for automatic EC number annotation. More specifically, the functional annotations of reviewed proteins in SwissProt are used to predict those of non-reviewed proteins in TrEMBL using label propagation on a complex network representation of protein sequence data. The GrAPFI algorithm first constructs an undirected weighted graph of the proteins using the domain composition of the reviewed proteins. Then, given an non-reviewed protein, a label propagation algorithm is applied to the protein graph in order to infer appropriate annotations.In this section, we first present some definitions and notations used in the paper.Graph: A graph is a collection of objects denoted as G=, where V is a set of vertices/nodes and E⊆V×V is a set of edges.Weighted Graph: A weighted graph is a graph which is represented as a three tuple G= where:V is a set of nodes,E⊆V×V is a set of edges,W is a weight matrix where each cell Wuv represents a numerical weight of the edge ⊆E.Labeled Graph: A labeled graph is a graph which is represented as a four tuple G= where:V is a set of nodes,E⊆V×V is a set of edges,L is a set of labels,I:V∪E→L is a labeling function.Directed Graph: A Directed graph G= is a collection of objects where V is a set of vertices/nodes and E⊆V×V is a set of edges with ordered pair of vertices  such that (u→v)∈E.Undirected Graph: An undirected graph is a collection of objects denoted as G=, where V is a set of vertices/nodes and E⊆V×V is a set of edges with unordered vertices  such that if (u→v)∈E exists then (v→u)∈E must exists.Neighbors: The neighbors of a node u are defined as N(u)={v|∈E}.Degree: The degree of a node in a graph is the number of edges which touch it. The degree of a node u in a graph G is denoted deg(u)=N(u).Average Degree: The average degree of a graph G= is a measure of how many edges are in the set E compared to number of vertices in the set V. The average degree of a graph G= is defined by Avgdeg=2|E|/|V|.u is identified by a set of labels L(u), has a set of neighbours N(u), and for every neighbour v it has an associated weight Wu,v. The overall aim is to propagate labels (i.e. annotations) from nodes having labels to similar nodes that lack labels.We present here a novel way of connecting protein sequences using their associated InterPro domains. Domains may be considered as natural building blocks of proteins. Due to evolution, protein domains may have gone through changes such as duplication, fusion, recombination to produce proteins with distinct structures and functions . Here, eP1,P2,P3,P4, and P5. Let us assume that these proteins are composed of domains D1=,D2=,D3=,D4=, and D5=, respectively.To illustrate the construction of the protein graph, let us consider five proteins with symbolic names D1= can be used in any other order D1=. Therefore, the composition is not strictly linear. The overlapping of domains are not considered as long as the overlapped domains has a new domain identification.Domain composition of a protein is the set of domains found in a protein sequence and considered irrespective of order of appearance in the sequence. For example the domain information in P1 and P2 contain two domains d1 and d3 in common. Therefore, proteins P1 and P2 may be linked and the number of shared domains may serve as a link weight given byP1 and P5 may be linked with a link weight of |∩|=||=2. In both cases, the link weight is 2. However, the link weight computed in this way does not reflect the relative strength of the relationship among the proteins. More specifically, in the first case the two proteins have |∪|=||=5 different domains, of which two are shared. In the second case, there are |∪|=11 different domains of which again two are shared. Although two domains are shared in each case, P1 is intuitively more aligned with P2 than P5. Therefore, instead of using the above raw similarity score, we used the Jaccard similarity index, or Jaccard similarity coefficient, to reflect better the similarity in composition. This is calculated as It is then evident that proteins Similarly, for P1 and P5, the link weight is calculated asUsing the Jaccard similarity index, the final graph is built in two simple steps. In the first step, the data files that contain reviewed protein information are parsed to collect the constituent domains of each protein. If the training data contains only sequences, InterProScan , 43 is uIt is worth mentioning that the order of the domains is not maintained while computing jaccard similarity index. Domain composition for each protein contains the set of unique InterPro signatures found in the sequence.Enzymes are usually labelled following the Enzyme Commission (EC) system , the widi for the query protein u. And δ is 1 if the function vi of the protein v is same as function i, otherwise, 0. The details of the label propagation algorithm is described in Algorithm 1. Overall, for a given input sequence, the annotation algorithm works according to the flow diagram shown in Fig. After building the graph from the reviewed proteins, the graph is ready to be used for the function annotation of new protein sequences. A neighborhood based label propagation algorithm is designed to perform the annotation task. Given the constituent domains of an input protein sequence, all of its neighboring proteins and their annotations are retrieved from the graph. Once the neighbors have been obtained, the weighted frequency of the labels are computed using the following formula:P with a set of domains D=. Our aim is to annotate this protein with an EC Number following the label propagation algorithm, as illustrated in Fig. P will have connection with proteins P2 and P4 in the running example graph. The dotted lines show the links from P to P2 and P4 in the graph along with the associated weights. Therefore, the protein P will have P2 and P4 as it’s neighbors. After finding the neighbors, the functional annotations of all the neighbors are propagated along with the corresponding weights. All of the functional annotations are ranked based on their cumulative weights. The top ranked function is selected as the best functional annotation for protein P. In this example, the weighted annotations for P are EC3,EC5,EC6,EC1,EC2 with cumulative weights of 0.70, 0.50, 0.50, 0.20, and 0.20, respectively. Therefore, the functional annotation for the protein P is EC3 as it has the highest weight among the propagated labels. Clearly, it is possible to select more than one high scoring functional annotations if we wish to propose more than one candidate annotation. Furthermore, node neighbours could be selected in other ways to reflect the requirements of the problem at hand.Let us consider a query protein


Escherichia coli and Homo sapiens, the lack of a substantial negative correlation between protein evolutionary rates and Gibbs free energies of unfolding, a direct measure of protein stability. We then analyze multiple new genome-scale data sets characterizing protein aggregation and interaction propensities, the properties that are likely optimized in evolution to alleviate deleterious effects associated with toxic protein misfolding and misinteractions. Our results demonstrate that the propensity of proteins to aggregate, the fraction of charged amino acids, and protein stickiness do correlate with protein abundances. Nevertheless, across multiple organisms and various data sets we do not observe substantial correlations between proteins’ aggregation- and stability-related properties and evolutionary rates. Therefore, diverse empirical data support the conclusion that the MAH and similar hypotheses do not play a major role in mediating a strong negative correlation between protein expression and the molecular clock, and thus in explaining the variability of evolutionary rates across cellular proteins.For more than a decade, the misfolding avoidance hypothesis (MAH) and related theories have dominated evolutionary discussions aimed at explaining the variance of the molecular clock across cellular proteins. In this study, we use various experimental data to further investigate the consistency of the MAH predictions with empirical evidence. We also critically discuss experimental results that motivated the MAH development and that are often viewed as evidence of its major contribution to the variability of protein evolutionary rates. We demonstrate, in  Evolutionary rates vary substantially across cellular proteins. Understanding the nature of the molecular clock and its variability across proteins is a foundational question in molecular evolution. The popular and currently dominant theory to explain the molecular clock variability is the misfolding avoidance hypothesis (MAH). The role and importance of the MAH is currently under active debate. In this article, we discuss how to appropriately test the MAH based on available empirical data. We then rigorously test the hypothesis using more than a dozen new genome-wide data sets that characterize protein stability and aggregation propensities. Our results demonstrate that the MAH is unlikely to play a major role in explaining the variability of the molecular clock across proteins.primarily due to increased selection against misfolded protein toxicity  protein abundance positively correlates with protein stability , and 2)  species . Razban’iscussed , the MAHE. coli and Homo sapiens proteins .The central message of Razban’s analysis is that the absence of the expected relationship between  and ΔG0 . Razban database . This anE. coli; H. sapiens). Thus, the mechanisms that make highly expressed proteins evolve more slowly are likely to be reflected in the properties of these proteins. But contrary to the MAH predictions, we did not observe, for E. coli and H. sapiens proteins, a negative correlation between E. coli became marginally significant but in the direction opposite to the one predicted by the MAH  . Therefocal/mol) .Finally, although the feasibility of a dominant MAH contribution was suggested by computational simulations , biologyhttps://github.com/protabit/protherm-conversion. We considered unfolding Gibbs free energies, E. coli and H. sapiens extracted from ProTherm are available in The protein stability data were obtained from the ProTherm database , which hE. coli, H. sapiens, and A. thaliana were calculated using the PAML package (Salmonella enterica, Mus musculus, and Brassica oleracea. Orthologs were identified as bidirectional best hits in pairwise local alignments calculated with Usearch (We used the rate of nonsynonymous substitutions,  package  relative Usearch . We consE. coli were obtained from H. sapiens, as it was demonstrated that mRNA expression in this tissue has the highest correlation with protein evolutionary rates (A. thaliana (Protein abundance data for all species were obtained from the whole-organism integrated data sets available in the PaxDB v.4 database . mRNA abry rates . mRNA abthaliana .Protein surface nonadhesiveness was obtained from Genome Biology and Evolution online.evab006_Supplementary_DataClick here for additional data file.


Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2.precision of at least 69% up to 100% and recall of 100%.ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature. Protein-protein interactions (PPI) are fundamental in many biological processes, including metabolism, information processing, decision making, transport, and structural organization. They are essential on the understanding of cellular physiology and constitute intricate networks of interactions which produce highly organized, dynamic cellular systems , 2.There are a variety of online resources, such as databases and functional genomics networks, devoted to protein interactions. Some databases, such as HINT , BioGRIDThese resources support the process of knowledge discovery on protein interactions. In this context, Li and colleagues devised, in , a humanAnother interesting example of knowledge discovery on protein interactions is the focus on understanding the potential of PPIs as targets, which has been addressed by many studies as –19. HereIn this work, we are particularly interested in protein-protein interactions in a fine degree of granularity, more specifically, in the residue and even atom level. In this context, Khashan and colleagues  proposedMorozova and colleagues , in contMelo and colleagues , in turnA variety of methods were devised to characterize, understand and detect patterns on protein-protein interfaces. Nonetheless, in spite of the relevant contributions of the majority of the works, strategies that depend on multiple structural superimposition might be prohibitively expensive for large scale processing. As the amount of biological data has been growing in a fast pace, scalable techniques are pivotal to perform such tasks in a real-world scenario. Furthermore, strategies that work at the residue granularity might not reveal the specific details involved in the molecular recognition process that takes place on protein-protein interfaces.To overcome these challenges, this article proposes ppiGReMLIN, a graph based strategy to detect conserved structural arrangements on protein-protein interactions. By structural arrangements, we consider not only specific interacting residues in PPIs, but more general biding motifs that may disclose more information on how interactions occur. Protein-protein interfaces are modeled as graphs in which atoms and non-covalent interactions represent nodes and edges respectively. Nodes and edges are labeled according to their physicochemical properties and distance criteria. The resulting graph dataset is encoded as feature vectors, which serve as input for a clustering analysis. The resulting set of clusters go through a frequent subgraph mining (FSM) to reveal substructures that are conserved across the whole dataset of protein-protein interactions. ppiGReMLIN requires a set of protein-protein complexes involving proteins of interests and is derived from GReMLIN (GRaph Mining strategy to infer proten-Ligand INteraction patterns) , which iAnticarsia gemmatalis Hübner is a pest that attacks soybean. The plant, when injured by this insect, produces the Kunitz trypsin inhibitor (KTI), which impairs the process of proteases degradation in the caterpillar gut [Anticarsia gut. It is important to note that our method is not specific to soybean and its insect pest Anticarsia gemmatalis Hübner and can be used in other datasets involving protein-protein complexes.ppiGReMLIN is based on ideas developed in some of our previous works, concerning the defense of plants against insects and pathogens. The insect llar gut , 28. InsThis section details ppiGReMLIN, our method to infer conserved structural arrangements on protein-protein interface at the atomic level. We describe the datasets, the problem modeling, the data mining tasks, and the evaluation strategy. Figure Data acquisition and modeling block  is one of many defoliating caterpillars affecting crop yields in agriculture. In north and south America, it infests mainly soybean, causing damage to plants, and potentially destroying entire crops [re crops . Agrochere crops , 31.The basis to construct the Serine protease (SP) dataset was a protease sequence from caterpillar digestive system sequenced in our previous work , availabc release on the mitochondrias [The BCL-2 proteins are key regulators of programmed cell death, acting in the regulation of cytochrome hondrias , 36. Thehondrias . Indeed,hondrias . Also, ahondrias , 40.For this dataset, we queried PDB for proteins with a minimum of 30% sequence similarity with the structure of Mcl-1 (extracted from pdb id 2KBW), an anti-apoptotic human protein of the BCL-2 family. Results were then filtered down to structures containing the query sequence target protein complexed with some other protein. In this proccess, 72 PDB entries were found, which composes our BCL-2 dataset.i and j, in the protein-protein interface, we say that i is in contact with j if j is inside of a sphere centered in i with radius r, called cutoff [We computed protein-protein interactions at the atomic level by using a cutoff based strategy Fig. a. Here wd cutoff , 42.A and B. Therefore, we have a bipartite graph G whose vertices can be segmented in 2 disjoint sets, P (nodes from A) and I (nodes from B), such that every edge in E connects a vertex in P to a vertex in I.In our modeling, nodes represent atoms and edges represent interactions between them. We consider only those interactions that connect atoms from different protein chains, which we name chains Nodes were labeled according to their physicochemical properties as acceptor (ACP), aromatic (ARM), donor (DON), hydrophobic (HPB), negative (NEG) or positive (POS) as in , 43–46. Some atoms are associated with more than one label due to their ability to interact in different ways with other atoms. For instance, nitrogen atoms ND1 and NE2 in a histidine residue can be labeled aromatic, positive, donor and acceptor according to our criteria. As a consequence of this manifold aspect of node labels, multiple edges with different labels can also be attributed to a single pair of nodes. Thus, we modeled interaction graphs as multigraphs in order to capture these physicochemical aspects of protein interactions. However, its important to notice that not all node labels were attributed to the nodes. After computing interactions, only those node labels that composed interactions with other nodes were considered as final labels for the respective nodes in the graph.Finally, for each complex (PDB entry), we computed the connected components, which serve as input data for the clustering analysis and FSM of ppiGReMLIN. So, a graph in our method corresponds to a connected component.A clustering analysis Fig. b takes aG1 that has nodes A, B, and C, labeled as donor (DN), acceptor/negative (AC/NG), donor/positive (DN/PS) respectively, and edges A-B and B-C, representing a hydrogen bond and a salt bridge respectively, as shown in Fig. G1, is shown in Table In order to perform the clustering analysis, we propose a counting matrix, in which graphs are represented based on the labels of each of their pair of nodes. Our representation is inspired in some fingerprints that characterize small molecules , 48. SupΘ), that can be manipulated more efficiently in terms of memory requirement and execution time [Singular Value Decomposition (SVD) was used to perform dimensionality and noise reduction in ppiGReMLIN, resulting in a compact representation of the input data for clustering analysis . These values were selected as a percentage p of the number of entries in each data matrix, starting at 1%, with and increment of 1%, up to 90%. Then, k was selected as the minimum value for which the respective similarity graph was fully connected. This is important since disconnected components in spectral clustering constitute themselves as clusters, which is not an ideal situation unless some prior information on the data indicates that [The appropriate number of clusters tes that , which in and k was done for all rank-d matrices obtained from SVD. For each pair  obtained over each d, we selected the one at which k was minimal, within the minimal threshold of d, considering 95% of variance in the reduced matrix Θ.Finally, on each dataset, evaluation of This block Fig. c takes aA FSM experiment was conducted to find conserved structural arrangements (frequent subgraphs) on protein-protein interactions for each group resulting from the clustering analysis.D=G0,G1,...,Gh,support(g) denotes the number or fraction of graphs in D that have g as a subgraph. The FSM task consists in finding any subgraph g whose support(g)≥minSup (a minimum threshold for the support). The FSM algorithm adopted was gSpan [In accordance with , consideas gSpan .Experiments were performed with support varying from 0.1 to 1.0, with step 0.1. Resulting subgraphs went through a filter in order to extract the maximal frequent subgraphs. In a general manner, FSM algorithms are able to detect frequent patterns and point out in which input graphs such patterns were found. However, it is not possible to directly map each node/edge of a frequent pattern to a specific node/edge in an input graph. In this work, being able to perform this mapping is relevant due to the biological semantic of patterns, so that the domain specialist knows which are the relevant atoms and interactions to allow protein-protein complexes to be formed. For details, see , 58.H′ of a graph H and a graph S, we employed the VF2 algorithm [In order to find a mapping between a subgraph lgorithm . HoweverL(H) of a simple graph H  was k=35, at which point the optimal number of clusters varies between 10 and 13. However, n=13 was selected as it occurs more frequently over a relatively larger range of d values  when compared with n=10 . Also, we believe the difference of the values in lower dimension instances of the data matrix is a result from the noise reduction feature offered by SVD decomposition.For the Serine protease dataset, the number of clusters selected was Serine protease and BCL datasets. As a general guideline, the minimum support value was set to 0.5. By increasing the support, graphs tend to be smaller in size, but more frequent in groups. Thereupon, the selection was made as a compromise between frequency of patterns and pattern size.This section presents how proper support values were selected for each dataset and the conserved substructures detected by ppiGReMLIN in For the serine protease dataset, at support 0.7, the largest patterns had 6 nodes and occurred in group 6. For two out of three patterns found in this group, two had real support equal to 0.83. By reducing the support value to 0.6, the number of patterns in this group went down to two, while growing in size to 7 nodes See Figure 1AFrom this point on we discuss some of the interesting patterns found by ppiGReMLIN and summarize other groups for the selected support. Figure The largest pattern from group 6 is depicted as F1 in Fig. Regarding the residue position in the protein chain sequences for pattern F1, the majority of aspartate interacting residues (located in 32 graphs) correspond to the aspartate present in the specificity pocket (S1 pocket) of trypsin and trypsin-like proteins See . The onlAnother interesting pattern, F2, is presented in Fig. Considering pattern F2, the interacting serine residues are part of the catalytic triad in serine proteases, commonly referred as SER195, HIS57 and ASP102. See . The intThe last patterns of Fig. Serine protease dataset, which generated a large number of substructures, in the order of 104 to 105. At support 0.6, the group with the highest number of patterns had over 50 thousand substructures, which were reduced to approximately 1,500 maximal patterns, from which only the 10 largest structures were selected for analysis.For the BCL-2 dataset, the FSM algorithm was performed with support values above 0.6 due to the size of graphs in this dataset being relatively larger (up to 30 vertices) than the ones in the The support selected for the BCL-2 dataset was 0.6, since it presented the largest graphs, with 10 vertices at group 6 and 11 vertices in group 7, as opposed to 8 and 10 vertices in the same groups respectively, at support value 0.7. See Figure 1BGroup 2 is composed of hydrophobic and aromatic atoms connected by hydrophobic and/or aromatic interactions. One of the two patterns found in this group ;II: the aspartate  at the bottom of S1 pocket;III: the histidine, usually HIS40, that play an important role in the zymogen activation in the enzymes;IV: a small structure composed of two nitrogen atoms connected to a single oxygen atom by means of hydrogen bonds was used in order to target a more complex structure found in serine-proteases know as the oxyanion hole.For the SP dataset, the residues considered relevant for the quantitative analysis were those experimentally determined already documented in the literature –64 .1: the leucine (L0), at the beginning of the motif LXXXXD, highly conserved in BH3 domains.2: the aspartate (D+5), at the end of the same motif.3: residue at postion −4 (multiple possible residues)4: residue at position +3 (multiple possible residues)5: residue at position +7 (multiple possible residues)For the BCL-2 dataset, five residues in specific positions in the BH3 domain of pro-apoptotic proteins were considered as relevant for the quantitative analysis, as they play an important role in the binding of BCL-2/BH3  and 100% (BCL-2), while recall is 100% for both datasets. For the SP dataset, the lower precision is due to the patterns in groups 2,4,5 and 11 as they do not contain residues considered relevant documented in the literature. However, we were able to find all the relevant structures in the ppiGReMLIN patterns, as shown by the overall recall (100%). Interestingly, some of the test structures were identified alone in specific groups, e.g., III in group 1, IV in group 7, and I in groups 6,8 and 9, for the SP dataset; and structure 2 in groups 6,8,11 and 12, for the BCL-2 dataset. This shows how ppiGReMLIN not only can identify interaction patterns, but also is able to single out some relevant interactions with specific residues in PPI’s, which also demonstrates the importance of the clustering step in the strategy workflow.The analysis of the results shows the overall The fact that we did not find patterns documented in the literature in groups 2,4,5 and 11 does not mean that there are no relevant binding patterns in these groups. This only means that the patterns found are not documented in the literature. There is a possibility that such patterns are relevant and related to protein-protein interactions. Thinking of proteins as a network of interaction between residues, there are very few residues at the binding site and even less residues at the active site. Nonetheless, there are a number of residues that can be important in this network of residues to help, for example, stabilize the residues that are at the binding site. We believe that the patterns in the groups 2,4,5 and 11 should be further investigated to understand whether they are relevant.According to Perona and Craik , positiooxyanion hole, composed of two amide nitrogen atoms, usually from SER195 and GLY193, in the protease chain interacting with a carbonyl oxygen atom in the inhibitor chain by means of hydrogen bonds. The role of the oxyanion hole in the catalysis is to stabilize the negative charge in the carbonyl group after it becomes a tetrahedral intermediate, i.e, the double bond between its carbon and oxygen atoms becomes a single-bond [Another interaction mentioned in the literature  was foungle-bond . The strAdditionally, the GLY193 residue, which is highly conserved in serine proteases, is frequent in the patterns described above. According to , it makeα-helix of regulators to be accommodated in the hydrophobic groove formed by the four BH domains in the suppressors [The molecular recognition between pro-apoptotic and anti-apoptotic members of the BCL-2 family is driven by the ability of the amphipathic BH3 pressors  Fig. 9)α-helix opressors .Fig. 9CThe BH3 domain of both effectors and activators, typically about 20 amino acids in length, is characterized by the presence of the motif LXXXXD. While interactions may occur along the BH3 chain, only a few of its residues have been characterized as critical to the specificity of binding towards suppressors. Apart from the leucine (L0), flanking the motif LXXXXD, hydrophobic residues at positions −4, +3 and +7, represent binding hotspots in the molecular recognition . The hydThe aspartate (D+5) in the motif LXXXXD, is the last of the critical residues mentioned in literature, described to interact with atoms from suppressor residues by means of hydrogen bonds or salt bridges . This inInterestingly, on the same structures described above, atoms from residues at position +1 were also found composing interactions. These residues were either arginine or lysine. F5 and F6 in Fig. This work proposed a graph based strategy to detect conserved structural arrangements on protein-protein interface, which we named ppiGReMLIN. In this method, protein-protein interfaces are modeled as graphs at the atomic level. Atoms and non-covalent interactions are represented by nodes and edges respectively, which are labeled according to their physicochemical properties and distance criteria. The resulting set of graphs is segmented in similar clusters that serve as the input for a FSM, revealing substructures that are conserved across the whole dataset of protein-protein interactions. Our method does not rely on sequence alignment nor structural superimposition, and can be used in large-scale datasets of protein-protein interactions.precision range from 69% up to 100%, with recall of 100%, considering the most relevant structures found in literature. For instance, from the serine protease dataset, patterns were found with support higher than 70%. Among these, structures such as the SER195 and GLY193 oxyanion, an important interaction in intermediate states of catalysis, and the ASP189 interactions in the binding specificity pocket of trypsin, were detected in more than a single cluster. Relevant interacting residues were also found for the BCL-2/BH3 dataset with support higher then 60%. Residues at critical positions relative to the motif LXXXXD, which characterizes the BH3 domain of apoptotic regulators, were found in many patterns across different groups. Additionally, relevant residues were also found in non-critical positions in BH3 domains, that influence the binding affinity of BCL-2 members.Results show the method is effective in finding conserved structures described in the literature in an automatic fashion. At the supports chosen for each of our main datasets, As future work, we plan to calculate water mediated interactions by considering structural water molecules. Also, we intend to make ppiGReMLIN available as a web-server and as web services, which will allow our method to be easily accessible for users and to be included in automatic pipelines.Additional file 1 Supplementary Material.


Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein–drug and protein–protein interactions. They are relevant for both protein stability and molecular recognition processes due to their natural occurrence in aromatic aminoacids  as well as in designed drugs since they are believed to contribute to optimizing both affinity and specificity of drug-like molecules. Despite the mentioned relevance, the impact of aromatic clusters on protein–protein and protein–drug complexes is still poorly characterized, especially in those that go beyond a dimer. In this work, we studied protein–drug and protein–protein complexes and systematically analyzed the presence and structure of their aromatic clusters. Our results show that aromatic clusters are highly prevalent in both protein–protein and protein–drug complexes, and suggest that protein–protein aromatic clusters have idealized interactions, probably because they were optimized by evolution, as compared to protein–drug clusters that were manually designed. Interestingly, the configuration, solvent accessibility and secondary structure of aromatic residues in protein–drug complexes shed light on the relation between these properties and compound affinity, allowing researchers to better design new molecules. Aromatic rings are important residues for biological interactions and appear to a large extent as part of protein–drug and protein–protein interactions. π–π (both stacking and T-shape), anion-π and cation-π are the main interaction types described in the literature . They arAromatic rings also appear in protein–protein interfaces playing a significant role as anchor residues. As shown by Rajamani et al. , bindingThe above-described relevance of aromatic interactions in protein structure, protein–protein, and protein–drug complexes, promoted the study of their structure and energetics. Previous work from our group, extended original studies of pairs of aromatic interactions, showing that inside proteins (intraprotein), aromatic rings  are found forming clusters beyond aromatic dimers. These show an additive energetic nature and display particular structures , 19. Thei. Finally, our results show that aromatic clusters in protein–protein could be more stable than protein–drug clusters as the former were optimized by evolution and not manually designed.In this work, we studied two datasets of protein–drug and protein–protein complexes and systematically analyzed the presence and structure of their aromatic clusters. In particular, we compared those found in protein–drug complexes with those found in protein–protein interfaces. Additionally, we compare our results with the previously reported intraprotein dataset . Our resWe constructed two datasets based on available structures in the PDB: (i) protein–drug complexes (protein–drug) and (ii) protein–protein complexes (protein–protein). First, we filtered the entire PDB in order to select only structures with ‘drug-like’ compounds. We removed those that appear in more than ten different crystals to eliminate natural compounds and also those having less than 100 Da to avoid small molecules that are too small. Additionally, we applied a 95% sequence identity clustering using CD-HIT  over theWe took a protein–drug complex for each cluster and each drug-like compound, to eliminate duplicate pairs when crystals have several structures and avoid biases due to a differential representation of protein families in the PDB. For the set of protein–protein complexes, we selected entries from the PDB that have only two chains and are explicitly mentioned in the PDB file header, which corresponds to a dimeric complex. Over these entries, we applied a clustering procedure at 95% of sequence identity ensuring not to repeat the same combination of two protein clusters .For both datasets, only complexes with a resolution of 2.5 Å or below were kept for further analysis. Detailed statistics of the filtering can be seen in Additional file First, we needed to detect aromatic rings. In protein molecules, benzene rings are found in Phe, Tyr, and Trp. Imidazole rings are found upon His and pyrrole in Trp. Aromatic ring selection in drug-like compounds was implemented using OpenBabel library  with pytAromatic interactions are known to be mainly described by the distance between the centre of each ring . BesidesIn order to characterize three or more interacting rings, we defined the aromatic clusters as each connected component in the underlying graph of interactions. We analyzed every entry in the database by detecting aromatic clusters based only on drug–residue aromatic interactions and residue–residue interactions. Only these interactions have been included to count for the number of interactions reported and define each cluster. Then, the number of interactions of each aromatic cluster in protein–drug is calculated as the number of drug–residue interactions and the number of residue–residue interactions. In the case of protein–protein, only residue–residue interactions that correspond to two rings belonging to different chains were used for detection and, as well as in protein–drug, but residue–residue interactions between the same chain were included to count the total number of interactions.Protein Kinase family (Pkinase:PF00069 and Pkinase_tyr:PF07714) 10.09%.Trypsins (Trypsin:PF00089) 4.66%.Nuclear Hormone Receptors (Hormone_recep:PF00104) 3.58%.Retroviral Aspartyl Proteases (RVP:PF00077) 2.24%.Despite the filter of complexes that we performed after clustering, we wanted to evaluate if there were biases in protein families. We searched the PFAM database  and we fImmunoglobulins (V-set:PF07686 and C1-set:PF07654) 2.1%Protein kinases (Pkinase:PF00069), 1.04%Small GTPases Ras proteins (Ras:PF00071) 1.03%Short-chain dehydrogenases (adh_short:PF00106) 0.91%On the other hand, the protein–protein dataset the most abundant families were:This means that no family is over-represented in neither protein–drug nor protein–protein.Additionally, we have computed the amino acid composition of our two datasets and compared it with the whole PDB. No significant differences were found  is the probability of a protein-protein residue in Sheet conformation to be interacting with a residue in Helix conformation. We also calculated the same preference for a given ring in a drug to be contacting a residue in a certain conformation.Solvent Accessible Surface Area (SASA) was computed using the plug-in based on the Connolly surface algorithm as implemented in the VMD program . The radAffinity data was taken from BindingDB  and mappTo analyze the structure and prevalence of aromatic interactions in protein–drug (PD) and protein–protein (PP) complexes, we surveyed the PDB and the complexes were filtered to have only one for each pair of protein–protein and protein–drug complexes. We found and analyzed 10,231 protein–drug complexes and 4837 protein–protein complexes to identify the presence of protein–drug/protein–protein aromatic clusters. Aromatic clusters are defined by the presence of at least two (i.e. a dimer), aromatic rings that are interacting according to Materials and Method based on our previous work . In the Interestingly, dimers are over-represented in protein–protein (59%) compared to protein–drug 31%) complexes  and Ladder (Lad) we analyzed how they behave in Protein–drug and Protein–Protein interactions . These residues also present an inverse preference to be in contact with residues in the other secondary structures. Although the preference is slightly higher for sheet conformation, as aromatic trimmers have been reported to stabilize the structure of beta-sheets .i associated with a given complex in the protein–drug dataset. This assignment resulted in 358 protein–drug complexes with annotated binding affinity which have aromatic clusters with more than 2 ring interactions and with a resolution higher than 2.5 Å. First, in Fig. i distributions vary as a function of the number of clusters (1 or 2) and the total number of ring interactions. First, as expected, when we look at complexes having only one cluster, the average Ki slightly decreases as the number of interactions increases. The trend is, however, not found when two clusters are present. Thus, no improvement in affinity is observed by adding more interactions. Also, comparing complexes with two or three interactions, if these are established using two clusters  and nanomolar (nM)  Fig. . When foIt was previously argued that having too many aromatic rings decreases drug developability , 5. Our In the present work, we have analyzed the occurrence of aromatic interactions in the structures deposited in the Protein Data Bank, focusing mostly on higher-order interactions that go beyond the pure dimer. We found that there are some differences in preference of interaction between protein–drug and protein–protein, while the former can adopt a traditional Pi-stacking interaction as well as T-shaped. The latter mostly prefers traditional T-shaped. On the affinity side, it is better to have more clusters with the same number of interactions. This information is valuable for optimization campaigns where medicinal chemists have to make decisions on which modifications to synthesize and test.Additional file 1. Supplementary tables and figures.Additional file 2. Protein-protein interactions with PDBID, chain and UniProtID.Additional file 3. Protein-drug interactions with PDBID, chain, residue name and Ki data if available.


Proteins are the workhorses of the cell and execute many of their functions by interacting with other proteins forming protein complexes. Multi-protein complexes are an admixture of subunits, change their interaction partners, and modulate their functions and cellular physiology in response to environmental changes. When two species mate, the hybrid offspring are usually inviable or sterile because of large-scale differences in the genetic makeup between the two parents causing incompatible genetic interactions. Such reciprocal-sign epistasis between inter-specific alleles is not limited to incompatible interactions between just one gene pair; and, usually involves multiple genes. Many of these multi-locus incompatibilities show visible defects, only in the presence of all the interactions, making it hard to characterize. Understanding the dynamics of protein-protein interactions (PPIs) leading to multi-protein complexes is better suited to characterize multi-locus incompatibilities, compared to studying them with traditional approaches of genetics and molecular biology. The advances in omics technologies, which includes genomics, transcriptomics, and proteomics can help achieve this end. This is especially relevant when studying non-model organisms. Here, we discuss the recent progress in the understanding of hybrid genetic incompatibility; omics technologies, and how together they have helped in characterizing protein complexes and in turn multi-locus incompatibilities. We also review advances in bioinformatic techniques suitable for this purpose and propose directions for leveraging the knowledge gained from model-organisms to identify genetic incompatibilities in non-model organisms. Reproductive isolation impedes gene flow between species or populations and is considered fundamental to speciation . GenomesFrom some of these studies, there is evidence for rapid evolution in these speciation genes. Co-evolution between a rapidly evolving gene (with important function) and its partner loci are sometimes due to molecular arms races containing bouts of positive selection that can lead to the formation of speciation genes . One majSaccharomyces cerevisiae populations can explain a significant proportion of the phenotypic variances under diverse environmental conditions. The allelic interactions between mitochondrial and nuclear genomes may be co-adapted to specific ecological niches that the yeasts occupy. Disturbing such naturally occurring interactions leads to breakdown of within-environment mitochondrial-nuclear epistasis, which provides fitness advantages in certain environments . A large fraction of PPIs in eukaryotic proteomes culminate as heteromeric-protein complexes, and are responsible for diverse biochemical activities essential to cellular homeostasis, growth, and proliferation. For example, over 60%  of the proteome , over 7,proteome , and oveproteome , are ideproteome ,B. The rproteome . This leproteome . Such miproteome . Proteinproteome . Also, pproteome . Howeverproteome . Even thCo-evolving protein-protein interactions depend on the phenotypic traits, which are species specific and maintained by reciprocal selection . These pvia the most energetically optimum path of bringing together proteins to form intermediate subcomplexes, which is further extended to form complete protein complexes  to cis-regulatory elements, which are short stretches of non-coding DNA, thus mutations on either of them can affect the mRNA abundance of target genes. While trans-factors are known to be under higher selection constraint than their cis-counterparts, they can evolve faster than other classes protein coding genes , Drosophila melanogaster (fly), Mus musculus (mouse), Strongylocentrotus purpuratus (sea urchin), and human. The identified protein complexes were compared with the data derived using a similar co-fractionation strategy in Xenopus laevis (frog), Nematostella vectensis (sea anemone), Dictyostelium discoideum (amoeba), and S. cerevisiae (yeast) to gain insight into biochemical evolution of protein interactions and conservation of protein complexes in species diverged by over billion years  contain the most accurate representation of the spectrum of protein complexes in vivo  can only be used to infer protein complexes when the information of protein functions or interactions is available. On the other hand, quantitative cross-linking mass spectrometry (QCLMS/QXL-MS) can be used to directly detect protein interaction sites and binding partners . ReactivCurrent high-throughput proteomics platforms come with several software tools for pre-screening, identification, assembly, and quantification of detected peptides. Such built-in tools are usually tailored for optimization of the data generated by mass spectrometers depending on their fragmentation processes and collision energies and generally do not work across platforms. MASCOT, OMSSA, SEQUEST, X!Tandem, and TOPPAS are some of the popularly used platforms for analyzing tandem mass spectrometry data. The popular tools that are used for analysis of peptides generated from SILAC or QCLMS/QXL-MS followed by LC-MS/MS are Census, MaxQuant, MsQuant, MASCOT Distille, and COFRADIC . One of Almost all the available methods for predicting protein complexes rely on protein-protein interactions. Experimental data are the primary source of these protein-protein interactions. Existing prediction methods can be classified into network-based approaches and functional information-based approaches. Although, methods from the two categories are used independently to predict protein complexes, they are also frequently used in combination . The netS. cerevisiae. In higher order organisms and non-model species direct protein-protein contacts can be derived using the from the covariation pattern of the protein abundances from CF-MS data. The three-dimensional arrangement of protein contacts leading to protein complexes can be determined by performing clustering analysis of correlation matrices derived from CF-MS profiles using machine learning algorithms. The CF-MS system has been successful in identifying complexomes of Caenorhabditis elegans, human cell lines, and 13 plant species of agricultural and scientific importance  has been resolved to a large extent by advance variants of MCL such as Multi-level regularized MCL  and ParaDespite these advances, the false positive rate in protein complex prediction is high. This is mainly due to high variability in complex core sizes and the sparsity of protein-protein interaction networks. Also, the protein complex compositions can vary between experiments for the same strains of the same species. These differences can be due to the experimental procedures, dissimilar coverage in data from different experiments and from the application of pre-screening software. Thus, it can lead to an ambiguity on the correct composition of protein complexes. To overcome these limitations, studies have tried to include functional information of interacting proteins. This information includes protein domain architecture, protein expression, gene expression, and gene ontology. Recent methods have indicated that including evolutionary information and genetic algorithms can improve the accuracy in protein complex predictions . BayesiaS. cerevisiae. This algorithm searches across the diagonal on the clustered protein interactome matrix for strongly interacting proteins, by traversing first from right to left and then from top to bottom. This is the forward phase of the algorithm, in the backward phase the direction of traversal is reversed, and detects and accounts for non-interacting proteins. Similar methods have also been developed for predicting consensus soluble protein complexes isolated from human HeLA S2 and HEK93 cells  technologies have the potential to work with any species, determine the natural variation at a genome-wide level and at unprecedented resolution, and also can provide us to with a comprehensive picture of regulatory variation. Over the past decade, such genomic technologies have been used to explore the extent of natural variability at the molecular level and the evolutionary forces that shape this variation . In seve species  as well  species .de novo assembly are two frequently used modes of genome assembly. Resequencing refers to sequenced reads performed when a reference genomes are available and de novo assembly is used when the reference genome is of poor quality or when no reference genome is available. De novo assembly is more computationally intensive compared to resequencing but has wider applications. The quality of a genome assembly provides a measure of the degree to which the sequence has been correctly assembled and the sequences are reliable, and thus of great importance. Assembly quality can be assessed using different statistics, which offer a measure of genome completeness and contiguity (de novo genome assembly (de novo assemblies especially in non-model organisms (de novo, as well as mapped to a reference genome or transcriptome and used to quantify gene expression changes between the organisms of interest (Resequencing and ntiguity . Excelleassembly  and use assembly . The recrganisms . Similarinterest . In addiinterest .Current genetic and molecular biology strategies are confined to two or three locus incompatibilities due to practical and technical restraints. Determining multi-locus incompatibilities is difficult due to the high level of epistasis in hybrids . Since pIn the long term, the goal is to develop experimental and computational tools that can confidently identify the molecular drivers of speciation. These changes can be at the genomic, transcriptomic, or at the proteomic levels. Since Charles Darwin, who illustrated “a tree of life” based on morphology, we have strived to further refine the branches in the “story of life on earth” with the addition of our knowledge about the biochemistries of metabolism and physiology, tissue histology and cellular structures, sub-cellular structures and organization, protein complexes, and with the recent great advances in genomic and transcriptomic sequencing. In addition to genetic incompatibilities and failures in protein interactions leading to a breakdown in protein complexes; protein-DNA and protein-RNA interactions  are alsoIn addition to identifying the molecular bases of past speciation events, the aim is to see within genomes, proteomes, and protein complexomes the signatures that may create blocks and barriers in future potential evolutionary trajectories. For example, the inner membrane of the mitochondria has one of the highest known protein:lipid ratios, which is proposed to make membrane integrity relatively fragile among biological membranes. It has been observed in yeast that almost all of the known cases of DM incompatibilities are mitochondrial-nuclear incompatibilities, and that there is a highly conserved AAA-ATPase homeostasis machinery essential for maintaining the integrity of the inner membrane . This suS. cerevisiae cells introgressed with CCM1-Saccharomyces bayanus helped discovering the general rules underlying PPR domain evolution (In the longer term, our knowledge about how past speciation events may have occurred in the “molecular story of life,” combined with a new potential ability to identify the potential barriers in possible future evolutionary trajectories, may allow us to bioengineer new species beyond these barriers and create organisms with increased fitness that Mother Nature has failed to create by chance over billions of years of evolution. One possible example is to bioengineer into yeast the vertebrate F1-F0 ATPase c-ring subunits, which only contains eight subunits compared to the 10 c-ring subunits of the yeast F1-F0 ATPase , which mvolution . LaboratKS and J-YL conceived the manuscript. KS, SCS, and J-YL wrote the manuscript and obtained funding. All authors contributed to the article and approved the submitted version.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.