The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods. Through these methods, a large number of proteins have been verified and recorded in biological databases, such as the Swiss-Prot database. In addition, these data serve as benchmark datasets for developing machine learning methods and useful in the computational identification and investigation of protein locations.The functions of proteins are closely related to their subcellular locations in cells. In studying proteins, determining their locations in cells is usually the first step, and these locations are used as guides for designing drugs. Thus, many experimental methods for identifying protein locations have been developed, such as Many computational methods based on machine learning for predicting protein subcellular locations have been proposed. For example, Chou and Cai  proposedFor the above issues, embedding-based methods can be applied to the transfer of high-dimensional one-hot encoding into distributed vectors for sequential and network data. Given that interacting proteins generally share similar locations, node2vec . The protein sequences do not include proteins with <50 amino acids or more than 5,000 amino acids and unknown amino acids. The included proteins are processed through CD-HIT , based on its GO terms, which is formulated byA protein m represents the number of GO terms and gi is defined as follows:where In this study, 22,729 GO terms are included, which induced a 22,729-D vector for each protein.VKEGG(p), with the formulaMoreover, with its KEGG pathways, it can also be encoded into a vector, n represents the number of KEGG pathways and ki is defined as follows:where Here, 328 KEGG pathways are used, inducing a 328-D vector for each protein.Two vectors based on GO terms and KEGG pathways are concatenated to a final vector. Thus, each protein is represented by a 23,057-D vector. We use Boruta feature selection  on this corpus to learn the embeddings of each GO/KEGG term.We first collect whole human proteins with GO/KEGG terms. Each GO or KEGG term is a word, and each protein is a sentence. The set of human proteins is a corpus. We run Word2vec program in genism   is the neighborhood of the node v, and e is the mapping function from nodes to embeddings.where https://snap.stanford.edu/node2vec/, and the dimension of the learned embeddings is set at 500. Finally, the network embeddings of each protein are obtained.In this study, we use node2vec implemented at x and y, their MI is computed byInstead of directly using combined features from network embeddings and functional embeddings for each protein, we further use minimum redundancy maximum relevance (mRMR)  and p denote the marginal probabilistic density and joint probabilistic density, respectively. A high MI indicates the strong associations of two variables. The mRMR feature list is produced by adding features one by one. Initially, it is empty. In each round, for each of features not in the list, its relevance to class labels, evaluated by MI value of the feature and class labels, and redundancy to features in the list, assessed by the mean of MI values of the feature and those in the list, are calculated. A feature with maximum difference of relevance to class labels and redundancy to features in the list is picked up and appended to the list. When all features are in the list, the mRMR feature list is complete. Here, we used the mRMR program provided in http://penglab.janelia.org/proj/mRMR/. It is executed with its default parameters.where The mRMR method can only output a feature list. Which features are optimum is still a problem. In view of this, the incremental feature selection (IFS)     and Matthew correlation coefficient (MCC) , we also plot four curves, one curve corresponds one classification algorithm, as shown in As for the last feature type , four curves are plotted in As mentioned above, the optimum RF classifier is all best for four different feature types. The optimum RF classifier on functional embeddings derived from KEGG/GO terms yields the best MCC value (0.876). This classifier is based on the top 239 features. The optimum RF classifier with the combined embeddings only yields an MCC value of 0.872 and is a little worse than the RF with only functional embeddings. However, it uses only the top 129 features, which is nearly half of the number of features of the optimum RF classifier with only functional embeddings (239). Thus, in this study, we use the combined network and functional embeddings as the final input features.We select the optimum RF classifier with the combined embeddings as the proposed method. As the sizes of 16 categories are of great difference, it is necessary to investigate the performance of such classifier on majority and minority categories. We set 100 as the threshold, that is, categories containing more than 100 proteins are deemed as majority categories, whereas other categories are termed as minority categories. In this case, we obtain seven majority categories and nine minority categories. The performance on majority and minority category of the proposed classifier is shown in In this study, we employed a feature selection procedure to improve the performance of different classification algorithms. To demonstrate the power of our proposed method, we compare our method with published methods, including BLAST and Deeploc. The results are listed in In this study, we present an embedding-based method to predict protein subcellular locations by integrating protein interactions and functional information. The proposed method first learns network embeddings from a protein–protein network and functional embeddings from associations between proteins and GO/KEGG terms. We demonstrate that our proposed method is superior to state-of-the-art methods, and the learned embeddings offer valuable biological insights.http://cn.expasy.org/).The datasets analyzed for this study can be found in the (Swiss-Prot) (TH and Y-DC designed the study. XP, HL, and TZ performed the experiments. XP, HL, ZL, and LC analyzed the results. XP and HL wrote the manuscript. 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.


Moreover, using sub-cellular proteomics data from Saccharomyces cerevisiae, we uncover a novel group of proteins trafficking from the ER to the early Golgi apparatus. Overall, we demonstrate the potential for novelty detection to yield biologically relevant niches that are missed by current approaches.The cell is compartmentalised into complex micro-environments allowing an array of specialised biological processes to be carried out in synchrony. Determining a protein’s sub-cellular localisation to one or more of these compartments can therefore be a first step in determining its function. High-throughput and high-accuracy mass spectrometry-based sub-cellular proteomic methods can now shed light on the localisation of thousands of proteins at once. Machine learning algorithms are then typically employed to make protein-organelle assignments. However, these algorithms are limited by insufficient and incomplete annotation. We propose a semi-supervised Bayesian approach to novelty detection, allowing the discovery of additional, previously unannotated sub-cellular niches. Inference in our model is performed in a Bayesian framework, allowing us to quantify uncertainty in the allocation of proteins to new sub-cellular niches, as well as in the number of newly discovered compartments. We apply our approach across 10 mass spectrometry based spatial proteomic datasets, representing a diverse range of experimental protocols. Application of our approach to  PLOS Computational Biology Methods paper.This is a Aberrant protein sub-cellular localisation has been implicated in numerous diseases, including cancers , obesitya priori knowledge of their sub-cellular proteome organisation, making it challenging to curate the marker set (training dataset) from the literature 18] propoa gondii  and idena gondii .Here, we propose an extension to TAGM to allow simultaneous protein-organelle assignments and novelty detection. One assumption of the existing TAGM model is that the number of sub-cellular niches is known. Here, we design a novelty detection algorithm based on allowing an unknown number of additional sub-cellular niches, as well as quantifying uncertainty in this number.overfitted mixtures allows us to specify a (possibly large) maximum number of clusters. As shown in . In the original formulation of the model it is supposed that there are K known sub-cellular compartments to which each protein could be localised . For simplicity of exposition, we refer to these K sub-cellular compartments as components, and introduce component labels zi, so that zi = k if the i-th protein localises to the k-th component. To fix notation, we denote by XL the set of proteins whose component labels are known, and by XU the set of unlabelled proteins. If protein i is in XU, we seek to evaluate the probability that zi = k for each k = 1, …, K; that is, for each unlabelled protein, we seek the probability of belonging to each component (given a model and the observed data).In this section, we briefly review the TAGM model proposed by . Let N dk-th component is modelled as multivariate normal with mean vector μk and covariance matrix Σk. However, many proteins are dispersed and do not fit this assumption. To model these “outliers”, [ϕ. Each protein xi is then described by an additional variable ϕi, with ϕi = 1 indicating that protein xi belongs to an organelle-derived component and ϕi = 0 indicating that protein xi is not well described by these known components. This outlier component is then modelled as a multivariate T distribution with degrees of freedom κ, mean vector M, and scale matrix V. Thus the model can be written as:The distribution of quantitative profiles associated with each protein that localises to the tliers”,  introducf denote the density of the multivariate normal with mean vector μ and covariance matrix Σ evaluated at x, and similarly let g denote the density of the multivariate T-distribution. For any i, the prior probability of the i-th protein localising to the k-th component is denoted by p(zi = k) = πk. Letting Let i, we set the prior probability of the i-th protein belonging to the outlier component as p(ϕi = 0) = ϵ, where ϵ is a parameter that we infer.For any κ = 4, M as the global empirical mean, and V as half the global empirical variance of the data, including labelled and unlabelled proteins. To extend this model to permit novelty detection, we specify the maximum number of components Kmax > K. Our proposed model then allows up to Knovelty = Kmax − K ≥ 0, new phenotypes to be detected. K components correspond to known sub-cellular niches and the second summation corresponds to the new phenotypes to be inferred. The parameter sets are then augmented to include these possibly new components; that is, we redefine Knovelty = 10 is ample to detect new phenotypes. To complete our Bayesian model, we need to specify priors. Detailed prior specifications and sensitivity analysis are provided in the As in , we fix mixtures , componeWe perform Bayesian inference using Markov-chain Monte-Carlo methods. We make modifications to the collapsed Gibbs sampler approach used previously in  to allowposterior similarity matrix (PSM) [N × N matrix where the th entry is the posterior probability that protein i and protein j reside in the same component. Throughout we use a heatmap representation of this quantity. The PSM is summarised into a clustering by maximising the posterior expected adjusted Rand index  . The PSMdetails; ). Formuli belongs to a new component is computed from the following equation:T is the number of Monte-Carlo iterations. Throughout, we refer to discovery probability.We may be interested in quantifying the uncertainty in whether a protein belongs to a new sub-cellular component. Indeed, it is important to distinguish whether a protein belongs to a new phenotype or if we simply have large uncertainty about its localisation. The probability that protein putative phenotypes, which we define as newly discovered clusters with at least 1 protein with discovery probability greater than 0.95.Applying Novelty TAGM to spatial proteomics datasets consists of several steps. After having run the algorithm on a dataset and assessing convergence, we proceed to explore the ouput of the method. We explore In a supervised framework the performance of computational methods can be assessed by using the training data, where a proportion of the training data is withheld from the classifier to be used for the assessment of predictive performance. In an unsupervised or semi-supervised framework we cannot validate in this way, since there is no “ground truth” with which to compare. Thus, we propose several approaches, using external information, for validation of our method.hyperLOPIT experiments, where the intention was to increase the resolution between chromatin and non-chromatin associated nuclear proteins [Removing the labels from an entire component and assessing the ability of our method to rediscover these labels is one form of validation. We consider this approach for several of the datasets; in particular, chromatin enrichment was performed in two of the proteins , 5, 7. AA further approach to validating our method is to use additional spatial proteomic information. The Human Protein Atlas (HPA) , 42 provThroughout, we perform GO enrichment analysis with FDR control performed according to the Benjamini-Höchberg procedure –45. The On occasion some cell lines have been analysed using multiple spatial proteomics technologies . In thesputative phenotypes, which we define as newly discovered clusters with at least 1 protein with discovery probability greater than 0.95. Our proposed methodology allows us to interrogate individual proteins to assess whether they belong to a newly discovered phenotype. Through the posterior similarity matrix (PSM) we can visualise the global patterns in the uncertainty in phenotype discovery (see supplement). We summarise this posterior similarity matrix into a single clustering by maximising the posterior expected adjusted rand index  hyperLOPIT dataset [hyperLOPIT dataset [Initially, we validated Novelty TAGM in a setting where we have a strong  dataset  and an m dataset . These ehyperLOPIT dataset was already shown to be in strong agreement with the microscopy data [nd and 3rd most frequent HPA annotations for proteins belonging to this phenotype. For phenotype 5, the most associated term is nucleoplasm from the HPA data and this is further supported by GO analysis  cells). Thus, we robustly identify new phenotypes across different spatial proteomics protocols. Hence, we have presented strong evidence for additional annotations in this dataset, beyond the original analysis of the data [hyperLOPIT datasets. Finally, through the discovery probability and by using the PSMs we have quantified uncertainty in these proposed phenotypes, enabling more rigorous interrogation of these datasets.In a similar vein to the analysis performed on the clusters . PhenotyhyperLOPIT data [cell periphery and fungal-type vacuole phenotype is uncovered along with a kinetochore phenotype, and a cytoskeleton phenotype. Phenotype 8 represents a joint Golgi and ER cluster with several enriched GO terms. Indeed, most of the proteins in this phenotype have roles in the early secretory pathway that involve either transport from the ER to the early Golgi apparatus, or retrograde transport from the Golgi to the ER [Novelty TAGM uncovers 8 putative phenotypes in the yeast PIT data . Four ofo the ER –50, . To be o the ER . The proo the ER , as wello the ER , and Cwpo the ER , howevero the ER . The locWe also uncover additional annotations for the HCMV infected and the control fibroblast spatial proteomics datasets ; such asThe Dynamic Organellar Maps (DOM) protocol was developed as a faster method for MS-based spatial proteomic mapping, albeit at the cost of lower organelle resolution , 57. Themitochondrial membrane phenotype, distinct from the already annotated mitochondrial class. Phenotype 2 represents a mixed cluster with nucleus-, ribosome- and cytosol-related enriched terms. The final phenotype is enriched for chromatin and chromosome, suggesting sub-nuclear resolution. Furthermore, as a result of quantifying uncertainty, we can see that there are potentially more sub-cellular structures in this data  to select just a single clustering, without taking into account the uncertainty in the number of phenotypes, and does not provide an estimate of individual protein-to-phenotype allocation uncertainty. Another difference is the input to both methods; Novelty TAGM uses the data directly, whereas phenoDisco takes the top principal components  as input. PhenoDisco also requires an additional parameter—the minimum group size. This parameter can be challenging to specify, since there is a trade-off between identifying functionally relevant phenotypes of different sizes and picking up small spurious protein clusters. Furthermore, phenoDisco struggles to scale to many of the datasets presented in this manuscript, because it requires iteratively refitting models and building of an outlier test statistic.Next, we compare an already available novelty detection algorithm, phenoDisco and Novelty TAGM to the HEK-293 spatial proteomics dataset interrogated by [phenoDisco; for example, we can scale the pointer size to the discovery probability. We note that both methods reveal 8 putative phenotypes in the data. phenoDisco, four of the phenotypes had at least 1 significant Gene Ontology term, whereas this was true for five of the Novelty TAGM phenotypes. phenoDisco phenotype 3, which is a lysosome-enriched phenotype, with the plasma membrane . On the other hand, Novelty TAGM phenotypes 2 and 3, enriched for chromatin and ribosome respectively, are associated with the mitochondria by phenoDisco. This demonstrates the ability of Novelty TAGM to derive more biologically meaningful phenotypes.To demonstrate the differences between the two approaches, we apply gated by . The PCAhyperLOPIT dataset resolves an endosomal cluster not previously explored, we perform a re-analysis of this dataset focusing on the endosomes. We curate a set of marker proteins for the endosomes and add these annotations to the U-2 OS hyperLOPIT dataset. After which, we apply our Bayesian generative classifier TAGM to the data with this additional annotation [Given the information that the U-2 OS notation . ProteinAll 7 proteins with uncertain assignment to our new endosome cluster are known to function in endosome dynamics. Rab5a and Rab5b  are isoforms of Rab5, a small GTPase which is considered a master organiser of the endocytic system, regulating clathrin-mediated endocytosis and early endosome dynamics –65. RN-tOf these 7 proteins with uncertain endosome assignment, only 4 have localisations annotated in HPA . The HPAWe have presented a semi-supervised Bayesian approach that simultaneously allows probabilistic allocation of proteins to organelles, detection of outlier proteins, as well as the discovery of novel sub-cellular structures. Our method unifies several approaches present in the literature, combining the ideas of supervised machine learning and unsupervised structure discovery. Formulating inference in a Bayesian framework allows for the quantification of uncertainty; in particular, the uncertainty in the number of newly discovered annotations.hyperLOPIT datasets. Our approach also revealed additional sub-cellular niches in the mESC hyperLOPIT and U-2 OS hyperLOPIT datasets.Application of our method across 10 different spatial proteomic datasets acquired using diverse fractionation and MS data acquisition protocols and displaying varying levels of resolution revealed additional annotation in every single dataset. Our analysis recovered the chromatin-associated protein phenotype and validated experimental design for chromatin enrichment in hyperLOPIT dataset, which were validated by Human Protein Atlas annotations. An additional endosome-enriched phenotype was uncovered and Novelty TAGM robustly identified an overlapping phenotype in U-2 OS LOPIT-DC data, providing strong evidence for endosomal resolution. Further biologically relevant annotations were uncovered in these, as well as other datasets. For example, a group of vesicle-associated proteins involved in transport from the ER to the early Golgi was identified in the yeast hyperLOPIT dataset; resolution of the ribosomal subunits was identified in the fibroblast dataset, and separate nuclear, cytosolic and ribosomal annotations were identified in the DOM datasets.Our method revealed resolution of 4 sub-nuclear compartments in the U-2 OS phenoDisco demonstrates clear differences between the approaches. Novelty TAGM, a fully Bayesian approach, quantifies uncertainty in both the number of newly discovered phenotypes and the individual protein-phenotype associations—phenoDisco provides no such information.A direct comparison with the state-of-the-art approach hyperLOPIT data allowed us to explore endosomal processes, which have not previously been considered with this dataset. We compare our results directly to immunofluorescence microscopy-based information from the HPA database and demonstrate the value of orthogonal spatial proteomics approaches to determine protein sub-cellular localisation. Our results provide insights on the sub-cellular localisation of proteins for which there is no information in the HPA Cell Atlas database.Improved annotation of the U-2 OS During our analysis, we observed that the posterior similarity matrices have potential sub-clustering structures. Many known organelles and sub-cellular niches have sub-compartmentalisation, thus methodology to detect these sub-compartments is in preparation. Furthermore, we have observed that different experiments and different data modalities provide complementary results. Thus, integrative approaches to spatial proteomics analysis are also desired.Our method is widely applicable within the field of spatial proteomics and builds upon state-of-the-art approaches. The computational algorithms presented here are disseminated as part of the Bioconductor project , 86 builS1 Text(PDF)Click here for additional data file.


Tomato spotted wilt orthotospovirus) is an economically important plant virus that infects multiple horticultural crops on a global scale. TSWV encodes a non-structural protein NSs that acts as a suppressor of host RNA silencing machinery during infection. Despite extensive structural and functional analyses having been carried out on TSWV NSs, its protein-interacting targets in host plants are still largely unknown. Here, we systemically investigated NSs-interacting proteins in Nicotiana benthamiana via affinity purification and mass spectrometry (AP-MS) analysis. Forty-three TSWV NSs-interacting candidates were identified in N. benthamiana. Gene Ontology (GO) and protein–protein interaction (PPI) network analyses were carried out on their closest homologs in tobacco (Nicotiana tabacum), tomatoes (Solanum lycopersicum) and Arabidopsis . The results showed that NSs preferentially interacts with plant defense-related proteins such as calmodulin (CaM), importin, carbonic anhydrase and two heat shock proteins (HSPs): HSP70 and HSP90. As two major nodes in the PPI network, CaM and importin subunit α were selected for the further verification of their interactions with NSs via yeast two-hybrid (Y2H) screening. Our work suggests that the downstream signaling, transportation and/or metabolic pathways of host-NSs-interacting proteins may play critical roles in NSs-facilitated TSWV infection.Tomato spotted wilt virus (TSWV; species  Tomato spotted wilt orthotospovirus) is the best known member in Orthotospovirus, which is the only genus with plant-infecting viruses in the family Tospoviridae [Bunyavirales, tospoviruses contain segmented RNA genomes with three single-stranded (ss) RNAs packaged in enveloped virus particles [Tomato spotted wilt virus  and another non-structural protein (NSs) are encoded by the S RNA [N and GC) envelope. TSWV infects plants via the thrips vector in the field [The TSWV L RNA encodes an RNA-dependent RNA polymerase (RdRp). The M RNA encodes a non-structural movement protein NSm, and the precursor of two structural glycoproteins Ghe S RNA . Both thhe S RNA . The thrhe field .Bunyaviruses [Tsw in peppers [Tsw-mediated resistance in peppers can be overcome by a single amino acid change in NSs at position 104 (T–A) [NSs proteins are widely found in plant- and vertebrate-infecting aviruses . NSs proaviruses . As a noaviruses . NSs is  peppers ,14. Tsw-04 (T–A) . The N-t04 (T–A) . Two con04 (T–A) .Nicotiana benthamiana via affinity purification and mass spectrometry (AP-MS) analysis. Gene Ontology (GO) and protein–protein interaction (PPI) network analyses were carried out on their closest homologs in Arabidopsis , tobacco (Nicotiana tabacum) and tomatoes (Solanum lycopersicum). Network analysis was carried out, followed by experimental validation by using the yeast two-hybrid (Y2H) assay. This approach of using AP-MS and network analysis combined with experimental validation offers an efficient approach for understanding the PPIs underlying virus–host interactions.Despite the advancement of structural and functional research on TSWV NSs, its protein-interacting targets in host plants are still largely unknown. In this research, we investigated the NSs-interacting proteins in N. benthamiana leaves at the four-leaf stage. Its binding proteins were extracted and analyzed by AP-MS. To identify the host proteins that specifically interact with TSWV NSs, overlapping candidates were selected from two independent AP-MS replicates. The list was then compared to the list of candidates that bind the V2 protein of Croton yellow vein mosaic virus (unpublished), to remove overlapping non-specific interactors. Eventually, 43 N. benthamiana proteins were found to specifically interact with TSWV NSs  and was transiently expressed in TSWV NSs . The lisN. benthamiana interactors, their closest homologs in tobacco, tomatoes and Arabidopsis were found by BLASTP and are listed in Arabidopsis, respectively. Therefore, only 42 inferred homologous proteins in each species are listed , a mitogen-activated protein kinase , a calcium-dependent protein kinase , a calmodulin (CaM) (U3MW48) and two serine/threonine protein kinases . Two heat shock proteins (HSPs), HSP70 (Q769C6) and HSP90 (A0A0M3SBS3), also interact with NSs. For these e listed .Arabidopsis homologs inferred from the N. benthamiana NSs-interacting proteins  were found to be enriched by about 30 fold , a KDEL-tailed cysteine endopeptidase (AT3G48340) and a glycinain-type cysteine proteinase (AT4G39090). Actually, all four Arabidopsis cysteine proteinases characterized in the GO molecular function test are localized in the lysosome, except the type-II metacaspase (AT1G79330). The chloroplast-localized Arabidopsis proteins in the list include a carbonic anhydrase (AT3G01500), PSBP1 (AT1G06680), a glutamate-tRNA ligase (AT5G64050), chloroplast photosystem II subunit O2 , a glutathione S-transferase , HSP90 (AT5G56000), a plastid RNA-binding protein (AT3G48500) and glucose-6-phosphate 1-dehydrogenase .Meaningful results were obtained when using tomato and ion test . In tomaion test , which iectively . The thrArabidopsis homologs inferred from N. benthamiana NSs-interacting proteins  and CaM 3 (U3MW48) were selected to verify their interactions with NSs via targeted Y2H. Both proteins interacted with NSs in the assays  of Rice tungro bacilliform virus and Mungbean yellow mosaic virus and transport them into the nucleus [Cauliflower mosaic virus translational transactivator protein P6, which suppresses plant RNA silencing in the nucleus [N. benthamiana importin subunit α has a similar function of transporting viral proteins. For example, it interacts with the CP of Beet black scorch virus and transports it into the nucleus [Potato mop-top virus Triple Gene Block1 (TGB1) protein is mediated by N. benthamiana importin subunit α, which facilitates systemic virus movement [Pelargonium line pattern virus p37 protein acts as an RNA silencing suppressor whose nuclear localization is also mediated by N. benthamiana importin subunit α [Importins are critical for the nuclear import of Agrobacterium virulence proteins . There a nucleus . Similar nucleus . N. bent nucleus . The nucmovement . The Pelubunit α . Taken tN. benthamiana NSs interactor list ; the genetic, biochemical and physiological outcomes of the interactions; the expression changes of downstream genes triggered by the interactions; and the regulatory/regulated proteins up-/downstream of the interaction cascades should be investigated to obtain more details. The obtained results would provide a comprehensive portrait of NSs’ activities in the plant cell.The TSWV NSs coding sequence (CDS) was previously described . The NSsN. benthamiana leaves by agroinfiltration. Leaf samples were collected two days after infiltration, and the expression of NSs-GFP was verified by Western blotting. AP-MS was carried out using the GFP-Trap beads  as previously described [N. benthamiana database (2020_05) [NSs-GFP was transiently expressed in escribed ,46. Brie2020_05)  using SE2020_05) .Croton yellow vein mosaic virus was used as the non-NSs AP-MS control. Overlapping NSs-interacting protein candidates were identified from the two NSs-GFP AP-MS replicates. Non-specific NSs interactors were then removed from the list, including mGFP5, ubiquitin and proteins that were found to also interact with V2 in the control samples. This step helped to exclude non-specific NSs-interacting proteins that are expressed at high levels in N. benthamiana.Two NSs-GFP AP-MS biological replicates as well as two non-NSs AP-MS control replicates were performed for NSs-GFP. Since the Gateway-compatible pEarleyGate 103 cannot express mGFP5 without gene insertion, a pEarleyGate 103 construct expressing an mGFP5-fused V2 protein from N. benthamiana proteins in the interacting list, importin subunit α (A1YUL9) and CaM 3 (U3MW48), were selected to verify their interactions with NSs via targeted Y2H. The Y2H procedure has been described previously [importin subunit α or CaM 3 CDS was cloned into the Gateway-compatible bait vector pBTM116-D9 with tryptophan selection marker and then used to transform a selected yeast line harboring pACT2-GW-NSs. Empty pBTM116-D9 was used as a negative control. Positive interactions were implied by the observation of the yeast’s growth on synthetic defined (SD) selection medium minus four elements of uracil, histidine, leucine and tryptophan (SDIV) and its tolerance to the His3p enzyme inhibitor 3-aminotriazole (3-AT).Two eviously . In brieN. benthamiana, the GO analysis of NSs-interacting proteins was performed using their closest homologs in tobacco, tomatoes and Arabidopsis. Tobacco is a close relative of N. benthamiana, and the genome of tomatoes has been well-studied compared to other species in the Solanaceae family. However, neither tobacco nor tomatoes have annotation information sufficient for a comprehensive GO analysis. Thus, Arabidopsis homologs were still used for all the GO enrichment tests for biological processes, molecular functions and cellular components. The test results for tobacco and tomato homologs were included if they contained meaningful information. All the tests were performed using the PANTHER (Version 15.0) GO Term Enrichment tools [Since there is currently no available ontology data and analysis tool for nt tools .Arabidopsis homologs were used for the protein–protein interaction (PPI) network analysis due to the availability of PPI data and analysis tools. The PPI analysis of the Arabidopsis homologs inferred from the N. benthamiana NSs-interacting proteins was performed using the Bio-Analytic Resource (BAR) Arabidopsis Interactions Viewer [s Viewer . The ques Viewer , IntAct s Viewer  and BAR s Viewer . The resN. benthamiana were deposited in the ProteomeXchange Consortium via the PRoteomics IDEntifications (PRIDE) [The original AP-MS dataset (RAW files) and results of NSs-interacting candidates in  (PRIDE)  partner


Arabidopsis thaliana uniformly and carry out a systematic analysis of sperm cell-specific FBP gene, i.e., 3p.AtFBP113 due to its reported high and preferential expression, for detailed functional annotation. The results revealed that 3p.AtFBP113 is located on the small arm of chromosome and encodes 397 amino acid long soluble, stable, and hydrophilic protein with the possibility of localization in various cellular compartments. The presence of the C-terminal F-box associated domain (FBA) with immunoglobulin-like fold anticipated its role in protein binding. Gene ontology based functional annotation and tissue-specific gene co-expression analysis further strengthened its role in protein binding and ubiquitination. Moreover, various potential post/co-translational modifications were anticipated and the predicted tertiary structure also showed the presence of characteristic domains and fold. Thus, the outcomes of the study will be useful in developing a better understating of the function of 3p.AtFBP113 during the process of pollen development, which will be helpful for targeting the gene for manipulation of male fertility that has immense importance in hybrid breeding.In plants, F-box proteins (FBPs) constitute one of the largest superfamilies of regulatory proteins. Most F-box proteins are shown to be an integral part of SCF complexes, which carry out the degradation of proteins and regulate diverse important biological processes. Anthers and pollen development have a huge importance in crop breeding. Despite the vast diversity of FBPs in Arabidopsis male reproductive organs, their role in anther and pollen development is not much explored. Moreover, a standard nomenclature for naming FBPs is also lacking. Here, we propose a standard nomenclature for naming the FBPs of  Caenorhabditis elegans genomes contain only 38, 23 and 326 FBP genes, respectively, where C. elegans is the only one among all animal species whose genome contains the largest number of FBP genes  constitute one of the largest superfamilies of regulatory proteins, which control diverse biological processes from growth to development . The FBPBP genes . DespiteBP genes . F-box pBP genes  and, henBP genes . On the BP genes . Some ofBP genes .In plants, most F-box proteins are shown to be an integral part of SCF (Skp1-Cul-FBP) complexes. Among several ubiquitin-protein ligase (E3) families, SCF complex is the most dominant type of E3 ubiquitin ligases in plants that consists of four components namely S-phase kinase-associated protein 1 (Skp1), Cullin (Cul), RING-box protein 1 (Rbx1), and F-box protein (FBP). In SCF complex, the Cul acts as a core scaffolding region, the Rbx1 provides docking site to the ubiquitin-conjugation enzyme (E2) that takes activated ubiquitin molecules from ubiquitin-activating ligase enzyme (E1), Skp1 functions as a bridge and connects the FBP to Cul. The FBP imparts specificity due to its modular C-terminal protein-protein interaction domain and selectively recruits protein substrates for degradation through ubiquitin/26S proteasome pathway (UPP) in a process known as proteolysis . ProteolArabidopsis thaliana. However, the role of only few FBP genes has been deciphered so far , respectively, by using gene identifier At3g49450. The eplant tool provides a user-friendly uniform interface for visualization of data by connecting to various web services available publically   . HMMTOP el (HMM) . The visel (HMM) .15.Several physicochemical properties of 3p.AtFBP113 protein like molecular weight, isoelectric point (pI), the total number of residues bearing positive charges and negative charges, instability index, aliphatic index, and grand average of hydropathicity (GRAVY) were theoretically determined with the ProtParam tool available at ExPASy16, InterProScan17, Conserved Domain Architecture Retrieval Tool (CDART)18, Simple Modular Architecture Research Tool (SMART)19, ScanProsite20, Pfam21, and SUPERFAMILY22. CDD is a resource of physically curated protein domain models, which uses 3-dimensional structure information for describing sequence-structure-function relationships ionships . InterPrionships . CDART sionships . SMART iionships . ScanProionships . Pfam daionships . SUPERFAionships .23 was used to detect the motifs present in 3p.AtFBP113 sequence. Folds are the general architecture of proteins and are detected using the PFP-FunDSeqE predictor web server24. The PFP-FunDSeqE web server predicts the folds in protein sequences by coupling information of the functional domain and sequential evolution  is a well-known tool for annotating and describing the functional attributes of genes, the biological processes in which they are involved and the cellular components they are residing in g scheme .29 with a score cut-off value of 0.400 was used for predicting interacting partners of 3p.AtFBP113 protein to infer about its probable functions.In the cellular environment, a protein usually interacts with many other proteins for the execution of its function, therefore, the prediction of protein functional network is important to gain insight into its function . STRING 3p.AtFBP113. The Expression Angler BAR tool identifies genes, which co-express or exhibit comparable expression patterns by calculating the correlation coefficient for expression patterns of all expression vectors of genes to the expression pattern of gene of interest (3p.AtFBP113 during the process. Their expression levels were obtained using the default parameter.The Expression Angler BAR tool (see text footnote 3) was used for analyzing, exploring, interpreting, and visualizing the microarray or RNA-seq data to comprehend the probable function of interest . In Expr30 was used for the prediction of the secondary structure of 3p.AtFBP113. PSIPRED is Position Specific Iterated-Blast based secondary structure prediction tool. It uses two feed-forward neural networks on PSI-Blast alignment output for prediction of secondary structures like alpha helixes, Beta pleated sheets and coils from the primary sequence of the protein  structure of 3p.AtFBP113, first protein BLAST was used for finding suitable homologous protein structure (with similarity > 30%) as a template in protein data bank library (PDB)equences . Robettab initio . Both sece field  and retuce field . The stece field  and ERRAce field . Finallyce field . The com3p.AtFBP113. The NCBI search showed that 3p.AtFBP113 is located in reverse orientation on chromosome 3 reference genome sequence NC_003074.8  spanning from 127 to 365 amino acids, while SMART and Pfam outputs showed a F-box associated domain type3 (FBA3) spanning from 233 to 357 amino at C-terminal. InterProScan database search revealed that 3p.AtFBP113 sequence contains at its C-terminus region either F-box associated interaction domain (127-365 aa) or FBA3 domain (234-357 aa). The CDD and CDART predicted it to be a member of either F-box-like domain superfamily or FBA1 domain superfamily. The InterProScan output showed it as a member of either F-box domain superfamily or F-box-like domain superfamily, whereas the Superfamily outcome anticipated it to belong to F-box domain superfamily of proteins. The domains, which are predicted by different bioinformatics tools, are shown in Glycosylation is one of the major modifications of proteins after their translation and glycoproteins carry out various important processes at the cellular level like folding of proteins, cell to cell interaction, cell recognition, and host-pathogen interaction . In 3p.A3p.AtFBP113 is involved in the biological processes like protein ubiquitination. At the molecular level, 3p.AtFBP113 is predicted to take part in protein binding, while at the cellular level it is anticipated to be a part of the proteasome core complex.ARGOT2.5 output returned various GO terms  among wh3p.AtFBP113 functions in the context of a well-organized biological network in the cellular environment, the STRING database was used for predicting the interacting partners of 3p.AtFBP113 protein. However, with cut-off value 0.04, we could not obtain reliable interacting protein partners. Next, using Expression Angler we obtained about 24 genes having similar expression patterns with that of 3p.AtFBP113 during the process of microgametogenesis  constitute one of the largest superfamilies of regulatory proteins. However, a standard and precise classification system for naming FBP genes is lacking in plants. In order to overcome the existing limitation, in this study, a nomenclature has been proposed for naming the FBP genes of thaliana . The prothaliana . Targetithaliana . In planthaliana  and funclization . The prelization . The analization . The poslization . The detlization , which alization . The inslization . Proteinlization . Virtuallization . Moreovelization . The immlization  and is klization . The prelization .Post/co-translational modifications introduce functional diversity in proteins and, therefore, regulate the cell environment . Protein3p.AtFBP113 via domain and motif prediction databases suggests its F-box protein with protein binding ability. The ARGOT2.5 output confirms the tentative annotation by identifying various GO terms among which the most significant terms were protein ubiquitination (GO:0016567), protein binding (GO:0005515), and proteasome complex, alpha-subunit complex (GO:0019773). In the cellular environment, the understating about biological and molecular functions a protein is performing requires the knowledge about the networks in which it is involved like PPI, gene regulation, and metabolic networks. However, reliable partners interacting with 3p.AtFBP113 could not be identified using the STRING database. Next, to further strengthen the aforementioned functional annotation in the biological context, digital co-expression analysis in a tissue-specific manner was carried to get insights into 3p.AtFBP113 probable function (3p.AtFBP113 in protein binding and ubiquitination pathway during microgametogenesis. Various studies have shown that gametogenesis is a highly active process involving a large number of proteins and F-box mediated protein regulation by SCF-ubiquitin dependent protein degradation pathway during the process ensures tight regulation and progression of the cell cycle (The functional annotation of function , which nfunction . The genll cycle .The knowledge of protein tertiary structure is of immense importance for understanding its molecular and biochemical functions . Due to 3p.AtFBP113 and its 3D structure. As to the best of our knowledge, none of the FBA domain containing F-box protein has been targeted for 3D structural resolution using X-ray crystallographic or NMR spectroscopic techniques, hence our 3D structure can serve as a starting point for unlocking the structure-function relationship for this largest subfamily of FBPs. Such efforts will not only provide better insight into 3p.AtFBP113 functions but also serve as a reference for elucidating the functions of other FBP genes expressing in sperm cells or pollen grains with the ultimate goal of improving crop yields to ensure food security.In this study, a new standard nomenclature system for naming the FBP genes has been proposed, which will introduce uniformity and will be helpful in communicating the research findings effectively. Besides, the present study is the first attempt in demonstrating the functional role of the sperm cell-specific F-box protein gene using advanced computational tools, which suggests it as an important player during the process of microgametogenesis and a potential target for hybrid breeding in future works. This study will serve as a roadmap for the experimental elucidation of the aforementioned functions of The original contributions presented in the study are included in the article/AM conceived the idea, designed the project, did all the experiments, analyzed and interpreted the data, and wrote the manuscript. AG, RA, FM, MB, SB, MH, RP, ZK, and HA provided their input and critically revised the manuscript. AG supervised the study and finalized the manuscript. All authors read and approved the final 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.


PMFs obtained by molecular dynamics simulations—their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. However, this physical justification is a matter of controversy since the beginning. Alternative interpretations based on Bayes’ theorem have been proposed, but the misleading formalism that invokes the inverse Boltzmann law remains recurrent in the literature. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. The theoretical development described in this study provides new insights into how statistical PMFs work, in comparison with our approach. To prove the concept, we have built interatomic distance-dependent scoring functions, based on the former and new equations, and compared their performance on an independent benchmark of 60,000 protein structures. The results demonstrate that our new formalism outperforms statistical PMFs in evaluating the quality of protein structural decoys. Therefore, this original type of score offers a possibility to improve the success of statistical PMFs in the various fields of structural biology where they are applied. The open-source code is available for download at https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score.For three decades now, knowledge-based scoring functions that operate through the “potential of mean force” (PMF) approach have continuously proven useful for studying protein structures. Although these statistical potentials are not to be confused with their physics-based counterparts of the same name— Free energy estimation may be achieved by generating ensembles of protein conformations, from which the lowest free energy structure can be calculated by using physics-inspired molecular force fields. However, such conformational sampling is computationally costly, which makes these physics-based methods only applicable to a few proteins at a time. Three decades ago, a faster approach has been proposed by M. J. Sippl r) distributions observed in a dataset of experimentally determined protein structures, as:i,jū(r) is the estimated free energy of interaction between atoms i and j, i,jfOBS(r) is the observed probability (i.e. frequency) of the atoms i and j being separated by a distance r (discretized into bins), i,jfREF(r) is a reference frequency aimed at eliminating the sampling bias, k the Boltzmann constant, and T the temperature. The pseudo-energy of the whole protein is thus computed by summing the i,jū(r) of every pairwise distance observed in the structure. In this article, for the thirtieth anniversary of this knowledge-based approach, we present a new information-theoretic view of its functioning, and propose an improvement in both theory and practice.Predicting the three-dimensional structure of a protein is only useful if the model produced is close enough to the native conformation of the macromolecule. According to Anfinsen’s hypothesis, the latter is assumed to be the one with the lowest free energy, in the native conditions Since 1990 i,jfOBS(r) and i,jfREF(r) on native conformations does not allow interpreting the score as a free energy variation between the unfolded and the folded states. Another point concerns the kT factor, which is (most often) not taken into account and replaced by an arbitrary value, thus further invalidating the physical interpretation. In 1997, Baker and co-workers qualitatively showed that statistical potentials should actually be seen as an application of Bayes’ theorem to the conditional probabilities of pairwise distances i,jfOBS(r)/i,jfREF(r) factor in Eq. p/p(r)—where p and p(r) are the probabilities of observing two atoms at a distance r, with and without the knowledge of the atom types (i and j), respectively—thus quantifying the Bayesian updating. Hamelryck and co-workers later proposed a quantitative explanation The relatively good correlation of these scores with the free energy variation of protein folding, as well as the presence of a logarithm in the formula, have lead authors to describe this approach as resulting from the inverse Boltzmann law. However, this physical explanation has been criticized and demonstrated as being invalid on several points Here, we propose a new formalism that is conceptually advantageous over the popular thirty-year-old statistical potentials, as it is disconnected from any physical interpretation, while being more relevant to probabilistic reasoning. As a proof of concept, we have built two scoring functions, respectively based on the new and the PMF equations, and compared their performance at ranking predicted structures of proteins by their quality. Using the reference dataset 3DRobot  22.1i,jfREF(r) is defined. The most straightforward way to do so is to calculate i,jfREF(r) as the weighted arithmetic mean of all i,jfOBS(r) i,jfOBS(r), except that the atom types are indistinct. Formally, the reference state could thus be written as x,xfOBS(r), where x is an atom of any type. Throughout the years, various improvements have been brought to this approach /i,jfREF(r)) takes the values +1 and −1, for frequency ratios 10/1 and 1/10, respectively. However, to the best of our knowledge, it has never been raised—in the context of structural biology—that Eq. i,jfOBS(r) and i,jfREF(r). Indeed, the statistical PMF formalism in Eq. kT = 1) as:L is the logarithmic mean of i,jfOBS(r) and i,jfREF(r). Given that i,jfREF(r) is computed based on the average of all i,jfOBS(r), calculating any type of mean between the two values i,jfOBS(r) and i,jfREF(r) is irrelevant. Instead, the frequency difference i,jfOBS(r) − i,jfREF(r) should simply be divided by i,jfREF(r). The latter would then properly play its part as a reference. Therefore, we propose here to change the PMF formalism, and compute the score of a whole protein structure as:There are two critical elements in Eq. In addition of being more statistically sound, this new formalism avoids any confusion with the Boltzmann distribution law, as it does no longer contain any logarithm. Another non-negligible advantage is that it does not require any “pseudo-count” calculation procedure. The latter is otherwise necessary, as the logarithm function is undefined for zero. Beyond its theoretical advantages, the practical validity of this new formalism is demonstrated in the present article through an extensive benchmarking of model quality assessment.f(r) = i,jfOBS(r) − i,jfREF(r) = i,jfOBS(r) − x,xfOBS(r). This change in the observed probabilities actually quantifies the information gain provided by the knowledge of the residue type. The more the surprise decreases, the more negative Δf(r) is, and the more native-like is the observed interaction. Conversely, an increase in how surprising the observation is (Δf(r) > 0) after knowing the residue type indicates a non-native interaction. To evaluate an entire protein model, all the Δf(r) for all atom pairs in the structure have to be added. However, summing all the Δf(r) requires distinguishing, for example, a 0.2–0.4 difference from a 0.7–0.9 one. This is achieved through a relative difference calculation, i.e. through dividing i,jfOBS(r) − i,jfREF(r) by a reference. In the PMF formalism as written in Eq. i,jfOBS(r) and i,jfREF(r). In Eq. i,jfREF(r) as a reference. We named the calculated score “total information gain” (TIG), which is expressed as a dimensionless quantity.Since the new formalism is disconnected from physics, the score produced can no longer be viewed as an approximation of the free energy, and one may wonder what property is measured here. In what follows, we propose an explanation of how our scoring function works. In a distance distribution obtained from native conformations, in which all atom types are indistinct, observing two atoms (belonging to residues separated by at least three positions) at a distance of 2 Å can be thought surprising. However, this observation becomes less surprising, when considering only the subdistribution of the cysteine atoms, as these residues can form disulfide bonds. This decrease in the surprise is measured in both Eq. kT = 1) as:i,jIOBS(r) and i,jIREF(r) are the surprisals of observing two atoms i and j at a distance r, for the observed and reference distributions, respectively, and Δi,jI(r) is the corresponding information gain. Thus, a total information gain is calculated by summing every Δi,jI(r) for every combination of atoms i and j found in the evaluated structural model. Also of note is the fact that the variation of Shannon entropy (i.e. the average amount of information) is simply obtained by dividing this total information gain by the total number of interatomic distances in the evaluated protein structure. However, this aspect will not be developed further in the present article.Here, we call attention to the fact that, independently of the probabilistic framework, the property quantified by Sippl’s PMFs should also be interpreted as an information gain (rather than a pseudo-energy), but only when restricting the definition of information to the Shannon “surprisal”. Indeed, Eq. i,jfOBS(r) and i,jfREF(r), or only the highest of the two frequencies. We refer to these scores as “MCK1” and “MCK2”, respectively. Formally, they are expressed as:Finally, to further demonstrate the irrelevance of the logarithmic mean in Eq. 2.2https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score.To test the formalism of Eq. http://dunbrack.fccc.edu/PISCES.php) we have downloaded a precompiled list of 3768 PDB chains of resolution ≤1.6 Å (X-ray structures only), R-factor ≤ 0.25, and sequence identity ≤20%; (ii) we only kept the 1973 protein chains of lengths ranging from 80 to 250 residues; (iii) to ensure independence from the benchmark dataset, we have eliminated the 56 protein chains that share >20% sequence identity with any of the 200 proteins from the 3DRobot dataset To build our scoring function, we have defined a set of native protein structures as follows: (i) from the PISCES website  has been calculated as the weighted arithmetic mean of all i,jfOBS(r) i and i + 1, i + 2, or i + 3 have not been taken into account To compare the formalisms of Eqs. 2.3Each scoring function has been assessed based on its ability to rank structural models by quality as measured by their TM-score Each scoring function was evaluated through a pairwise ranking of the decoys for each of the 200 proteins from 3DRobot, or each of the 133 CASP13 targets. This allowed to calculate the accuracy of each method as the proportion (in %) of correct pairwise rankings. As the difficulty of ranking models may vary depending on their qualities, four subsets of the 3DRobot and CASP13 datasets have been defined, based on the TM-score to the native: “near-native”, “good”, “medium”, and “poor” quality models are defined by three thresholds at 0.8, 0.6, 0.4, respectively. Since comparing two very similar models is pointless, another threshold for the minimal TM-score difference between the compared models has been defined at 0.1. The other performance criterion used in this study is the average ranking, as predicted by the scoring function, for the aforementioned “near-native” and “good” categories of models , as well as for the “poor” ones . The statistical significance of the observed differences between accuracies was determined by comparing the distributions of correct and wrong rankings, using the Wilcoxon signed-rank test, with an α error of 0.05. The exact same procedure has been carried out using the global distance test total score (GDT_TS) i,jfREF(r), DOPE and GOAP take into account either the radius of gyration or the molecular volume of each protein structure from the training set, which eliminates the bias on the interatomic distances—the distance distributions may vary a lot, depending on the sizes of the proteins included in the dataset. Thus, the difference between DOPE/GOAP and PMF/TIG lies in (i) the training datasets, (ii) the calculation of the reference state and (iii), only in the case of GOAP, the dependence on orientation.To compare both the PMF and TIG scores with an external reference from the literature, we have included the DOPE 33.1i,jfOBS(r) and i,jfREF(r) as a statistical reference. On the whole, the results obtained with the mock scores are also significantly better than those produced by DOPE. This indicates that the logarithmic mean could be advantageously replaced by other types of means, such as the arithmetic mean used in MCK1.The TIG scoring function is supposed to be more accurate than Sippl’s statistical potentials Eq. , as it ii.e. which use less information), such as DFIRE This article describes how Sippl’s formalism can be comprehended and improved, in light of probability and information theories. The simple TIG and mock scores have been designed for that purpose. However, to give the reader an idea of the performance that a more complex scoring function can achieve, our benchmark includes a sixth method, GOAP, which ranks among the best statistical potentials i.e. native structures of altered quality. Thus, cases of success and failure of TIG, selected from both CASP13 and 3DRobot, are presented in The ranking of the methods is similar to the CASP13 benchmark, although every accuracy is higher . On this3.2i.e. the higher the rank), the better; conversely, for models of “poor” quality, the lower the rank, the better. Taken as a whole, these results further validate the new formalism, as TIG significantly outperforms the other methods except GOAP. Indeed, TIG is better because it (i) ranks higher the near-native models, as well as the good models  and (ii) ranks lower the models of poor quality. Again, the mock scores systematically outperform PMF, and DOPE is the only score that rivals or bests TIG (at ranking good models). In agreement with the accuracies reported in To confirm the above benchmarking results, a second test has been performed. It consists in observing how the models are ranked by a scoring function, depending on their actual quality as measured by either the TM-score or the GDT_TS. The results of this second benchmarking procedure are presented in As for the first test, the results contain some discrepancies between the TM-score- and GTD_TS-based categories. This shows the difficulty of defining thresholds to categorize model quality. As both the TM-score and GDT_TS take values between 0 and 1, we used the same thresholds for these two scores. The >0.8 limit for the near-native models was defined based on a major and recent study, which defined conformations with a TM-score >0.7 as “high-accuracy” predicted structures 3.3To further validate the information gain-based approach, the correlations between the scores produced by PMF or TIG and the corresponding TM-scores have been investigated, for the decoys of 3DRobot. Averaged on the 200 proteins (×300 decoys), the Pearson correlation coefficients are −0.719 and −0.782, for PMF and TIG, respectively. This makes TIG equal to dDFIRE, by referring to performances previously reported in the literature 3.4i.e. the negative score well) is always stronger for TIG than for PMF. This is simply due to the fact that, for x and y ∈ ]0, 1], the −ln(x/y) function from Eq. x − y)/y from Eq. As the new formalism has been designed to be independent of any physics-based interpretation, it is interesting to analyze the score profiles of our TIG function, given that we will not attempt to draw any analogy with physical interatomic potentials . Compared with the PMF profiles, a first observation is that most profiles are actually very similar. Therefore, i,jfREF(r) = 0, which requires arbitrarily setting a default value of the score . However, due to the logarithm in the formula, only PMF is undefined for i,jfOBS(r) = 0, which also requires a default value (again set to +10). For i,jfOBS(r) = 0, the TIG scoring function is defined and takes the value +1, which makes its repulsive part composed of two plateaus, at +1 and +10. The +1 plateau corresponds to an interatomic distance that has never been observed for the particular atom pair, but otherwise exists in the training set of native conformations. The +10 plateau, however, corresponds to an interatomic distance that has never been observed within the experimental structures, whatever the type of atoms. Although it seems useful to distinguish these two cases, with a higher penalty for the second one, this repulsive part with either one or two plateaus presumably does not affect the results of our benchmarking procedure. Indeed, only protein models of very poor quality would contain such abnormal interatomic distances. Nevertheless, the importance of this parameter—which can be applied to both TIG and PMF formalisms—remains to be investigated. Interestingly, the right sides of the plots indicate that the information gain is limited for two residues spaced by >10 Å, as the score fluctuates around zero. This suggests that similar performances could be achieved at a lower computational cost, by restricting calculations to shorter interatomic distances (see section below). Finally, from a qualitative point of view, these profiles produced by TIG do not seem to show any unexpected features. For the Asp-Glu and Lys-Arg residue pairs  is computed from a subdistribution of i,jfREF(r). When the prior and posterior distributions are of equal complexity, the logarithmic mean of i,jfOBS(r) and i,jfREF(r) holds relevant. However, the advantage of dividing by a logarithmic mean in Eq. The new formalism presented here was developed to be more statistically relevant than Sippl’s PMFs. Thus, the better performances observed on the benchmark were actually expected. Through the inclusion of the two mock scoring functions, this study was also aimed at shedding light on how the statistical PMFs actually work by summing relative frequency differences, which correspond to information gains. It should be noted that we used here a general definition of information that quantifies the Bayesian updating and is, therefore, different than the particular Shannon surprisal . Importantly, the conceptual improvement brought here is only valid when In their original form, as devised by Sippl thirty years ago, the statistical potentials used only the distance between each pair of atoms to represent protein structures. Through the lens of probability theory, Bakers’s and Hamelryck’s research groups later showed how any other descriptors can be successfully used: typically, solvent accessibility or torsion angles. More exotic structural properties have also been exploited, e.g. lipid bilayer depth to build a potential aimed at evaluating structural models of transmembrane proteins i.e. models whose behavior depends on a relatively small number of combinations of parameters. Although this theoretical framework has gained popularity in recent years for explaining phenomena in physics and biology (see The development of a distance-dependent scoring function relies on several parameters, such as the distance bin width, the minimum and maximum distance thresholds, and the minimum number of sequence positions separating the residues of the two interacting atoms. Behind the setting of these values lies the question of how to treat long-range interactions and local contacts. Here, we considered distances ranging from 0 to 15 Å in order to be comparable with DOPE, but authors use a 4–8 Å range, following the aforementioned PCNs approach logy see , it remaWe proved our concept for the ranking of predicted structures according to their quality. However, there are several other applications that could be explored—like protein–protein docking, for example. An interesting use of statistical potentials consists in training them on a particular type of native protein conformations, in order to gain insight into the rules that govern the relative positioning of residues within these protein structures. For example, this has recently been done for transmembrane protein structures Guillaume Postic: Conceptualization, Formal analysis, Methodology, Software, Supervision, Validation, Writing - original draft, Writing - review & editing. Nathalie Janel: Funding acquisition, Writing - review & editing. Pierre Tufféry: Formal analysis, Funding acquisition, Validation, Writing - review & editing. Gautier Moroy: Formal analysis, Validation, Writing - original draft, Writing - review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein’s tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at  Protein tertiary structure comparison is very important in many applications of modern structural biology, drug design, drug discovery, in studies of protein-ligand binding, protein-protein interactions, and other fields. This is especially significant because the structure of a protein is more protected than the protein sequence . Many woO(N) time (The distance matrix of α carbon can be seen extensively used in (N) time .An image is basically a matrix of N × N dimension with corresponding data in each cell. Thus the distance matrix can be used as an image. Basically, three types of features can be generated from an image: pixel-based, filter-based and computationally generated features. Pixel-based features, for example, histograms are simplistic and dependent on the capability of classification algorithms. Filter-based methodologies transform the original image to use feature extraction methods. Refined algorithms are used to segment and other various algorithms are used to detect different features. Using ideas from computer vision and utilizing it in protein structure retrieval is not uncommon in the field. ProteinDBS server  implemenThe human body uses protein for repairing tissues, making enzymes, hormones, and other biological chemicals. It is an essential building block of bones, muscles, cartilage, skin, and blood. On the other hand, a ligand is a material that has the potentiality to bind to and forms a composite with a biomolecule in order to carry out a biological function. In protein-ligand binding, the ligand is usually a molecule which produces a signal by binding to a locus on a target protein. The binding typically results in a change of conformational isomerism (conformation) of the target protein. The evolution of the protein’s responsibility depends on the development of specific sites which are designed to bind ligand molecules. Ligand binding ability is important for the management of biological functions. Ligand binding interactions change the protein state and function. Protein-ligand binding prediction is very important in many applications of modern structural biology, drug design, drug discovery, and other fields.Many experimental techniques can be used to investigate various aspects of protein-ligand binding. X-ray crystallography, nuclear magnetic resonance, Laue X-ray diffraction, small-angle X-ray scattering and cryo-electron microscopy provide atomic-resolution or near-atomic-resolution structures of the unbound proteins and the protein-ligand complexes, which can be used to study the changes in structure and/or dynamics between the free and bound forms as well as relevant binding events. Although experimental techniques can investigate thermodynamic profiles for a ligand-protein complex, the experimental procedures for determination of binding affinity are laborious, time-consuming, and expensive. Modern rational drug design usually involves the HTS of a large compound library comprising hundreds or thousands of compounds to find the lead molecules, but this is still not realistic to use experimental methods alone. Different methods like isothermal titration calorimetry , surfaceIn this paper, we propose the combination of LBP histogram, Gabor Filtered LBP Histogram, Separate Row Multiplication Matrix with Uniform LBP Histogram, neighbor Block Subtraction Matrix with Uniform LBP Histogram and Atom Bond features to be used for protein similarity measurement. We extract the distance matrix of α carbon of a protein from PDB file and use the distance matrix as an image to extract our first four features, and the atom bond is extracted from the PDB files. We have used a large variety of classification algorithms to test the extracted features. We also show the results and comparative study of different implementation methodology of CoMOGrad and PHOG. The method we have proposed can produce a better result on some classification algorithm over the previous methods on the same benchmark. In addition, we have proposed a supervised learning algorithm for predicting Protein-Ligand Binding which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms. Our proposed method uses the features proposed in this paper.Our methodology is divided into two parts. Firstly, we have generated image-based features using protein tertiary structures and performed feature analysis based on the prediction power on the structural class prediction problem. In this section, we present the materials and methods for both of the problems. The dataset, features, necessary algorithms and performance measurements are described accordingly for each of the problems.In this section, we present the methodology of structural class prediction. Atom bond features are generated from the protein tertiary structures given as PDB files. Images are created from the distance matrix calculated using α carbon atom coordinates of the amino acids of the protein structures in the given dataset. From each image of protein, we have derived five features. Stratified Remove Folds was used to test the capability and efficiency of the dataset. There are in total seven different classes of protein structures. Synthetic minority over-sampling technique (SMOTE) is used to handle class imbalance problem. The block diagram of the methodology is given in We have used 40 percent ID filtered subset of PDB-style files for SCOPe domains version 2.03  as our dWe have generated images of protein structures according to the methodology described in CoMOGrad and PHOG . Only α The dimension of protein images is based on the total number of α carbon they have. So, every individual protein images are different from the other in dimension. Therefore, the images were scaled to the same dimension. CoMOGrad and PHOG have used Bi-cubic interpolation and wavelet transform to scale all the protein images into 128 × 128 dimension . During We have generated five different feature groups. Our first four feature groups are different types of histograms and the fifth feature group is about the prognosis of the atoms. The histograms were taken from both scaled and unscaled images.8 = 256 possible patterns, thus the values range from 0 to maximum 255 in each pixel of the image. The total number of bins of the histogram is thus 256. We would get 256 attributes from each image. We have used zero-padding technique to generate LBP.Local binary pattern histogram was first proposed by Gabor Filter is titled after Dennis Gabor. It is used for texture segmentation , opticalFirst of all, we’ve identified unique atoms amidst all the protein PDB files. From each protein PDB file, we’ve counted occurrences of each atom. Then we’ve taken the percentage as features of each atom among all the atoms that each protein has. Then we’ve taken the first 100 sequential atoms and used their atomic mass as the feature. Then we’ve counted the bond that each pair of atoms has in a particular protein using atomic distance based on a threshold value. Finally, we’ve taken the percentage as the feature of the bond of each unique pair of atoms among all the bonds that the protein has.The image is split into 3 × 3 matrices. From each matrix, we get three rows with the dimension of 1 × 3. By multiplying each row with the same 3 × 3 matrix, we get three result matrix consisting of 1 × 3 dimension. Each cell is divided by 100. The results are then put in the 3 × 3 matrix in accordance with the row numbers. The color intensity of an image is between 0 to 255. So, if the value of any cell of the result matrix is greater than 255, then the value is replaced with 255. After applying this technique, the uniform LBP is applied. From 8 = 256 possible patterns with 58 of them being uniform. For estimating the histogram, every uniform pattern gets a separate bin while a single bin is allotted for all non-uniform patterns. Therefore, from a uniform binary pattern, we get the histogram of the total bin size of 59.Another variation of the LBP is called uniform pattern . Some biBlocks are of the same dimension, 3 × 3. Two blocks of matrices are considered neighbors for this method if the center cells are neighboring. Because of this, the value of the last two columns of the first block and the first two columns of the second block are the same. The two blocks of matrices are subtracted and the result is set in the place of the first block. If any of the cells have any negative number, then 0 is placed instead of the negative value. The replacement of value is made because the histogram bin begins from zero. Uniform LBP is then used to compute the histogram.Summary of all the feature groups used in this paper is given in x denotes the highest number of instances among all the classes and y is denoted by a class which we will SMOTE, then the expression for the percentage calculation is From It is to be noted that SMOTE was used on only train dataset after separating the train and test dataset. So, there are no artificial instances in the test dataset.We have used five classifiers for the analysis of features applied to solve structural class prediction problem: K-Nearest Neighbor (KNN), Naive Bayesian Classifier, support vector machines (SVM), Adaptive Boosting (AdaBoost) and Random Forest. A concise description of the classifiers is given in this section.K-nearest neighbor algorithm (KNN)  is a simNaive Bayesian classifier  is basedSupport vector machine  works byAdaptive Boosting classifier  is a metRandom Forest  is an enProtein-ligand binding prediction is a binary class classification problem. We have used image-based features for each protein and ligand dataset. Our methodology learns threshold values from the training data and uses these in test data prediction. We have used the same set of features that were generated and analyzed for the structural class prediction problem to solve the ligand-binding problem. In this section, we present the necessary materials and methods that were used for the ligand-binding problem.We’ve used Computer Vision and Pattern Discovery for Bioimages Group @ BII as our dataset. In our dataset, 3,000 protein-ligand complexes were determined experimentally with 3D structures available. Each protein and its ligand are of one-to-one correspondence, that is, they can bind to each other and make Protein-Ligand complex. The dataset has 3,000 pairs of protein and ligand where the same name/ID of protein and ligand interacts/binds with each other.We’ve used OpenCV  library Among the PDB files, 33 ligands have only one atom, which will create a 1 × 1 image having no significance for feature extraction. So, we had to compromise those 33 ligands as well as 33 corresponding proteins from training. The given dataset has only positive instances (the pairs of protein and ligand where they bind with each other). But there were no negative instances (the pairs of protein and ligand where they do not bind with each other). The missing negative instances have created our dataset highly imbalanced. To overcome this imbalance, we’ve generated negative instances in two different ways.Random Negative Undersampling: We have 2,967 protein PDB and 2,967 ligand PDB where 8,803,089 pairs are possible. Among these, 2,967 pairs are given as positive instances and the rest 8,800,122 pairs are unknown/unseen instances. From the unseen pairs, we’ve taken 2,967 pairs randomly as negative instances to make our dataset balanced.Clustering-Based Undersampling: Using the positive instances , we’ve created 10 clusters. Then we’ve searched for 2,967 unseen pairs randomly as negative instances where they belong to those 10 clusters. We’ve made sure that each cluster has the same number of positive and negative instances to make the dataset balanced See .We’ve developed a similarity-based clustering method to predict the binding class. Distance is used to measure similarity. Our methodology is given in From the PDB dataset of proteins and ligands, firstly we have generated images and converted to 128 × 128 images for each protein and ligand. From these images, we have generated two different features.CoMOGrad and PHOG: CoMOGrad stands for Co-occurrence Matrix of the Oriented Gradient of Distance Matrices and PHOG stands for Pyramid Histogram of Oriented Gradient . This meHybrid Local Binary Pattern: LBP  is a proDistance can only be calculated between proteins or between ligands. We’ve used KNN and Clustering method to calculate these distances.RELATEDLIGANDS  with 2.4 GHz speed and 18.5 GB memory. Java language was used for data preprocessing including feature generation using OpenCV software library, negative data generation and data merging using Eclipse IDE with Java 8 standard edition. Python language was used to implement our algorithm using the Spyder IDE. Weka tool was used to run the traditional classification algorithms for the comparison with our algorithm. 10-fold cross-validation method has been used to get the performance of our model.We also used Scale-invariant feature transform (SIFT)  methodolWe’ve tried outlier detection method to handle the absence of negative data in Ligand-Binding dataset. But it gave overfitting problem. Details are in c = 1.0 and ε = 1.0 w−2. Data was normalized before supplying to the classifier. J48 decision tree classifier was used in Adaboost classifier as the weak base classifier. Classifier number of iterations was set to 100 for Random Forest.A different sets of parameters were used for each classifier used in this research. A linear searching was used with no distance weighting for KNN. In the case of the Naive Bayesian Classifier, SVM, a polynomial kernel was used with We’ve used the “StratifiedRemoveFolds” filter which is available in Weka for creating Train and Test set. This is a supervised instance filtering process that takes a dataset and outputs a specified fold for cross-validation After running the experiments for our five feature groups ABCDE classifies the highest percentage of correct instances in Random Forest, Adaboost(J48) and SVM among all other feature groups. Feature scaled E and D individually provides the highest accuracy in Naive Bayesian and KNN. As the whole combination of all feature groups, accuracy gives the highest percentage than any other feature group, thus we conclude that the best-performing feature group combination is ABCDE and the best classifier is SVM classifier.In this section, we compare the performance of our proposed method with CoMOGrad and PHOG . For comWe have revealed the precedence of our methodology over CoMOGrad and PHOG .Sensitivity is the true positive rate regarding the positive instances. As we had to generate the negative data artificially, sensitivity is the actual scale of performance measuring as the positive data were the actual data. Using the thresholds gained using the negative data, the sensitivity of our algorithm is very good comparing to other existing algorithms shown in We have generated three different datasets based on three different features. Hybrid LBP gives 736 long feature vectors from protein images and 677 long feature vectors from ligand images. So, for one protein-ligand pair, we’ve got 1,413 (736 + 677) attributes and one Binding Class value as one instance. The above mentioned two types of negative data (random and Clustering-Based Undersampling) were generated using Hybrid LBP for balancing the data. CoMOGrad and PHOG gives 1,021 or 1,020 long feature vectors from protein image, but for ligand images, it gives 1,020 long feature vectors. We assumed “0” as the last feature in protein where features were 1,020 long, to make it a 1,021 long feature. So, for one protein-ligand pair we’ve got 2,041  attributes and one Binding Class value as one instance. Random negative undersampling was used in CoMOGrad and PHOG but Clustering-Based Undersampling was not possible as some clusters couldn’t get any unseen pairs of protein and ligand. Our method was used based on three nearest neighbors and shown on the above table and chart.Accuracy is not shown because it is dependent on the artificial negative data. However, specificity is shown to correlate with sensitivity, not to judge the algorithms as it is a true negative rate. On the other hand, the F1 score is also valuable because of being a harmonic mean of sensitivity & precision where both scores are based on positive data. For more details, please check the We can see that AdaBoost works better than our algorithm in terms of sensitivity in ComoGrad and PHOG dataset. Because Ligand data were so small in terms of the number of atoms that ComoGrad and PHOG gave zeros for most of the ligands. This is an overfitting problem as high sensitivity is offset by low specificity. But our algorithm’s overall performance is better than other machine learning algorithms in the three different feature datasets.In this paper, we showed how accurately we can detect protein classes using the combination of different image-based feature groups generated from protein images. We also propose a simple similarity-based clustering method to predict protein-ligand binding without using deep-learning or neural-networks. This simple distance-based algorithm is quite effective compared to complex machine learning algorithms. Our main limitation was the missing negative data. If we had the actual negative data, we could’ve determined the perfect thresholds for each category of distances, and that would give us a more accurate prediction. Another problem was the dimensions of small Ligands as we’re using image-based features. As the advancement of deep learning, neural network, and many other deep learning techniques are being used to classify images, many remarkably interesting applications can be made. For our future advancement, we wish to introduce new features to improve accuracy, use new tools and explore other fields of computer vision such as human emotion detection. Besides, we will try to extract some unique features from the ligand dataset so that the dimensionality problem does not affect our protein-ligand binding prediction.10.7717/peerj-cs.253/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj-cs.253/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj-cs.253/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj-cs.253/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj-cs.253/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj-cs.253/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj-cs.253/supp-7Supplemental Information 7Click here for additional data file.


Their interaction is a fascinating and complex trait of the eukaryotic cell that MitImpact explores with its third major release. MitImpact expands its collection of genomic, clinical, and functional annotations of all non-synonymous substitutions of the human mitochondrial genome with new information on putative Compensated Pathogenic Deviations and co-varying amino acid sites of the Respiratory Chain subunits. It further provides evidence of energetic and structural residue compensation by techniques of molecular dynamics simulation. MitImpact is freely accessible at  The genetics of the mitochondria received a high impulse approximately thirty years ago when a missense variant in the mitochondrial MT-ND4 gene was discovered to cause Leber's Optic Neuropathy  . Since tAround 1500 proteins appear to be necessary for the mitochondrial biogenesis and metabolism ,7; the hVariations on mitochondrial proteins associate with a broad range of phenotypes that differ in terms of age at onset, single/multi-organ involvement and inheritance pattern . To dateCompensated Pathogenic Deviations (CPDs)   were fouNumerous lines of evidence showed that the nuclear and mitochondrial genomes need to co-evolve to ensure the efficient functioning of the OXPHOS complexes ,31–33. TThe vast availability of genomics data, computational tools, and specialized databases contributed significantly to achieving these findings. Since its release in 2015, MitImpact  is one pin-vitro , Complex IV  or Complex V (MT-ATP6 + MT-ATP8), and intra-protein when both residues were located in the same protein (thirteen pairs), and then not in MT-ND1, which was concatenated by default to all proteins. From the TOP500 inter-protein pairs, in terms of cMI or mfDCA scores, we retained only the pairs whose residues did not fall in the same concatenated protein. From the TOP500 intra-protein pairs, we considered only the pairs where neither residue fell in the MT-ND1 concatenated sequence. Intra-protein interactions were assessed through EVMutation  and, consequently, assess the protein stability and protein–protein interaction energy alteration. Alternative amino acids that caused a ΔΔG to exceed the suggested cutoffs  for the single mutant were tagged as disruptive .MitImpact 3 stores functional assessments of sixteen predictors, seven meta-predictors, and four cancer-specific predictors for 24.190 amino acid variations in the 13 protein-coding genes of the CPD. Forty-one of these variants were classified as CPDs  and interface-protein . This last subclass contains double mutants at the interaction interface between RC subunits, including nuclear proteins. An example case study where the energetic destabilization caused by a variant in a gene (i.e. S45P in ND3) can or cannot be restored by a variant in another gene  is described in The co-varying sites were then studied energetically. All the pairs encompassing residues that compensate for the energetic destabilization caused by the other members of the pairs were split into inter-protein , is predicted by the previous energetic analysis to be stabilized by three co-occurring other residues located in position 126 of the interacting MT-ND3 protein. Similarly, the MT-ND1 V144I variant is associated with Leber hereditary optic neuropathy . The analysis of the molecular dynamics trajectories of this mutant combined with the N10S variant on the interacting MT-ND3 protein showed how the co-occurrence of both mutations could partially restore the fundamental dynamical properties, such as stability and binding affinity (Figure 56000, isMitImpact 3 updates its knowledge base and becomes the largest collection of functional predictions of mitochondrial non-synonymous variants. Among all available sixteen pathogenicity predictors, seven meta-predictors, four cancer-specific predictors, our APOGEE  stands oThese techniques have limitations, as standard procedures to benchmark their results do not exist. The immediate consequence is that different computational methods can yield incongruent results to an extent that is not currently accountable. Caution must also be used with the analyses based on molecular dynamics simulation. These yield results that are indeed more accurate and detailed than those of software predictors, but under the strong hypothesis that the causes of a variant's pathogenic character are ascribed to structural alterations .Nevertheless, MitImpact 3 is one of the first specialized bioinformatics resources that adds information on binary epistatic events and their consequences to classical variant annotations and functional predictions, thereby disclosing sites that significantly co-vary through the mammalian phylogeny. Moreover, it stores the results of a great computational effort, which consisted of studying hundreds of co-varying amino acid pairs from energetic and molecular dynamics points of view. The aim of this study, which is still in progress, is that of assessing the structural consequences of known pathogenic variants and pinpointing any eventual compensatory effects. Within the frame of this task, we will create a submission form on the website where interested users can request to analyze energetically and structurally their sets of variants searching for epistatic interactions.Future developments will deal with the low congruence of methods to assess the co-variation of RC subunits residues. We will use other algorithms and will define a more reliable consensus set of results. We have also planned to release an updated version of APOGEE , which wMitImpact has been supporting molecular and computational biologists, geneticists, and medical doctors for five years in conducting their work, and will keep doing it freely.http://mitimpact.css-mendel.it. Raw I-COMS score matrices and protein alignments are available at https://mitimpact.css-mendel.it/static/supporting_data/ICOMS_mitimpact3.rar.MitImpact is freely available at Atomic coordinates and structure factors of the human RC I transmembrane arm have been retrieved from the PDB with the following accession number 5xtc. Energetic compensation was calculated on smaller bovine models retrieved from PDB with the following accession numbers: 1be3, 1bgy, 1bmf, 1cow, 1e1q, 1e1r, 1e79, 1efr, 1h8e, 1h8h, 1l0l, 1l0n, 1mab, 1nbm, 1ntk, 1ntm, 1ntz, 1nu1, 1occ, 1oco, 1ocr, 1ocz, 1ohh, 1pp9, 1ppj, 1qcr, 1qo1, 1sqb, 1sqp, 1sqq, 1sqv, 1sqx, 1v54, 1v55, 1w0j, 1w0k, 2a06, 2bcc, 2ck3, 2cly, 2dyr, 2dys, 2eij, 2eik, 2eil, 2eim, 2ein, 2f43, 2fyu, 2jdi, 2jiz, 2jj1, 2jj2, 2occ, 2v7q, 2w6e, 2w6f, 2w6g, 2w6h, 2w6i, 2w6j, 2wss, 2xnd, 2y69, 2ybb, 2zxw, 3abk, 3abl, 3abm, 3ag1, 3ag2, 3ag3, 3ag4, 3asn, 3aso, 3bcc, 3wg7, 3x2q, 4asu, 4b2q, 4d6t, 4d6u, 4tsf, 4tt3, 4yxw, 4z1m, 5ara, 5are, 5arh, 5ari, 5b1a, 5b1b, 5b3s, 5fij, 5fik, 5fil, 5gpn, 5iy5, 5klv, 5lc5, 5ldw, 5ldx, 5lnk, 5luf, 5nmi, 5w97, 5wau, 5x19, 5x1b, 5x1f, 5xdq.gkaa1032_Supplemental_FilesClick here for additional data file.