Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.  •Evaluating protein group FDR estimation methods with entrapment and simulated data.•Accurate & sensitive protein group FDR method on databases with protein isoforms.•Tool for combining multiple large-scale MaxQuant searches on protein group-level.•Analysis on ProteomicsDB identified >1200 human genes with multiple protein groups. Distinguishing between protein products of a gene is complicated by many peptides that such isoforms have in common. Grouping indistinguishable proteins alleviates this issue but leads to problems with estimating false discovery rates (FDRs) in large-scale experiments as false positives accumulate. Here, protein group FDR estimation methods were evaluated on accuracy and sensitivity. Our new Picked Protein Group FDR method performed best and reanalysis of the draft human proteome in ProteomicsDB found >1200 genes with multiple protein products. Today, it is not at all uncommon to perform experiments resulting in hundreds, or even thousands, of MS data files , 2, 3, 4ideAtlas , estimatideAtlas , 9, 10. ideAtlas , such asideAtlas  or SNPs ideAtlas .i.e., target) peptide/protein sequences to serve as a model for false hits. The underlying assumption is that the database search engine identification score distributions of decoy and incorrect target peptide-spectrum matches (PSMs), peptides, and proteins are the same. Violations of this assumption can lead to inaccurate FDR estimates, referred to as loss of FDR control. A well-working FDR estimation procedure should both be accurate, that is, reflecting the true proportion of false discoveries, and sensitive, that is, maximizing the number of acceptable discoveries at a defined threshold . When large-scale datasets begun to appear in the literature, it was soon recognized that naively compiling lists of identified proteins by combining large numbers of experiments led to loss of control of the protein FDR  . Brieflyet al.  employedi.e., unique) peptides may be identified for protein isoforms of the same gene. Therefore, proteins are typically only grouped if a protein’s identified peptides are a subset of the identified peptides of another protein. The identification of even a single unique peptide for each of the isoforms would prevent isoforms to be grouped even if they had (many) identified peptides in common. This may lead to the identification of multiple protein groups for a given gene and, in some cases, even particular protein isoforms.As proteomic technology has improved over time to enable profiling proteomes at more and more depth, the above simplification may no longer be acceptable in many circumstances. Indeed, it has been estimated that up to 50% of all peptide sequences are shared between multiple protein sequences when considering all protein isoforms resulting from alternative RNA splicing . In turnProtein grouping has become very popular because it provides some of the desired granularity for distinguishing different protein products from the same gene. However, the subject of estimating protein group-level FDR has received little attention , 21, 22.Note that gene-level FDRs and protein group-level FDRs differ fundamentally due to the type of entities in the lists they are calculated on, that is, genes and protein groups respectively and can, thus, generally not be compared to each other. However, one can calculate gene-level FDRs on a list of protein groups by, for example, only retaining the best scoring protein group per gene. The reverse, that is, calculating protein group-level FDRs from a list of genes, is only possible in the most trivial sense, where each gene is its own protein group. In the following, we will exclusively use protein group-level FDRs.https://github.com/kusterlab/picked_group_fdr.To address the above issues, we introduce the Picked Protein Group FDR method, along with an accompanying software tool, which extends the Picked Protein FDR approach to protein groups. In particular, we show that this method identifies up to 4% more protein groups than the method used by MaxQuant while simultaneously correctly controlling the protein group-level FDR. Furthermore, we show that the method scales to very large numbers of experiments, exemplified by the reanalysis of the entire human section of ProteomicsDB. The tool comes with a graphical user interface operating under Windows as well as a Python package. The software also contains the option to merge results from multiple searches into a single list of quantified protein groups  and provides an output similar to MaxQuant’s proteinGroups.txt. Instructions for download and use can be found at et al.  including both Mascot and MaxQuant search results for all proteases formed the ing size , totalinWang_base and PrDB datasets, we produced three lists of PSMs that were filtered to only include PSMs with peptide sequences that mapped to the canonical Swiss-Prot, the Swiss-Prot with isoforms databases and the Swiss-Prot+TrEMBL with isoforms, respectively. These protein sequences were obtained from ProteomicsDB . Note that these filtered lists of PSMs will slightly differ from the results of a normal search against these respective databases. In the filtered lists, PSMs with sequences not present in the reduced databases are discarded instead of matched to another sequence within the reduced database. As this equally affects incorrect target and decoy PSMs, this should not lead to any biases for FDR estimation.For both the To assess if protein group-level FDRs are well-calibrated for the protein group-level FDR estimation methods presented below, entrapment searches were performed . BrieflyA):1.The target database was in-silico digested with Trypsin/P as protease, without missed cleavages. Only peptides longer than six amino acids were retained.2.S (see below) of peptides was randomly selected to remain unchanged, thereby creating shared peptides between the target and entrapment databases . Trypsin/P was specified as protease and no missed cleavages were allowed, to reflect the construction of the entrapment databases. The peptide-level FDR threshold was set to 10% (searching with 100% peptide-level FDR was prohibitively slow) and the protein-level FDR threshold to 100%. All other parameters were set to default values. These datasets are referred to Wang_trap_0.5 and Wang_trap_0.04, respectively.Entrapment databases were generated for two different fractions For protein-level FDR estimation methods, it is common to supply the list of PSMs without applying a peptide-level FDR threshold , 9. ThisHowever, for MaxQuant’s method, we noted a decrease of up to 9% in the number of identified protein groups at 1% protein group-level FDR for the 100% peptide-level FDR cutoff compared to MaxQuant’s default 1% peptide-level FDR cutoff . This deWang datasets, the evidence.txt MaxQuant output files were processed by a custom python script to create a percolator input file by extracting the following features: Andromeda score, Andromeda delta score, peptide length, charge (one-hot encoded), mass, enzymatic N-terminal, enzymatic C-terminal, missed cleavages, number of modifications, delta mass, and absolute delta mass. This file was subsequently processed by Percolator v3.04. The resulting PSM target and decoy output files were merged back with the original evidence.txt and the Q-value and PEP columns were updated with the new values provided by the Percolator analysis.For the three PrDB dataset, PSMs were grouped by project (n = 77) and the same features as above were extracted, minus the delta mass and absolute delta mass features. These two columns were incomplete in ProteomicsDB and were, therefore, discarded. Percolator was applied to each project separately, so that the weights of the support vector machine could be adjusted for each project. The percolator results were extracted on peptide level, where only the best scoring PSM per peptide sequence was retained. The resulting peptide lists (one for each project) were merged into a single list, using the -log10 of the peptide as the score and only retaining the best PSM for each peptide. Peptide-level q-values were then recalculated on this final list of (unique) peptide sequences.For the proteotypicity was used to select peptides, that is, conditional probabilities of detecting a peptide, given that the corresponding protein was identified. Furthermore, the probabilities of a protein to be detected were used, to replicate the typical observation that some proteins are identified in almost every experiment, whereas others might only be identified in only one or a few of experiments. Both the proteotypicity values (protein_probs) and protein detection probabilities (peptide_probs) were calculated from the data in ProteomicsDB. The simulation is described in pseudo-code below.Entrapment searches provide a good way to assess the validity of FDR estimates, but as it requires researching the data, this becomes prohibitively computationally expensive for datasets comprising hundreds of experiments. Therefore, lists of peptides were simulated with respective scores and labels (correct/incorrect) that recapitulate experimental results to a degree that they agree with the observations made with entrapment searches on a qualitative level. Specifically, the n_protein_mean = 10.000, n_protein_stdev = 1000, tp_score_mean = 2.5 tp_score_stdev = 0.7, fp_score_mean = 0.0, fp_score_stdev = 0.7, incorrect_ratio = 0.6, peptide_fdr = 0.01. Data was simulated for 10, 100, 200, 300, 400, and 500 experiments, to assess the performance of the different protein group-level FDR estimation methods at different scales. Additionally, datasets were simulated where the combined list of experiments had a 10% peptide-level FDR per raw file or a global 1% peptide-level FDR.For the simulated datasets in this article, the input parameters were estimated from the Wang_base dataset, where one experiment corresponds to one of the 29 tissues analyzed: A common practice to alleviate the problem of distributing shared peptide identifications between multiple protein sequences is to group proteins into so-called protein groups. In this article, we considered the following options:Each protein is considered its own protein group. In databases with many protein isoforms or homologous proteins, this will result in many shared peptides between the protein groups.Proteins are grouped if the peptides for one protein form a subset of the peptides for a second protein. This will, for example, group isoforms of the same gene if no isoform-specific peptides are identified. This method is, for example, used by MaxQuant.The idea behind this new two-step procedure is to prevent that protein groups are split into multiple groups due to the presence of low-confident PSMs. To achieve this, first, a regular subset protein grouping (sG) is performed, producing a list of protein groups, PG1. Next, we filter the list of PSMs using a PSM-level threshold equivalent to a 1% protein group-level FDR, which is calculated based on PG1. Then, a second sG is performed using this filtered list of PSMs, producing a second list of protein groups PG2. Finally, PG2 is supplemented with protein groups from PG1 that did not contain proteins already present in a protein group in PG2. This last step ensures that protein groups above 1% protein group-level FDR also have FDR estimates.After protein grouping, peptides have to be assigned to protein groups. This is simple if all proteins the peptides could originate from are all in the same protein group. However, one needs to decide on how to deal with peptides shared between proteins that are not in the same protein group. Two options are considered in this article:The rS strategy forces the assignment of a shared peptide to one of its associated protein groups based on which of these had the highest number of unique peptides. In case of a tie for the highest number of unique peptides, the tie is broken randomly.Here, all peptides that are shared between protein groups are discarded.To rank protein groups by confidence of identification, protein group scores are computed based on the identification probabilities of the constituent peptides. Here, three options for protein group scoring were evaluated.The protein group score used by MaxQuant is based on multiplying peptide PEPs. This score was re-implemented in Python and verified by comparing it to the protein group score that MaxQuant itself reported for each protein group. In reproducing the protein group scores reported by MaxQuant, we noted that the peptide PEPs were first divided by a constant, which appeared to be chosen with a view to maximizing the number of identified protein groups. Therefore, we implemented a grid search to optimize this constant before calculating the protein group scores.This takes the −log10(PEP) of the best scoring PSM for the protein as the protein group’s score.This takes the −log10(PEP) of the best scoring PSM after rescoring with Percolator as the protein group’s score.Before protein group-level FDR estimation, one can optionally perform a target-decoy competition step on the protein group-level. This competition has been shown to resolve problems with decreased sensitivity that results from an asymmetry between decoy proteins and falsely identified target proteins as noted in the introduction.All proteins are passed to the FDR calculation without any protein-level target-decoy competition taking place.This is a target-decoy competition step at the protein level. For each target protein, one notes down the corresponding decoy protein that was constructed by reversing or shuffling the target protein sequence. These two paired-up proteins are considered each other’s counterpart protein and only the highest scoring out of the two is retained.This is a target-decoy competition step at the protein group level. First, we mark all leading proteins for each protein group, that is, proteins which cover all identified peptides associated with the protein group. When going down the list of protein groups (sorted by decreasing identification score), protein groups are removed for which one or more counterpart leading protein was observed in the current (higher scoring) protein group. Note that if no protein grouping (nG) is done, this procedure is equal to the pT.The null hypothesis for the identification of a protein group  was defiTo assess which protein groups were correctly identified, we make use of the protein group scores defined above and use the number of decoy protein groups as an estimate for the number of incorrect target protein groups. First, protein groups are sorted by protein group score, starting with the best scoring protein group. The protein group-level FDR for a protein group is estimated as the ratio of the number of decoy protein groups and target protein groups with a better score than the current protein group.The options listed above for peptide-level FDR threshold, protein grouping, usage of shared peptides, protein scoring, and protein group FDR calculation can be combined in any desired constellation. Below is a summary of the methods used in the main text.MaxQuant•Grouping: subset grouping (sG)•Shared peptides: Occam’s razor (rS)•Scoring: multiplication of MaxQuant PEPs (mmP)•TDS: classic (cT)Savitski•Grouping: no grouping (nG)•Shared peptides: discard (dS)•Scoring: best Percolator PEP (bpP)•TDS: picked (pT)Picked Protein Group FDR•Grouping: rescued subset grouping (rsG)•Shared peptides: discard (dS)•Scoring: best Percolator PEP (bpP)•TDS: picked group (pgT)Savitski + Classic FDR•Grouping: no grouping (nG)•Shared peptides: discard (dS)•Scoring: best Percolator PEP (bpP)•TDS: classic (cT)Discard + Picked Group•Grouping: subset grouping (sG)•Shared peptides: discard (dS)•Scoring: best Percolator PEP (bpP)•TDS: picked group (pgT)Razor + Picked Group•Grouping: subset grouping (sG)•Shared peptides: Occam’s razor (rS)•Scoring: best Percolator PEP (bpP)•TDS: picked group (pgT)Classic Protein Group FDR•Grouping: rescued subset grouping (rsG)•Shared peptides: discard (dS)•Scoring: best Percolator PEP (bpP)•TDS: classic (cT)versus 154k) in the number of peptide identifications at 1% peptide-level FDR compared to using the MaxQuant PEPs (E). Therefore, the best Percolator PEP (bpP) was selected as the default choice for protein scoring. Results are also shown for mmP and best MaxQuant PEPs where these were relevant.We observed that MaxQuant PSM-level PEPs are less well-calibrated than those generated by Percolator, which led to anticonservative protein group-level FDR estimates for the MaxQuant PEPs but not for the PEPs generated by Percolator . Using tant PEPs E. Therefoptions at each stage has to be chosen. We define a method as a particular combination of chosen options. Any one method takes a list of PSMs as input and generates a list of protein groups with associated protein group-level FDRs as output. We used two large-scale datasets to evaluate several protein group-level FDR estimation methods in terms of accuracy and sensitivity  a novel Picked Protein Group FDR method which will be introduced further below. We demonstrate that the two state-of-the-art methods have issues either with calibration or sensitivity and that the Picked Protein Group FDR method resolves both of these problems (A).Protein group-level FDR estimation can be broken down into multiple stages, where one of several available sitivity . Specifiproblems A.Fig. 1OB) groups proteins if the identified peptides of one protein are a subset of the identified peptides of another protein. Rescued subset protein grouping (rsG) is an extension of sG that will be introduced in more detail further below. Second, peptides shared between protein groups can either be discarded shared peptides (dS) or assigned using Occam’s razor , that is, assigned to the protein group with the highest number of identified peptides, with ties broken randomly. Next, one has to calculate a score for each protein based on the scores of peptide identifications . As the default choice, we used bpP among all PSMs for a protein group, while also showing results for mmP and best MaxQuant PEPs where relevant for the comparison of methods. Finally, one has to choose TDS for protein groups. In the classic TDS (cT), all target and decoy protein groups are passed onto the FDR estimation stage. In the pT (3D), the score of each target protein is compared to its shuffled or reversed decoy counterpart protein and only the best scoring out of the two proteins is retained. The picked group TDS (pgT) is an evolution of the pT that specifically deals with protein groups and which will also be introduced further below.The different methods, stages of data processing, and associated options are described in detail in the Fig. 2n the pT  and 3D, https://pypi.org/project/picked-group-fdr/). The results shown below were generated with v0.3.0.As input to the protein group FDR estimation methods, we used the MaxQuant evidence.txt results filtered at 1% peptide-level FDR and 100% protein-level FDR and used Percolator to re-score the PSMs see . All optet al., based on the pT, has been shown to avoid the accumulation of decoy matches at the gene level in large datasets, while correctly controlling protein-level FDR . This modest increase is realized despite the fact that the Savitski method does not perform protein grouping (nG) and dS, whereas MaxQuant’s method uses sG and rS, both of which can enhance sensitivity. When we applied the pT to results of searches against protein databases including isoforms (Swiss-Prot+Isoforms) and unreviewed protein sequences (Swiss-Prot+Isoforms+TrEMBL), the sensitivity of the Savitski method drops compared to MaxQuant’s method (A). We also obtained the counter-intuitive result that fewer proteins are identified the larger the database that is used. This drop in sensitivity can largely be attributed to the discarding of the shared peptides in the Savitski method. This is because the fraction of shared peptides increases drastically with the increasing sequence redundancy of tryptic peptides obtained by in-silico digestion (B). For the most redundant database, Swiss-Prot+Isoforms+TrEMBL, 63% of all peptides are shared by at least two protein sequences. This effect is also evident in the MaxQuant search results, where 75% of the peptides were shared by two or more protein sequences for this database , we observed a 3% gain in identified proteins for the Savitski method compared to MaxQuant’s method A. This ms method A. We alsigestion B. For thdatabase .A), whereas REV_E forms a different protein group of its own. Therefore, we extended the pT to handle such cases that arise from protein grouping and we term this extension the pgT. This strategy first sorts the protein groups by descending protein identification score. Then, while going down the sorted protein group list, all protein groups that contain at least one counterpart protein of a leading protein in the current protein group (but with a lower score) are eliminated . However, when we changed this method to use rS, the results showed anticonservative FDR estimates. A similar behavior was observed for the MaxQuant method, because it also uses rS.To verify that the pgT leads to well-calibrated protein group-level FDRs, we searched the database  , this effect was not as apparent anymore, as the effect of rS is reduced.The anticonservative behavior when using rS is a result of false positives that remain unaccounted for by the decoy model, as explained in the introduction and in Figure 4using rS . When reB). This is because a group of proteins with one (or more) high-confident shared peptide identifications can be split over two protein groups owing to the presence of low-confident peptides that are unique to particular isoforms and are present in the same group (C). In such cases, high-confidence shared peptides are shared by two protein groups and are discarded by pgT. This results in neither protein group being identified, in turn, leading to reduced sensitivity of protein group identification.Combining pgT with dS led to well-calibrated FDR estimates but the loss in the number of identified protein groups was not completely resolved B. This ime group C. In sucD, orange), and (2) dS, leading to reduced sensitivity , we propose an extension to sG that we term rsG (A). First, a regular sG is performed and the PSM-level score cutoff corresponding to a 1% protein group-level FDR is computed. Second, using this cutoff, the high-confident PSMs are retained from the original list and sG is performed on this filtered list of PSMs. The final list of protein groups consists of the protein groups from the second grouping, supplemented with protein groups from the first grouping for which none of its proteins were already in a protein group from the second grouping. By removing the effect of low-confident PSMs on the protein grouping procedure, more high-confidence peptides can be uniquely mapped to a protein group. This reduces the fraction of discarded precursors from 0.21 for sG to 0.15 for rsG, a 30% decrease (B). On the Wang_trap_0.5 dataset, pgTDS combined with dS and rsG  shows well-calibrated FDRs (C). This combination of options in this new method will henceforth be referred to as the Picked Protein Group FDR method.To overcome the shortcomings of (1) using rS, leading to anticonservative protein FDR estimates D, orangesitivity D, purpleterm rsG A. First,decrease B. On theted FDRs C. This cWang_base dataset using the new Picked Protein Group FDR method and compared the results to those obtained by MaxQuant’s method , the Savitski method , and rsG with the cT . For the database with the smallest ratio of shared peptides (Swiss-Prot), the pT of the Savitski method led to the expected moderate increase in identified proteins at 1% protein group-level FDR compared to MaxQuant’s method which uses the cT. The same was observed for the Picked Protein Group FDR method, which showed an increase of 4% in the number of identified protein groups relative to MaxQuant’s method. When including isoforms into the analysis (Swiss-Prot+Isoforms), the number of identified proteins using the Savitski method drops by 21%, as observed above. In contrast, this is not the case for methods that use rsG, for which an increase of 4 to 5% in the number of identified protein groups was observed compared to the Swiss-Prot database. This is because one can now identify multiple protein groups per gene. For the Swiss-Prot+Isoforms+TrEMBL database, the number of protein groups at 1% FDR roughly doubled when using the Picked Protein Group FDR method compared to the Savitski method (D). There was also an increase of 4% in the number of identified protein groups by switching from cT to pgT and a 2% increase compared to MaxQuant’s method. However, as demonstrated above, the FDR estimates of MaxQuant’s method are likely not well-calibrated due to the use of rS. Hence, the actual FDR might be higher than the reported 1% on this list of protein groups. In summary, the Picked Protein Group FDR method obtained the highest number of identified protein groups across the three differently sized databases while correctly controlling protein group-level FDR. This led to 15,600 confidently identified protein groups in this human proteome represented by 29 healthy tissues.We analyzed the i method D. There Wang_trap_0.5 and Wang_trap_0.04 datasets. To this end, we estimated the appropriate input parameters for the simulation from the entrapment experiments using the Wang et al. dataset. We then simulated PSMs for the two entrapment searches (4% and 50% shared peptide ratios), where each experiment was controlled at 10% or 1% peptide-level FDR. The results of the different protein group FDR methods for simulated and entrapment datasets were similar for both shared peptide ratios . At the same time, the number of protein groups at 1% FDR for the Picked Protein Group FDR method increased as the number of combined experiments increased . Furthermore, and as expected, using a 10% peptide-level FDR threshold per experiment greatly exacerbated the anticonservative behavior of methods employing rS  when searching against Swiss-Prot+Isoforms+TrEMBL but identified an almost identical number when searching the canonical Swiss-Prot database only . Furthermore, the Picked Protein Group FDR method showed the expected behavior of identifying more protein groups when searching Swiss-Prot+Isoforms+TrEMBL  than Swiss-Prot+isoforms  or Swiss-Prot . At the gene level, we observed the expected and desired behavior that the same genes were identified when searching Swiss-Prot+Isoforms or Swiss-Prot . Searching Swiss-Prot+Isoforms, the Picked Protein Group FDR method resulted in 1230 genes with multiple identified protein groups , the number of identified protein groups was increased by up to 500 (+4%) compared to MaxQuant’s method. The reanalysis of the human section of ProteomicsDB resulted in 15,600 identified genes, similar to the number previously obtained with the Savitski method. Out of these identified genes, 1230 had multiple identified protein groups when searched against the SwissProt database with isoforms included. This number is substantially higher than the 246 genes previously reported by Abascal et al. . These aet al. . Here, wTo resolve sensitivity and calibration issues of the state-of-the-art methods, the Picked Protein Group FDR method introduces rsG for protein grouping and pgT for target-decoy competition of protein groups. rsG extends regular subset grouping with a second protein grouping step in which low-confident PSMs are ignored. This is comparable to the current best practice of applying a global 1% peptide-level FDR cutoff before protein grouping but has the benefit of producing FDR estimates for protein groups without peptides below the peptide-level FDR cutoff. The pgT extends the pT to handle protein groups. It is easy to implement and is identical to the pT when protein grouping is not performed. However, it should be noted that the pgT is a heuristic rule that relies on the similarity in composition, for example, number of proteins and shared peptides, of incorrect target protein groups and decoy protein groups. One can indeed construct  examples where pgT produces undesirable results, for example, an incorrect target protein group consisting of one protein eliminating a decoy protein group with 10 decoy proteins that happen to share one peptide. Nevertheless, we demonstrated here through our calibration experiments that, in practice, the protein groups competing against each other are similar enough to ensure a fair competition and, thereby, accurate FDR estimations.Furthermore, we demonstrated that the use of rS can lead to anticonservative protein group-level FDR estimates. Fortunately, we observed in our simulation experiments that this bias will likely be minor if the best practice of applying a global 1% peptide-level FDR cutoff before protein grouping is used. We acknowledge that rS increase the number of peptides for a protein group and thereby stabilize protein abundance estimates. However, it cannot be guaranteed that the assignment to one of the protein groups in question is indeed correct. Using rS might, therefore, lead to a false sense of confidence in the presence and abundance of specific isoforms.More concerningly for protein quantification, isoform-specific peptides are frequently only identified in a small fraction of samples. This often leads to high levels of missing values for isoforms, regardless of whether rS are used or not. One way to address this issue could be to take the abundances of the peptides shared between isoforms into account , 35. As In summary, the current study presents a method for protein group FDR estimation that is both correct and sensitive. The accompanying software as well as the data simulation scripts are open-source, providing the proteomics community useful new tools to design, develop, and test methods for estimating protein group-level FDRs. The authors also expect that the ability of the software to generate consistent protein group identifications when combining search results from different (and possibly large) datasets will make proteomic experiments more comparable without the need for expending large computational resources.et al. dataset are available on PRIDE (PXD010154). The MaxQuant search results and result files of the Picked Protein Group FDR analysis are available on Zenodo (10.5281/zenodo.7157677). The software and graphical user interface are freely available on GitHub (https://github.com/kusterlab/picked_group_fdr) and as a Python package (https://pypi.org/project/picked-group-fdr/). This includes the scripts to reproduce the figures in this article as well as those for simulating PSMs for large-scale datasets.Raw files for the Wang This article contains M. W. and B. K. are founders and shareholders of OmicScouts GmbH and MSAID GmbH, both operating in the field of proteomics. They have no operational role in either company. All other authors declare that they have no conflicts of interest with the contents of this article.


Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome-wide protein interaction (PPI) data is growing as high-throughput experiments become more common. The aim of this research is that it provides a dual-tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer-based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three-dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double-tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two-dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual-tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure. Protein is an important part of living things because it is made up of 20 types of natural amino acids. Proteins have different secondary structures because the order and number of these amino acids make them unique. Proposals for a classification of protein structures in the literature say that proteins are usually broken down into three main groups: A protein's secondary structure can be broken down into four groups: All-I, All-I, All-I, and All-I+I. The classification of secondary structure is important for more in-depth study of protein tertiary structure and how proteins work together. There has been a big rise in the size of protein databases over the last few years, but experimental annotation of protein structure classification has not kept up. So, there is a real need to make it easier to predict which structural proteins will be found and which ones will not.Proteins are the building blocks of life. They are found in all living cells and play a role in almost all of life's activities. Because most proteins do not do their job alone, they work together with other proteins to form protein complexes . As a reSome experimental techniques, like tandem affinity purification and mass spectrometry (TAP-MS) , 5 and yWith the rise of high-throughput experiments, more and more data about protein-protein interactions (PPI) is being collected every day. This makes it easier to predict protein complexes by using computational methods. Because there are many problems with biological experiment technology, computational methods are being used more and more in this field. They are good because they are fast and cheap. They can quickly predict some protein complexes with a high level of confidence, which makes up for the flaws in biological experiments , 9. TherThere are usually two steps to figuring out how proteins will look in the future. In the first place, proteins of different lengths can be represented by feature vectors of the same length through feature extraction. Then, the feature vectors are put into a specific algorithm for making predictions. Mathematical, statistical, and spectral analysis methods have been used to look at protein sequence features. These methods include amino acid composition characteristics , 3, pseuAfter converting the protein sequence into a distance matrix, it can be seen that it looks like a texture picture. Grey-level cooccurrence matrix and grey-level histogram statistics are used to figure out what it looks like. In this research, the authors have used computational methods for the prediction of protein complexes. The future scope of this proposed method is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. However, the result of the input classifier's classification is not the same. Using the wavelet transform, this paper comes up with a way to get the features out of the distance matrix. The dual-tree complex wavelet transform was created to solve some problems with the traditional dimensional discrete real wavelet transform in image processing , 14. It Furthermore, it uses the six directional subbands that were found at each scale to figure out their energies and standard deviations and build feature vectors based on that. The double-tree complex wavelet transform is used in this paper to finish extracting the features from the transformed protein distance matrix. The following sections show that this method is very good at classifying proteins with secondary structures.The present article has been planned into six sections. The introduction is described in A protein complex is a collection of proteins that gather together to complete a specific biological function or biological process by interacting with each other at a particular time and space. Therefore, protein complexes play a crucial role in the normal functioning of organisms. Common protein complexes include RNA polymerase for RNA synthesis during transcription and proteasome for molecular degradation. Biological methods for detecting protein complexes mainly include tandem affinity purification, mass spectrometry technology, and yeast two-hybrid technology. Below, these two biological methods are briefly introduced.Tandem affinity purification and mass spectrometry are essential tools in current proteomics research. The primary step is to embed a protein tag and introduce the target protein. The proteins that interact with the target protein under physiological conditions can be eluted together and then identified by mass spectrometry technology. The natural conditions under physiological conditions can be quickly obtained protein complexes , 5.Yeast two-hybrid technology detects protein complexes. First, the known coding protein DNA sequence is ligated to an expression vector with transcriptional regulator DNA; then, the introduced yeast cells are combined with the promoter region upstream of the reporter gene. Next, as a “bait” protein, the DNA known to encode the transcription activation domain is ligated with different fragments in the cDNA library to be screened to obtain a “prey” vector; finally, the reporter gene expression is activated, and a protein complex is obtained , 7.It is possible to look at protein complexes directly with techniques like tandem affinity purification, mass spectrometry, and yeast two-hybrid, but there are a lot of false positives and false negatives in the results. Some protein complexes are also hard to find because of the limitations of practical technology. In the postgenome era, there are problems that cannot be solved because they take a long time and cost a lot of money. With the rise of high-throughput experiments, the amount of information about how many different kinds of proteins interact with each other across the whole genome has grown. This has made it easier for computer programs to predict protein complexes.For example, computer-based methods for predicting how proteins work together can make up for the inaccuracies in biological experiments. There are a lot of high-confidence protein complexes that can be found on large biological networks very quickly. In the current computer programs, the relationship between proteins is usually shown as a network that does not go anywhere. This is called G = . All three of these things are called “G,” “V,” and “E.” They all refer to the protein interaction network, “V,” and “E.” At the moment, both domestic and foreign scientists have come up with a number of computer-based algorithms for predicting the structure of protein complexes. This paper breaks down these methods into the following seven groups: based on dense subgraphs, prediction algorithms based on the core-subsidiary structure, and algorithms that use dynamic networks to predict what will happen, learning algorithms that use supervised learning, algorithms that move from function to interaction, algorithms that use data from multiple sources, and other methods can be used to make predictions about how people will act together.There are seven types of algorithms for making predictions. The algorithms based on local dense subgraphs are the oldest and most common of these seven types. Because most proteins need to work together to form complexes to do their jobs in the body, the proteomes in the complexes are nodes in the interaction network, or dense subgraphs. Protein interaction networks have been shown to be modular by a lot of different studies , 5.Network topology shows that modules in a PPI network are made up of proteins that are close together. From a biological point of view, modules in a PPI network are groups of proteins that work together to perform a specific biological task. When you look at the modular structure  in the PPI network, you can figure out what kinds of protein complexes are likely. If the edges in the PPI network are weighted, methods based on dense subgraphs can be broken down into two groups: algorithms based on unweighted networks and algorithms based on additive networks.In 2003, Bader and Hogue came up with the MCODE  method, Network-based clustering algorithms have been used to look for dense subgraphs in protein interaction networks that are likely to be protein complexes –17. For When we look at unweighted protein interaction networks, you only look at whether there is a relationship between two proteins. False positives and false negatives are found in the protein interaction data because of the incompleteness of current biological experiments. Liu et al. devised a method for grouping items associated with the entire graph. The weight of the relevant edge is determined first by the number of known neighbors the node has. This value is then modified repeatedly. The PPI network's whole graph is then identified, and the weight of each edge is determined. This is completed next. Finally, assemble them into a complex based on their scores. The confidence in the unweighted network that was built by this is also low. Because of this, some studies have made the PPI network more reliable by putting more weight on the edges of the PPI network topology, gene expression data, protein function, and other information, and on this basis, they came up with a weighted network-based complex prediction algorithm.Weighted networks were made in the early days by figuring out how many proteins that interact with each other have neighbors who know each other . Literatα atoms of protein sequences in both datasets. In the process of downloading the data, it was found that some protein data in the literature did not exist in the PDB database, so this paper selected the complete protein sequence of the three-dimensional data of the Cα atom and finally obtained the first dataset containing a total of 197 proteins, of which 48 All-α class, 60 All-β, 45 α β class, and 44 α+β class, from now on referred to as dataset A; the second dataset contains a total of 1 656 proteins, of which 440 All-α class, 437 All-beta classes, 342 alpha-beta classes, and 437 alpha+beta classes, after this referred to as dataset B. The secondary structure data of each protein contained in the two datasets are shown in Two datasets are used in this paper, from the literature  and liteα atoms to calculate the distance between all Cα atoms on each protein chain to form a matrix, so that the matrix contains sufficient 3D structural information of the protein structure in addition to the chirality [The key to establishing the prediction method is to extract the protein sequence features and use the protein backbone to describe its secondary structure, that is, use the three-dimensional coordinates of the Chirality . TherefoL ber1, r2, ⋯rn represents the protein sequence Psi amino acid residues. So then, its skeleton can be defined ascαli is the three-dimensional coordinate vector of the Cα atom of the Lth amino acid residue, and then the protein sequence Pi is converted into a distance matrix DM = {dmi = dist1 ≤ m, n ≤ L}, where dist is the Euclidean distance.Let the protein sequence psi of length In this paper, the distance matrix of proteins is regarded as a texture image; that is, each element in the matrix corresponds to an image pixel, and the value of each element is mapped to the grey value of the corresponding pixel. It can be seen from The texture is important information and feature of an image, and it is an effective method to use the texture feature of an image to classify. The methods of extracting image texture features include grayscale histogram, grayscale cooccurrence matrix, and wavelet transform-based methods. Still, the grayscale histogram of the image only counts the first-order information of the picture, and the grayscale cooccurrence matrix only describes the coarse granularity. Because of the texture characteristics, two-dimensional wavelet transform has the defects of translation change and limited direction selectivity. The dual-tree complex wavelet transform proposed by Kingsbury  has apprThe two-dimensional dual-tree complex wavelet is defined asi is an imaginary number, i2 = −1, and ϑi and ϑj are orthogonal or biorthogonal real wavelets, respectively, and form a pair of Hibert transforms. Dual-tree complex wavelet transform can be implemented by discrete wavelet transform DWT, one DWT produces the real part and the other DWT produces the imaginary part. Its decomposition process is shown in where Wln at each scale, where l = 1,2,3,4, and n = 1, 2,3,4,5,6. Calculate the energy El, n, and the standard deviation σ according to equations  and μl, n is the mean of wln. Using a combination of standard deviation and energy features, the following 48-dimensional feature vector is obtained:As can be seen from F can be obtained for protein sequences of different lengths.After the protein sequence is transformed through the above steps, a 48-dimensional feature vector K is 5. Tenfold cross-validation is adopted for each experiment, and the average value of the results of the five experiments is calculated as the final result. After calculating the two feature vectors, the normalized Euclidean distance metric method in [Extract the features of the proteins in the two datasets according to the above method, and input them into the KNN classifier, where ethod in  is used.To test the prediction performance of the method, this paper adopts five indicators: sensitivity, specificity, accuracy, Mathew's correlation coefficient, and overall accuracies.To test the prediction performance of the method, this paper adopts five indicators: sensitivity, specificity, accuracy, Mathew's correlation coefficient, and overall accuracies.α and All-β in dataset A, the specificity reaches 100%. This is because the dual-tree complex wavelet transform uses two trees to transform the image, enhancing texture information expression [Extract the statistical features of the grey histogram ; that isExtract the grey-level cooccurrence matrix features; that is, calculate the grey-level cooccurrence matrix in the three directions of 0°, 45°, and 135° according to the distance matrix and then calculate the contrast, correlation, and energy of the grey-level cooccurrence matrix in each direction and uniformity of these four features, and finally get a 12-dimensional feature vectorK in KNN is all 5. Tables Extract the wavelet energy feature; that is, use the sym4 wavelet packet to decompose the distance matrix in four levels and calculate the energy percentage Ea of the corresponding approximate coefficient and the corresponding horizontal detail coefficient Eh, vertical coefficient Ev, and diagonal detail coefficient energy percentage Ed; these results in a 13-dimensional feature vector. The above feature vectors are input into the KNN classifier, respectively, for classification, and the value of According to those mentioned above dual-tree complex wavelet feature extraction and KNN classification method, the prediction results on datasets A and B are shown in Tables pression . For theIt can be seen from Tables To prove that the effect of the feature extraction method in this paper does not depend on the classification algorithm, SVM classifier classification is also used in the experiment. With the help of an easy-to-use and fast and effective SVM software package LIBSVM developed and designed by Professor Lin Zhiren of the National Taiwan University, the SVM in main parameters  are obtained by grid search method, RBF is selected as the kernel function, ten-fold cross-validation is adopted in each experiment, and the average of five experimental results is calculated as the final result.As can be seen from As can be seen from Figures In this paper, a dual-tree complex wavelet transform is used to find out about the structure of proteins. For the texture information in the distance matrix of proteins, the dual-tree complex wavelet transform is used because it has good direction selectivity and very little data redundancy. This study introduces a new feature known as a dual-tree complex wavelet, which improves the texture granularity and directionality of the standard feature extraction approach known as secondary structure. The dual-tree complex wavelet transform uses two trees to transform the image, enhancing texture information expression. It is important to avoid the problem that the grey cooccurrence matrix of a traditional extracted image does not show how the overall spatial distribution of the texture of the image looks, so KNN and SVM are used to classify the extracted feature vectors. Classification has been checked, and the prediction results look good. They reached 98.50 percent and 99.29 percent, respectively, when they used the SVM to classify the two datasets. There are a lot of ways to look for features in protein sequences, like the classic pseudoamino acid component method. When we look into the future, we can try to improve the traditional process, combine it with this method, and use it with protein data that has texture features.


While a number of computational approaches have been proposed to explore potential biological interactions, they have been limited to specific interactions, and have not been readily accessible for non-experts or use in bioinformatics pipelines. Here we present CSM-Potential, a geometric deep learning approach to identify regions of a protein surface that are likely to mediate protein-protein and protein–ligand interactions in order to provide a link between 3D structure and biological function. Our method has shown robust performance, outperforming existing methods for both predictive tasks. By assessing the performance of CSM-Potential on independent blind tests, we show that our method was able to achieve ROC AUC values of up to 0.81 for the identification of potential protein-protein binding sites, and up to 0.96 accuracy on biological ligand classification. Our method is freely available as a user-friendly and easy-to-use web server and API at   Recent breakthroughs in protein structure prediction by AlphaFold ,2 and RoSignificant efforts have been made towards identification of interacting sites on proteins. For prediction of PPI binding sites previous methods have ranged from those using only information about the protein sequence as input ,6, to mohttp://biosig.unimelb.edu.au/csm_potential.In this study, we present CSM-Potential, a geometric deep learning approach to identify areas of a protein surface that are likely to participate in protein-protein interactions (PPIs) and protein–ligand interactions Figure . Our metExperimentally characterised structures of PPI complexes were extracted from the PRISM database of non-redundant PPIs  for a toThe database used in this work for biological ligand classification was compiled based on all structures cofactor-binding proteins available in the Protein Data Bank (PDB)   on interaction. Here, we consider as part of the interface the region of the surface that becomes inaccessible to solvent molecules upon complex formation. This was done by comparing the difference in SASA, at the residue level, between the individual protomers (unbound protein) and within the complex as follow :\documenResidues at the surface of the unbound partners which were not at the surface on the bound complex (solvent inaccessible) were then defined as being part of the PPI interface.For our final dataset of protein–ligand interactions and following previous work , after shttp://biosig.unimelb.edu.au/csm_potential). The server back end is developed using Python via the Flask Framework (version 1.0.2), while the front end was built using Materialize framework version 1.0.0. The web server is hosted on a Linux Server running Apache2.We have implemented CSM-Potential as a user-friendly and freely available web server  and To train a predictive model to explore biological ligand binding sites, here we used the training and validation set described in the Materials and Methods section. Performance on the validation set was assessed at each epoch  and used to select the best network after each epoch. Finally, we sampled each pocket 100 times and averaged the resulting 100 predictions to obtain the final prediction, similar to previous work. At the end of our training procedure, our approach achieved a balanced accuracy of up to 0.75 and ROC AUC of 0.87.Here, we compared the results of our PPI binding site predictive model with those reported for MaSIF-site  on all 3For assessing the performance of our predictive model for the classification of biological ligands binding sites, we first investigated how accurate CSM-Potential performed on the blind-test for each specific ligand separately  as an easy-to-use webserver and API at gkac381_Supplemental_FileClick here for additional data file.


The widespread clinical application of genome-wide sequencing has resulted in many new diagnoses for rare genetic conditions, but testing regularly identifies variants of uncertain significance (VUS). The remarkable rise in the amount of genomic data has been paralleled by a rise in the number of protein structures that are now publicly available, which may have clinical utility for the interpretation of missense and in-frame insertions or deletions.Within a UK National Health Service genomic medicine diagnostic laboratory, we investigated the number of VUS over a 5-year period that were evaluated using protein structural analysis and how often this analysis aided variant classification.We found 99 novel missense and in-frame variants across 67 genes that were initially classified as VUS by our diagnostic laboratory using standard variant classification guidelines and for which further analysis of protein structure was requested. Evidence from protein structural analysis was used in the re-assessment of 64 variants, of which 47 were subsequently reclassified as pathogenic or likely pathogenic and 17 remained as VUS. We identified several case studies where protein structural analysis aided variant interpretation by predicting disease mechanisms that were consistent with the observed phenotypes, including loss-of-function through thermodynamic destabilisation or disruption of ligand binding, and gain-of-function through de-repression or escape from proteasomal degradation.We have shown that using in silico protein structural analysis can aid classification of VUS and give insights into the mechanisms of pathogenicity. Based on our experience, we propose a generic evidence-based workflow for incorporating protein structural information into diagnostic practice to facilitate variant classification.The online version contains supplementary material available at 10.1186/s13073-022-01082-2. In an era of rapidly expanding genomic data, the interpretation and classification of novel missense variants remains a perennial challenge for diagnostic laboratories. Over the years, numerous in silico tools have been developed to assess the impact and pathogenicity of amino acid substitutions and aid variant classification. These tools have developed broadly along two lines : first, https://www.rcsb.org). Currently, around 17% of residues in the human proteome are represented in experimentally solved protein structures, although the use of comparative modelling can increase the proportion of residues for which reliable structural analysis can be performed to around 50% [The great advances in genomic medicine over the last decade have been paralleled by a concomitant rise in the number of experimentally derived protein structures publicly available in the Research Collaboratory for Structural Bioinformatics (RSCB) Protein Data Bank (PDB)   genomics laboratory over a 5-year period following implementation of the American College of Medical Genetics and Genomics (ACMG) and Association of Molecular Pathologists guidelines on variant classification , and subAll variants were identified in patients referred to the Exeter Genomics Laboratory  from 2016 to 2020 for genetic testing in a variety of conditions. Informed consent for genetic testing was provided by patients or their parents in accordance with the requirements for clinical diagnostic testing provided through the NHS. All testing was conducted in accordance with the Declaration of Helsinki. Variants were detected by targeted next generation sequencing either of whole exomes or of custom gene panels. For cases referred to the laboratory for panel analysis, a clinical summary was reviewed by a healthcare scientist prior to panel selection to ensure that the referral was appropriate and adhered to any relevant best-practice guidelines. For cases referred to the laboratory for exome sequencing, the case was reviewed by a registered clinical scientist prior to testing to select cases with a likely monogenic disease aetiology. Following testing, all results were discussed with the referring clinician before a report was issued. Probes for in-solution capture and enrichment of target sequences were supplied either by Agilent Technologies  or Twist Bioscience  and used according to the manufacturer’s instructions. Sequencing was carried out on an Illumina NextSeq500 or 550 instrument using 150-bp paired end reads . Variants were called in human genome build GRCh37 and annotated using Alamut Batch software . Following evaluation by clinical scientists, variants received a preliminary classification based on the available evidence according to current ACMG/ACGS guidelines. In cases where variants were classified as VUS, we then employed the option of referral for further in-house analysis of protein structure to gain insight into the likely impact of the variant. All variants reported in this manuscript have been deposited in the DECIPHER database .https://www.uniprot.org) entry for the protein in question, which provided links either to experimental data for the protein itself in the PDB database [https://swissmodel.expasy.org/repository). Where no experimental or pre-calculated models were available, protein sequence data was submitted to one or more of the protein modelling servers, SWISS-MODEL [https://swissmodel.expasy.org), Phyre2 [http://www.sbg.bio.ic.ac.uk/phyre2) and I-TASSER [https://zhanggroup.org/I-TASSER), for identification of suitable templates for comparative modelling and subsequent construction of predicted models. For comparative single-templated modelling using the SWISS-MODEL server, models were normally constructed using the template yielding the highest overall quality score (GMQE) over the residue of interest and meeting general requirements for reliable prediction . For models generated by multi-templated modelling, only residues predicted with ≥ 90% confidence were used in further analyses. In all cases, protein structures were downloaded as PDB files, and the structural context of the residue(s) affected by the variant investigated in detail using Swiss-PdbViewer [G) > 3 kcal/mol was regarded as severely destabilising, 1–3 kcal/mol as destabilising and < 1 kcal/mol as neutral or benign.For most of the period covered by this analysis, the availability of structural data was queried via the Universal Protein Knowledgebase (UniProtKB)  , which provides integrated annotation of experimental structural data for a protein; VarSite [https://www.ebi.ac.uk/thornton-srv/databases/VarSite) and VarMap [https://www.ebi.ac.uk/thornton-srv/databases/VarMap), which provide data on structural conservation and homology-based annotation of residue function; and Missense3D [http://missense3d.bc.ic.ac.uk/missense3d), which performs in silico mutagenesis, either from a user-specified PDB file  or from generation of a comparative model using the Phyre2 server, and then evaluates the effect of missense variants on protein structure by a number of criteria. These tools are now routinely used as part of our structural analysis workflow.Later in the period covered by this analysis, additional online tools became available that have been designed to integrate and streamline parts of the above workflow. These include PDBe-KB  (https:/ VarSite  (https:/d VarMap  (https:/ssense3D  (http://ARSL) in which evidence was applied under the PP3 criterion remained as VUS. Using 3-dimensional (3D) protein modelling within PM1 enables precise positioning of a variant within the functional region of a protein and may also offer mechanistic insights about how a variant alters domain function. We incorporated the use of FoldX into our routine analysis pathway in February 2017, in order to assess the impact of variants on the thermodynamic stability of protein structure in 80 of the cases listed; this tool was used to analyse 80 variants, and reported ΔΔG values are included in Additional file From cases referred to the Exeter Genomic Laboratory for genetic diagnosis , we identified a total of 99 unique rare or novel variants  for which protein structural analysis was performed. In all cases, these were variants for which there was support for pathogenicity from one or more other strands of evidence  but which remained as VUS following preliminary classification  or supporting (34) based on professional judgement, while 17 cases remained classified as VUS. No variants were reclassified as benign or likely benign following structural analysis. This was largely due to ascertainment bias in the cohort of variants for which structural analysis was requested, all of which had some existing evidence in support of pathogenicity. However, due to the intrinsic limitations of comparative modelling, whereby an apparent lack of structural damage in silico does not necessarily constitute positive evidence against the possibility of such an effect in vivo, we exercised a conservative approach when applying modelling data to variant classification to minimise misclassification. A summary of evidence from structural analysis of each variant, and whether this was or was not used in the final classification, is included in Additional file Novel variants are by definition unique and uncharacterised, so their analysis is inevitably highly context-specific and can require a high degree of manual intervention and curation. We therefore developed a standardised, evidence-based workflow for the systematic analysis of structural and sequence data as an aid to diagnostic classification of genetic variants Fig. . AlthougLoss-of-function is a common mechanism of disease, but in contrast to protein-truncating variants that result in nonsense-mediated decay, it can be difficult to evaluate whether missense or in-frame deletion/insertion variants will result in impaired protein function. However, thermodynamic destabilisation is a generic mechanism through which missense changes can cause misfolding and/or degradation of a folded protein domain , 28, whiCASR variant NM_000388.3:c.488C > G, p.(Pro163Arg), which was identified in a patient referred for genetic testing for Familial Hypocalciuric Hypercalcaemia (FHH). The proband presented with hypercalcaemia and no evidence of parathyroid adenoma or hyperplasia. A calcium infusion test strongly supported a diagnosis of a calcium-sensing receptor disorder rather than primary hyperparathyroidism [CASR encodes the calcium-sensing receptor CaSR, a G protein-coupled transmembrane receptor which senses and regulates the level of extracellular calcium. Inactivating variants in CASR cause numerous hypercalcaemic disorders of differing severity, including FHH and neonatal severe hyperparathyroidism, through a loss-of-function mechanism. Conversely, specific activating missense variants cause the opposite phenotype, hypocalcaemia (Bartter syndrome type V) through a gain-of-function mechanism [G values for the variant as ~ 13 kcal/mol in both the active and inactive forms, i.e. an extremely destabilising change [An example of the value of this analysis was yroidism . The hetechanism . Experimechanism . Inspectg change . This reGNAO1 NM_020988.2:c.980C > A, p.(Thr327Lys), identified in a child referred for exome sequencing with global developmental delay, central axial hypotonia and hypermobility. GNAO1 encodes a 354-residue member of the Gα family of guanine nucleotide-binding proteins, and Thr327 is annotated as lying in the last of five guanine-nucleotide binding motifs, or G-boxes. Although its precise function is unclear, pathogenic variants in GNAO1 are known to cause early infantile epileptic encephalopathy type 17 (EIEE17) [Another potential mechanism by which missense variants can cause loss-of-function is through disruption of ligand binding, which can most easily be determined through evaluation of the protein structure. One example of this mechanism was a novel heterozygous variant, (EIEE17) , and PDB(EIEE17) . Modelli A, p.Thr27Lys, id A, p.Thr27Lys, idIn many cases, the benefit of protein structure modelling in variant classification—above and beyond assessing pathogenicity—is to provide evidence supporting a mechanism of pathogenicity for the variant that is consistent with the known mechanism of that disease. Unlike loss-of-function, however, variants that cause gain-of-function are often hard to interpret as their effects on structure are likely to be more variable, potentially more subtle, and may require retention of the active form and structure of a protein. Moreover, there is some evidence that pathogenicity predictors generally perform worse for non-loss-of-function mechanisms , 39, so MAP2K1 NM_002755.4:c.149 T > C p.(Leu50Pro), which was identified as a de novo variant in an infant presenting with facial dysmorphism , skin abnormalities, joint hypermobility, macrocephaly and global developmental delay. Variants in MAP2K1 are known to cause cardiofaciocutaneous syndrome 3  [MAPK1 encodes MAP/ERK kinase 1 (MEK1), which is activated in response to a variety of extracellular signals, and mutagenesis experiments have demonstrated the importance of a short region in the N-terminal of the protein, lying outside the catalytic domain, in maintaining the kinase in an inactive basal state [MAP2K1, namely p.(Phe53Ser) [G value of 4.4 kcal/mol, predicting the variant to be severely destabilising to this region of the protein. A value of 2.7 kcal/mol was predicted for the nearby CFC3-associated variant, p.(Phe53Ser) variant, indicating that the p.(Leu50Pro) variant likely has a similar effect in vivo, consistent with disease mechanism and the observed phenotype. Furthermore, an analysis of all variants reported in the Human Gene Mutation Database (HGMD) [One example where limited destabilisation can lead to gain-of-function was that of a novel heterozygous missense variant, 615,279) , in whical state , 42. Thihe53Ser) . The cryhe53Ser) . Residuee (HGMD)  showed tWNK1 gene, NM_018979.3:c.1903G > A, p.(Asp635Asn), which was identified in a patient with a family history of hyperkalaemia and a suspected clinical diagnosis of Gordon’s syndrome. WNK1 encodes a serine/threonine kinase that plays an important role in the regulation of electrolyte homeostasis, cell signalling, survival and proliferation, and specific missense variants can cause pseudohypoaldosteronism type IIC , characterised by hypertension and hyperkalaemia, though with normal renal function, and metabolic acidosis [WNK1 missense variant reported in association with PHA2, namely p.(Glu630Lys) [WNK4 , which cause a related form of PHA2, have been shown to exhibit decreased interaction with KLHL3 [WNK1 also affects the degron motif, suggesting a common mechanism of action of these variants. Moreover, around the time of our analysis, a series of pathogenic variants within the degron (or acidic motif) of WNK1 were reported in a cohort of patients with inherited hyperkalaemic hyperchloremic acidosis [A gain-of-function can also occur because of an increased level of active protein, either through increased protein production or reduced protein degradation. An example of this mechanism was that of a novel variant in the acidosis , 46. At u630Lys) , and no u630Lys) –51. Manyu630Lys) , 53. Impu630Lys) , 54–57. u630Lys)  and Scanu630Lys)  to searcu630Lys) . Variantth KLHL3 –63, whilacidosis , which iIn many cases, a genetic variant can be classified as pathogenic/likely pathogenic, or benign/likely benign using standard automated or semi-automated pipelines or evidence-based algorithms , 18 withProtein modelling typically requires a substantial level of manual intervention and data curation, and thus, we were prompted to develop a systematic and standardised framework for the incorporation of data from structural analysis into the variant classification pathway Fig. . The proOur first step in using structural analysis is to determine whether the position of interest is present in an experimental protein structure, as this will normally represent the highest quality of data for further analysis  and analysed in 3D molecular viewers such as PyMOL or Swiss-PdbViewer. Further analysis of the effects of variants on protein structure can be assessed using tools such as FoldX, although it should be noted that FoldX assumes exposed protein surfaces to be in contact with water, and therefore particular caution should be applied when analysing variants within transmembrane regions. Other analysis tools such as DynaMut2 , Venus  and Misshttps://alphafold.ebi.ac.uk).Where experimental structures do not exist, there may be existing predicted models of the human protein which are of sufficient quality for further analysis , with higher levels of identity leading to higher quality of modelling. Alignments between template and query sequence should also be inspected to ensure that the residue of interest is actually modelled directly onto the template sequence, rather than being inserted into the structure as a loop of low confidence prediction. In the case of multi-templated models produced by Phyre2, AlphaFold or other methods, modelled sequences are annotated with a quality or confidence score, and this should always be taken into account when using the models. In the case of Phyre2 models, we normally exclude residues which have been modelled at < 90% confidence, while the recommendation for AlphaFold models is to exclude residues with a pLDDT score < 70. Moreover, in multi-domain proteins, in which individual domains are often joined by flexible linkers, there may be considerable error in how AlphaFold and other methods predict the manner in which these domains interact in 3D space. In AlphaFold models, this uncertainty is expressed in the ‘predicted aligned error’ metric, and should be taken into account particularly when assessing the potential impact of variants which may lie at inter-domain interfaces.One potential limitation of models predicted by deep-learning methods is that they do not include ligands and typically provide only a single ‘best’ structure for the protein sequence in question. As discussed above, many proteins exist in multiple conformations depending upon ligand binding, post-translational modifications or other processes, and where experimental structures occur it may be informative to perform variant modelling in these different structural forms to fully understand the impact on the protein and its activity. For example, in silico analysis of the hypomorphic ABCC6 variant p.(Arg391Gly) using structures of multiple conformations of the related ABCC1 protein revealed a conformation-specific effect of the variant, thus identifying a molecular basis for a defect in transporter function and explaining the variant’s association with late-onset pseudoxanthoma elasticum . AnotherFollowing template selection and homology modelling, PDB files can then be analysed in essentially the same way as experimental structures. In the case of FoldX, while this tool has not been formally validated for use on comparative or homology models, it has been shown that such models can provide similar utility to experimental structures in the analysis of variant impact , and thaNotwithstanding the ever-expanding resources of experimental and predicted structural data for a protein, there may still be cases where no high-quality models are available over the residue(s) of interest. This may occur either where the protein sequence shares very little homology with proteins or domains of known structure, or where there is in fact no stable underlying structure, i.e. the protein or region is intrinsically disordered. However, this does not necessarily preclude further analysis, as the tendency for a protein sequence to adopt either a stable secondary structure  or to be intrinsically disordered can be predicted with reasonable accuracy from sequence alone. For the former, tools such as PredictProtein , PSIPREDHBB p., which causes sickle cell anaemia. A number of bioinformatic tools have been developed to help predict protein aggregation [Aside from the availability of relevant structures, there are some notable limitations to using a protein structural approach in a clinical diagnostic setting. Although many missense variants will have only a very minor effect on the protein and be insufficiently deleterious to result in a phenotype, some variants that appear in silico to have little or no deleterious effect on the structure of a fully folded mature protein might nevertheless have a major impact on protein folding and/or function in vivo. Variants that lie at the surface of a protein could potentially affect protein–protein interactions, but their impact is likely only to be fully understood either if there is detailed information about the nature of the interaction, or a structure for the relevant protein complex. Searching for such data has recently been greatly facilitated by the development of the PDBe-KB server, which collates data from all known structures of the protein, including sites of ligand and macromolecular interactions, and the VarMap and VarSite databases, which provide functional annotation based on structural homology. Alternatively, surface substitutions may lead to the creation of hydrophobic patches which may result in protein aggregation, such as in the case of the haemoglobin S variant, regation , though regation , and theWe have shown that 3D structural analysis of proteins provides additional clinical utility in the classification of missense variants, particularly in cases of poorly studied genes or those with few known pathogenic variants. We identified numerous missense variants that are predicted to cause complete loss-of-function, either through affecting ligand binding or by severely destabilising the protein; we also identified missense variants that were predicted to cause gain-of-function in critical functional areas of a protein domain. Such evidence can be used directly in variant classification through PM1 in the existing ACMG/ACGS guidelines and is particularly important for determining whether the predicted variant effect is consistent with the known disease-gene mechanism and observed phenotype. In the expectation that this type of analysis will become more widespread, we have outlined some of the most relevant software tools for use in a diagnostic context and presented a generic workflow for implementation of structural analysis in the diagnostic pathway. We hope this work will catalyse the development of a standardised, best practice approach and that wider use of protein structural analysis will ultimately lead to fewer variants remaining classified as VUS.Additional file 1. List of variants subjected to structural analysis, with details of protein modelling, effects observed and contribution of evidence to final variant classification.


Epistasis is a term originating from genomics and describes the non-additivity of effects of gene interactions on functional parameters . Both wiIn protein sciences, epistasis reflects non-linearity effects, ie., non-additive impacts, resulting from interactions between mutations within a protein sequence . The evoThe prediction of protein structure has been an on-going challenge for computational methods, including artificial intelligence. However, the recent development of structure prediction deep learning (DL) tools such as alphafold2 , ESMfoldAfter protein folding powered by DeepMind, Meta and/or Baker’s team, the next challenge is to accurately predict epistasis ie., the impact of non-linear interactions of mutations within the protein sequence. A recent review of machine learning and deep learning strategies examines how epistatic effects influence the success rate of protein engineering projects by comparing fifteen state-of-the-art approaches and provides a general workflow for non-experts when using such learning strategies .The aim of this Research Topic is to give an overview of recent advances and to discuss the current understanding of epistatic phenomena. Particular attention is paid to better understanding and modeling of epistatic phenomena that can impair the prediction of a property of interest.The articles in this Research Topic illustrate the role of non-linear interactions between players through four particularly interesting examples: negative epistasis for an RNA enzyme; higher-order interactions in complex phenotypes revealed by the BowSaw tool; synergistic effect between Lysine acetylation levels, mSWI/SNF activity and BRD9 inhibition in many cellular contexts, and positive epistasis during the acquisition of resistance of pathogenic bacteria to antibiotics.in vitro evolution). Moreover, extensive pairwise and higher-order epistasis prevent straightforward prediction of the effect of multiple mutations that is needed for rational design. Beck et al. used high-throughput experimental data from variants of a self-cleaving ribozyme to train a predictive model through machine learning approaches. Using only sequence and activity data, they showed that a machine learning approach can be used for RNA design even for RNA molecules with unknown structures. This work is very important because self-cleaving ribozymes can be used to engineer control of gene expression thanks to their capacity to alter RNA processing and stability. Beck et al. unveiled negative epistasis in the RNA data suggesting that additional information, eg. thermodynamic stability of helices, might be necessary for increasing accuracy when predicting effects of long-range distance mutations. This will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.Machine learning approaches based on large experimental datasets allow the behavior of some complex biological systems to be predicted. As we know, the rational design of RNA enzyme (ribozyme) activity is challenging, and many ribozyme-based systems are only engineered or improved by random mutagenesis and selection  model in order to extract multiple candidate explanatory rules. Applying BowSaw to a study on the role of the gut microbiome in Crohn’s disease, DiMucci et al. shows that it can find a previously unreported combination of microbial taxa that is broadly and precisely associated with Crohn’s disease samples in the data set: they unveil higher-order interactions in complex phenotypes.Machine learning also enables the inference and classification of cellular, organismal and ecological phenotypes based on large datasets, e.g., from genomic, transcriptomic and metagenomic analyses. Loehr et al. have created a cellular model allowing the study of lysine acetylation-dependent protein interaction networks. Using cell lines in which histone acetylation was dependent on acetate supplementation, they showed that the loss of lysine acetylation remodels the composition of the chromatin and the protein-protein interactions of BRDs. Their model may help to understand the functional characterization of BRD-containing proteins across distinct cellular contexts. Synergy between Kac levels, mSWI/SNF activity and BRD9 inhibition appears to be present in many cellular contexts. Loehr et al. created an inexpensive and flexible cellular model allowing the study of Kac-dependent protein interaction networks.Protein-protein interaction networks are often affected by post-translational modifications. Since proteins rarely act alone, these interactions and their regulation play key roles in a cell. For example, acetylated histones not only affect chromatin condensation but also act as anchor points for bromodomain (BRD)-containing adapter proteins. These domains, and the proteins containing them, can act as chromatin scaffolds that organize large interaction networks which regulate transcription. Mehta et al. showed that in the case of the pathogen Francisella tularensis, exposure to two different drugs, sequentially or in combination, resulted in a generalist mutation followed by further mutations that alternated between adaptation to one drug or the other, hence overcoming resistance. Mehta et al. showed that clonal interference, weak pleiotropy and positive epistasis also contributed to combinatorial evolution. The finding suggests that the use of this non-interacting drug pair against F. tularensis may render both drugs ineffective because of mutational switch-backs that accelerate evolution of dual resistance. A better understanding of how drug combinations affect adaptation to multi-drug resistance is required to help overcome antibiotic resistance.The increasing resistance of pathogenic bacteria to antibiotics is a major challenge to the health of humans, livestock and wildlife. One approach to overcome resistance is to use drug combinations. However, Our understanding of how to account for the effects of genetic variation and in particular the impact of non-linear interactions both within and between genes, is still in its infancy . As show


Our previously published tools implemented our method of normalized phylogenetic sequence profiling to detect functional associations between non-homologous proteins. However, many proteins consist of multiple protein domains subjected to different selective pressures, so using protein domain as the unit of analysis improves the detection of similar phylogenetic patterns. Here we analyze sequence conservation patterns across the whole tree of life for every protein domain from a set of widely studied organisms. The resulting new interactive webserver, DEPCOD , performs searches with either a selected pre-defined protein domain or a user-supplied sequence as a query to detect other domains from the same organism that have similar conservation patterns. Top similarities on two evolutionary scales (the whole tree of life or eukaryotic genomes) are displayed along with known protein interactions and shared complexes, pathway enrichment among the hits, and detailed visualization of sources of detected similarities. DEPCOD reveals functional relationships between often non-homologous domains that could not be detected using whole-protein sequences. The web server is accessible at   A shared evolutionary history of two proteins across various organisms may suggest similar functions, shared cellular pathways and protein complexes, or functional interactions between these proteins regardless of whether they are homologous to each other . Our firPhylogenetic profiling depends on detecting conservation between individual representatives of the same sequence family in taxonomically distant organisms. However, different domains within the same protein often evolve under different evolutionary constrains and occur in various combinations in different species, especially between taxa at higher levels of taxonomic hierarchy. Protein domains are the functional modules of proteins that can fold, function, and evolve often independently of other domains in the same protein. Variation of particular protein domains or even abrupt changes of domain architecture during evolution may often be due to the relaxation of past functional requirements and changing evolutionary pressures on domain function as organisms specialize for new niches or evolve displacing pathways. These differences of conservation patterns among different domains of the same protein reduce the level of sequence similarity in the analyses of conservation at the level of the whole protein. Therefore, focusing on protein domains as independent evolutionary units should bring more biological relevance and clearer correlations in detecting similar patterns of sequence conservation. Every protein domain in a given genome can be assigned a phylogenetic profile of its relative conservation, variation, or loss based on its sequence similarity to the homologs across hundreds of diverse eukaryotic and prokaryotic species. This phylogenetic pattern can be used to search for non-homologous protein domains with a similar pattern of conservation or loss. This general approach has been discussed and implemented previously, but mainly in the context of domain identification within protein sequences  and predHere, we developed a new interactive web server, DEPCOD , which allows the user to submit a query protein domain from an individual protein in a selected organism and (a) instantly identify the taxa of organisms that have conserved, varied, or lost this domain; (b) detect other protein domains in the same organism that have a correlated pattern of sequence conservation across a wide range of taxonomically diverse species; (c) understand functions of these domains, known physical interactions and shared protein complexes with the query and (d) inspect possible sources and evolutionary relevance of the similarity between their conservation patterns.Eukaryota, Bacteria, and Archaea; (ii) incorporation of phylogeny of the searched genomes into the correlation of conservation patterns; (iii) visualization of known BioGRID  expanded scope of species in evolutionary profiles at two scales: 244 eukaryotic genomes or 506 genomes from all three domains of life:  BioGRID  and hu.M BioGRID ,26, KEGG BioGRID ,28 and R BioGRID  pathway H. sapiens, M. musculus, D. melanogaster, C. elegans, S. cerevisae, A. thaliana, E. coli, B. subtilis), we split the whole sequence of each protein into PFAM domains  for each domain across a wide range of sequenced genomes in a fashion similar to our previously described approach ,9. In brZ-scores. We chose Pearson R over an alternative of Spearman correlation coefficient (a more robust but less sensitive measure of correlation) since in our tests Pearson R produced more accurate and functionally relevant results. As DEPCOD profiles are based on a larger number of target genomes, we introduced a new modification into the calculation of Pearson correlation coefficient, which downweighs closely related species among 506 genomes by weighing each target genome proportionally to the number of genomes sampled from the same taxonomic clade in the NCBI taxonomy ) and recall (TP/(TP + FN)) were calculated for each position n in the list, where TP, FP and FN are the numbers of true positives, false positives, and false negatives among top n hits in the list.To evaluate the accuracy of detection of functional associations, we generated precision/recall curves using protein pairs sharing the same functional pathway as a reference of true associations. We used GO biological process level 3 ,26, KEGGEukaryota, Bacteria and Archaea), whereas ‘Eukaryotes’ option is focused on eukaryotic species, which would be more relevant for eukaryote-specific query domains or for more detailed analysis of conservation patterns among eukaryotes only.DEPCOD webpage  includesH. sapiens, M.musculus, D. melanogaster, C. elegans, S. cerevisae and A. thaliana for the search among profiles based on eukaryotic genomes, with the addition of bacterial genomes of E. coli and B. subtilis for the search among profiles based on the whole tree of life. After the organism is selected, the user can use the autocomplete search menu to quickly choose the query domain from the pre-defined list of protein domains in the given genome. The user can also select the number  of top correlated protein domains to display in the output. Alternatively, the user can provide an arbitrary amino acid sequence as a query, either by pasting this sequence in the window or by uploading a sequence file.The area on the left of the webpage  includesClicking the Submit button starts the search and generates the output in the main area of the webpage , which iZ-scores of their statistical significance (‘Significance Score’) are shown by color in two leftmost columns of the heatmap adjacent to the hit names. The third column (‘Correlated and significant’) highlights the most confident hits which satisfy the suggested cutoffs of Pearson R > 0.6 and significance Z-score > 4.0 as a combined criterion of a confident profile similarity, based on extensive manual inspection of DEPCOD results. However, the user is encouraged to carefully inspect a wider range of hits beyond the ones highlighted in this column for possible functional associations with the query.Examples of DEPCOD output are shown in Figures Mouseover of hit names displays the description of the protein and the amino acid positions of the given domain with the protein. Clicking on a hit name redirects to the corresponding entry for this specific sequence in the PFAM database, which allows for a deeper inspection of this individual domain of a given protein, the PFAM domain family that it belongs to, and its biological function. The heatmap in shades of blue on the right displays normalized similarity scores for a given domain across all genomes in the chosen evolutionary range. Taxonomic tree on top is derived from NCBI Taxonomy database ,32 and hAs another new functionality in DEPCOD, the enrichment of functional protein categories and pathways Figure  is displThe URL for the whole result webpage can be quickly copied for the user's records or sharing using button ‘Share’ in the top section of the page.In the example of DEPCOD output shown in Figure The focus of DEPCOD on individual domains improves phylogenetic profiling by preventing possible effects produced by the changes of domain architecture that are frequent in the evolution of multidomain proteins. This focus allows DEPCOD to detect profile similarities between functionally associated domains that are challenging to detect from whole-protein sequences. As an example, the kinesin domain of STARD9 protein is a part of multidomain protein architecture that varies among protein homologs across different species. As shown in Figure This variability of domain architecture creates a challenge for the accurate assessment of sequence similarity between whole-protein sequences, often resulting in weaker phylogenetic profiles across multiple species and therefore weaker profile similarity between whole proteins. As a result, some of the similarities between phylogenetic profiles detected at the domain level  are missR > 0.75, Figure Archaea and a drop of conservation signal in Bacteria.The analysis of phylogenetic profiles based on only eukaryotic species allows for a higher-resolution survey of correlations based on eukaryotic evolution, especially for the domains whose homologs are restricted to eukaryotes. However, expanding the range of species from eukaryotes to 506 species across the tree of life allows for the detection of many additional profile correlations for the domains with prokaryotic homologs. As an example, eukaryotic phylogenetic profiles of RNA_pol_Rpb1_1 domain of human POLR3A, RNA_pol_Rpb2_1 domain of POLR1B, and RIO1 domain of RIOK1 protein had only modest similarity (Pearson R < 0.5) to the profile of tRNA-synt_1 of LARS protein as a query Figure . In factR > 0.6 for all PFAM domains in human genome. Figure We performed a comprehensive evaluation of detection accuracy for the modes of DEPCOD that use phylogenetic profiles calculated (a) among eukaryotes only and (b) across the whole tree of life, and further compared these DEPCOD modes to the PhyloGene approach  that useOn genome-wide scale, similarities between DEPCOD phylogenetic profiles are associated with both specific protein functions and specific evolutionary patterns of sequence conservation among protein domains. Figure Domains with animal-specific distribution (clusters 6–8) are enriched in well-known animal-specific functions associated with the nervous system  and animal development , but also in less expected functions such as focal adhesion, regulation of actin cytoskeleton, and calcium signaling, suggesting animal-specific evolutionary innovations in these more general eukaryotic pathways. Finally, mammalian-specific domains (cluster 9) are enriched in blood and adaptive immunity functions , consistent with the known expansion and specialization of these functions in mammals. These similarities of phylogenetic conservation patterns among human domains with specific biological functions suggest that DEPCOD is a valuable tool for both detailed analyses of domain evolution and the prediction of potential functional associations between protein domains.As a further development of the normalized phylogenetic profiling approach initially implemented in our PhyloGene webserver, DEPCOD focuses on sequence conservation patterns of protein domains as mobile evolutionary units. This more focused analysis allows the detection of similarities between phylogenetic profiles and prediction of potential functional associations between non-homologous domains beyond the capabilities of the whole-protein approach. Importantly, the new DEPCOD server provides various types of additional information to help the user inspect biological functions of the detected domains, their known physical interactions and protein complexes shared with the query, and aid in detailed manual analyses of possible sources and evolutionary relevance of the detected conservation similarities. As a result, DEPCOD is an informative and efficient web server for the analyses of evolutionary and functional associations between protein domains.Bacteria and Archaea) and ‘Bacteria’ (phylogenetic profiles based on Bacteria only). In these two modes, we will add new prokaryotic organisms as query species. Third, we will further extend the set of organisms available as query species in all DEPCOD modes and expand the number of organisms used in phylogenetic profiles.In the future, we plan to focus on a few methodological directions to extend and improve DEPCOD. First, we will develop a separate mode for the detection of anti-correlations between phylogenetic profiles. These anti-correlations correspond to mutual exclusion or, at a more gradual scale, opposite patterns of sequence conservation among homologs of the two compared domains across the range of species, which may suggest redundant or otherwise related functions of the two domains. Second, to fully leverage the rich evolutionary information among prokaryotes, in addition to the modes of ‘Eukaryotes’ and ‘All clades’, we plan to introduce the modes of ‘Prokaryotes’ (phylogenetic profiles based on http://genetics.mgh.harvard.edu/DEPCOD.DEPCOD is freely available at gkac349_Supplemental_FilesClick here for additional data file.