The PRMT5–MEP50 methyltransferase complex plays a key role in various cancers and is regulated by different protein–protein interactions. Several proteins have been reported to act as adaptor proteins that recruit substrate proteins to the active site of PRMT5 for the methylation of arginine residues. To define the interaction between these adaptor proteins and PRMT5, we employed peptide truncation and mutation studies and prepared truncated protein constructs. We report the characterisation of the interface between the TIM barrel of PRMT5 and the adaptor proteins pICln, RioK1 and COPR5, and identify the consensus amino acid sequence GQF[D/E]DA[E/D] involved in binding. Protein crystallography revealed that the RioK1 derived peptide interacts with a novel PPI site. Determination of a PPI interface: The adaptor protein binding site of PRMT5 has been identified and studied by using synthetic peptides. A cocrystal structure of the PRMT5 TIM‐barrel domain and a RioK1‐derived peptide highlights the site of interaction. Methylation of terminal arginine nitrogens by protein arginine methyltransferases (PRMTs) plays important roles in various cellular processes, including the establishment of cancer.PRMT5 contains three domains, that is, the catalytic site‐containing Rossmann fold, a β‐barrel domain used for dimerization, and the TIM barrel, which acts as interaction site for MEP50 Figure A.3a, 3a, 3cHerein, we describe the use of synthetic peptides to study the PRMT5–adaptor protein interaction by biochemical methods and protein crystallography. An adaptor protein consensus sequence was derived by sequence alignment and synthetic peptides derived from this consensus were prepared. The site of interaction was initially determined by truncations of PRMT5 itself and measuring the affinity for synthetic adaptor protein peptides. Truncation and alanine scanning of the adaptor protein peptides allowed identification of the residues contributing to the interaction. A co‐crystal structure of a RioK1 derived peptide with the PRMT5 TIM barrel domain confirmed the results of our mutation and truncation studies. Our findings are in agreement with the recent preliminary report by Sellers et al.Homo sapiens protein data, resulting in the identification of two new sequence matches: COPR5  and DAB1 , 2 (KD 1.14 μM) and 3 (KD 0.55 μM), but not 4 , corresponding to the TIM barrel domain, was either expressed alone or co‐expressed as a complex with MEP50  led to a significant loss of affinity. To explore N‐terminal truncation, C‐terminally labelled 7 was synthesised which bound with a similar affinity as peptide 1. Truncating the N‐terminus showed that loss of the first three residues improved the binding (compound 8) but any further truncation led to a substantial loss of affinity (compounds 9 and 10). Compound 8, with the nine amino acid sequence VPGQFDDAD, constituted the smallest but still efficiently binding peptide fragment allowing for the interaction with PRMT5 (KD=208 nM). Extensions of either the N‐ or C‐termini led to improvements over peptide 1 but not in comparison to 8. Peptides derived from pICln showed similar trends where deletion of the negatively charged C‐terminal section resulted in loss of binding (compare compound 14 to 2) while extension led to an increase (compare 15 to 2). In contrast to the RioK1 derived peptides extension on the N‐terminus of the pICln peptide resulted in a twofold decrease in affinity (compare 16 to 2).In order to further define the requirements for pICln/RioK1/COPR5‐PRMT5 binding, we synthesised a series of RioK1 and pICln derived peptides Table  to deter17–22) and tested for binding to the TIM–MEP50 complex. No binding was observed in the performed FP assay  were able to compete with the fluorescently labelled 11, indicating that all peptides share the same binding site . The side chains of Val12 and Asp18 also contributed to the interaction (compounds 34 and 28), and substitution of Ala for Gly14 resulted in loss of affinity indicating that Gly is important for the correct conformation of the peptide (peptide 32). Mutation of the residues in the SRV and AD region, had either no, or a moderately positive impact on the interaction with the protein .To identify hot spot residues which provide the main contribution to the interaction,26 Table  which re23, using data to a resolution of 2.55 Å (PDB ID: 7BOC).More insight into the structural basis of the identified PRMT5‐pICln/RioK1/COPR5 interface was obtained from a co‐crystal structure of the TIM barrel domain in complex with peptide PPI web‐service, where a virtual alanine scan with a measurement of the change in the binding free energy (ΔΔG) of the protein–protein complex (PDB ID: 4GQB) was performed (Table S6).G values , and thus, high contribution to the PPI, were present in the disordered protein fragments , supporting the observation of the MEP50 stabilisation effect on the TIM barrel.The protein structure shows an unusual seven‐stranded β‐sheet with one antiparallel strand for the TIM barrel which is not observed in other PRMT5–MEP50 complexes.3a, 3c27, Table 9, Table The peptide is wedged into a shallow groove of the TIM barrel, formed between Lys240/241, Lys248 and Tyr286  should be addressed to the authors.SupplementaryClick here for additional data file.


Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques . In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies . Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles. Increased understanding of the events taking place during intracellular signaling has revealed a highly dynamic protein landscape for eukaryotic cells, especially in pathological settings  ,2,3. MosBearing this in mind, the recent advances in next-generation sequencing (NGS) and high-resolution mass spectrometry (MS/MS) have opened up strategies methodologically which provide novel insights useful for understanding the complexity of intracellular processes ,9. AmongThe development of mass spectrometry (MS/MS)-based proteomics coupled with protein extraction at subcellular level  as well This study has two main purposes: i. Evaluation of protein extraction or complementary characterization with MS/MS-based proteomics and affinity proteomics (SEC-MAP), which may be useful for bioinformatic orthogonal integration with deep transcriptomics characterization; ii. generation of systematic pipeline for comprehensive orthogonal integration of protein subcellular localization, affinity proteomics (SEC-MAP), MS/MS data sets, and RNA-seq information in human lymphoma cell line, like a model for mapping cancer protein interactions. Moreover, the combination of these methodologies helps to deepen the knowledge about the architecture of the cells and the complexity of the spatial organization of the proteome, which can be a tool for interpreting a multiscale map of protein systems—which is relevant for deciphering cell signaling pathways linked to genetic perturbations/alterations, therapeutic interventions, or another external stimulus.v/v) fetal bovine serum  and 1% (v/v) penicillin/streptavidin . The growth was monitored daily and cells were incubated at 37 °C/5% CO2 with media renewal every 2–3 days.Human Ramos cell line  was cultured in RPMI 1640 medium supplemented with L-glutamine , 10%  at 1200 rpm. All the different protein extraction buffers were supplemented with phosphatase and protease inhibitors: tris [2-carboxyethyl]phosphine]  1 mM, phenylmethylsulfonyl fluoride  1 mM, sodium fluoride  1 mM, sodium orthovanadate  1 mM, β-glycerophosphate  1 mM and sodium pyrophosphate tetrabasic decahydrate  1 mM. All centrifugation and incubation steps were carried out at 4 °C. The extracted proteins from each strategy were stored at −20 °C until further use.For protein extraction, 40 × 10w/v) Coomassie brilliant blue and silver stains solutions according to standard laboratory procedure  vs. high/low values of log [FPKM]. Finally, development biology and chromatin organization have been represented in three proteins groups according to log [LFQ] and log [FPKM] values .Bearing in mind similar patterns in the correlation between log[LFQ] and log[FPKM], the t-SNE plots provide insights from the functional point of view (within group of proteins): i. vesicle-mediated transport display high values on both parameters , ii. orgThe synergistic integration of multi-omics datasets is highly dependent on multiple methodological aspects, which are also critical in the design and development of an algorithm for deepening the biological knowledge of intracellular signaling pathways. In this study, a simple approach has been designed and developed for the orthogonal integration of SEC-MAP, LC-MS/MS, and RNA-seq datasets corresponding to a Ramos cell line as a model. This orthogonal integration allowed—in a particular cellular situation—to decipher protein expression, isoforms, quantification at peptide and transcript levels, protein localization, protein interactions which also could be done at multiple cellular differentiation stages and/or physiological situations; thus, it could help uncover novel insights about the cellular dynamics and response to external stimulus.Initially, sample preparation, as protein extraction procedures, has been revealed which is critical for orthogonal integration of multi-omics because of the expected influence in relative protein abundance, protein chemistry, protein stability, protein structure, tryptic digestion, protein solubility, and PTMs. It has been shown that protein extraction procedures have to be optimized for multi-omics integration because it could be an advantage/disadvantage in multi-pronged proteomics characterization (such as LC-MS/MS and SEC-MAP). Additionally, the specificity and selectivity of immunoassays are also affected by chemical labeling for detecting antigen-antibody interaction, which may hinder the epitopes and/or alter protein structure. Therefore, it is another critical factor for immunoassay validation of findings from RNA-seq and LC-MS/MS.Overall, the importance of protein extraction procedures for further integration of proteomics with other omics datasets has been revealed. For example, protocols #2 and #3 have urea in the lysis buffer, which decreases the efficiency of biotin labeling and subsequent SEC-MAP analysis . AnotherRegarding Human Proteome Project, it is a huge effort to detect “missing proteins”; which are classified into five groups (1–5) according to protein evidence (PE) ; then, pEscherichia coli, HEK293, osteosarcoma cells, liver human tissue and others) focused mainly on analysis of protein interactions , the difference between transcriptomics-proteomics levels or in combination with spatial proteomics [Recently, several studies employing SEC  are successfully detected at the expected MW and the subcellular localization as their confirmed existence by transcriptomics and MS/MS datasets. There is another group of proteins that are detected at the reported subcellular localization but at different MW, such as, BLC-2, RELA, MAPK1, MDM2, TP53, BLNK, SYK, CD19, LYN, ZAP70, PLCG2, suggesting the existence of protein–protein interactions and protein complexes. Thus, this approach is amenable for studying protein interaction networks and/or intracellular signaling dynamic as confirmed previously by our group in this study by correctly identifying the protein players in the previously reported pathways in this B lymphocyte cell line  ,63,64,65Multi-pronged proteomics characterization is highly dependent on protein extraction procedures, being a key step for functional outcomes from detection of proteins/multi-protein complexes in samples. Furthermore, it also reveals critical orthogonal integration of multi-omics data sets  and also provides complimentary info such as multi-protein complexes and subcellular localization. Moreover, the reported pipeline for multi-omics integration is useful for the HPP, as it systematically explores the compatibility of multi-omics data sets. Currently, the RNA-Seq technique provides us with information on the presence-absence of a particular protein of interest; meanwhile, MS/MS and SEC-MAP add data on protein localization, isoforms, and also, protein complexation status (complex-monomer-degraded). Consequently, SEC-MAP analysis seems to be a useful tool for orthogonal multi-omics integration, as it provides detailed complementary information about MW, subcellular localization, isoforms, PTMs, and protein complexes. Furthermore, SEC-MAP analysis seems feasible for determining predicted or unknown protein complexes and/or protein interactions in any sample .With multi-omics orthogonal integration, it seems feasible to identify if a drug could target a protein complex of interest. It might help in the selection of drugs for a particular pathology.


PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D  and predictions of protein function  terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.Since 1992   PredictProtein (PP)PredictProtein ( at the European Molecular Biology Laboratory (EMBL) in 1992  and position-specific scoring matrices (PSSMs) computed by a combination of pairwise BLAST , PSI-BLAFmax values of 37 ± 2%, 52 ± 2% and 58 ± 2% for BPO , MFO (molecular function), and CCO (cellular component), respectively  using ProtBert  embeddinproteins ,46. On aproteins . Althougproteins  at 82%),in-house implementation of ProtVec (ProNA2020  predicts ProtVec . Per-res ProtVec . ProNA20Most time-consuming for PP was the search for related proteins in ever growing databases. MMseqs2  finds rePredictProtein.org by submitting a protein sequence. Results are available in seconds for sequences that had been submitted recently (cf. PPcache next section), or within up to 30 min if predictions are recomputed. Per-residue predictions are displayed online via ProtVista .All results displayed on and downloadable from PP are available through Docker  and as sRNA-binding; GO:0003723; ECO:0000213) and CCO  matching annotations found in UniProt , goPredSim predicted protein folding (GO:0006457) and protein ubiquitination (GO:0016567) suggesting the nucleoprotein to be involved in biological processes requiring protein binding. ProNA2020 (We demonstrate PredictProtein.org tools through predictions of the novel coronavirus SARS-CoV-2 (NCBI:txid2697049) nucleoprotein  supportroNA2020  predictsroNA2020  both preroNA2020 ; 6WJI (6roNA2020 ). Both sPredictProtein (PP) has been offering predictions for proteins. PP is the oldest prediction Internet server, online for 6-times as long as most other servers   servers . It pion servers . For theFreely accessible webserverPredictProtein.org; Source and docker images: github.com/rostlab.gkab354_Supplemental_FilesClick here for additional data file.


Saccharomyces cerevisiae. This approach enabled us to apply quasi-potentials, the negative logarithm of the probabilities, to calibrate the elevation of the interaction landscapes with young cells as a reference state. Our approach detected opposite landscape changes based on protein abundances from transcript levels, especially for intra-essential gene interactions. We showed that essential proteins play different roles from hub proteins on the age-dependent interaction potential landscapes. We verified that hub proteins tend to avoid other hub proteins, but essential proteins prefer to interact with other essential proteins. Overall, we showed that the interaction potential landscape is promising for inferring network profile change during aging and that the essential hub proteins may play an important role in the uncoupling between protein and transcript levels during replicative aging.We proposed a novel interaction potential landscape approach to map the systems-level profile changes of gene networks during replicative aging in  Saccharomyces cerevisiae, provided mechanistic insights on cellular aging1, and contributed to the understanding of aging in other organisms including humans5. Deletions of single genes, such as FOB16, and suppression or overexpression of genes, such as SIR28, could change the yeast RLS. Other factors that affect lifespan, such as caloric restriction (CR), were also found dependent on the genotypes9. A recent report found overexpression of both SIR2 and HAP4 genes leads to a cell state with considerably long RLS10.Studies of replicative lifespan (RLS) in budding yeast, 13. It was shown that cellular aging can be viewed as an emergent phenomenon of a gene interaction network with stochastic loss of interactions to essential proteins14. A recent study showed that the uncoupling between protein and transcript levels is strongly connected to yeast aging15. The time-series transcriptome and proteome data of this work also involved extensive numbers of essential proteins and genes15.Gene network is critical to understand the pleiotropic nature of aging12. The translational efficiencies of different proteins vary up to hundreds of folds in yeast, however, protein and mRNA levels were highly correlated13. Increased noises in both transcriptome and proteome during aging could be part of the cascading events leading to replicative aging and eventual cell death.The pleiotropic nature of yeast aging is also reflected at the transcriptome and proteome levels. Transcriptional fidelity is associated with aging and longevity in yeast14. The PIN is often modeled as a graph composed of proteins as vertices and interactions as edges18. The protein–protein interactions (PPIs) are important factors in the genotype-to-phenotype relationships19. It was proposed that aging preferentially attacks key regulatory nodes important for the stability of the PIN20. Age-specific PIN was found to suggest key players in aging21.The protein interaction network (PIN) offers a backbone for cellular functions. Proteins with similar functions tend to interact with each other and form functional modules22. Nevertheless, we propose in this work that a “quasi-potential” approach could be utilized to weight the PPIs using the age-dependent transcriptome and proteome dataset of the yeast S. cerevisiae15 and corresponding PIN22. We constructed the Protein Interaction Potential landscapes (PIPLs) based on the age-dependent potentials. We observed opposite landscape changes from protein levels and those based on transcript levels. The results also suggest that interactions to essential hub proteins may play an important role in the uncoupling of proteome and transcriptome during aging.The PPI must bear a weight (strength) depending on both biophysical properties and spatial/temporal distributions of the proteins. In the crowded environment of the cell a PPI is also the subject of competitions and/or inferences by other molecules including the solvents. This situation complicates the PPI detections and, in many cases, only the “binary” PINs—i.e., “1” for interaction and “0” for no interaction—can be estimated, such as those reported in the BioGRID15.We hypothesize that the uncoupling of proteome and transcriptome in replicative aging may lead to protein–protein interaction profile changes, which motivated us to construct age-dependent PIPLs using the age-dependent proteome and transcriptome data along the yeast replicative aging23. Note that the quasi-potential used for the landscapes was calculated as the negative logarithm of the interaction probability based on the law of mass action . Therefore, as compared with the ground state, the ridges on the landscape indicate that the interacting pairs need to acquire “high potential”, whereas basins represent that the interacting pairs possess “low potential” for interactions to occur.To gauge changes in the interaction profiles during replicative aging, we constructed the age-dependent PIPLs on the dimension of RLS. We used RLS ratios between the RLS of single-gene deletion mutants and those of the wild type cells cultivated under the same YPD (2% glucose) conditions, which is referred to as the normalized RLS in present work. The normalized RLS of essential proteins (genes) is 0, as the cell will not be viable after the deletion of a single essential gene. The probabilistic nature of the landscape enabled us to apply quasi-potentials to quantify the elevations of the landscape. We choose the young cells collected at the age of 7.8 h as the ground state Fig. , a term To estimate the changing elevations on landscapes during aging, we used two approaches: One based on the proteome Fig. a and one24. Elevations of the landscape were estimated again with both protein and transcript levels.To better understand the role of essential genes during aging, we examined the interaction potential landscapes by node degrees Fig. a, becausBased on protein abundances, in the old cells at 72.3 h, the hub-versus-hub protein interactions also fell into a basin ΔU = –31) with the quasi-potential weaker than the essential-versus-essential protein interactions (ΔU = –83) shown in Fig.  with theWe further examined the PIPL on both the node degrees and normalized RLSs Fig. b. The in26. It may also be owing to the fact that many proteins in yeast have long half-lives27. A significant amount of proteins underwent non-exponential degradations in the cells and that the protein degradation rate seemed to be age-dependent28. The protein turnover rates were correlated protein functions and activities29, and the long-lived proteins may accumulate to high abundances in old cells even though their expression levels had been declined. Here, two independent protein turnover data sets, the CellSys2017 set29 and the CellRep2014 set27 had been used to construct the age-dependent PIPLs on the dimensions of normalized RLSs versus protein turnover rates . Considerable differences can be found between the results of both sets  levels than to the protein abundances, with respect to the protein–protein interactions. For example, whereas based on the transcript levels for both turnover sets, the interactions with essential genes from all gene groups with different turnovers were in high potential, low probability states, in the protein abundance-based landscapes the interactions between certain groups of genes and the essential genes were dropped to low potential, high probability basins. Moreover, using the CellSys2017 set the low-turnover proteins also showed dropped quasi-potentials or increased interaction probabilities with the essential genes.15. Overall, in all the landscapes we observed large fluctuations attributed to the essential genes  and their interactions with the low-turnover proteins. The interactions between essential genes and high-turnover proteins are relatively stable during aging, according to the protein-abundance-based PIPLs, but not in the gene-expression-based PIPLs.Based on the transcript levels (bottom panels), the landscapes using both turnover sets showed elevated quasi-potentials, especially at old age Fig. . However30. This approach had been recently applied to estimate the association strengths between different categories of proteins using a human PIN17. Figure To further understand why lower relative quasi-potential (or higher probability) have been found in the essential-versus-essential interactions based on the proteomics, despite the transcript level trend predicted the opposite direction, we constructed another proteome-wide, global landscape model which calculates the probabilities of that the empirical PIN having more interactions than the random null models. As for the age-dependent landscape models, quasi-potentials were calculated as the negative logarithm of the probabilities to infer basins and hills on this landscape. Similar method had been previously applied to a smaller yeast PIN and observed that the high-degree, hub proteins tend to avoid interactions with the other hub proteins, which was suggested to contribute to the stability of the PIN30. Originally observed in yeast PIN, it had been proposed that the hub-proteins in the PIN tend to be essential proteins and vice versa24. This centrality-lethality rule hypothesis had been confirmed in the PINs of other organisms31 as well as the genetic interaction network (GIN) of yeast32. On the global landscape on the RLS space, however, the global landscape indicated that the essential proteins strongly favor the interactions with other essential proteins but tend to avoid the interactions with non-essential proteins  tend to avoid interactions with other hub proteins yet favor the interactions with non-hub proteins Fig. a, in agrDHH1 (YDL160C) has 3,605 interactions in the PIN but is nonessential and the ΔDHH1 mutant has a normalized RLS of 0.825 compared to the wild type.It is worth noting that the essential genes did have higher degrees (average of 63.3) than non-essential genes (average of 32.7). Based on the landscape on the node degree-RLS space Fig. c, we alsSaccharomyces Genome Database34. These ORFs encode ca. 1000 essential genes, with any one of which deleted the cell will not be viable38. We compared the abundances of 4 categories of proteins (genes) including essential hub, essential non-hub, nonessential hub, and nonessential non-hub proteins. The protein or transcript abundances were referenced to the ground state, the young cells with the age of 7.8 h, consistent with the PIPLs shown above. We used the node degree k ≥ 100 as a cutoff (which covered the top 5.8% high connectivity proteins) to classify the hub and non-hub proteins  on the 40, and the long estimated RLS in Fig. 2.Consistent with the evaluation of the relative-quasi potentials , the node degrees were weighted to the protein or transcript abundance values in Fig. K), i.e., the decay rate (d) is 0 for all PPIs. However, in reality, K might be affected or altered by many factors, such as post-translational modifications, protein aggregations, pH, ionic strengths, and other cofactors. It is difficult to measure the variations of K for each of the protein–protein interactions. We used an exponential decay model aiming to estimate the potential topological changes of the landscapes attributed to the variations of the dissociation constants, K.All PIPLs shown above were constructed using constant protein–protein interaction dissociation constants  were selected from this PIN, comprising 110,290 interactions among 5784 proteins.The empirical protein–protein interaction network (PIN) of 17. 22,188 null network models were built in a way such that all vertices were randomly reshuffled, yet the node degrees preserved as the original PIN. These network models were also termed the MS02 null models47.The PIN used in the present work is regarded as a simple graph, i.e., an undirected graph with no self-loops or multi-interactions. We recently proposed the network permutation-based association study (NetPAS) approach to evaluate the interaction strength between different protein (gene) sets by comparing the original PIN and its random null modelsS. cerevisiae under the YPD (2% glucose) culture had been reported1.The replicative lifespans (RLSs) of 4698 single-gene deletion strains of S. cerevisiae came from a recent report, which comprises the abundances of 1494 proteins and expression levels of 4904 genes collected at 12 different ages ranging from 7.8 to 72.3 h15. We used the young cells at 7.8 h as the reference (ground state) to evaluate the PIPLs of the older cells at the other 11 ages. The two data sets cover different genes (proteins), as shown in Figure One set of proteomics and transcriptomics data of 29 comprised of 3160 proteins with 1341 in the aging proteomics and 2526 in the aging transcriptomics data sets. The CellRep2014 set27 included 3715 proteins with 1331 in the aging proteomics and 2988 in the aging transcriptomics data sets.Two sets of in vivo protein turnover time or half-lives have been used. The CellSys2017 set36. Specifically, two morphologies related to a recent morphological landscape of yeast replicative aging had been analyzed in the protein interaction landscapes48, including the cell size ratio (C118_C) and bud axis ratio (C114_C) at the late stages. Although the definition of the axis ratio in SCMD2 was different than that used in the morphological landscape, it could provide a rough identification of the roundness of the cell shapes.We also generated the PIPLs on dimensions of morphological parameters and the RLS of the yeast single-gene deletion mutations. See in the S. cerevisiae were taken from the SCMD236 that comprises 1112 genes, 1033 of which have interactions recorded in the PIN.The essential gene set of We used quantiles to catalog the gene groups in the PIPLs. The yeast PIN covered 3939 genes with the RLS records of the single-gene deletion mutations. The normalized RLS refers to the RLS ratios between the mutant and the control wide type strain and had been adopted in the landscapes. Among the genes covered by the PIN, 926 non-essential and 403 essential genes had the age-dependent protein abundance records, and 2991 non-essential and 882 essential genes had the age-dependent transcript records, respectively. In both the age-dependent protein abundance and transcript level sets, the non-essential genes were evenly classified into 20 groups based on the quantiles of the normalized RLSs, and the essential genes were classified into one group with the normalized RLS of 0. For the protein node degrees, half-lives, and morphology data sets, all genes were classified into 20 evenly distributed quantiles based on the respective measures. The same classification approach for the RLS groups was applied to the global protein interaction landscape, and all genes included in the PIN and with normalized RLS data had been used.i is the expression level of the i-th gene49—we suggest xi could also be the protein abundance of the i-th protein. The vector St continuously changes during the aging process.Aging is a dynamic process reflected in the variations of both protein abundances and transcript levels along the aging process. It had been suggested that a configuration of the cell state could be formalized as a vectorK is the dissociation constant and [x]t and [y]t is the concentration of free x and y at time t. K may variate along aging, e.g., via an exponential modeld is the decay rate, and t is time (age), and Kt and K0 are the dissociation constants at age t and 0, respectively. The decay rate may be affected by posttranslational modifications, pH and ionic strength variations during aging, and other factors. The dissociation constant K remains unchanged during aging if d is 0; K exponentially decays or increases if d > 0 or d < 0, respectively. Under this condition and by using the young cell (ground state) at time 0 as the reference state or ground state, we could get:The interaction between a pair of proteins x and y can be written asK0, which was canceled by comparing with the ground state. Therefore, using a reference state at time 0 , the interactions between different protein pairs could be treated equally, if the variation rates of the dissociation constants are the same.Note that Eq.  does not50. The quasi-potential49 can be calculated from the probabilities usingBesides the above approximation, we used a quasi-potential, the negative logarithm of the probabilities, to describe the barriers that the cells must overcome for the transition from one attractor state to the othert using U0 as a reference can be calculated asAgain, because all potentials here are relative to the ground state, the relative quasi potential UEquation  providesi, yi), i = 1, 2, …, n, the relative quasi-potential from all interactions between X and Y would beIn this work, the relative quasi potential of interactions between two categories of proteins (or genes) is evaluated. The probability is not additive as we cannot sum up the probabilities of different interactions. However, the logarithm function and hence the quasi potential U is additive. Therefore, it is straightforward to calculate the relative quasi potential of interactions between two sets of genes X and Y based on Eq. . For insd) for the dissociation constants of the PPIs  for more interactions between certain categories of genes in the PIN than in the null models. The NetPAS method17 was used to calculate the p-values using 22,188 null models. The protein-interaction quasi potential can also be calculated using Eq. (p = 0 cases could be interpreted as p < 1/22,188, which was converted to p = 1/22,189 in practice to avoid the infinite log0 values. The resulted quasi-potentials were then normalized to  for the landscape.Here we calculated the protein-interaction quasi potentials based on the probability (sing Eq. . The bassing Eq. . As 22,1Supplementary Figures.


Can quality measures identify low-quality physician groups when performance is correlated across multiple measures or multiple years?In this cross-sectional study of a commercially insured population with diabetes or cardiovascular disease, we found weak consistency of low performance scores across multiple measures but moderate to strong consistency of scores over multiple years. One percent or fewer of physician groups performed in the bottom quartile for all diabetes measures or all cardiovascular disease measures in any given year, while 4% to 11% were in the bottom quartile in all 4 years for most measures.These results suggest that consistency in poor performance depends on the statistical properties of the measures. This cross-sectional study assesses the use of consistently low performance scores for physician groups across multiple performance measures and over multiple years to identify low-quality physician groups. There has been a growth in the use of performance-based payment models in the past decade, but inherently noisy and stochastic quality measures complicate the assessment of the quality of physician groups. Examining consistently low performance across multiple measures or multiple years could potentially identify a subset of low-quality physician groups.To identify low-performing physician groups based on consistently low performance after adjusting for patient characteristics across multiple measures or multiple years for 10 commonly used quality measures for diabetes and cardiovascular disease (CVD).This cross-sectional study used medical and pharmacy claims and laboratory data for enrollees ages 18 to 65 years with diabetes or CVD in an Aetna health insurance plan between 2016 and 2019. Each physician group’s risk-adjusted performance for a given year was estimated using mixed-effects linear probability regression models. Performance was correlated across measures and time, and the proportion of physician groups that performed in the bottom quartile was examined across multiple measures or multiple years. Data analysis was conducted between September 2020 and May 2021.Primary care physician groups.1c (HbA1c) testing, low-density lipoprotein testing, statin use, HbA1c control, low-density lipoprotein control, and hospital-based utilization.Performance scores of 6 quality measures for diabetes and 4 for CVD, including hemoglobin Ar = −0.10) to moderate , but year-to-year correlations for all measures were moderate to strong. One percent or fewer of physician groups performed in the bottom quartile for all 6 diabetes measures or all 4 cardiovascular disease measures in any given year, while 14 (4.0%) to 39 groups (11.1%) were in the bottom quartile in all 4 years for any given measure other than hospital-based utilization for CVD (1.1%).A total of 786 641 unique enrollees treated by 890 physician groups were included; 414 655 (52.7%) of the enrollees were men and the mean (SD) age was 53 (9.5) years. After adjusting for age, sex, and clinical and social risk variables, correlations among individual measures were weak (eg, performance-adjusted correlation between any statin use and LDL testing for patients with diabetes, A subset of physician groups that was consistently low performing could be identified by considering performance measures across multiple years. Considering the consistency of group performance could contribute a novel method to identify physician groups most likely to benefit from limited resources. In the past decade, there has been a shift away from fee-for-service and toward population-based payment models that reward physician groups based on performance on quality measures. However, the multidimensionality and stochastic nature of quality measures may complicate assessment and, more specifically, the identification of inadequately performing physician groups. In particular, groups may be incorrectly identified as poor performing purely by chance in common forms of cross-sectional analyses.4 In the US, physician practices spend more than $15.4 billion annually on reporting quality measures alone.4 With health care expenditures rising, it is increasingly critical that resources for tracking quality are used effectively.In addition, the burden of quality measurement on physicians and the substantial investments made in measurement development have been a continuous concern.12 Other analyses have investigated the quality of health plans using the Healthcare Effectiveness Data and Information Set (HEDIS), finding variation and lack of correlation among performance measures.14Previous work has assessed consistency in quality measurement in hospitals and health plans. Various national organizations and payers have examined performance across multiple measures to rate hospitals and also explored whether hospitals are ranked differently from 1 year to another.15 We propose the use of consistently low performance across multiple quality measures or multiple years as a method to identify low-performing physician groups.Previous research has also focused on hospital or health plan quality performance and has mostly studied variation and changes in ranking , but there has been less work on identification of low performance. It is important to measure quality at the physician group level because, increasingly, groups are responsible for population-based outcomes. In addition, rankings are often not useful because of the inherent noisiness of measures.In this study, we used medical and pharmacy claims and laboratory data from an Aetna health insurance plan to examine whether consistently low performance across multiple quality measures or multiple years can identify low-performing physician groups. We measured performance using commonly used measures for 2 common chronic conditions that have been the focus of many quality measurement and improvement efforts: diabetes and cardiovascular disease (CVD).International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code for diabetes or CVD) who were continuously enrolled for at least 1 calendar year between 2016 and 2019.We used medical claims, pharmacy claims, and laboratory results from an Aetna health insurance plan between 2016 and 2019. Our sample included adults between ages 18 and 65 years with either diabetes or CVD (based on 2013 HEDIS eligibility criteria: 1 or more inpatient visit or 2 or more outpatient visits with an 16We attributed each enrollee to the physician group (defined by tax identification number [TIN]) accounting for the plurality of the enrollee’s office visits during a given year  reporting guideline for cross-sectional studies.We obtained institutional review board approval from Harvard University’s Committee on the Use of Human Subjects. Informed consent was not required because data sets were deidentified. Analysis was conducted between September 2020 and May 2021. This article is compliant with the Strengthening the Reporting of Observational Studies in Epidemiology , 2 disease control measures , and 1 utilization-based outcome measure . For CVD, we included measures for LDL testing, statin use, LDL control, and no ED, observation, or hospital admissions for MACE. Some measures were limited to subsets of enrollees with relevant pharmacy benefits with the same insurer (42.7% for diabetes cohort [235 546 of 551 415 enrollees] and 41.2% for CVD cohort [291 597 of 708 171 enrollees]) and available laboratory results (54.3% for diabetes cohort [299 215 of 551 415] and 42.6% for CVD cohort [301 671 of 708 171]). The clinical and area-level characteristics of enrollees with and without laboratory or pharmacy data were similar  that were coded using the CMS Chronic Condition Warehouse definitions,26 and a comorbidity score based on a DxCG Intelligence version 5.0.0 (Cotiviti) prediction model. We used zip code–level sociodemographic characteristics from the 2010 US Census  and 5-year estimates from the 2010-2014 American Community Survey (percentage of population below poverty). Using methods from a previous study, we also assigned enrollees to urban, suburban, and rural classifications based on zip code.27Because patient characteristics are often associated with performance,We used inverse probability weighting to account for observed differences between enrollees with and without pharmacy or laboratory data. Specifically, to standardize the samples across measures that required laboratory or pharmacy data, we weighted each patient as the inverse of the probability that they had laboratory (for disease control) or pharmacy (for statin use) measures, respectively. These weights were estimated using a logit model that included all of the covariates described above and used the availability of data as the dependent variable for the relevant measures.29 for computing adjusted quality performance. Prior work has shown that social risk adjustment can affect the variance and rankings of physician group performance on disease control and outcome measures, but is less of a factor for process measures.22 For consistency, we adjusted all quality measures, including process measures. In this approach, we removed the association of high-risk patients sorting to low-performing physician groups by basing our adjustment on within-group associations. In the first step, we fit inverse probability weighted linear regression models estimating the adherence of an enrollee attributed to a physician group in a given year as a function of patient-level age and gender, patient-level comorbidities and DxCG composite, zip code–level sociodemographic characteristics, and physician group fixed effects. We then computed an enrollee-year–level risk score as the projected performance for each measure estimated from only the coefficients on the enrollee characteristics .Because low-performing groups may be more likely to treat high-risk patients, we followed a 2-step social risk adjustment methodology from earlier studiesIn the second step, we computed group-level performance scores. We estimated patient-level mixed-effects linear probability regression models that related the performance on a measure to the risk-score computed in step 1 and to physician group random effects. We then computed an adjusted score that represented a group’s estimated performance in a given year for an enrollee with average clinical and social risk. A more detailed explanation of this 2-step adjustment approach can be found in eAppendix 3 in the within and between physician groups, as well as the sample size. We report reliability for practices based on the median number of enrollees.To estimate the degree of variation in performance at the physician group level, we computed intraclass correlation coefficients (ICCs). The ICC represents the fraction of total variation in performance that is explained by differences between physician groups. The ICC can be low if there is little variation between groups or if the within-group variance is high. Measures with low ICCs generally have less ability to distinguish performance at the group level and are thus less useful for identifying low-performing clinicians. We also computed reliability for each measure. Reliability represents a measure’s reproducibility and is a function of the measure of variation P < .05 was treated as statistically significant, and all statistical tests were 2-sided.To examine consistency across measures and years, we computed pairwise and year-to-year  correlations among physician groups’ risk-adjusted performance on measures. We tabulated the proportion of physician groups that performed in the bottom quartile of scores across multiple measures or multiple years. We defined low-performing physician groups as falling into the bottom quartile of quality performance. After excluding those that did not have complete data for all 4 years, 353 physician groups were included in the analysis of consistency in performance across years. All analyses were performed using Stata statistical software version 15.1 (StataCorp). Our final sample included 786 641 unique enrollees  treated by 890 physician groups . A total of 414 655 (52.7%) of enrollees were men and the mean (SD) age was 53 (9.5) years.1c testing (90.9% [89.2%-94.5%]), LDL testing (89.4% [86.9%-92.5%]), and avoidance of hospital-based utilization (78.9% [77.6%-80.3%]) (1c control (65.2% [62.9%-67.5%]), LDL control (66.4% [64.5%-68.3%]), and statin use (57.8% [56.5%-62.0%]). The IQRs, which illustrate the variability of adjusted performance across measures, were narrow: the difference between the upper and lower quartiles ranged from 2.7% for hospital-based utilization to 5.6% for LDL testing. Group-level variation explained a small proportion of the total variation in performance on the measures for diabetes: the ICCs, which indicate ability to distinguish performance, were lowest for hospital-based utilization (ranging from 0.6%-0.7% between 2016 and 2019) and highest for HbA1c testing (4.7%-5.8%). Measure reliability was high for testing measures but moderate for hospital-based utilization for a group with the median number of attributed enrollees.For diabetes, median (interquartile range [IQR]) rates of adjusted performance were high on HbA-80.3%]) . PerformSimilar to diabetes, median (IQR) rates of performance were high on LDL testing (79.3% [74.3%-84.9%]) and utilization-based outcomes (94.6% [92.2%-96.4%]) for CVD. Physician groups had lower performance on LDL control (40.3% [35.5%-44.7%]) and statin use (48.8% [41.1%-59.6%]). The IQR was lowest for hospital-based utilization (a range of 4.2%) and highest for statin use (18.5%). Again, ICCs were low; they were lowest for hospital-based utilization (0.3%-0.6%) and highest for LDL testing (3.1%-4.3%). Measure reliability was highest for testing and lowest for hospital-based utilization.r = 0.43 between LDL testing for diabetes and LDL testing for CVD). Testing measures had low correlations with their corresponding control measures. The highest positive correlation was in the CVD cohort between LDL control and statin use. Avoidance of hospital-based utilization was negatively correlated with LDL control and statin use for CVD.There were weak to moderate correlations between most of the individual quality measures, and only 4 of 45 pairwise correlations were greater than 0.5 (eTable 2 in the r = 0.81 for HbA1c testing and 0.68 for LDL testing) and CVD (mean r = 0.82 for LDL testing) (r = 0.51) and CVD (mean r = 0.46).We observed stronger consistency in performance on individual measures across years. Year-to-year correlations were highest for testing measures for both diabetes (mean testing) . They weWe observed minimal consistency in poor adjusted performance across multiple measures. Fewer than 0.2% of groups performed in the bottom quartile for all 6 diabetes measures in any given year . Similar1c testing for diabetes and LDL testing and hospital-based utilization for both diabetes and CVD. Including only disease control and statin use measures, about 1% or fewer were consistently low performing on all 5  was highest for testing measures and lowest for hospital-based utilization, particularly in the CVD cohort. ICCs and reliability were lowest for hospital-based utilization measures (most out of physicians’ control).In sensitivity analyses, we found that consistency of low performance across multiple measures or multiple years was similar if performance was unadjusted  . Pooling data across multiple years and/or creating composite measures are alternative approaches to improve precision in inherently noisy measures.39 we observed low to moderate correlations between most individual measures. Although we did not find many groups that were consistently low performing across multiple measures, we did find a subset that were consistently low performing across multiple years. Rather than spreading efforts to track performance over a large number of practices, using this methodology to target a condensed number of underperforming groups could be the first line of defense against further declining or sustained low quality of care.41 Although all of the groups in this subset may not necessarily be low quality , those that are truly low quality are most likely to be in this subset. Further scrutiny of these groups, perhaps through medical record reviews or site visits, could inform actionable initiatives that include financial incentives  or nonfinancial approaches to improve care. Similarly, high performers could be examined for best practices and possibly receive leniency on some data gathering requirements, allowing them to redirect some of their funds for data collection toward other areas of care.As in previous studies,42 and absolute thresholds may be more appropriate in some settings.43This study has several limitations. First, the data came from 1 commercial health insurance plan that may have higher proportions of enrollees in certain regions of the US. This health plan may not cover a large portion of physician groups, and our quality metrics were computed based on a subset of each group’s patient panel. However, commercial data made it possible to examine quality in nonelderly populations and to construct disease control measures, which are typically uncommon in administrative data. Second, because we focused on quality of care for 2 chronic diseases, our analysis only included 10 measures. Third, following common practice, we used billing arrangements to identify physician groups. Fourth, because we did not have access to enrollee sociodemographic characteristics, we used zip code–level characteristics to adjust for social risk. Although we adjusted for both clinical (enrollee-level) and sociodemographic (area-level) characteristics, there may still be residual effects that we were unable to capture. Fifth, our results cannot be generalized to small practices, and to evaluate consistency of performance across multiple years we could only include physician groups with complete data across all 4 years. Sixth, we used relative thresholds to define poor performance. This approach is commonly used in value-based payments. However, performance classification can be sensitive to approach,Moving forward with the use of quality performance to assess and reward health care professionals, it is important to consider the noisy nature of measures. In this article, we were able to identify a subset of physician groups based on their consistently low performance across multiple years. Consistency in performance could be applied to many other settings and could also be used to identify high-quality physicians. As quality measurement and incentives continue to be developed and are often directed at the organizational level, considering the consistency of group performance could afford a novel method to identify groups most likely to benefit from limited resources.


The subcellular locations of proteins are closely related to their functions. In the past few decades, the application of machine learning algorithms to predict protein subcellular locations has been an important topic in proteomics. However, most studies in this field used only amino acid sequences as the data source. Only a few works focused on other protein data types. For example, three-dimensional structures, which contain far more functional protein information than sequences, remain to be explored. In this work, we extracted various handcrafted features to describe the protein structures from physical, chemical, and topological aspects, as well as the learned features obtained by deep neural networks. We then used these features to classify the protein subcellular locations. Our experimental results demonstrated that some of these structural features have a certain effect on the protein location classification, and can help improve the performance of sequence-based location predictors. Our method provides a new view for the analysis of protein spatial distribution, and is anticipated to be used in revealing the relationships between protein structures and functions. Given that subcellular/organelle structures in cells provide specific physiological and functional environments, the determination of the subcellular locations of proteins is believed to be an important aspect of the understanding of their functions ,2. In thMost of the methods that were developed for the prediction of protein subcellular localizations employed amino acid sequences as the information source because the protein sequence data are abundant and easy to access . The theThree-dimensional (3D) protein structures, as a complex representation of proteins, contain spatial folding information  that largely manipulate biological functions, such as binding specificity and signal transduction. Given that the 3D structures stored in the protein data bank (PDB) database ,9 are inSignal patches in the tertiary structures can be indicators for subcellular location ,20,21. AIn this paper, we seek to construct a comprehensive representation for 3D protein structures, which can perform well in the classification of protein subcellular locations. These features are designed or have learned to describe information underlying the 3D structures in terms of the topological structures, arrangement, and physicochemical properties of atoms and other organelle-related characteristics. According to our experimental results, some of the structural features were demonstrated to be effective in protein location prediction, and can increase the classification performance when combined with sequence features. This work provides a new perspective on protein subcellular location research.https://www.rcsb.org, accessed on 28 October 2021), which stores hundreds of thousands of biological macromolecules obtained through experimental methods, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The dataset was generated by applying PISCES  [In this study, we constructed a new protein structure benchmark dataset from the PDB database   with an PA, USA)  in orderhttps://www.uniprot.org, accessed on 28 October 2021), a widely used protein sequence database [These proteins localize specifically to one of four major subcellular locations, namely the nucleoplasm, plasma membrane, cytosol, and mitochondria. The subcellular locations were derived from the Swiss-Prot database  features. The total number of sub-structures was over one million, and the large number of sub-structures hindered the implementation of direct clustering, so we used a two-step clustering method. In the first step, hierarchical clustering was performed on each protein individually, and the clustering centroids of the sub-structures for each protein were obtained. The local clustering centroids were believed to be representative of the source proteins. The second step performed a hierarchical clustering on only the local clustering centroids of all of the proteins, and obtained the global clustering centroids. The distance thresholds of the two hierarchical clusterings were determined by a grid search.Finally, the sub-structure frequency features were constructed by assigning each sub-structure to the closest cluster centroid and counting the proportion of sub-structures of one protein across the clusters.The chemical properties of the surfaces of protein molecules determine the interaction between the proteins and the outside world . As suchThe surface amino acids of a protein are defined by measuring whether the amino acid is fully surrounded. We took the Cα atom of each amino acid as the center of gravity, and constructed a spherical coordinate system e. The spfi), jα is the proportion of the j-th amino acid, and i-th chemical property of the j-th amino acid in The 20-dimensional AAC features of the surface amino acids were calculated. In addition, according to the chemical properties of the amino acid  Supplem, we calcThe topology of a protein structure is a simplified description of its fold, including the amino acid nodes and their relative positions and links . Here, wThe Gauss integrals’ features were obtained using Gauss integrals  to calculate the global writhe of the space curve . We usedPersistent homology features  show theCompared with the manually designed features, deep learning methods can automatically extract intrinsic information from the 3D protein structures. Here, we used deep convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) to learn the structural features.Given that some CNN architectures have been demonstrated to be effective in extracting protein structure information, we used transfer learning based on a previous CNN network of protein function classification . The inpOne alternative way to show the 3D structure of a protein is a graph; as such, we attempted to use the GCN model  to creatThe architecture of the graph neural network is illustrated in In order to test the ability of the above descriptors to distinguish protein subcellular locations, we used t-distributed stochastic neighbor embedding (t-SNE) to visualize the dimension reduction projection of the handcrafted descriptors . The cytg and c in the SVMs, and number of trees in the RFs was determined by a grid search. A 10-fold cross validation was employed, in which the protein dataset was randomly partitioned into 10 equally sized subsets. Of the 10 subsets, a single subset was retained as the validation set, and the remaining nine subsets were used as training data. The process was repeated 10 times, with each of the subsets being used once as the validation set. We used the accuracies and F1 scores obtained by the 10-fold cross validation to evaluate the model performance.In addition, we employed a support vector machine (SVM) and random forest (RF) to build protein location classifiers for these handcrafted descriptors. Libsvm-3.24 was used to build the SVM models . The modIn order to build an efficient feature set of protein structures for location analysis, we performed feature fusing and tested whether multiple descriptors can yield better results. Two fusion methods, namely feature concatenating and decision voting, were used. The first method concatenated the feature vectors after the feature selection, and then fed them into classification models, while the second method averaged the output scores from the models trained on individual descriptors. Here, the derived CNN and GCN features were also used in the feature fusion. The heat maps in Furthermore, we tested the combinations of three or four descriptors. The combination of the sub-structure frequency, Gauss integrals, and chemical features was the best-performing, and can achieve improvements of 3.27% accuracy and 0.028 F1 score compared to the best single descriptor a. The coWe also investigated the improvements achieved when incorporating the structure information with protein sequence features. Here, two sequence-based predictors of protein subcellular location, namely, Hum-mPLoc 3.0  and Deept-test was conducted on the output probability scores to measure whether the differences between the models before and after incorporating the structure features are significant. Most of the cases obtained a p-value of <0.001. This finding demonstrated that feature extraction from 3D protein structures could capture a large amount of information that cannot be learned from sequences. Second, the fusion at the feature level achieved a better performance than that at the decision level, as the multi-kernel mechanism can work well in feature fusion. Third, the kernel weights which searched for Hum-mPLoc 3.0 and the structure features were 0.83 and 0.17, respectively, while those for DeepLoc and structure features were 0.57 and 0.43, respectively. This finding indicated that the two parts of the information played important roles in the protein location classification.The results of the 10-fold cross validation are presented in In order to obtain an intuitional observation of the predictions, we also listed some specific protein structures in In this work, we constructed a multi-view representation for 3D protein structures and demonstrated its utility for protein subcellular location analysis. This work has two main contributions. First, this study built a structure representation from a wide variety of perspectives, which facilitated the search of location-related information in the protein structures. Some approaches, including the calculation of sub-structure frequency and the GCN framework, were designed and first used in profiling the protein localization. Our experimental results suggest that some methods can achieve promising results. Second, this study demonstrated that incorporating 3D structure information can improve the performance of protein subcellular localization classification. Hence, protein structures might contain some location-related information  that do not exist in amino acid sequences.Currently, the discovery of the relationship between 3D protein structures and subcellular locations is still in its infancy stage. Our future work will search for more effective structure descriptors and further explore the use of deep neural networks in this field. Although the performance of the current neural networks is not good, the models are likely to benefit from more structure data in future works. The predictions of the entire human proteome by AlphaFold, which is a state-of-the-art machine learning model for protein structure prediction, have been made freely and openly available very recently , giving


Complexome profiling (CP) is a state-of-the-art approach that combines separation of native proteins by electrophoresis, size exclusion chromatography or density gradient centrifugation with tandem mass spectrometry identification and quantification. Resulting data are computationally clustered to visualize the inventory, abundance and arrangement of multiprotein complexes in a biological sample. Since its formal introduction a decade ago, this method has been mostly applied to explore not only the composition and abundance of mitochondrial oxidative phosphorylation (OXPHOS) complexes in several species but also to identify novel protein interactors involved in their assembly, maintenance and functions. Besides, complexome profiling has been utilized to study the dynamics of OXPHOS complexes, as well as the impact of an increasing number of mutations leading to mitochondrial disorders or rearrangements of the whole mitochondrial complexome. Here, we summarize the major findings obtained by this approach; emphasize its advantages and current limitations; discuss multiple examples on how this tool could be applied to further investigate pathophysiological mechanisms and comment on the latest advances and opportunity areas to keep developing this methodology. Proteins, the so-called “workhorses” of life, are the tools that make living machines work . The vasIn situations where impaired interaction of the elements of a protein complex affect its proper formation; e.g., due to genetic mutations, the related cell process(es) could be compromised and result in biological dysfunction. Higher complexity in multicellular organisms is of course accompanied by larger complexomes than those from unicellular species. Therefore, alterations in protein complexes may affect not only multiple cell processes, but also lead to severe pathophysiological issues at the tissue/organ level. An integral elucidation of both complexomes and protein interaction networks; i.e., interactome  under diin vivo by using novel and more reliable methods.Numerous biochemical, biophysical, structural, genetic, microscopy and mass spectrometry (MS) approaches have been applied to characterize protein complexes. Although these methods have different principles, in most cases, they require experimental interventions, cell/tissue fractionation, protein extraction and/or time-consuming protocols prior to data collection and analyses. Besides, the amount of information obtained is often limited to one or a small set of protein complexes. The recent breakthrough in quantitative high-throughput technologies and bioinformatic tools has substantially increased the efficiency and quality of the large-scale identification of protein interactors . AccordiA systematic approach known as complexome profiling (CP) has recently been introduced as a straightforward and unbiased way to explore protein complexes . CP combThe first requisite to carry out a CP experiment is the collection of biological material; e.g., tissue pieces/biopsies, cultured cells, enriched cell fractions, purified organelles, etc. These materials might also come from previous experimental interventions. Homogenization and cell fractionation methods should aim to keep the native state of protein complexes. For solubilization of membrane proteins, it is recommended to use mild, non-ionic detergents; e.g., Triton X-100, NP-40, dodecyl-maltoside, octyl-glucoside or digitonin . Next, nPrevious to MS analysis, peptides are usually separated based on their hydrophobicity by reversed-phase high-performance liquid chromatography (RP-HPLC) followed by electrospray ionization (ESI) to produce gas phase ions . The majde novo peptide sequencing   or intenAs a ground-breaking “-omics” method, CP has been rapidly developing and spreading among the scientific community. It is thus not striking that in less than a decade, multiple strategies have complemented or even improved its scope. At the same time, several limitations of CP have been circumvented gradually by including novel MS-based strategies, specific adaptions in sample processing and new tools for data analysis. All variants of CP do share a common goal nonetheless: to unravel the composition of protein complexes, as well as their abundance, stabilities, apparent molecular masses and stoichiometries.in vivo  and density gradient ultracentrifugation (DGU) have been implemented in different CP setups. Using SEC, native proteins are separated by filtration through a gel matrix (resin), which consists of spherical porous beads of different sizes depending on the desired range of molecular masses . Elution13C, 2H, 18O, 15N) or unlabeled, respectively -captured protein complexes . After sectively . The speectively .A major challenge of DGU relies on retrieving of fractions without manual disturbance of the resolved layers. To account for this issue, several strategies and devices have been developed, including commercially available automatic fractionators and freezing the gradient after centrifugation followed by cryo-slicing to fractionate samples consistently . LocatioAdditional untargeted MS-based assays have been described in the literature, which share the same principles of a CP experiment. The group of Matthias Mann developed protein correlation profiling (PCP) by combining DGU and quantitative MS to identify and validate several components of the human centrosome . A few ybona fide component of the cytochrome c oxidase  has recently been established as the first website for depositing and sharing CP data . A standIt should be mentioned that the data available through CEDAR are just a fraction of the CP studies reported in the literature. At present, ∼145 publications mentioning the term “complexome” (or complexomic/complexomics) have been found in PubMed (NCBI) records; ∼62 publications correspond to CP-related studies, reviews, methods and software tools . In ∼40 1FO-ATP synthase (complex V). Complexes I, III and IV couple the energy released during electron transfer to oxygen to the generation of an electrochemical gradient of protons across the inner mitochondrial membrane (IMM). The proton gradient not only drives the synthesis of ATP, but also other processes such as metabolite transport, protein import, redox and Ca2+ homeostasis, fusion/fission, signaling and cell death. These organelles do also contain their own genome, also known as mitochondrial DNA (mtDNA), which encodes for a few but essential subunits of OXPHOS complexes. The roles of mitochondria are thus not limited to their energy duties. These organelles are multi-functional “hubs” critical to almost every cell process.Mitochondria are traditionally known as the eukaryotes’ “powerhouses” since they contain all the enzymes required to generate ATP via the oxidative phosphorylation (OXPHOS) pathway. This process is catalyzed canonically by four respiratory chain complexes  and a FDespite of the great progress in molecular characterization and structure elucidation of numerous mitochondrial protein complexes, mostly OXPHOS- and mitoribosome-related proteins, a large number of mitochondrial PPIs remain elusive. In the last years, however, CP has proven valuable for identifying novel protein interactors, validating previous results and shedding light on intricate assembly pathways. In this section, we summarize these findings and describe how CP has boosted the analysis of protein complexes in the mitochondrial research field. Expectedly, most of these findings are again related to components or mediators of the assembly of OXPHOS complexes, energy metabolism-linked proteins and mitoribosomes.TMEM126B knockdown in 143B osteosarcoma cells led to ∼95% specific decrease in CI-containing supercomplexes, i.e., supramolecular associations of complexes I, III and IV. These results thus proposed TMEM126B as a CI assembly factor. In an earlier report using HEK293 cells, Wessels and co-workers identified with a similar approach two other assembly factors of CI: C6ORF66 and C3ORF60 . CI is the largest redox enzyme of the mitochondrial respiratory chain constituted by ∼45 subunits depending on the species. CI generates proton-motive force driven by the transfer of electrons from NADH to ubiquinone . Althoug C3ORF60 , currentP and PD, before forming the entire enzyme. This study also provided insight into the specific involvement of earlier reported assembly factors and novel interactors. Complex IV (CIV) assembly-related chaperone COA1 and TMEM186 were clearly found associated with membrane arm intermediates that also interact with TMEM126B, NDUFAF1 and ECSIT. At present, all these proteins have been recognized as true components of the so-called mitochondrial CI assembly (MCIA) complex , which occurs only in fungi. Deletion of Coi1 resulted in severe alteration of mitochondrial function, diminished amounts of CIV-associated heme, defective assembly of complexes III, IV and their supercomplexes, as well as accumulation of assembly intermediates. Vidoni and co-workers reported that the short isoform of myofibrillogenesis regulator 1 (MR-1S) associates with chaperones PET100 and PET117 to mediate human CIV assembly  and apolipoprotein O-like protein (APOOL/MIC27) as potential components of the human MIB complex . Huynen m-AAA protease-binding protein in complexome profiles (2+ uniporter (MCU) complexes.The IMM contains other key protein complexes involved in proteolytic events, fusion/fission of mitochondrial membranes, regulation of cristae morphology and cell signaling. Some of these proteins belong to the SPFH  family and usually arrange as large scaffolds. To have a clearer picture, Wai and co-workers implemented CP to unveil the interaction partners of stomatin-like protein 2 (SLP2) and at least two proteases, PARL and YME1L, in immortalized embryonic fibroblasts mitochondria . SLP2 woprofiles . MAIP1 wA. thaliana H oxidoreductases. This species also expresses an alternative oxidase and its complexes III and IV are firmly associated as supercomplexes. In comparison to A. thaliana, the abundance of complexes II and V was particularly low, suggesting a shift in stoichiometry of the OXPHOS complexes in the IMM of this plant. Rugen and co-workers took a more focused CP strategy to investigate the composition of the mitoribosome in A. thaliana  and complexes II, III and V remained unaffected. Notably, even if complex V forms large macromolecular assemblies that are independent of respirasomes, its interconnection to other OXPHOS complexes became apparent by the accumulation in COX4I1/2 double knockout cells not only of the F1 segment, but also of the early FO intermediate containing subunits ATP5MF, ATP5PB, ATP5ME and ATP5MG.Respiratory complex IV is a terminal oxidase that transfers electrons from soluble cytochrome tability . The lacMT-CO1 and MT-CO2 that resulted in truncated versions of subunits COX1 or COX2, respectively , in cybrids with truncated COX1 and COX2 no CIV was formed but S0 remained stable. In the absence of COX1, the only detected assembly intermediate contained subunits COX4 and COX5, whereas the lack of signals of all other unassembled CIV subunits indicated their degradation. Conversely, cells lacking COX2 displayed, in addition to the heterodimer intermediate formed by COX4 and COX5, a larger intermediate containing also subunit COX1. Importantly, these authors described the formation of a heavier S0 containing several subunits and assembly factors of CIV, which has also been corroborated by bidirectional co-IP assays. The association of this partially assembled unconventional intermediate of CIV bound to S0 exemplified how alternative biogenesis of respirasomes could occur in disease-related conditions.Similarly, Lobo-Jarne and co-workers analyzed the effects of the lack of CIV holoenzyme on the stability of respiratory complexes and respirasomes using trans-mitochondrial cybrids of human osteosarcoma 143B cells harboring nonsense mutations in mitochondrial genes ectively . In contThe intricate assembly process of OXPHOS complexes requires not only expression and import of functional subunits as well as ancillary proteins produced by cytosolic ribosomes, but also proper expression and insertion of the mtDNA-encoded components. Therefore, diseases featured by energy metabolism alterations may also result from mitochondrial translation deficits. In mammals, the 13 mtDNA-encoded subunits of OXPHOS complexes are synthesized by the mitochondrial translation machinery . This inPerrault syndrome is a rare genetic mitochondrial disorder with a relatively mild phenotype characterized by sensorineural deafness and ovarian dysgenesis. Genetic mutations in several genes encoding mitochondrial and peroxisomal proteins have been identified as direct causes for this disease, including CLPP, HSD17B4, LARS2, HARS2 and TWNK. Chatzispyrou et al. identified two unrelated female patients presenting Perrault syndrome symptoms where whole exome sequencing revealed no mutations in any of the enlisted genes causing this syndrome . Instead1 segment and to FO early subassemblies that are not detected in control cell lines also indicated mitochondrial translation impairment in these patients.A study of two subjects with mutations in MRPS2, a structural component of the mt-SSU, has linked these mutations to combined OXPHOS complex defects with sensorineural hearing loss . The comA comparable decrease in the abundance of the mt-SSU has been found in a knockout cell line of METTL15, a N4-methylcytidine methyltransferase responsible for modification of the 12S mitochondrial rRNA . The abs0) cells, devoid of mtDNA, are incapable to respire due to the lack of essential subunits of OXPHOS complexes thus depend entirely on anaerobic ATP synthesis , but not supercomplexes S0 and S1, has been found as well as partial destabilization of the complexes α-ketoglutarate dehydrogenase and branched-chain α-keto acid dehydrogenase , which is caused by mutations in CLPB, an ATP-dependent refoldase (dissagregase) of the mitochondrial intermembrane space. In a recent study comparing two subjects with bi-allelic mutations in patients . BesidesThe implementation of CP analysis to help tackle the many open questions regarding PPIs is not limited to mitochondrial biology. CP is swiftly spreading to other scientific fields and already utilized to investigate protein complexes from other cell compartments and different species. However, there are still key limitations in the current setups, which open the door for further development and expanding its reach. In this final section we discussed not only the most recent advances in CP, but also its application to study protein complexes beyond the mitochondrion.The biochemical native separation techniques used to date for complexome analysis cover wide molecular mass ranges. Although each of these methods can easily be adapted to enhance the separation resolution of a certain mass window of interest, alternative strategies or modifications to the currently applied methods are envisioned, especially to improve separation resolution of large complexes.Nicothiana benthamiana with a similar resolution to the one of density gradients to distinguish ribosomal subunits from monosomes and free proteins (Replacement of detergents by amphiphilic polymers (amphipols) could also help improve the analysis of membrane protein complexes. Amphipol-enclosed transmembrane complexes are more stable during separation than the ones solubilized with detergents . Moreoveproteins . FFF couResolution of complexome profiles might be significantly improved by increasing the number of fractions in native-PAGE- and SEC-based CP setups. The more fractions the more MS data acquisition time is required however. Hence, a major limitation of CP is LC-MS time; especially if analysis of multiple experimental conditions is intended. Duplexing and triplexing with conventional SILAC/SILAM as well as multiplexing using isobaric labeling compounds are promising strategies for increasing the throughput of CP while reducing MS measuring times . IncorpoOn the one hand, SILAC/SILAM-based quantifications enable more precise comparisons between samples or conditions than label-free quantification methods, and software tools have already been developed to facilitate data analysis . SILAC iMultiplexed CP (MCP), on the other hand, employs a set of isobaric labeling compounds, such as tandem mass tags that allows pooling a higher number of samples together after tryptic digestion. With commercially available tags, it is now possible to combine peptide solutions from up to 16 samples together before LC-MS/MS analyses. The quantitative advantage of using multiplexing isobaric compounds is that these molecules contain a reporter ion that is cleaved off during high collision dissociation together with precursor ion fragmentation. Therefore, quantifications of the reporter ions are performed at the MS2 level in the same spectrum used for peptide identification. Nevertheless, state-of-the-art equipment capable of MS3 or quantifications based on complement reporter ions are required to avoid quantification distortion due to interfering co-isolated ions .Cross-linking mass spectrometry (XL-MS) has emerged as a powerful technology for interactome and structural biology studies in mitochondria . ChemicaBefore structure analysis, determination of the optimal concentrations of cross-linkers and protein is strictly required to abate artifacts. To help circumvent this, a hybrid workflow termed in-gel cross-linking (IGX-MS) has recently been developed . In thisIGX-MS not only makes time-consuming experimental optimization steps  nearly obsolete but also decreases the required protein amounts as well as undesired over-length cross-links. In contrast to classical in-solution XL-MS workflows, IGX-MS allows the differentiation of conformation- and interaction-specific distance restraints as it has been shown for various protein complexes, among them complexes I and V from bovine heart mitochondria. IGX-MS seems specially promising for future modeling studies that aim at characterizing co-occurring protein complexes with different stoichiometries or assembly states.It has lately been demonstrated that CP and XL-MS are highly complementary and could provide valuable insights into the macromolecular organization of, for instance, the mitochondrial complexome. By combining CP and XL-MS data, Hevler and co-workers build a detailed atomic model of bovine CIV associated with dimeric apoptosis-inducing factor 1 (AIFM1) . CP analChaetomium thermophilum  and computational modeling. This structural proteomics approach allowed simultaneous description of the abundance, PPIs and structure profiling of 1/3 of the proteome of mophilum . These r , electroTwo research groups aiming to characterize the protein composition of chromatin complexes have made use of separation of protein complexes by size and LC-MS/MS analysis followed by correlation profiling. One utilized sucrose density gradients for separation of complexes containing chromatin-associated protein Wdr82 . The othConversely, CP variants to study RNA-protein complexes have been developed. Yet, these approaches have been particularly used for studying ribonucleoproteins (RNPs). Applications of RNP complexomics are generously reviewed in SEC is another method that has successfully been used for separation of RNPs from complex mixtures . It provClassic CP has also been useful for dissecting protein composition of mitoribosomes as they enter regular BN-gels . For larA potential issue for characterization of RNA-protein complexes may result from the poor stability of RNA molecules, which are also prone to degradation by RNases. In this case, RNA-protein interactions would be lost and leading to identification of not only sub-assemblies but also artifacts. To avoid this, prior to CP, separation of these complexes should be thus performed using RNase-free materials and RNase inhibitors to keep stable both the protein and RNA interactors . AlternaMany species still lack functional annotation for a large portion of their genomes despite their significance as pathogens or drivers of major ecological processes. This is often owed to poor conservation, missing orthologues and the additional pitfall that the most unique biology that is not found in standard models, remains enigmatic. Systematic knowledge of PPIs can allow us to place these proteins into pathways, compartments and interaction networks. CP represents a clear choice to help narrow this gap and has the added benefit that no genetic intervention is required, which allows immediate assessment of species without a well-developed toolkit.Plasmodium species. Integration with machine-learning led to identification of ∼20,000 putative PPIs arranged into ∼600 protein clusters, which were used to map the interactome  from the single-cell green alga nhardtii . AuthorsCandidatus Methanoperedens, CP has proven invaluable (Finally, even in microorganisms that are not currently obtainable in axenic cultures, such as the archaeon valuable . In thisATP6V1E1 and ATP6V1A, encoding subunits E and A from the V1 segment of the V-type ATPase, respectively. Primary skin fibroblasts from three patients, one with a mutation in E1 and two with mutations in subunit A were compared to controls and to an earlier described subject presenting cutis laxa with a mutation in subunit ATP6V0A2 of the VO segment. To further investigate this, enriched Golgi preparations have been analyzed by BN-PAGE-based CP. Migration patterns of healthy controls revealed three assembly intermediates of the V1 segment, the VO segment and a highly abundant, fully assembled V1VO-ATPase complex. In contrast, in patient fibroblasts with mutations in subunits of the V1 segment, only the VO intermediate and traces of the holo-complex have been detected. Comparably, the mutation in ATP6V0A2 decreased the abundance of intermediates of both domains as well as the holo-complex. The destabilization of the fully assembled V1VO-ATPase evidenced by CP contributed to detailing the molecular consequences and pathogenicity of these mutations.Although to date the vast majority of CP analyses in patients have been focused on disorders affecting mitochondria, its application is broadening nonetheless. The effects of pathogenic missense mutations in structural subunits of the V-type ATPase on the stability of the holoenzyme have been characterized by this method . By wholS. cerevisiae as a model system and fibroblasts from a Zellweger syndrome patient, Nübel and co-workers linked accumulation of miss-targeted peroxisomal membrane proteins into mitochondria to metabolic and morphological abnormalities of this organelle (In a recent CP study of peroxisome-deficient cells using peroxins deletion strains of rganelle . ComplexThese two studies exemplified how CP offers a straightforward option to shed light on the pathophysiological roles of critical proteins located in cell compartments other than mitochondria and the specific consequences of impairments in protein complexes and PPIs resulting from genetic and/or biochemical defects. CP thus holds a great potential to help not only unveil the molecular mechanisms of diseases, but also develop novel treatments.In a very short time, CP has revolutionized the way we study mitochondrial protein complexes and significantly increased the amount of data collected from single unbiased experiments. Research findings obtained by this methodology include, for instance, identification of novel OXPHOS-related subunits and assembly factors; stepwise description of intricate assembly pathways; molecular evidence on the role of accessory subunits and existence of tissue-specific subunit isoforms of CI; better insight on the formation of respirasomes and the supramolecular organization of the MICOS/MIB complexes as well as the unique composition of OXPHOS complexes and mitoribosomes in different species. Notably, CP has also been invaluable to better understand the effects of mutations found in mitochondrial genes from patients as well as in disease models. Recent developments in duplexing and multiplexing CP experiments not only enable accurate proteomic quantifications, but also greatly increase the feasibility of including this approach in future clinical diagnosis. We have also discussed innovative strategies exemplifying how CP easily complements other state-of-the-art methods such as XL-MS, RNAseq and cryo-EM. However, there is still a lot of room for improving this method and keep expanding its reach. It is projected that CP will further evolve with the advent of the next generation of LC-MS instruments and proteomic strategies as well as upcoming complexome data analysis software. Finally, we also encourage the scientific community to apply CP for studying protein complexes from cell compartments other than the mitochondrion and share publicly the resultant complexome datasets through CEDAR.


Here, we present an online platform for G protein research with reference data and tools for analysis, visualization and design of scientific studies across disciplines and areas. This platform may help translate new pharmacological, structural and genomic data into insights on G protein signaling vital for human physiology and medicine. The G protein database is accessible at  The theoretical G protein ‘couplome’ in human spans the potential interaction of these 16 G proteins with ∼800 receptors totaling 12 800 couplings or non-couplings. Recently, breakthroughs in biosensor development (https://gproteindb.org/structure/gprot_statistics) which make up a majority of the new and nearly all cryo-EM GPCR structures  module in conjunction with yadcf.js (https://yadcf-showcase.appspot.com) which support sorting and filtering. The visualizations were written in JavaScript with the largest use of the D3.js framework (https://d3js.org) to generate SVG figures and animations. While initial versions of some resources were published in . GPCR–G protein couplings have a confidence filter that by default restricts the quantitative couplings to those supported by a second dataset, while the qualitative (primary versus secondary) couplings in Guide to Pharmacology are instead typically supported by multiple literature references. The requirement for shared coupling is applied on the G protein level—also for families—to avoid the issue of apparent support of family couplings when subtypes differ. G protein coupling selectivity profiles were visualized in a Venn diagram (https://gproteindb.org/signprot/statistics_venn) which, by GPCR class, intersects the receptor sets that couple to the four G protein families. This differs from (https://gproteindb.org/signprot/statistics_tree). This differs from  integrates data from quantitative profiling studies (Emax/EC50), pEC50 and Emax values can differentiate receptors with strong or no/weak coupling to a G protein subtype or family. Furthermore, a ‘G protein selectivity Venn’ (https://gproteindb.org/signprot/statistics_venn) can discriminate the receptors in each GPCR class by their profile of combined G protein family couplings  maps G protein family couplings onto receptor classification trees – one for each GPCR class and further classified through an alphabetic listing of ligand types (e.g. peptide or lipid) and receptor families sharing endogenous ligands (e.g. serotonin receptors)  Figure . These t‘Structures’ (https://gproteindb.org/structure/g_protein_structure_browser) include all unbound and GPCR complex structures from the Protein Data Bank  of >3000 GPCR–G protein complexes that are not yet covered by experimental structures but can be modelled based on a similar template from the same GPCR class and G protein family. The structural topology of residue positions can be mapped in a ‘snakeplot’ . The refined and modeled structure complexes expand the GPCR–G protein ‘couplome’ that can be studied across basic and applied structure-based research.The G protein ata Bank . We addie Figure . Structue Figure . The obte Figure  or expor’ Figure  from the‘Interface interactions and profiling’ (https://gproteindb.org/signprot/matrix) to identify residue interactions and their frequencies  from literature annotations  summarizes sequence, structural and mutagenesis data. Residue positions are mapped to a ‘snakeplot’ which can be custom colored or display stored functional data about the receptor interface (below), genetic variants   can be customized to cover specific G proteins, sequence segments or common residue number positions (‘Generic residue number tables’ (https://gproteindb.org/residue/residuetable_gprot) tabulate G protein-specific and common residue positions  Figure . The ‘G ositions   features data-driven prediction of receptor residue determinants of G protein activation  and that these make up the majority of receptor cryo-EM structures, the annotated structures presented herein will help substantially to keep track of new structures and selecting study templates. Furthermore, as cryo-EM structure determination is challenging at a high resolution, the refinement based on other experimental templates model-in many missing sidechains or loop segments. While the vast majority of GPCR–G protein complexes lack an experimental structure, our structure models greatly expand the ‘couplome’ for which we can generate structure-based hypotheses across mutagenesis, dynamics, kinetics, molecular mechanistic and drug design studies. The ‘Interface interactions and profiling’ tool may shed new light on how receptors bind G proteins at the interface  and GitHub (https://github.com/protwis/gpcrdb_data). Documentation is available at https://docs.gpcrdb.org. All open-source code can be obtained from GitHub (https://github.com/protwis/protwis) under the permissive Apache 2.0 License (https://www.apache.org/licenses/LICENSE-2.0).All data is available via the web (Section ‘GproteinDb’ in gkab852_Supplemental_FileClick here for additional data file.