Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with  Judgment of what constitutes a stable complex is based on available scientific literature, experimental evidence and a consensus decision made by two curators. Homomultimers are only curated if it has been demonstrated experimentally that multimerization is required for their function or in cases where a heterodimeric complex exists and at least one of the two participants forms an orthologous homodimeric complex. Polymers are excluded and large, multi-complex machineries are reduced to their functional subcomplexes because the final assemblies are often dynamic, rather than a single instance of a functional unit existing at any moment of time. The subcomplexes of large assemblies are annotated with GO terms relating to the larger machineries, for example, all spliceosome sub-complexes are annotated with the GO term GO:0005681  to facilitate searching. Unlike other compendia of complexes, such as CORUM  is an important model organism for our understanding of the biology of all eukaryotic organisms and significant effort has gone into identifying all its stable complexes. However, until recently, information about such complexes was scattered across many publications and in different databases. An early effort to concatenate these data was the, now deprecated, MIPS yeast complex database   and also a compendium of 408 literature-derived, manually curated complexes based on small-scale experiments  (database . Domain-database  and geneome.org)  provide ome.org) ,15 proviome.org) ,17 or afome.org)  but onlyome.org) . In 2009CYC2008) . While bet al. and complexes predicted based on all experimental protein–protein interactions in the IntAct molecular interaction database CYC2008—408 manually curated complexes YHTP2008—400 predicted complexes IntAct-LT—332 predicted complexes derived from low-throughput experiments in IntAct IntAct-HT—689 predicted complexes derived from high-throughput experiments in IntAct To enable direct comparison of protein complex components represented in the Complex Portal and IntAct, gene locus IDs in CYC2008 and YHTP2008 were mapped to UniProt ACs using the UniProt Mapping service web application (UniProt Release November 2019). Ambiguous mappings, where a locus could be mapped to more than one UniProt entry with the same sequence, were expanded to include all potential mapping pairs.Complex Portal data were exported in ComplexTab format. Where complexes are part of larger assemblies (sub-complexes) these were expanded to provide a list of unique UniProtKB identifiers. Sets of paralogous ribosomal proteins were expanded to a full list, therefore all potential UniProtKB identifiers were included in the analyses. The expansion of paralogous proteins leads to an over-inflation of the subunit count per complex for the two ribosomal subunits but is the only way to include all proteins in the comparative analysis. As stoichiometry information is only available in a limited number of Complex Portal and IntAct entries and often missing due to a lack of available evidence, it was ignored and comparisons were based on unique protein identifiers only. Nonprotein complex members such as nucleic acids and small molecules were not included as these are not provided in full by any resource other than the Complex Portal.n = 3.IntAct complexes were derived from all yeast–yeast interactions in IntAct release 228. Interactions were exported in MI-TAB2.7 format and split into those derived from papers with 100 or less interactions/paper and those with >100 interactions/paper. Complexes were predicted using the Cytoscape App ClusterONE  using deFor the selection of genetic interactions, we used the global yeast genetic interaction network, the first comprehensive genetic interaction map in any organism . The netInteracting protein pairs in a complex (i.e. co-complex pairs) were inferred by matrix expansion of all complexes. UniProt identifiers were mapped to ORFs in order to compare inferred physical interactions and genetic interactions as the latter are provided as ORFs. Background pairs (i.e. ‘no co-complex pairs’) were defined as those pairs of proteins present only in different complexes. The fractions of co-complex and background pairs with positive and negative interactions were calculated, considering only pairs of proteins whose genes were present in the genetic interaction network . Statistical significance was calculated by Fisher’s exact tests.In addition to genetic interactions, we evaluated the overlap of co-complex relationships with the co-expression, co-localization and co-annotation functional standards. In all cases, only protein pairs for which functional data were available were considered. The co-expression standard was derived from the MEFIT co-expression network, which integrates data from multiple microarray datasets . Pairs wz-score normalized using the median and the standard deviation of the values for the background proteins. The median z-score value of the proteins in complexes was used for the graphical representation of the result. Statistical significance was evaluated using two-sided Mann–Whitney U tests. For each binary feature, fold enrichment was calculated as the ratio of complex members with that feature divided by the ratio of noncomplex members with that feature. Statistical significance was calculated by two-sided Fisher’s exact tests.To obtain a comprehensive view of the differences between those proteins participating in complexes and those that do not, the following characteristics were compared: genetic interaction degree calculated on array genes and averaging estimates across the different alleles of a gene , co-exprU tests.The relative difference in transcript counts, expression variance, protein abundance and protein half life was calculated for co-complex and background pairs. For every pair and measure, we calculated the maximum (MAX) and minimum (MIN) value within the pair. The relative difference was then calculated as (MAX-MIN)/MAX. The larger this score is, the larger the difference between the pair of proteins/genes. Statistical significance was calculated using two-sided Mann–Whitney http://thecellmap.org .Data manipulation and visualizations were performed in R , Python and Excel. Unique versus shared sets of complexes were identified using Venny . Some complexes that are included in these sources have not been included in the Complex Portal because they have since been identified as part of a bigger complex or they lack clear experimental evidence for their existence and their functional role in vivo. These putative complexes are kept in a separate list and are periodically revisited to see if more evidence has been published. Collaborations with SGD are ongoing and we update existing entries and add new ones when new evidence comes to light.Compared to other resources, the Complex Portal provides added value through its greater scope of annotation. Each complex entry has a manually annotated description of their function and physical properties and includes stoichiometry and topological information when available. The Evidence and Conclusion Ontology (ECO)  is used et al. in 2009. We compare these two manually curated datasets with each other and with corresponding experimentally derived predicted complexes from YHTP2008 and IntAct release 228 (16 November 2019). The IntAct data were split into low- and high-throughput publications setting a cut-off at 100 interactions per publication. See Table The yeast complex dataset published in the Complex Portal is the first manually annotated yeast complex dataset since the publication of CYC2008 by Pu The two manually curated datasets share 1543 proteins : 387 proteins are unique to the Complex Portal and 81 unique to CYC2008 Table ; overallThe Complex Portal contains 589 yeast complexes compared to 408 in the CYC2008 dataset, a 44% increase Table . They shThe IntAct yeast interactome contains a total of 124 918 yeast–yeast binary interactions containing 5850 unique proteins or 97% of the yeast proteome (proteome = 6049 proteins) and 18 interactions between a yeast protein and a yeast complex. A topological clustering analysis of the IntAct yeast interactome was performed using the Cytoscape App ClusterONE, restricting accepted clusters to those with three or more proteins. The resulting clusters encompassed only just over half the proteome  and predicted 332 complexes from low-throughput publications (IntAct-LT) and 689 complexes from high throughput publications (IntAct-HT)  evidence. The overlap between any curated and predicted dataset never exceeded 20% in any comparison with a Jaccard Index of 1.0 Figure . The IntThe low level of overlap between manually-curated and predicted complexes may be the result of a combination of factors: First, experimentally derived interactomes contain a lot more proteins than the complex datasets but incorporate fewer validated evidence than the often thoroughly and even functionally validated interaction evidence used to define curated complexes. Secondly, the need for a reductionist representation of the interactome, where multiprotein associations are reduced to binary pairs via spoke expansion methods introduces a bias in the internal topology of PPI evidence networks, potentially generating spurious associations. Finally, prediction algorithms are restricted to predicting heteromers and ClusterOne restricts clusters to size 3 and larger; therefore, any heterodimeric complexes are not included in the predicted datasets and were removed from the above comparisons for the overlap of complexes between the five datasets.The properties of protein complex members were characterized using a panel of numerical and binary features . Genes cMore than 70% of proteins in each dataset are only found in a single complex , and theP < 0.0005, There are five proteins that are found in ≥4 complexes in the Complex Portal where the complexes are annotated to two or more unrelated pathways or complexes and three of these proteins are also found in more than one subcellular location when part of multiple complexes. Four of these proteins, H4 (P02309), LTV1 (P34078), SKP1 (P52286) and TAF14 (P35189), are regulatory subunits and one, PP12 (P32598), is a protein phosphatase . These fGenetic interactions identify combinations of genes that yield unexpected phenotypes when simultaneously mutated. Negative genetic interactions identify cases with more severe phenotypes than expected given the individual mutant phenotypes, whereas in positive genetic interactions the resulting phenotype is healthier than expected. Both types of genetic interactions are a powerful tool for the characterization of genes and to elucidate the functional wiring of the cell .P < 0.05, Figure Since genetic interactions identify potentially functional relationships between genes, we evaluated whether gene pairs coding for proteins within the same complex were enriched in genetic interactions using the global genetic interaction network . GeneticP < 0.01 for all pairwise comparisons). This suggests that, in protein complexes with unknown internal connectivity, the analysis of genetic interaction profiles of the individual components may discriminate direct from indirect contacts.Identifying the direct physical contacts within protein complexes can reveal sub-complex modules, improve the characterization of protein function, and help to interpret how mutations affect the phenotype. The Complex Portal is the only dataset that describes the internal connectivity of complexes, with detailed information for 237 complexes that have 3 or more participants. The functional relevance of these data were evaluated by comparing genetic interaction profiles  of direct and indirect contacts within protein complexes. These profiles are quantitative phenotypic signatures and revealed a higher similarity for gene pairs coding for proteins in direct contact . However, these show a poor overlap when compared to predicted complexes. This may be due to large-scale affinity purification data producing clusters of apparently highly connected proteins as well as the presence of transient interactions in these datasets. This poor overlap also highlights that experimental protein–protein interactomes are a limited predictor for functional complexes which highlights the continuing need for a manually curated complex database.Most proteins are found in only one complex and those found in two or more complexes tend to have the same function in multiple complexes. Only five proteins found in four or more complexes are linked to different processes showing that protein function is fairly conserved when they are part of complexes.We highlight that there is a relative enrichment of multimolecular machines in the nucleus and the nucleolus. These complexes are often involved in nucleic acid-related metabolic processes like replication, transcription and translation, plus other processes where multimolecular assemblies are the predominant functional drivers such as ER to Golgi and trans-Golgi transports.We found that the co-complex pairs overlap significantly with genetic interaction, co-expression, co-localization and co-annotation datasets, which highlights the functional relevance of co-complex membership and the potential of protein complex datasets to address questions of biological interest. Members of the same complex also tended to present more similar regulation patterns that reflect the role of the protein complex as a coordinated biological unit. Genes coding for co-complex pairs in physical contact exhibited more similar patterns of genetic interactions, illustrating that the structural organization within complexes is key to interpret the results of functional studies. Importantly, contact information within complexes is only available in Complex Portal and not in the other complex datasets.Escherichia coli, and continually improve our coverage of the human and mouse complexes, it will also be possible to improve our understanding of the evolution of these assemblies .The complete yeast complexome is available for download from gkab077_Supplemental_FilesClick here for additional data file.


Protein calories consumed by people all over the world approximate 15–20% of their energy intake. This makes protein a major nutritional imperative. Today, we are facing an unprecedented challenge to produce and distribute adequate protein to feed over nine billion people by 2050, in an environmentally sustainable and affordable way. Plant-based proteins present a promising solution to our nutritional needs due to their long history of crop use and cultivation, lower cost of production, and easy access in many parts of the world. However, plant proteins have comparatively poor functionality, defined as poor solubility, foaming, emulsifying, and gelling properties, limiting their use in food products. Relative to animal proteins, including dairy products, plant protein technology is still in its infancy. To bridge this gap, advances in plant protein ingredient development and the knowledge to construct plant-based foods are sorely needed. This review focuses on some salient features in the science and technology of plant proteins, providing the current state of the art and highlighting new research directions. It focuses on how manipulating plant protein structures during protein extraction, fractionation, and modification can considerably enhance protein functionality. To create novel plant-based foods, important considerations such as protein–polysaccharide interactions, the inclusion of plant protein-generated flavors, and some novel techniques to structure plant proteins are discussed. Finally, the attention to nutrition as a compass to navigate the plant protein roadmap is also considered. With global populations projected to increase above nine billion people by 2050 , we faceThis review is an attempt to stimulate interest and presents a roadmap to accelerate plant protein science and technology, focusing on plant protein ingredient development and future food creation . In eachAlthough the focus of this review on plant proteins is in the context of advanced nations, it is important to recognize that over one billion people may suffer from protein deficiency . This prProteins are macromolecules comprising linear polymers of amino acid residues joined by peptide bonds which have various structural, functional and nutritional properties that are useful for the food industry in food formulations . The phyDue to the growing interest in producing plant-based foods, it is important to choose a suitable plant protein with good functionality to mimic animal proteins. Knowledge of the structure and functional properties of animal proteins provides opportunities to emerging plant protein sources, with a focus on the development of plant-derived protein ingredients and their use in formulated foods. In a food system, proteins are the most significant functional component because of their structuring, texturizing, emulsifying, foaming, hydration, and nutritive properties . ModifyiBroadly, based on the structure, proteins can be classified as globular , fibrous , or flexible (casein), depending on their physicochemical properties such as amino acid residue quantity and sequence on the polymer chain . The fibPlant proteins are mainly made of globular proteins, present as multimers that are covalently linked together, and are classified as albumins (soluble in water), globulins , prolamins (soluble in aqueous ethanol solutions), and glutelins  . AlbuminFactors that affect the solubility of globular proteins include (a) differences in molecular weight, (b) molecular flexibility, (c) the presence of hydrophobic, basic and acidic subunits on the protein surface and (d) association and dissociation of subunits ,22. For Globular proteins act as good emulsifying and foaming agents due to non-polar regions on their surfaces that promote adsorption to oil–water or air–water interfaces. Albumins from pea and chickpea have lower overall emulsifying properties than globulins, due to low molecular flexibility and hydrophobicity ,30. On tGlobular proteins can also be employed as gelling agents, because when heated, they unfold, exposing non-polar and sulfhydryl groups to the surrounding aqueous phase, causing aggregation via hydrophobic and disulfide bond formation . GelatioSome examples of animal-based globular proteins used in the food industry are whey  and albumins (egg white), which are water-soluble and have excellent foaming, emulsifying, and gelling properties ,39. AlthFibrous or meat proteins have a complex hierarchical construction of fibrous protein bundles embedded inside connective tissue formed of triple helices of collagen, and can be classified as sarcoplasmic, stromal , and myofibrillar  ,43. DepeFilamentous proteins have flexible and disorderly structures; for instance, casein has a random coil structure, with hydrophobic and hydrophilic patches . Casein Simulating the characteristics of gelatin and casein has proven extremely challenging because most known natural plant proteins do not have flexible random-coil structures or micellar structures, respectively. Assembling plant proteins into these superstructures is therefore a key problem in the field. Although certain polysaccharides that could simulate the properties of these flexible proteins exist, more attention could be paid to recreate these flexible structures in plant proteins. This could take shape by introducing random coils in globular plant protein structures or the assembly of various plant proteins to mimic casein micellar structures.To fully realize the potential of plant proteins as described in the previous section, the proteins must be extracted and fractionated intact. This is challenging compared to some animal proteins. For instance, egg white albumin is relatively easy to separate from the egg yolk, and dairy casein is highly resistant to various forms of processing. Historically, alkaline/acid extraction and precipitation has been extensively used to isolate and fractionate proteins. However, early observations show that the alkaline treatment of protein leads to the production of lysinoalanine, a toxic amino acid . Most coThe overview of plant protein extraction was well covered in a recent review . DependiTo further improve the extraction efficiency, additional physical and/or enzymatic techniques can be used. These include ultrasound, pulsed electric field, microwave, high-pressure, pectinases, carbohydrases and proteases . The genAfter extraction, the proteins are purified and separated from the extraction solvent. For alkaline-extracted proteins, isoelectric precipitation is the most common method to obtain high-concentration protein isolates. This process takes advantage of the proteins aggregating at their isoelectric points, which can be separated by decanting and centrifugation. As mentioned earlier, this process has detrimental effects on protein structure and functionality, and typically only the globulin fraction is obtained. A gentler alternative to isoelectric precipitation is ultrafiltration and/or diafiltration, whereby specific protein fractions, including albumins, can be isolated by molecular weight without sacrificing protein concentration and functionality . HoweverIn our opinion, a promising commercial isolation technique to obtain functional plant proteins is membrane filtration. To improve cost efficiency, one could take a leaf from the dairy industry whereby whey protein isolate is produced in bulk using ultrafiltration . Some imWet extraction and fractionation are typically energy- and water-intensive processes and also generate proteinaceous effluents . To imprMost commercial protein ingredients are made up of various protein fractions, such as albumins, globulins, glutelins and prolamins. Even most legume protein isolates rich in globulin contain various globulin subfractions, such as legumin, vicilin and convicilin in pea . These fPlant proteins typically have relatively poor nutritional and functional properties compared to animal proteins, including dairy. This is exacerbated due to the harsh commercial protein isolation conditions, as described in the previous section, which cause some protein denaturation, aggregation, and loss in functionality. Due to aggregation, these non-functional proteins are usually insoluble and may have deleterious effects on the overall performance of the protein ingredient. These non-functional proteins may be separated by simple centrifugation . This enTo improve functionality, the plant proteins may be modified. Two recent reviews comprehensively cover this area ,9. AkharWe would also like to introduce another paradigm in protein modification: the transformation of denatured and/or aggregated non-functional plant proteins into functional plant proteins. Until we adopt better protein isolation techniques, commercial plant protein isolates contain a large fraction of non-functional proteins. Therefore, functionalizing these “inert” plant proteins will unlock greater value for plant protein ingredients. Native and non-functional proteins are in very different conformational states; therefore, the approaches needed to modify each of them are likely to be different. For instance, techniques to improve foaming and emulsifying behavior by protein denaturation will not likely influence the already denatured non-functional proteins. The modification objectives for native and denatured proteins are anticipated to be different; therefore, it is better to separate the non-functional proteins from the native proteins. Some methods to transform non-functional proteins include thermal treatment  and micrHigh-pressure processing (HPP) is a nonthermal processing method where food is subjected to high hydrostatic pressures (200–600 MPa) and held for a certain amount of time (3–5 min). All food components experience the same pressure quasi-instantaneously. Currently, the primary use of HPP is cold pasteurization and shelf-life extension, but HPP has recently been explored for food structure engineering . This isHigh-pressure homogenization (HPH) is a technology that produces a homogenous size distribution of particles suspended in a liquid by forcing through a narrow orifice or valve . HPH canThe solubility of the protein increased, as well as higher emulsifying and foaming properties observed because of decreased particle sizes. For example, Saricaoglu et al. reported an increase in hazelnut protein and lentil protein solubility when the pressure reached 100 MPa ,97. HoweHPH also leads to a change in protein structure. For instance, Yang et al. reported an increase in α-helix and β-turns, and a decrease in β-sheets in faba bean protein was observed after HPH at ~206.8 MPa . HPH cauExtrusion is a process in which a material is forced to flow, under a variety of conditions of mixing, heating and shearing, through a die designed to form or puff-dry ingredients . The higThe high temperature during extrusion can cause protein unfolding due to the breakage of hydrogen bonds . The intExtrusion can also be used as a pre-treatment for other protein modification methods, such as ultrafiltration and enzymatic hydrolysis. Dehulled black beans were processed through extrusion before being extracted and concentrated by ultrafiltration, followed by spray-drying . Peanut Sonication or ultrasound processing is one of the emerging technologies that has shown potential in the food industry . This prw/v pea protein isolate solutions at different amplitude levels of 30, 60 and 90% for 30 min at 20 kHz [w/w wheat and soy protein isolate solutions at a frequency of 20 kHz and amplitude of 95% for 2 min, and also for 0.5% w/v walnut protein isolate solutions at sonication powers of 200, 400 or 600 W for either 15 or 30 min [Arzeni et al. reported that protein aggregates were violently agitated and collided during sonication, resulting in smaller broken particles and wider size distribution . This wat 20 kHz . In addit 20 kHz . The sizr 30 min ,119. Howr 30 min . For insr 30 min . The autr 30 min .Sonication can induce structural changes due to the disruption of non-covalent bonds. The process can destroy the secondary structure and partially denature the tertiary and quaternary structure of proteins without any significant changes in their primary structure . The redChemical modifications include reactions with chemical agents and modification by pH alteration . DerivatGlycation is a common food-grade reaction which is widely used to improve protein functionalities because it is safe, and it usually does not require exogenous chemicals. Glycation can be achieved chemically through Maillard reactions or it can be obtained by cross-linking enzymes such as transglutaminase or laccase . The latSeveral studies have discussed this in-depth, but this section focuses on only a few examples of the glycation-induced structural modifications in several plant-based proteins, resulting in enhanced functionality. For instance, after dry-state glycation, sesame protein had higher solubility and emulsifying ability at a wide range of studied pH, due to secondary structural changes . The foa4OH, can readily be used to create alkaline conditions [Acidic or alkaline treatments can cause changes in the structural and functional characteristics of proteins because the pH of the liquid matrix in which proteins are dissolved is one of the major effective variables on their structure. During pH-shifting, the extreme acid–base environment results in protein conformational change, commonly known as a “molten globule” structure, which improves the emulsification properties of globular proteins ,138,139.nditions ,142. HydDue to the structural alteration, pea protein following pH-shifting treatment under alkaline conditions demonstrated better oxidative stability in oil-in-water emulsions than native pea protein. Changing the pH of wheat gluten to basic levels using NaOH resulted in changes in their molecular and secondary structure, as well as improved extensibility and tensile properties of the resultant films . They prTherefore, for plant-based proteins, most studies have employed pH shifting in conjunction with ultrasonic treatment. Soy protein has improved functionality such as enhanced solubility, surface hydrophobicity and emulsifying ability with the combination of pH shifting and ultrasonication . In anotProtein modification using enzymatic methods can be classified into enzymatic hydrolysis or enzymatic cross-linking. Enzymatic hydrolysis is carried out by breaking protein peptide bonds to improve its biological and nutritional value, creating hydrolysates of high-added value . On the w/v when transglutaminase was added at 10 U/g [Transglutaminase and other oxidative enzymes are used to induce the cross-linking of proteins, to improve the textural properties of the proteins by building up the polypeptides into stronger structures . To datet 10 U/g . The addt 10 U/g . The aut®; however, no significant difference was observed for the emulsifying stability, foaming capacity and stability [Enzymatic hydrolysis is able to modify structural properties; for instance, the structure of rice bran protein was unfolded, which exposed the buried hydrophobic groups to the outer surface of the molecule . Zang ettability .Enzymatic hydrolysis breaks down the peptide bonds in the protein; therefore, the molecular weight of the protein is also reduced. Klost and Drusch reported that convicilin (~70 kDa), legumin (~60 kDa) and vicilin (~50 kDa) of pea protein hydrolysate were no longer detected in the SDS-PAGE profile after tryptic hydrolysis , althoug®, Flavourzyme® and Neutrase® . The hydrolysates were fractionated using ultrafiltration with molecular weight cut-off membranes of 30, 10 and 3 kDa [® . at an enzyme-to-substrate ratio of 3:100 under a high hydrostatic pressure of 80–300 MPa [® [® rather than Trypsin [Enzymatic hydrolysis can also be used as a pre- or post-treatment for other protein modification methods, such as ultrafiltration and high-pressure processing, respectively. Aguilar et al. produced black bean protein hydrolysates using Alcalasend 3 kDa . Guan et–300 MPa . The hig0 MPa [® . Wang et Trypsin . Zhao an Trypsin .Interestingly, a compound known as plastein was found to be a product of protease-induced aggregation of protein hydrolysates, or from peptide-rich mixtures. These aggregates were initially believed to be formed through the reformation of peptide linkages  and wereThe previous sections have described the gaps in plant protein ingredient science and technology. However, we usually eat foods, and not individual ingredients. After obtaining highly functional plant proteins, the challenge is to transform these ingredients into delicious and nutritious foods. The following sections describe some important factors: the role of protein–polysaccharide interactions, the ability to structure plant proteins into fibers and gels, the inclusion of flavors derived from plant proteins, and nutrition to guide the development of plant-based foods.Most foods are a complex mixture of various components. In addition to proteins, polysaccharides make up the predominant component in most plant-based ingredients. Polysaccharides are sugar polymers linked by glycosidic bonds and include a vast family such as starch, cellulose, pectins, agars, carrageenans, alginates and gums . By capiFrom a search of the literature, a total of 49 articles relating to plant proteins with polysaccharides were found published between 1990 and 2021, with the majority (30 articles) published in the last five years. Although this is reflective of the present emphasis on plant protein research, the number of studies is still a small fraction of the entire plant protein research field. Hence, there is a great opportunity to explore deeper into this area. In addition to advancing basic knowledge, polysaccharides could help overcome some functional shortcomings of plant proteins, with the potential to replace animal proteins ,172. TheThe solubility of plant proteins is relatively low; therefore, the addition of polysaccharides has been employed to improve overall biopolymer solubility, and this is often coupled with a processing or modification step. Some examples include simple complexation ,174, sonIn addition to solubility, polysaccharides also improve the viscosity ,179, foaPolysaccharides do not always improve plant protein performance . In someReplicating the characteristics of muscle tissue comprising muscle fibers, connective tissue, and adipose tissue arranged into complicated hierarchical systems with viscoelastic textural characteristics has proven challenging. The physicochemical and sensory characteristics of traditional meats are largely determined by the structural arrangement of these tissues. During cooking, the thermally unfolded proteins are cross-linked into a continuous gel structure . The firThe fibrous structuring of plant proteins using thermomechanical processing can be classified under two main principles. The first principle is based on phase separation within a multi-phase protein mixture ,219. TheThe second principle is based on the complex conformational changes and molecular interactions of the protein upon thermomechanical processing under high-moisture (40–80%) conditions such as extrusion . As mentIn addition to anisotropic fibrous structures, many foods are structured in the form of homogenous gels. These include yogurts, cheeses, tofu, tempeh, etc. Protein gelation occurs via various mechanisms. Globular protein gelation involves protein denaturation, aggregation, and network formation, whereas casein gels proceed via the aggregation of casein micelles. There are different methods to induce gel formation, including heat gelation, cold gelation by pH change , the addition of salts, enzymatic cross-linking, or pressure-induced gelation. Various types of gels can also be formed, such as hydrogels, oleogels (oil gels), aerogels and emulsion gels. Readers are referred to the excellent review by Cao and Mezzenga for a comprehensive examination of food gels .Plant protein gelation has long been utilized in traditional foods such as tofu and tempeh. One active area is the use of other plant proteins beside soy to produce traditional foods. Some examples include tofu and bean curds made from pea and various legumes ,229,230.Notably, most traditional foods structured by proteins are in the form of either hydrogels or emulsion gels. This is unsurprising, because proteins tend to have biological activity only in aqueous environments. In the design of future plant-based foods, a main challenge is mimicking the texture and mouthfeel of animal-based fats, because most plant-based lipids exist in the form of liquid oils. Solid fats also contain saturated fats of which excessive consumption has been associated with elevated cardiovascular disease risk and could lead to other health complications . In contFlavor is one of the sensory attributes that affects a consumer’s eating quality and food purchasing decision. Numerous studies on meaty flavor chemistry have discovered thousands of volatile compounds from meat or model systems consisting of meat ingredients. Due to the rising trend in alternative proteins, there is interest in developing this meaty flavor from non-meat sources such as plant proteins. These meaty flavors can be generated via the Maillard reaction, a process whereby free amino compounds  and reducing sugars  are reacted together under specific conditions to produce melanoidins . The plaThe most abundant flavor compounds formed during the Maillard reaction are aliphatic aldehydes, ketones, diketones, and lower fatty acids . HoweverThere have been published reports using several plant proteins to generate meaty flavors such as pea protein , quinoa Since the 1970s, nutritional scientists have claimed the existence of a “protein gap”. With the projected increase in population, providing sufficient protein for human health remains a topic of considerable importance. With an increasing demand for plant-based proteins, it is important to develop science and technology that will conserve both the functionality and nutrition of plant proteins. Historically, protein quality has been measured using either animals or humans. For example, net protein utilization (NPU) is a measure of the protein retained as a fraction of the protein consumed. Protein quality is dependent on digestibility (D) and biological value (BV); thus, NPU is a single value that represents both D and BV. Due to the procedure being onerous and time-consuming, it is customary to now use the protein digestibility-corrected amino acid score (PDCAAS) and digestible indispensable amino acid score (DIAAS). PDCAAS and DIAAS are used to measure the protein digestible quality based on both human amino acid requirements and digestible quality. PDCAAS is measured as the product of a dietary protein’s amino acid score to its actual fecal nitrogen digestibility (level of nitrogen excreted in feces relative to the level ingested). In contrast to the PDCAAS, DIAAS utilizes the ileal digestibility coefficients of each amino acid instead of the actual fecal nitrogen digestibility of the protein, to estimate the true ileal digestibility of the essential amino acids . The essApart from the bioavailability of proteins during gastrointestinal digestion, the nutritional value of protein sources is influenced by the rate/kinetics of protein digestion. Proteins are classified as slow proteins and fast proteins based on the kinetics of protein digestion ,261. WheThere are few strategies to improve the quality of plant-based proteins for human consumption. Firstly, to increase the protein’s nutritional quality to PDCAAS 1.00, which is comparable to animal-derived foods, the anti-nutrients are removed from the isolated plant-based proteins . Secondly, increasing the amount of plant-based protein consumed per meal is predicted to effectively compensate for their reduced anabolic response in comparison to animal protein . Having This review has presented a roadmap to accelerate plant protein science and technology, focusing on plant protein ingredient development and the creation of delicious and nutritious plant-based future foods. The areas for further improvement include plant protein extraction, fractionation, and modification. More research is also needed in understanding plant protein–polysaccharide interactions, developing different structuring techniques, incorporating plant protein-generated flavors, and improving plant protein nutritional value. An area that needs future attention is the potential impact that different forms of fractionation and improved functionality may have on its nutritional quality. Finally, although the focus has been on plant proteins, it is vital to note that we usually eat whole foods and not individual ingredients; hence, other components that make up future foods will also be needed to be considered.


Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions.We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures.Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins. A parent node may have more than one child and a child node may have more than one parent and different relations to its different parents. Figure Gene Ontology (GO) provides a set of structured and controlled vocabularies that describe gene products and molecular properties . GO inclGO terms have been used to annotate many biomedical databases  , UniProtMost previous methods of estimating the semantic similarity of GO terms are based on the information content (IC). Such pioneered methods –7 and thRecently, some researchers employed word embeddings, which have been developed in the area of natural language processing, to learn vectors representing GO terms and proteins, and use the learned vectors to compute the semantic similarity between GO terms and the functional similarity between proteins –27. ThesIn this paper, we proposed a method to compute the semantic similarity of GO terms by transforming a GO graph into vector representations by using a graph embeddings technique. Specifically, we first transformed a GO graph into vector representations where each node in the GO graph is represented by a vector of low-rank features. After that transformation, the semantic similarity of GO terms and the functional similarity of proteins are computed by the cosine distance of their corresponding vectors. Graph embeddings are capable of capturing the structural information connecting the nodes in the entire GO graph. On the one hand, when compared with the structure-based information content methods, which mainly consider the nearest common ancestors of two nodes, graph embeddings consider the information from every path between two nodes. Graph embeddings therefore can fully portray the relationship of two nodes in the entire graph. On the other hand, when compared with the corpus-based methods, including the traditional information content methods and the word embedding methods, graph embeddings can employ the expert knowledge stored in the graphical structure. In our experiments, we use the node2vec model  as the rThere are several ways to evaluate the quality of the semantic similarity between GO terms and of the functional similarity between proteins. One way is to compare them with human similarity ratings , 15, 30 We conducted two kinds of experiments to evaluate the quality of the learned vectors of GO2Vec: (1) evaluation of protein similarities on the CESSM dataset and (2) prediction of protein-protein interactions (PPI) on Yeast and Human networks. The results of GO2Vec are compared with information content-based methods  for the GO and GOA graph transformations as well as the semantic similarity computation of GO terms and proteins. For the GO graph transformation of each GO ontology, we do not use the GO annotations see Fig. . For theFor the node2vec model, we applied its code in our experiments by trying different settings for the parameters and report the best performance. The setting that achieves the best results is as follows: 100 dimensions, 20 walks per node, 100-length per walk and 20 walks per node, undirected binary edges.We implemented several versions of GO2Vec to computer the functional similarity between proteins on both the GO and GOA graphs in both ways described in “ECC similarity [Pfam similarity [ECC similarity is computed by the Enzyme Comparison Class (ECC) metric [Pfam similarity is computed through the Jaccard measure, indicating the similarity between two proteins by the ratio between the number of Pfam families [Pfam families they have.The performance of protein similarity on CESSM is evaluated under two metrics: milarity  and Pfammilarity . The ECC) metric . The Pfafamilies  they shaPfam metric, GO2Vec achieves the best performance on all the three ontologies in the two metrics. Specifically, GO2Vec_mhd_goa achieves four best results and GO2Vec_mhd_go achieves one best result. This indicates that graph embeddings can better capture information that is useful for computing the semantic similarity of GO terms and proteins compared to information content-based methods and corpus-based word embeddings method.Table Let us look at the comparison between using the GOA graph and using the GO graph. The models that use a GOA graph achieves better performance than the ones that use a GO graph in most ontologies in most evaluation metrics. This indicates that GO annotations provide useful information for computing the semantic similarity between GO terms and the functional similarity between proteins.In this experiment, we aimed to evaluate the learned vectors by predicting the protein-protein interactions in two species whose datasets are collected from the STRING database : Yeast aWe got from the STRING database  two dataWe followed the setting of Onto2Vec to merge all the three categories of ontologies and GO annotations into a large graph. Like the CESSM experiment, we also implement several versions of GO2Vec for the PPI prediction, and their denotations are the same as those used in CESSM experiments. See “For the node2vec model, we used the same setting as the one used in the first kind of experiment.TP denotes the number of true positives, FP the number of false positives, TN the number of true negatives, and FN the number of false negatives.The performance of PPI prediction is evaluated under the metric of area under the ROC curve (AUC), where ROC stands for the receiver operating characteristic, which is widely used to evaluate the performance of classification and prediction tasks. ROC is defined by the relation between the true-positive rate (TPR) and the false-positive rate (FPR). TPR is defined as Table Compare the performance of using the GO graph and using the GOA graph. GO2Vec_mhd_goa performs better than GO2Vec_mhd_go in both datasets. This indicates again that GO annotations successfully provide useful information for computing the semantic similarity between GO terms and the functional similarity between proteins.There are two potential limitations in our method. First, GO2Vec transforms directed graphs into undirected graphs, which might result in a loss of structural information. Second, GO2Vec treats the edges of term-term relations and term-protein annotations equal in a GOA graph, while the term-term relations and the term-protein annotations might not be equal in reality. We will investigate the two issues in our future work.In this paper, we employed the technique of graph embeddings to transform the GO and GOA graphs into vector representations so as to compute the semantic similarity between GO terms and the functional similarity between proteins in an Euclidean space. To evaluate the quality of our method, we conducted two kinds of experiments, namely protein similarity on the CESSM dataset and protein-protein interaction prediction, and compared our method with the traditional information content-based methods and the recent corpus-based word embedding methods. Experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from GO and GOA graphs. Experiments also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.Recent years have witnessed an advancement of unsupervised feature learning from sequences of words  represent a graph where T denotes the set of nodes and E⊆(T×T) denotes the set of edges. The goal is to learn a mapping function ℝk, where the parameter k specifies the dimensions of the vector representations. f can be represented by a matrix of parameters with the size |T|×k. For each node t∈T, N(t)⊂T denotes the set of neighbourhood nodes of node t, generated through a sampling strategy.Let (N(t) for a node t conditioned on its vector representation, given by f. The node2vec model aims to optimize Eq. , which mTo make the optimization problem resolvable, the node2vec model makes two assumptions:t, the likelihood of observing a neighborhood node t′ is independent of observing any other neighborhood node. This is expressed by Eq. : breadth-first sampling and depth-first sampling.The problem of sampling neighborhoods of a source node is viewed as a problem of performing a local search. To achieve this, the node2vec model adopts a flexible sampling strategy that allows the model to smoothly interpolate between two extreme sampling strategies for generating neighborhood sets t, the node2vec model simulates a random walk of fixed length l. Let ci denote the i-th node in the walk, starting with c0=t. Node ci is generated by the following distribution: Given a source node πtx is the transition probability between nodes t and x, and Z is the normalizing constant.where Figures k-dimensional vector. We can then compute the semantic similarity of GO terms by computing the distance of their corresponding vectors. That is, vi and vj denote the vector representations of terms ti and tj, respectively, the semantic similarity sim between terms ti and tj is given by the distance dist between their vectors vi and vj in the Euclidean space. The distance dist can be computed by the cosine distance: After using node2vec for transformation, each GO term is represented by a There are two ways to compute the semantic similarity of proteins. One way is directly through the learned vectors of proteins, similar to the one for the semantic similarity between GO terms. The other way is through the learned vectors of GO terms.wm and wn denote the learned vectors of protein pm and pn. The functional similarity fun between two proteins is defined by the cosine distance cos of their corresponding vectors wm and wn, as shown by Eq. . Since a set of GO terms can be represented by its corresponding set of vectors, the semantic similarity of two proteins can be computed by the distance of the two sets of vectors. Let Vm denote the set of vectors that correspond to Tm, and Vn correspond to Tn. Then, the functional similarity between two proteins is given by the semantic similarity between two sets of vectors, that is, the distance between the corresponding sets of vectors: Since a protein is annotated by several GO terms under each category of GO graphs, we can view protein There are several measures that can be used to compute the semantic similarity between two sets of vectors , 42. In dist measures the distance of the two vectors in the space. The smaller the dist score is, the closer the two vectors are. Since GO terms are transformed into vectors, the dist score can be used to estimate the spatial relation of two GO terms ti and tj. dist is defined by the opposite of the distance function: the larger the dist is, the closer the terms ti and tj are. Therefore, we get a variant of the modified Hausdorff distance [pm and pn from two sets of vector representations of GO terms. Specifically, the modified Hausdorff distance of two proteins is defined by fun= Given two points in a vector space , distance  for compVm| denotes the number of vectors in Vm. In Eq.  denotes the distance of two vectors, and in this paper, we use the cosine distance: dist=cos.where |. In Eq. , dist(vmThe first way can be only used in the GOA graph transformation while the second way can be used in both the GO and GOA graph transformations.t is defined by the negative log-likelihood in Eq. (Resnik’s semantic similarity is based on the information content (IC) of a given term in an ontology. The IC of a term d in Eq. . 10\docp(t) is the probability of encountering an instance of the term t. According to this information, Resnik similarity is defined as where tm is the most informative common ancestor of t1 and t2 in the ontology.where Lin similarity  is definJang&Conrath similarity  is insteT1 and T2 be the set of GO terms that annotate proteins p1 and p2, respectively. simGIC similarity is defined by the Jaccard index as Eq. , maximum (MAX), and best-match average (BMA), and they are defined by Eqs. , 17), a, a17), aw1, w2,..., wK, the skip-gram model aims to maximize the average log-likelihood of Function  is the vector of protein p, v(wi) is the vector of word wi, and W represents the set of words in the descriptive axiom of protein p.where w2vGO  also use


The postsynaptic density (PSD) is a dense protein network playing a key role in information processing during learning and memory, and is also indicated in a number of neurological disorders. Efforts to characterize its detailed molecular organization are encumbered by the large variability of the abundance of its constituent proteins both spatially, in different brain areas, and temporally, during development, circadian rhythm, and also in response to various stimuli. In this study we ran large-scale stochastic simulations of protein binding events to predict the presence and distribution of PSD complexes. We simulated the interactions of seven major PSD proteins  based on previously published, experimentally determined protein abundance data from 22 different brain areas and 42 patients . Our results demonstrate that the relative ratio of the emerging protein complexes can be sensitive to even subtle changes in protein abundances and thus explicit simulations are invaluable to understand the relationships between protein availability and complex formation. Our observations are compatible with a scenario where larger supercomplexes are formed from available smaller binary and ternary associations of PSD proteins. Specifically, Homer1 and Shank3 self-association reactions substantially promote the emergence of very large protein complexes. The described simulations represent a first approximation to assess PSD complex abundance, and as such, use significant simplifications. Therefore, their direct biological relevance might be limited but we believe that the major qualitative findings can contribute to the understanding of the molecular features of the postsynapse. Chemical and electrical synapses connect neurons in the brain. In chemical synapses the information is sent via molecules from one neuron (presynaptic one) to the other neuron (postsynaptic one). The messenger molecule called neurotransmitter is released from the presynaptic neuron’s active zone and binds to receptor molecules sitting on the postsynaptic neuron’s cell surface. This part of the postsynaptic neuron is the dendrite. Inside the dendrite there is an electron dense region full of proteins binding to each other forming large protein complexes. These complexes make sure that the receptor molecules are on the right place usually in front of the active zone. The protein dense region of the postsynaptic cell in the dendrites is called the postsynaptic density. We have performed extensive simulations on the formation of postsynaptic protein complexes using a well-defined set of proteins and a large number of publicly available input data sets on protein abundance. We used a simulator implementing the Gillespie algorithm to simulate binding and unbinding events proteins. We found that the relationship between single protein and protein complex abundances can be non-trivial, since similar complex distributions can emerge from distinct relative protein abundances and quite different protein complexes can be formed from almost similar initial protein abundances. Our results are compatible with the idea that the association-dissociation of smaller subcomplexes lead to the formation of large supercomplexes. The emergence of supercomplexes is largely facilitated by the self-association of Homer1 and Shank3 proteins. Our results are qualitatively in agreement with the formation of the experimentally observed ‘nanodomains’ in the postsynaptic density. Complexity of the human brain is often attributed to the diversity of the neuronal network. However, there is growing experimental evidence showing that individual synapses are highly diverse in terms of the relative abundance of their constituent proteins . These oAs demonstrated recently, network-based analysis of the synaptic proteome is a powerful tool and can suggest novel associations between diseases . To get While knock-out experiments can provide valuable information on the role of a given PSD protein at the level of the full brain/organism, like the contribution of PSD-95 to learning processes . HoweverThe Simulation based Complex Prediction (SiComPre) method has been proposed to predict compositions and abundances of protein complexes from the abundance of individual proteins . The metwww.cytocast.com). This tool is available upon licensing or collaboration with the developer company. The performance of SiComPre was analyzed in detail and it was shown that it already overcomes several limitations of current methods predicting protein complexes by protein-protein interactions and simulations [SiComPre has been used to predict the formation of protein complexes in yeast and human cells , and chaulations .in vivo PSD complexes because only a small subset of PSD proteins and their interactions are considered. Nonetheless, we clearly demonstrate the added value of protein complex modeling in the interpretation of protein abundance data and the new biological insights it brings. A custom simulation with any combination of the simulated protein abundances can be run through our server available at psdcomplexsim.cytocast.com.In the present work we have used Cytocast Cell Simulator to model protein complex formation in the PSD using published mRNA abundance data in 22 different brain areas from a 42 human individuals, totaling 524 sets overall  . The datIn this study we investigated a small subset of the most well-characterized PSD proteins. Evidently, even the diversity of these can not be captured in our simulations as all of these proteins have multiple isoforms with different partner binding properties, while we have considered only the representative ones as defined in UniProt. Moreover, all these proteins have additional binding partners, some of which might not even be characterized yet. Nevertheless, we believe that this subset is a suitable candidate for a first simulation study as described here. Below we briefly introduce the molecules selected for our protein complex simulations.Mg2+ binding sites blocking the cation channel in the absence of depolarization. The receptor is one of the most important components affecting Long Term Potentiation through its Ca2+ permeability and second messenger pathways [The NMDA receptor NMDAR was recognized as a coincidence detector by Donald Hebb in 1949 as it has voltage-dependent pathways . It is apathways .Ca2+ permeability and the absence of voltage-dependent behavior. Both NMDAR and AMPAR are required for normal functioning of glutamaterg synapses.The AMPA receptor AMPAR is also an abundant ionotropic glutamate receptor having both similar and different subunits compared to NMDAR, resulting in different PSD-95 is a member of the MAGUK (membrane-associated guanylate kinase) family and is the most abundant scaffold protein in the postsynaptic density. It contains 3 PDZ domains as well as SH3 and a GK domain, all of which mediate protein-protein interactions. The domains PDZ1–2 and PDZ3-SH3-GK are considered to form two supramodules in which the conformational changes occurring in one domain upon ligand binding affect the behavior of neighboring domains , 24. AmoSynGAP is a Ras GTP-ase activating protein capable of interacting with PSD-95 with its C-terminal segments . It has The GKAP (DLGAP1) protein is almost completely intrinsically disordered. It contains multiple binding sites for the GK domain and DYNLL and has a helical GH1 domain near its C-terminus.  Its C-teShank3 is a member of the Shank (SH3 and multiple anykrin repeat domains protein) family, containing an ankyrin repeat region, and SH3, a PDZ and a SAM domain, as well as a proline-rich segment . Shank3 Homer1 contains an N-terminal EVH1 domain and a long coiled-coil segment which mediates homodimerization and also tetramerization of Homer1 molecules. The EVH1 domain can bind proline-rich segments on Shank proteins, metabotropic glutamate receptors as well as IP3 and ryanodine receptors . The HomThese proteins span the entire PSD from the membrane receptors and establish connections with the cytoskeleton and the inner membrane system of the neurons.For the present study we chose a well-defined and well-described subset of PSD proteins . The sevvia its SAM domain. In the ‘H4’ set we have only considered Homer1 tetramerization, and in the ‘Simple’ settings neither Homer1 tetramerization nor Shank3 self-association was included. Below, unless explicitly noted otherwise, we report the results of the H4SM set as the one with closest to our current understanding of the intracellular behavior of these proteins.To account for the effect of protein homomultimerization, we have performed simulations with three different setups with regard to protein self-association: in the set designated ‘H4SM’, we have considered Homer1 tetramerization through its coiled coil region and Shank3 polymerization To analyze the output of the simulations, we have assigned an ID to each resulting protein complex . Note thAbundance of the complexes formed in each simulation are shown in We have analyzed the diversity of input protein abundances and the resulting complexes. It should be noted that in the input there are only seven kinds of proteins with relatively large copy numbers and in the output there are a high number of possible complexes but with much lower copy numbers than the input constituent proteins, precluding direct comparison of their diversity. Both the simulation inputs and outputs can be described by appropriate vectors containing the protein (input) and complex (output) abundance data. We have independently clustered both the input and output vectors and analyzed whether the obtained clusters represent the same sets of experiments. Surprisingly, the input and output clusters show only limited correspondence to each other , indicatBesides the differences in the normalized distance matrices, we have calculated the differences between the inputs and outputs based on their first two principal components. The cosine of the angle between the points shows exactly how the relative positions of the two points have changed, not counting the distance. Comparing each region with each region, we find that the heatmap of the distances and the heatmap of the cosines on show a similar pattern. Where the change in distance is greater, the cosine is smaller. Nonetheless, there may be nuanced differences due to the different sensitivities of the measures created. See Fig D in The most dominant complexes are the binary complexes SynGAP1/PSD-95 (id:75) and AMPAR/PSD-95. These observations resonate with the key role of PSD-95 in the synaptomic theory .Principal component analysis  corroborIn order to complement the PCA results we also run an another dimension reduction method called tSNE based on joint probabilities through input and output data. tSNE separates the regions better but it is still not capable of meaningfully reproduce the regions .To analyze this phenomenon in more detail, we compared the input and output vectors of each individual experiment by generating two normalized distance matrices. One is based on the input data and the other on the output complex abundance. To assess how the output complex occurrence of a given experiment deviates from the expectations based on its input protein abundance, we subtracted the two distance matrices and identified the largest differences .We have selected the three experiments with the highest differences in their input and output and discuss these in detail below.The highest difference in the output relative to the input was observed for brain region 238 (H376.IX.51_MFC), representing data from the anterior medial prefrontal cortex. In this region PSD-95 makes up more than 75% of all proteins, which is unusual as it rarely exceeds 55%. This huge ratio of PSD-95 is subject-specific. The subject H376.IX.51 has similar amount of PSD-95 around 75% in its every region, but it is not disclosed in the data source whether there is any neural disease diagnosed for this individual .Considering the receptors, the abundance of AMPAR is much higher that that of NMDAR, which is negligible. Shank3 is the second-most abundant protein while GKAP is in only present in small amounts. The output complexes are dominated by PSD-95/GKAP,PSD-95/SYNGAP and AMPAR/PSD-95 binary interactions. Shank3 is mainly found in Shank3 dimers.We have identified the two regions with inputs (protein abundance data) most similar to region 238 and compared their respective outputs (complex distribution) with each other. Although the inputs are highly similar see , remarkaAll three regions are from the same individual, one sample from the ventrolateral prefrontal cortex (region 244) and another from the orbital frontal cortex (region 239). In these regions, the abundances of Homer1 and SynGAP1 show the largest difference compared to region 238. However, the most evident difference between the regions is in the presence of Shank3 dimers which are absent from regions 244. Instead, Shank3 proteins participate in several larger complexes or in the binary complex GKAP/Shank3. We can clearly see that a slightly less GKAP does not result in higher Shank3 dimer ratios, indicating a nontrivial relationship between input ptrotein abundance and complex formation in our simulations.Experiment 254, corresponding to the primary visual cortex (H376.IX.52_V1C) and 339 from the primary sensory cortex (H376.VIII.51_S1C) exhibit the second and third largest all-against-all distance differences between their input and output data vectors. Interestingly, these two sets have only slightly different input protein abundances but are not the closest sets of each other in this respect. The two inputs differ in the ratios of proteins SynGAP1, GKAP and AMPAR. The main difference between the sets 254 and 339 lies in their PSD-95/GKAP and SynGAP1/PSD-95 ratio as we can expect from the input differences. However, the SynGAP1/PSD-95/GKAP ternary complex can be expected to emerge in both simulations but less SynGAP1 and more GKAP does not lead to forming their ternary complex with PSD-95. In this case (experiment 254) higher ratio of the PSD-95(2)/GKAP precludes GKAP to join the SynGAP1/PSD-95 binary complex.For both regions, we have identified the experiments with highest similarity in the input abundances. For region 254  and 10 (PSD-95/GKAP) while having the largest ratio . Here, aOur results are dominated by relatively small complexes of 2–4 proteins. However, the potential interactions between the molecules allow for the formation of much larger associations, like complex 88 that contains four different kinds of proteins including seven copies of Shank3 see The supercomplexes formed are typically unique and are present in very low copy numbers, usually 1. It seems still true that the number and distribution of the most abundant smaller associations does not change significantly, although the proteins are in constant dynamic interchange between the complexes. The weighted average of complex size remains in the range of 2.7–2.8 irrespective of the simulation time, meaning that binary and ternary complexes remain the most abundant.Comparing the complex abundances of regions 238, 239 and 244 with the original simulation time and t = 1/6  demonstrates that the percentages of the complexes remain similar throughout the full simulation .Principal component analysis of the outputs including the longer simulations for the three selected regions  corroborates the above observation that simulation time has negligible effect on the complex abundances, especially when compared to the changes observed for variations in the input protein distributions.Notwithstanding the fact that the most abundant complexes remain the same when running the simulations six times longer, the maximum size of appearing complexes differ. The largest complex at t = 1/6 contained 11 proteins, the one at t = 1/2 had 8 and for the full simulation, 12 proteins throughout every region. Meanwhile, the average complex size remained 2 proteins.Longer simulation time does not change the occurrence of the most abundant complexes, that are in fact much smaller than the large ones with very low abundance. The small, abundant complexes can be regarded as building blocks for the larger ones formed via their association. Thus, the simulation time affects the largest complexes in a way that the longer the simulation, the more small complexes stack together into supercomplexes without changing the dynamical equilibrium of the smaller blocks in the system.Tracking the formation of large complexes is a nontrivial task as identification of the complexes during assembly and disassembly can not be easily solved. Therefore, our current implementation does not contain such a feature that would allow such analysis. Nevertheless, analysis of selected runs at a higher time resolution strongly suggests a scenario where a number of smaller complexes associate to form larger ones instead of each complex growing gradually by the addition of single components.via the EVH1 domain of each monomer.Our simulations were ran in three variants, the most realistic one termed H4SM—discussed so far—considering Homer1 tetramerization via its C-terminal coiled coil region and Shank3 multimerization through its SAM domain, H4 considering only the former and also a variant where neither of these higher-order associations were included. Homer1 can form a dimer of dimers, and in the Simple simulation only dimers were considered, still capable of forming bivalent interactions In our H4SM simulations, the largest complex observed contains 12 proteins. Primary complexes typically associate through Shank3 multimerization with the occasional involvement of Homer1 tetramers. The supercomplex containing the most receptor molecules (2) observed during our simulations is unexpectedly held together only by one GKAP molecule. Receptors are not present in the largest complexes generated by Shank3 multimer chains.via the association of primary ones.Omission of Shank multimerization in our H4 simulations precludes the formation of very large supercomplexes. The largest complex observed in the H4 simulations is shown in in vivo conditions most often the abundance of binding partners is the rate limiting factor [In the analysis throughout this paper, we used nominal binding—unbinding rates for each reaction. We made this simplification as there is no data on measurements. Although equilibrium dissociation constants (kDs) of the simulated interactions can be measured in-vitro and some (sometimes conflicting) data is available on these  using experimentally determined binding and unbinding rates. Currently, we are very far from this scenario and these two aspects mutually exclude each other. Even for the 7-protein system investigated there are very few ribed in . AlteratData described in  has beenThe data set contained RNA-Seq RPKM (reads per kilobase per million) values averaged to genes. From these values protein abundance data were generated as below:The median RNA-Seq value of the PSD-95 were calculated.This value (66.82 rpkm) were assigned to the protein abundance 300 RNA-Seq values were multiplied by 300/66.82—assuming linear relationship based on The model is suitable for making a primary estimate. Approximating the protein abundances by mRNA expression shows interregional separation at the level of mRNA synthesis, while other separations could emerge during the translation of the proteins. In the case of actual protein abundance data, the procedure can be repeated in the same way without changes. The distribution of the data is not uniform in the sense that there are brain regions for which data are not available from all of the patients. Because of this we handled all experiments separately and aimed at identifying the major differences between the results.Cytocast implements a version of the Gillespie algorithm, which is a Monte Carlo based method to simulate every reaction in order to have a non-deterministic approach of abundance change of molecules or in our case complexes.Steps of Gillespie-algorithm :ni,0 and possible reaction numbers qSet the initial conditions as the number of starting molecules (here protein abundance) r1, r2)Generating two random variables between 0 and 1. .Compute the propensity function for the whole system:t + τ whereCompute the time when the next chemical reaction takes place as ith reaction occurs if the conditions are true:Calculate which reaction occurs then adjust the numbers of the proteins (one less protein from the inputs and one more for the output (complex)). The KD) is the ratio of unbinding (koff) to binding rate (kon).The equilibrium dissociation constant . Although the ratio of the rates affects the size of the emerging complexes, our intention is to investigate the effect of varying protein abundances, which in this case will be the main determinant of the results. Previous works have shown that this simplification can lead to biologically relevant results , 19.The compartment was set up in 3 dimensions and had a spherical shape in order to approach the shape of a general dendrite. The sphere contained 1024 subvolumes. This shape and size gives enough space for the proteins to diffuse but small enough to observe sufficient number of interactions.http://psdcomplexsim.cytocast.com/) calculates protein complexes of the seven major postsynaptic proteins  as a function of their individual abundances. The abundance of the constituent proteins can be set to any desired value using the form. By default, Homer proteins are modeled as dimers and Shank polymerization (via its SAM domain) is not considered, you can change these settings by ticking the appropriate boxes. The default length of the simulation run with this service is 1 AU. The output contains all the protein complexes formed from the user set abundances of each proteins. The tool allows the reproduction of the results described in the present paper and enables users to test any combination of protein abundances.The online tool  and the last one has the lowest impact. The calculations were made by scikit-learn [In the it-learn .Another dimension reduction method is the t-distributed Stochastic Neighbor Embedding (tSNE), which is based mainly on joint probabilities created by the dataset. tSNE converts Eucledian distances into Gaussian probabilities where the probability is high if the two points are close.Calculating the joint probabilities:For low-dimensional spaces, a Student-t distribution is calculated. Using the two probabilities an error function can be defined which is then minimized by an optimization approach . We usedThe K-means clustering is one of the simplest algorithms for creating similarity classes so called clusters based on closeness in a parameter space. The number of clusters is a key parameter as the same difference can be seemingly larger when a higher number of clusters is used. In this study we have performed K-means clusterings for K = 2,3,4,5 and 6  :k mean points.Randomize the n is assigned to the nearest mean.Each data point Calculate the new mean of the clusters by the assigned data points.Reassign each data point to the new nearest mean.Repeat step 3 and 4 until every mean point remains unchanged.Then we generated a one-hot labeled vector for each cluster. The coordinates of the vector represent each dataset, and the coordinate is 0 if the dataset is not in the cluster and one if the data is in the cluster.Then the distance of two clusters can be formalized as:u,v are cluster vectors and N is the number of datapoints (dimension of the cluster vectorspace). This is a conservative measure in the sense that points that are not included in any of the two clusters are also considered to be similar. For this reason, even smaller values represent a larger difference compared to considering only the union of the two clusters.Where High inter-cluster distances indicate that the input and the output data are in a nontrivial relationship.The brain regions were associated by a point in a multidimensional space, one space for the input data and another space for the output data. For the input data the axis were the protein abundances while in the output data the axis were the occurred complexes. Eucledian distances between each brain regions were calculated in the input data and in the output data as well. Then those distances were normalised between zero and one to eliminate artefacts caused by the different vector spaces before checking how a distance changed. The two distance matrices were then subtracted to identify the largest changes in region-to-region similarities based on protein and complex abundances.The real question of simulations, however, is what complexes appear in a given simulation and in what quantities. It is difficult to compare two simulations based only by the specific numbers of complexes, since our starting materials the proteins, are also present in less or more amounts. Thus, for the comparison, the relative abundances of the complexes were taken into account. The pie charts show that a given complex occurs at what percentage of the whole complexes. The complexes with the smallest percentages were put together in the other group by a threshold of amount. Thus,the complexes which were the most common according to the simulation in the given brain region can be determined. The complex IDs are generated for one simulation set containing all the regions to be compared. Therefore, when comparing different metaparameters on same regions, the complex IDs emerged have to be associated with the original IDs of the same complex, in the charts the colors are also associated with the original IDs.S1 TextFig A. Analysis of the relationship between the input and output data based on cluster distances obtained for different cluster numbers generated by K-means clustering. Fig B. Silhouette scores of kMeans algorithm on input data. The red line indicates the average score. Higher scores indicate more reliable clustering. Each cluster is colored differently. Although the average score is highest at k = 2 for the output complexes, the smaller cluster is virtually non-existent at this value. Thus, practically the the k = 3 and k = 4 cases can be used for informative comparisons. Fig C. Silhouette scores of kMeans algorithm on output data. Fig D. Correlations of Relative Positions of PCA points. The heatmap shows how much the direction of the relative positions between each brain region differs from input positions to output positions regarding to their first two principal components. The value is absolute. It is illustrated at the bottom of the figure that the cosine of the intervening angle of the positions was taken. X is a reference point in the input and output field of while the A is an another point in the input field and A’ is the same region as A just in the output field. Fig E. The distribution of data for each brain region along first principal component. Fig F. Distribution of complexes obtained for the three main regions with setup of non-uniform binding values and the two closest—in case of input protein abundances—regions for each (similarly to the main figures): A,D) H376.IX.51_MFC G,J) H376.VI.50_V1C M,P) H376.VIII.51_S1C. Similar to the region H376.IX.51_MFC are the regions: B,E: 239 and C,F:244. Similar to the region H376.VI.50_V1C are the regions: H,K:342 and I,L:255. Similar to the region H376.VIII.51_S1C are the regions: N,Q:328 and O,R:340. The regions with similar protein abundances (lines) remained similar however small perturbations still caused small changes as we observed originally. Changes between the uniform and non-uniform unbinding values makes differences between the complex abundances. The most abundant complexes remain the most abundant but the ratio changed and the less abundant complexes changed indicating that non-uniform binding rates affect more the time a bigger complex needs to evolve.For example on subfigure B there is the complex GKAP/Shank3 while on subfigure E for non-uniform unbinding values instead of the binary complex GKAP/Shank3 there is the quaternary complex of PSD-95(2)/GKAP/Shank3. This change is caused by the smaller (kD = 0.02μM) unbinding value of GKAP-Shank3 binding. Similar phenomenon can be observed at the subfigures M-P and N-Q where the PSD-95/GKAP/Shank3 and PSD-95(2)/GKAP/Shank3 complexes emerged due to the smaller unbinding value of PSD-95-GKAP binding.(PDF)Click here for additional data file.S1 TableTable A. Input Proteins with Uniprot ID and Domains. Table B. Domain-Domain Interactions. Table C. ID of Brain Regions. Table D. Function of Brain Regions. Table E. Input protein abundances. Table F. Protein complex IDs. Table G. Output of H4SM Original (rows:complexes columns:regions). Table H. Output of H4SM t = 1/2. Table I. Output of H4SM t = 1/6. Table J. input PCA coordinatesTable K. output PCA coordinates. Table L. equilibrium dissociation constants found in literature.(XLSX)Click here for additional data file.S1 DatasetThe averaged and sorted outputs of each region of the corresponding simulation. H4: Homer tetramerization, SM: Shank3 polymerization and t time.(ZIP)Click here for additional data file.


Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein–protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from  If the structure of the protein or protein–protein complex is known the effect of introducing a mutation in a protein can be studied using a variety of computational approaches.In order to simplify the initial search for a putative desired protein–protein interface the computational Match_Motif tool has been developed. The approach is entirely based on interfaces of known protein–protein complexes. The user provides a desired initial geometric arrangement of two proteins that should include a putative contact surface. It may originate from a manual placement or can be obtained as putative complex by some protein–protein docking software. The tool then searches through a database of structural interaction motifs (based on the arrangement of short backbone segments in known complexes). All motifs that structurally match to a corresponding motif at the desired interface are collected. The desired contact surface can then be re‐modeled based on the side chains of the matching motifs. The procedure takes a few seconds on a standard workstation computer. Since the final contact surface is based on motifs extracted from known favorable protein–protein interactions it likely also provides stable binding of the desired interface or at least provides a basis for further adjustment or refinement using more demanding methods. In a first part, the workflow of the method will be presented followed by several test applications to recover known highly stable interfaces or to design putative new arrangements. Additional possible applications of the tool and directions of further improvement are also discussed.2For the design of assemblies of proteins with a desired function often a specific binding geometry of interacting protein partners is required. Hence, for a given pair of stable proteins that act as building blocks a stable binding in a preselected arrangement is desired. In the Match_Motif approach the user needs to provide such desired geometry as an input. Based on the backbone geometry of both partners at the interface, the algorithm searches for structural motifs of the target arrangement and searches for similar motifs in a database extracted from known stable complexes. A structural motif is here defined as four consecutive interface residues in one and another four consecutive residues in the other partner within a threshold distance of 10 Å. The motif is characterized by the distances between backbone atoms within each partner (within each of the four residues) and between the two short stretches of residues  and root‐mean‐square deviation (RMSD) of the superposition of target motif and database motif . Once a match has been found it is stored and for constructing a new interface structure the interface segment is replaced by the database motif . Upon completion of the algorithm, typically several side chains at the target interface are replaced by the side chains found in the matching motifs from the database. Since the side chains in the database motifs are all extracted from known stable protein–protein complexes, it is likely that the redesigned interface also forms a stable complex or at least is a good starting point for further adjustments or refinements. The process steps and workflow of the Match_Motif approach are outlined in Figure 2.13Q87 example case). In addition to the superimposed matching motifs, the Match_Motif approach also provides a new alternative complex structure that can include several substitutions of residues at the interface  included a Glu‐> Leu, an Arg‐> Ala, and a Thr‐> Ile substitution. In case of PDB 3Q87, with a larger interface, six substitutions at the buried interface were accepted . Hence, both substitutions toward larger and sometimes smaller residues are observed such that the average size of interface residues does not change significantly. The approach in its present form does not include further (possibly costly) energetic evaluation of the substitutions. However, for the user it is possible to not only evaluate the suggested alternative interface but to inspect independently all suggested matching motifs  and further evaluate the suggested changes using other computational tools or direct experimental testing.As a first application, the method was tested on a diverse set of 10 known protein–protein complexes with different interfaces . In a systematic docking run the surface of the protein partners is systematically scanned for potential interaction geometries usually resulting in thousands of local energy minimum solutions. For the PDB 1HE1 case, the solution ranked at position 100 (top100 solution) significantly differs from the native geometry . Here, the interface redesign resulted in an alternative complex structure that upon systematic redocking gave a score of −13.4 RT units and scored at position top9 . Such case is illustrated in Figure 3The large collection of proteins and protein–protein complexes with known structure forms a rich basis of extracting recurrent motifs at protein–protein interfaces. Such local motifs of contacting consecutive residues on both partner proteins occur not only in homologous complexes but can also form locally as part of interfaces in unrelated complexes. The Match_Motif approach rapidly compares all backbone interaction motifs at a desired interface with the known available motifs with sterically well‐fitting intermolecular contacts. For a target interface of sufficient size that places both proteins at a reasonable distance to form a stable complex typically in the order of ~100 structural interface motifs can be found. The process requires only a few seconds but could be further accelerated by a smarter algorithm to search through available motifs. In the future, this could be achieved for example by first searching through a structural classification of motifs that contains only a few representatives followed by an extended search through only the members of a few matching classes.It should be emphasized that the present approach can be used to redesign a known interface by suggesting appropriate substitutions. However, in such a case, it may also be useful to directly search for evolutionary conserved related complexes44.12 or one partner containing <40 residues were not included , it resulted in 255,000 interface motifs. The coordinates of each motif and the distance between all pairs of Cα backbone atoms are stored in the database. A second database with a slightly reduced set of structural motifs was generated by introducing a cutoff for the mean of Cα‐Cα distance deviations (dRMSD) between motifs. A motif was only retained if it deviates from all other motifs by a sum of Cα‐Cα distances larger than the cutoff of 1 Å. Second, the set was further reduced by including only interface motifs with at least three nonpolar or aromatic residues. The reduced set contains ~57,000 motifs and allows rapid selection and was used for all applications in the present study.In the present study, an interaction motif is considered as four consecutive residues in one protein partner and four consecutive residues below a distance threshold in the second partner. The distance threshold needs to be large enough to include all interaction motifs such that residues in one four‐residue segment are in contact with residues in the four‐residue segment in the partner protein. A test with different threshold distances indicated that for distances larger than 11 Å, an increasing number of motifs are found that have no or only little contact between residues of the partner proteins. On the other side for threshold distance smaller than 11 Å, some motifs with large interacting side chains are excluded from the database. Hence, with a 11 Å cutoff structural motifs were extracted from a set of 1,686 protein–protein complexes extracted from the PDBbind database.4.2sander program of the Amber18 packagepmemd.cuda module of Amber18. The FF14SB force fieldFor a given protein–protein complex, the program Match_Motif identifies the interface regions of the protein partners. The interface region typically includes all residues within a distance threshold for interatom pairs between the protein partners (default is 10 Å). Similar to the collection of interface motifs extracted from known protein–protein complexes in a next step, all segments of four consecutive residues in one partner and four residue segments in the second partner within a center‐of‐mass distance below a threshold  are collected. The collected motifs are compared with all the motifs in the interaction motif database with respect to the backbone dRMSD. All structural motifs from the database below a dRMSD threshold are considered as possible matches and can replace the corresponding interface segment . As a final check the RMSD of the target motif (backbone) relative to the database motif is calculated and used for selection (with a default threshold of 1 Å). With this threshold for a protein–protein interface (of a known complex) with typical size between 20 and 200 matches can be collected. The matching segments are stored in a separate file and can be inspected visually. However, the program also outputs a PDB‐file with an alternative interface based on the matching motifs. An interface residue is replaced by the most frequently found residues for a given position found in all matching motifs if it does not overlap with other residues in the partner proteins. To remove residual overlap and optimize the alternative interface arrangement an energy minimization employing a molecular mechanics program is performed. For the present cases, it was performed using first the https://www.groups.ph.tum.de/t38/downloads/.The Match_Motif program, a manual, and the data file of structural interface motifs as well as example applications can be downloaded from Martin Zacharias: Conceptualization, investigation, writing, resources, funding acquisition.