Edible seaweed, also known as sea vegetables, are macroalgae that can be eaten and used in cooking. They contain enriched bioactive compounds such as polysaccharides, which have a variety of functions such as antioxidants, anti-aging, and immune regulation. This study was a voyage of biological discovery that sought to thoroughly examine all of the DNA (known as a genome) of 12 edible seaweeds. The collected genomic sequence data are publicly accessible as a critical functional reference for the discovery of bioactive products and genome-assisted breeding for edible seaweeds. Secreted proteins frequently circulate throughout the body and, thus, have access to the majority of organs and tissues in animals. We investigate the potential secreted short peptides in seaweed using a comparative analysis of protein families that interact with human cancer genes. Some of the secreted proteins may be therapeutic agents interacting with surface receptors in human cells due to their circulating function. In summary, our data integration predicted the first shortlist of secreted peptides of edible seaweeds, which may provide novel information about anti-tumour mechanisms, thus accelerating the study of seaweed biology and improving seaweed nutraceutical practice.Seaweeds are multicellular marine macroalgae with natural compounds that have potential anticancer activity. To date, the identification of those compounds has relied on purification and assay, yet few have been documented. Additionally, the genomes and associated proteomes of edible seaweeds that have been identified thus far are scattered among different resources and with no systematic summary available, which hinders the development of a large-scale omics analysis. To enable this, we constructed a comprehensive genomics resource for the edible seaweeds. These data could be used for systematic metabolomics and a proteome search for anti-cancer compound and peptides. In brief, we integrated and annotated 12 publicly available edible seaweed genomes . In addition, we integrate the new seaweed genomic resources with established cancer bioinformatics pipelines to help identify potential seaweed proteins that could help mitigate the development of cancer. We present 7892 protein domains that were predicted to be associated with cancer proteins based on a protein domain–domain interaction. The most enriched protein families were associated with protein phosphorylation and insulin signalling, both of which are recognised to be crucial molecular components for patient survival in various cancers. In addition, we found 6692 seaweed proteins that could interact with over 100 tumour suppressor proteins, of which 147 are predicted to be secreted proteins. In conclusion, our genomics resource not only may be helpful in exploring the genomics features of these edible seaweed but also may provide a new avenue to explore the molecular mechanisms for seaweed-associated inhibition of human cancer development. According to World Health Organization estimates, there were approximately 19.3 million new cancer cases in 2020, of which 10.06 million were in males and 9.23 million were in females [Many cancer-associated deaths can be avoided by choosing a healthy diet such as fruits and vegetables . AdditioUndaria pinnatifida was generated by using PacBio and Hi-C technologies [In general, genome, proteomics, and metabolomics have all made significant contributions to our understanding of seaweed biology. Although the above experimental evidence links many seaweed bioactive compounds with anti-cancer effects, there is still a lack of a big picture for seaweed biology. With the advance in high-throughput sequencing technologies, many seaweed genomes have been sequenced, which has provided us with an encyclopedia of molecular functions ,20,21,22As well known, the cell is far more complicated than the sum of its genes, proteins, or metabolites. In general, the anti-cancer effects are the activity of all those components and their interactions that add up to those edible seaweed. However, these data were dispersed throughout the literature and a systematic review is lacking. To explore the complexity of seaweed, we aim to collect genome and proteome data for edible seaweeds, which will advance our knowledge about how these compounds may safeguard cancers. In addition, these omics data may provide reliable opportunities to explore the molecular interactions between seaweed compounds and cancer cells. Once identified, cross-species comparisons can be used to determine similarities and differences between seaweeds and, therefore, to predict their anti-cancer effects. Given this, we have carried out the first systematic data integration and published it as a web database for genome-level molecular interaction investigation in an attempt to identify all possible molecular interactions theoretically related to cancer biology. Specifically, we aimed to computationally connect seaweed protein-coding genes with known TSG and OCG proteins in humans. We explored (1) the interactional preference of seaweed proteins with tumour suppressor and oncogene proteins, (2) common interaction patterns between the 12 seaweeds, and (3) common cancer proteins with the most abundant interactions with seaweed proteins.Macrocystis pyrifera (giant kelp) genome was highly fragmented, with a contig N50 of only 2573 bp. Therefore, we excluded this assembly and focused on the remaining 12 genomes  did not, and therefore, we utilised an in-house gene prediction pipeline to predict the protein sequences. All proteins from the 12 seaweed genomes were annotated with known protein family/domain information from the most updated Pfam database [Among the 12 genomes, 5 had existing predicted protein-coding proteomes available. The remaining  to 30,694 proteins (K-strain). The two cultivars of Saccharina japonica followed, with 25,245 and 23,598 proteins. The smallest proteome was obtained from the sea lettuce Ulva mutabilis, including 7468 proteins. The number of proteins associated with Pfam domains generally correlated with the proteome sizes (Saccharina japonica proteomes had relatively lower ratios of proteins annotated by Pfam (0.437 and 0.452), which might imply the potential novel functions in their proteomes. In general, seaweeds with larger proteomes and more Pfam annotations tended to have more proteins interacting with cancer proteins  were found in the total number of TSGs, the numbers of proteins interacting with dual role genes are over half of that number with TSGs in all the twelve proteomes. This may confirm our previous observation, where the dual role domains tended to have more interactions with seaweed proteins. To explore the potential anti-cancer effect, we also collected 6692 proteins interacting with 100 or more TSGs . As one of the most well-studied post-translational modifications, protein phosphorylation is like a switch that controls many fundamental cellular processes, especially cancer-related cell growth [p-value < 10−39). Another functional category of interest was insulin signalling . There is some sporadic evidence for an association between seaweed and human insulin. For example, in healthy postmenopausal women, serum concentrations of insulin-like growth factor 1 (IGF-1) change following seaweed supplements [Laminaria digitata was found to induce apoptosis in HT-29 colon cancer cells via an insulin-like growth factor [The putative interactome between the seaweed and human is important to identify the potential therapeutic targets. Since our putative interactomes mainly focused on TSGs and OCGs, we further explored the importance of these cancer proteins based on the number of interactions with seaweed proteins. Focusing on the top 50 TSGs/OCGs, we found those cancer proteins involved at least 23,720 interactions. We then conducted functional enrichment analyses to provide an overview the molecular functions . Of mostl growth . After fplements . Recent plements . In addih factor . In summTRIO (15%), STK11 (13%), PPKCI (11%), PRKAA1 (8%), and PAK5 (5%). The most important thing is that these top mutated genes can significantly affect the cancer prognostic survival time . We further divided the patients into two groups by analysing the clinical data of those lung cancer patients. One group was patients with somatic mutations; the cancer genomic data of the other group of patients did not involve any mutations in these 50 genes. Interestingly, these two groups of patients had significant differences in the composition of driver genes  E. TMB isAs the global population grows and life expectancy increases, cancer will become more common, and the global cancer burden will further increase, especially in low- and middle-income countries. However, many deaths caused by cancer can be avoided. About one-third of cancer deaths are due to five major behavioural and dietary risk factors: high body mass index, low intake of fruits and vegetables, lack of exercise, tobacco use, and alcohol consumption. The nutraceutical approach promises to maintain human well-being and prevent cancer . As wellAlthough many seaweeds have anti-cancer effects, the mechanism of action at the molecular level, such as protein–protein interaction, is largely unknown. However, this information could help inform potential targets for nutritional or pharmacological studies. Different types of secondary metabolites  have been isolated from marine algae and found to have a wide range of biological activities such as antimicrobial, antitumor, anti-inflammatory, antioxidant, antiviral, antimalarial, antitubercular, and anti-aging ,42. In aThere has been a lack of systematic comparison of the similarities and differences of the anti-cancer effects among seaweeds. Our work identified common interacting protein domains found in various seaweeds. At the same time, it also helped to elucidate some species-specific interactions. Based on our observation, the 12 seaweed–cancer interactomes consist of a similar set of cancer proteins. However, a massive variation of seaweed proteins interact with cancer proteins. With more edible seaweeds identified, we could expect more discoveries about the anti-cancer effects such as immune-boosting . RegardiPseudomonas aeruginosa is a promising anti-cancer therapeutic option in adult and paediatric solid tumours [The biological availability of nutritional and functional food components is one of the key assumptions for this study. Secreted proteins could circulate throughout the human body and, therefore, are the therapeutics with access to most organs and tissues . Many se tumours . Through tumours . Therefo tumours . This research has used our newly established edible seaweed resource to predict protein interactomes between seaweed and human; however, there still exists large knowledge gaps in translating the static genome information into dynamic biological interaction. Since the anti-cancer effects of seaweeds have, to date, been primarily associated with secondary metabolite activity, the standardised proteome data will be helpful in exploring a broader systems biology analysis that includes secondary metabolites with potential anti-cancer effects. It should be noted that seaweeds may also contain natural or environmentally derived products that are detrimental to human health. For example, seaweeds have an incredible ability to absorb trace nutrients from seawater and store them in its tissues . This isWe downloaded the genome assembly in Fasta format for 12 seaweeds with genomic DNA assemblies available. By following the link in their associated manuscript, we further downloaded eight proteomes, which contained all proteins sequences in Fasta format. However, four genomes did not provide gene regions and protein sequences, and therefore, a gene prediction pipeline was performed to extract putative protein sequences from the genome assembly. For gene prediction, we used the ab initio program Augustus (Version 3.4.0) . To avoiThe resulting database has a convenient web-based interface to facilitate textual searches. All the interactome are downloadable for advanced bioinformatics data mining. The database was implement using MYSQL system and Perl CGI scripts. To explore the protein–protein interactions with cancer proteins at the genome level, we proposed a computational framework by starting with human genes of known functions as TSGs and OCGs. This study collected cancer genes with literature evidence from TSGene 2.0 . In TSGeOne of the most common mechanisms of protein functioning is through interacting with other proteins in the cells. The protein–protein interactions are accomplished by functional domains interacting with other protein domains physically. Therefore, high-quality protein–protein interaction relationships could be summarised from biological pathway databases, such as KEGG and Reactome databases. Thus, the domain–domain interaction consists of non-redundant protein–domain interactions derived from the well-studied protein–protein interactions. In this study, the putative protein–protein interactions between the seaweeds and cancer genomes were based on protein domain–domain interactions. Cladosiphon okamuranus (O-strain) genome as an example, we constructed a protein–domain interaction network linking C. okamuranus (O-strain) proteins to human cancer proteins. Since we already assigned the protein-coding genes to protein domains, the high confidence domain–domain interactions from the database DOMINE were used to link the protein domains from the algal genomes to the protein domains in human cancer genes [Taking the er genes .To predict the potentially secreted proteins, we utilised a computational approach by a combination of SignalP  and TMHMp-values of 0.01 were identified as over-representative annotations.For those leading cancer genes with the most abundant interactions with seaweed genomes, we further investigate the enriched functional categories using MetaScape . In brieBased on the existing public platform cBio Portal , we deteIn this study, we have shared 12 genomes and proteomes of edible seaweed via a user-friendly web interface. To provide evidence for potential anti-cancer proteins, we also constructed putative interactomes constituting seaweeds and human cancer proteins. The computational results highlighted potential protein families/domains with anti-cancer effects. We found that dual role domains were the leading functional blocks that bridge the interactions, which include secreted seaweed protein interacting partners. In addition, a high number of cancer proteins that interacted with seaweed proteins were associated patient survival. In conclusion, our online portal may provide a data infrastructure to explore functional interactions between oncogenesis and anti-cancer effects in those seaweed genomes.


Experimental biologists, biocurators, and computational biologists all play a role in characterizing a protein’s function. The discovery of protein function in the laboratory by experimental scientists is the foundation of our knowledge about proteins. Experimental findings are compiled in knowledgebases by biocurators to provide standardized, readily accessible, and computationally amenable information. Computational biologists train their methods using these data to predict protein function and guide subsequent experiments. To understand the state of affairs in this ecosystem, centered here around protein function prediction, we surveyed scientists from these three constituent communities.We show that the three communities have common but also idiosyncratic perspectives on the field. Most strikingly, experimentalists rarely use state-of-the-art prediction software, but when presented with predictions, report many to be surprising and useful. Ontologies appear to be highly valued by biocurators, less so by experimentalists and computational biologists, yet controlled vocabularies bridge the communities and simplify the prediction task. Additionally, many software tools are not readily accessible and the predictions presented to the users can be broad and uninformative. We conclude that to meet both the social and technical challenges in the field, a more productive and meaningful interaction between members of the core communities is necessary.Data cannot be shared for ethical/privacy reasons.Bioinformatics Advances online. Although this strategy is reasonable, functional annotations accumulate slowly and remain far from complete even in model species. Filling this knowledge gap not only requires accurate prediction, but also systematization of knowledge , computaHistorically, protein function has been described in the literature using natural language, but to unify terminology across all domains of life and to standardize computational pipelines, major knowledgebases, such as UniProtKB , often rThere are three core domain-expertise communities that are associated with protein function prediction. The first community is experimental biologists, who determine the function of proteins using experimental assays . The secIn this work, we set out to understand how members of each of the three communities work on understanding protein function, how they perceive computational function prediction, how well they understand and use the work produced by members of the other communities, and how much they interact with each other. To do so we surveyed members of each of these communities using general as well as individualized questionnaires. Here we summarize major findings from these surveys that we believe describe the state of affairs in the field. We argue that a deeper understanding of how this scientific field operates, and where the bottlenecks may be, will lead to an improved quality of interaction between three constituent communities and ultimately advance the technical component of the field itself.The assessment of the field of protein function prediction was carried out using five online questionnaires—three for experimental scientists and one each for biocurators and computational biologists. The consent forms and the surveys were presented to the participants using Qualtrics , and eacWe reasoned that only protein function experts have deep insight into the field and therefore targeted active doctorate-level participants. We required interest and published work in the protein function domain, measured by the reputation of the publishing venue. Participants were recruited via emails that briefly described the purpose of the study and provided the expectations for the online questionnaires. The initial email was personalized and included a link to the consent form and subsequent survey materials. We also assured potential participants that their privacy would be protected.en masse; (iii) completing the questionnaires in full required a commitment not many respondents could provide.We approached 169 scientists from 94 institutions using our understanding of the field as well as recommendations from colleagues with knowledge in the field . Of the The three questionnaires for the experimental scientists are referred to here as the ‘general’, ‘specific’ and ‘comparative’ questionnaires. The general questionnaire inquired about the background information and overall attitude toward protein function annotation and prediction (Section). The specific questionnaire was designed to assess the quality of the state-of-the-art function predictions on the proteins that are experimentally studied by consented domain experts; therefore, it was tailored to each surveyed participant (Section). We examined each person’s publications for proteins they investigated and asked each scientist to provide two proteins in which they believe they have the most expertise; and these proteins were subsequently used in the questionnaire. Finally, the comparative questionnaire was designed to assess the quality of the predictions of state-of-the-art methods relative to the predictions from baseline methods in the field (Section). The comparative questionnaire was also individualized.The general questionnaire was designed to understand the research background of the subjects, the level of their familiarity with bioinformatics software and databases, their understanding of GO annotations, and their need for protein function prediction. Overall, the questionnaire contained 28 multiple choice questions and 10 free-text questions. Each participant was asked to answer the same set of questions. The median time of completion was 10 min.α-hemoglobin and p53, for which we expected every participant to have rudimentary knowledge of their function, were identical for all participants. The other two proteins were unique to each participant and were selected from their own publications. We expected the researchers to have in-depth knowledge of the function of proteins they actively studied.The specific questionnaire was developed to understand the performance of function prediction algorithms, as seen by experimentalists. This questionnaire was individualized with the rationale that only an expert can judge the quality, usefulness and surprise of computational predictions of functions of proteins in their field. Each participant was first asked to self-assess their knowledge and then to assess the performance of one of the state-of-the-art algorithms on four proteins. Two of the proteins, The state-of-the-art algorithm was selected randomly for each researcher from four tools that have been found to perform well . These mResearchers were presented with up to 25 highest-scoring terms for each of the proteins on each domain of GO . They were then asked to click on one or more of the following characterizations for each predicted term: (i) ‘known’, (ii) ‘useful’, (iii) ‘surprising, possible’, (iv) ‘surprising, doubtful’ and (v) ‘wrong’. We subsequently asked them to tell us if a predictor did a good job and then to use a text field to offer any additional information about the protein that was absent from the set of terms outputted by the predictor. We next asked them to describe the steps they typically take when they study function of a protein.Overall, the specific questionnaire presented 23 questions to the researchers. Thirteen questions were answered by clicking among pre-specified options and 10 were textual fields with free-form answers. However, as described above, the multiple choice questions contained a number of predicted terms , thus requiring significant time to complete. The median time to completion was 50 min.The comparative questionnaire was developed to contrast the predictions from the state-of-the-art algorithm researchers saw in the specific questionnaire against the predictions from one of the baseline algorithms. We considered three different methods as baselines and randomly picked one for each researcher: (i) Naïve , (ii) TOOverall, the comparative questionnaire presented seven questions to the researchers. Six questions were answered by clicking among pre-specified options and one was a textual field with free-form answers. The median time to completion of this survey was 6 min.The questionnaires for biocurators and computational biologists were developed to understand the background of the participants, their familiarity with databases and software used in bioinformatics, their opinion of the GO terms and evidence codes, their level of interaction with other communities, the feedback that they have received about their research products, and their thoughts about the Critical Assessment of Functional Annotation (CAFA). In contrast to experimentalists, biocurators and computational biologists were not required to enter their name in the survey and therefore could stay anonymous.We asked both groups whether they think protein function prediction is an important problem and what they think are the bottlenecks in the field. Since both communities are related to CAFA , we alsoAn additional set of questions were asked of the biocurators about their reaction to the output of a protein function prediction algorithm (a list of GO terms and scores) to hemoglobin. This was to assess how they, as the developers of ontologies, would react to such usage of GO terms. The goal was to identify the differences in the intended usage and actual usage of ontologies as perceived by the biocurators. Biocurators were asked how useful they think these predictions are when used to convey protein function in that manner, how important it is for the bioinformatics community to build such algorithms, what should computational biologists keep in mind about ontologies when developing tools, how useful these predictions are for experimental biologists, and what experimental biologists should keep in mind when using such predictions.The median time for the completion of the surveys was 46 min (38 questions) for the biocurators and 45 min (36 questions) for the computational biologists.t-test, as appropriate.Asymmetric 68% confidence intervals in all evaluations were estimated using bootstrapping with 1000 iterations of the entire cohort . StatistTo learn about the participants we were working with, we asked about their years of research experience and the field(s) of expertise. As shown in To establish the type of expertise, we asked about the familiarity with common protein annotation databases and software. The participants were first offered a list of databases and asked to specify the level of familiarity with them, then they were asked to list additional databases they work with. Overall, biocurators appear to be more familiar with all databases, and especially with model-organism-specific databases . A more In terms of bioinformatics software, we found that the participants from all three communities use numerous software packages. Of the tools related to function prediction, all were equally versed with BLAST  participants responded with a ‘yes’, whereas 14/22 (64%) responded with a ‘no’ . This isThe researchers who answered with a ‘yes’ for the use of function prediction tools were further asked to list the tools that they use for this purpose. The answers included PSI-BLAST  and I-TAOverall, we find that a small fraction of experimental researchers actually use advanced machine learning tools to understand protein function. Along the same lines, we identified some misunderstanding among experimental researchers as to what protein function prediction tools are.Here we summarize the reactions of experimental researchers to the predictions of state-of-the-art tools. Each participant was given up to 25 of the highest-scoring predictions by one of the computational tools (Section). That is, for each of the four proteins  they were shown up to 25 predictions for each of the three GO domains . For each such predicted term we provided the numerical score given by the algorithm and offered the following checkboxes: ‘known’, ‘useful’, ‘surprising, possible’, ‘surprising, doubtful’ and ‘wrong’, where a selection of multiple boxes or no boxes at all was allowed.The summary of respondents’ assessments of individual predictions is shown in The experimental scientists were then asked to assess if the software has done a good job. A plurality (40%) of researchers responded that the algorithm has in fact done a good job with an additional 10% answering that the algorithm has done a somewhat good job. About 20% responded ‘no’, whereas about 30% of participants decided to either not answer this question or answer it with uncertainty .The participants were also asked to write free-text answers to describe what they think about the predictions. A representative set of criticisms includes ‘Extremely superficial annotation—some is super-obvious and likely only useful in superficial classifications’, ‘I don’t think any of this is really all that useful–most of what comes out is things like the protein binds a metal ion, well most proteins do that, its kinda like saying that they’re made of amino acids’, ‘I found these descriptions redundant and unhelpful  in many ways’, and ‘This is an enzymatic domain  that has lost key catalytic residues is likely to be inactive. It regulates other enzymes instead.’ These answers highlight the difficulty of the protein function prediction task, the difficulty of presenting the results to a downstream user as well as fairly high expectations from experimental scientists. The high expectations are exemplified by the last of the quotes above where a protein that looks like an enzyme has in fact evolved to lose the catalytic ability by mutations of a few key amino acids. To our knowledge, such fine-grained prediction by the machine learning and other computational tools has not yet been achieved.Extensive evaluation of protein function prediction revealed small but a consistently better performance of the state-of-the-art algorithms compared to the baseline methods , this is a surprising finding that suggests some examination is needed of the metrics used for quality assessment of protein function prediction, the ontologies used to describe function, as well as the ways predictions are presented to experimental scientists.The biocuration community has developed ontologies to unify protein function studies across species and to facilitate computation . At the We first asked ‘How useful do you think is a GO annotation for an experimental scientist?’. While most participants classified GO as at least ‘somewhat useful’, the answers varied between the core communities, with experimental scientists showing a preference for literature search in the process of knowledge acquisition and having a different trend than biocurators and computational biologists . We nextThe free-text answers about protein function prediction further suggest that some experimentalists tied the outputs of a function prediction program to the perceived utility of GO itself. Some respondents see that as both a fault of the prediction algorithm and a fault of the GO term; e.g. experimental scientists noted ‘The resolution/granularity of GO annotation is too low to be useful’ or ‘Terms are very broad. It would be useful to have more specific biological functions. I was surprised that nothing related to hypoxia came up for [protein].’ Another experimentalist noted ‘GO has become an insider joke for many in the field, likely however due to misuse of what the annotation tells us. I.e. use of root terms such as ‘biological process’ has been ridiculed repeatedly, but likely due to unclear use of GO. Has become too standard to just add to show some sort of analysis of a used dataset, but needs much more consideration in standard use (a fact that lots of people don’t seem to be aware of).’General questions regarding function prediction were also asked of biocurators and computational biologists in their surveys. Supporting the views from experimentalists, one of the biocurators noted about their interaction with experimentalists ‘They are frustrated by the vast number of terms returned with a enrichment analysis—often more than the number of genes in the query list.’ One computational biologist noted ‘Function is a loose term, therefore can only be predicted loosely.’ This exemplifies numerous problems in the field and relates to the bottlenecks (Section).Our next question was designed to examine the motivation of function prediction among biocurators and computational biologists, who best understand the function prediction field. We asked ‘Do you think that developing tools for protein function prediction is an important problem?’ and allowed the respondents to select answers from ‘not important’ to ‘key to driving biology’. Both groups answered the question with effectively the same distribution, with about 60% of them giving the highest importance and 30% saying ‘quite important’.We next asked ‘What do you think are the chief bottlenecks in protein function prediction?’, where we allowed each participant to give multiple answers or no answers at all. We also provided a free-text field for those who selected ‘other’. We find that more biocurators than computational biologists believe that ‘methodology’ and ‘evaluation’ are the chief bottlenecks. On the other hand, more computational biologists than biocurators believe that ‘available data’ and ‘ontologies’ are the chief bottlenecks . The latAmong the researchers who selected ‘other’, the following answers stood out: ‘I would like to see progress in explainability—why did an algorithm come to its prediction. With some explainability, functional predictions would be better accepted by experimentalists’, ‘Awareness of the area, other than in general terms. Use of the tools already developed in the area. Incorporation of predictions generated by these tools into standard databases like GO and HPO’, ‘Lack of negative experimental examples […]’ and ‘Protein Identifier Cross-Referencing’.Overall, we conclude that all factors have been often enough identified as the chief bottlenecks, including factors that extend beyond technical aspects of the field. Therefore, tangibly advancing any number of these areas is likely to be significant.Functional annotation of proteins is highly interdisciplinary, and making progress requires concerted work of researchers with domain knowledge in different disciplines. We, therefore, surveyed the participants regarding the level of collaborative work they perform with members of other core communities . The mosAmong the free-text answers on how they interact with others and handle feedback from the userbase, some biocurators reported ‘Extensive feedback and interactions. These include suggestions for enhancement to the tools we maintain, reports of problems, requests for help which sometimes indicates we are missing a tools that would be useful to provide to our users’, ‘They need extra terms added and we do so. We also get feedback on the submission process and we do our best to include the suggestions depending on resources and whether there is wider demand in the community for that new feature’, ‘A lot of the random requests about file formats/URLs are not addressable as we don’t have the budget to pull someone away from a central project to work on the request […] or translating the ontology to another language ’, and ‘Our group […] has held roughly 50 onsite workshops with experts to build our ontology and have had over 7000 requests on our github tracker, so interaction has been pretty intense.’Computational biologists described their interaction as ‘We receive continuous feedback from users regarding many facets of our software  We do our best to track and address user feedback using standard GitHub tools such as issues, pull requests and automated tests enabled by GitHub Actions’ and ‘In one project, the agrobiotech company would run large-scale data-generating and validation experiments co-designed with us, and one of their staff meets weekly with my group throughout the project period discussing both technical ideas, results and refinements. In another project, it was more like one week a month kind of interactions as we progressed through different components of the project.’Over the past 10 years, we have been organizing the CAFA challenge . BrieflyWe first asked ‘To what extent do you think CAFA is useful?’ to which 60% of biocurators and 80% of computational biologists answered ‘highly useful’, 35% and 15% answered ‘somewhat useful’, and 5% of computational biologists did not find CAFA useful at all. About 10% of participating biocurators have never heard of CAFA . These rSome free-text answers about CAFA include ‘CAFA has been extremely successful in driving this field—by ensuring methods are benchmarked properly and evaluated against each other. Also, in helping predictors test different strategies to see how well they perform. It has also raised the profile of this research field and helped explain the value to the wider biological community through some very high impact publications. By showing which strategies work well, it has helped secure funding for those initiatives’ and ‘CAFA is indispensable to building a community and advancing the science. I look forward to more types of CAFA challenges toward defining and solving new problems related to protein function prediction (the recent addition of more ontologies was a good example). I also look forward to more CAFA-led community efforts to outreach, such as community-prediction of SARS-CoV-2 proteins when they were first sequenced last year or that of proteins found in meta genomes.’There is scant literature assessing community experience in the field of bioinformatics. A 2019 investigation of 24 490 omics software packages revealed that more than a quarter were inaccessible via the original link provided in the publication  or a speWhile these previous studies point out usability issues in individual tools as well as the collective body of tools produced by the bioinformatics community, we are not aware of any research about the perception, characterization and use of protein function prediction by experimentalists. A study closest to ours was a qualitative survey of 11 life scientists that investigated their needs for bioinformatics software, but it did not investigate the perception of the quality of the methods (We present key findings from a study of three core communities involved in the research of protein function: experimentalists, who generate new knowledge in the field by directly testing hypotheses; biocurators, who codify and standardize functional annotations; and computational biologists, who develop algorithms to predict function. To characterize a protein’s function in a way that is informative and useful, members of all three communities need to work together with an understanding of the type of work and the importance of their peers in other communities. In this work, we found that while members of the three core domains have produced work that has advanced our understanding of protein function, there are a few areas in which collaboration can be improved.One interesting finding was that experimentalists rarely use state-of-the-art machine learning software to predict function. While we did not survey for the reasons, it is reasonable to assume that several underlying causes are at play. First, the knowledge that such tools exist. While they are published in scientific literature, many of these tools do not gain visibility unless adopted by large go-to platforms such as those provided by NCBI or EBI. Second, the usability of prediction software. Software developed in research labs often lingers in the form of prototypes, tends to be cumbersome to use and is often not maintained regularly. Third, the question of need. While new machine learning software may have higher precision and recall than, say, BLAST or HHpred, BLAST and HHpred are often perceived as sufficient for the task at hand. Fourth, the trust and understanding. Even if experimentalists have access to new machine learning software, they still tend to trust highly cited baseline methods rather than a recently published method. Despite the reluctance and lack of time to find and apply advanced software, many predictions from such algorithms were found by experimentalists to be surprising and useful.The survey of engagement between the domain communities offers some more information. Clearly, there is engagement between members of all communities. Computational biologists and experimentalists seem to have less interaction than any of these two groups with biocurators. Again, the reasons for this discrepancy are unclear, and may include that the inherent nature of biocuration requires work with experimentalists to properly capture function annotation and at the same time with computational biologists to provide data formatted in a computationally amenable manner. In contrast, computational biologists seem not to interact as much with experimentalists, which may be related to the fact that algorithmic advances are often more valued than software production in their home departments. Another reason is the lack of understanding of the needs of experimental communities and the different definitions of function used by experimentalists. Finally, the structure of academia is such that many interactions occur within a departmental level, and with like-minded peers, as opposed to interdisciplinary colleagues. These structural disincentives may also contribute to the lack of more productive and more meaningful interaction.There are limitations to this work that include a relatively small selection of participants. Most of the answers provided by the consented participants were opinions and also incorporate their own interpretation of the intent of the question. We have found minor issues with the surveys in that some questions have in fact been misunderstood. Finally, despite our intent for otherwise, many participants took significant time to complete this work. The length of the survey may have confounded some of the findings; e.g. the comparative questionnaire for the experimentalists has taken much shorter time than other surveys. As the last of the three components, it is conceivable that some participants have lost focus by the end of the process.That said, through this work we have discovered interesting trends in the interdisciplinary work within and between the three communities involved in functional annotation of proteins. While each community is producing admirable results, in the interest of accurate, reliable and sustained functional annotation, the quality of mutual interaction can be improved. Specifically, the state-of-the-art algorithms should be made visible and usable for experimentalists, which would stimulate more dialogue between experimental and computational groups. This would require adjustments in the way many computational groups are operating, and is best incentivized by funding agencies. Also, educating computational groups on the typical needs of the experimental communities, and conversely, experimentalists with basic computational skills would help to remove silos between the communities. The over- and misinterpretation of ontologies also seems to be a problem, and the biocuration community is best positioned to address that.To further advance the studies of protein function, and by extension understand the molecular underpinnings of life, we deem it important that both technical and social challenges of the field must be overcome.vbac057_Supplementary_DataClick here for additional data file.


Lastly, we discuss challenges and highlight opportunities for the protein design field.The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from  This functional versatility and the fact that proteins are nanoscopic, biodegradable materials have motivated tremendous efforts toward designing artificial proteins for medical and industrial applications. In fact, human-designed proteins are widely used across medicine, agriculture and manufacturing, constituting the active form of four out of the top five top selling pharmaceuticals in 2021 de-novo protein design, including the design of long  functional proteins. Motivated by the enormous success of structure prediction methods sequence-first, by training large generative language models on vast sequence databases In recent months, however, an explosion of methods leveraging advances in machine learning provide a fresh alternative for de novo protein sequences selectable for tailored properties that may benefit the protein design community and make use of this novel pipeline to discover sequences that may generate natural products.For comprehensive reviews of protein engineering or machine learning methods for protein research, we refer to the works by Yang 1.We describe recent advances in protein design, namely those shifting from a physical-based function paradigm to one that uses deep learning architectures for sequence and structure generation. We aim to give practitioners a waymark to novel tools and their intended uses.2.de novo sequences, with ProtT5 We offer a novel pipeline capable of generating protein sequences with tailored properties. In short, we couple ProtGPT2 3.We dig into the pipeline by presenting a use case for the selection of factory proteins with the predicted ability to produce natural products.4.We discuss challenges to the design of marketable proteins with controllable properties.In particular, in the following:2de novo design of proteins was traditionally approached as an optimization problem where an algorithm searched the global minima of an energy function de novo generated proteins in the last 20 years The in-silicoMore recently however, deep learning (DL) approaches have provided a new venue for protein design research by showing high accuracy in prediction tasks. Highly publicized progress from one such tool came from DeepMind’s AlphaFold language, offering an alternative route to the explicit extraction of evolutionary information from multiple sequence alignments (MSAs), historically done by collecting statistics on the co-evolution of residues within MSAs embeddings) did not outperform traditional exploitations of direct physicochemical and evolutionary knowledge downstream prediction methods of protein function and structure competing with those that exploit MSAs. However, what is encoded in embeddings, including how much evolutionary information as defined in MSAs  is implicitly captured, remains a subject of debate, even in light of correlation between MSAs and pLM embeddings in accuracy for 3D structure prediction In parallel to AlphaFold, natural language processing (NLP) methods were leveraged to learn novel protein representations by learning the protein While these pLMs excel at embedding representations of input sequences, other types of pLMs (discussed below) are now capable of directly generating new sequences. With increasing experimental validation of “black box” DL methods, the protein design field now has a unique opportunity to generate sequences, interpret DL representations, and build pipelines aiding at various stages of protein design and drug development, from initial library generation to refinement and optimization.3hallucination . The performance of these methods is usually evaluated by native sequence recovery (NSR), i.e., the percentage of wild-type amino acids recovered for an input sequence by the design method. While this metric imposes some limitations, given that the identity percentage does not necessarily correlate with expression or functional levels SPIN2SPROF[59], tried to remedy information loss by leveraging a 2D input and a 2D Convolutional Neural Network (CNN), which brought NSR to 39.8 %. 2D CNN models excelled for years in computer vision tasks as they handle local and global information better than FNNs ProDCoNNAnandresidual blocks, which inspired DenseCPDCNN protein landscapeTIMEDThe first category of methods we describe focus on solving the traditional protein design problem, i.e., finding a sequence that optimally adopts a desired backbone , Panel 1TERMinatorProteinSolverIngrahamStructured Transformer, where the GNN encoder learnt protein structures represented by graphs, while the decoder sampled sequences conditioned on the encoder-learn structure representations. Another encoder-decoder, ABACUS-RMIFProDESIGN-LEGVP-GNN) OrellanaMcPartlonFold2seqESM-IF1ProteinMPNNde novo proteins up to date PiFoldresidue featurizer”, achieving up to 70 times speedup while providing a NSR of 51.7 %.Protein 3D structures can also be represented by graphs where nodes are residues (or atoms) and edges represent structural proximity. Graph neural networks (GNN) directly harness graph representations, and thus attempts like Anand2 to learn from distance maps, and included a learned coordinate recovery module replacing ADMM IG-VAELaiRamaNetDECO-VAESCUBAGENESISProteinSGMFoldingDiffProtDiff and SMCDiffMost of the early work in the second category of methods generating structure encoding objects , Panel 2ProteinGANProGenkeywords). These tags could be used to control the generation process, e.g. by selecting for acyltransferase activity. Transformer models can also be “fine-tuned” to achieve a desired goal, practically an alternative technique to using tags for controllable generation. ProGen was employed for the generation of Lysozimes using fine-tuning on five fold diverse Lysozimes families resulting in about 50,000 sequences. Generated sequences showed enzymatic activity in the range of natural counterparts, and one sequence was purified and its structure resolved via X-ray crystallography ProtTransDARK3ProtGPT2 leveraged a GPT2-like model RITAProGen2TranceptionEVEReLSOAnother emerging protein design branch focuses on sequence generation , Panel 3Hallucinationhallucinate” 100-amino acid long de-novo protein sequences leveraging trRosetta. The term hallucination was inspired by DeepDream Constrained hallucinationNornRFjointRoneyProtein DiffusionThe last category of design methods encompasses those capable of concomitantly designing sequence and structure , Panel 44the end to be marketable devices primed to ace clinical trials or pass environmental regulations. A step towards this goal would be DL models that prompted with a set of biological mechanism and industry-relevant properties  output a sequence or structure satisfying the selected criteria with high precision in a timely fashion. Whilst this may not yet be possible, we present an offline attempt  by combining a generative protein sequence model  a nonsense sequence set created by shuffling residues within the sequences of the sampled UniRef50 set (e.g. “SEQVENCE” may become “QEEVNCSE”) which we call “random”. We then predict structure and function using the same methods and script applied to the generated sequences for both sets.In particular, we unconditionally  generated a set of 100,000 protein sequences using ProtGPT2, and predicted secondary structure https://github.com/hefeda/PGP) that allows to query the generated protein set in quasi-natural language via the predicted features. To cite two examples, a user could shortlist protein sequences with more than 200 residues that have at least 30 % of residues involved in alpha-helical secondary structure and that locate on the outer cell membrane according to GO annotations, or select for sequences shorter than 100 residues with transmembrane strand content and binding to small molecules. The resulting sequences filtered by the desired query displayed in the Jupyter Notebook are linked to LambdaPP Qualitatively comparing distributions of several predicted features for the three sets  suggestsWhilst not an end-to-end solution, carefully crafted combinations of lightning-fast DL generators and predictors, easy query mechanisms, and rich visual tools allow to push the envelope in discovery of novel candidates, as explored in the following use case.5molecular glues as they work by gluing a protein of interest to a regulating protein, often an enzyme called the effector protein. Multiple approved drugs with previously unknown mechanisms of action have now been identified to work through this mechanism Proteins can serve as factories increasingly engineered to manufacture other products, such as the triterpene squalene secondary metabolite biosynthetic process” (GO:0044550) or its children terms, using a cutoff of embedding euclidean distance of up to 0.6, resulting in 1,345 sequences  to naturally occurring BGC proteins . First, we filter sequences by those with the Gene Ontology (GO) annotation “proteins .Fig. 3Unin-silico workflow of generating sequences, with the aim of evolving specific BGCs to produce desired molecules after further in-vitro confirmation, for instance by applying directed evolution We focus on one generated protein  remains difficult, an unfortunate reality since proteins would have the potential to tackle many emerging biomedical and environmental challenges end-to-end nature and the ability to combine different losses, i.e. to condition the output (e.g. sequence) on the input (e.g. structure) using a mathematical formulation which takes into account aspects (e.g. thermostability) interesting in the application context (e.g. biopharma). DL models for protein design are optimized end-to-end on large biological collections, which are often limited by experimental shortcomings, for instance capturing a static notion of protein 3D structure, as opposed to its dynamic nature . Thus, while DL models for protein design may encode biological mechanisms required to propose viable biologics, they cannot yet be blindly utilized without experimental validation, and do not yet explicitly account for marketable variables. Similarly, as protein function lacks rigorous definition  and scale Two interesting aspects of DL solutions are their in-silico with in-vitro approaches, where physical experiments, accounting for all variables, inform DL models in a differentiable manner may come from advancements in automated labs An opportunity to connect Looking forward: protein design community on a rigorous journey to establish goals. In the first half of the 1990s, at a time when having solved the protein folding challenge sporadically made headlines, the Critical Assessment of Structure Prediction (CASP) in-silico predictive methods for protein structure needed to prove advancement. In combination with establishing data standardization and a single data repository, this led to multiple revolutionary approaches, from early uses of single-frequency models , to complex co-evolutionary representations through direct-coupling analysis in-silico tools predict known function from sequence, rather than their ability to infer proteins (sequences or structures) that perform a desired, sometimes non-naturally found function. Conversely, model developers score their tools by metrics like Natural Sequence Recovery (NSR), which validate a model’s ability to link structure to sequence, but often not a model’s ability to generate diverse sequences fitting a desired structure As the protein design field moves to more complex DL approaches, benchmarks, which promoted the success of structure prediction tools, appear to be lacking. Arguably, this is due to the underlying complexity of defining, in principle, what protein design is supposed to be, especially as it is moving from theoretical exercise to practical applications. Is it sequence design? Is it structure design? Is it sequence and structure that culminate in function? In fact, function would most likely be the design goal from a practical standpoint. Attempts at measuring advancements in protein function prediction exist, e.g. CAFA Ultimately, by overcoming the challenges of measuring advances, deep learning is set to enable protein engineers to design sequence, structure, and function with controllable properties.http://data.bioembeddings.com/public/design. Code-base and Notebooks for analysis are available at https://github.com/hefeda/PGP. An online version of Table 1 can be found at https://github.com/hefeda/design_tools.pLM generated and UniRef50 sampled sequence sets and predictions are available at Noelia Ferruz: Conceptualization, Methodology, Software, Investigation, Writing – original draft, Writing – review & editing, Visualization. Michael Heinzinger: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Mehmet Akdel: Validation, Investigation, Writing – original draft, Writing – review & editing, Visualization. Alexander Goncearenco: Validation, Writing – original draft, Visualization. Luca Naef: Conceptualization, Validation, Writing – original draft, Visualization. Christian Dallago: Conceptualization, Methodology, Validation, Resources, Writing – original draft, Writing – review & editing, Visualization.The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: CD was employed by VantAI and NVIDIA at different periods during the time of writing. MA, AG, LN are employees of VantAI. NVIDIA and VantAI had no influence on the contents of this manuscript.


Caenorhabditis elegans (C. elegans) model, we can surpass the challenges posed by the functional redundancy of SIN3 isoforms. In this regard, we have previously demonstrated the role of SIN-3 in uncoupling autophagy and longevity in C. elegans. In order to understand the mechanism of action of SIN3 in these processes, we carried out a comparative analysis of the SIN3 protein interactome from model organisms of different phyla. We identified conserved, expanded, and contracted gene classes. The C. elegans SIN-3 interactome -revealed the presence of  well-known proteins, such as DAF-16, SIR-2.1, SGK-1, and AKT-1/2, involved in autophagy, apoptosis, and longevity. Overall, our analyses propose  potential mechanisms by which SIN3 participates in multiple biological processes and their conservation across species and identifies candidate genes for further experimental analysis.SIN3/HDAC is a multi-protein complex that acts as a regulatory unit and functions as a co-repressor/co-activator and a general transcription factor. SIN3 acts as a scaffold in the complex, binding directly to HDAC1/2 and other proteins and plays crucial roles in regulating apoptosis, differentiation, cell proliferation, development, and cell cycle. However, its exact mechanism of action remains elusive. Using the  SIN3 protein is a transcriptional regulator that functions as the central scaffold unit of the multi-protein SIN3/HDAC co-repressor complex3. This SIN3 core complex and its interaction partners play a significant role in several critical pathways such as autophagy, apoptosis and longevity.Cell death and aging are known to play a crucial role in development, health and disease. Biological processes such as cell death and aging are tightly controlled by gene expression, mediated by complex interactions between chromatin, epigenetic modifiers, and transcription regulatory proteins (TRPs). TRPs such as the SIN3 protein are associated with DNA-binding transcription factors in addition to different co-activator and co-repressor complexes. These protein–protein interactions function as writers, erasers, readers and modifiers of the chromatin design for the functioning of a cell4. These evolutionarily conserved domains enable the protein to control transcription6. Though SIN3 has multiple roles in apoptosis, differentiation, cellular proliferation, development, cell cycle, cancer and aging10, the exact mechanism of regulation mediated by the protein is unknown.A typical SIN3 protein harbours several paired amphipathic α-helix (PAH) domains, a prominent HDAC interacting domain (HID), and a highly conserved region (HCR)C. elegans makes it a suitable model for understanding the functional diversity of SIN3.The functional diversity of SIN3 can be facilitated by the diversity in its interactome. However, the mechanistic studies are impeded by the presence of multiple isoforms of SIN3 in mammals and their functional redundancy. The presence of SIN-3 and the absence of isoforms of SIN3 in C. elegans, SIN-3 protein plays a vital role in male sensory organ development11, muscle integrity, motility, and longevity. It also controls several physiological parameters such as stress tolerance, protein homeostasis, muscle and mitochondrial functioning, cuticle integrity, accumulation of age-associated pigments, fecundity and fertility12. Remarkably, the protein modulates autophagy and lifespan in an unconventional way. Usually, an increase in autophagy leads to lifespan extension. However, reactive oxygen species (ROS) and intracellular oxidative stress in sin-3 deletion mutants are associated with uncoupling of autophagy and longevity such that increased autophagy leads to a shorter lifespan13. In order to understand the factors involved in SIN3 regulated autophagy and longevity, we carried out an analysis of the interactome of SIN3 in different model organisms from different phyla. Further analysis of gene ontology and the expansion/contraction of the gene classes led to the identification of conserved factors important for these functions that can serve as candidate genes for further analysis.In https://www.rstudio.com/)14, Microsoft Excel 2013 (https://www.microsoft.com/)15.NCBI Protein database, Clustal Omega web server, MAFFT, MEGA-X v10, NCBI Conserved domain database, G-BLOCKS online web server v0.91b, GeneMANIA, DAVID Bioinformatics Resource v6.8, NCBI Conserved domain database, Ensembl genome, Orthofinder (version 2.3.12), RStudio v2021.09.0+351.pro6  for proper sequence alignment and domain comparison19. The percent identity matrix was obtained for all the SIN3 homologs. Similarly, the percent identity matrix for SIN3 protein domains, such as PAH, SIN3A_C, and HDAC interaction domain, was also prepared for domain-specific comparison.The FASTA sequences of SIN3 homologs were downloaded from the NCBI Protein database20. L-INS-i is one of the most accurate multiple sequence alignments, particularly suitable to align 10–100 protein sequences. The amino acid alignment was curated in the G-BLOCKS online web server v0.91b21 with default parameters . Phylogenetic tree was constructed through Maximum likelihood (ML) using MEGA-X v1022. Statistical significance was increased by bootstrapping with 1000 replicates23. The phylogenetic tree for SIN3 protein domains, PAH, SIN3A_C and HID, was obtained in the same manner.The FASTA sequences of SIN3 homologs were downloaded and aligned in the MAFFT (L-INS-I method) online web server v724 and analyzed for the different functional domains present in the SIN3 proteins.The FASTA sequences of SIN3 homologs were submitted to the NCBI Conserved domain databasehttp://Genemania.org) was used to examine the various physical interactions of SIN3 homologs with a limitation on the maximum resultant genes, set to 10025. After selecting the specific organism under study, the official gene symbol of the protein of interest was submitted to the database. All default network attributes were de-selected, and only physical interactions were selected for generating the final SIN3 protein interactome.GeneMANIA  Bioinformatics Resource v6.8 was used for analyzing the physical protein interactors of C. elegans (WBcel235), S. cerevisiae (R64-1-1), D. melanogaster (BDGP6.32), D. rerio (GRCz11), H. sapiens (GRCh38.p13), and M. musculus (GRCm39) from the Ensembl genome (ensembl.org) were used. Orthofinder (version 2.3.12) was run with default parameters to find orthogroups among the whole deduced proteomes of all six organisms listed above32. If there are multiple predicted proteins/transcripts for a gene, the primary transcript of the predicted protein was chosen for analysis.For evaluating the orthology of proteins of interest, the predicted proteomes of SIN3 is well-conserved across phyla due to the significant similarities in the functional domains of the protein. These domains and other protein motifs enable SIN3 to participate in multiple protein–protein interactions, hence regulating multiple pathways. The analysis we have carried out identifies candidate protein–protein interactions of SIN3 involved in the eukaryotic regulation of crucial biological processes like apoptosis, autophagy and longevity.C. elegans SIN-3 protein (ceSIN3) and SIN3 isoforms present in other model organisms. Previous studies have reported that ceSIN3 has homology with human and mouse SIN313. Therefore, we carried out phylogenomic analyses of the SIN-3 protein of C. elegans and five other model organisms, namely, S. cerevisiae, D. melanogaster, D. rerio, H. sapiens, and M. musculus, to deduce the level of protein homology in the context of the functional domains. ceSIN3 is an orthologous member of the SIN3A phylogenetic family 35. Therefore, the current study demonstrates that ceSIN3 protein shares homology with human and mice SIN3. It also shows significant similarity with the SIN3 isoforms of S. cerevisiae, D. melanogaster, and D. rerio.To determine the quantitative similarity, a percent identity (PID) analysis was carried out using ClustalW sequence alignment. ed Table . It is gceSIN3 with the rest of the proteins is restricted to specific regions. While the PAH and HID domains contain the well-aligned regions of the protein, it was also seen that the C. elegans protein has large stretches of unaligned regions that seem to be only partially conserved in SIN3 proteins of yeast and Drosophila . The current study reveals that the PAH domain of ceSIN3 has ≥ 28% identity with all the three PAH domains of SIN3 isoforms. The cePAH domain is remarkably similar to the PAH1 (≥ 50%) domain of the other SIN3 homologs, especially the yeast Sin3 PAH1 , p38-MAPK pathway , and insulin signaling pathway  are conserved across phyla and expanded in proteomes of three or more species. These pathway proteins are crucial for cellular processes preceding apoptosis and longevity, and are expanded in the eukaryotic system. The ERK-MAPK pathway is the primary signaling network involved in regulating cell division, growth, development, apoptosis39 and tumor formation, and the p38-MAPK pathway works in response to stress to regulate cellular functions such as differentiation, apoptosis, and senescence40. The insulin/IGF signaling (IIS) pathway is involved in stress resistance and longevity along with nutrient regulation and growth41. Interestingly, orthogroups of signaling proteins  are also expanded in the nematode proteome, revealing the need for increased signaling proteins even in lower eukaryotes.Protein orthogroups involved in signaling pathways are well conserved in higher eukaryotes. While the number of proteins attributed to the different orthogroups is similar for the invertebrates, 42.Orthogroups of nematode proteins that show poor conservation in other model organisms are mainly associated with the main apoptotic pathway. While the main cell death protein CED-3 (caspase 2/3/6 homolog) protein is expanded in higher eukaryotes, rest of the apoptosis proteins  are contracted in the species included in the study. This is likely because of the functional redundancy of the proteins associated with essential functions. For example, CEP-1, a functional p53 homolog, has very low sequence identity with p53 isoforms of different model organisms43; even the MAPK cascade components are only conserved in function44 but not in terms of sequence identity. Certain zebrafish proteins are over-represented due to genome duplication, which precedes the teleost evolution. As a result, many mammalian genes have more than one ortholog in the zebrafish genome46.Most of the orthogroups do not have any yeast protein as the unicellular organism lacks homologs for most eukaryotic proteins involved in apoptosis, autophagy, and longevity. Yeast does not have distinct cell death pathways similar to mammalian ones50. Therefore, identifying the interaction repertoire of transcriptional regulator SIN-3 will give a better understanding of the regulation of various biological processes in C. elegans51.Protein–protein interactions are crucial for the basic functionality of cells (40). The detailed study of the protein interactome holds the key to identifying biological pathways and predicting protein functions53. Therefore, the protein–protein interactions of these eukaryotic systems can be extrapolated to ceSIN3 ortholog with computational methods54. GeneMANIA was used to obtain protein–protein interactions of SIN3 in different eukaryotic systems. The database extracted 100 physical interactions, represented in the form of a network image  are involved in important processes like sex differentiation, multicellular development, cell differentiation and cell signaling pathways, thereby enabling SIN-3 mediated regulation via protein–protein interaction.Orthology of the nematode proteins in the SIN-3 interactome, obtained via GeneMANIA and literature based evidence method, was checked in other model organisms included in the analysis using the Orthofinder tool. It was found that many of the SIN-3 interactors found in sms Fig. . This coceSIN3 shares a closer relationship with SIN3A isoforms. Therefore, it was imperative to evaluate the nematode protein for conservation of SIN3A/SIN3B specific protein interactions in model organisms which harbor multiple isoforms of the SIN3 protein. While it was observed that very few SIN3A/SIN3B specific interactions are conserved in ceSIN3, no particular preference for SIN3A specific interactions is seen in humans, mice, or zebrafish. This might be due to the similarity in the protein sequence identity of the ceSIN3 protein with SIN3A and SIN3B isoforms.It has been well-established that both SIN3 isoforms, SIN3A and SIN3B, form distinct protein complexes. Phylogenetic analysis has revealed that 58 to identify the participation of SIN-3 protein interactors in apoptosis, autophagy and longevity. GO ‘biological process’ classification of ceSIN3 protein interactome reveals that 53% interactors are associated with the regulation of transcription, the majority being transcription factors .GO term analysis revealed that some ns Table . HLH-3 aceSIN3 interactors. The FOXO , MAPK and Jak-STAT  interacting with ceSIN3 protein contain a stretch of Glutamine-rich repeats or Aspartate repeats (Table Motifs are used as scaffolds by proteins and transcription factors interacting with SIN3 PAH domains. LxxLL (x indicates any amino acid) motif is an acidic leucine (L)–rich protein binding motif used by proteins to interact with the PAH1 domain of SIN3 proteints Table .Table 5CceSIN3 interactome , daf-16 , sgk-1 (ortholog of human SGK2), and sir-2.1 mutant (ortholog of human SIRT1), show elevated autophagy, programmed cell death and shortened lifespan like that of sin-3 mutants. Other mutants such as akt-2, akt-1, ces-2, and bar-1 also show phenotypes related to apoptosis and longevity. These interactor proteins seem to have a well-established connection with the apoptosis-autophagy-longevity axis 13.Consequential mutant phenotypes provide evidence-based annotations for a majority of proteins. Such an approach helps to investigate the presence of SIN3 mutant-like phenotypes. Therefore, the mutant phenotypes associated with apoptosis, autophagy, and longevity were curated for all the proteins in the ome Fig. . We usedceSIN3 and other eukaryotic SIN3 isoforms provides a basis for the genetic and functional correlation of the C. elegans protein with the other homologs. Protein–protein interactions of ceSIN3 protein revealed many interacting proteins associated with the regulation of programmed cell death, autophagy, and adult life span. Some of these interacting proteins, such as SIR-2.1, AKT-1/2, BAR-1, and CES-2, contain the SIN3 PAH domain interaction (LXXLL) motif, while others, like SMA-9, FOZI-1, and CEH-36, harbor Glutamine rich repeats known to be involved in protein–protein interactions. A few of these protein interactors also display mutant phenotypes similar to that of sin-3 mutants, indicating their potential in regulating apoptosis, autophagy, and longevity. However, experimental evidence will provide better insight. It is important to check the protein interactions of the ceSIN3 protein by performing a protein immunoprecipitation (IP) assay followed by Mass Spectroscopic (MS) analysis. This will confirm SIN3 protein interactions, which can be genetically studied for their role in apoptosis, autophagy, and longevity.Our study identifies novel protein interactors of the SIN3 protein. The protein homology and phylogenetic relationship between the Supplementary Information.


While multi-channel fluorescence microscopy is a vital imaging method in biological studies, the number of channels that can be imaged simultaneously is limited by technical and hardware limitations such as emission spectra cross-talk. One solution is using deep neural networks to model the localization relationship between two proteins so that the localization of one protein can be digitally predicted. Furthermore, the input and predicted localization implicitly reflect the modeled relationship. Accordingly, observing the response of the prediction via manipulating input localization could provide an informative way to analyze the modeled relationships between the input and the predicted proteins.We propose a protein localization prediction (PLP) method using a cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN) to digitally generate additional channels. 4DR-GAN models the joint probability distribution of input and output proteins by simultaneously incorporating the protein localization signals in four dimensions including space and time. Because protein localization often correlates with protein activation state, based on accurate PLP, we further propose two novel tools: digital activation (DA) and digital inactivation (DI) to digitally activate and inactivate a protein, in order to observing the response of the predicted protein localization. Compared with genetic approaches, these tools allow precise spatial and temporal control. A comprehensive experiment on six pairs of proteins shows that 4DR-GAN achieves higher-quality PLP than Pix2Pix, and the DA and DI responses are consistent with the known protein functions. The proposed PLP method helps simultaneously visualize additional proteins, and the developed DA and DI tools provide guidance to study localization-based protein functions.https://github.com/YangJiaoUSA/4DR-GAN.The open-source code is available at Bioinformatics online. Fluorescence microscopy, where samples are labeled with fluorescent probes (a.k.a. fluorophores), is one of the most versatile optical imaging methods. It allows visualization and quantification of various aspects of the target proteins, including their level, localization, behavior and interaction with other proteins. Usually, each protein of interest is labeled by one type of fluorophore, and its signals are collected as one channel. In laser-scanning confocal microscopy, labeled samples can be imaged in a 3D volume with three spatial axes. Application to live tissues or organisms generates time-lapse datasets with the time axis. Furthermore, if multiple proteins are labeled and imaged, additional channels are obtained, which result in information-rich 5D datasets.in vivo concentration of target proteins. When it comes to live imaging, the choice of fluorophores is particularly limited because signals from live samples are much weaker. These limitations contribute to the difficulties of simultaneously imaging more than two proteins in live samples. Beyond the choice of compatible fluorophores, the number of channels is also bound by the availability of laser lines and detectors of a microscope, the demand for acquisition speed, and the availability of genetically labeled proteins. Without a proper tool, simultaneously observing and even studying multiple proteins has been quite a challenge.Although multi-channel imaging is a powerful tool that is used to understand protein functions, in practice, the number of proteins that can be imaged simultaneously is limited. This is because the emission spectra of individual fluorophores are often too wide to be sufficiently separated. Additionally, the choice of fluorophores is limited by the quantum yield and photostability of fluorophores  are able to take an input image and generate the desired output image. A cGAN usually has a generator and a discriminator that are both convolutional neural networks. The generator uses network parameters to implicitly model the joint probability distribution of the inputs and the outputs so that it generates the desired output for any given new input. Theoretically, if enough samples and training time are offered, the modeled probability distribution can match the true distribution  method using a new cGAN named 4D Reslicing Generative Adversarial Network (4DR-GAN). 4DR-GAN models the joint probability distribution of imaged and unimaged proteins by incorporating the correlations between two protein localizations manifested in four dimensions, three in space and one in time. To our knowledge, this is the first work on applying cGANs to 4D information modeling. The generator of the 4DR-GAN is an end-to-end network that takes a 4D image as an input and incorporates spatial and temporal information via two encoding paths. Subsequently, the 5D feature maps are extracted from the two paths, and they are resliced to the same shape to be paired in space and time. The paired features are reconstructed to produce a 4D output image with identical size as the input. Altogether, this 4DR-GAN enables accurate prediction of protein localization that cannot be imaged together. Furthermore, with the new capability of accurate PLP of fluorescence images, it opens the door to digitally manipulating a protein’s localization and activation. When manipulating the input protein, the response of the predicted protein reveals the protein relationships. In this regard, we further propose two novel tools, digital activation (DA) and digital inactivation (DI). DA allows to observe the predicted protein response when digitally increases protein localization or protein activity, while DI serves the same purpose by digitally decreases protein localization or protein activity.DA and DI present advantages when compared with genetic knockout and knockdown in terms of protein activity manipulation. Essential in testing the function of a gene, genetic knockout removes the gene from the genome, and gene knockdown stops or decreases the expression of the targeted genes. However, gene knockout and knockdown have drawbacks that mostly originate from their limited spatial and temporal control capabilities. Applying genetic knockdown or knockout to undesired tissues or stages often complicates the analysis of gene functions. Another drawback is that these genetic approaches are unable to manipulate gene functions at subcellular levels, which is an important aspect in understanding the differential protein functions at multiple subcellular compartments. In contrast, DA and DI can manipulate gene functions with precise spatial and temporal control and induce immediate effects, allowing gene function to be digitally removed or activated in any cells and subcellular regions at any time point. If the protein manipulation consistently leads to changes in prediction, the changes reflect the local or global relationship between the input and the predicted proteins, making DA and DI desirable tools for protein functional relationship study.To evaluate the effectiveness of PLP along with DA and DI, we used 5D datasets from live imaging of Drosophila embryos that revealed the localization of two proteins in separate channels. These datasets offer rich temporal information since the subcellular localization of proteins change rapidly in a developing embryo. The high spatial and temporal resolutions of these datasets  allow us to test prediction accuracy at subcellular levels. The proteins involved are well studied in their localizations and functions, and therefore, they offer a variety of evaluation criteria. We summarize our contributions in the following three aspects:To visualize more proteins simultaneously in fluorescence microscopy, we propose a PLP method to predict the localization of unimaged protein from imaged proteins using 4DR-GANs, a new cGAN developed solely for this work. 4DR-GAN can simultaneously incorporate 4D information for the purpose of PLP.Based on PLP, we developed two new tools to digitally manipulate protein localization and activation: DA and DI. These tools allow to precise spatial and temporal manipulation and induce an immediate response. A consistent response could provide clues to the functional relationship between the two proteins.A comprehensive experiment on six pairs of PLP shows the effectiveness of 4DR-GAN and the success of PLP. Compared with the existing network, the protein localization and dynamic behavior in our prediction results are closer to the ground truth (GT). Through performing DA and DI on multiple groups of proteins, we obtained responses in the predicted protein localization that are consistent with the known protein functions.G) that predicts the protein localization in 4D images, and a 4D-consistency discriminator (D) that assesses the realness of the prediction in terms of localization, temporal consistency, and the input-target correlation. t frames, and XYT-Z view as z frames.The fundamental role of 4DR-GAN is to incorporate spatial and temporal information simultaneously in the input 4D image and produce realistic 4D output. In PLP, 4DR-GAN takes one protein localization as input, and predicts another protein localization as output. 4DR-GAN consists of a 4D-reslicing generator  view such as D justifies the localization and the temporal consistency of proteins, as well as the interaction between proteins.The discriminator We applied this 4DR-GAN to the 5D datasets collected from live imaging of Drosophila early embryos involving three proteins: Myosin (Myo), E-Cadherin (E-Cad) and Ajuba (Jub). Embryos were dechorionated in 4% sodium hypochlorite, washed in water, and mounted in glass-bottom Petri dishes by the natural affinity between the vitelline membrane and the glass. The dish chamber was then filled with water and covered by an oxygen-permeable membrane. The imaging was performed with a Zeiss LSM 800 confocal microscope equipped with high sensitivity GaAsp detectors. The 488- and 561-nm lasers were used to excite GFP and mCherry, respectively. Images were acquired using a plan-Apochromat 63×/1.40 oil objective with the pinhole set at 1 Airy unit and the pixel size set at 0.124 μm. The z-stacks start from the embryo surface to 7 μm deep with 0.5 μm increments. The time interval between stacks is 10 s.The original 5D fluorescence images have two channels and slight variations in shapes. The two channels are split as input and target, and then resized and cropped into training and testing samples. Accordingly, we performed four groups of PLP: from Myo to E-Cad, from E-Cad to Myo, from Jub to Myo, and from Jub to E-Cad.X- and Y-axis, 16 pixels on Z-axis and 10 time frames. Meanwhile, two channels were involved in each sample, where one channel was used as input, and another channel was as the target output (GT). These samples were cropped from 12 5D fluorescence images. For validation, 129 samples were cropped from another three images. Three images containing the Myo localization with a size of 256 × 256 × 16 × 40 × 1 were used for testing.For each group of proteins, 516 samples with a size of 256 × 256 × 16 × 10 × 2 were used for training. Each sample had 256 pixels on the We evaluate the PLP from 4DR-GAN with three approaches and compare the results with Pix2Pix prediction. Section 3.1 demonstrates the similarity and difference between prediction and GT based on the characteristics of imaged biological structures. Section 3.2 quantifies the similarity between the prediction and GT images using Fréchet Inception Distance (FID). Section 3.3 quantitatively evaluates the protein distribution and behavior as well as their changes with time. Section 3.4 shows the application of PLP in DA and DI.Because the function and activation state of the protein determine the subcellular localization, we evaluated the similarity between the prediction and target GT, using key biological characteristics of the subcellular localization. Our datasets recorded the ventral cells of fly embryos . Myo is t frames  and temporal (XYT) consistency. The discriminator is the key to the improvement because it comprehensively justifies the localization and the temporal consistency of proteins, as well as the interaction between proteins. Although the 4DR-GAN XYZ-path focuses on encoding XYZ dimensions in the generator, the discriminator forces the generator to learn the T dimension to reduce the adversarial loss.We observe that the score scales vary with networks used in FID. It is common for different pre-trained networks to result in scores that differ by a few orders of magnitude  of filamentous Myo is correlated with higher contractile force . By connThe above analysis shows that, by integrating the information from all four dimensions, 4DR-GAN can accurately predict protein subcellular localization and concentration. Protein localization and concentration are not only the input and output of PLP, but are also closely related to protein activation states. For example, a higher concentration of Myo indicates more activated Myo proteins and correlates with higher physical tension generated by Myo. Therefore, we reason that it is possible to digitally control protein activities, by altering their localization and concentration in the images. This inspired us to develop effective DA and DI methods  and inactivate E-Cad (low intensity uniform membrane). These experimental cases suggest that 4DR-GAN can learn complex spatial and temporal relationships.in vivo protein concentration and fluorophore limitations, let alone imaging three proteins. With PLP, we successfully predicted E-Cad from the imaged Myo channel in Myo-Jub datasets, which leads to high-quality signals for all three proteins. PLP can also be instrumental in case of hardware limitations such as the availability of laser lines and detectors on a microscope by predicting other channels from imaged channels.The proposed PLP method will benefit a variety of fluorescence microscopies, especially live imaging where fluorophore choices are limited. For example, in our experiments, imaging Jub and E-Cad together is already challenging due to low Based on 4DR-GAN-based PLP, DA and DI are two novel tools we propose for protein functional relationship study. A key feature of DA and DI is the capacity to precisely manipulate a protein localization in space and time. When doing so, DA and DI reflect the immediate effect on the output protein localization and therefore shed light on the protein relationships locally and globally. The experimental results not only demonstrate that DA and DI can predict the correct consequences of protein loss-of-function and gain-of-function, but also suggest that the 4DR-GAN-based PLP learns correct protein relationships. DA and DI require manipulation designs appropriate for protein functions. For unknown protein functions, multiple designs should be considered and analyzed.Another key feature of DA and DI is that the outputs respond to input manipulation regardless of causality between proteins or the structure labeled by the protein. When the upstream protein is manipulated digitally, it mimics biological loss-of-function and gain-of-function experiments and the downstream protein responds in the predicted localization. However, the reverse experiment is different. When manipulation is applied to the downstream protein, the upstream protein does not respond in biological experiments, but it will respond in digital experiments. Specifically, when the downstream protein is digitally manipulated into a certain localization, the prediction provides a clue on the localization of the upstream protein localization that is required to drive the downstream protein into this certain localization. Therefore, DA and DI provide additional information in such cases. Overall, the realization of DA and DI provides a convenient and low-cost way to study protein functions and relationships and guides experimental designs in biological studies of unknown proteins.Besides DA and DI, there are other possible applications of PLP. For example, in the cases of predicting the localizations of E-Cad from that of Myo and backward, we observe that the prediction of E-Cad from Myo has better perceptual quality than the prediction of Myo. The high prediction quality of E-Cad could imply that Myo is a major factor affecting E-Cad localization, while the information from E-Cad alone is insufficient and factors other than E-Cad contribute significantly to Myo localization. This observation also suggests potential future works of PLP. By adopting multiple informational sources, the performance of PLP for proteins that have multiple factors can be improved. In addition, analyzing the prediction quality may provide clues to the causality between proteins or cellular structures labeled by proteins.This work was supported by the UNLV TTGRA and NIH Pathway to Independence Award [K99/R00 HD088764]. The publication fees for this article were partially supported by the UNLV University Libraries Open Article Fund.Conflict of Interest: none declared.btac719_Supplementary_DataClick here for additional data file.