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Synopsis The connection between structure and function is one of the fundamental tenets of biology: a biological unit’s structure determines its function, and, conversely, its function depends upon its structure. Historically, important advances have been made either when understanding of structure leads to questions about function or when understanding of function raises questions about the structures involved. Consequently, considering the connections between structure and function from a broader perspective might lead to the development of novel hypotheses that move our understanding of the fundamental connections between structure and function forward. Better integration of structure and function is a key component in the broader goal of reintegrating biology within and across scales. Here, we provide examples of how integrating studies of structure and function as well as comparing structure–function relationships across biological scales can lead to scientific advances. We also emphasize the potential of integrating studies of structure and function across scales for bio-inspired design and for improving biology education.

RNA binding proteins play key roles in many aspects of RNA metabolism and function, including splicing, transport, translation, localization, stability and degradation. Within the past few years, proteomics studies have identified dozens of enzymes in intermediary metabolism that bind to RNA. The wide occurrence and conservation of RNA binding ability across distant branches of the evolutionary tree suggest that these moonlighting enzymes are involved in connections between intermediary metabolism and gene expression that comprise far more extensive regulatory networks than previously thought. There are many outstanding questions about the molecular structures and mechanisms involved, the effects of these interactions on enzyme and RNA functions, and the factors that regulate the interactions. The effects on RNA function are likely to be wider than regulation of translation, and some enzyme–RNA interactions have been found to regulate the enzyme's catalytic activity. Several enzyme–RNA interactions have been shown to be affected by cellular factors that change under different intracellular and environmental conditions, including concentrations of substrates and cofactors. Understanding the molecular mechanisms involved in the interactions between the enzymes and RNA, the factors involved in regulation, and the effects of the enzyme–RNA interactions on both the enzyme and RNA functions will lead to a better understanding of the role of the many newly identified enzyme–RNA interactions in connecting intermediary metabolism and gene expression.

As more genome sequences are elucidated, there is an increasing need for information about the functions of the millions of proteins they encode. The function of a newly sequenced protein is often estimated by sequence alignment with the sequences of proteins with known functions. However, protein superfamilies can contain members that share significant amino acid sequence and structural homology yet catalyze different reactions or act on different substrates. Some homologous proteins differ by having a second or even third function, called moonlighting proteins. More recently, it was found that most protein superfamilies also include pseudoenzymes, a protein, or a domain within a protein, that has a three-dimensional fold that resembles a conventional catalytically active enzyme, but has no catalytic activity. In this review, we discuss several examples of protein families that contain enzymes, pseudoenzymes, and moonlighting proteins. It is becoming clear that pseudoenzymes and moonlighting proteins are widespread in the evolutionary tree, and in many protein families, and they are often very similar in sequence and structure to their monofunctional and catalytically active counterparts. A greater understanding is needed to clarify when similarities and differences in amino acid sequences and structures correspond to similarities and differences in biochemical functions and cellular roles. This information can help improve programs that identify protein functions from sequence or structure and assist in more accurate annotation of sequence and structural databases, as well as in our understanding of the broad diversity of protein functions.

The numerous interconnected biochemical pathways that make up the metabolism of a living cell comprise a fuzzy logic system because of its high level of complexity and our inability to fully understand, predict, and model the many activities, how they interact, and their regulation. Each cell contains thousands of proteins with changing levels of expression, levels of activity, and patterns of interactions. Adding more layers of complexity is the number of proteins that have multiple functions. Moonlighting proteins include a wide variety of proteins where two or more functions are performed by one polypeptide chain. In this article, we discuss examples of proteins with variable functions that contribute to the fuzziness of cellular metabolism.

Biological databases represent an extraordinary collective volume of work. Diligently built up over decades and comprising many millions of contributions from the biomedical research community, biological databases provide worldwide access to a massive number of records (also known as entries) [1]. Starting from individual laboratories, genomes are sequenced, assembled, annotated, and ultimately submitted to primary nucleotide databases such as GenBank [2], European Nucleotide Archive (ENA) [3], and DNA Data Bank of Japan (DDBJ) [4] (collectively known as the International Nucleotide Sequence Database Collaboration, INSDC). Protein records, which are the translations of these nucleotide records, are deposited into central protein databases such as the UniProt KnowledgeBase (UniProtKB) [5] and the Protein Data Bank (PDB) [6]. Sequence records are further accumulated into different databases for more specialized purposes: RFam [7] and PFam [8] for RNA and protein families, respectively; DictyBase [9] and PomBase [10] for model organisms; as well as ArrayExpress [11] and Gene Expression Omnibus (GEO) [12] for gene expression profiles. These databases are selected as examples; the list is not intended to be exhaustive. However, they are representative of biological databases that have been named in the “golden set” of the 24th Nucleic Acids Research database issue (in 2016). The introduction of that issue highlights the databases that “consistently served as authoritative, comprehensive, and convenient data resources widely used by the entire community and offer some lessons on what makes a successful database” [13]. In addition, the associated information about sequences is also propagated into non-sequence databases, such as PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) for scientific literature or Gene Ontology (GO) [14] for function annotations. These databases in turn benefit individual studies, many of which use these publicly available records as the basis for their own research.

Pathogenic and commensal, or probiotic, bacteria employ adhesins on the cell surface to attach to and interact with the host. Dozens of the adhesins that play key roles in binding to host cells or extracellular matrix were originally identified as intracellular chaperones or enzymes in glycolysis or other central metabolic pathways. Proteins that have two very different functions, often in two different subcellular locations, are referred to as moonlighting proteins. The intracellular/surface moonlighting proteins do not contain signal sequences for secretion or known sequence motifs for binding to the cell surface, so in most cases is not known how these proteins are secreted or how they become attached to the cell surface. A secretion system in which a large portion of the pool of each protein remains inside the cell while some of the pool of the protein is partitioned to the cell surface has not been identified. This may involve a novel version of a known secretion system or it may involve a novel secretion system. Understanding the processes by which intracellular/cell surface moonlighting proteins are targeted to the cell surface could provide novel protein targets for the development of small molecules that block secretion and/or association with the cell surface and could serve as lead compounds for the development of novel antibacterial therapeutics.

We have previously shown that glioblastoma stem cells (GSCs) are enriched in the hypoxic tumor microenvironment, and that monocarboxylate transporter-4 (MCT4) is critical for mediating GSC signaling in hypoxia. Basigin is involved in many physiological functions during early stages of development and in cancer and is required for functional plasma membrane expression of MCT4. We sought to determine if disruption of the MCT-Basigin interaction may be achieved with a small molecule. Using a cell-based drug-screening assay, we identified Acriflavine (ACF), a small molecule that inhibits the binding between Basigin and MCT4. Surface plasmon resonance and cellular thermal-shift-assays confirmed ACF binding to basigin in vitro and in live glioblastoma cells, respectively. ACF significantly inhibited growth and self-renewal potential of several glioblastoma neurosphere lines in vitro, and this activity was further augmented by hypoxia. Finally, treatment of mice bearing GSC-derived xenografts resulted in significant inhibition of tumor progression in early and late-stage disease. ACF treatment inhibited intratumoral expression of VEGF and tumor vascularization. Our work serves as a proof-of-concept as it shows, for the first time, that disruption of MCT binding to their chaperon, Basigin, may be an effective approach to target GSC and to inhibit angiogenesis and tumor progression.

During the past few decades, it's become clear that many enzymes evolved not only to act as specific, finely tuned and carefully regulated catalysts, but also to perform a second, completely different function in the cell. In general, these moonlighting proteins have a single polypeptide chain that performs two or more distinct and physiologically relevant biochemical or biophysical functions. This mini-review describes examples of moonlighting proteins that have been found within the past few years, including some that play key roles in human and animal diseases and in the regulation of biochemical pathways in food crops. Several belong to two of the most common subclasses of moonlighting proteins: trigger enzymes and intracellular/surface moonlighting proteins, but a few represent less often observed combinations of functions. These examples also help illustrate some of the current methods used for identifying proteins with multiple functions. In general, a greater understanding about the functions and molecular mechanisms of moonlighting proteins, their roles in the regulation of cellular processes, and their involvement in health and disease could aid in many areas including developing new antibiotics, predicting the functions of the millions of proteins being identified through genome sequencing projects, designing novel proteins, using biological circuitry analysis to construct bacterial strains that are better producers of materials for industrial use, and developing methods to tweak biochemical pathways for increasing yields of food crops.

The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only). We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

In the cell, expression levels, allosteric modulators, post-translational modifications, sequestration, and other factors can affect the level of protein function. For moonlighting proteins, cellular factors like these can also affect the kind of protein function. This minireview discusses examples of moonlighting proteins that illustrate how a single protein can have different functions in different cell types, in different intracellular locations, or under varying cellular conditions. This variability in the kind of protein activity, added to the variability in the amount of protein activity, contributes to the difficulty in predicting the behavior of proteins in the cell.

Tosatto, Silvio/0000-0003-4525-7793; Zhang, Feng/0000-0003-3447-897X; Gonzalez, Jose Maria Fernandez/0000-0002-4806-5140; Devignes, Marie-Dominique/0000-0002-0399-8713; Wass, Mark/0000-0001-5428-6479; Falda, Marco/0000-0003-2642-519X; Thurlby, Natalie/0000-0002-1007-0286; Zosa, Elaine/0000-0003-2482-0663; Dessimoz, Christophe/0000-0002-2170-853X; Yunes, Jeffrey/0000-0003-1869-3231; Hamid, Md Nafiz/0000-0001-8681-6526; Hoehndorf, Robert/0000-0001-8149-5890; Dogan, Tunca/0000-0002-1298-9763; NOTARO, MARCO/0000-0003-4309-2200; Cozzetto, Domenico/0000-0001-6752-5432; Lewis, Kimberley/0000-0003-3010-8453; Roche, Daniel/0000-0002-9204-1840; Martin, Maria-Jesus/0000-0001-5454-2815; Tress, Michael/0000-0001-9046-6370; Tolvanen, Martti/0000-0003-3434-7646; Cheng, Jianlin/0000-0003-0305-2853; Rose, Peter/0000-0001-9981-9750; Renaux, Alexandre/0000-0002-4339-2791; Kacsoh, Balint/0000-0001-9171-0611; O'Donovan, Claire/0000-0001-8051-7429; Kulmanov, Maxat/0000-0003-1710-1820; Friedberg, Iddo/0000-0002-1789-8000; Zhou, Naihui/0000-0001-6268-6149, WOS: 000498615000001, PubMed ID: 31744546, Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens., National Science FoundationNational Science Foundation (NSF) [DBI1564756, DBI-1458359, DBI-1458390, DMS1614777, CMMI1825941, NSF 1458390]; Gordon and Betty Moore FoundationGordon and Betty Moore Foundation [GBMF 4552]; National Institutes of Health NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P20 GM113132]; Cystic Fibrosis Foundation [CFRDP STANTO19R0]; BBSRCBiotechnology and Biological Sciences Research Council (BBSRC) [BB/K004131/1, BB/F00964X/1, BB/M025047/1, BB/M015009/1]; Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT)Consejo Nacional de Ciencia y Tecnologia (CONACyT) [14-INV-088, PINV15-315]; NSFNational Science Foundation (NSF) [1660648, DBI 1759934, IIS1763246, DBI-1458477, 0965768, DMR-1420073, DBI-1458443]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM093123, DP1MH110234, UL1 TR002319, U24 TR002306]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC 2155 "RESIST"German Research Foundation (DFG) [39087428]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01GM123055, R01GM60595, R15GM120650, GM083107, GM116960, AI134678, NIH R35-GM128637, R00-GM097033]; ERCEuropean Research Council (ERC) [StG 757700]; Spanish Ministry of Science, Innovation and Universities [BFU2017-89833-P]; Severo Ochoa award; Centre of Excellence project "BioProspecting of Adriatic Sea"; Croatian Government; European Regional Development FundEuropean Union (EU) [KK.01.1.1.01.0002]; ATT Tieto kayttoon grant; Academy of FinlandAcademy of Finland; University of Turku; CSC-IT Center for Science Ltd.; University of Miami; National Cancer Institute of the National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI) [U01CA198942]; Helsinki Institute for Life Sciences; Academy of FinlandAcademy of Finland [292589]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [31671367, 31471245, 91631301, 61872094, 61572139]; National Key Research and Development Program of China [2016YFC1000505, 2017YFC0908402]; Italian Ministry of Education, University and Research (MIUR) PRIN 2017 projectMinistry of Education, Universities and Research (MIUR) [2017483NH8]; Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX01]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019431/1, BB/L020505/1, BB/L002817/1]; Elsevier; Extreme Science and Engineering Discovery Environment (XSEDE) award [MCB160101, MCB160124]; Ministry of Education, Science and Technological Development of the Republic of Serbia [173001]; Taiwan Ministry of Science and Technology [106-2221-E-004-011-MY2]; Montana State University; Bavarian Ministry for Education; Simons Foundation; NIH NINDSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [1R21NS103831-01]; University of Illinois at Chicago (UIC) Cancer Center award; UIC College of Liberal Arts and Sciences Faculty Award; UIC International Development Award; Yad Hanadiv [9660/2019]; National Institute of General Medical Science of the National Institute of Health [GM066099, GM079656]; Research Supporting Plan (PSR) of University of Milan [PSR2018-DIP-010-MFRAS]; Swiss National Science FoundationSwiss National Science Foundation (SNSF) [150654]; EMBL-European Bioinformatics Institute core funds; CAFA BBSRC [BB/N004876/1]; European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grantEuropean Union (EU) [778247]; COST ActionEuropean Cooperation in Science and Technology (COST) [BM1405]; NIH/NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R01 GM071749]; National Human Genome Research Institute of the National of Health [U41 HG007234]; INB Grant (ISCIII-SGEFI/ERDF) [PT17/0009/0001]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [EEEAG-116E930]; KanSil [2016K121540]; Universita degli Studi di Milano; 111 ProjectMinistry of Education, China - 111 Project [B18015]; key project of Shanghai Science Technology [16JC1420402]; ZJLab; project Ribes Network POR-FESR 3S4H [TOPP-ALFREVE18-01]; PRID/SID of University of Padova [TOPP-SID19-01]; NIGMSUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [R15GM120650]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [URF/1/3454-01-01, URF/1/3790-01-01]; "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education; National Center for High-performance ComputingIstanbul Technical University, The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnologia Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto kayttoon grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC -IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124.; The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660/2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Universita degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443.

Hsp90 is a highly expressed and ubiquitous chaperone in eukaryotes and bacteria. It works with hundreds of client proteins and is regulated by dozens of co-chaperones. Its functions in folding, stabilizing, assembling and disassembling proteins and complexes that are involved in many key processes in the cell, including antigen cross-presentation, stabilization of the cytoskeleton, signaling pathways, stabilization of steroid receptors and other transcription factors, assembly and disassembly of transcription machinery, DNA repair, and the cell cycle. This ubiquitous and versatile intracellular protein is found to have even more functions outside the cell. In this review we discuss the idea that Hsp90 is a moonlighting protein with roles as a secreted cytokine and as a cell surface apoptotic signal and receptor for bacterial cells and lipopolysaccharide.

One of the goals of proteomics is to predict the functions of all proteins. Many protein functions involve interactions with other biological molecules. Some of the most important interactions are those of proteins with small molecule ligands. Several experimental and computational approaches have been proposed previously to predict the locations of ligand binding regions of proteins. The aim of this thesis is to test a new approach to identify the ligand binding regions on the surface of X-ray crystal structures of proteins with unknown functions. The hypothesis that was tested was that X-ray crystallographic B-factors, also known as temperature factors, of atoms located on the surface of a protein X-ray crystal structure, in combination with information about the relative sizes of surface pockets, can help in identifying the functional regions of proteins. We used as our test set of protein structures a non-redundant dataset of 328 proteins for which X-ray crystal structures have been solved for both ligand-bound and unbound forms. The main steps involved identifying the surface pocket that, out of the ten largest surface pockets, contains the largest number of atoms with low B-factor values, and then checking to see if that pocket corresponds to the known ligand-binding pocket. If so, the crystallographic B-factor values could be used as a parameter to aid in predicting the ligand-binding regions of many uncharacterized proteins. For the majority of proteins in our study, that is, for 66% of the proteins in our working dataset, the proposed methodology was able to identify correctly the ligand-binding pockets in the protein crystal structures of the unliganded forms of the proteins.

Pseudoenzymes are noncatalytic homologues of enzymes and are found in most enzyme families. Although lacking catalytic activity and sometimes referred to as ‘dead' enzymes, they instead resemble phoenixes because the loss of a catalytic function during evolution was associated with the development of vital new functions. They are important in regulating the activity and location of catalytically active homologues, scaffolding the assembly of signaling complexes, and regulating transcription or translation. They are key actors in cell proliferation and differentiation, proteostasis, and many other biochemical pathways and processes. They perform their functions in diverse ways, but many retain some aspects of the function of their catalytically active homologues. In some pseudoenzymes, their functions are very different from other members of their protein families, suggesting some arose from ancient moonlighting proteins during evolution. Much less is known about pseudoenzymes than their catalytically active counterparts, but a growing appreciation of their key roles in many important biochemical processes and signaling pathways has led to increased investigation in recent years. It is clear that there is still much more to learn about the structures, functions, and cellular roles of these phoenix-like proteins.

Proteomics studies that characterize hundreds or thousands of proteins in parallel can play an important part in the identification of moonlighting proteins, proteins that perform two or more distinct and physiologically relevant biochemical or biophysical functions. Functional assays, including ligand-binding assays, can find a surprising second function for a protein that was previously identified as performing a different function, for example, a DNA-binding ability for an enzyme in amino acid metabolism. The results of large-scale assays of protein-protein interactions, gene knockouts, or subcellular protein localizations, or bioinformatics analysis of amino acid sequences and three-dimensional structures, can also be used to predict that a protein has additional functions, but in these cases it is important to use biochemical and biophysical methods to confirm the protein can perform each function.

Periplasmic ligand-binding proteins (PBPs) bind ligands with a high affinity and specificity. They undergo a large conformational change upon ligand binding, and they have a robust protein fold. These physical features have made them ideal candidates for use in protein engineering projects to develop novel biosensors and signaling molecules. The Escherichia coli MppA (murein peptide permease A) PBP binds the murein tripeptide, l-alanyl-γ-d-glutamyl-meso-diaminopimelate, (l-Ala-γ-d-Glu-meso-Dap), which contains both a D-amino acid and a gamma linkage between two of the amino acids. We have solved a high-resolution X-ray crystal structure of E. coli MppA at 1.5 Å resolution in the unliganded, open conformation. Now, structures are available for this member of the PBP protein family in both the liganded/closed form and the unliganded/open form.

The human body is a complex biological machine with billions of cells and vast numbers of biochemical processes – but our genome only contains 22,000 protein-encoding genes. Moonlighting proteins provide one way to increase the number of cellular activities. Moonlighting proteins exhibit more than one physiologically relevant biochemical or biophysical function within one polypeptide chain. Already more than 300 moonlighting proteins have been identified, and they include a diverse set of proteins with a large variety of functions. This article discusses examples of moonlighting proteins, how one protein structure can perform two different functions, and how the multiple functions can be regulated. In addition to learning more about what our proteins do and how they work together in complex multilayered interaction networks and processes in our bodies, the study of moonlighting proteins can inform future synthetic biology projects in making proteins that perform new functions and new combinations of functions, for example, for synthesising new materials, delivering drugs into cells, and in bioremediation.

MoonProt 2.0 (http://moonlightingproteins.org) is an updated, comprehensive and open-access database storing expert-curated annotations for moonlighting proteins. Moonlighting proteins contain two or more physiologically relevant distinct functions performed by a single polypeptide chain. Here, we describe developments in the MoonProt website and database since our previous report in the Database Issue of Nucleic Acids Research. For this V 2.0 release, we expanded the number of proteins annotated to 370 and modified several dozen protein annotations with additional or updated information, including more links to protein structures in the Protein Data Bank, compared with the previous release. The new entries include more examples from humans and several model organisms, more proteins involved in disease, and proteins with different combinations of functions. The updated web interface includes a search function using BLAST to enable users to search the database for proteins that share amino acid sequence similarity with a protein of interest. The updated website also includes additional background information about moonlighting proteins and an expanded list of links to published articles about moonlighting proteins.

Members of the GroEL/HSP60 protein family have been studied for many years because of their critical roles as ATP-dependent molecular chaperones, so it might come as a surprise that some have important functions in ATP-poor conditions, for example, when secreted outside the cell. At least some members of each of the HSP10, HSP70, HSP90, HSP100 and HSP110 heat shock protein families are also ‘moonlighting proteins’. Moonlighting proteins exhibit more than one physiologically relevant biochemical or biophysical function within one polypeptide chain. In this class of multifunctional proteins, the multiple functions are not due to gene fusions or multiple proteolytic fragments. Several hundred moonlighting proteins have been identified, and they include a diverse set of proteins with a large variety of functions. Some participate in multiple biochemical processes by using an active site pocket for catalysis and a different part of the protein's surface to interact with other proteins. Moonlighting proteins play a central role in many diseases, and the development of novel treatments would be aided by more information addressing current questions, for example, how some are targeted to multiple cellular locations and how a single function can be targeted by therapeutics without targeting a function not involved in disease. This article is part of the theme issue ‘Heat shock proteins as modulators and therapeutic targets of chronic disease: an integrated perspective’.

Bacteria use cell surface proteins and secreted proteins to interact with host tissues. Several dozen previously published proteomics studies have identified cell surface proteins for pathogens. In this issue, Celebioglu and Svensson (Proteomics 2017, XXX, XXX-XXX) use 2-D gel electrophoresis and mass spectrometry to identify secreted and cell surface proteins of a commensual gut bacterium, Lactobacillus acidophilus NCFM. Some of the proteins are known to have functions in the cytoplasm, and their presence on the cell surface suggests they might be moonlighting proteins. In addition, comparisons of proteins used by pathogenic and probiotic species to interact with their hosts could lead to improved treatments of infections and chronic diseases that are associated with an imbalance of pathogenic and probiotic gut bacteria. This article is protected by copyright. All rights reserved

Moonlighting proteins comprise a class of multifunctional proteins in which a single polypeptide chain performs multiple physiologically relevant biochemical or biophysical functions. Almost 300 proteins have been found to moonlight. The known examples of moonlighting proteins include diverse types of proteins, including receptors, enzymes, transcription factors, adhesins and scaffolds, and different combinations of functions are observed. Moonlighting proteins are expressed throughout the evolutionary tree and function in many different biochemical pathways. Some moonlighting proteins can perform both functions simultaneously, but for others, the protein's function changes in response to changes in the environment. The diverse examples of moonlighting proteins already identified, and the potential benefits moonlighting proteins might provide to the organism, such as through coordinating cellular activities, suggest that many more moonlighting proteins are likely to be found. Continuing studies of the structures and functions of moonlighting proteins will aid in predicting the functions of proteins identified through genome sequencing projects, in interpreting results from proteomics experiments, in understanding how different biochemical pathways interact in systems biology, in annotating protein sequence and structure databases, in studies of protein evolution and in the design of proteins with novel functions.

The development of specialized nanoparticles for use in the detection and treatment of cancer is increasing. Methods are being proposed and tested that could target treatments more directly to cancer cells, which could lead to higher efficacy and reduced toxicity, possibly even eliminating the adverse effects of damage to the immune system and the loss of quick replicating cells. In this mini-review we focus on recent studies that employ folate nanoconjugates to target the folate receptor. Folate receptors are highly overexpressed on the surface of many tumor types. This expression can be exploited to target imaging molecules and therapeutic compounds directly to cancerous tissues.Keywords: cancer; conjugates; doxorubicin; folate; folic acid; folate receptor; gold; nanoaggregates; 99mTc-EC20; nanotechnology(Published: 7 December 2012)Citation: Nano Reviews 2012, 3: 18496 - http://dx.doi.org/10.3402/nano.v3i0.18496

Type I phosphomannose isomerases (PMIs) are zinc-dependent metalloenzymes involved in the reversible isomerization of D-mannose 6-phosphate (M6P) and D-fructose 6-phosphate (F6P). 5-Phospho-D-arabinonohydroxamic acid (5PAH), an inhibitor endowed with nanomolar affinity for yeast (Type I) and Pseudomonas aeruginosa (Type II) PMIs (Roux et al., Biochemistry 2004; 43:2926-2934), strongly inhibits human (Type I) PMI (for which we report an improved expression and purification procedure), as well as Escherichia coli (Type I) PMI. Its K(i) value of 41 nM for human PMI is the lowest value ever reported for an inhibitor of PMI. 5-Phospho-D-arabinonhydrazide, a neutral analogue of the reaction intermediate 1,2-cis-enediol, is about 15 times less efficient at inhibiting both enzymes, in accord with the anionic nature of the postulated high-energy reaction intermediate. Using the polarizable molecular mechanics, sum of interactions between fragments ab initio computed (SIBFA) procedure, computed structures of the complexes between Candida albicans (Type I) PMI and the cyclic substrate β-D-mannopyranose 6-phosphate (β-M6P) and between the enzyme and the high-energy intermediate analogue inhibitor 5PAH are reported. Their analysis allows us to identify clearly the nature of each individual active site amino acid and to formulate a hypothesis for the overall mechanism of the reaction catalyzed by Type I PMIs, that is, the ring-opening and isomerization steps, respectively. Following enzyme-catalyzed ring-opening of β-M6P by zinc-coordinated water and Gln111 ligands, Lys136 is identified as the probable catalytic base involved in proton transfer between the two carbon atoms C1 and C2 of the substrate D-mannose 6-phosphate.

The examples above illustrate just some of the ways in which the continuing study of moonlighting proteins is important: developing novel therapeutics, identifying previously unknown cellular processes, elucidating the connections among biochemical pathways, understanding novel protein mechanisms, improving the prediction of protein functions, and providing information about the evolution of protein structure and function as well as examples for the design of new proteins. The references listed above and especially the collection of papers in this Research Topic are recommended for providing more in-depth examples and analysis of these topics. Conflict of interest statement The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

In the past few decades, hundreds of moonlighting proteins have been identified that perform two or more distinct and physiologically relevant biochemical or biophysical functions that are not due to gene fusions, multiple RNA splice variants, or pleiotropic effects. For this special issue on protein species, this article discusses three topics related to moonlighting proteins that illustrate how small changes or differences in protein covalent structures can result in different functions. Examples are given of moonlighting proteins that switch between functions after undergoing post-translational modifications (PTMs), proteins that share high levels of amino acid sequence identity to a moonlighting protein but share only one of its functions, and several “neomorphic moonlighting proteins” in which a single amino acid mutation results in the addition of a new function. Biological significance For this special issue on protein species, this article discusses three topics related to moonlighting proteins : Post-translational modifications (PTMs) that can cause a switch between functions, homologs that share only one of multiple functions, and proteins in which a single amino acid mutation results in the creation of a new function. The examples included illustrate that even in an average protein of hundreds of amino acids, a relatively small difference in sequence or PTMs can result in a large difference in function, which can be important in predicting protein functions, regulation of protein functions, and in the evolution of new functions.

Moonlighting proteins comprise a class of multifunctional proteins in which a single polypeptide chain performs multiple biochemical functions that are not due to gene fusions, multiple RNA splice variants or pleiotropic effects. The known moonlighting proteins perform a variety of diverse functions in many different cell types and species, and information about their structures and functions is scattered in many publications. We have constructed the manually curated, searchable, internet-based MoonProt Database (http://www.moonlightingproteins.org) with information about the over 200 proteins that have been experimentally verified to be moonlighting proteins. The availability of this organized information provides a more complete picture of what is currently known about moonlighting proteins. The database will also aid researchers in other fields, including determining the functions of genes identified in genome sequencing projects, interpreting data from proteomics projects and annotating protein sequence and structural databases. In addition, information about the structures and functions of moonlighting proteins can be helpful in understanding how novel protein functional sites evolved on an ancient protein scaffold, which can also help in the design of proteins with novel functions.

Phosphoglucose isomerase (PGI; E.C. 5.3.1.9) is a cytosolic enzyme that catalyzes the reversible isomerization of G6P and F6P. Its activity is important for glycolysis; gluconeogenesis; the pentose phosphate pathway; and the glycosylation of proteins, lipids, and other molecules. Recently, attention to PGI has grown because PGI is a moonlighting protein with a second function outside of the cell. PGI is the same protein as the extracellular growth factors neuroleukin (Chaput et al. 1988; Faik et al. 1988), autocrine motility factor (Watanabe et al. 1996), and differentiation and maturation mediator (Xu et al. 1996). It stimulates antibody secretion, promotes the survival of embryonic spinal neurons in vitro (Gurney et al. 1986a,b), affects an increase in tumor cell motility, and causes the differentiation of human myelogenous leukemia cells (Chiao et al. 1999) and a human myelocytic cell line (HL-60 cells) to terminal monocytic cells (Leung and Chiao 1985; Abolhassani et al. 1990). Recently, PGI also has been found to be the self-antigen in a mouse model of rheumatoid arthritis (Matsumoto et al. 1999; Schaller et al. 2001). One lab has identified antibodies to PGI in patients suffering from rheumatoid arthritis (Schaller et al. 2001). The enzymatic activity of PGI was first described over 70 years ago (Lohmann 1933). Biochemical characterization of PGI enzyme activity by several labs led to a proposed multistep mechanism involving general acid–base catalysis (Rose and O’Connell 1961; Rose 1975, 1981; Willem et al. 1990). Both G6P and F6P are substrates for PGI activity. Because G6P and F6P exist predominantly in their cyclic forms in solution (Swenson and Barker 1971), it is believed that first the cyclic form of G6P or F6P binds to the active site. PGI catalyzes ring opening of the substrate to yield the open chain form. The reaction then proceeds through the isomerization step using acid–base catalysis with proton transfer. PGI is a dimer with 557 amino acids in each subunit and has two equivalent active sites. X-ray crystal structures have been reported of PGI without bound ligand or with a substrate (F6P) or competitive inhibitors of catalytic activity bound in the active site pocket. X-ray crystal structures of mammalian (rabbit, pig, and human) PGI have been solved with 6PGA (Jeffery et al. 2000), 5PAA (Jeffery et al. 2001; Davies and Muirhead 2002; Davies et al. 2003), 5PAH (Arsenieva et al. 2002), or F6P (Lee et al. 2001), or with no bound ligand (Arsenieva and Jeffery 2002; Davies and Muirhead 2002, 2003). A crystal structure of human PGI with a sulfate ion was also reported (Read et al. 2001). Other crystal structures of PGI included two structures from a bacterium, Bacillus stearothermophilus, with no bound ligand (Sun et al. 1999) or complexed with 5PAA (Chou et al. 2000). Three crystal structures of PGI enzyme from Pyrococcus furiosus were also solved with no bound ligand (Berrisford et al. 2003), with 5PAA, and with 6PGA (Swan et al. 2003). Interestingly, the PGI enzyme from P. furiosus does not share sequence or structural homology with either mammalian or B. stearothermophilus PGI, and its activity is probably the result of convergent evolution. The X-ray crystal structures of PGI have helped to identify active site amino acids, ordered water molecules, and conformational changes involved in the ligand binding, ring opening, and isomerization steps of the multistep catalytic mechanism. Comparison of a crystal structure of rabbit PGI without a bound ligand to structures with ligands provided information about induced fit of the enzyme upon substrate binding. A crystal structure of rabbit PGI complexed with F6P provided information about the roles of Glu216, His388, Lys518, and an ordered water molecule in the ring opening step (Lee et al. 2001). Crystal structures of rabbit PGI complexed with 5PAA and 5PAH were used to identify the role of Glu357 as the active site amino acid residue that transfers a proton between C1 and C2 during the isomerization step (Jeffery et al. 2001) and the role of a water molecule in transferring a proton between the hydroxyl groups on C1 and C2 (Arsenieva et al. 2002). Arg272 was proposed to have a role in helping stabilize the cis-enedio-l(ate) intermediate. Comparison of the PGI/5PAA, PGI/5PAH, and PGI/F6P complexes also indicated that the substrate undergoes a rotation about the C3–C4 bond between the ring opening and isomerization steps to position C1 and C2 near to Glu357. Also, a helix containing amino acid residues 512–520 moves in toward the substrate to help hold it in place for the isomerization step. However, less clear is the state of the active site and substrate between the ring opening and isomerization steps. To complete the details of the catalytic mechanism of PGI, a structure of PGI complexed with an open chain substrate, or an analog, is required. Here, we report an X-ray crystal structure of rabbit PGI complexed with an analog of the open chain form of G6P (S6P), bound in the active site. This structure was solved at 2.0 A resolution.

Recently determined X-ray crystal structures of moonlighting proteins are helping to elucidate how a protein can evolve two different functions and, in some cases, switch between its two functions in response to cellular conditions. X-ray crystal structures of the I-AniI homing endonuclease/maturase and the PutA proline dehydrogenase/transcription factor have provided evidence that these proteins utilize separate protein surfaces for their multiple functions. Also, the structure of the DegP (HtrA) protease/chaperone has revealed information about the mechanism of its chaperone activity and suggests how the protein regulates its protease activity. Comparing the structure of eta-crystallin/retinal dehydrogenase with structures of its single-function enzyme homologs provides clues to changes in the protein structure that may have improved its ability to serve as a crystallin, but at the same time may have adversely affected its catalytic activity.

Proteomics projects involving biochemical, genetic and computational methods are being used to elucidate the functions of thousands of proteins in healthy and disease states. The results provide valuable information both for understanding basic physiological processes and for the development of novel therapeutics. However, the ability of a protein to moonlight, or to have more than one function, can complicate the interpretation and application of the results from these proteomics studies. This review provides examples of how moonlighting proteins can affect the results of proteomics projects being used to determine protein function based on patterns of protein expression, protein–protein interactions, analysis of protein sequences and structures, and other methods.

Phosphoglucose isomerase (PGI; EC 5.3.1.9) is the second enzyme in glycolysis, where it catalyzes the isomerization of d -glucose-6-phosphate to d -fructose-6-phosphate. It is the same protein as autocrine motility factor, differentiation and maturation mediator, and neuroleukin. Here, we report a new X-ray crystal structure of rabbit PGI (rPGI) without ligands bound in its active site. The structure was solved at 1.8 A resolution by isomorphous phasing with a previously solved X-ray crystal structure of the rPGI dimer containing 6-phosphogluconate in its active site. Comparison of the new structure to previously reported structures enables identification of conformational changes that occur during binding of substrate or inhibitor molecules. Ligand binding causes an induced fit of regions containing amino acid residues 209–215, 245–259 and 385–389. This conformational change differs from the change previously reported to occur between the ring-opening and isomerization steps, in which the helix containing residues 513–521 moves toward the bound substrate. Differences between the liganded and unliganded structures are limited to the region within and close to the active-site pocket.

Monocarboxylate transporters, constitute a family (SLC16) of proton-linked plasma membrane transporters that carry molecules containing a single carboxylate group across biological membranes. Basigin (CD147), is involved in many physiological functions during early stages of development and in cancer. Basigin has been shown to be required for functional plasma membrane expression of Monocarboxylate transporter-1 and Monocarboxylate transporter-4. Using a cell-based screening assay, we identified acriflavine, a small molecule that inhibits the binding between Basigin and Monocarboxylate transporters in vitro and in vivo. Surface Plasmon Resonance analysis confirmed direct binding of acriflavine to Basigin’s immunoglobulin extracellular domain with a low binding constant (kD) of 0.16µM. Acriflavine inhibits normoxic growth of glioma stem cells in vitro and this activity is augmented by hypoxia or by expression of oxygen-stable mutant forms of HIF-1α or HIF-2α. Treatment of mice bearing established glioma stem cell-derived xenografts resulted in inhibition of tumor growth. Acriflavine treatment inhibited intratumoral expression of the angiogenic cytokine vascular endothelial growth factor and tumor vascularization. Our work shows that disruption of monocarboxylate transporter binding to Basigin is an effective approach to target glioma stem cells. Citation Format: Raffaella Spina, Dillon M. Voss, Kah Suan Lim, Constance J. Jeffery, Eli E. Bar. Disruption of Monocarboxylate transporter-4 Basigin interaction as an effective strategy to inhibit hypoxic response, tumor growth and vascularization, and stem cell phenotype in human glioblastoma in vitro and in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5023. doi:10.1158/1538-7445.AM2017-5023

The Escherichia coli aspartate receptor is a dimer with two transmembrane sequences per monomer that connect a periplasmic ligand binding domain to a cytoplasmic signaling domain. The method of 'hydrophobic-biased' random mutagenesis, that we describe here, was used to construct mutant aspartate receptors in which either the entire transmembrane sequence or seven residues near the center of the transmembrane sequence were replaced with hydrophobic and polar random residues. Some of these receptors responded to aspartate in an in vivo chemotaxis assay, while others did not. The acceptable substitutions included hydrophobic to polar residues, small to larger residues, and large to smaller residues. However, one mutant receptor that had only a few hydrophobic substitutions did not respond to aspartate. These results add to our understanding of sequence specificity in the transmembrane regions of proteins with more than one transmembrane sequence. This work also demonstrates a method of constructing families of mutant proteins containing random residues with chosen characteristics.