A First-Class Degrader Candidate Targeting Both KRAS G12D and G12V Mediated by CANDDY Technology Independent of Ubiquitination.

"Undruggable" targets such as KRAS are particularly challenging in the development of drugs. We devised a novel chemical knockdown strategy, CANDDY (Chemical knockdown with Affinity aNd Degradation DYnamics) technology, which promotes protein degradation using small molecules (CANDDY molecules) that are conjugated to a degradation tag (CANDDY tag) modified from proteasome inhibitors. We demonstrated that CANDDY tags allowed for direct proteasomal target degradation independent of ubiquitination. We synthesized a KRAS-degrading CANDDY molecule, TUS-007, which induced degradation in KRAS mutants (G12D and G12V) and wild-type KRAS. We confirmed the tumor suppression effect of TUS-007 in subcutaneous xenograft models of human colon cells (KRAS G12V) with intraperitoneal administrations and in orthotopic xenograft models of human pancreatic cells (KRAS G12D) with oral administrations. Thus, CANDDY technology has the potential to therapeutically target previously undruggable proteins, providing a simpler and more practical drug targeting approach and avoiding the difficulties in matchmaking between the E3 enzyme and the target.

Development of a novel conditional knockdown mouse based on YB-1 protein degradation.

To clarify the pathogenic mechanism of disease and establish effective therapies, animal disease models that can be dynamically analyzed are urgently required. Knockout mouse models and conditional genetically engineered mouse models were developed to analyze genes and proteins involved in disease. However, these methods have drawbacks, including embryonic lethality, side effects and low efficiency. To address this issue, we created a novel transgenic mouse model in which the YB1 gene was fused with a destabilizing domain (DD), named the YB1-DD mouse. YB-1 is widely expressed throughout development and has been implicated as a cell survival factor. Newly synthesized DD proteins are degraded through the proteasome pathway, but their degradation can be blocked with trimethoprim (TMP). In this study, we established a novel conditional knockdown mouse model that enables targeting of protein degradation directly; this model resulted in dose-dependent regulation of the target protein YB-1 by the ligand TMP in YB1 heterozygous mice. Since this conditional knockdown mouse model appears to be functional, it has potential as a useful disease model based on direct protein degradation control.

Cell-Free Technologies for Proteomics and Protein Engineering.

Cell-free translation systems facilitate rapid production of specific proteins and are particularly suited as high-throughput methods for whole-genome protein synthesis. Moreover, these systems do not rely on living cells, thereby allowing the synthesis of unstable or cytotoxic proteins in vitro. In this review, we describe the principles and potential applications of cell-free protein translation systems and the future prospects of proteomics approaches using next-generation sequencing and cell-free expression technologies.

Catalytic subunits of the phosphatase calcineurin interact with NF-κB-inducing kinase (NIK) and attenuate NIK-dependent gene expression.

Nuclear factor (NF)-κB-inducing kinase (NIK) is a serine/threonine kinase that activates NF-κB pathways, thereby regulating a wide variety of immune systems. Aberrant NIK activation causes tumor malignancy, suggesting a requirement for precise regulation of NIK activity. To explore novel interacting proteins of NIK, we performed in vitro virus screening and identified the catalytic subunit Aα isoform of serine/threonine phosphatase calcineurin (CnAα) as a novel NIK-interacting protein. The interaction of NIK with CnAα in living cells was confirmed by co-immunoprecipitation. Calcineurin catalytic subunit Aß isoform (CnAß) also bound to NIK. Experiments using domain deletion mutants suggested that CnAα and CnAß interact with both the kinase domain and C-terminal region of NIK. Moreover, the phosphatase domain of CnAα is responsible for the interaction with NIK. Intriguingly, we found that TRAF3, a critical regulator of NIK activity, also binds to CnAα and CnAß. Depletion of CnAα and CnAß significantly enhanced lymphotoxin-ß receptor (LtßR)-mediated expression of the NIK-dependent gene Spi-B and activation of RelA and RelB, suggesting that CnAα and CnAß attenuate NF-κB activation mediated by LtßR-NIK signaling. Overall, these findings suggest a possible role of CnAα and CnAß in modifying NIK functions.

Next-generation technologies for multiomics approaches including interactome sequencing.

The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research (e.g., genomics, transcriptomics, metabolomics, proteomics, and interactomics). We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research.

Analysis of transcription factor networks using IVV method.

We have developed a simple and totally in vitro selection procedure based on cell-free cotranslation using a highly stable and efficient in vitro virus (IVV). Cell-free cotranslation of tagged bait and prey proteins is advantageous for the formation of protein complexes and allows high-throughput analysis of protein-protein interactions (PPI) as a result of providing in vitro instead of in vivo preparation of bait proteins. The use of plural selection rounds and a two-step purification of the IVV selection, followed by in vitro post-selection, is advantageous for decreasing false positives. This simple IVV selection system based on cell-free cotranslation is applicable to high-throughput and comprehensive analysis of transcription factor networks.

Next-generation sequencing coupled with a cell-free display technology for reliable interactome of translational factors.

Next-generation sequencing (NGS) has been applied to various kinds of omics studies, resulting in many biological and medical discoveries. However, high-throughput protein-protein interactome datasets derived from detection by sequencing are scarce, because protein-protein interaction analysis requires many cell manipulations to examine the interactions. The low reliability of the high-throughput data is also a problem. Here, we describe a cell-free display technology combined with NGS that can improve both the coverage and reliability of interactome datasets. This in vitro method is suitable for exploring the interactome networks of transcription factors.

Mitochondria-nucleus shuttling FK506-binding protein 51 interacts with TRAF proteins and facilitates the RIG-I-like receptor-mediated expression of type I IFN.

Virus-derived double-stranded RNAs (dsRNAs) are sensed in the cytosol by retinoic acid-inducible gene (RIG)-I-like receptors (RLRs). These induce the expression of type I IFN and proinflammatory cytokines through signaling pathways mediated by the mitochondrial antiviral signaling (MAVS) protein. TNF receptor-associated factor (TRAF) family proteins are reported to facilitate the RLR-dependent expression of type I IFN by interacting with MAVS. However, the precise regulatory mechanisms remain unclear. Here, we show the role of FK506-binding protein 51 (FKBP51) in regulating the dsRNA-dependent expression of type I IFN. The binding of FKBP51 to TRAF6 was first identified by "in vitro virus" selection and was subsequently confirmed with a coimmunoprecipitation assay in HEK293T cells. The TRAF-C domain of TRAF6 is required for its interaction, although FKBP51 does not contain the consensus motif for interaction with the TRAF-C domain. Besides TRAF6, we found that FKBP51 also interacts with TRAF3. The depletion of FKBP51 reduced the expression of type I IFN induced by dsRNA transfection or Newcastle disease virus infection in murine fibroblasts. Consistent with this, the FKBP51 depletion attenuated dsRNA-mediated phosphorylations of IRF3 and JNK and nuclear translocation of RelA. Interestingly, dsRNA stimulation promoted the accumulation of FKBP51 in the mitochondria. Moreover, the overexpression of FKBP51 inhibited RLR-dependent transcriptional activation, suggesting a scaffolding function for FKBP51 in the MAVS-mediated signaling pathway. Overall, we have demonstrated that FKBP51 interacts with TRAF proteins and facilitates the expression of type I IFN induced by cytosolic dsRNA. These findings suggest a novel role for FKBP51 in the innate immune response to viral infection.

Towards personalized medicine mediated by in vitro virus-based interactome approaches.

We have developed a simple in vitro virus (IVV) selection system based on cell-free co-translation, using a highly stable and efficient mRNA display method. The IVV system is applicable to the high-throughput and comprehensive analysis of proteins and protein-ligand interactions. Huge amounts of genomic sequence data have been generated over the last decade. The accumulated genetic alterations and the interactome networks identified within cells represent a universal feature of a disease, and knowledge of these aspects can help to determine the optimal therapy for the disease. The concept of the "integrome" has been developed as a means of integrating large amounts of data. We have developed an interactome analysis method aimed at providing individually-targeted health care. We also consider future prospects for this system.

Efficiency of puromycin-based technologies mediated by release factors and a ribosome recycling factor.

Two puromycin-based techniques, in vitro virus (IVV) and C-terminal labelling of proteins, were developed based on the observation that puromycin binds the C-terminus of a protein. Puromycin technology is a useful tool for the detection of proteins and analysis of protein-protein interactions (PPIs); however, problems arise due to the existence of stop codons in the native mRNAs. Release factors (RFs) that enter the A-site of the ribosome at stop codons compete with puromycin. To overcome this issue, we have used a highly controllable reconstituted cell-free system for puromycin-based techniques, and observed efficient IVV formation and C-terminal labelling using templates possessing a stop codon. The optimal conditions of IVV formation using templates possessing a stop codon was RF (-), while that of C-terminal labelling was RF (-) and the ribosome recycling factor (RRF) (+). Thus, we have overcome the experimental limitations of conventional IVV. In addition, we discovered that RRF significantly increases the efficiency of C-terminal protein labelling, but not IVV formation.

Next-generation sequencing coupled with a cell-free display technology for high-throughput production of reliable interactome data.

Next-generation sequencing (NGS) has been applied to various kinds of omics studies, resulting in many biological and medical discoveries. However, high-throughput protein-protein interactome datasets derived from detection by sequencing are scarce, because protein-protein interaction analysis requires many cell manipulations to examine the interactions. The low reliability of the high-throughput data is also a problem. Here, we describe a cell-free display technology combined with NGS that can improve both the coverage and reliability of interactome datasets. The completely cell-free method gives a high-throughput and a large detection space, testing the interactions without using clones. The quantitative information provided by NGS reduces the number of false positives. The method is suitable for the in vitro detection of proteins that interact not only with the bait protein, but also with DNA, RNA and chemical compounds. Thus, it could become a universal approach for exploring the large space of protein sequences and interactome networks.

PRD: A protein-RNA interaction database.

UNLABELLED: Although protein-RNA interactions (PRIs) are involved in various important cellular processes, compiled data on PRIs are still limited. This contrasts with protein-protein interactions, which have been intensively recorded in public databases and subjected to network level analysis. Here, we introduce PRD, an online database of PRIs, dispersed across several sources, including scientific literature. Currently, over 10,000 interactions have been stored in PRD using PSI-MI 2.5, which is a standard model for describing detailed molecular interactions, with an emphasis on gene level data. Users can browse all recorded interactions and execute flexible keyword searches against the database via a web interface. Our database is not only a reference of PRIs, but will also be a valuable resource for studying characteristics of PRI networks. AVAILABILITY: PRD can be freely accessed at http://pri.hgc.jp/

IRView: a database and viewer for protein interacting regions.

UNLABELLED: Protein-protein interactions (PPIs) are mediated through specific regions on proteins. Some proteins have two or more protein interacting regions (IRs) and some IRs are competitively used for interactions with different proteins. IRView currently contains data for 3417 IRs in human and mouse proteins. The data were obtained from different sources and combined with annotated region data from InterPro. Information on non-synonymous single nucleotide polymorphism sites and variable regions owing to alternative mRNA splicing is also included. The IRView web interface displays all IR data, including user-uploaded data, on reference sequences so that the positional relationship between IRs can be easily understood. IRView should be useful for analyzing underlying relationships between the proteins behind the PPI networks. AVAILABILITY: IRView is publicly available on the web at http://ir.hgc.jp/

Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions.

BACKGROUND: High-throughput methods for detecting protein-protein interactions enable us to obtain large interaction networks, and also allow us to computationally identify the associations of proteins as protein complexes. Although there are methods to extract protein complexes as sets of proteins from interaction networks, the extracted complexes may include false positives because they do not account for the structural limitations of the proteins and thus do not check that the proteins in the extracted complex can simultaneously bind to each other. In addition, there have been few searches for deeper insights into the protein complexes, such as of the topology of the protein-protein interactions or into the domain-domain interactions that mediate the protein interactions. RESULTS: Here, we introduce a combinatorial approach for prediction of protein complexes focusing not only on determining member proteins in complexes but also on the DDI/PPI organization of the complexes. Our method analyzes complex candidates predicted by the existing methods. It searches for optimal combinations of domain-domain interactions in the candidates based on an assumption that the proteins in a candidate can form a true protein complex if each of the domains is used by a single protein interaction. This optimization problem was mathematically formulated and solved using binary integer linear programming. By using publicly available sets of yeast protein-protein interactions and domain-domain interactions, we succeeded in extracting protein complex candidates with an accuracy that is twice the average accuracy of the existing methods, MCL, MCODE, or clustering coefficient. Although the configuring parameters for each algorithm resulted in slightly improved precisions, our method always showed better precision for most values of the parameters. CONCLUSIONS: Our combinatorial approach can provide better accuracy for prediction of protein complexes and also enables to identify both direct PPIs and DDIs that mediate them in complexes.

A comprehensive resource of interacting protein regions for refining human transcription factor networks.

Large-scale data sets of protein-protein interactions (PPIs) are a valuable resource for mapping and analysis of the topological and dynamic features of interactome networks. The currently available large-scale PPI data sets only contain information on interaction partners. The data presented in this study also include the sequences involved in the interactions (i.e., the interacting regions, IRs) suggested to correspond to functional and structural domains. Here we present the first large-scale IR data set obtained using mRNA display for 50 human transcription factors (TFs), including 12 transcription-related proteins. The core data set (966 IRs; 943 PPIs) displays a verification rate of 70%. Analysis of the IR data set revealed the existence of IRs that interact with multiple partners. Furthermore, these IRs were preferentially associated with intrinsic disorder. This finding supports the hypothesis that intrinsically disordered regions play a major role in the dynamics and diversity of TF networks through their ability to structurally adapt to and bind with multiple partners. Accordingly, this domain-based interaction resource represents an important step in refining protein interactions and networks at the domain level and in associating network analysis with biological structure and function.

mRNA display selection of a high-affinity, Bcl-X(L)-specific binding peptide.

Bcl-X(L), an antiapoptotic member of the Bcl-2 family, is a mitochondrial protein that inhibits activation of Bax and Bak, which commit the cell to apoptosis, and it therefore represents a potential target for drug discovery. Peptides have potential as therapeutic molecules because they can be designed to engage a larger portion of the target protein with higher specificity. In the present study, we selected 16-mer peptides that interact with Bcl-X(L) from random and degenerate peptide libraries using mRNA display. The selected peptides have sequence similarity with the Bcl-2 family BH3 domains, and one of them has higher affinity (IC(50)=0.9 microM) than Bak BH3 (IC(50)=11.8 microM) for Bcl-X(L) in vitro. We also found that GFP fusions of the selected peptides specifically interact with Bcl-X(L), localize in mitochondria, and induce cell death. Further, a chimeric molecule, in which the BH3 domain of Bak protein was replaced with a selected peptide, retained the ability to bind specifically to Bcl-X(L). These results demonstrate that this selected peptide specifically antagonizes the function of Bcl-X(L) and overcomes the effects of Bcl-X(L) in intact cells. We suggest that mRNA display is a powerful technique to identify peptide inhibitors with high affinity and specificity for disease-related proteins.

In vitro selection of GTP-binding proteins by block shuffling of estrogen-receptor fragments.

To what extent has alternative splicing contributed to the evolution of protein-function diversity? We previously constructed a pool of block-deletion mutants of the human estrogen receptor alpha ligand binding domain by random multi-recombinant PCR. Here we performed iterative in vitro selection of GTP-binding proteins by using the library of mRNA-displayed proteins and GTP-affinity chromatography combined with quantitative real-time PCR. We obtained a novel GTP-binding protein with moderate affinity and substrate-specificity. The results of our in vitro simulation imply that alternative splicing may have contributed substantially to the diversification of protein function during evolution.

Rapid antibody selection by mRNA display on a microfluidic chip.

In vitro antibody-display technologies are powerful approaches for isolating monoclonal antibodies from recombinant antibody libraries. However, these display techniques require several rounds of affinity selection which is time-consuming. Here, we combined mRNA display with a microfluidic system for in vitro selection and evolution of antibodies and achieved ultrahigh enrichment efficiency of 10(6)- to 10(8)-fold per round. After only one or two rounds of selection, antibodies with high affinity and specificity were obtained from naive and randomized single-chain Fv libraries of approximately 10(12) molecules. Furthermore, we confirmed that not only protein-protein (antigen-antibody) interactions, but also protein-DNA and protein-drug interactions were selected with ultrahigh efficiencies. This method will facilitate high-throughput preparation of antibodies and identification of protein interactions in proteomic and therapeutic fields.

Six classes of nuclear localization signals specific to different binding grooves of importin alpha.

The importin alpha/beta pathway mediates nuclear import of proteins containing the classical nuclear localization signals (NLSs). Although the consensus sequences of the classical NLSs have been defined, there are still many NLSs that do not match the consensus rule and many nonfunctional sequences that match the consensus. We report here six different NLS classes that specifically bind to distinct binding pockets of importin alpha. By screening of random peptide libraries using an mRNA display, we selected peptides bound by importin alpha and identified six classes of NLSs, including three novel classes. Two noncanonical classes (class 3 and class 4) specifically bound the minor binding pocket of importin alpha, whereas the classical monopartite NLSs (class 1 and class 2) bound to the major binding pocket. Using a newly developed universal green fluorescent protein expression system, we found that these NLS classes, including plant-specific class 5 NLSs and bipartite NLSs, fundamentally require the regions outside the core basic residues for their activity and have specific residues or patterns that confer the activities differently between yeast, plants, and mammals. Furthermore, amino acid replacement analyses revealed that the consensus basic patterns of the classical NLSs are not essential for activity, thereby generating more unconventional patterns, including redox-sensitive NLSs. These results explain the causes of the NLS diversity. The defined consensus patterns and properties of importin alpha-dependent NLSs provide useful information for identifying NLSs.