Subunit cooperation in the Get1/2 receptor promotes tail-anchored membrane protein insertion

The guided entry of tail-anchored protein (GET) pathway, in which the Get3 ATPase delivers an essential class of tail-anchored membrane proteins (TAs) to the Get1/2 receptor at the endoplasmic reticulum, provides a conserved mechanism for TA biogenesis in eukaryotic cells. The membrane-associated events of this pathway remain poorly understood. Here we show that complex assembly between the cytosolic domains (CDs) of Get1 and Get2 strongly enhances the affinity of the individual subunits for Get3•TA, thus enabling efficient capture of the targeting complex. In addition to the known role of Get1CD in remodeling Get3 conformation, two molecular recognition features (MoRFs) in Get2CD induce Get3 opening, and both subunits are required for optimal TA release from Get3. Mutation of the MoRFs attenuates TA insertion into the ER in vivo. Our results demonstrate extensive cooperation between the Get1/2 receptor subunits in the capture and remodeling of the targeting complex, and emphasize the role of MoRFs in receptor function during membrane protein biogenesis.

“Texas-Sized” Molecular Boxes: From Chemistry to Applications

The design and synthesis of novel macrocyclic host molecules continues to attract attention because such species play important roles in supramolecular chemistry. However, the discovery of new classes of macrocycles presents a considerable challenge due to the need to embody by design effective molecular recognition features, as well as ideally the development of synthetic routes that permit further functionalization. In 2010, we reported a new class of macrocyclic hosts: a set of tetracationic imidazolium macrocycles, which we termed “Texas-sized” molecular boxes (TxSBs) in homage to Stoddart’s classic “blue box” (CBPQT4+). Compared with the rigid blue box, the first generation TxSB displayed considerably greater conformational flexibility and a relatively large central cavity, making it a good host for a variety of electron-rich guests. In this review, we provide a comprehensive summary of TxSB chemistry, detailing our recent progress in the area of anion-responsive supramolecular self-assembly and applications of the underlying chemistry to water purification, information storage, and controlled drug release. Our objective is to provide not only a review of the fundamental findings, but also to outline future research directions where TxSBs and their constructs may have a role to play.

Dynamic Covalent Bond Constrained Ureas for Multimode Fluorescence Switching, Thermally Induced Emission, and Chemical Signaling Cascades

Urea groups play an important role in organic, supramolecular, and materials chemistry owing to unique structural and molecular recognition features. Herein a strategy of reversible covalent bond constrained ureas was...

GAI MoRFs Regulate Cleft and Channel Binding Pathways for Gibberellin in GID1A

The hormone gibberellin (GA) promotes arabidopsis growth by enhancing binding between GA Insensitive DELLA transcriptional repressors and GA Insensitive Dwarf 1 (GID1) receptors to regulate DELLA degradation. The binding mechanism for GA was elucidated by employing a computational study of dissociations of the N-terminus of the DELLA family member GAI (GA Insensitive transcriptional repressor) from the GID1A receptor in the presence and absence of bound GA, and of GA from GID1A in the presence and absence of GAI. The tRAMD method was employed to deduce egression pathways for a diverse set of GA molecules (GA (x) ). Two pathways in the form of a newly identified cleft and a previously identified channel are prevalent. The cleft pathway is open in the absence of GAI. Upon GAI binding, the cleft route is blocked, resulting in a slower process for GA (x) to exit and enter the binding pocket through the channel. Several binding pocket residues are identified as gate-keepers to the channel. Molecular recognition features (MoRFs) found in the disordered signaling protein GAI affect GA (x) binding and GID1A dynamics. A three-step synergistic binding cycle is proposed where GAI MoRFs regulate the process. Rapid binding takes place through the cleft where little to no distinctions are made between major and less active forms of GA (x) . After GAI is bound to the GA (x) [[EQUATION]] GID1A complex, the channel supports a rectification process that increases the retention of major active forms of GA within the binding pocket. Both the cleft and channel contact residues to GA (x) are markedly conserved in a GID1 phylogeny, suggesting this binding process in the GID1 [[EQUATION]] DELLA GA-receptor complex represents a general paradigm for GA binding. Non-specific GA binding assists binding of GAI, which then helps to select the major active forms of the hormone and induce a downstream signalling cascade in response to bioactive GA.

Trivalent PROTACs enhance protein degradation through cooperativity and avidity

Bivalent small-molecule degraders, or proteolysis targeting chimeras (PROTACs), work by simultaneously binding a target protein and E3 ubiquitin ligase to produce a ternary complex. To drive target ubiquitination and degradation at low catalytic concentrations, degraders must form appropriately positioned complexes of sufficient stability, aided by intra-complex interactions. We hypothesized these molecular recognition features could be enhanced by increasing binding valency. Here we present trivalent PROTACs as a strategy to boost protein degradation. Our design for a trivalent PROTAC consisted of two BET bromodomain inhibitors and an E3 ligase ligand, each separately tethered via a branched linker. In screening, we identified SIM1, a VHL-based PROTAC, as a highly potent BET degrader, capable of low picomolar degradation for all family members, with preference for BRD2. In functional comparison studies to bivalent PROTACs or inhibitors, SIM1 showed more sustained anti-cancer activity across numerous therapeutically relevant cell lines. Biophysical, biochemical, and cellular mechanistic studies showed SIM1 induces conformational changes upon binding to the BET protein to simultaneously engage with high avidity both its bromodomains in a cis intramolecular fashion. The resulting 1:1:1 complex showed positive cooperativity, high stability and prolonged cellular residence time. We provide proof-of-concept for augmenting the binding valency of proximity-induced modalities as a strategy to leverage both cooperativity and avidity within the ternary complex to advance functional outcomes.

Trivalent PROTACs Enhance Protein Degradation Through Cooperativity and Avidity

<p><b>Bivalent small-molecule degraders, or proteolysis targeting chimeras (PROTACs), work by simultaneously binding a target protein and E3 ubiquitin ligase to produce a ternary complex. To drive target ubiquitination and degradation at low catalytic concentrations, degraders must form appropriately positioned complexes of sufficient stability, aided by intra-complex interactions. We hypothesized these molecular recognition features could be enhanced by increasing binding valency. Here we present trivalent PROTACs as a strategy to boost protein degradation. Our design for a trivalent PROTAC consisted of two BET bromodomain inhibitors and an E3 ligase ligand, each separately tethered via a branched linker. In screening, we identified SIM1, a VHL-based PROTAC, as a highly potent BET degrader, capable of low picomolar degradation for all family members, with preference for BRD2. In functional comparison studies to bivalent PROTACs or inhibitors, SIM1 showed more sustained anti-cancer activity across numerous therapeutically relevant cell lines. Biophysical, biochemical, and cellular mechanistic studies showed SIM1 induces conformational changes upon binding to the BET protein to simultaneously engage with high avidity both its bromodomains in a cis intramolecular fashion. The resulting 1:1:1 complex showed positive cooperativity, high stability and prolonged cellular residence time. We provide proof-of-concept for augmenting the binding valency of proximity-induced modalities as a strategy to leverage both cooperativity and avidity within the ternary complex to advance functional outcomes.</b></p>

Trivalent PROTACs Enhance Protein Degradation Through Cooperativity and Avidity

<p><b>Bivalent small-molecule degraders, or proteolysis targeting chimeras (PROTACs), work by simultaneously binding a target protein and E3 ubiquitin ligase to produce a ternary complex. To drive target ubiquitination and degradation at low catalytic concentrations, degraders must form appropriately positioned complexes of sufficient stability, aided by intra-complex interactions. We hypothesized these molecular recognition features could be enhanced by increasing binding valency. Here we present trivalent PROTACs as a strategy to boost protein degradation. Our design for a trivalent PROTAC consisted of two BET bromodomain inhibitors and an E3 ligase ligand, each separately tethered via a branched linker. In screening, we identified SIM1, a VHL-based PROTAC, as a highly potent BET degrader, capable of low picomolar degradation for all family members, with preference for BRD2. In functional comparison studies to bivalent PROTACs or inhibitors, SIM1 showed more sustained anti-cancer activity across numerous therapeutically relevant cell lines. Biophysical, biochemical, and cellular mechanistic studies showed SIM1 induces conformational changes upon binding to the BET protein to simultaneously engage with high avidity both its bromodomains in a cis intramolecular fashion. The resulting 1:1:1 complex showed positive cooperativity, high stability and prolonged cellular residence time. We provide proof-of-concept for augmenting the binding valency of proximity-induced modalities as a strategy to leverage both cooperativity and avidity within the ternary complex to advance functional outcomes.</b></p>

MoRFPred_en: Sequence-based prediction of MoRFs using an ensemble learning strategy

Molecular recognition features (MoRFs) usually act as “hub” sites in the interaction networks of intrinsically disordered proteins (IDPs). Because an increasing number of serious diseases have been found to be associated with disordered proteins, identifying MoRFs has become increasingly important. In this study, we propose an ensemble learning strategy, named MoRFPred_en, to predict MoRFs from protein sequences. This approach combines four submodels that utilize different sequence-derived features for the prediction, including a multichannel one-dimensional convolutional neural network (CNN_1D multichannel) based model, two deep two-dimensional convolutional neural network (DCNN_2D) based models, and a support vector machine (SVM) based model. When compared with other methods on the same datasets, the MoRFPred_en approach produced better results than existing state-of-the-art MoRF prediction methods, achieving an AUC of 0.762 on the VALIDATION419 dataset, 0.795 on the TEST45 dataset, and 0.776 on the TEST49 dataset. Availability: http://vivace.bi.a.u-tokyo.ac.jp:8008/fang/MoRFPred_en.php .

Computational prediction of MoRFs based on protein sequences and minimax probability machine

Background Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods. Results In this study, a new sequence-based method, named as MoRFMPM, is proposed for predicting MoRFs. MoRFMPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRFCHiBi on the same test sets, MoRFMPM not only obtains higher AUC, but also obtains higher TPR at low FPR. Conclusions The features used in MoRFMPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRFMPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable.

Identifying molecular recognition features in intrinsically disordered regions of proteins by transfer learning

Motivation Protein intrinsic disorder describes the tendency of sequence residues to not fold into a rigid three-dimensional shape by themselves. However, some of these disordered regions can transition from disorder to order when interacting with another molecule in segments known as molecular recognition features (MoRFs). Previous analysis has shown that these MoRF regions are indirectly encoded within the prediction of residue disorder as low-confidence predictions [i.e. in a semi-disordered state P(D)≈0.5]. Thus, what has been learned for disorder prediction may be transferable to MoRF prediction. Transferring the internal characterization of protein disorder for the prediction of MoRF residues would allow us to take advantage of the large training set available for disorder prediction, enabling the training of larger analytical models than is currently feasible on the small number of currently available annotated MoRF proteins. In this paper, we propose a new method for MoRF prediction by transfer learning from the SPOT-Disorder2 ensemble models built for disorder prediction. Results We confirm that directly training on the MoRF set with a randomly initialized model yields substantially poorer performance on independent test sets than by using the transfer-learning-based method SPOT-MoRF, for both deep and simple networks. Its comparison to current state-of-the-art techniques reveals its superior performance in identifying MoRF binding regions in proteins across two independent testing sets, including our new dataset of >800 protein chains. These test chains share <30% sequence similarity to all training and validation proteins used in SPOT-Disorder2 and SPOT-MoRF, and provide a much-needed large-scale update on the performance of current MoRF predictors. The method is expected to be useful in locating functional disordered regions in proteins. Availability and implementation SPOT-MoRF and its data are available as a web server and as a standalone program at: http://sparks-lab.org/jack/server/SPOT-MoRF/index.php. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.