Conformational selection in protein binding and function

Protein binding and function often involves conformational changes. Advanced NMR experiments indicate that these conformational changes can occur in the absence of ligand molecules (or with bound ligands), and that the ligands may 'select' protein conformations for binding (or unbinding). In this review, we argue that this conformational selection requires transition times for ligand binding and unbinding that are small compared to the dwell times of proteins in different conformations, which is plausible for small ligand molecules. Such a separation of timescales leads to a decoupling and temporal ordering of binding/unbinding events and conformational changes. We propose that conformational-selection and induced-change processes (such as induced fit) are two sides of the same coin, because the temporal ordering is reversed in binding and unbinding direction. Conformational-selection processes can be characterized by a conformational excitation that occurs prior to a binding or unbinding event, while induced-change processes exhibit a characteristic conformational relaxation that occurs after a binding or unbinding event. We discuss how the ordering of events can be determined from relaxation rates and effective on- and off-rates determined in mixing experiments, and from the conformational exchange rates measured in advanced NMR or single-molecule FRET experiments. For larger ligand molecules such as peptides, conformational changes and binding events can be intricately coupled and exhibit aspects of conformational-selection and induced-change processes in both binding and unbinding direction.Comment: review article; 10 pages, 4 figures, Protein Sci. 201

Cell cycle regulation of a Xenopus Wee1-like kinase

Using a polymerase chain reaction-based strategy, we have isolated a gene encoding a Wee1-like kinase from Xenopus eggs. The recombinant Xenopus Wee1 protein efficiently phosphorylates Cdc2 exclusively on Tyr- 15 in a cyclin-dependent manner. The addition of exogenous Wee1 protein to Xenopus cell cycle extracts results in a dose-dependent delay of mitotic initiation that is accompanied by enhanced tyrosine phosphorylation of Cdc2. The activity of the Wee1 protein is highly regulated during the cell cycle: the interphase, underphosphorylated form of Wee1 (68 kDa) phosphorylates Cdc2 very efficiently, whereas the mitotic, hyperphosphorylated version (75 kDa) is weakly active as a Cdc2-specific tyrosine kinase. The down-modulation of Wee1 at mitosis is directly attributable to phosphorylation, since dephosphorylation with protein phosphatase 2A restores its kinase activity. During interphase, the activity of this Wee1 homolog does not vary in response to the presence of unreplicated DNA. The mitosis-specific phosphorylation of Wee1 is due to at least two distinct kinases: the Cdc2 protein and another activity (kinase X) that may correspond to an MPM-2 epitope kinase. These studies indicate that the down-regulation of Wee1-like kinase activity at mitosis is a multistep process that occurs after other biochemical reactions have signaled the successful completion of S phase

RNNs Implicitly Implement Tensor Product Representations

Recurrent neural networks (RNNs) can learn continuous vector representations of symbolic structures such as sequences and sentences; these representations often exhibit linear regularities (analogies). Such regularities motivate our hypothesis that RNNs that show such regularities implicitly compile symbolic structures into tensor product representations (TPRs; Smolensky, 1990), which additively combine tensor products of vectors representing roles (e.g., sequence positions) and vectors representing fillers (e.g., particular words). To test this hypothesis, we introduce Tensor Product Decomposition Networks (TPDNs), which use TPRs to approximate existing vector representations. We demonstrate using synthetic data that TPDNs can successfully approximate linear and tree-based RNN autoencoder representations, suggesting that these representations exhibit interpretable compositional structure; we explore the settings that lead RNNs to induce such structure-sensitive representations. By contrast, further TPDN experiments show that the representations of four models trained to encode naturally-occurring sentences can be largely approximated with a bag of words, with only marginal improvements from more sophisticated structures. We conclude that TPDNs provide a powerful method for interpreting vector representations, and that standard RNNs can induce compositional sequence representations that are remarkably well approximated by TPRs; at the same time, existing training tasks for sentence representation learning may not be sufficient for inducing robust structural representations.Comment: Accepted to ICLR 201

The Ohio Grain Marketing Legislation of 1982 and 1983

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Distinct roles of transcription factors EGL-46 and DAF-19 in specifying the functionality of a polycystin-expressing sensory neuron necessary for C. elegans male vulva location behavior

Caenorhabditis elegans polycystins LOV-1 and PKD-2 are expressed in the male-specific HOB neuron, and are necessary for sensation of the hermaphrodite vulva during mating. We demonstrate that male vulva location behavior and expression of lov-1 and pkd-2 in the ciliated sensory neuron HOB require the activities of transcription factor EGL-46 and to some extent also EGL-44. This EGL-46- regulated program is specific to HOB and is distinct from a general ciliogenic pathway functioning in all ciliated neurons. The ciliogenic pathway regulator DAF-19 affects downstream components of the HOB-specific program indirectly and is independent of EGL-46 activity. The sensory function of HOB requires the combined action of these two distinct regulatory pathways