The beta subunit of the signal recognition particle receptor is a transmembrane GTPase that anchors the alpha subunit, a peripheral membrane GTPase, to the endoplasmic reticulum membrane.

The signal recognition particle receptor (SR) is required for the cotranslational targeting of both secretory and membrane proteins to the endoplasmic reticulum (ER) membrane. During targeting, the SR interacts with the signal recognition particle (SRP) which is bound to the signal sequence of the nascent protein chain. This interaction catalyzes the GTP-dependent transfer of the nascent chain from SRP to the protein translocation apparatus in the ER membrane. The SR is a heterodimeric protein comprised of a 69-kD subunit (SR alpha) and a 30-kD subunit (SR beta) which are associated with the ER membrane in an unknown manner. SR alpha and the 54-kD subunits of SRP (SRP54) each contain related GTPase domains which are required for SR and SRP function. Molecular cloning and sequencing of a cDNA encoding SR beta revealed that SR beta is a transmembrane protein and, like SR alpha and SRP54, is a member of the GTPase superfamily. Although SR beta defines its own GTPase subfamily, it is distantly related to ARF and Sar1. Using UV cross-linking, we confirm that SR beta binds GTP specifically. Proteolytic digestion experiments show that SR alpha is required for the interaction of SRP with SR. SR alpha appears to be peripherally associated with the ER membrane, and we suggest that SR beta, as an integral membrane protein, mediates the membrane association of SR alpha. The discovery of its guanine nucleotide-binding domain, however, makes it likely that its role is more complex than that of a passive anchor for SR alpha. These findings suggest that a cascade of three directly interacting GTPases functions during protein targeting to the ER membrane

Membrane and Protein Interactions of the Pleckstrin Homology Domain Superfamily.

The human genome encodes about 285 proteins that contain at least one annotated pleckstrin homology (PH) domain. As the first phosphoinositide binding module domain to be discovered, the PH domain recruits diverse protein architectures to cellular membranes. PH domains constitute one of the largest protein superfamilies, and have diverged to regulate many different signaling proteins and modules such as Dbl homology (DH) and Tec homology (TH) domains. The ligands of approximately 70 PH domains have been validated by binding assays and complexed structures, allowing meaningful extrapolation across the entire superfamily. Here the Membrane Optimal Docking Area (MODA) program is used at a genome-wide level to identify all membrane docking PH structures and map their lipid-binding determinants. In addition to the linear sequence motifs which are employed for phosphoinositide recognition, the three dimensional structural features that allow peripheral membrane domains to approach and insert into the bilayer are pinpointed and can be predicted ab initio. The analysis shows that conserved structural surfaces distinguish which PH domains associate with membrane from those that do not. Moreover, the results indicate that lipid-binding PH domains can be classified into different functional subgroups based on the type of membrane insertion elements they project towards the bilayer

Structure of the substrate-engaged SecA-SecY protein translocation machine.

The Sec61/SecY channel allows the translocation of many proteins across the eukaryotic endoplasmic reticulum membrane or the prokaryotic plasma membrane. In bacteria, most secretory proteins are transported post-translationally through the SecY channel by the SecA ATPase. How a polypeptide is moved through the SecA-SecY complex is poorly understood, as structural information is lacking. Here, we report an electron cryo-microscopy (cryo-EM) structure of a translocating SecA-SecY complex in a lipid environment. The translocating polypeptide chain can be traced through both SecA and SecY. In the captured transition state of ATP hydrolysis, SecAs two-helix finger is close to the polypeptide, while SecAs clamp interacts with the polypeptide in a sequence-independent manner by inducing a short β-strand. Taking into account previous biochemical and biophysical data, our structure is consistent with a model in which the two-helix finger and clamp cooperate during the ATPase cycle to move a polypeptide through the channel

Solution structure of a repeated unit of the ABA-1 nematode polyprotein allergen of ascaris reveals a novel fold and two discrete lipid-binding sites

Parasitic nematode worms cause serious health problems in humans and other animals. They can induce allergic-type immune responses, which can be harmful but may at the same time protect against the infections. Allergens are proteins that trigger allergic reactions and these parasites produce a type that is confined to nematodes, the nematode polyprotein allergens (NPAs). These are synthesized as large precursor proteins comprising repeating units of similar amino acid sequence that are subsequently cleaved into multiple copies of the allergen protein. NPAs bind small lipids such as fatty acids and retinol (Vitamin A) and probably transport these sensitive and insoluble compounds between the tissues of the worms. Nematodes cannot synthesize these lipids, so NPAs may also be crucial for extracting nutrients from their hosts. They may also be involved in altering immune responses by controlling the lipids by which the immune and inflammatory cells communicate. We describe the molecular structure of one unit of an NPA, the well-known ABA-1 allergen of Ascaris and find its structure to be of a type not previously found for lipid-binding proteins, and we describe the unusual sites where lipids bind within this structur

Computational Analysis and Prediction of Intrinsic Disorder and Intrinsic Disorder Functions in Proteins

COMPUTATIONAL ANALYSIS AND PREDICTION OF INTRINSIC DISORDER AND INTRINSIC DISORDER FUNCTIONS IN PROTEINS By Akila Imesha Katuwawala A dissertation submitted in partial fulfillment of the requirements for the degree of Engineering, Doctor of Philosophy with a concentration in Computer Science at Virginia Commonwealth University. Virginia Commonwealth University, 2021 Director: Lukasz Kurgan, Professor, Department of Computer Science Proteins, as a fundamental class of biomolecules, have been studied from various perspectives over the past two centuries. The traditional notion is that proteins require fixed and stable three-dimensional structures to carry out biological functions. However, there is mounting evidence regarding a “special” class of proteins, named intrinsically disordered proteins, which do not have fixed three-dimensional structures though they perform a number of important biological functions. Computational approaches have been a vital component to study these intrinsically disordered proteins over the past few decades. Prediction of the intrinsic disorder and functions of intrinsic disorder from protein sequences is one such important computational approach that has recently gained attention, particularly in the advent of the development of modern machine learning techniques. This dissertation runs along two basic themes, namely, prediction of the intrinsic disorder and prediction of the intrinsic disorder functions. The work related to the prediction of intrinsic disorder covers a novel approach to evaluate the predictive performance of the current computational disorder predictors. This approach evaluates the intrinsic disorder predictors at the individual protein level compared to the traditional studies that evaluate them over large protein datasets. We address several interesting aspects concerning the differences in the protein-level vs. dataset-level predictive quality, complementarity and predictive performance of the current predictors. Based on the findings from this assessment we have conceptualized, developed, tested and deployed an innovative platform called DISOselect that recommends the most suitable computational disorder predictors for a given protein, with an underlying goal to maximize the predictive performance. DISOselect provides advice on whether a given disorder predictor would provide an accurate prediction for a given protein of user’s interest, and recommends the most suitable disorder predictor together with an estimate of its expected predictive quality. The second theme, prediction of the intrinsic disorder functions, includes first-of-its-kind evaluation of the current computational disorder predictors on two functional sub-classes of the intrinsically disordered proteins. This study introduces several novel evaluation strategies to assess predictive performance of disorder prediction methods and focuses on the evaluation for disorder functions associated with interactions with partner molecules. Results of this analysis motivated us to conceptualize, design, test and deploy a new and accurate machine learning-based predictor of the disordered lipid-binding residues, DisoLipPred. We empirically show that the strong predictive performance of DisoLipPred stems from several innovative design features and that its predictions complements results produced by current disorder predictors, disorder function predictors and predictors of transmembrane regions. We deploy DisoLipPred as a convenient webserver and discuss its predictions on the yeast proteome