Ab-Initio Membrane Protein Amphipathic Helix Structure Prediction Using Deep Neural Networks

Emerging concepts of myosin 18A isoform mechanobiology in organismal and immune system physiology, development, and function

Alternative and combinatorial splicing of myosin 18A (MYO18A) gene transcripts results in expression of MYO18A protein isoforms and isoform variants with different membrane and subcellular localizations, and functional properties. MYO18A proteins are members of the myosin superfamily consisting of a myosin-like motor domain, an IQ motif, and a coiled-coil domain. MYO18A isoforms, however, lack the ability to hydrolyze ATP and do not perform ATP-dependent motor activity. MYO18A isoforms are distinguished by different amino- and carboxy-terminal extensions and domains. The domain organization and functions of MYO18Aα, MYO18Aβ, and MYO18Aγ have been studied experimentally. MYO18Aα and MYO18Aβ have a common carboxy-terminal extension but differ by the presence or absence of an amino-terminal KE repeat and PDZ domain, respectively. The amino- and carboxy-terminal extensions of MYO18Aγ contain unique proline and serine-rich domains. Computationally predicted MYO18Aε and MYO18Aδ isoforms contain the carboxy-terminal serine-rich extension but differ by the presence or absence of the amino-terminal KE/PDZ extension. Additional isoform variants within each category arise by alternative utilization or inclusion/exclusion of small exons. MYO18Aα variants are expressed in somatic cells and mature immune cells, whereas MYO18Aβ variants occur mainly in myeloid and natural killer cells. MYO18Aγ expression is selective to cardiac and skeletal muscle. In the present review perspective, we discuss current and emerging concepts of the functional specialization of MYO18A proteins in membrane and cytoskeletal dynamics, cellular communication and signaling, endocytic and exocytic organelle movement, viral infection, and as the SP-R210 receptor for surfactant protein A.

A membrane-sensing mechanism links lipid metabolism to protein degradation at the nuclear envelope

Lipid composition determines organelle identity; however, whether the lipid composition of the inner nuclear membrane (INM) domain of the ER contributes to its identity is not known. Here, we show that the INM lipid environment of animal cells is under local control by CTDNEP1, the master regulator of the phosphatidic acid phosphatase lipin 1. Loss of CTDNEP1 reduces association of an INM-specific diacylglycerol (DAG) biosensor and results in a decreased percentage of polyunsaturated containing DAG species. Alterations in DAG metabolism impact the levels of the resident INM protein Sun2, which is under local proteasomal regulation. We identify a lipid-binding amphipathic helix (AH) in the nucleoplasmic domain of Sun2 that prefers membrane packing defects. INM dissociation of the Sun2 AH is linked to its proteasomal degradation. We suggest that direct lipid-protein interactions contribute to sculpting the INM proteome and that INM identity is adaptable to lipid metabolism, which has broad implications on disease mechanisms associated with the nuclear envelope.

Independent Membrane Binding Properties of the Caspase Generated Fragments of the Beaded Filament Structural Protein 1 (BFSP1) Involves an Amphipathic Helix

BACKGROUND

BFSP1 (beaded filament structural protein 1) is a plasma membrane, Aquaporin 0 (AQP0/MIP)-associated intermediate filament protein expressed in the eye lens. BFSP1 is myristoylated, a post-translation modification that requires caspase cleavage at D433. Bioinformatic analyses suggested that the sequences 434-452 were α-helical and amphipathic.

METHODS AND RESULTS

By CD spectroscopy, we show that the addition of trifluoroethanol induced a switch from an intrinsically disordered to a more α-helical conformation for the residues 434-467. Recombinantly produced BFSP1 fragments containing this amphipathic helix bind to lens lipid bilayers as determined by surface plasmon resonance (SPR). Lastly, we demonstrate by transient transfection of non-lens MCF7 cells that these same BFSP1 C-terminal sequences localise to plasma membranes and to cytoplasmic vesicles. These can be co-labelled with the vital dye, lysotracker, but other cell compartments, such as the nuclear and mitochondrial membranes, were negative. The N-terminal myristoylation of the amphipathic helix appeared not to change either the lipid affinity or membrane localisation of the BFSP1 polypeptides or fragments we assessed by SPR and transient transfection, but it did appear to enhance its helical content.

CONCLUSIONS

These data support the conclusion that C-terminal sequences of human BFSP1 distal to the caspase site at G433 have independent membrane binding properties via an adjacent amphipathic helix.

ReSMAP: Web Server for Predicting Residue-Specific Membrane-Association Propensities of Intrinsically Disordered Proteins

The functional processes of many proteins involve the association of their intrinsically disordered regions (IDRs) with acidic membranes. We have identified the membrane-association characteristics of IDRs using extensive molecular dynamics (MD) simulations and validated them with NMR spectroscopy. These studies have led to not only deep insight into functional mechanisms of IDRs but also to intimate knowledge regarding the sequence determinants of membrane-association propensities. Here we turned this knowledge into a web server called ReSMAP, for predicting the residue-specific membrane-association propensities from IDR sequences. The membrane-association propensities are calculated from a sequence-based partition function, trained on the MD simulation results of seven IDRs. Robustness of the prediction is demonstrated by leaving one IDR out of the training set. We anticipate there will be many applications for the ReSMAP web server, including rapid screening of IDR sequences for membrane association.

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks

MOTIVATION

Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural features in coiled-coil: coiled-coil domain (CCD), oligomeric state and register. However, most of the existing computational tools only focus on one of them.

RESULTS

Here, we describe a new deep learning model, CoCoPRED, which is based on convolutional layers, bidirectional long short-term memory, and attention mechanism. It has three networks, i.e. CCD network, oligomeric state network, and register network, corresponding to the three types of structural features in coiled-coil. This means CoCoPRED has the ability of fulfilling comprehensive prediction for coiled-coil proteins. Through the 5-fold cross-validation experiment, we demonstrate that CoCoPRED can achieve better performance than the state-of-the-art models on both CCD prediction and oligomeric state prediction. Further analysis suggests the CCD prediction may be a performance indicator of the oligomeric state prediction in CoCoPRED. The attention heads in CoCoPRED indicate that registers a, b and e are more crucial for the oligomeric state prediction.

AVAILABILITY AND IMPLEMENTATION

CoCoPRED is available at http://www.csbio.sjtu.edu.cn/bioinf/CoCoPRED. The datasets used in this research can also be downloaded from the website.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.