PredMP: a web server for de novo prediction and visualization of membrane proteins.

MOTIVATION: PredMP is the first web service, to our knowledge, that aims at de novo prediction of the membrane protein (MP) 3D structure followed by the embedding of the MP into the lipid bilayer for visualization. Our approach is based on a high-throughput Deep Transfer Learning (DTL) method that first predicts MP contacts by learning from non-MPs and then predicts the 3D model of the MP using the predicted contacts as distance restraints. This algorithm is derived from our previous Deep Learning (DL) method originally developed for soluble protein contact prediction, which has been officially ranked No. 1 in CASP12. The DTL framework in our approach overcomes the challenge that there are only a limited number of solved MP structures for training the deep learning model. There are three modules in the PredMP server: (i) The DTL framework followed by the contact-assisted folding protocol has already been implemented in RaptorX-Contact, which serves as the key module for 3D model generation; (ii) The 1D annotation module, implemented in RaptorX-Property, is used to predict the secondary structure and disordered regions; and (iii) the visualization module to display the predicted MPs embedded in the lipid bilayer guided by the predicted transmembrane topology. RESULTS: Tested on 510 non-redundant MPs, our server predicts correct folds for ∼290 MPs, which significantly outperforms existing methods. Tested on a blind and live benchmark CAMEO from September 2016 to January 2018, PredMP can successfully model all 10 MPs belonging to the hard category. AVAILABILITY AND IMPLEMENTATION: PredMP is freely accessed on the web at http://www.predmp.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

On the Interpretation of Force-Induced Unfolding Studies of Membrane Proteins Using Fast Simulations.

Single-molecule force spectroscopy has proven extremely beneficial in elucidating folding pathways for membrane proteins. Here, we simulate these measurements, conducting hundreds of unfolding trajectories using our fast Upside algorithm for slow enough speeds to reproduce key experimental features that may be missed using all-atom methods. The speed also enables us to determine the logarithmic dependence of pulling velocities on the rupture levels to better compare to experimental values. For simulations of atomic force microscope measurements in which force is applied vertically to the C-terminus of bacteriorhodopsin, we reproduce the major experimental features including even the back-and-forth unfolding of single helical turns. When pulling laterally on GlpG to mimic the experiment, we observe quite different behavior depending on the stiffness of the spring. With a soft spring, as used in the experimental studies with magnetic tweezers, the force remains nearly constant after the initial unfolding event, and a few pathways and a high degree of cooperativity are observed in both the experiment and simulation. With a stiff spring, however, the force drops to near zero after each major unfolding event, and numerous intermediates are observed along a wide variety of pathways. Hence, the mode of force application significantly alters the perception of the folding landscape, including the number of intermediates and the degree of folding cooperativity, important issues that should be considered when designing experiments and interpreting unfolding data.

A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations.

We use the statistics of a large and curated training set of transmembrane helical proteins to develop a knowledge-based potential that accounts for the dependence on both the depth of burial of the protein in the membrane and the degree of side-chain exposure. Additionally, the statistical potential includes depth-dependent energies for unsatisfied backbone hydrogen bond donors and acceptors, which are found to be relatively small, ∼2 RT. Our potential accurately places known proteins within the bilayer. The potential is applied to the mechanosensing MscL channel in membranes of varying thickness and curvature, as well as to the prediction of protein structure. The potential is incorporated into our new Upside molecular dynamics algorithm. Notably, we account for the exchange of protein-lipid interactions for protein-protein interactions as helices contact each other, thereby avoiding overestimating the energetics of helix association within the membrane. Simulations of most multimeric complexes find that isolated monomers and the oligomers retain the same orientation in the membrane, suggesting that the assembly of prepositioned monomers presents a viable mechanism of oligomerization.

3D LITHOGRAPHY. Atomic gold-enabled three-dimensional lithography for silicon mesostructures.

Three-dimensional (3D) mesostructured semiconductors show promising properties and applications; however, to date, few methods exist to synthesize or fabricate such materials. Metal can diffuse along semiconductor surfaces, and even trace amounts can change the surface behavior. We exploited the phenomena for 3D mesoscale lithography, by showing one example where iterated deposition-diffusion-incorporation of gold over silicon nanowires forms etchant-resistant patterns. This process is facet-selective, producing mesostructured silicon spicules with skeletonlike morphology, 3D tectonic motifs, and reduced symmetries. Atom-probe tomography, coupled with other quantitative measurements, indicates the existence and the role of individual gold atoms in forming 3D lithographic resists. Compared to other more uniform silicon structures, the anisotropic spicule requires greater force for detachment from collagen hydrogels, suggesting enhanced interfacial interactions at the mesoscale.