Association of Protein Helices and Assembly of Foldamers: Stories in Membrane and Aqueous Environments

Solvents play an important role in association and assembly of molecules. Here we studied solvent effects on proteins and organic chemicals in different contexts. First, X-ray crystal structures show that helix dimers in membrane- and water-soluble proteins have distinct behaviors in packing and sequence selection. Transmembrane dimers are stabilized by compact packing and hydrogen bonding between small residues. Meanwhile, water-soluble dimers utilize hydrophobic residues for packing irrespective of the size of the interface and tight dimers are rare. Secondly, we apply the results learned above to a complex system in which a designed protein binds to single-walled carbon-nanotube in aqueous environments. Previous designs of the hexameric helical bundles utilized leucine and alanine residues to make two distinct helix-helix interfaces. Our molecular dynamics simulations showed that the alanine-comprising interface is much more labile than the leucine-comprising one. This result can be interpreted by the scarcity of tight soluble helix dimers as mentioned above. Thus more stable modular helix-helix interfaces have to be employed to design peptides binding to carbon-nanotubes with higher affinities. Lastly, we describe a serendipitous discovery of the crystalline framework structure by an amphiphilic triarylamide foldamer. Foldamers are peptide-like polymers of non-natural monomers arranged in defined sequence and chain length that are able to adopt protein-like secondary and tertiary structures. In contrast with traditional metal-organic and organic frameworks, which exploit strong directional coordination and hydrogen bonding for assembly in organic solvents, the crystal herein is built up from a combination of noncovalent hydrophobic, hydrogen-bonded, and electrostatic interactions in aqueous solution. The structure is in honeycomb geometry with each cubicle as a truncated octahedron. A new supramolecular synthon, in which hydrogen bonding and π-π stacking are encompassed, was discovered in the crystal structure. Through NMR experiments we probed the oligomeric states of the foldamer in the early stages prior to crystallization. The hierarchic crystal structure was discussed in terms of supramolecular synthons in crystal engineering

Deep Residual Learning for Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.Comment: Tech repor

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224x224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102x faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.Comment: This manuscript is the accepted version for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015. See Changelo