Amyloid Proteins Structure, Dynamics, Interactions and Early Stages of Self-Assembly

The self-assembly and aggregation of amyloid protein are associated with several neurodegenerative diseases. The evidence indicates that the oligomeric intermediates, formed prior to the final fibrillary product, are the primary culprits of neurotoxicity. Although tremendous efforts have been dedicated for the characterization of structures, dynamics and toxic-related hallmarks of the oligomers, to date, yet the mechanism of such assembly from disordered monomers and their structure remain elusive. In this dissertation, I focused on understanding the dimerization process of amyloid proteins and peptides of different sizes and I combined experimental studies with high-power computer simulations. The AFM force spectroscopy experiments showed that within dimers misfolded states of peptides were characterized by a lifetime as large as ∼1 s. Compared with the conformational dynamics of monomers, dimerization stabilized the misfolded states by many orders of magnitude. To characterize structure of the dimers, the all-atom Molecular Dynamics (MD) simulations were employed. These MD simulations indeed revealed the stabilization of dimers when they form antiparallel of β-sheet conformation. The hydrogen bonds, salt bridges, and weakly polar interactions further stabilized the dimer structure. The simulations led to several structures, so to distinguish between them and identify the one that was observed in the experiment, a novel computational approach termed Monte Carlo Pulling (MCP) was developed. The key property of this approach is the ability to simulate the AFM force spectroscopy experiment at conditions identical ones used in the experiment enabling us to identify the appropriate computational model of the dimer by direct comparison with the AFM experiment. A comparison of experimental results with the computational data for two amyloid peptides allowed us for the first time to identify the dimers analyzed in the experiment and characterize their structure. These studies demonstrated that although hydrogen bonds were the major contributors to dimer dissociation, the aromatic-aromatic interaction also contributed to the dimer rupture process. Entirely unexpected results were obtained in the application of this combined approach to characterization of dimers formed by full-size Aβ42 dimers. The dimers were stabilized primarily by interactions within the central hydrophobic regions and C-terminal region with a contribution from local hydrogen bonding. The dimers were dynamic as evidenced by the existence of a set of conformations and computational analyses of the dimer dissociation process. Although Aβ42 protein formed stable dimers, but their structure was entirely different from the ones reported for the Aβ42 protein in fibrils. In fact a set of structures was identified and we hypothesize that different structures can be nuclei for the Aβ42 assembly in different morphologies. To characterize dimerization of such large amyloid protein as α-Synuclein (α-Syn) (140 residues), a novel combined approach was utilized. The structure and dynamics of the dimers was characterized by high-speed AFM and Monte Carlo modeling was used to characterize the protein structure. These studies showed that the hydrophobic region of α-Syn facilitated the formation of compact structures. Surprisingly, the dynamics of one α-Syn dimers shared a number of similar features with the dissociation process in Aβ42 simulations. Altogether, our results revealed structure of transiently existing dimeric forms of amyloid proteins. Given the fact that the dimers are the very first oligomers of amyloids, this novel information is indispensable drug design activity and development of novel therapeutic tools for early diagnostic of AD and PD and opens prospects for understanding molecular mechanisms of early onset of AD and PD and development of the preventive means for these devastating diseases

EnsNet: Ensconce Text in the Wild

A new method is proposed for removing text from natural images. The challenge is to first accurately localize text on the stroke-level and then replace it with a visually plausible background. Unlike previous methods that require image patches to erase scene text, our method, namely ensconce network (EnsNet), can operate end-to-end on a single image without any prior knowledge. The overall structure is an end-to-end trainable FCN-ResNet-18 network with a conditional generative adversarial network (cGAN). The feature of the former is first enhanced by a novel lateral connection structure and then refined by four carefully designed losses: multiscale regression loss and content loss, which capture the global discrepancy of different level features; texture loss and total variation loss, which primarily target filling the text region and preserving the reality of the background. The latter is a novel local-sensitive GAN, which attentively assesses the local consistency of the text erased regions. Both qualitative and quantitative sensitivity experiments on synthetic images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet is essential to achieve a good performance. Moreover, our EnsNet can significantly outperform previous state-of-the-art methods in terms of all metrics. In addition, a qualitative experiment conducted on the SMBNet dataset further demonstrates that the proposed method can also preform well on general object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can preform at 333 fps on an i5-8600 CPU device.Comment: 8 pages, 8 figures, 2 tables, accepted to appear in AAAI 201