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Spider silks have been a focus of research for almost two decades due to their outstanding mechanical and biophysical properties. Recent advances in genetic engineering have led to the synthesis of recombinant spider silks, thus helping to unravel a fundamental understanding of structure–function–property relationships. The relationships between molecular composition, secondary structures and mechanical properties found in different types of spider silks are described, along with a discussion of artificial spinning of these proteins and their bioapplications, including the role of silks in biomineralization and fabrication of biomaterials with controlled properties. Keywords: Spider silk; Proteins; Secondary structure; Self-assembly; Genetic engineering, National Institutes of Health (U.S.) (Grant EB014976), National Institutes of Health (U.S.) (Grant EB002520), National Institutes of Health (U.S.) (Grant EY020856), National Institutes of Health (U.S.) (Grant DE017207), National Institutes of Health (U.S.) (Grant EB014283)

Accurate prediction and validation of the assembly of bioinspired peptide sequences into fibers with defined mechanical characteristics would aid significantly in designing and creating materials with desired properties. This process may also be utilized to provide insight into how the molecular architecture of many natural protein fibers is assembled. In this work, computational modeling and experimentation are used in tandem to determine how peptide terminal modification affects a fiber-forming core domain. Modeling shows that increased terminal molecular weight and hydrophilicity improve peptide chain alignment under shearing conditions and promote consolidation of semicrystalline domains. Mechanical analysis shows acute improvements to strength and elasticity, but significantly reduced extensibility and overall toughness. These results highlight an important entropic function that terminal domains of fiber-forming peptides exhibit as chain alignment promoters, which ultimately has notable consequences on the mechanical behavior of the final fiber products., National Institutes of Health (U.S.) (U01 EB014976), Texas Advanced Computing Center (grant number TG-DMR140101), Texas Advanced Computing Center (grant number TG-MSS090007)

Silk is a promising material for biomedical applications, and much research is focused on how application-specific, mechanical properties of silk can be designed synthetically through proper amino acid sequences and processing parameters. This protocol describes an iterative process between research disciplines that combines simulation, genetic synthesis, and fiber analysis to better design silk fibers with specific mechanical properties. Computational methods are used to assess the protein polymer structure as it forms an interconnected fiber network through shearing and how this process affects fiber mechanical properties. Model outcomes are validated experimentally with the genetic design of protein polymers that match the simulation structures, fiber fabrication from these polymers, and mechanical testing of these fibers. Through iterative feedback between computation, genetic synthesis, and fiber mechanical testing, this protocol will enable a priori prediction capability of recombinant material mechanical properties via insights from the resulting molecular architecture of the fiber network based entirely on the initial protein monomer composition. This style of protocol may be applied to other fields where a research team seeks to design a biomaterial with biomedical application-specific properties. This protocol highlights when and how the three research groups (simulation, synthesis, and engineering) should be interacting to arrive at the most effective method for predictive design of their material. Keywords: computational modeling; genetic synthesis; recombinant silk; spinning, National Institutes of Health (U.S.) (Grant U01 EB014976-05)

Real-time computer vision applications have difficult runtime constraints within which to execute. Implementing on a CPU provides a baseline for performance. But using custom parallel hardware such as graphics processing units (GPUs) and field programmable gate arrays (FPGAs) represents a cost effective method to achieve greater performance. Greater performance can move an algorithm from non-real-time into the realm of real-time. This opens numerous possibilities for interaction that did not exist before. Tasks such as face detection can be used to set focus points in cameras if performed in real-time. Similarly, body part tracking can be used as input for consumer televisions or video game systems when run in real-time. Acceleration using heterogeneous hardware is attractive because algorithms exhibit different models of computation at different stages of execution. Each platform can be exploited to execute when most efficient. However, it can be difficult to combine these platforms into a single application. This is due to the lack of reusable components and communication abstractions for these devices. This work describes a framework to lower the barrier for computer vision application acceleration called the Smart Frame Grabber Framework. This framework is a collection of reusable hardware acceleration components that are commonly used for accelerating computer vision applications using CPUs and FPGAs. It allows applications to be easily partitioned across multiple heterogenous compute devices. At the heart of this framework is a communication and synchronization platform called RIFFA: A Reusable Integration Framework for FPGA Accelerators. Using the Smart Frame Grabber Framework, researchers can design and build a hardware accelerated computer vision application in considerably less time and with less upfront effort than it would take using existing vendor provided tools alone

Real-time computer vision applications have difficult runtime constraints within which to execute. Implementing on a CPU provides a baseline for performance. But using custom parallel hardware such as graphics processing units (GPUs) and field programmable gate arrays (FPGAs) represents a cost effective method to achieve greater performance. Greater performance can move an algorithm from non-real-time into the realm of real-time. This opens numerous possibilities for interaction that did not exist before. Tasks such as face detection can be used to set focus points in cameras if performed in real-time. Similarly, body part tracking can be used as input for consumer televisions or video game systems when run in real-time. Acceleration using heterogeneous hardware is attractive because algorithms exhibit different models of computation at different stages of execution. Each platform can be exploited to execute when most efficient. However, it can be difficult to combine these platforms into a single application. This is due to the lack of reusable components and communication abstractions for these devices. This work describes a framework to lower the barrier for computer vision application acceleration called the Smart Frame Grabber Framework. This framework is a collection of reusable hardware acceleration components that are commonly used for accelerating computer vision applications using CPUs and FPGAs. It allows applications to be easily partitioned across multiple heterogenous compute devices. At the heart of this framework is a communication and synchronization platform called RIFFA: A Reusable Integration Framework for FPGA Accelerators. Using the Smart Frame Grabber Framework, researchers can design and build a hardware accelerated computer vision application in considerably less time and with less upfront effort than it would take using existing vendor provided tools alone

De novo assembly is a widely used methodology in bioinformatics. However, the conventional short-read-based de novo assembly is incapable of reliably reconstructing the large-scale structures of human genomes. Recently, a novel optical label-based technology has enabled reliable large-scale de novo assembly. Despite its advantage in large-scale genome analysis, this new technology requires a more computationally intensive alignment algorithm than its conventional counterpart. For example, the runtime of reconstructing a human genome is on the order of 10,000 hours on a sequential CPU. Therefore, in order to practically apply this new technology in genome research, accelerated approaches are desirable. In this article, we present three different accelerated approaches, multicore CPU, GPU, and FPGA. Against the sequential software baseline, our multicore CPU design achieved an 8.4× speedup, while the GPU and FPGA designs achieved 13.6× and 115× speedups, respectively. We also discuss the details of the design space exploration of this new assembly algorithm on these three different devices. Finally, we compare these devices in performance, optimization techniques, prices, and design efforts.

Bioengineered spider silk block copolymers were studied to understand the effect of protein chain length and sequence chemistry on the formation of secondary structure and materials assembly. Using a combination of in vitro protein design and assembly studies, we demonstrate that silk block copolymers possessing multiple repetitive units self-assemble into lamellar microstructures. Additionally, the study provides insights into the assembly behavior of spider silk block copolymers in concentrated salt solutions., National Institutes of Health (U.S.) (NIH U01 EB014976)

Bioengineered spider silk block copolymers were studied to understand the effect of protein chain length and sequence chemistry on the formation of secondary structure and materials assembly. Using a combination of in vitro protein design and assembly studies, we demonstrate that silk block copolymers possessing multiple repetitive units self-assemble into lamellar microstructures. Additionally, the study provides insights into the assembly behavior of spider silk block copolymers in concentrated salt solutions., National Institutes of Health (U.S.) (NIH U01 EB014976)

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified., National Institutes of Health (U.S.) (NIH (U01 EB014967)), National Science Foundation (U.S.) (NSF award No. ECS–0335765), National Research Foundation of Korea (2013R1A1A010091), European Research Council (ERC StG Ideas 2011 BIHSNAM no. 279985 on ‘Bio-Inspired hierarchical super-nanomaterials’), European Research Council (ERC PoC 2013-1 REPLICA2 no. 619448 on ‘Large-area replication of biological anti-adhesive nanosurfaces’), European Research Council (ERC PoC 2013-2 KNOTOUGH no. 632277 on ‘Super-tough knotted fibers’), European Commission (Graphene Flagship (WP10 ‘Nanocomposites’, no. 604391)), Autonomous Province of Trento (‘Graphene Nanocomposites’, no. S116/2012-242637 and reg. delib. no. 2266)