3,093 research outputs found
Distance-based Protein Folding Powered by Deep Learning
Contact-assisted protein folding has made very good progress, but two
challenges remain. One is accurate contact prediction for proteins lack of many
sequence homologs and the other is that time-consuming folding simulation is
often needed to predict good 3D models from predicted contacts. We show that
protein distance matrix can be predicted well by deep learning and then
directly used to construct 3D models without folding simulation at all. Using
distance geometry to construct 3D models from our predicted distance matrices,
we successfully folded 21 of the 37 CASP12 hard targets with a median family
size of 58 effective sequence homologs within 4 hours on a Linux computer of 20
CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot
fold any of them in the absence of folding simulation and the best CASP12 group
folded 11 of them by integrating predicted contacts into complex,
fragment-based folding simulation. The rigorous experimental validation on 15
CASP13 targets show that among the 3 hardest targets of new fold our
distance-based folding servers successfully folded 2 large ones with <150
sequence homologs while the other servers failed on all three, and that our ab
initio folding server also predicted the best, high-quality 3D model for a
large homology modeling target. Further experimental validation in CAMEO shows
that our ab initio folding server predicted correct fold for a membrane protein
of new fold with 200 residues and 229 sequence homologs while all the other
servers failed. These results imply that deep learning offers an efficient and
accurate solution for ab initio folding on a personal computer
Dependable Digitally-Assisted Mixed-Signal IPs Based on Integrated Self-Test & Self-Calibration
Heterogeneous SoC devices, including sensors, analogue and mixed-signal front-end circuits and the availability of massive digital processing capability, are being increasingly used in safety-critical applications like in the automotive, medical, and the security arena. Already a significant amount of attention has been paid in literature with respect to the dependability of the digital parts in heterogeneous SoCs. This is in contrast to especially the sensors and front-end mixed-signal electronics; these are however particular sensitive to external influences over time and hence determining their dependability. This paper provides an integrated SoC/IP approach to enhance the dependability. It will give an example of a digitally-assisted mixed-signal front-end IP which is being evaluated under its mission profile of an automotive tyre pressure monitoring system. It will be shown how internal monitoring and digitally-controlled adaptation by using embedded processors can help in terms of improving the dependability of this mixed-signal part under harsh conditions for a long time
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