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Label-free nanopore sensors have emerged as a new generation technology of DNA sequencing and have been widely used for single molecule analysis. Since the first α-hemolysin biological nanopore, various types of nanopores made of different materials have been under extensive development. Noise represents a common challenge among all types of nanopore sensors. The nanopore noise can be decomposed into four components in the frequency domain (1/f noise, white noise, dielectric noise, and amplifier noise). In this work, we reviewed and summarized the physical models, origins, and reduction methods for each of these noise components. For the first time, we quantitatively benchmarked the root mean square (RMS) noise levels for different types of nanopores, demonstrating a clear material-dependent RMS noise. We anticipate this review article will enhance the understanding of nanopore sensor noises and provide an informative tutorial for developing future nanopore sensors with a high signal-to-noise ratio. 

Abstract We present the results for CAPRI Round 54, the 5th joint CASP‐CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo‐trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High‐quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2‐Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2‐Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.