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Abstract This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins’ ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution. 

In this paper we present spatio-temporally controlled electrochemical stimulation of aqueous samples using an integrated CMOS microelectrode array with 131,072 pixels. We demonstrate programmable gold electrodeposition in arbitrary spatial patterns, controllable electrolysis to produce microscale hydrogen bubbles, and spatially targeted electrochemical pH modulation. Dense spatially-addressable electrochemical stimulation is important for a wide range of bioelectronics applications. 

Biotechnology applications are increasingly turning to CMOS integrated biosensor arrays for massive parallelism and increased throughput in biomolecular diagnostics. Yet many opportunities still remain to take advantage of the spatially-resolved nature of dense semiconductor platforms to open up new imaging dimensions which complement traditional microscopy. To better understand the emergence of spatial organization in living systems, we require techniques that dynamically probe the spatial structure of assemblies of millions of cells or more. Optical microscopy is the dominant technique, but large field-of-view microscopes have an inherent tradeoff with resolving fine features. Confocal microscopes can image cellular-scale 3D structures, but their bright illumination can impart severe phototoxicity, and observing large areas can be prohibitively slow. Here we present an integrated CMOS sensor array with 131,072 pixels, which is designed to electrochemically image and interface with bacterial biofilms. 

While classical electrochemical impedance spectroscopy (EIS) focuses on measurements from a single working electrode, dense active microelectrode arrays offer opportunities for new modes of sensing. Here we present experimental results with an integrated sensor array for electrochemical imaging. The system uses a 100 x 100 custom CMOS electrode array with 10 micron pixels, which measures impedance at frequencies up to 100 MHz. The sensor chip is uniquely designed to take advantage of the electrostatic coupling between groups of nearby pixels to re-shape the local electric field. Multiple bias voltages and clock phases create new types of signal diversity that will enable enhanced sensing modes for computational imaging and impedance tomography. 

This paper presents a 100 x 100 super-resolution integrated sensor array for microscale electrochemical impedance spectroscopy (EIS) imaging. The system is implemented in 180 nm CMOS with 10 x 10 micron pixels. Rather than treating each electrode independently, the sensor is designed to measure the mutual capacitance between programmable sets of pixels. Multiple spatially-resolved measurements can then be computationally combined to produce super-resolution impedance images. Experimental measurements of sub-cellular permittivity distributions within single algae cells demonstrate the potential of this new approach.