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Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning–enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers—functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis. 

Increasing demand of using everyday clothing in wearable sensing and display has synergistically advanced the field of electronic textiles, or e-textiles. A variety of types of e-textiles have been formed into stretchy fabrics in a manner that can maintain their intrinsic properties of stretchability, breathability, and wearability to fit comfortably across different sizes and shapes of the human body. These unique features have been leveraged to ensure accuracy in capturing physical, chemical, and electrophysiological signals from the skin under ambulatory conditions, while also displaying the sensing data or other immediate information in daily life. Here, we review the emerging trends and recent advances in e-textiles in wearable sensing and display, with a focus on their materials, constructions, and implementations. We also describe perspectives on the remaining challenges of e-textiles to guide future research directions toward wider adoption in practice. 

In this paper, we introduce the design and implementation of a low-cost, small-scale autonomous vehicle equipped with an onboard computer, a camera, a Lidar, and some other accessories. We implement various autonomous driving-related modules including mapping and localization, object detection, obstacle avoidance, and path planning. In order to better test the system, we focus on the autonomous parking scenario. In this scenario, the vehicle is able to move from an appointed start point to the desired parking lot autonomously by following a path planned by the hybrid A* algorithm. The vehicle is able to detect objects and avoid obstacles on its path and achieve autonomous parking. 

Abstract Machine learning has been increasingly used for protein engineering. However, because the general sequence contexts they capture are not specific to the protein being engineered, the accuracy of existing machine learning algorithms is rather limited. Here, we report ECNet (evolutionary context-integrated neural network), a deep-learning algorithm that exploits evolutionary contexts to predict functional fitness for protein engineering. This algorithm integrates local evolutionary context from homologous sequences that explicitly model residue-residue epistasis for the protein of interest with the global evolutionary context that encodes rich semantic and structural features from the enormous protein sequence universe. As such, it enables accurate mapping from sequence to function and provides generalization from low-order mutants to higher-order mutants. We show that ECNet predicts the sequence-function relationship more accurately as compared to existing machine learning algorithms by using ~50 deep mutational scanning and random mutagenesis datasets. Moreover, we used ECNet to guide the engineering of TEM-1 β-lactamase and identified variants with improved ampicillin resistance with high success rates.