Publications by authors named "Zhaoqing Yan"

Directed protein evolution is central to biomedical applications but faces challenges like experimental complexity, inefficient multi-property optimization, and local maxima traps. While methods using protein language models (PLMs) can provide modeled fitness landscape guidance, they struggle to generalize across diverse protein families and map to protein activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models to rapidly improve protein activity.

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Article Synopsis
  • Directed evolution of proteins is crucial for various fields but is traditionally labor-intensive and limited in efficiency.
  • The introduction of EVOLVEpro, a few-shot active learning framework, enhances protein activity optimization using protein language models and activity predictors, achieving significant improvements in fewer rounds.
  • EVOLVEpro demonstrated substantial advancements across different proteins and applications, potentially transforming AI-guided protein engineering in biology and medicine.
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Rapid, simple, and low-cost diagnostic technologies are crucial tools for combatting infectious disease. We describe a class of aptamer-based RNA switches or aptaswitches that recognize target nucleic acid molecules and initiate folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide an intense fluorescent readout without intervening enzymes, generating signals in as little as 5 minutes and enabling detection by eye with minimal equipment.

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Rapid, simple, and low-cost diagnostic technologies are crucial tools for combatting infectious disease. Here, we describe a class of aptamer-based RNA switches called aptaswitches that recognize specific target nucleic acid molecules and respond by initiating folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide a fast and intense fluorescent readout, generating signals in as little as 5 minutes and enabling detection by eye with minimal equipment.

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Translation activators are an important class of riboregulators that respond to nucleic acid signals by activating gene expression. Toehold switches and single-nucleotide-specific programmable riboregulators (SNIPRs) are two types of translation activators that can detect nearly any nucleic acid sequence using interactions initiated by single-stranded domains known as toeholds. Toehold switches operate with high dynamic range, orthogonality, and programmability, making them capable of detecting a variety of pathogens in paper-based cell-free diagnostic assays.

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Applications of RNA-based molecular logic have been hampered by sequence constraints imposed on the input and output of the circuits. Here we show that the sequence constraints can be substantially reduced by appropriately encoded multi-arm junctions of single-stranded RNA structures. To conditionally activate RNA translation, we integrated multi-arm junctions, self-assembled upstream of a regulated gene and designed to unfold sequentially in response to different RNA inputs, with motifs of loop-initiated RNA activators that function independently of the sequence of the input RNAs and that reduce interference with the output gene.

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The common drawbacks of most traditional electrochemiluminescence (ECL) immunoassays are the strict storage conditions for the ECL electrode and the steric hindrance caused by bovine serum albumin and antigen. The strict storage conditions require that the modified electrode must be stored at 4 °C before measurement, which may cause the degradation of protein molecules and low reproducibility as the time goes by. The steric hindrance can hinder electron transfer between the electrode and the electrochemical active substance unable to transmit proteins on the electrode surface.

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