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Biology subjects

Conover, M.

Publications and source records attributed to Conover, M..

2 recordsLinked to original sources

Photothermal Recycling Biosensing for Continuous, Sensitive Molecular Quantification

Continuous biochemical sensing provides valuable insights into an individuals physiological state and the mechanisms underlying pathophysiological changes. However, most existing bioanalytical methods are not compatible with continuous biochemical sensing. A major technical challenge lies in achieving rapid measurement readouts while maintaining high specificity and sensitivity in complex biological fluids. Sensitive molecular detection typically requires slow analyte-binder dissociation and long incubation to reach equilibrium, whereas rapid and frequent measurements demand fast association-dissociation kinetics that are difficult to reconcile for low-abundance analytes. To address this challenge, we introduce a sensing mechanism termed photothermal recycling (PTR), which mimics the thermal cycling process in polymerase chain reaction. Using plasmonic photothermal effects, PTR rapidly recycles binders to enable frequent measurements. We demonstrate a digital PTR assay capable of multi-hour biochemical monitoring with subpicomolar(pM) sensitivity in buffer, diluted serum, and saliva. This approach leverages localized thermal energy to dynamically modulate biomolecular recognition, offering a new bioanalytical paradigm for continuous biochemical sensing across diverse application settings.

bioengineering↗

AngularQA: Protein Model Quality Assessment with LSTM Networks

Quality Assessment (QA) plays an important role in protein structure prediction. Traditional protein QA methods suffer from searching databases or comparing with other models for making predictions, which usually fail. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (C) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure at each time-step, without using any database. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

bioinformatics↗