Recent advances in computational methods for biosensor design

Khoshbin, Zahra; Housaindokht, Mohammad Reza; Izadyar, Mohammad; Bozorgmehr, Mohammad Reza; Verdian, Asma

doi:10.1002/bit.27618

Cited by 22 publications

(6 citation statements)

References 171 publications

(220 reference statements)

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“…To achieve this goal, protein-ligand docking is a valuable technique (Figure 3A). [64,65] Predicting the most likely way a ligand would bind to a bioreceptor, as well as the number and types of interactions established between the two agents are some of the advantages of this technique.…”

Section: Exploiting Computational Approaches To Tailor Bioreceptor St...mentioning

confidence: 99%

Unlocking the Potential of Field Effect Transistor (FET) Biosensors: A Perspective on Methodological Advances in Computational and Molecular Biology

Rexha,

Perta,

Roscioni

et al. 2023

Advanced Sensor Research

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Field‐effect transistor (FET)‐based sensors are increasingly gaining relevance in diagnostic, healthcare, and environmental monitoring applications. A FET operates by transducing chemical interactions between a surface‐immobilized bioreceptor and the target analyte into a detectable electrical signal. FET biosensors can detect and monitor molecules (i.e., biomarkers, small molecules, viruses, bacterias) present in liquid samples, making a “liquid gate” configuration of FETs the most suitable approach. However, this FET architecture presents dimensional constraints that affect bioreceptors’ stability and immobilization in the liquid phase. To overcome these limitations, herein, a combination of computational and molecular biology techniques for improving bioreceptors' applicability in biosensing is proposed. This results in the optimized and problem‐tailored protein receptors for specific FET biosensors applications, thus enhancing their overall performance. The interplay between the computational and experimental approaches will represent a ground‐breaking solution for the development of next‐generation biosensors.

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Section: Exploiting Computational Approaches To Tailor Bioreceptor St...mentioning

confidence: 99%

Unlocking the Potential of Field Effect Transistor (FET) Biosensors: A Perspective on Methodological Advances in Computational and Molecular Biology

Rexha,

Perta,

Roscioni

et al. 2023

Advanced Sensor Research

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“…Therefore, improving the accuracy of such binding mode prediction is a vital goal in the field of computer‐aided drug discovery (CADD). Additionally, understanding the substrate‐enzyme binding mode can facilitate the rational design of industrial enzymes or bio‐sensors in the realm of enzyme engineering and bio‐sensor design 3–5 . Molecular docking, molecular dynamics (MD) simulations, and artificial intelligence (AI)‐based approaches represent the three main strategies employed for protein–ligand binding pose prediction.…”

Section: Introductionmentioning

confidence: 99%

zPoseScore model for accurate and robust protein–ligand docking pose scoring in CASP15

Shen,

Liu,

Wang

et al. 2023

Proteins

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We introduce a deep learning‐based ligand pose scoring model called zPoseScore for predicting protein–ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the “zFormer” module, inspired by AlphaFold2's Evoformer, to efficiently describe protein–ligand interactions. This module enables the extraction of protein–ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic‐level protein–ligand feature encoding and fusion to output refined ligand poses and ligand per‐atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF‐2016 protein–ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein–ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side‐chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein–ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

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“…With the rapid development of functional materials, integration technology, and sensing principles, flexible sensors have been widely used in healthcare monitoring, biomedicine, and human-machine interaction [1]. Long-term stability, and appropriate stretchability are the fundamental characteristics for flexible sensors to mimic human flexible perception.…”

Section: Introductionmentioning

confidence: 99%

Large-area flexible MWCNT/PDMS pressure sensor for ergonomic design with aid of deep learning

Zhong¹,

Fu²,

Chen³

et al. 2022

Nanotechnology

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The achievement of well-performing pressure sensors with low pressure detection, high sensitivity, large-scale integration, and effective analysis of the subsequent data remains a major challenge in the development of flexible piezoresistive sensors. In this study, a simple and extendable sensor preparation strategy was proposed to fabricate flexible sensors on the basis of multiwalled carbon nanotube/polydimethylsiloxane (MWCNT/PDMS) composites. A dispersant of tetrahydrofuran (THF) was added to solve the agglomeration of MWCNTs in PDMS, and the resistance of the obtained MWCNT/PDMS conductive unit with 7.5 wt.% MWCNTs were as low as 180 Ω/hemisphere. Sensitivity (0.004 kPa−1), excellent response stability, fast response time (36 ms), and excellent electromechanical properties were demonstrated within the pressure range from 0 kPa to 100 kPa. A large-area flexible sensor with 8×10 pixels was successfully adopted to detect the pressure distribution on the human back and to verify its applicability. Combining the sensor array with deep learning, inclination of human sitting was easily recognized with high accuracy, indicating that the combined technology can be used to guide ergonomic design.

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Recent advances in computational methods for biosensor design

Cited by 22 publications

References 171 publications

Unlocking the Potential of Field Effect Transistor (FET) Biosensors: A Perspective on Methodological Advances in Computational and Molecular Biology

Unlocking the Potential of Field Effect Transistor (FET) Biosensors: A Perspective on Methodological Advances in Computational and Molecular Biology

zPoseScore model for accurate and robust protein–ligand docking pose scoring in CASP15

Large-area flexible MWCNT/PDMS pressure sensor for ergonomic design with aid of deep learning

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