Dataset Information

The RESP AI model accelerates the identification of tight-binding antibodies.

ABSTRACT: High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representation trained on over 3 million human B-cell receptor sequences to encode antibody sequences. We then develop a variational Bayesian neural network to perform ordinal regression on a set of the directed evolution sequences binned by off-rate and quantify their likelihood to be tight binders against an antigen. Importantly, this model can assess sequences not present in the directed evolution library and thus greatly expand the search space to uncover the best sequences for experimental evaluation. We demonstrate the power of this pipeline by achieving a 17-fold improvement in the K_D of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development.

SUBMITTER: Parkinson J

PROVIDER: S-EPMC9884274 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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The RESP AI model accelerates the identification of tight-binding antibodies.

Parkinson Jonathan J Hard Ryan R Wang Wei W

Nature communications 20230128 1

High-affinity antibodies are often identified through directed evolution, which may require many iterations of mutagenesis and selection to find an optimal candidate. Deep learning techniques hold the potential to accelerate this process but the existing methods cannot provide the confidence interval or uncertainty needed to assess the reliability of the predictions. Here we present a pipeline called RESP for efficient identification of high affinity antibodies. We develop a learned representati ...[more]

PMID: 36709319

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Project description:BackgroundBronchoscopy is a key step in the diagnosis and treatment of respiratory diseases. However, the level of expertise varies among different bronchoscopists. Artificial intelligence (AI) may help them identify bronchial lumens. Thus, a bronchoscopy quality-control system based on AI was built to improve the performance of bronchoscopists.MethodsThis single-center observational study consecutively collected bronchoscopy videos from Shanghai Chest Hospital and segmented each video into 31 different anatomical locations to develop an AI-assisted system based on a convolutional neural network (CNN) model. We then designed a single-center trial to compare the accuracy of lumen recognition by bronchoscopists with and without the assistance of the AI system.ResultsA total of 28,441 qualified images of bronchial lumen were used to train the CNNs. In the cross-validation set, the optimal accuracy of the six models was between 91.83% and 96.62%. In the test set, the visual geometry group 16 (VGG-16) achieved optimal performance with an accuracy of 91.88%, and an area under the curve of 0.995. In the clinical evaluation, the accuracy rate of the AI system alone was 54.30% (202/372). For the identification of bronchi except for segmental bronchi, the accuracy was 82.69% (129/156). In group 1, the recognition accuracy rates of doctors A, B, a and b alone were 42.47%, 34.68%, 28.76%, and 29.57%, respectively, but increased to 57.53%, 54.57%, 54.57%, and 46.24% respectively when combined with the AI system. Similarly, in group 2, the recognition accuracy rates of doctors C, D, c, and d were 37.90%, 41.40%, 30.91%, and 33.60% respectively, but increased to 51.61%, 47.85%, 53.49%, and 54.30% respectively, when combined with the AI system. Except for doctor D, the accuracy of doctors in recognizing lumen was significantly higher with AI assistance than without AI assistance, regardless of their experience (P<0.001).ConclusionsOur AI system could better recognize bronchial lumen and reduce differences in the operation levels of different bronchoscopists. It could be used to improve the quality of everyday bronchoscopies.

Dataset Information

The RESP AI model accelerates the identification of tight-binding antibodies.

Publications

The RESP AI model accelerates the identification of tight-binding antibodies.

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