3 ms·

Artificial intelligence aids discovery of super tight-binding antibodies

1 points by mdp2021 4y ago

mdp2021 4y ago
I unfortunately currently suffer from a constraint hindering production of a full, good abridgement; in a stub: > The approach starts... with researchers generating an initial library of about half a million possible antibody sequences and screening them for their affinity to a specific protein target. But instead of repeating this process over and over again, they feed the dataset into a Bayesian neural network which can analyze the information and use it to predict the binding affinity of other sequences. // «With our machine learning tools, these subsequent rounds of sequence mutation and selection can be carried out quickly and efficiently on a computer rather than in the lab» Original research article: The RESP AI model accelerates the identification of tight-binding antibodies - https://www.nature.com/articles/s41467-023-36028-8 https://www.nature.com/articles/s41467-023-36028-8 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 KD of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development Description of the AI system: > Machine learning-based approaches face at least two major challenges. The first one is the lack of estimated uncertainty in the predictions [...] Gaussian process models have been suggested as an alternative that does provide well-calibrated confidence intervals, but they scale poorly to large datasets [...] // The second key challenge is the selection of an appropriate representation for the input. [...] // In this paper, we develop an easy to implement machine learning-assisted pipeline for the identification of high-affinity antibodies that addresses these challenges. We train an autoencoder model on over 3 million B-cell receptor sequences and show that this learned representation provides better results for a task of interest than state-of-the-art embedding schemes. Next, we develop a Bayesian neural network trained to perform ordinal regression to model the relationship between sequence and binding affinity or off-rate using the directed evolution data. Importantly, this model provides an estimate of the uncertainty in its predictions that cannot be achieved by the current deep learning methods. To benchmark this approach, we show it achieves competitive accuracy on a literature dataset, while providing useful uncertainty information not provided by the deep learning model in the original associated study. Finally, we perform in silico mutagenesis using a simulated annealing strategy to explore sequences not present in the mutation libraries and assess their binding affinities for experimental evaluation. Together, the autoencoder, the Bayesian network for ordinal regression and the search strategy form the key computational components of our RESP pipeline

1 points by mdp2021 4y ago

mdp2021 4y ago

I unfortunately currently suffer from a constraint hindering production of a full, good abridgement; in a stub: > The approach starts... with researchers generating an initial library of about half a million possible antibody sequences and screening them for their affinity to a specific protein target. But instead of repeating this process over and over again, they feed the dataset into a Bayesian neural network which can analyze the information and use it to predict the binding affinity of other sequences. // «With our machine learning tools, these subsequent rounds of sequence mutation and selection can be carried out quickly and efficiently on a computer rather than in the lab» Original research article: The RESP AI model accelerates the identification of tight-binding antibodies - https://www.nature.com/articles/s41467-023-36028-8 https://www.nature.com/articles/s41467-023-36028-8 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 KD of the PD-L1 antibody Atezolizumab and this success illustrates the potential of RESP in facilitating general antibody development Description of the AI system: > Machine learning-based approaches face at least two major challenges. The first one is the lack of estimated uncertainty in the predictions [...] Gaussian process models have been suggested as an alternative that does provide well-calibrated confidence intervals, but they scale poorly to large datasets [...] // The second key challenge is the selection of an appropriate representation for the input. [...] // In this paper, we develop an easy to implement machine learning-assisted pipeline for the identification of high-affinity antibodies that addresses these challenges. We train an autoencoder model on over 3 million B-cell receptor sequences and show that this learned representation provides better results for a task of interest than state-of-the-art embedding schemes. Next, we develop a Bayesian neural network trained to perform ordinal regression to model the relationship between sequence and binding affinity or off-rate using the directed evolution data. Importantly, this model provides an estimate of the uncertainty in its predictions that cannot be achieved by the current deep learning methods. To benchmark this approach, we show it achieves competitive accuracy on a literature dataset, while providing useful uncertainty information not provided by the deep learning model in the original associated study. Finally, we perform in silico mutagenesis using a simulated annealing strategy to explore sequences not present in the mutation libraries and assess their binding affinities for experimental evaluation. Together, the autoencoder, the Bayesian network for ordinal regression and the search strategy form the key computational components of our RESP pipeline