Antibody structure prediction using interpretable deep learning

Ruffolo, Jeffrey A.; Sulam, Jeremias; Gray, Jeffrey J.

doi:10.1016/j.patter.2021.100406

Cited by 149 publications

(149 citation statements)

References 65 publications

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“…DeepAb structural models were generated using the open-source version of the code (available at https://github.com/RosettaCommons/DeepAb ). As suggested in their paper ( Ruffolo et al , 2021 ), we generated five decoys per structure. This took around 10 min per antibody on an 8-core Intel i7-10700 CPU.…”

Section: Methodsmentioning

confidence: 99%

“…However, experimental structure determination is time-consuming and expensive so it is not always practical or even possible to use routinely. Computational modelling tools have allowed researchers to bridge this gap by predicting large numbers of antibody structures to a high level of accuracy ( Leem et al , 2016 ; Ruffolo et al , 2021 ). For example, models of antibody structures have recently been used for virtual screening ( Schneider et al , 2021 ) and to identify coronavirus-binding antibodies that bind the same epitope with very different sequences ( Robinson et al , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…DeepH3 ( Ruffolo et al , 2020 ), an antibody-specific version of TrRosetta. Recently, DeepAb ( Ruffolo et al , 2021 ), an improved version of DeepH3, was shown to outperform all currently available antibody structure prediction methods. DeepAb and DeepH3 are similar to TrRosetta and the original version of AlphaFold in that deep learning is used to obtain inter-residue geometries that are then fed into an energy minimization method to produce the final structure.…”

Section: Introductionmentioning

confidence: 99%

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ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

et al. 2022

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Motivation Antibodies are a key component of the immune system and have been extensively used as biotherapeutics. Accurate knowledge of their structure is central to understanding their antigen binding function. The key area for antigen binding and the main area of structural variation in antibodies is concentrated in the six complementarity determining regions (CDRs), with the most important for binding and most variable being the CDR-H3 loop. The sequence and structural variability of CDR-H3 make it particularly challenging to model. Recently deep learning methods have offered a step change in our ability to predict protein structures. Results In this work we present ABlooper, an end-to-end equivariant deep-learning based CDR loop structure prediction tool. ABlooper rapidly predicts the structure of CDR loops with high accuracy and provides a confidence estimate for each of its predictions. On the models of the Rosetta Antibody Benchmark, ABlooper makes predictions with an average CDR-H3 RMSD of 2.49 Å, which drops to 2.05 Å when considering only its 75% most confident predictions. Availability https://github.com/oxpig/ABlooper. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

et al. 2022

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“…Deep learning has been applied to predict a variety of antibody properties. 19 For example, DeepH3 20 and DeepAb 21 were developed to predict antibody structure. Deep learning was also implemented to predict antibody binders to a target antigen.…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning has been applied to predict a variety of antibody properties. 19 For example, DeepH3 20 and DeepAb 21 were developed to predict antibody structure.…”

Section: Introductionmentioning

confidence: 99%

DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

Lai

2022

Preprint

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Predicting high concentration antibody viscosity is essential for developing subcutaneous administration. Computer simulations provide promising tools to reach this aim. One such model is the spatial charge map (SCM) proposed by Agrawal and coworkers (mAbs. 2015, 8(1):43–48). SCM applies molecular dynamics simulations to calculate a score for the screening of antibody viscosity at high concentrations. However, molecular dynamics simulations are computationally costly and require structural information, a significant application bottleneck. In this work, high throughput computing was performed to calculate the SCM scores for 6596 nonredundant antibody variable regions. A convolutional neural network surrogate model, DeepSCM, requiring only sequence information, was then developed based on this dataset. The linear correlation coefficient of the DeepSCM and SCM scores achieved 0.9 on the test set (N=1320). The DeepSCM model was applied to screen the viscosity of 38 therapeutic antibodies that SCM correctly classified and resulted in only one misclassification. The DeepSCM model will facilitate high concentration antibody viscosity screening. The code and parameters are freely available at https://github.com/Lailabcode/DeepSCM.

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An outlook on structural biology after AlphaFold: tools, limits and perspectives

Rosignoli,

Pacelli,

Manganiello

et al. 2024

FEBS Open Bio

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AlphaFold and similar groundbreaking, AI‐based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab‐initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI‐based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI‐driven advancements. Indeed, user‐driven intervention is still as pivotal in the structure prediction process as in complementing state‐of‐the‐art algorithms with functional and biological knowledge.

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Antibody structure prediction using interpretable deep learning

Cited by 149 publications

References 65 publications

ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

DeepSCM: an efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

An outlook on structural biology after AlphaFold: tools, limits and perspectives

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