tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs

Wu, Jin V.; Wu, Fandi; Jiang, Biaobin; Liu, Wei; Zhao, Peilin

doi:10.1101/2022.11.10.515918

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…Given the high degree of similarity in the H chains among 2C1, 2C5, and 2D5, we employed tFold-Ab to generate models for these Fabs. 30 Despite the less conserved CDR sequences in the L chain compared to the H chain, they demonstrated similar hydrophobic surfaces ( Figures S5 A and S5B). An analysis of the density map highlighted that F198 inserts into the hydrophobic pocket of 2C1, which is constituted by conserved residues including W47 H , I51 H , I52 H , K63 H , and P95 L .…”

Section: Resultsmentioning

confidence: 97%

Potent human monoclonal antibodies targeting Epstein-Barr virus gp42 reveal vulnerable sites for virus infection

Zhao,

Fang,

et al. 2024

Cell Reports Medicine

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Section: Resultsmentioning

confidence: 97%

Potent human monoclonal antibodies targeting Epstein-Barr virus gp42 reveal vulnerable sites for virus infection

Zhao,

Fang,

et al. 2024

Cell Reports Medicine

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“…In our initial version [89], tFold-Ab employed a two-stage architecture. It utilized a language model ( e .…”

Section: Fig A1mentioning

confidence: 99%

Fast and accurate modeling and design of antibody-antigen complex using tFold

Wu,

Zhao,

et al. 2024

Preprint

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Accurate prediction of antibody-antigen complex structures holds significant potential for advancing biomedical research and the design of therapeutic antibodies. Currently, structure prediction for protein monomers has achieved considerable success, and promising progress has been made in extending this achievement to the prediction of protein complexes. However, despite these advancements, fast and accurate prediction of antibody-antigen complex structures remains a challenging and unresolved issue. Existing end-to-end prediction methods, which rely on homology and templates, exhibit sub-optimal accuracy due to the absence of co-evolutionary constraints. Meanwhile, conventional docking-based methods face difficulties in identifying the contact interface between the antigen and antibody and require known structures of individual components as inputs. In this study, we present a fully end-to-end approach for three-dimensional (3D) atomic-level structure predictions of antibodies and antibody-antigen complexes, referred to as tFold-Ab and tFold-Ag, respectively. tFold leverages a large protein language model to extract both intra-chain and inter-chain residue-residue contact information, as well as evolutionary relationships, avoiding the time-consuming multiple sequence alignment (MSA) search. Combined with specially designed modules such as the AI-driven flexible docking module, it achieves superior performance and significantly enhanced speed in predicting both antibody (1.6\% RMSD reduction in the CDR-H3 region, thousand times faster) and antibody-antigen complex structures (37\% increase in DockQ score, over 10 times faster), compared to AlphaFold-Multimer. Given the performance and speed advantages, we further extend the capability of tFold for structure-based virtual screening of binding antibodies, as well as de novo co-design of both structure and sequence for therapeutic antibodies. The experiment results demonstrate the potential of tFold as a high-throughput tool to enhance processes involved in these tasks.To facilitate public access, we release code and offer a web service for antibody and antigen-antibody complex structure prediction, which is available at \url{https://drug.ai.tencent.com/en}.

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“…Therefore, we checked to what extent different networks can produce physically plausible out-of-distribution sequences. For this purpose, we randomly generated ten CDR-H3s, between lengths [10][11][12][13][14][15][16][17][18][19] for each of the sequences in the Rosetta test set. This was supposed to be a the most extreme case of introducing the diversity, as opposed to following the natural substitution profiles 32 or sampling novel sequences from the OAS 22,23 .…”

Section: Heavy-onlymentioning

confidence: 99%

“…To address this issue, but also to draw from AlphaFold2 and its derivatives (Ahdritz et al 2022; Lin et al 2022), many antibody-specific deep learning structure prediction methods have been introduced (Wilman et al 2022; Lin et al 2022). The algorithms started by addressing CDR-H3 loop prediction such as DeepH3 (Ruffolo et al 2020) and AbLooper (Abanades, Georges, et al 2022), after extending these to the entire variable domain via NanoNet (Cohen, Halfon, and Schneidman-Duhovny 2022) and the entire Fv molecule by DeepAb (Ruffolo, Sulam, and Gray 2022), IgFold (Ruffolo et al 2022), AbodyBuilder2 (Abanades, Wong, et al 2022), EquiFold (Lee et al 2022) and tFold-Ab (Wu et al 2022). As opposed to the homology methods that reported CDR-H3 root mean squared deviation (RMSD) accuracies in the region of 3-4Å (Almagro et al 2014), the deep learning methods achieve an RMSD of 2-3Å RMSD.…”

Section: Introductionmentioning

confidence: 99%

Structural pre-training improves physical accuracy of antibody structure prediction using deep learning

Kończak¹,

Janusz²,

Młokosiewicz³

et al. 2022

Preprint

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Protein folding problem obtained a practical solution recently, owing to advances in deep learning. There are classes of proteins though, such as antibodies, that are structurally unique, where the general solution still lacks. In particular, the prediction of the CDR-H3 loop, which is an instrumental part of an antibody in its antigen recognition abilities, remains a challenge. Antibody-specific deep learning frameworks were proposed to tackle this problem noting great progress, both on accuracy and speed fronts. Oftentimes though, the original networks produce physically implausible bond geometries that then need to undergo a time-consuming energy minimization process. Here we hypothesized that pre-training the network on a large, augmented set of models with correct physical geometries, rather than a small set of real antibody X-ray structures, would allow the network to learn better bond geometries. We show that fine-tuning such a pre-trained network on a task of shape prediction on real X-ray structures improves the number of correct peptide bond distances. We further demonstrate that pre-training allows the network to produce physically plausible shapes on an artificial set of CDR-H3s, showing the ability to generalize to the vast antibody sequence space. We hope that our strategy will benefit the development of deep learning antibody models that rapidly generate physically plausible geometries, without the burden of time-consuming energy minimization.

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tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs

Cited by 9 publications

References 51 publications

Potent human monoclonal antibodies targeting Epstein-Barr virus gp42 reveal vulnerable sites for virus infection

Potent human monoclonal antibodies targeting Epstein-Barr virus gp42 reveal vulnerable sites for virus infection

Fast and accurate modeling and design of antibody-antigen complex using tFold

Structural pre-training improves physical accuracy of antibody structure prediction using deep learning

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