Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms

Barozet, Amélie; Bianciotto, Marc; Siméon, Thierry; Minoux, Hervé; Cortés, Juan

doi:10.1016/j.imlet.2018.06.002

Cited by 8 publications

(5 citation statements)

References 28 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is known that the CDR3 loops of TCR undergo a conformational change upon pMHC binding 26 . Similar scenarios also occur in BCRs upon antigen binding 36 . The conformational changes of AIR structures can impact the AIR-antigen binding; however, such changes cannot be predicted by AlphaFold2.…”

Section: Discussionmentioning

confidence: 75%

“…We downloaded the raw single-cell V(D)J sequencing data, including RNA and TCR/BCR sequencing data for nasopharyngeal carcinoma 28 (SRP262300) and inflammatory bowel disease 36 (SRP181666) from the Sequence Read Archive (SRA) 37 (https://www.ncbi.nlm.nih.gov/sra). Then we used the Cell Ranger pipeline (v6.1.2, 10x Genomics, Pleasanton, CA) to analyze the single-cell sequencing data.…”

Section: Curation Of the Data For Classification Of The Immune Repert...mentioning

confidence: 99%

See 1 more Smart Citation

DeepAIR: a deep-learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Zhao

et al. 2022

Preprint

View full text Add to dashboard Cite

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens is one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep-learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR consists of three feature encoders (a trainable-embedding-layer-based gene encoder, a transformer-based sequence encoder, and a pre-trained AlphaFold2-based structure encoder), a gating-based attention mechanism to extract important features, and a tensor fusion mechanism to integrate obtained features. We train and evaluate DeepAIR on three downstream prediction tasks, including the prediction of AIR-antigen binding affinity, the prediction of AIR-antigen binding reactivity, and the classification of the immune repertoire. On five representative datasets, DeepAIR shows outstanding prediction performance in terms of AUC (area under the ROC curve) in predicting the binding reactivity to various antigens, as well as the classification of immune repertoire for nasopharyngeal carcinoma (NPC) and inflammatory bowel disease (IBD). DeepAIR is freely available for academic purposes at https://github.com/TencentAILabHealthcare/DeepAIR. We anticipate that DeepAIR can serve as a useful tool for characterizing and profiling antigen binding AIRs, thereby informing the design of personalized immunotherapy.

show abstract

Section: Discussionmentioning

confidence: 75%

Section: Curation Of the Data For Classification Of The Immune Repert...mentioning

confidence: 99%

DeepAIR: a deep-learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Zhao

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…5 c) by the antibody (green in Fig. 5 c) is close regulated by the hypervariable loops at the binding interface of the antibody [ 60 ], as highlighted by grey in the figure. The local conformational dynamics of the flexible loops at the binding interface can impede its association with the viral protein.…”

Section: Resultsmentioning

confidence: 99%

Classification of protein–protein association rates based on biophysical informatics

Dhusia

2021

BMC Bioinformatics

View full text Add to dashboard Cite

Background Proteins form various complexes to carry out their versatile functions in cells. The dynamic properties of protein complex formation are mainly characterized by the association rates which measures how fast these complexes can be formed. It was experimentally observed that the association rates span an extremely wide range with over ten orders of magnitudes. Identification of association rates within this spectrum for specific protein complexes is therefore essential for us to understand their functional roles. Results To tackle this problem, we integrate physics-based coarse-grained simulations into a neural-network-based classification model to estimate the range of association rates for protein complexes in a large-scale benchmark set. The cross-validation results show that, when an optimal threshold was selected, we can reach the best performance with specificity, precision, sensitivity and overall accuracy all higher than 70%. The quality of our cross-validation data has also been testified by further statistical analysis. Additionally, given an independent testing set, we can successfully predict the group of association rates for eight protein complexes out of ten. Finally, the analysis of failed cases suggests the future implementation of conformational dynamics into simulation can further improve model. Conclusions In summary, this study demonstrated that a new modeling framework that combines biophysical simulations with bioinformatics approaches is able to identify protein–protein interactions with low association rates from those with higher association rates. This method thereby can serve as a useful addition to a collection of existing experimental approaches that measure biomolecular recognition.

show abstract

“…However, it is known that the CDR3 loops of TCR undergo a conformational change upon pMHC binding (5). Similar scenarios also occur in BCRs upon antigen binding (49). The conformational changes of AIR structures can affect the AIR-antigen binding; however, these changes cannot be predicted by AlphaFold2.…”

Section: Discussionmentioning

confidence: 99%

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Zhao,

He,

et al. 2023

Sci. Adv.

View full text Add to dashboard Cite

Structural docking between the adaptive immune receptors (AIRs), including T cell receptors (TCRs) and B cell receptors (BCRs), and their cognate antigens are one of the most fundamental processes in adaptive immunity. However, current methods for predicting AIR-antigen binding largely rely on sequence-derived features of AIRs, omitting the structure features that are essential for binding affinity. In this study, we present a deep learning framework, termed DeepAIR, for the accurate prediction of AIR-antigen binding by integrating both sequence and structure features of AIRs. DeepAIR achieves a Pearson’s correlation of 0.813 in predicting the binding affinity of TCR, and a median area under the receiver-operating characteristic curve (AUC) of 0.904 and 0.942 in predicting the binding reactivity of TCR and BCR, respectively. Meanwhile, using TCR and BCR repertoire, DeepAIR correctly identifies every patient with nasopharyngeal carcinoma and inflammatory bowel disease in test data. Thus, DeepAIR improves the AIR-antigen binding prediction that facilitates the study of adaptive immunity.

show abstract

Conformational changes in antibody Fab fragments upon binding and their consequences on the performance of docking algorithms

Abstract: This study is aimed to help scientists working on antibody analysis and design by giving insights into the nature and the extent of conformational changes at different levels upon antigen binding.

Cited by 8 publications

References 28 publications

DeepAIR: a deep-learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

DeepAIR: a deep-learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Classification of protein–protein association rates based on biophysical informatics

DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis

Contact Info

Product

Resources

About