Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Tomaralimab (OPN-305) is the first humanized immunoglobulin G4 monoclonal antibody against TLR2 and is designed to prevent inflammation that is driven by inappropriate or excessive activation of innate immune pathways. Here, we constructed a homology model of Tomaralimab and its complex with TLR2 at different mapped epitopes and unraveled their behavior at the atomistic level. Furthermore, we predicted a novel epitope (leucine-rich region 9–12) near the lipopeptide-binding site that can be targeted and studied for the utility of therapeutic antibodies. A geometric deep learning algorithm was used to envisage Tomaralimab binding affinity changes upon mutation. There was a significant difference in binding affinity for Tomaralimab following epitope-mutated alanine substitutions of Val266, Pro294, Arg295, Asn319, Pro326, and His372. Using deep learning-based ΔΔG prediction, we computationally contrasted human TLR2–TLR2, TLR2–TLR1, and TLR2–TLR6 dimerization. These results reveal the mechanism that underlies Tomaralimab binding to TLR2 and should help to design structure-based mimics or bispecific antibodies that can be used to inhibit both lipopeptide-binding and TLR2 dimerization.

show abstract

“…With the deep learning algorithm, the changes in binding affinity upon changes in amino acids can be modeled quickly and accurately. 45 …”

Section: Resultsmentioning

confidence: 99%

“…An understanding of protein–protein binding affinity values is vital to understanding biological phenomena in a cell, such as how missense mutations alter the protein–protein binding. With the deep learning algorithm, the changes in binding affinity upon changes in amino acids can be modeled quickly and accurately …”

Section: Resultsmentioning

confidence: 99%

Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning

Ahmad

Choi

2022

ACS Omega

View full text Add to dashboard Cite

show abstract

“…The TopNetTree model outperformed TopGBT (topology-based GBT), TopCNN (topology-based CNN) models, and previously published methods on the AB-Bind dataset and SKEMPI database. A similar method, GeoPPI 175 consists of two components, a graph neural network trained on topology features from protein structure via self-supervised learning and a gradient-boosting tree (GBT) trained on learned features of both wild-type residue and its mutant to predict ΔΔG upon AA replacement.…”

Section: Learnability Of Antibody–antigen Bindingmentioning

confidence: 99%

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Akbar

Bashour

Rawat

et al. 2022

mAbs

View full text Add to dashboard Cite

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

show abstract

“…One of the driving force is the ever-increasing data accumulated in various PPI data sets, including ASEdb, PINT SKEMPI, SKEMPI 2.0, DACUM, AB-Bind, and PROXiMATE . Based on them, various learning models have been proposed, , such as mCSM, ELASPIC, BindProf, MutaBind, iSEE, MuPIPR, ProAffiMuSeq, GeoPPI, etc. These models have demonstrated great promise in the prediction of binding affinity change of PPIs upon mutations.…”

Section: Introductionmentioning

confidence: 99%

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein–Protein Binding Affinity Changes upon Mutation

Liu

Feng

Wu³

et al. 2022

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Protein–protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein–protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G 1, G) can be generated for the graph representation G of protein–protein complex by using different graphs G 1, which reveal G 1-related inner connections within the graph representation G of protein–protein complex. Further, for a specific graph G 1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.

show abstract

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity

Cited by 69 publications

References 65 publications

Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning

Unraveling the Tomaralimab Epitope on the Toll-like Receptor 2 via Molecular Dynamics and Deep Learning

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein–Protein Binding Affinity Changes upon Mutation

Contact Info

Product

Resources

About