A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics

Rollins, Zachary A.; Huang, Jun; Tagkopoulos, Ilias; Faller, Roland; George, Steven C.

doi:10.1016/j.csbj.2022.06.048

Cited by 4 publications

(1 citation statement)

References 63 publications

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“…The descriptors were arithmetically averaged over the 3D conformational ensemble, and selected in the following sequential manner before training of the regressors: (i) ranked by random forest feature importance; (ii) recursive features selected, and (iii) exhaustive features selected from the top 10 ranked descriptors after recursion (i.e. 10 chose 1–10) ( Rollins et al 2022 ). Next, the features were trained on seven scikit-learn ( Pedregosa et al 2011 ) regressors using k-fold ( k = 10) cross-validation, hyperparameter searched (skopt), ranked by mean squared error, and refit ( Supplementary Table A2 ).…”

Section: Methodsmentioning

confidence: 99%

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions

Rollins,

Widatalla,

Waight

et al. 2024

Bioinformatics

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Motivation Pre-trained protein language and/or structural models are often fine-tuned on drug development properties (ie, developability properties) to accelerate drug discovery initiatives. However, these models generally rely on a single structural conformation and/or a single sequence as a molecular representation. We present a physics-based model whereby 3D conformational ensemble representations are fused by a transformer-based architecture and concatenated to a language representation to predict antibody protein properties. AbLEF enables the direct infusion of thermodynamic information into latent space and this enhances property prediction by explicitly infusing dynamic molecular behavior that occurs during experimental measurement. Results We showcase the AbLEF model on two developability properties: hydrophobic interaction chromatography retention time (HIC-RT) and temperature of aggregation (Tagg). We find that (1) 3D conformational ensembles that are generated from molecular simulation can further improve antibody property prediction for small datasets, (2) the performance benefit from 3D conformational ensembles matches shallow machine learning methods in the small data regime, and (3) fine-tuned large protein language models can match smaller antibody-specific language models at predicting antibody properties. Availability and implementation AbLEF codebase is available at https://github.com/merck/AbLEF. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions

Rollins,

Widatalla,

Waight

et al. 2024

Bioinformatics

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A Computational Strategy for the Rapid Identification and Ranking of Patient‐Specific T Cell Receptors Bound to Neoantigens

Rollins,

Curtis,

George

et al. 2024

Macromol. Rapid Commun.

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T cell receptor (TCR) recognition of a peptide–major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR‐pMHC protein pairs is a bottleneck in the implementation of TCR‐based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next‐generation sequencing, protein–protein structure prediction, molecular dynamics, and TCR ranking. A pipeline to evaluate patient‐specific, sequence‐based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein–protein structure of the TCRs to the target CEA peptide–MHC is predicted using Modeller and ColabFold. TCR‐pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× reduction in equilibration time of TCR‐pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (≈0.20 nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard‐Jones contacts between the TCR and pMHC. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state‐of‐the‐art machine learning‐based methods trained to predict the probability of TCR‐pMHC binding.

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A dynamic biomimetic model of the membrane-bound CD4-CD3-TCR complex during pMHC disengagement

Rollins

Faller

George

2023

Biophysical Journal

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A computational algorithm to assess the physiochemical determinants of T cell receptor dissociation kinetics

Cited by 4 publications

References 63 publications

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions

AbLEF: antibody language ensemble fusion for thermodynamically empowered property predictions

A Computational Strategy for the Rapid Identification and Ranking of Patient‐Specific T Cell Receptors Bound to Neoantigens

A dynamic biomimetic model of the membrane-bound CD4-CD3-TCR complex during pMHC disengagement

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