Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA

Rory M Crean; Christopher R Pudney; David K Cole; Marc W Van der Kamp

doi:10.1021/acs.jcim.1c00765

Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA

Rory M Crean^*, Christopher R Pudney, David K Cole, Marc W Van der Kamp^*

^*Corresponding author for this work

School of Biochemistry

Research output: Contribution to journal › Article (Academic Journal) › peer-review

15 Citations (Scopus)

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Abstract

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

Original language	English
Pages (from-to)	577–590
Number of pages	14
Journal	Journal of Chemical Information and Modeling
Volume	62
Issue number	3
DOIs	https://doi.org/10.1021/acs.jcim.1c00765
Publication status	Published - 20 Jan 2022

Bibliographical note

Funding Information:
R.M.C.’s Ph.D. studentship was funded by a Engineering and Physical Sciences Research Council (EPSRC) Training Grant (EP/L016354/1). M.W.v.d.K. thanks BBSRC for funding (BBSRC David Phillips Fellowship, BB/M026280/1). This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath, as well as the computational facilities of the Advanced Computing Research Centre of the University of Bristol. Further, this project used computing time on ARCHER, granted via the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim ( http://hecbiosim.ac.uk ), supported by EPSRC (grant no. EP/L000253/1).

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© 2022 American Chemical Society. All rights reserved.

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title = "Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA",

abstract = "Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.",

author = "Crean, {Rory M} and Pudney, {Christopher R} and Cole, {David K} and {Van der Kamp}, {Marc W}",

note = "Funding Information: R.M.C.{\textquoteright}s Ph.D. studentship was funded by a Engineering and Physical Sciences Research Council (EPSRC) Training Grant (EP/L016354/1). M.W.v.d.K. thanks BBSRC for funding (BBSRC David Phillips Fellowship, BB/M026280/1). This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath, as well as the computational facilities of the Advanced Computing Research Centre of the University of Bristol. Further, this project used computing time on ARCHER, granted via the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim ( http://hecbiosim.ac.uk ), supported by EPSRC (grant no. EP/L000253/1). Publisher Copyright: {\textcopyright} 2022 American Chemical Society. All rights reserved.",

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N1 - Funding Information: R.M.C.’s Ph.D. studentship was funded by a Engineering and Physical Sciences Research Council (EPSRC) Training Grant (EP/L016354/1). M.W.v.d.K. thanks BBSRC for funding (BBSRC David Phillips Fellowship, BB/M026280/1). This research made use of the Balena High Performance Computing (HPC) Service at the University of Bath, as well as the computational facilities of the Advanced Computing Research Centre of the University of Bristol. Further, this project used computing time on ARCHER, granted via the UK High-End Computing Consortium for Biomolecular Simulation, HECBioSim ( http://hecbiosim.ac.uk ), supported by EPSRC (grant no. EP/L000253/1). Publisher Copyright: © 2022 American Chemical Society. All rights reserved.

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N2 - Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

AB - Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

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M3 - Article (Academic Journal)

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Reliable in silico ranking of engineered therapeutic TCR binding affinities with MMPB/GBSA

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