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

Research output: Contribution to journalArticlepeer-review

6 Citations (SciVal)


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 languageEnglish
Pages (from-to)577-590
Number of pages14
JournalJournal of Chemical Information and Modeling
Issue number3
Early online date20 Jan 2022
Publication statusPublished - 14 Feb 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 ( ), supported by EPSRC (grant no. EP/L000253/1).

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences


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