On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations
The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Antibodies |
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| Online Access: | https://www.mdpi.com/2073-4468/11/3/51 |
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| author | Simone Conti Edmond Y. Lau Victor Ovchinnikov |
| author_facet | Simone Conti Edmond Y. Lau Victor Ovchinnikov |
| author_sort | Simone Conti |
| collection | DOAJ |
| description | The accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process. |
| first_indexed | 2024-03-10T00:54:23Z |
| format | Article |
| id | doaj.art-fc7fe7d53b6b4756b75a28e8208902e6 |
| institution | Directory Open Access Journal |
| issn | 2073-4468 |
| language | English |
| last_indexed | 2024-03-10T00:54:23Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Antibodies |
| spelling | doaj.art-fc7fe7d53b6b4756b75a28e8208902e62023-11-23T14:46:10ZengMDPI AGAntibodies2073-44682022-08-011135110.3390/antib11030051On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation SimulationsSimone Conti0Edmond Y. Lau1Victor Ovchinnikov2Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USAPhysical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94551, USADepartment of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USAThe accurate and efficient calculation of protein-protein binding affinities is an essential component in antibody and antigen design and optimization, and in computer modeling of antibody affinity maturation. Such calculations remain challenging despite advances in computer hardware and algorithms, primarily because proteins are flexible molecules, and thus, require explicit or implicit incorporation of multiple conformational states into the computational procedure. The astronomical size of the amino acid sequence space further compounds the challenge by requiring predictions to be computed within a short time so that many sequence variants can be tested. In this study, we compare three classes of methods for antibody/antigen (Ab/Ag) binding affinity calculations: (i) a method that relies on the physical separation of the Ab/Ag complex in equilibrium molecular dynamics (MD) simulations, (ii) a collection of 18 scoring functions that act on an ensemble of structures created using homology modeling software, and (iii) methods based on the molecular mechanics-generalized Born surface area (MM-GBSA) energy decomposition, in which the individual contributions of the energy terms are scaled to optimize agreement with the experiment. When applied to a set of 49 antibody mutations in two Ab/HIV gp120 complexes, all of the methods are found to have modest accuracy, with the highest Pearson correlations reaching about 0.6. In particular, the most computationally intensive method, i.e., MD simulation, did not outperform several scoring functions. The optimized energy decomposition methods provided marginally higher accuracy, but at the expense of requiring experimental data for parametrization. Within each method class, we examined the effect of the number of independent computational replicates, i.e., modeled structures or reinitialized MD simulations, on the prediction accuracy. We suggest using about ten modeled structures for scoring methods, and about five simulation replicates for MD simulations as a rule of thumb for obtaining reasonable convergence. We anticipate that our study will be a useful resource for practitioners working to incorporate binding affinity calculations within their protein design and optimization process.https://www.mdpi.com/2073-4468/11/3/51molecular dynamics simulationfree energyprotein-protein interactionscoring functionprotein-protein docking |
| spellingShingle | Simone Conti Edmond Y. Lau Victor Ovchinnikov On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations Antibodies molecular dynamics simulation free energy protein-protein interaction scoring function protein-protein docking |
| title | On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations |
| title_full | On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations |
| title_fullStr | On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations |
| title_full_unstemmed | On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations |
| title_short | On the Rapid Calculation of Binding Affinities for Antigen and Antibody Design and Affinity Maturation Simulations |
| title_sort | on the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations |
| topic | molecular dynamics simulation free energy protein-protein interaction scoring function protein-protein docking |
| url | https://www.mdpi.com/2073-4468/11/3/51 |
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