Table 2.
Key methods applied to pMHC binding affinity prediction
| Publication (Ref.) | Tool/Method | Validation (size) | Dataset Composition |
|---|---|---|---|
| Rognan et al., 1999 (75) | FRESNO scoring function | Correlation (84) | Class I: HLA-A*02:01, HLA-A*02:04, H-2K |
| Altuvia et al., 2004 (83) | PREDEP, residue-contact matrices | Classification and Correlation (>1000) | Class I: 2 HLA-A, 4 HLA-B, H-2D, H-2K, H-2L |
| Tong et al., 2006 (84) | calibrated scoring function | Classification (139) | Class II: HLA-DQ3.2β |
| Tong et al., 2006 (85) | calibrated scoring function | Classification and Correlation (84) | Class II: HLA-DRB1*0402, HLA-DQB1*0503 |
| Liao et al., 2011 (86) | Modified version of FRESNO scoring function | Correlation (>100) | Class I and II: HLA-A2, HLA-DR15, HLA-DR1, and HLA-DR4 |
| Knapp et al., 2011 (87) | PeptX, genetic algorithm | Classification (>1000)* | Class I: HLA-A*02:01 |
| Yanover et al., 2011 (88) | Rosetta | Classification (>1000)* | Class I: 7 HLA-A, 12 HLA-B |
| Atanasova et al., 2011 (89) | EpiDOCK, generated quantitative matrices | Classification (4540) | Class II: 12 HLA-DRB1 |
| Doytchinova et al., 2002 (90) | QSAR | Correlation (266) | Class I: HLA-A*02:01 |
| Doytchinova et al., 2004 (91) | QSAR | Correlation (90) | Class I: HLA-A*02:01 |
| Jojic et al., 2006 (92) | custom scoring function with calibrated weights | Classification and Correlation (>500) | Class I: 4 HLA-A, 5 HLA-B |
| Antes et al., 2006 (82) | DynaPred, SVM using quantitative matrices and MD-derived energy features | Classification (>1000) | Class I: HLA-A*02:01 |
| Bordner et al., 2006 (78) | SVM using scoring function terms | Classification (331) | Class I: HLA-A*02:01, H-2Kb |
| Tian et al., 2009 (93) | QSAR | Correlation (152) | Class I: HLA-A*02:01 |
| Bordner, 2010 (69) | random forest using scoring function terms | Classification (>1000) | Class II: various human and murine allotypes |
| Saethang et al., 2013 (94) | random forest using residue-residue contacts and topological descriptors | Classification (>1000) | Class I: HLA-A2 |
| Mukherjee et al., 2016 (95) | learning statistical pair potentials to use as features for Gaussian process regression | Classification and Correlation (>10000) | Any Class I with experimental binding affinity data |
| Davies et al., 2003 (96) | simulated annealing, AMBER force field | Classification (>10) | Class II: 4 HLA-DR1 |
| Zhang et al., 2010 (97) | position specific free energy contributions using MD and MM/PBSA | Correlation (3882) | Class II: HLA-DRB1*0101 |
| Polydorides et al., 2016 (98) | Proteus, computational suite for the optimization of protein and ligand conformations | Correlation (1)** | Class II: HLA-DQ8 |
| Wan et al., 2015 (99) | MD, MM/PB(GB)SA | Correlation (12) | Class I: HLA-A*02:01 |
| Knapp et al., 2016 (100) | hierarchical natural move Monte Carlo simulations | Correlation (32) | Class I: HLA-A*02:01 |
Correlation: study reports computing affinity values that can be directly compared with experiment. Classification: study reports affinity predictions for the purpose of classifying peptides as binders or non-binders given an appropriate threshold.
*
These studies searched for strong binding peptides, instead of producing a score related to affinity. See text.
**
Only reported an example use case using their tool