A novel method for predicting the binding sites for druglike compounds on the surface of proteins was developed on the basis of the specific amino acid composition observed at the ligand-binding sites of ligand-protein complexes determined by X-ray analysis. A profile representing the preference of each of the 20 standard amino acids at the binding sites of druglike molecules was obtained for a small set of high-quality complex structures. An index termed propensity for ligand binding (PLB) was created from these profiles. The PLB index was used to predict the propensity of binding for 804 ligands at all potential binding sites on the proteins whose structures were determined by X-ray analysis. If the sites with the first two highest PLB indices are taken into consideration, the successfully predicted sites reached a high percentage of 86. The PLB prediction is relatively simple, but the validation study showed that it is both fast and accurate to detect ligand-binding sites, especially the binding sites of druglike molecules. Therefore, the PLB index can be used to predict the ligand-binding sites of uncharacterized protein structures and also to identify novel drug-binding sites of known drug targets.

The optimization of antibodies is a desirable goal towards the development of better therapeutic strategies. The antibody 11K2 was previously developed as a therapeutic tool for inflammatory diseases, and displays very high affinity (4.6 pM) for its antigen the chemokine MCP-1 (monocyte chemo-attractant protein-1). We have employed a virtual library of mutations of 11K2 to identify antibody variants of potentially higher affinity, and to establish benchmarks in the engineering of a mature therapeutic antibody. The most promising candidates identified in the virtual screening were examined by surface plasmon resonance to validate the computational predictions, and to characterize their binding affinity and key thermodynamic properties in detail. Only mutations in the light-chain of the antibody are effective at enhancing its affinity for the antigen in vitro, suggesting that the interaction surface of the heavy-chain (dominated by the hot-spot residue Phe101) is not amenable to optimization. The single-mutation with the highest affinity is L-N31R (4.6-fold higher affinity than wild-type antibody). Importantly, all the single-mutations showing increase affinity incorporate a charged residue (Arg, Asp, or Glu). The characterization of the relevant thermodynamic parameters clarifies the energetic mechanism. Essentially, the formation of new electrostatic interactions early in the binding reaction coordinate (transition state or earlier) benefits the durability of the antibody-antigen complex. The combination of in silico calculations and thermodynamic analysis is an effective strategy to improve the affinity of a matured therapeutic antibody.

No abstract

We identified specific amino acid propensities at the interfaces of antigen-antibody interactions in non-redundant qualified antigen-antibody complex structures from Protein Data Bank. Propensities were expressed by the frequency of each of the 20 x 20 standard amino acid pairs that appeared at the interfaces of the complexes and were named the antibody-specific epitope propensity (ASEP) index. Using this index, we developed a novel method of predicting epitope residues for individual antibodies by narrowing down candidate epitope residues which was predicted by the conventional method. The 74 benchmarked antigens were used in ASEP prediction. The efficiency of this method was assessed using the leave-one-out approach. On elimination of residues with ASEP indices in the lowest 10% of all measured, true positives were enriched for 49 antigens. On subsequent elimination of residues with ASEP indices in the lowest 50%, true positives were enriched for 40 of the 74 antigens assessed. The ASEP index is the first benchmark proposed to predict epitope residues for an individual antibody. Used in combination with mutation experiments, this index has the potential to markedly increase the success ratio of epitope analysis.