Atsushi Yoshimori

MIAX: A new paradigm for modeling biomacromolecular interactions and complex formation in condensed phases

A new paradigm is proposed for modeling biomacromolecular interactions and complex formation in solution (protein–protein interactions so far in this report) that constitutes the scaffold of the automatic system MIAX (acronym for Macromolecular Interaction Assessment X). It combines in a rational way a series of computational methodologies, the goal being the prediction of the most native‐like protein complex that may be formed when two isolated (unbound) protein monomers interact in a liquid environment. The overall strategy consists of first inferring putative precomplex structures by identification of binding sites or epitopes on the proteins surfaces and a simultaneous rigid‐body docking process using geometric instances alone. Precomplex configurations are defined here as all those decoys the interfaces of which comply substantially with the inferred binding sites and whose free energy values are lower. Retaining all those precomplex configurations with low energies leads to a reasonable number of decoys for which a flexible treatment is amenable. A novel algorithm is introduced here for automatically inferring binding sites in proteins given their 3‐D structure. The procedure combines an unsupervised learning algorithm based on the self‐organizing map or Kohonen network with a 2‐D Fourier spectral analysis. To model interaction, the potential function proposed here plays a central role in the system and is constituted by empirical terms expressing well‐characterized factors influencing biomacromolecular interaction processes, essentially electrostatic, van der Waals, and hydrophobic. Each of these procedures is validated by comparing results with observed instances. Finally, the more demanding process of flexible docking is performed in MIAX embedding the potential function in a simulated annealing optimization procedure. Whereas search of the entire configuration hyperspace is a major factor precluding hitherto systems from efficiently modeling macromolecular interaction modes and complex structures, the paradigm presented here may constitute a step forward in the field because it is shown that a rational treatment of the information available from the 3‐D structure of the interacting monomers combined with conveniently selected computational techniques can assist to elude search of regions of low probability in configuration space and indeed lead to a highly efficient system oriented to solve this intriguing and fundamental biologic problem. Proteins 2002;48:696–732.

A combined bioinformatic approach oriented to the analysis and design of peptides with high affinity to MHC class I molecules

We report on a new method to compute the antigenic degree of peptides from available experimental data on peptide binding affinity to class I MHC molecules. The methodology is a combination of two strategies at different levels of information. The first, at the primary structure level, consists in expressing the peptides binding activity as a profile of amino acid contributions, amino acid similarity being accounted for by their characteristic physicochemical properties and their position within the sequence. The higher level of the strategy is based on a meticulous analysis of the contact interface of the peptides with the cleft constituting the receptor region of a particular class I MHC molecule. Interaction interfaces are inferred by docking the peptide onto the receptor groove of the MHC molecule; evaluation of the affinity of the peptide to the receptor is then performed by analysis of the electrostatic and hydrophobic energies on points of the interaction interface. The result is a robust system for analysis of peptide affinity to class I MHC molecules since while the first analysis dictates the composition of active sequences at the amino acid level, the second translates this information to the atomic level, where the molecular interaction can be analyzed in terms of the intrinsic interatomic forces and energies. Evaluation results for the methodology are encouraging since high affinity peptides are reflected by high scores at both levels of information, and are proportionally lower for peptides of medium and lower affinity for which interaction surfaces show relatively lower electrostatic complementarity and hydrophobic correlation than for the former.