In this paper, we present SMoG (Small Molecule Growth), a novel, straightforward method for de novo lead design and the evidence for its effectiveness. It is based on a simple model for ligand-protein interactions and a scoring that is directly related to the free energy through a knowledge-based potential. A large number of structures are examined by an efficient metropolis Monte Carlo molecular growth algorithm that generates molecules through the adjoining of functional groups directly in the binding region. Thus SMoG is a method that is able to rank a large number of potential compounds according to binding free energy in a short time. In this sense, SMoG represents a step toward an ideal computational tool for ligand design.

Pairwise contact energies do not explicitly take protein secondary structure into account, and so provide an incomplete description of conformational energy. In order to construct a Hamiltonian that specifically relates to protein backbone conformations, a simplified backbone angle is used. The pseudodihedral angle (the torsion angle between planes defined by 4 consecutive a-carbon atoms) provides a simplified backbone representation and continues to manifest information about secondary-structure elements: the pseudo-Ramachandran plot contains helical and sheetlike regions. The distribution of pseudodihedral angles is highly sensitive to the identity of the central pair of amino acids. Therefore, a sequence-dependent, knowledge-based potential energy was found according to a quasichemical approximation. These functions form complementary additions to the contact potentials currently in use. This pseudodihedral potential greatly enhances the ability to design sequences that are specific to a given conformation and also improves the ability to discriminate a native conformation from many other conformations.

This paper describes the derivation of a Knowledge-Based Potential for intermolecular interactions from the statistical information stored in the Cambridge Structural Database. We develop a statistical mechanical method that relates the occurrences of intermolecular contacts in the database to their energies. Our approach allows us to quantify (in the form of energy) the geometrical preferences of interactions. We use our method to construct energy maps for a hydrogen bond between carbonyl oxygen and amino hydrogen. Our results demonstrate high orientational selectivity of this type of hydrogen bonding.

In this paper, we summarize three ligand design studies performed using the program SMoG, which was developed in our lab. The aim of this presentation is to communicate through examples the potential of this method:  the richness of the molecules that can be developed and the ease with which they are found. In particular, we present suggestions for ligands to Src SH3 domain (specificity pocket and LP site) and CD4.

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