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.

No abstract

No abstract

We present an efficient new algorithm that enumerates all possible conformations of a protein that satisfy a given set of distance restraints. Rapid growth of all possible self-avoiding conformations on the diamond lattice provides construction of a-carbon representations of a protein fold. We investigated the dependence of the number of conformations on pairwise distance restraints for the proteins crambin, pancreatic trypsin inhibitor, and ubiquitin. Knowledge of between one and two contacts per monomer is shown to be sufficient to restrict the number of candidate structures to approximately 1 , OOO conformations. Pairwise RMS deviations of atomic position comparisons between pairs of these 1,000 structures revealed that these conformations can be grouped into about 25 families of structures. These results suggest a new approach to assessing alternative protein folds given a very limited number of distance restraints. Such restraints are available from several experimental techniques such as NMR, NOESY, energy transfer fluorescence spectroscopy, and crosslinking experiments. This work focuses on exhaustive enumeration of protein structures with emphasis on the possible use of NOESY-determined distance restraints.Keywords: lattice models of proteins; NMR restraints; protein folding; protein structure prediction Determining the solution structure of proteins from highresolution NMR data by restrained molecular dynamical simulated annealing (MDSA) uses distance information determined from NOE data (Briinger et al

No abstract