A new procedure for optimizing the potential function for proteins is presented, and the folding of lattice-chain protein models was studied with optimized potentials. The pairwise contact energies between residues in the lattice protein models were optimized by an iterative algorithm that maximizes the free-energy difference between the (given) native structure and the non-native states of the protein. Optimization of the energy parameters for a variety of sequences of the model protein was carried out. The statistical-mechanical properties of the model proteins were analyzed with different sets of energy parameters to investigate the effects of optimization of the energy parameters on protein folding. For many sequences, optimization of the potential leads to strongly foldable protein models that can be folded to the target native structures in relatively short Monte Carlo simulations. It is found that consistency among the different components of the interactions in the native structure of a protein is a necessary condition for the existence of an exact and efficient folding potential. The results of this work reveal some crucial correlations between the sequence and the native structure of a protein, which determine the unique folding of the protein.
We propose three analytical models that describe the characteristics of proteins that can be folded into unique native structures. Model I is characterized by a mean-field single-residue energy which favors the native state and has a large energy gap between the native and non-native states; Model II involves mean-field cooperative interactions among the residues in the native states, and Model III is characterized by the mean-field single-residue energy at a low degree of folding and by the cooperative interactions among native residues at a high degree of folding. The thermodynamics of all three protein models exhibit two-state transition behavior, in which the non-native state is dominated by large entropy while the native state is determined by low energy. The folding kinetics of the models are studied by means of the master equation method. While the kinetics of folding of all three models are driven by the energetic biases of individual residues which favor the native state, the different interaction modes lead to different folding rates. It is found that the models with long-range cooperativity (i.e., Models II and III) fold several orders of magnitude faster than the model with only localized interactions (Model I). The intramolecular interactions that are responsible for the different properties of these models are examined, and the ways that these models may be used for developing the force fields for realistic proteins are discussed.
Chymase plays an important and diverse role in the homeostasis of a number of cardiovascular processes. Herein, we describe the identification of potent, selective chymase inhibitors, developed using fragment-based, structure-guided linking and optimization techniques. High-concentration biophysical screening methods followed by high-throughput crystallography identified an oxindole fragment bound to the S1 pocket of the protein exhibiting a novel interaction pattern hitherto not observed in chymase inhibitors. X-ray crystallographic structures were used to guide the elaboration/linking of the fragment, ultimately leading to a potent inhibitor that was >100-fold selective over cathepsin G and that mitigated a number of liabilities associated with poor physicochemical properties of the series it was derived from.
A statistical mechanical approach to the protein folding problem is developed based on computer simulations. The properties of proteins related to conformation and folding are determined from the density of states of the protein. A new simulation procedure, the Entropy Sampling Monte Carlo method, is used to determine accurately the density of states of the protein. To enhance the efficiency of sampling the conformational space of a protein, two techniques (a conformational-biased chain regrowth procedure and a jump-walking method) were introduced into the simulation. Applications of the approach to study a number of model polypeptides and a small protein, Bovine Pancreatic Trypsin Inhibitor, have been carried out. The results obtained demonstrate that the new approach is more powerful and produces richer information about the thermodynamics and folding behavior of proteins than conventional simulation methods.
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