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We propose a new algorithm for obtaining proton titration curves of ionizable residues. The algorithm is a pH replica-exchange method (PHREM), which is based on the constant pH algorithm of Mongan et al. (J Comput Chem 2004;25:2038-2048). In the original replica-exchange method, simulations of different replicas are performed at different temperatures, and the temperatures are exchanged between the replicas. In our PHREM, simulations of different replicas are performed at different pH values, and the pHs are exchanged between the replicas. The PHREM was applied to a blocked amino acid and to two protein systems (snake cardiotoxin and turkey ovomucoid third domain), in conjunction with a generalized Born implicit solvent. The performance and accuracy of this algorithm and the original constant pH method (PHMD) were compared. For a single set of simulations at different pHs, the use of PHREM yields more accurate Hill coefficients of titratable residues. By performing multiple sets of constant pH simulations started with different initial states, the accuracy of predicted pK(a) values and Hill coefficients obtained with PHREM and PHMD methods becomes comparable. However, the PHREM algorithm exhibits better samplings of the protonation states of titratable residues and less scatter of the titration points and thus better precision of measured pK(a) values and Hill coefficients. In addition, PHREM exhibits faster convergence of individual simulations than the original constant pH algorithm., (Copyright © 2011 Wiley-Liss, Inc.)

The ability to predict and characterize distributions of reactivities over families and even superfamilies of proteins opens the door to an array of analyses regarding functional evolution. In this article, insights into functional evolution in the Kazal inhibitor superfamily are gained by analyzing and comparing predicted association free energy distributions against six serine proteinases, over a number of groups of inhibitors: all possible Kazal inhibitors, natural avian ovomucoid first and third domains, and sets of Kazal inhibitors with statistically weighted combinations of residues. The results indicate that, despite the great hypervariability of residues in the 10 proteinase-binding positions, avian ovomucoid third domains evolved to inhibit enzymes similar to the six enzymes selected, whereas the orthologous first domains are not inhibitors of these enzymes on purpose. Hypervariability arises because of similarity in energetic contribution from multiple residue types; conservation is in terms of functionality, with "good" residues, which make positive or less deleterious contributions to the binding, selected more frequently, and yielding overall the same distributional characteristics. Further analysis of the distributions indicates that while nature did optimize inhibitor strength, the objective may not have been the strongest possible inhibitor against one enzyme but rather an inhibitor that is relatively strong against a number of enzymes., ((c) 2006 Wiley-Liss, Inc.)

A computational methodology for protein pK(a) predictions, based on ab initio quantum mechanical treatment of part of the protein and linear Poisson-Boltzmann equation treatment of the bulk solvent, is presented. The method is used to predict and interpret the pK(a) values of the five carboxyl residues (Asp7, Glu10, Glu19, Asp27, and Glu43) in the serine protease inhibitor turkey ovomucoid third domain. All the predicted pK(a) values are within 0.5 pH units of experiment, with a root-mean-square deviation of 0.31 pH units. We show that the decreased pK(a) values observed for some of the residues are primarily due to hydrogen bonds to the carboxyl oxygens. Hydrogen bonds involving amide protons are shown to be particularly important, and the effect of hydrogen bonding is shown to be nonadditive. Hydrophobic effects are also shown to be important in raising the pK(a). Interactions with charged residues are shown to have relatively little effect on the carboxyl pK(a) values in this protein, in general agreement with experiment., (Copyright 2004 Wiley-Liss, Inc.)

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., (Copyright 2002 Wiley-Liss, Inc.)

A new method for calculating compactness (Z) that uses look-up table-based algorithms for area and volume computations is introduced. These algorithms can be used in any iterative area or volume calculation, are more than 1000 times faster than the original algorithms, and have equal or better precision. With the faster algorithms it is now possible to calculate the compactness of all continuous units in a protein, and to precisely locate the optimal compact units without the screening functions and limited resolution used previously. These methods have been incorporated into a fully automatic domain finding algorithm, and this method has been applied to the 21 proteins originally analyzed as well as 12 additional proteins. This method is robust, and yields similar units even when applied to coordinates of protein crystals grown under different experimental conditions.

We present a method to search for possible binding modes of molecular fragments at a specific site of a potential drug target of known structure. Our method is based on a Monte Carlo (MC) algorithm applied to the translational and rotational degrees of freedom of the probe fragment. Starting from a randomly generated initial configuration, favorable binding modes are generated using a two-step process. An MC run is first performed in which the energy in the Metropolis algorithm is substituted by a score function that measures the average distance of the probe to the target surface. This has the effect of making buried probes move toward the target surface and also allows enhanced sampling of deep pockets. In a second MC run, a pairwise atom potential function is used, and the temperature parameter is slowly lowered during the run (Simulated Annealing). We repeat this procedure starting from a large number of different randomly generated initial configurations in order to find all energetically favorable docking modes in a specified region around the target. We test this method using two inhibitor-receptor systems: Streptomyces griseus proteinase B in complex with the third domain of the ovomucoid inhibitor from turkey, and dihydrofolate reductase from E. coli in complex with methotrexate. The method could consistently reproduce the complex found in the crystal structure searching from random initial positions in cubes ranging from 25 A to 50 A about the binding site. In the case of SGPB, we were also successful in docking to the native structure. In addition, we were successful in docking small probes in a search that included the entire protein surface.