5CINECA Supercomputing Centre. 6University of Modena and Reggio Emilia.7Christian-Albrechts-University Kiel.8Michigan State University. International course and report were conceived by Pietro Cozzini and Glen E. Kellogg. * To whom correspondence should be addressed. For G.E.K.: Department of Medicinal Chemistry, Virginia Commonwealth University, Box 980540, Richmond, VA 23298-0540; (phone) 804-828-6452; (fax) 804-827-3664; (e-mail) glen.kellogg@vcu.edu. For P.C.: Department of General and Inorganic Chemistry, University of Parma, Via G.P. Usberti 17/A 43100, Parma, Italy; (phone) +39-0521-905669; (fax) +39-0521-905556; (e-mail) pietro.cozzini@unipr.it. NIH Public Access IntroductionStructure-based drug discovery has played an important role in medicinal chemistry 1 beginning nearly when the first X-ray crystal structure of the myoglobin and hemoglobin proteins at nearatomic resolution were described by Perutz, Kendrew and colleagues. 2-5 Even though only static structures were (and still generally are) used for most Structure-Based Drug Design (SBDD), and indeed most molecular modeling, the importance of flexibility was recognized immediately: hemoglobin has two rather different structures, "tense" and "relaxed", depending on its oxygenation, although in recent years a family of relaxed hemoglobin structures with different tertiary structure conformations have been reported. 6 In fact, all proteins are inherently flexible systems. This flexibility is frequently essential for function (e.g., as in hemoglobin). Proteins have an intrinsic ability to undergo functionally relevant conformational transitions under native state conditions, 7,8 on a wide range of scales, both in time and space. 9 In adenylate kinase large conformational changes due to movements of the nucleotide 'lids'-rate-limiting for overall catalytic turnover 10,11 -are 'linked' with relatively small-amplitude atomic fluctuations on the ps timescale such that changes in the local backbone conformation are required for lid closure. 12 Nuclear receptors are modular proteins where a significant degree of conformational flexibility is essential to biological function. Most of the pharmacology of nuclear receptor ligands has been discussed on the basis of their ability to stabilize (or displace) a short α-helix segment (known as H12 or AF-2) localized at the carboxy terminus of the receptor in (or from) its conformation in the protein "active" form. 13-15 Available X-ray crystal structures show a surprisingly wide range of structural diversity in ligands binding to, and inhibiting, nuclear receptor proteins such as the farnesoid X-receptor (FXR). 16,17 Protein dynamics is also a key component of intramolecular and intermolecular communication/signaling mechanisms and an essential requirement for the function of Gprotein coupled receptors (GPCRs), which are the largest known superfamily of membrane proteins. GPCRs regulate cell activity by transmitting extracellular signals to the inside of cells and respond to these signals by catalyzing nucleotide e...

The prediction of the binding affinity between a protein and ligands is one of the most challenging issues for computational biochemistry and drug discovery. While the enthalpic contribution to binding is routinely available with molecular mechanics methods, the entropic contribution is more difficult to estimate. We describe and apply a relatively simple and intuitive calculation procedure for estimating the free energy of binding for 53 protein-ligand complexes formed by 17 proteins of known three-dimensional structure and characterized by different active site polarity. HINT, a software model based on experimental LogP(o/w) values for small organic molecules, was used to evaluate and score all atom-atom hydropathic interactions between the protein and the ligands. These total scores (H(TOTAL)), which have been previously shown to correlate with DeltaG(interaction) for protein-protein interactions, correlate with DeltaG(binding) for protein-ligand complexes in the present study with a standard error of +/-2.6 kcal mol(-1) from the equation DeltaG(binding) = -0.001 95 H(TOTAL) - 5.543. A more sophisticated model, utilizing categorized (by interaction class) HINT scores, produces a superior standard error of +/-1.8 kcal mol(-1). It is shown that within families of ligands for the same protein binding site, better models can be obtained with standard errors approaching +/-1.0 kcal mol(-1). Standardized methods for preparing crystallographic models for hydropathic analysis are also described. Particular attention is paid to the relationship between the ionization state of the ligands and the pH conditions under which the binding measurements are made. Sources and potential remedies of experimental and modeling errors affecting prediction of DeltaG(binding) are discussed.

Structural water molecules within protein active sites are relevant for ligand-protein recognition because they modify the active site geometry and contribute to binding affinity. In this work an analysis of the interactions between 23 ligands and dimeric HIV-1 protease is reported. The X-ray structures of these complexes show the presence of four types of structural water molecules: water 301 (on the symmetry axis), water 313, water 313bis, and peripheral waters. Except for water 301, these are generally complemented with a symmetry-related set. The GRID program was used both for checking water locations and for placing water molecules that appear to be missing from the complexes due to crystallographic uncertainty. Hydropathic analysis of the energetic contributions using HINT indicates a significant improvement of the correlation between HINT scores and the experimentally determined binding constants when the appropriate bridging water molecules are taken into account. In the absence of water r 2) 0.30 with a standard error of (1.30 kcal mol-1 and when the energetic contributions of the constrained waters are included r 2) 0.61 with a standard error of (0.98 kcal mol-1. HINT was shown to be able to map quantitatively the contribution of individual structural waters to binding energy. The order of relevance for the various types of water is water 301 > water 313 > water 313bis > peripheral waters. Thus, to obtain the most reliable free energy predictions, the contributions of structural water molecules should be included. However, care must be taken to include the effects of water molecules that add information value and not just noise.