The understanding and optimization of protein-ligand interactions are instrumental to medicinal chemists investigating potential drug candidates. Over the past couple of decades, many powerful standalone tools for computer-aided drug discovery have been developed in academia providing insight into protein-ligand interactions. As programs are developed by various research groups, a consistent user-friendly graphical working environment combining computational techniques such as docking, scoring, molecular dynamics simulations, and free energy calculations is needed. Utilizing PyMOL we have developed such a graphical user interface incorporating individual academic packages designed for protein preparation (AMBER package and Reduce), molecular mechanics applications (AMBER package), and docking and scoring (AutoDock Vina and SLIDE). In addition to amassing several computational tools under one interface, the computational platform also provides a user-friendly combination of different programs. For example, utilizing a molecular dynamics (MD) simulation performed with AMBER as input for ensemble docking with AutoDock Vina. The overarching goal of this work was to provide a computational platform that facilitates medicinal chemists, many who are not experts in computational methodologies, to utilize several common computational techniques germane to drug discovery. Furthermore, our software is open source and is aimed to initiate collaborative efforts among computational researchers to combine other open source computational methods under a single, easily understandable graphical user interface.

In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. We show that these metrics correlate well with the RMSD between two configurations if this RMSD is obtained from a global minimization over all translations, rotations and permutations of atomic indices. We introduce a Monte Carlo approach to obtain this global minimum of the RMSD between configurations.

Cyclooxygenase (COX-1/COX-2)-catalyzed eicosanoid formation plays a key role in inflammation-associated diseases. Natural forms of vitamin E are recently shown to be metabolized to long-chain carboxychromanols and their sulfated counterparts. Here we find that vitamin E forms differentially inhibit COX-2-catalyzed prostaglandin E 2 in IL-1␤-stimulated A549 cells without affecting COX-2 expression, showing the relative potency of ␥-tocotrienol Ϸ ␦-tocopherol > ␥-tocopherol Ͼ Ͼ ␣-or ␤-tocopherol. The cellular inhibition is partially diminished by sesamin, which blocks the metabolism of vitamin E, suggesting that their metabolites may be inhibitory. Consistently, conditioned media enriched with long-chain carboxychromanols, but not their sulfated counterparts or vitamin E, reduce COX-2 activity in COX-preinduced cells with 5 M arachidonic acid as substrate. Under this condition, 9-or 13-carboxychromanol, the vitamin E metabolites that contain a chromanol linked with a 9-or 13-carbon-length carboxylated side chain, inhibits COX-2 with an IC 50 of 6 or 4 M, respectively. But 13-carboxychromanol inhibits purified COX-1 and COX-2 much more potently than shorter side-chain analogs or vitamin E forms by competitively inhibiting their cyclooxygenase activity with K i of 3.9 and 10.7 M, respectively, without affecting the peroxidase activity. Computer simulation consistently indicates that 13-carboxychromanol binds more strongly than 9-carboxychromanol to the substrate-binding site of COX-1. Therefore, long-chain carboxychromanols, including 13-carboxychromanol, are novel cyclooxygenase inhibitors, may serve as anti-inflammation and anticancer agents, and may contribute to the beneficial effects of certain forms of vitamin E.cancer ͉ inflammation ͉ PGE2 ͉ tocopherol ͉ tocotrienol C yclooxygenases (COX-1 and COX-2) catalyze the conversion of arachidonic acid (AA) to prostaglandin H 2 (PGH 2 ), the common precursor to prostaglandins and thromboxanes that are important lipid mediators for regulation of many physiological and pathophysiological responses (1). COXs are bifunctional enzymes that carry out two sequential activities-i.e., the cyclooxygenase activity, which leads to the formation of prostaglandin G 2 (PGG 2 ), and the peroxidase activity, which reduces PGG 2 to PGH 2 (2). COX-1 is constitutively expressed in many tissues, including platelets where thromboxanes are generated by this enzyme to promote platelet aggregation. COX-2 is often induced under acute/chronic inflammatory conditions and is mainly responsible for the generation of proinflammatory eicosanoids, including prostaglandin E 2 (PGE 2 ) (3). COX inhibitors, which are nonsteroidal anti-inflammatory drugs (NSAIDs), have been used for the relief of fever, pain, and inflammation (4). Chronic inflammation has been identified as a significant factor in the development of cancer (5). It is well established that NSAIDs are effective chemoprevention agents for cancer (6), although their long-term use has been questioned due to the associated gastrointestinal sid...

As a direct simulation of a multistep proton transfer reaction involving protein residues, the proton relay shuttle between A and I forms of green fluorescent protein (GFP) is simulated in atomic detail by using a special molecular dynamics simulation technique. Electronic excitation of neutral chromophore in wild-type GFP is generally followed by excited-state proton transfer to a nearby glutamic acid residue via a water molecule and a serine residue. Here we show that the second and third transfer steps occur ultrafast on time scales of several tens of femtoseconds. Proton back-shuttle in the ground state is slower and occurs in a different sequence of events. The simulations provide atomic models of various intermediates and yield realistic rate constants for proton transfer events. In particular, we argue that the I form observed spectroscopically under equilibrium conditions may differ from the I form observed as a fast intermediate by an anti to syn rotation of the carboxyl proton of neutral Glu-222.B ecause of its unique photophysical properties-strong green fluorescence without need for an additional cofactorgreen fluorescent protein (GFP) has become a very powerful marker for gene expression, cellular localization, and dynamic intracellular events over the past 5 years (1). Its rich photophysical behavior was characterized quite well by a great variety of spectroscopic techniques (1-4), and its three-dimensional structure was determined by x-ray crystallography (5, 6). Wild-type GFP exhibits two absorption maxima ''A'' and ''B'' at 395 nanometers (nm) and at 475 nm (1) that are present roughly in a 6:1 ratio (1), and that correspond to forms of the protein with either neutral (A) or anionic (B) chromophores. Time-resolved fluorescence spectroscopy identified an additional form ''I*'' that is populated as a fast intermediate after excitation of the A form, A* 3 I* (2, 3). The mechanism of interconversion is likely caused by excited-state proton transfer from the chromophore to a nearby protein residue (2, 3) because the new form emits at a similar wave length as B* and because the transfer rate slows down upon deuteration (2); see Fig. 1 for an overview.Although ''I form'' originally denoted only this fast intermediate, it has become common to use ''I form'' as a term for GFP species that absorb at wavelengths red-shifted with respect to the B form. Surprisingly, recent hole-burning experiments demonstrated that an I form is already present in wild-type GFP samples at room temperature under equilibrium conditions (4). It is currently unclear whether this is the same I form observed in the time-resolved fluorescence experiments. Although the I form has so far not been described in atomic detail, a structural model of I was suggested on the basis of comparisons between the crystal structures of wild-type (A form) and mutant GFP proteins containing the B form (7, 8); see Fig. 2. In this model, Glu-222 is assumed to be protonated in the I form (7), thus requiring a rise of the pK a of Glu-222 by more than 4 ...

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

A very efficient scheme is presented to simulate proton transport by classical molecular dynamics simulation coupled with quantum mechanically derived proton hopping. Simulated proton transfer rates and proton diffusion constants for an excess proton in a box of water molecules are in good agreement with experimental data and with previous simulations that employed empirical valence bond (EVB) theory. For the first time, the proton occupancy of an aspartic acid residue in water was computed directly by MD simulations. Locally enhanced sampling or multi copy techniques were used to facilitate proton release in simulations of an imidazole ring in a solvent box. Summarizing, a quasiclassical description of proton transfer dynamics has been able to capture important kinetic and thermodynamic features of these systems at less than 50% computational overhead compared to standard molecular dynamics simulations. The method can be easily generalized to simulate the protonation equilibria of a large number of titratable sites. This should make it an attractive method to study proton transport in large biological systems.

In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein and in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years due to implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with tunnels and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.

We present a concept for the in silico simulation of adverse effects triggered by drugs and chemicals. The underlying philosophy combines flexible docking (software Yeti) for the identification of the binding mode(s) and 6D-QSAR (software Quasar) for their quantification. The results obtained for 106 diverse molecules binding to the estrogen receptor (q2 = 0.903; p2 = 0.885) suggest that our approach is suitable for the identification of an endocrine-disrupting potential associated with drugs and chemicals.