Christoph A. Sotriffer

Target Flexibility: An Emerging Consideration in Drug Discovery and Design†

Department of General and Inorganic Chemistry, UniVersity of Parma, Via G.P. Usberti 17/A 43100, Parma, Italy, National Institute for Biosystems and Biostructures, Rome, Italy, Department of Medicinal Chemistry and Institute for Structural Biology & Drug DiscoVery, Virginia Commonwealth UniVersity, Richmond, Virginia 23298-0540, Department of Pharmaceutics, UniVersity of Parma, Via GP Usberti 27/A, 43100 Parma, Italy, High Performance Systems, CINECA Supercomputing Centre, Casalecchio di Reno, Bologna, Italy, Dulbecco Telethon Institute, Department of Chemistry, UniVersity of Modena and Reggio Emilia, Via Campi 183, 41100 Modena, Italy, Department of Mathematics and Natural Sciences, Pharmaceutical Institute, Christian-Albrechts-UniVersity, Gutenbergstrasse 76, 24118 Kiel, Germany, Departments of Biochemistry & Molecular Biology, Computer Science & Engineering, and Physics & Astronomy, Michigan State UniVersity, East Lansing, Michigan 48824-1319, Department of Molecular Biology, MB-5, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037-1000, Molecular Modeling and Bioinformatics Unit, Institute of Biomedical Research, Scientific Park of Barcelona, Department of Biochemistry and Molecular Biology, UniVersity of Barcelona, Josep Samitier 1-5, Barcelona 08028, Spain, Department of Experimental Medicine, UniVersity of Parma, Via Volturno, 39, 43100, Parma, Italy, Department of Chemical, Food, Pharmaceutical and Pharmacological Sciences, UniVersity of Piemonte Orientale “Amedeo AVogadro”, Via BoVio 6, 28100 NoVara, Italy, Institute of Pharmacy and Food Chemistry, UniVersity of Wurzburg, Am Hubland, D-97074 Wurzburg, Germany

Thalassiolins A–C: new marine-derived inhibitors of HIV cDNA integrase

Human immunodeficiency virus (HIV) replication requires integration of viral cDNA into the host genome, a process mediated by the viral enzyme integrase. We describe a new series of HIV integrase inhibitors, thalassiolins A-C (1-3), isolated from the Caribbean sea grass Thalassia testudinum. The thalassiolins are distinguished from other flavones previously studied by the substitution of a sulfated beta-D-glucose at the 7-position, a substituent that imparts increased potency against integrase in biochemical assays. The most active of these molecules, thalassiolin A (1), displays in vitro inhibition of the integrase catalyzed strand transfer reaction (IC50=0.4 microM) and an antiviral IC50 of 30 microM. Molecular modeling studies indicate a favorable binding mode is probable at the catalytic core domain of HIV-1 integrase.

SFCscore: Scoring functions for affinity prediction of protein–ligand complexes

Empirical scoring functions to calculate binding affinities of protein–ligand complexes have been calibrated based on experimental structure and affinity data collected from public and industrial sources. Public data were taken from the AffinDB database, whereas access to industrial data was gained through the Scoring Function Consortium (SFC), a collaborative effort with various pharmaceutical companies and the Cambridge Crystallographic Data Center. More than 850 complexes were obtained by the data collection procedure and subsequently used to setup different training sets for the parameterization of new scoring functions. Over 60 different descriptors were evaluated for all complexes, including terms accounting for interactions with and among aromatic ring systems as well as many surface‐dependent terms. After exploratory correlation and regression analyses, stepwise variable selection procedures and systematic searches, the most suitable descriptors were chosen as variables to calibrate regression functions by means of multiple linear regression or partial least squares analysis. Eight different functions are presented herein. Cross‐validated r2 (Q2) values of up to 0.72 and standard errors (sPRESS) generally below 1.15 pKi units suggest highly predictive functions. Extensive unbiased validation was carried out by testing the functions on large data sets from the PDBbind database as used by Wang et al. (J Chem Inf Comput Sci 2004;44:2114–2125) in a comparative analysis of other scoring functions. Superior performance of the SFCscore functions is observed in many cases, but the results also illustrate the need for further improvements. Proteins 2008.

AffinDB: a freely accessible database of affinities for protein–ligand complexes from the PDB

AffinDB is a database of affinity data for structurally resolved protein–ligand complexes from the Protein Data Bank (PDB). It is freely accessible at . Affinity data are collected from the scientific literature, both from primary sources describing the original experimental work of affinity determination and from secondary references which report affinity values determined by others. AffinDB currently contains over 730 affinity entries covering more than 450 different protein–ligand complexes. Besides the affinity value, PDB summary information and additional data are provided, including the experimental conditions of the affinity measurement (if available in the corresponding reference); 2D drawing, SMILES code and molecular weight of the ligand; links to other databases, and bibliographic information. AffinDB can be queried by PDB code or by any combination of affinity range, temperature and pH value of the measurement, ligand molecular weight, and publication data (author, journal and year). Search results can be saved as tabular reports in text files. The database is supposed to be a valuable resource for researchers interested in biomolecular recognition and the development of tools for correlating structural data with affinities, as needed, for example, in structure-based drug design.

Structure-Based Search for New Inhibitors of Cholinesterases

Cholinesterases are important biological targets responsible for regulation of cholinergic transmission, and their inhibitors are used for the treatment of Alzheimer’s disease. To design new cholinesterase inhibitors, of different structure-based design strategies was followed, including the modification of compounds from a previously developed library and a fragment-based design approach. This led to the selection of heterodimeric structures as potential inhibitors. Synthesis and biological evaluation of selected candidates confirmed that the designed compounds were acetylcholinesterase inhibitors with IC50 values in the mid-nanomolar to low micromolar range, and some of them were also butyrylcholinesterase inhibitors.

Probing flexibility and “induced-fit” phenomena in aldose reductase by comparative crystal structure analysis and molecular dynamics simulations†

Aldose reductase is a promising target for the treatment of diabetic complications, and as such, has become the focus of various drug design projects. As revealed by a survey of available crystal structures, the protein shows pronounced induced‐fit effects upon ligand binding. Although helping to explain the enzymes substrate promiscuity, phenomena of this kind are still responsible for significant complications in structure‐based design efforts directed to aldose reductase. Accordingly, a deeper understanding of the principles governing conformational alterations in this enzyme would be of utmost practical importance. As a first step in addressing this issue, molecular dynamics (MD) simulations have been carried out. The ultrahigh resolution crystal structure of aldose reductase complexed with inhibitor IDD594 served as ideal starting point for a set of different simulations of nanosecond time scale: the native complexed state with bound inhibitor, the uncomplexed state (after removal of the inhibitor) at standard temperature, and the uncomplexed state at elevated temperature. The reference simulation of the complex exhibits extraordinary stability of the overall fold, whereas two distinct conformational substates are found for the binding‐site region. In contrast, already at standard temperature pronounced changes are observed in the binding region during the simulation of the uncomplexed state. Leu300, for example, closes the access to the pocket opened by IDD594. On the other hand, conformations around the catalytic site are highly conserved, with the His110–Tyr48–NADP+ orientation being stabilized by a water molecule. Detailed analysis of the trajectories allows to reveal a set of distinct conformational substates that may prove useful as alternative structural templates in virtual screening for new aldose reductase inhibitors. Proteins 2004.

SFCscoreRF: A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes

A major shortcoming of empirical scoring functions for protein-ligand complexes is the low degree of correlation between predicted and experimental binding affinities, as frequently observed not only for large and diverse data sets but also for SAR series of individual targets. Improvements can be envisaged by developing new descriptors, employing larger training sets of higher quality, and resorting to more sophisticated regression methods. Herein, we describe the use of SFCscore descriptors to develop an improved scoring function by means of a PDBbind training set of 1005 complexes in combination with random forest for regression. This provided SFCscore(RF) as a new scoring function with significantly improved performance on the PDBbind and CSAR-NRC HiQ benchmarks in comparison to previously developed SFCscore functions. A leave-cluster-out cross-validation and performance in the CSAR 2012 scoring exercise point out remaining limitations but also directions for further improvements of SFCscore(RF) and empirical scoring functions in general.

Development, validation, and application of adapted PEOE charges to estimate pKa values of functional groups in protein-ligand complexes.

For routine pKa calculations of protein–ligand complexes in drug design, the PEOE method to compute partial charges was modified. The new method is applicable to a large scope of proteins and ligands. The adapted charges were parameterized using experimental free energies of solvation of amino acids and small organic ligands. For a data set of 80 small organic molecules, a correlation coefficient of r2 = 0.78 between calculated and experimental solvation free energies was obtained. Continuum electrostatics pKa calculations based on the Poisson–Boltzmann equation were carried out on a validation set of nine proteins for which 132 experimental pKa values are known. In total, an overall RMSD of 0.88 log units between calculated and experimentally determined data is achieved. In particular, the predictions of significantly shifted pKa values are satisfactory, and reasonable estimates of protonation states in the active sites of lysozyme and xylanase could be obtained. Application of the charge‐assignment and pKa‐calculation procedure to protein–ligand complexes provides clear structural interpretations of experimentally observed changes of protonation states of functional groups upon complex formation. This information is essential for the interpretation of thermodynamic data of protein–ligand complex formation and provides the basis for the reliable factorization of the free energy of binding in enthalpic and entropic contributions. The modified charge‐assignment procedure forms the basis for future automated pKa calculations of protein–ligand complexes. Proteins 2006;

Physicochemical descriptors to discriminate protein–protein interactions in permanent and transient complexes selected by means of machine learning algorithms

Analyzing protein–protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein–protein recognition. For this purpose, descriptors explaining the nature of different protein–protein complexes are desirable. In this work, the authors introduced Epic Protein Interface Classification as a framework handling the preparation, processing, and analysis of protein–protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines, C4.5 Decision Trees, K Nearest Neighbors, and Naïve Bayes algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms, to extract discriminating features from the protein–protein complexes. To compare protein–protein complexes to each other, the authors represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors, DrugScore pair potential vectors and SFCscore descriptor vectors. We classified two different datasets: (A) 172 protein–protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein–protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein–protein complexes and introduce an approach for scoring the importance of the extracted features. Proteins 2006.

Accounting for Induced-Fit Effects in Docking: What is Possible and What is Not?

Proteins can undergo a variety of conformational changes upon ligand binding. Although different mechanisms may play a role, the phenomenon is commonly referred to as induced fit to indicate that the tight structural complementarity of the interaction partners is a consequence of the binding event. Docking methods need to take into account this ability of the ligand and the protein to mutually adapt to each other when forming a complex. Handling the ligand as flexible is already common practice in docking applications. This is not yet the case for the protein. In fact, the accurate prediction of protein conformational changes upon ligand binding is still a major challenge, even more if computational speed is an issue, as for example in virtual screening applications. However, significant progress has been made over the past years and many valuable approaches have become available to address the protein flexibility problem and to provide more reliable docking predictions for complexes governed by significant induced-fit effects. This review provides a brief overview of the current situation, the most recent advances, and the remaining limitations of flexible protein docking, with particular focus on approaches handling protein flexibility simultaneously with ligand placement in the docking process.