Andreas Steffen

Comparison of Molecular Fingerprint Methods on the Basis of Biological Profile Data

In this study we evaluated a set of molecular fingerprint methods with respect to their capability to reproduce similarities in the biological activity space. The evaluation presented in this paper is therefore different from many other fingerprint studies, in which the enrichment of active compounds binding to the same target as selected query structures was studied. Conversely, our data set was extracted from the BioPrint database, which contains uniformly derived biological activity profiles of mainly marketed drugs for a range of biological assays relevant for the pharmaceutical industry. We compared calculated molecular fingerprint similarity values between all compound pairs of the data set with the corresponding similarities in the biological activity space and additionally analyzed agreements of generated clusterings. A closer analysis of the compound pairs with a high biological activity similarity revealed that fingerprint methods such as CHEMGPS or TRUST4, which describe global features of a molecule such as physicochemical properties and pharmacophore patterns, might be better suited to describe similarity of biological activity profiles than purely structural fingerprint methods. It is therefore suggested that the usage of these fingerprint methods could increase the probability of finding molecules with a similar biological activity profile but yet a different chemical structure.

AIScore — Chemically Diverse Empirical Scoring Function Employing Quantum Chemical Binding Energies of Hydrogen-Bonded Complexes

In this work we report on a novel scoring function that is based on the LUDI model and focuses on the prediction of binding affinities. AIScore extends the original FlexX scoring function using a chemically diverse set of hydrogen-bonded interactions derived from extensive quantum chemical ab initio calculations. Furthermore, we introduce an algorithmic extension for the treatment of multifurcated hydrogen bonds (XFurcate). Charged and resonance-assisted hydrogen bond energies and hydrophobic interactions as well as a scaling factor for implicit solvation were fitted to experimental data. To this end, we assembled a set of 101 protein-ligand complexes with known experimental binding affinities. Tightly bound water molecules in the active site were considered to be an integral part of the binding pocket. Compared to the original FlexX scoring function, AIScore significantly improves the prediction of the binding free energies of the complexes in their native crystal structures. In combination with XFurcate, AIScore yields a Pearson correlation coefficient of R P = 0.87 on the training set. In a validation run on the PDBbind test set we achieved an R P value of 0.46 for 799 attractively scored complexes, compared to a value of R P = 0.17 and 739 bound complexes obtained with the FlexX original scoring function. The redocking capability of AIScore, on the other hand, does not fully reach the good performance of the original FlexX scoring function. This finding suggests that AIScore should rather be used for postscoring in combination with the standard FlexX incremental ligand construction scheme.

On the ease of predicting the thermodynamic properties of beta-cyclodextrin inclusion complexes

BackgroundIn this study we investigated the predictability of three thermodynamic quantities related to complex formation. As a model system we chose the host-guest complexes of β-cyclodextrin (β-CD) with different guest molecules. A training dataset comprised of 176 β-CD guest molecules with experimentally determined thermodynamic quantities was taken from the literature. We compared the performance of three different statistical regression methods – principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression combined with forward feature selection (SVMR/FSS) – with respect to their ability to generate predictive quantitative structure property relationship (QSPR) models for ΔG°, ΔH° and ΔS° on the basis of computed molecular descriptors.ResultsWe found that SVMR/FFS marginally outperforms PLSR and PCR in the prediction of ΔG°, with PLSR performing slightly better than PCR. PLSR and PCR proved to be more stable in a nested cross-validation protocol. Whereas ΔG° can be predicted in good agreement with experimental values, none of the methods led to comparably good predictive models for ΔH°. In using the methods outlined in this study, we found that ΔS° appears almost unpredictable. In order to understand the differences in the ease of predicting the quantities, we performed a detailed analysis. As a result we can show that free energies are less sensitive (than enthalpy or entropy) to the small structural variations of guest molecules. This property, as well as the lower sensitivity of ΔG° to experimental conditions, are possible explanations for its greater predictability.ConclusionThis study shows that the ease of predicting ΔG° cannot be explained by the predictability of either ΔH° or ΔS°. Our analysis suggests that the poor predictability of TΔS° and, to a lesser extent, ΔH° has to do with a stronger dependence of these quantities on the structural details of the complex and only to a lesser extent on experimental error.

Flexible Docking of Ligands into Synthetic Receptors Using a Two-Sided Incremental Construction Algorithm

We present a new algorithm for the fast and reliable structure prediction of synthetic receptor-ligand complexes. Our method is based on the protein-ligand docking program FlexX and extends our recently introduced docking technique for synthetic receptors, which has been implemented in the program FlexR. To handle the flexibility of the relevant molecules, we apply a novel docking strategy that uses an adaptive two-sided incremental construction algorithm which incorporates the structural flexibility of both the ligand and synthetic receptor. We follow an adaptive strategy, in which one molecule is expanded by attaching its next fragment in all possible torsion angles, whereas the other (partially assembled) molecule serves as a rigid binding partner. Then the roles of the molecules are exchanged. Geometric filters are used to discard partial conformations that cannot realize a targeted interaction pattern derived in a graph-based precomputation phase. The process is repeated until the entire complex is built up. Our algorithm produces promising results on a test data set comprising 10 complexes of synthetic receptors and ligands. The method generated near-native solutions compared to crystal structures in all but one case. It is able to generate solutions within a couple of minutes and has the potential of being used as a virtual screening tool for searching for suitable guest molecules for a given synthetic receptor in large databases of guests and vice versa.