Biomedical subjects

Nikolaus Stiefl

Publications and source records attributed to Nikolaus Stiefl.

12 recordsLinked to original sources

PubMed2007

Evaluation of machine-learning methods for ligand-based virtual screening.

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

Artificial Intelligence↗

PubMed2006

Aziridide-based inhibitors of cathepsin L: synthesis, inhibition activity, and docking studies.

A comprehensive screening of N-acylated aziridine (aziridide) based cysteine protease inhibitors containing either Boc-Leu-Caa (Caa=cyclic amino acid), Boc-Gly-Caa, or Boc-Phe-Ala attached to the aziridine nitrogen atom revealed Boc-(S)-Leu-(S)-Azy-(S,S)-Azi(OBn)(2) (18 a) as a highly potent cathepsin L (CL) inhibitor (K(i)=13 nM) (Azy=aziridine-2-carboxylate, Azi=aziridine-2,3-dicarboxylate). Docking studies, which also accounted for the unusual bonding situations (the flexibility and hybridization of the aziridides) predict that the inhibitor adopts a Y shape and spans across the entire active site cleft, binding into both the nonprimed and primed sites of CL.

Animals↗

PubMed2005

A new lead for nonpeptidic active-site-directed inhibitors of the severe acute respiratory syndrome coronavirus main protease discovered by a combination of screening and docking methods.

The coronavirus main protease, M(pro), is considered to be a major target for drugs suitable for combating coronavirus infections including severe acute respiratory syndrome (SARS). An HPLC-based screening of electrophilic compounds that was performed to identify potential M(pro) inhibitors revealed etacrynic acid tert-butylamide (6a) as an effective nonpeptidic inhibitor. Docking studies suggested a binding mode in which the phenyl ring acts as a spacer bridging the inhibitor's activated double bond and its hydrophobic tert-butyl moiety. The latter is supposed to fit into the S4 pocket of the target protease. Furthermore, these studies revealed etacrynic acid amide (6b) as a promising lead for nonpeptidic active-site-directed M(pro) inhibitors. In a fluorimetric enzyme assay using a novel fluorescence resonance energy transfer (FRET) pair labeled substrate, compound 6b showed a K(i) value of 35.3 muM. Since the novel lead compound does not target the S1', S1, and S2 subsites of the enzyme's substrate-binding pockets, there is room for improvement that underlines the lead character of compound 6b.

Amides↗

PubMed2005

Screening of electrophilic compounds yields an aziridinyl peptide as new active-site directed SARS-CoV main protease inhibitor.

The coronavirus main protease, M(pro), is considered a major target for drugs suitable to combat coronavirus infections including the severe acute respiratory syndrome (SARS). In this study, comprehensive HPLC- and FRET-substrate-based screenings of various electrophilic compounds were performed to identify potential M(pro) inhibitors. The data revealed that the coronaviral main protease is inhibited by aziridine- and oxirane-2-carboxylates. Among the trans-configured aziridine-2,3-dicarboxylates the Gly-Gly-containing peptide 2c was found to be the most potent inhibitor.

Aziridines↗

PubMed2004

Probing the pharmacophore for allosteric ligands of muscarinic M2 receptors: SAR and QSAR studies in a series of bisquaternary salts of caracurine V and related ring systems.

Allosteric effects on muscarinic acetylcholine M(2) receptors were examined in a series of bisquaternary salts of the Strychnos alkaloid caracurine V (6) and related iso-caracurine V, tetrahydrocaracurine V, and bisnortoxiferine ring systems. The compounds inhibited dissociation of the orthosteric antagonist [(3)H]N-methylscopolamine (NMS) from porcine cardiac M(2) receptors with EC(0.5,diss) values from 4 to 3270 nM. The majority of compounds hardly changed [(3)H]NMS equilibrium binding, indicating similar binding affinities in free and NMS-occupied M(2) receptors. The most potent agents were found in the caracurine V, iso-caracurine V, and tetrahydrocaracurine V series and carried nonpolar alkyl groups with a maximal chain length of three carbon atoms. 3D QSAR (CoMSIA) analysis explained the wide range of binding affinities by steric and electrostatic properties of the side chains. Furthermore, the findings suggest that the spatial orientation of the "caracurine" aromatic rings compared with the bisnortoxiferine ring skeleton is favorable to optimal allostere-receptor interactions.

Alkaloids↗

PubMed2003

Mapping property distributions of molecular surfaces: algorithm and evaluation of a novel 3D quantitative structure-activity relationship technique.

A novel molecular descriptor called MaP (mapping property distributions of molecular surfaces) is presented. It combines facile computation, translational and rotational invariance, and straightforward interpretability of the computed models. A three-step procedure is used to compute the MaP descriptor. First, an approximation to the molecular surface with equally distributed surface points is computed. Next, molecular properties are projected onto this surface. Finally, the distribution of surface properties is encoded into a translationally and rotationally invariant molecular descriptor that is based on radial distribution functions (distance-dependent count statistics). The calculated descriptor is correlated with biological data through chemometric regression techniques in combination with a variable selection. The latter is used to identify variables that are highly relevant for the model and hence for its interpretation. Three applications of the new descriptor are presented, each representing a different area of 3D-QSAR. For reasons of comparability, the new descriptor was tested on the steroid "benchmark" data set. Furthermore, a highly diverse data set with potentially eye-irritating compounds was studied, and third, a set of flexible structures with a modulating effect on the muscarinic M(2) receptor were studied. Not only were all models highly predictive but interpretation of the back-projected variables into the original molecular space led to biologically and chemically relevant conclusions.

Acetates↗

PubMed2003

Synthesis, biological activity, and docking studies of new acetylcholinesterase inhibitors of the bispyridinium type.

A novel series of acetylcholinesterase (AChE) inhibitors of the bispyridinium type was synthesized and the inhibitory activity against AChE and butyrylcholinesterase (BChE) measured. In essence, the substitution pattern influenced the inhibitory potency against AChE, where the most active bispyridiniumoxime (TMB-4) was bisbenzyl substituted followed by monobenzyl substituted, bismethyl substituted, and unsubstituted derivatives of TMB-4. Hence, the bisbenzyl ether of TMB-4 was further investigated. In order to obtain diverse lipophilic and electronic properties for these bisbenzyl bispyridinium derivatives (so-called DUO series), the lateral ring substitution was systematically varied. The lowest IC(50) value against AChE found thus far in the DUO series was 0.34 microM. Docking studies were carried out to elucidate the differences in biological activity. A general binding mode for nearly all compounds could be identified by these investigations. In this binding mode, the docked ligands span the narrow, deeply buried active-site gorge, interacting with Trp84 at the bottom of the gorge, Tyr334 or Phe331 halfway down the gorge, and Trp279 at the peripheral anionic site at the mouth of the gorge. For specific ligands, additional interactions were found which helped to explain their deviating activity. Based on the promising characteristics of the novel acetylcholinesterase inhibitors presented, a series of structurally related, optimized candidates will be developed.

Acetylcholinesterase↗

PubMedDate not supplied

Validation tools for variable subset regression.

Variable selection is applied frequently in QSAR research. Since the selection process influences the characteristics of the finally chosen model, thorough validation of the selection technique is very important. Here, a validation protocol is presented briefly and two of the tools which are part of this protocol are introduced in more detail. The first tool, which is based on permutation testing, allows to assess the inflation of internal figures of merit (such as the cross-validated prediction error). The other tool, based on noise addition, can be used to determine the complexity and with it the stability of models generated by variable selection. The obtained statistical information is important in deciding whether or not to trust the predictive abilities of a specific model. The graphical output of the validation tools is easily accessible and provides a reliable impression of model performance. Among others, the tools were employed to study the influence of leave-one-out and leave-multiple-out cross-validation on model characteristics. Here, it was confirmed that leave-multiple-out cross-validation yields more stable models. To study the performance of the entire validation protocol, it was applied to eight different QSAR data sets with default settings. In all cases internal and external model performance was good, indicating that the protocol serves its purpose quite well.

Quantitative Structure-Activity Relationship↗

PubMedDate not supplied

Structure-based validation of the 3D-QSAR technique MaP.

For three target proteins with different binding pocket characteristics (size and shape, hydrophobicity, hydrogen-bonding) a structure-based validation of the translationally and rotationally invariant 3D-QSAR technique MaP is performed (MaP: Mapping Property distributions of molecular surfaces). The structure-based validation procedure comprises two steps: first, QSAR models are derived without using the information of the target protein. Second, the models are back-projected into the crystal structure of the binding pockets and interpreted. It is demonstrated that MaP is able to identify characteristics important for ligand binding in the cases studied here. Moreover, it is demonstrated that MaP is a versatile 3D-QSAR technique since good, predictive models could be obtained for all three data sets showing distinct characteristics.

Cholinesterase Inhibitors↗

PubMedDate not supplied

A knowledge-based weighting approach to ligand-based virtual screening.

On the basis of the recently introduced reduced graph concept of ErG (extending reduced graphs), a straightforward weighting approach to include additional (e.g., structural or SAR) knowledge into similarity searching procedures for virtual screening (wErG) is proposed. This simple procedure is exemplified with three data sets, for which interaction patterns available from X-ray structures of native or peptidomimetic ligands with their target protein are used to significantly improve retrieval rates of known actives from the MDL Drug Report database. The results are compared to those of other virtual screening techniques such as Daylight fingerprints, FTrees, UNITY, and various FlexX docking protocols. Here, it is shown that wErG exhibits a very good and stable performance independent of the target structure. On the basis of this (and the fact that ErG retrieves structurally more dissimilar compounds due to its potential to perform scaffold-hopping), the combination of wErG and FlexX is successfully explored. Overall, wErG is not only an easily applicable weighting procedure that efficiently identifies actives in large data sets but it is also straightforward to understand for both medicinal and computational chemists and can, therefore, be driven by several aspects of project-related knowledge (e.g., X-ray, NMR, SAR, and site-directed mutagenesis) in a very early stage of the hit identification process.

Computational Biology↗

PubMedDate not supplied

ErG: 2D pharmacophore descriptions for scaffold hopping.

An extended reduced graph approach (ErG) is presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties. The basic idea of the method can be described as a hybrid approach of reduced graphs (Gillet et al. J. Chem. Inf. Comput. Sci. 2003, 43, 338-345) and binding property pairs (Kearsley et al. J. Chem. Inf. Comput. Sci. 1996, 36, 118-127). However, specific extension modifications to correctly describe the pharmacophoric properties, size, and shape of the molecules under study result in a very stable and good performance as compared to DAYLIGHT fingerprints (DFP). This is exemplified for 11 activity classes of the MDL Drug Data Report database, for which ErG performs as well or better than DFP in 10 cases. On the basis of the example data sets, the ability of ErG to switch from one chemotype to another (often referred to as "scaffold hopping") is highlighted. Additionally, possible pitfalls of reduced graph approaches as well as suitable solutions are discussed with the help of example structures. Overall, it is shown that ErG is a widely applicable method capable of identifying structurally diverse actives for a given active search query. This diversity is achieved by a high degree of molecular abstraction, which in turn results in a low dimensional descriptor vector that allows very low computation times for similarity searches.

Journal Article↗

PubMedDate not supplied

Evaluation of extended parameter sets for the 3D-QSAR technique MaP: implications for interpretability and model quality exemplified by antimalarially active naphthylisoquinoline alkaloids.

The 3D-QSAR technique MaP (Mapping Property distributions of molecular surfaces) characterises biologically active compounds in terms of the distribution of their surface properties (H-bond donor, H-bond acceptor, hydrophilic, weakly hydrophobic, strongly hydrophobic). The MaP descriptor is alignment-independent and yields chemically intuitive models. In this study, the impact of different operational parameters on the interpretability and model quality was investigated. Based on a set of antimalarially active naphthylisoquinoline alkaloids the effect of hydrophobicity assignment as well as the differentiation of H-bond propensity was evaluated according to a full factorial design. It turns out, that including different categories for H-bond donor strength significantly improved interpretability, reduced model complexity, and made possible the derivation of a novel pharmacophore hypothesis for this dataset. Further analysis of the factorial design reveals, that MaP models are robust to parameter changes and generate consistent models for different parameter settings.

Algorithms↗