Simona Distinto

Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Within the last few years a considerable amount of evaluative studies has been published that investigate the performance of 3D virtual screening approaches. Thereby, in particular assessments of protein–ligand docking are facing remarkable interest in the scientific community. However, comparing virtual screening approaches is a non-trivial task. Several publications, especially in the field of molecular docking, suffer from shortcomings that are likely to affect the significance of the results considerably. These quality issues often arise from poor study design, biasing, by using improper or inexpressive enrichment descriptors, and from errors in interpretation of the data output. In this review we analyze recent literature evaluating 3D virtual screening methods, with focus on molecular docking. We highlight problematic issues and provide guidelines on how to improve the quality of computational studies. Since 3D virtual screening protocols are in general assessed by their ability to discriminate between active and inactive compounds, we summarize the impact of the composition and preparation of test sets on the outcome of evaluations. Moreover, we investigate the significance of both classic enrichment parameters and advanced descriptors for the performance of 3D virtual screening methods. Furthermore, we review the significance and suitability of RMSD as a measure for the accuracy of protein–ligand docking algorithms and of conformational space sub sampling algorithms.

Identification and Characterization of New DNA G-Quadruplex Binders Selected by a Combination of Ligand and Structure-Based Virtual Screening Approaches

Nowadays, it has been demonstrated that DNA G-quadruplex arrangements are involved in cellular aging and cancer, thus boosting the discovery of selective binders for these DNA secondary structures. By taking advantage of available structural and biological information on these structures, we performed a high throughput in silico screening of commercially available molecules databases by merging ligand- and structure-based approaches by means of docking experiments. Compounds selected by the virtual screening procedure were then tested for their ability to interact with the human telomeric G-quadruplex folding by circular dichroism, fluorescence spectroscopy, and photodynamic techniques. Interestingly, our screening succeeded in retrieving a new promising scaffold for G-quadruplex binders characterized by a psoralen moiety.

Discovery of Novel PPAR Ligands by a Virtual Screening Approach Based on Pharmacophore Modeling, 3D Shape, and Electrostatic Similarity Screening

Peroxisome proliferator-activated receptors (PPARs) are important targets for drugs used in the treatment of atherosclerosis, dyslipidaemia, obesity, type 2 diabetes, and other diseases caused by abnormal regulation of the glucose and lipid metabolism. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR ligands. From the resulting 10 virtual screening hits, five tested positive in human PPAR ligand-binding domain (hPPAR-LBD) transactivation assays and showed affinities for PPAR in a competitive binding assay. Compounds 5, 7, and 8 were identified as PPAR-alpha agonists, whereas compounds 2 and 9 showed agonistic activity for hPPAR-gamma. Moreover, compound 9 was identified as a PPAR-delta antagonist. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for PPAR ligands that could be useful for drug development in the area of atherosclerosis, dyslipidaemia, and type 2 diabetes.

Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach.

We report the first application of ligand-based virtual screening (VS) methods for discovering new compounds able to inhibit both human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT)-associated functions, DNA polymerase and ribonuclease H (RNase H) activities. The overall VS campaign consisted of two consecutive screening processes. In the first, the VS platform Rapid Overlay of Chemical Structures (ROCS) was used to perform in silico shape-based similarity screening on the NCI compounds database in which a hydrazone derivative, previously shown to inhibit the HIV-1 RT, was chosen. As a result, 34 hit molecules were selected and assayed on both RT-associated functions. In the second, the 4 most potent RT inhibitors identified were selected as queries for parallel VS performed by combining shape-based, 2D-fingerprint and 3D-pharmacophore VS methods. Overall, a set of molecules characterized by new different scaffolds were identified as novel inhibitors of both HIV-1 RT-associated activities in the low micromolar range.

Molecular Aspects of the RT/drug Interactions. Perspective of Dual Inhibitors

The HIV-1 reverse transcriptase (RT) is one of the most attracting targets for the development of early phase infection inhibitors. Although many RT inhibitors have been approved for the treatment of HIV-1 infection, they all target the polymerase function of this enzyme. So far, no drugs are available for the inhibition of the RT associated ribonuclease H function (RNase H), which plays an essential role in the HIV replication cycle. Moreover it should be reported that many of the known RT inhibitors, targeting the polymerase function, enhance the RNase H activity, indicating that, although spatially distinct, a close relation occurs between the two functions. The aim of this review is to summarise the efforts in the design of new inhibitors either characterized by a novel mechanism of action or capable of blocking both RT associated functions, as well as pointing out the main binding features of the known RT inhibitors.

Alizarine derivatives as new dual inhibitors of the HIV-1 reverse transcriptase-associated DNA polymerase and RNase H activities effective also on the RNase H activity of non-nucleoside resistant reverse transcriptases

HIV‐1 reverse transcriptase (RT) has two associated activities, DNA polymerase and RNase H, both essential for viral replication and validated drug targets. Although all RT inhibitors approved for therapy target DNA polymerase activity, the search for new RT inhibitors that target the RNase H function and are possibly active on RTs resistant to the known non‐nucleoside inhibitors (NNRTI) is a viable approach for anti‐HIV drug development. In this study, several alizarine derivatives were synthesized and tested for both HIV‐1 RT‐associated activities. Alizarine analogues K‐49 and KNA‐53 showed IC50 values for both RT‐associated functions of ∼ 10 μm. When tested on the K103N RT, both derivatives inhibited the RT‐associated functions equally, whereas when tested on the Y181C RT, KNA‐53 inhibited the RNase H function and was inactive on the polymerase function. Mechanism of action studies showed that these derivatives do not intercalate into DNA and do not chelate the divalent cofactor Mg2+. Kinetic studies demonstrated that they are noncompetitive inhibitors, they do not bind to the RNase H active site or to the classical NNRTI binding pocket, even though efavirenz binding negatively influenced K‐49/KNA‐53 binding and vice versa. This behavior suggested that the alizarine derivatives binding site might be close to the NNRTI binding pocket. Docking experiments and molecular dynamic simulation confirmed the experimental data and the ability of these compounds to occupy a binding pocket close to the NNRTI site.

Synthesis, semipreparative HPLC separation, biological evaluation, and 3D-QSAR of hydrazothiazole derivatives as human monoamine oxidase B inhibitors

The present study reports on synthesis in high yields (70-99%), HPLC enantioseparation, inhibitory activity against human monoamino oxidases, and molecular modeling including 3D-QSAR studies, of a large series of (4-aryl-thiazol-2-yl)hydrazones (1-45). Most of the synthesized compounds proved to be potent and selective inhibitors of hMAO-B isoform in the micromolar or nanomolar range, thus demonstrating that hydrazothiazole could be considered a good pharmacophore to design new hMAO-B inhibitors. Due to the presence in some derivatives of a chiral center, we also performed a semipreparative chromatographic enantioseparation of these compounds obtained by a stereoconservative pattern. The separated enantiomers were submitted to in vitro biological evaluation to point out the stereorecognition of the active site of the enzyme towards these structures. Finally, a 3D-QSAR study was carried out using Comparative Molecular Field Analysis (CoMFA), aiming to deduce rational guidelines for the further structural modification of these lead compounds.

Critical comparison of virtual screening methods against the MUV data set.

In the current work, we measure the performance of seven ligand-based virtual screening tools--five similarity search methods and two pharmacophore elucidators--against the MUV data set. For the similarity search tools, single active molecules as well as active compound sets clustered in terms of their chemical diversity were used as templates. Their score was calculated against all inactive and active compounds in their target class. Subsequently, the scores were used to calculate different performance metrics including enrichment factors and AUC values. We also studied the effect of data fusion on the results. To measure the performance of the pharmacophore tools, a set of active molecules was picked either random- or chemical diversity-based from each target class to build a pharmacophore model which was then used to screen the remaining compounds in the set. Our results indicate that template sets selected by their chemical diversity are the best choice for similarity search tools, whereas the optimal training sets for pharmacophore elucidators are based on random selection underscoring that pharmacophore modeling cannot be easily automated. We also suggest a number of improvements for future benchmark sets and discuss activity cliffs as a potential problem in ligand-based virtual screening.

Development of anti-viral agents using molecular modeling and virtual screening techniques.

Computational chemistry has always played a key role in anti-viral drug development. The challenges and the quickly rising public interest when a virus is becoming a threat has significantly influenced computational drug discovery. The most obvious example is anti-AIDS research, where HIV protease and reverse transcriptase have triggered enormous efforts in developing and improving computational methods. Methods applied to anti-viral research include (i) ligand-based approaches that rely on known active compounds to extrapolate biological activity, such as machine learning techniques or classical QSAR, (ii) structure-based methods that rely on an experimentally determined 3D structure of the targets, such as molecular docking or molecular dynamics, and (iii) universal approaches that can be applied in a structure- or ligand-based way, such as 3D QSAR or 3D pharmacophore elucidation. In this review we summarize these molecular modeling approaches as they were applied to fight anti-viral diseases and highlight their importance for anti-viral research. We discuss the role of computational chemistry in the development of small molecules as agents against HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, the influenza virus M2 channel protein, influenza virus neuraminidase, the SARS coronavirus main proteinase and spike protein, thymidine kinases of herpes viruses, hepatitis c virus proteins and other flaviviruses as well as human rhinovirus coat protein and proteases, and other picornaviridae. We highlight how computational approaches have helped in discovering anti-viral activities of natural products and give an overview on polypharmacology approaches that help to optimize drugs against several viruses or help to optimize the metabolic profile of and anti-viral drug.

3-Acetyl-2,5-diaryl-2,3-dihydro-1,3,4-oxadiazoles: a new scaffold for the selective inhibition of monoamine oxidase B.

3-Acetyl-2,5-diaryl-2,3-dihydro-1,3,4-oxadiazoles were designed, synthesized, and tested as inhibitors against human monoamine oxidase (MAO) A and B isoforms. Several compounds, obtained as racemates, were identified as selective MAO-B inhibitors. The enantiomers of some derivatives were separated by enantioselective HPLC and tested. The R-enantiomers always showed the highest activity. Docking study and molecular dynamic simulations demonstrated the putative binding mode. We conclude that these 1,3,4-oxadiazoles derivatives are promising reversible and selective MAO-B inhibitors.