Cleavage entropy as quantitative measure of protease specificity.

A purely information theory-guided approach to quantitatively characterize protease specificity is established. We calculate an entropy value for each protease subpocket based on sequences of cleaved substrates extracted from the MEROPS database. We compare our results with known subpocket specificity profiles for individual proteases and protease groups (e.g. serine proteases, metallo proteases) and reflect them quantitatively. Summation of subpocket-wise cleavage entropy contributions yields a measure for overall protease substrate specificity. This total cleavage entropy allows ranking of different proteases with respect to their specificity, separating unspecific digestive enzymes showing high total cleavage entropy from specific proteases involved in signaling cascades. The development of a quantitative cleavage entropy score allows an unbiased comparison of subpocket-wise and overall protease specificity. Thus, it enables assessment of relative importance of physicochemical and structural descriptors in protease recognition. We present an exemplary application of cleavage entropy in tracing substrate specificity in protease evolution. This highlights the wide range of substrate promiscuity within homologue proteases and hence the heavy impact of a limited number of mutations on individual substrate specificity.

Dynamic regulation of phenylalanine hydroxylase by simulated redox manipulation.

Recent clinical studies revealed increased phenylalanine levels and phenylalanine to tyrosine ratios in patients suffering from infection, inflammation and general immune activity. These data implicated down-regulation of activity of phenylalanine hydroxylase by oxidative stress upon in vivo immune activation. Though the structural damage of oxidative stress is expected to be comparably small, a structural rationale for this experimental finding was lacking. Hence, we investigated the impact of side chain oxidation at two vicinal cysteine residues on local conformational flexibility in the protein by comparative molecular dynamics simulations. Analysis of backbone dynamics revealed a highly flexible loop region (Tyr138-loop) in proximity to the active center of phenylalanine hydroxylase. We observed elevated loop dynamics in connection with a loop movement towards the active site in the oxidized state, thereby partially blocking access for the substrate phenylalanine. These findings were confirmed by extensive replica exchange molecular dynamics simulations and serve as a first structural explanation for decreased enzyme turnover in situations of oxidative stress.

Qualitative prediction of blood-brain barrier permeability on a large and refined dataset.

The prediction of blood-brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood-brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood-brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (n(trees) = 5) based on only four descriptors yields a validated accuracy of 88%.

Identification of novel functional inhibitors of acid sphingomyelinase.

We describe a hitherto unknown feature for 27 small drug-like molecules, namely functional inhibition of acid sphingomyelinase (ASM). These entities named FIASMAs (Functional Inhibitors of Acid SphingoMyelinAse), therefore, can be potentially used to treat diseases associated with enhanced activity of ASM, such as Alzheimer's disease, major depression, radiation- and chemotherapy-induced apoptosis and endotoxic shock syndrome. Residual activity of ASM measured in the presence of 10 µM drug concentration shows a bimodal distribution; thus the tested drugs can be classified into two groups with lower and higher inhibitory activity. All FIASMAs share distinct physicochemical properties in showing lipophilic and weakly basic properties. Hierarchical clustering of Tanimoto coefficients revealed that FIASMAs occur among drugs of various chemical scaffolds. Moreover, FIASMAs more frequently violate Lipinski's Rule-of-Five than compounds without effect on ASM. Inhibition of ASM appears to be associated with good permeability across the blood-brain barrier. In the present investigation, we developed a novel structure-property-activity relationship by using a random forest-based binary classification learner. Virtual screening revealed that only six out of 768 (0.78%) compounds of natural products functionally inhibit ASM, whereas this inhibitory activity occurs in 135 out of 2028 (6.66%) drugs licensed for medical use in humans.

Minor groove binders and drugs targeting proteins cover complementary regions in chemical shape space.

DNA minor groove binders (MGBs) are known to influence gene expression and are therefore widely studied to explore their therapeutic potential. We identified shape-based virtual screening with ROCS as a highly effective computational approach to enrich known MGBs in top-ranked molecules. Discovery of ten previously unknown MGBs by shape-based screening further confirmed the relevance of ligand shape for minor groove affinity. Based on experimental testing we propose three simple rules (at least two positive charges, four nitrogen atoms, and one aromatic ring) as filters to reach even better enrichment of true positives in ROCS hit lists. Interestingly, shape-based ranking of MGBs versus FDA-approved drugs again leads to high enrichment rates, indicating complementary coverage of chemical shape space and indicating minor groove affinity to be unfavorable for approval of drugs targeting proteins.

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.

One concept, three implementations of 3D pharmacophore-based virtual screening: distinct coverage of chemical search space.

Feature-based pharmacophore modeling is a well-established concept to support early stage drug discovery, where large virtual databases are filtered for potential drug candidates. The concept is implemented in popular molecular modeling software, including Catalyst, Phase, and MOE. With these software tools we performed a comparative virtual screening campaign on HSP90 and FXIa, taken from the 'maximum unbiased validation' data set. Despite the straightforward concept that pharmacophores are based on, we observed an unexpectedly high degree of variation among the hit lists obtained. By harmonizing the pharmacophore feature definitions of the investigated approaches, the exclusion volume sphere settings, and the screening parameters, we have derived a rationale for the observed differences, providing insight on the strengths and weaknesses of these algorithms. Application of more than one of these software tools in parallel will result in a widened coverage of chemical space. This is not only rooted in the dissimilarity of feature definitions but also in different algorithmic search strategies.

How to optimize shape-based virtual screening: choosing the right query and including chemical information.

Shape-based molecular similarity approaches have been established as important and popular virtual screening techniques. Recent applications have shown successful screening campaigns using different parameters and query selection. It is common sense that pure volume overlap scoring (or "shape-based screening") under-represents chemical or pharmacophoric information of a molecule. Using the "Directory of Useful Decoys" (DUD) as a benchmark set, we systematically evaluate how (i) the choice of query conformations, (ii) the selection of the active compound to be used as a query structure, and (iii) the inclusion of chemical information (i.e., the pharmacophoric properties of the query molecule) affect screening performance. Varying these parameters bears remarkable potential for improvements and delivers the best screening performance reported using these tools so far. From these insights, guidelines on how to reach optimum performance during virtual screening are developed.

Hydrogen-bonding patterns of minor groove-binder-DNA complexes reveal criteria for discovery of new scaffolds.

Minor groove-binding ligands are able to control gene expression and are of great interest for therapeutic applications. We extracted hydrogen-bonding geometries from all available structures of minor groove-binder-DNA complexes of two noncovalent binding modes, namely 1:1 (including hairpin and cyclic ligands) and 2:1 ligand/DNA binding. Positions of the ligand atoms involved in hydrogen bonding deviate from idealized hydrogen bond geometries and do not exploit the possibilities indicated by water molecules. Therefore, we suggest the inclusion of shape-based descriptors rather than hydrogen-bond patterns in virtual screening protocols for the identification of innovative minor groove-binding scaffolds.

Discovery of novel CB2 receptor ligands by a pharmacophore-based virtual screening workflow.

Cannabinoid receptor 2 (CB(2) receptor) ligands are potential candidates for the therapy of chronic pain, inflammatory disorders, atherosclerosis, and osteoporosis. We describe the development of pharmacophore models for CB(2) receptor ligands, as well as a pharmacophore-based virtual screening workflow, which resulted in 14 hits for experimental follow-up. Seven compounds were identified with K(i) values below 25 microM. The CB(2) receptor-selective pyridine tetrahydrocannabinol analogue 8 (K(i) = 1.78 microM) was identified as a CB(2) partial agonist. Acetamides 12 (K(i) = 1.35 microM) and 18 (K(i) = 2.1 microM) represent new scaffolds for CB(2) receptor-selective antagonists and inverse agonists, respectively. Overall, our pharmacophore-based workflow yielded three novel scaffolds for the chemical development of CB(2) receptor ligands.

Sequence-specific positions of water molecules at the interface between DNA and minor groove binders.

Ligands able to specifically recognize DNA sequences are of fundamental interest as transcription-controlling drugs. Herein, we analyze the positions of water molecules relative to B-DNA base pairs in the minor groove of X-ray and NMR protein data bank (PDB) structures. The patterns observed for water molecules at the interface between DNA and a ligand are compared with those obtained for structures without a ligand. Although the ligand end groups are often charged, and therefore highly hydrated, they do not alter the water patterns, which show considerable differences for the AT and CG base pairs. For AT they are much more precise than for CG in both ligand-containing and ligand-free structures. This behavior strongly indicates that the release of water molecules upon ligand binding leads to a gain of entropy and explains why this effect is especially pronounced for A-tract B-DNA sequences.

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.

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening.

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.

DNA minor groove pharmacophores describing sequence specific properties.

The more that is known about human and other genome sequences and the correlation between gene expression and the course of a disease, the more evident it seems to be that DNA is chosen as a drug target instead of proteins which are built with the information encoded by DNA. According to this approach, small minor groove binding molecules have been designed to bind the DNA sequence specifically and thereby downregulate genes. Because of their lack of druglikeness, we plan to use them as templates for forthcoming virtual screening experiments to discover molecules with the same bioactivity and a different scaffold. In this proof of principle study, carried out with the software tool Catalyst, we present a model work for description of a ligand-DNA complex with the aid of pharmacophore modeling methods. The successful reproduction of sequence specificity of a polyamidic minor groove binding ligand is the precondition for later model application to virtual screening.