Thermolysin Inhibitors Research Articles

Zinc-Chelating Compounds as Inhibitors of Human and Bacterial Zinc Metalloproteases.

Inhibition of bacterial virulence is believed to be a new treatment option for bacterial infections. In the present study, we tested dipicolylamine (DPA), tripicolylamine (TPA), tris pyridine ethylene diamine (TPED), pyridine and thiophene derivatives as putative inhibitors of the bacterial virulence factors thermolysin (TLN), pseudolysin (PLN) and aureolysin (ALN) and the human zinc metalloproteases, matrix metalloprotease-9 (MMP-9) and matrix metalloprotease-14 (MMP-14). These compounds have nitrogen or sulfur as putative donor atoms for zinc chelation. In general, the compounds showed stronger inhibition of MMP-14 and PLN than of the other enzymes, with Ki values in the lower μM range. Except for DPA, none of the compounds showed significantly stronger inhibition of the virulence factors than of the human zinc metalloproteases. TPA and Zn230 were the only compounds that inhibited all five zinc metalloproteinases with a Ki value in the lower μM range. The thiophene compounds gave weak or no inhibition. Docking indicated that some of the compounds coordinated zinc by one oxygen atom from a hydroxyl or carbonyl group, or by oxygen atoms both from a hydroxyl group and a carbonyl group, and not by pyridine nitrogen as in DPA and TPA.

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases.

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

Paying the Price of Desolvation in Solvent-Exposed Protein Pockets: Impact of Distal Solubilizing Groups on Affinity and Binding Thermodynamics in a Series of Thermolysin Inhibitors.

In lead optimization, open, solvent-exposed protein pockets are often disregarded as prospective binding sites. Because of bulk-solvent proximity, researchers are instead enticed to attach charged polar groups at inhibitor scaffolds to improve solubility and pharmacokinetic properties. It is rarely considered that solvent effects from water reorganization in the first hydration shell of protein-ligand complexes can have a significant impact on binding. We investigate the thermodynamic fingerprint of thermolysin inhibitors featuring terminal charged ammonium groups that are gradually pulled from a distal, solvent-exposed position into the flat, bowl-shaped S2' pocket. Even for the most remote attachment, costs for partial desolvation of the polar group next to the protein-solvent interface are difficult to compensate by interactions with the protein or surrounding water molecules. Through direct comparison with hydrophobic analogues, a significant 180-fold affinity loss was recorded, which questions popular strategies to attach polar ligand-solubilizing groups at the exposed terminus of substituents accommodated in flat open pockets.

Elucidating the Origin of Long Residence Time Binding for Inhibitors of the Metalloprotease Thermolysin.

Kinetic parameters of protein-ligand interactions are progressively acknowledged as valuable information for rational drug discovery. However, a targeted optimization of binding kinetics is not easy to achieve, and further systematic studies are necessary to increase the understanding about molecular mechanisms involved. We determined association and dissociation rate constants for 17 inhibitors of the metalloprotease thermolysin by surface plasmon resonance spectroscopy and correlated kinetic data with high-resolution crystal structures in complex with the protein. From the structure-kinetics relationship, we conclude that the strength of interaction with Asn112 correlates with the rate-limiting step of dissociation. This residue is located at the beginning of a β-strand motif that lines the binding cleft and is commonly believed to align a substrate for catalysis. A reduced mobility of the Asn112 side chain owing to an enhanced engagement in charge-assisted hydrogen bonds prevents the conformational adjustment associated with ligand release and transformation of the enzyme to its open state. This hypothesis is supported by kinetic data of ZFPLA, a known pseudopeptidic inhibitor of thermolysin, which blocks the conformational transition of Asn112. Interference with this retrograde induced-fit mechanism results in variation of the residence time of thermolysin inhibitors by a factor of 74 000. The high conservation of this structural motif within the M4 and M13 metalloprotease families underpins the importance of this feature and has significant implications for drug discovery.

Rational Design of Thermodynamic and Kinetic Binding Profiles by Optimizing Surface Water Networks Coating Protein-Bound Ligands.

A previously studied congeneric series of thermolysin inhibitors addressing the solvent-accessible S2' pocket with different hydrophobic substituents showed modulations of the surface water layers coating the protein-bound inhibitors. Increasing stabilization of water molecules resulted in an enthalpically more favorable binding signature, overall enhancing affinity. Based on this observation, we optimized the series by designing tailored P2' substituents to improve and further stabilize the surface water network. MD simulations were applied to predict the putative water pattern around the bound ligands. Subsequently, the inhibitors were synthesized and characterized by high-resolution crystallography, microcalorimetry, and surface plasmon resonance. One of the designed inhibitors established the most pronounced water network of all inhibitors tested so far, composed of several fused water polygons, and showed 50-fold affinity enhancement with respect to the original methylated parent ligand. Notably, the inhibitor forming the most perfect water network also showed significantly prolonged residence time compared to the other tested inhibitors.

An approach to identify new antihypertensive agents using Thermolysin as model: In silico study based on QSARINS and docking

Thermolysin is a bacterial proteolytic enzyme, considered by many authors as a pharmacological and biological model of other mammalian enzymes, with similar structural characteristics, such as angiotensin converting enzyme and neutral endopeptidase. Inhibitors of these enzymes are considered therapeutic targets for common diseases, such as hypertension and heart failure. In this report, a mathematical model of Multiple Linear Regression, for ordinary least squares, and genetic algorithm, for selection of variables, are developed and implemented in QSARINS software, with appropriate parameters for its fitting. The model is extensively validated according to OECD standards, so that its robustness, stability, low correlation of descriptors and good predictive power are proven. In addition, it is found that the model fit is not the product of a random correlation. Two possible outliers are identified in the model application domain but, in a molecular docking study, they show good activity, so we decide to keep both in our database. Finally, 141 and 69 compounds (2.5⩽pKi<3.5 and pKi⩾3.5, respectively) are identified as potential Thermolysin inhibitors, concluding that the proposed computational tools are an efficient method for the identification of new drugs that could inhibit this enzyme.

N-tuple topological/geometric cutoffs for 3D N-linear algebraic molecular codifications: variability, linear independence and QSAR analysis

Novel N-tuple topological/geometric cutoffs to consider specific inter-atomic relations in the QuBiLS-MIDAS framework are introduced in this manuscript. These molecular cutoffs permit the taking into account of relations between more than two atoms by using (dis-)similarity multi-metrics and the concepts related with topological and Euclidean-geometric distances. To this end, the kth two-, three- and four-tuple topological and geometric neighbourhood quotient (NQ) total (or local-fragment) spatial-(dis)similarity matrices are defined, to represent 3D information corresponding to the relations between two, three and four atoms of the molecular structures that satisfy certain cutoff criteria. First, an analysis of a diverse chemical space for the most common values of topological/Euclidean-geometric distances, bond/dihedral angles, triangle/quadrilateral perimeters, triangle area and volume was performed in order to determine the intervals to take into account in the cutoff procedures. A variability analysis based on Shannon’s entropy reveals that better distribution patterns are attained with the descriptors based on the cutoffs proposed (QuBiLS-MIDAS NQ-MDs) with regard to the results obtained when all inter-atomic relations are considered (QuBiLS-MIDAS KA-MDs – ‘Keep All’). A principal component analysis shows that the novel molecular cutoffs codify chemical information captured by the respective QuBiLS-MIDAS KA-MDs, as well as information not captured by the latter. Lastly, a QSAR study to obtain deeper knowledge of the contribution of the proposed methods was carried out, using four molecular datasets (steroids (STER), angiotensin converting enzyme (ACE), thermolysin inhibitors (THER) and thrombin inhibitors (THR)) widely used as benchmarks in the evaluation of several methodologies. One to four variable QSAR models based on multiple linear regression were developed for each compound dataset following the original division into training and test sets. The results obtained reveal that the novel cutoff procedures yield superior performances relative to those of the QuBiLS-MIDAS KA-MDs in the prediction of the biological activities considered. From the results achieved, it can be suggested that the proposed N-tuple topological/geometric cutoffs constitute a relevant criteria for generating MDs codifying particular atomic relations, ultimately useful in enhancing the modelling capacity of the QuBiLS-MIDAS 3D-MDs.

Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets.

BackgroundRecently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed.ResultsTo this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficientsleft( {Q_{loo}^{2} } right) reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficientsleft( {Q_{ext}^{2} } right) are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the Q_{ext}^{2} values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them.ConclusionsAs conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasetsElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0122-x) contains supplementary material, which is available to authorized users.

Development and validation of hydrophobic molecular fields derived from the quantum mechanical IEF/PCM-MST solvation models in 3D-QSAR.

Since the development of structure-activity relationships about 50 years ago, 3D-QSAR methods belong to the most refined ligand-based in silico techniques for prediction of biological data using physicochemical molecular fields. In this scenario, this study reports the development and validation of quantum mechanical (QM)-based hydrophobic descriptors derived from the parametrized MST continuum solvation model to be used in 3D-QSAR studies within the framework of the Hydrophobic Pharmacophore (HyPhar) method. To this end, five sets of compounds reported in the literature (dopamine D2/D4 antagonists, antifungal 2-aryl-4-chromanones, and inhibitors of GSK-3, cruzain and thermolysin) have been revisited. The results derived from the QM/MST-based hydrophobic descriptors have been compared with previous CoMFA and CoMSIA studies, and examined in light of the available X-ray crystallographic structures of the targets. The analysis reveals that the combination of electrostatic and nonelectrostatic components of the octanol/water partition coefficient yields pharmacophoric models fully comparable with the predictive potential of standard 3D-QSAR techniques. Moreover, the graphical representation of the hydrophobic maps provides a direct linkage with the pattern of interactions found in crystallographic structures. Overall, the introduction of the QM/MST-based descriptors, which could be easily adapted to other continuum solvation formalisms, paves the way to novel computational strategies for disclosing structure-activity relationships in drug design. © 2016 Wiley Periodicals, Inc.

Synthesis, experimental evaluation and molecular modelling of hydroxamate derivatives as zinc metalloproteinase inhibitors

Enzymes of the M4 family of zinc-metalloproteinases are virulence factors secreted from gram-positive or gram-negative bacteria, and putative drug targets in the treatment of bacterial infections. In order to have a therapeutic value such inhibitors should not interfere with endogenous zinc-metalloproteinases. In the present study we have synthesised a series of hydroxamate derivatives and validated the compounds as inhibitors of the M4 enzymes thermolysin and pseudolysin, and the endogenous metalloproteinases ADAM-17, MMP-2 and MMP-9 using experimental binding studies and molecular modelling. In general, the compounds are stronger inhibitors of the MMPs than of the M4 enzymes, however, an interesting exception is LM2. The compounds bound stronger to pseudolysin than to thermolysin, and the molecular modelling studies showed that occupation of the S2′ subpocket by an aromatic group is favourable for strong interactions with pseudolysin.

The synthesis of optically enriched 2-benzyl- 3-nitropropionic amide

AbstractThe present communication describes a valuable procedure for the short and facile synthesis of a thermolysin inhibitor, i.e. optically enriched 2-benzyl-3-nitropropionic amide, successfully synthesized by virtue of a diastereoisomeric mixture intermediate, starting from commercially available

Additivity or cooperativity: which model can predict the influence of simultaneous incorporation of two or more functionalities in a ligand molecule?

Predicting how binding affinity responds to ligand structural modifications in structure-activity relationship studies (SAR) is a major challenge in medicinal chemistry. This is particularly true when two or more of these modifications are carried out simultaneously. In this study, we present binding affinity data from several series of thermolysin inhibitors in which simultaneous structural modifications were investigated to determine whether they are cooperative or additive. Data revealed that, while additivity is at work in some cases, cooperativity is more commonly demonstrated. Cooperativity and additivity were then correlated with ligand descriptors, such as the spacing and the topological features of the modified groups, in a manner that may provide guidance as to when each model should be utilized. Cooperativity was particularly associated with contiguous groups and small unbranched hydrophobic side chain. Additivity, on the other hand, was associated with moderately distant hydrophobic group combinations and side chain branching. Such correlations can improve the predictability of SAR studies and can provide a starting point for additional investigations that may lead to further significant enhancements in the current scoring functions.

Corrigendum: Methyl, Ethyl, Propyl, Butyl: Futile But Not for Water, as the Correlation of Structure and Thermodynamic Signature Shows in a Congeneric Series of Thermolysin Inhibitors

Corrigendum: Methyl, Ethyl, Propyl, Butyl: Futile But Not for Water, as the Correlation of Structure and Thermodynamic Signature Shows in a Congeneric Series of Thermolysin Inhibitors

Methyl, Ethyl, Propyl, Butyl: Futile But Not for Water, as the Correlation of Structure and Thermodynamic Signature Shows in a Congeneric Series of Thermolysin Inhibitors

Water is ubiquitously present in any biological system and has therefore to be regarded as an additional binding partner in the protein-ligand binding process. Upon complex formation, a new solvent-exposed surface is generated and water molecules from the first solvation layer will arrange around this newly formed surface. So far, the influence of such water arrangements on the ligand binding properties is unknown. In this study, the binding modes of nine congeneric phosphonamidate-type inhibitors with systematically varied, size-increasing hydrophobic P2 ' substituents (from methyl to phenylethyl) addressing the hydrophobic, solvent-exposed S2 ' pocket of thermolysin were analyzed by high-resolution crystal structures and correlated with their thermodynamic binding profiles as measured by isothermal titration calorimetry. Overall, ΔΔG spreads over 7.0 kJ mol(-1) , ΔΔH varies by 15.8 kJ mol(-1) , and -TΔΔS by 12.1 kJ mol(-1) . Throughout the series, these changes correlate remarkably well with the geometric differences of water molecules arranged adjacent to the P2 ' substituents. Ligands with medium-sized P2 ' substituents exhibit highest affinities, presumably because of their optimal solvation patterns around these complexes. The addition, removal, or rearrangement of even a single methyl group can result in a strong modulation of the adjacent water network pattern shifting from enthalpy to entropy-driven binding. In conclusion, the quality of a water network assembled around a protein-ligand complex influences the enthalpy/entropy signature and can even modulate affinity to a surprising extent.

Quantum Mechanics-Based Properties for 3D-QSAR

We have used a set of four local properties based on semiempirical molecular orbital calculations (electron density (ρ), hydrogen bond donor field (HDF), hydrogen bond acceptor field (HAF), and molecular lipophilicity potential (MLP)) for 3D-QSAR studies to overcome the limitations of the current force field-based molecular interaction fields (MIFs). These properties can be calculated rapidly and are thus amenable to high-throughput industrial applications. Their statistical performance was compared with that of conventional 3D-QSAR approaches using nine data sets (angiotensin converting enzyme inhibitors (ACE), acetylcholinesterase inhibitors (AchE), benzodiazepine receptor ligands (BZR), cyclooxygenase-2 inhibitors (COX2), dihydrofolate reductase inhibitors (DHFR), glycogen phosphorylase b inhibitors (GPB), thermolysin inhibitors (THER), thrombin inhibitors (THR), and serine protease factor Xa inhibitors (fXa)). The 3D-QSAR models generated were tested thoroughly for robustness and predictive ability. The average performance of the quantum mechanical molecular interaction field (QM-MIF) models for the nine data sets is better than that of the conventional force field-based MIFs. In the individual data sets, the QM-MIF models always perform better than, or as well as, the conventional approaches. It is particularly encouraging that the relative performance of the QM-MIF models improves in the external validation. In addition, the models generated showed statistical stability with respect to model building procedure variations such as grid spacing size and grid orientation. QM-MIF contour maps reproduce the features important for ligand binding for the example data set (factor Xa inhibitors), demonstrating the intuitive chemical interpretability of QM-MIFs.

Comparative Occupancy Analysis (CoOAn) - A Straightforward and Directly Applicable 3D-QSAR Formalism to Extract Molecular Features Obligatory for Designing Potent Leads.

A simple and directly applicable 3D-QSAR method, termed Comparative Occupancy Analysis (CoOAn), has been developed. The method is based on the comparison of local occupancies of fragments of an aligned set of molecules in a 3D-grid space. The formalism commendably extracts the crucial position-specific molecular features and correlates them quantitatively to their biological endpoints. The method has been effectively applied and efficaciously validated on three large and diverse datasetsthrombin, glycogen phosphorylase b (GPB), and thermolysin inhibitors. Several robust and statistically significant predictive 3D-QSAR models were developed while simultaneously considering the influence of grid spacing on the accuracy of the results. The models, generated by the G/PLS chemometric method, not only unswervingly identified the obligatory chemical features but advantageously detected those that are unfavourable or detrimental for the molecular activity. The CoOAn models can profitably be used to optimize existing molecules as well as to design new leads with more desirable (and/or less detrimental) features. The activity-modulating features (together with their distance-constraints) extracted by the methodology can also be incorporated into a pharmacophore-type query to search a chemical database for novel leads.

Water Makes the Difference: Rearrangement of Water Solvation Layer Triggers Non‐additivity of Functional Group Contributions in Protein–Ligand Binding

The binding of four congeneric peptide-like thermolysin inhibitors has been studied by high-resolution crystal structure analysis and isothermal titration calorimetry. The ligands differ only by a terminal carboxylate and/or methyl group. A surprising non-additivity of functional group contributions for the carboxylate and/or methyl groups is detected. Adding the methyl first and then the carboxylate group results in a small Gibbs free energy increase and minor enthalpy/entropy partitioning for the first modification, whereas the second involves a strong affinity increase combined with large enthalpy/entropy changes. However, first adding the carboxylate and then the methyl group yields reverse effects: the acidic group attachment now causes minor effects, whereas the added methyl group provokes large changes. As all crystal structures show virtually identical binding modes, affinity changes are related to rearrangements of the first solvation layer next to the S(2)' pocket. About 20-25 water molecules are visible next to the studied complexes. The added COO(-) groups perturb the local water network in both carboxylated complexes, and the attached methyl groups provide favorable interaction sites for water molecules. Apart from one example, a contiguously connected water network between protein and ligand functional groups is observed in all complexes. In the complex with the carboxylated ligand, which still lacks the terminal methyl group, the water network is unfavorably ruptured. This results in a surprising thermodynamic signature showing only a minor affinity increase upon COO(-) group attachment. Because the further added methyl group provides a favorable interaction site for water, the network can be reestablished, and a strong affinity increase with a large enthalpy/entropy signature is then detected.

Binding Modes and Pharmacophore Modelling of Thermolysin Inhibitors

In the present paper 25 known thermolysin inhibitors were docked into thermolysin using the Internal Coordinate Mechanics (ICM) software. Pharmacophore models based on thermolysin binding modes and activity profiles were generated using the LigandScout program. The docking studies indicated that all 25 inhibitors coordinated the catalytic zinc in bidentate or monodentate geometry. A 'three-point' pharmacophore model was proposed which consisted of a hydrophobic group, a negative ionizable group and a hydrogen bond acceptor group. Finally the pharmacophore model has been tested against a small compound library containing 18 highly, moderately, less active as well as inactive compounds. The screening indicated that the pharmacophore model could, identify highly active compounds in front of inactive or less active ones.

Identification of novel quinazolin-4(3 H)-ones as inhibitors of thermolysin, the prototype of the M4 family of proteinases

A combinatorial series of novel quinazolin-4(3 H)-ones were synthesised and their structures were established based on spectroscopic data (IR, NMR, EI-MS, and FAB-MS). The compounds were tested for inhibition of the zinc metalloproteinase thermolysin (TLN) utilizing a chemical array-based approach. Some of the compounds were found to inhibit TLN, with IC 50 values ranging from 0.0115 μM (compound 3) to 122,637 μM (compound 29). Compound 3 [3-phenyl-2-(trifluoromethyl) quinazolin-4(3 H)-one] (IC 50 = 0.0115 μM) and compound 35 [3-(isopropylideneamino)-2,2-dimethyl-2,3-dihydroquinazolin-4 (1 H)-one] (IC 50 = 0.2477 μM) were found to be the most potent inhibitors.

Local Indices for Similarity Analysis (LISA)—A 3D-QSAR Formalism Based on Local Molecular Similarity

A simple quantitative structure activity relationship (QSAR) approach termed local indices for similarity analysis (LISA) has been developed. In this technique, the global molecular similarity is broken up as local similarity at each grid point surrounding the molecules and is used as a QSAR descriptor. In this way, a view of the molecular sites permitting favorable and rational changes to enhance activity is obtained. The local similarity index, calculated on the basis of Petke's formula, segregates the regions into "equivalent", "favored similar", and "disfavored similar" (alternatively "favored dissimilar") potentials with respect to a reference molecule in the data set. The method has been tested on three large and diverse data sets-thrombin, glycogen phosphorylase b, and thermolysin inhibitors. The QSAR models derived using genetic algorithm incorporated partial least square analysis statistics are found to be comparable to the ones obtained by the standard three-dimensional (3D)-QSAR methods, such as comparative molecular field analysis and comparative molecular similarity indices analysis. The graphical interpretation of the LISA models is straightforward, and the outcome of the models corroborates well with literature data. The LISA models give insight into the binding mechanisms of the ligand with the enzyme and allow fine-tuning of the molecules at the local level to improve their activity.