Modeling conformational flexibility of kinases in inactive states.

Kinase structures in the inactive "DFG-out" state provide a wealth of druggable binding site variants. The conformational plasticity of this state can be mainly described by different conformations of binding site-forming elements such as DFG motif, A-loop, P-loop, and αC-helix. Compared to DFG-in structures, DFG-out structures are largely underrepresented in the Protein Data Bank (PDB). Thus, structure-based drug design efforts for DFG-out inhibitors may benefit from an efficient approach to generate an ensemble of DFG-out structures. Accordingly, the presented modeling pipeline systematically generates homology models of kinases in several DFG-out conformations based on a sophisticated creation of template structures that represent the major states of the flexible structural elements. Eighteen template classes were initially selected from all available kinase structures in the PDB and subsequently employed for modeling the entire kinome in different DFG-out variants by fusing individual structural elements to multiple chimeric template structures. Molecular dynamics simulations revealed that conformational transitions between the different DFG-out states generally do not occur within trajectories of a few hundred nanoseconds length. This underlines the benefits of the presented homology modeling pipeline to generate relevant conformations of "DFG-out" kinase structures for subsequent in silico screening or binding site analysis studies.

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

Conformational flexibility of DENV NS2B/NS3pro: from the inhibitor effect to the serotype influence.

The dengue virus (DENV) has four well-known serotypes, namely DENV1 to DENV4, which together cause 50-100 million infections worldwide each year. DENV NS2B/NS3pro is a protease recognized as a valid target for DENV antiviral drug discovery. However, NS2B/NS3pro conformational flexibility, involving in particular the NS2B region, is not yet completely understood and, hence, a big challenge for any virtual screening (VS) campaign. Molecular dynamics (MD) simulations were performed in this study to explore the DENV3 NS2B/NS3pro binding-site flexibility and obtain guidelines for further VS studies. MD simulations were done with and without the Bz-nKRR-H inhibitor, showing that the NS2B region stays close to the NS3pro core even in the ligand-free structure. Binding-site conformational states obtained from the simulations were clustered and further analysed using GRID/PCA, identifying four conformations of potential importance for VS studies. A virtual screening applied to a set of 31 peptide-based DENV NS2B/NS3pro inhibitors, taken from literature, illustrated that selective alternative pharmacophore models can be constructed based on conformations derived from MD simulations. For the first time, the NS2B/NS3pro binding-site flexibility was evaluated for all DENV serotypes using homology models followed by MD simulations. Interestingly, the number of NS2B/NS3pro conformational states differed depending on the serotype. Binding-site differences could be identified that may be crucial to subsequent VS studies.

An ordered water channel in Staphylococcus aureus FabI: unraveling the mechanism of substrate recognition and reduction.

One third of all drugs in clinical use owe their pharmacological activity to the functional inhibition of enzymes, highlighting the importance of enzymatic targets for drug development. Because of the close relationship between inhibition and catalysis, understanding the recognition and turnover of enzymatic substrates is essential for rational drug design. Although the Staphylococcus aureus enoyl-acyl carrier protein reductase (saFabI) involved in bacterial fatty acid biosynthesis constitutes a very promising target for the development of novel, urgently needed anti-staphylococcal agents, the substrate binding mode and catalytic mechanism remained unclear for this enzyme. Using a combined crystallographic, kinetic, and computational approach, we have explored the chemical properties of the saFabI binding cavity, obtaining a consistent mechanistic model for substrate binding and turnover. We identified a water-molecule network linking the active site with a water basin inside the homo-tetrameric protein, which seems to be crucial for the closure of the flexible substrate binding loop as well as for an effective hydride and proton transfer during catalysis. On the basis of our results, we also derive a new model for the FabI-ACP complex that reveals how the ACP-bound acyl-substrate is injected into the FabI binding crevice. These findings support the future development of novel FabI inhibitors that target the FabI-ACP interface leading to the disruption of the interaction between these two proteins.

Anti-trypanosomal activities and structural chemical properties of selected compound classes.

Potent compounds do not necessarily make the best drugs in the market. Consequently, with the aim to describe tools that may be fundamental for refining the screening of candidates for animal and preclinical studies and further development, molecules of different structural classes synthesized within the frame of a broad screening platform were evaluated for their trypanocidal activities, cytotoxicities against murine macrophages J774.1 and selectivity indices, as well as for their ligand efficiencies and structural chemical properties. To advance into their modes of action, we also describe the morphological and ultrastructural changes exerted by selected members of each compound class on the parasite Trypanosoma brucei. Our data suggest that the potential organelles targeted are either the flagellar pocket (compound 77, N-Arylpyridinium salt; 15, amino acid derivative with piperazine moieties), the endoplasmic reticulum membrane systems (37, bisquaternary bisnaphthalimide; 77, N-Arylpyridinium salt; 68, piperidine derivative), or mitochondria and kinetoplasts (88, N-Arylpyridinium salt; 68, piperidine derivative). Amino acid derivatives with fumaric acid and piperazine moieties (4, 15) weakly inhibiting cysteine proteases seem to preferentially target acidic compartments. Our results suggest that ligand efficiency indices may be helpful to learn about the relationship between potency and chemical characteristics of the compounds. Interestingly, the correlations found between the physico-chemical parameters of the selected compounds and those of commercial molecules that target specific organelles indicate that our rationale might be helpful to drive compound design toward high activities and acceptable pharmacokinetic properties for all compound families.

MycPermCheck: the Mycobacterium tuberculosis permeability prediction tool for small molecules.

MOTIVATION: With >8 million new cases in 2010, particularly documented in developing countries, tuberculosis (TB) is still a highly present pandemic and often terminal. This is also due to the emergence of antibiotic-resistant strains (MDR-TB and XDR-TB) of the primary causative TB agent Mycobacterium tuberculosis (MTB). Efforts to develop new effective drugs against MTB are restrained by the unique and largely impermeable composition of the mycobacterial cell wall. RESULTS: Based on a database of antimycobacterial substances (CDD TB), 3815 compounds were classified as active and thus permeable. A data mining approach was conducted to gather the physico-chemical similarities of these substances and delimit them from a generic dataset of drug-like molecules. On the basis of the differences in these datasets, a regression model was generated and implemented into the online tool MycPermCheck to predict the permeability probability of small organic compounds. DISCUSSION: Given the current lack of precise molecular criteria determining mycobacterial permeability, MycPermCheck represents an unprecedented prediction tool intended to support antimycobacterial drug discovery. It follows a novel knowledge-driven approach to estimate the permeability probability of small organic compounds. As such, MycPermCheck can be used intuitively as an additional selection criterion for potential new inhibitors against MTB. Based on the validation results, its performance is expected to be of high practical value for virtual screening purposes. AVAILABILITY: The online tool is freely accessible under the URL http://www.mycpermcheck.aksotriffer.pharmazie.uni-wuerzburg.de

A Long Residence Time Enoyl-Reductase Inhibitor Explores an Extended Binding Region with Isoenzyme-Dependent Tautomer Adaptation and Differential Substrate-Binding Loop Closure.

The enoyl-acyl carrier protein (ACP) reductase (ENR) is a key enzyme within the bacterial fatty-acid synthesis pathway. It has been demonstrated that small-molecule inhibitors carrying the diphenylether (DPE) scaffold bear a great potential for the development of highly specific and effective drugs against this enzyme class. Interestingly, different substitution patterns of the DPE scaffold have been shown to lead to varying effects on the kinetic and thermodynamic behavior toward ENRs from different organisms. Here, we investigated the effect of a 4'-pyridone substituent in the context of the slow tight-binding inhibitor SKTS1 on the inhibition of the Staphylococcus aureus enoyl-ACP-reductase saFabI and the closely related isoenzyme from Mycobacterium tuberculosis, InhA, and explored a new interaction site of DPE inhibitors within the substrate-binding pocket. Using high-resolution crystal structures of both complexes in combination with molecular dynamics (MD) simulations, kinetic measurements, and quantum mechanical (QM) calculations, we provide evidence that the 4'-pyridone substituent adopts different tautomeric forms when bound to the two ENRs. We furthermore elucidate the structural determinants leading to significant differences in the residence time of SKTS1 on both enzymes.

Kinome-Wide Profiling Prediction of Small Molecules.

Extensive kinase profiling data, covering more than half of the human kinome, are available nowadays and allow the construction of activity prediction models of high practical utility. Proteochemometric (PCM) approaches use compound and protein descriptors, which enables the extrapolation of bioactivity values to thus far unexplored kinases. In this study, the potential of PCM to make large-scale predictions on the entire kinome is explored, considering the applicability on novel compounds and kinases, including clinically relevant mutants. A rigorous validation indicates high predictive power on left-out kinases and superiority over individual kinase QSAR models for new compounds. Furthermore, external validation on clinically relevant mutant kinases reveals an excellent predictive power for mutations spread across the ATP binding site.

Activity-based classification circumvents affinity prediction problems for pyrrolidine carboxamide inhibitors of InhA.

Developing reliable structure-based activity prediction models for a particular ligand series can be challenging if the target is flexible and the affinity range of the training compounds is narrow. For a data set of 44 pyrrolidine carboxamide inhibitors of the mycobacterial enoyl-ACP-reductase InhA this proved to be case, as scoring methods of various origin and complexity did not succeed in providing practically useful correlations with experimental inhibition data. In contrast, logistic regression models for activity-based classification trained with combinations of scoring functions led to good separation of the more active inhibitors from the weakest compounds. The approach is suggested as an alternative in cases where classical scoring and ranking procedures fail.

From Cancer to Pain Target by Automated Selectivity Inversion of a Clinical Candidate.

Elimination of inadvertent binding is crucial for inhibitor design targeting conserved protein classes like kinases. Compounds in clinical trials provide a rich source for initiating drug design efforts by exploiting such secondary binding events. Considering both aspects, we shifted the selectivity of tozasertib, originally developed against AurA as cancer target, toward the pain target TrkA. First, selectivity-determining features in binding pockets were identified by fusing interaction grids of several key and off-target conformations. A focused library was subsequently created and prioritized using a multiobjective selection scheme that filters for selective and highly active compounds based on orthogonal methods grounded in computational chemistry and machine learning. Eighteen high-ranking compounds were synthesized and experimentally tested. The top-ranked compound has 10000-fold improved selectivity versus AurA, nanomolar cellular activity, and is highly selective in a kinase panel. This was achieved in a single round of automated in silico optimization, highlighting the power of recent advances in computer-aided drug design to automate design and selection processes.

Overcoming safety challenges in CO therapy - Extracorporeal CO delivery under precise feedback control of systemic carboxyhemoglobin levels.

Carbon monoxide (CO) has demonstrated therapeutic potential in multiple inflammatory conditions including intensive care applications such as organ transplantation or sepsis. Approaches to translate these findings into future therapies, however, have been challenged by multiple hurdles including handling and toxicity issues associated with systemic CO delivery. Here, we describe a membrane-controlled Extracorporeal Carbon Monoxide Release System (ECCORS) for easy implementation into Extracorporeal Membrane Oxygenation (ECMO) setups, which are being used to treat cardiac and respiratory diseases in various intensive care applications. Functionalities of the ECCORS were investigated in a pig model of veno-arterial ECMO. By precisely controlling CO generation and delivery as a function of systemic carboxyhemoglobin levels, the system allows for an immediate onset of therapeutic CO-levels while preventing CO-toxicity. Systemic carboxyhemoglobin levels were profiled in real-time by monitoring exhaled CO levels as well as by pulse oximetry, enabling self-contained and automatic feedback control of CO generation within ECCORS. Machine learning based mathematical modeling was performed to increase the predictive power of this approach, laying foundation for high precision systemic CO delivery concepts of tomorrow.

Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay.

Kinome-wide screening would have the advantage of providing structure-activity relationships against hundreds of targets simultaneously. Here, we report the generation of ligand-based activity prediction models for over 280 kinases by employing Machine Learning methods on an extensive data set of proprietary bioactivity data combined with open data. High quality (AUC > 0.7) was achieved for ∼200 kinases by (1) combining open with proprietary data, (2) choosing Random Forest over alternative tested Machine Learning methods, and (3) balancing the training data sets. Tests on left-out and external data indicate a high value for virtual screening projects. Importantly, the derived models are evenly distributed across the kinome tree, allowing reliable profiling prediction for all kinase branches. The prediction quality was further improved by employing experimental bioactivity fingerprints of a small kinase subset. Overall, the generated models can support various hit identification tasks, including virtual screening, compound repurposing, and the detection of potential off-targets.

Mapping the pharmaceutical design space by amorphous ionic liquid strategies.

Poor water solubility of drugs fuels complex formulations and jeopardizes patient access to medication. Simplifying these complexities we systematically synthesized a library of 36 sterically demanding counterions and mapped the pharmaceutical design space for amorphous ionic liquid strategies for Selurampanel, a poorly water soluble drug used against migraine. Patients would benefit from a rapid uptake after oral administration to alleviate migraine symptoms. Therefore, we probed the ionic liquids for the flux, supersaturation period and hygroscopicity leading to algorithms linking molecular counterion descriptors to predicted pharmaceutical outcome. By that, 30- or 800-fold improvements of the supersaturation period and fluxes were achieved as were immediate to sustained release profiles through structural counterions' optimization compared to the crystalline free acid of Selurampanel. Guided by ionic liquid structure, in vivo profiles ranged from rapid bioavailability and high maximal plasma concentrations to sustained patterns. In conclusion, the study outlined and predicted the accessible pharmaceutical design space of amorphous ionic liquid based and excipient-free formulations pointing to the enormous pharmaceutical potential of ionic liquid designs.

Slow-Onset Inhibition of Mycobacterium tuberculosis InhA: Revealing Molecular Determinants of Residence Time by MD Simulations.

An important kinetic parameter for drug efficacy is the residence time of a compound at a drug target, which is related to the dissociation rate constant koff. For the essential antimycobacterial target InhA, this parameter is most likely governed by the ordering of the flexible substrate binding loop (SBL). Whereas the diphenyl ether inhibitors 6PP and triclosan (TCL) do not show loop ordering and thus, no slow-binding inhibition and high koff values, the slightly modified PT70 leads to an ordered loop and a residence time of 24 minutes. To assess the structural differences of the complexes from a dynamic point of view, molecular dynamics (MD) simulations with a total sampling time of 3.0 µs were performed for three ligand-bound and two ligand-free (perturbed) InhA systems. The individual simulations show comparable conformational features with respect to both the binding pocket and the SBL, allowing to define five recurring conformational families. Based on their different occurrence frequencies in the simulated systems, the conformational preferences could be linked to structural differences of the respective ligands to reveal important determinants of residence time. The most abundant conformation besides the stable EI* state is characterized by a shift of Ile202 and Val203 toward the hydrophobic pocket of InhA. The analyses revealed potential directions for avoiding this conformational change and, thus, hindering rapid dissociation: (1) an anchor group in 2'-position of the B-ring for scaffold stabilization, (2) proper occupation of the hydrophobic pocket, and (3) the introduction of a barricade substituent in 5'-position of the diphenyl ether B-ring.

Predicting critical micelle concentration and micelle molecular weight of polysorbate 80 using compendial methods.

This manuscript addresses the capability of compendial methods in controlling polysorbate 80 (PS80) functionality. Based on the analysis of sixteen batches, functionality related characteristics (FRC) including critical micelle concentration (CMC), cloud point, hydrophilic-lipophilic balance (HLB) value and micelle molecular weight were correlated to chemical composition including fatty acids before and after hydrolysis, content of non-esterified polyethylene glycols and sorbitan polyethoxylates, sorbitan- and isosorbide polyethoxylate fatty acid mono- and diesters, polyoxyethylene diesters, and peroxide values. Batches from some suppliers had a high variability in functionality related characteristic (FRC), questioning the ability of the current monograph in controlling these. Interestingly, the combined use of the input parameters oleic acid content and peroxide value - both of which being monographed methods - resulted in a model adequately predicting CMC. Confining the batches to those complying with specifications for peroxide value proved oleic acid content alone as being predictive for CMC. Similarly, a four parameter model based on chemical analyses alone was instrumental in predicting the molecular weight of PS80 micelles. Improved models based on analytical outcome from fingerprint analyses are also presented. A road map controlling PS80 batches with respect to FRC and based on chemical analyses alone is provided for the formulator.

Acetylcholinesterase inhibitors with photoswitchable inhibition of ß-amyloid aggregation.

Photochromic cholinesterase inhibitors were obtained from cis-1,2-α-dithienylethene-based compounds by incorporating one or two aminopolymethylene tacrine groups. All target compounds are potent acetyl- (AChE) and butyrylcholinesterase (BChE) inhibitors in the nanomolar concentration range. Compound 11b bearing an octylene linker exhibited interactions with both the catalytic active site (CAS) and the peripheral anionic site (PAS) of AChE. Yet upon irradiation with light, the mechanism of interaction varied from one photochromic form to another, which was investigated by kinetic studies and proved "photoswitchable". The AChE-induced ß-amyloid (Aß) aggregation assay gave further experimental support to this finding: Aß1-40 aggregation catalyzed by the PAS of AChE might be inhibited by compound 11b in a concentration-dependent manner and seems to occur only with one photochromic form. Computational docking studies provided potential binding modes of the compound. Docking studies and molecular dynamics (MD) simulations for the ring-open and -closed form indicate a difference in binding. Although both forms can interact with the PAS, more stable interactions are observed for the ring-open form based upon stabilization of a water molecule network within the enzyme, whereas the ring-closed form lacks the required conformational flexibility for an analogous binding mode. The photoswitchable inhibitor identified might serve as valuable molecular tool to investigate the different biological properties of AChE as well as its role in pathogenesis of AD in in vitro assays.

The ITS2 Database.

The internal transcribed spacer 2 (ITS2) has been used as a phylogenetic marker for more than two decades. As ITS2 research mainly focused on the very variable ITS2 sequence, it confined this marker to low-level phylogenetics only. However, the combination of the ITS2 sequence and its highly conserved secondary structure improves the phylogenetic resolution(1) and allows phylogenetic inference at multiple taxonomic ranks, including species delimitation(2-8). The ITS2 Database(9) presents an exhaustive dataset of internal transcribed spacer 2 sequences from NCBI GenBank(11) accurately reannotated(10). Following an annotation by profile Hidden Markov Models (HMMs), the secondary structure of each sequence is predicted. First, it is tested whether a minimum energy based fold(12) (direct fold) results in a correct, four helix conformation. If this is not the case, the structure is predicted by homology modeling(13). In homology modeling, an already known secondary structure is transferred to another ITS2 sequence, whose secondary structure was not able to fold correctly in a direct fold. The ITS2 Database is not only a database for storage and retrieval of ITS2 sequence-structures. It also provides several tools to process your own ITS2 sequences, including annotation, structural prediction, motif detection and BLAST(14) search on the combined sequence-structure information. Moreover, it integrates trimmed versions of 4SALE(15,16) and ProfDistS(17) for multiple sequence-structure alignment calculation and Neighbor Joining(18) tree reconstruction. Together they form a coherent analysis pipeline from an initial set of sequences to a phylogeny based on sequence and secondary structure. In a nutshell, this workbench simplifies first phylogenetic analyses to only a few mouse-clicks, while additionally providing tools and data for comprehensive large-scale analyses.

Internal transcribed spacer 2 (nu ITS2 rRNA) sequence-structure phylogenetics: towards an automated reconstruction of the green algal tree of life.

BACKGROUND: Chloroplast-encoded genes (matK and rbcL) have been formally proposed for use in DNA barcoding efforts targeting embryophytes. Extending such a protocol to chlorophytan green algae, though, is fraught with problems including non homology (matK) and heterogeneity that prevents the creation of a universal PCR toolkit (rbcL). Some have advocated the use of the nuclear-encoded, internal transcribed spacer two (ITS2) as an alternative to the traditional chloroplast markers. However, the ITS2 is broadly perceived to be insufficiently conserved or to be confounded by introgression or biparental inheritance patterns, precluding its broad use in phylogenetic reconstruction or as a DNA barcode. A growing body of evidence has shown that simultaneous analysis of nucleotide data with secondary structure information can overcome at least some of the limitations of ITS2. The goal of this investigation was to assess the feasibility of an automated, sequence-structure approach for analysis of IT2 data from a large sampling of phylum Chlorophyta. METHODOLOGY/PRINCIPAL FINDINGS: Sequences and secondary structures from 591 chlorophycean, 741 trebouxiophycean and 938 ulvophycean algae, all obtained from the ITS2 Database, were aligned using a sequence structure-specific scoring matrix. Phylogenetic relationships were reconstructed by Profile Neighbor-Joining coupled with a sequence structure-specific, general time reversible substitution model. Results from analyses of the ITS2 data were robust at multiple nodes and showed considerable congruence with results from published phylogenetic analyses. CONCLUSIONS/SIGNIFICANCE: Our observations on the power of automated, sequence-structure analyses of ITS2 to reconstruct phylum-level phylogenies of the green algae validate this approach to assessing diversity for large sets of chlorophytan taxa. Moreover, our results indicate that objections to the use of ITS2 for DNA barcoding should be weighed against the utility of an automated, data analysis approach with demonstrated power to reconstruct evolutionary patterns for highly divergent lineages.

A molecular phylogeny of Hypnales (Bryophyta) inferred from ITS2 sequence-structure data.

BACKGROUND: Hypnales comprise over 50% of all pleurocarpous mosses. They provide a young radiation complicating phylogenetic analyses. To resolve the hypnalean phylogeny, it is necessary to use a phylogenetic marker providing highly variable features to resolve species on the one hand and conserved features enabling a backbone analysis on the other. Therefore we used highly variable internal transcribed spacer 2 (ITS2) sequences and conserved secondary structures, as deposited with the ITS2 Database, simultaneously. FINDINGS: We built an accurate and in parts robustly resolved large scale phylogeny for 1,634 currently available hypnalean ITS2 sequence-structure pairs. CONCLUSIONS: Profile Neighbor-Joining revealed a possible hypnalean backbone, indicating that most of the hypnalean taxa classified as different moss families are polyphyletic assemblages awaiting taxonomic changes.

Evolutionary distances in the twilight zone--a rational kernel approach.

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.