Capturing time-dependent activation of genes and stress-response pathways using transcriptomics in iPSC-derived renal proximal tubule cells

Transcriptomic analysis is a powerful method in the utilization of New Approach Methods (NAMs) for identifying mechanisms of toxicity and application to hazard characterization. With this regard, mapping toxicological events to time of exposure would be helpful to characterize early events. Here, we investigated time-dependent changes in gene expression levels in iPSC-derived renal proximal tubular-like cells (PTL) treated with five diverse compounds using TempO-Seq transcriptomics with the aims to evaluate the application of PTL for toxicity prediction and to report on temporal effects for the activation of cellular stress response pathways. PTL were treated with either 50 μM amiodarone, 10 μM sodium arsenate, 5 nM rotenone, or 300 nM tunicamycin over a temporal time course between 1 and 24 h. The TGFβ-type I receptor kinase inhibitor GW788388 (1 μM) was used as a negative control. Pathway analysis revealed the induction of key stress-response pathways, including Nrf2 oxidative stress response, unfolding protein response, and metal stress response. Early response genes per pathway were identified much earlier than 24 h and included HMOX1, ATF3, DDIT3, and several MT1 isotypes. GW788388 did not induce any genes within the stress response pathways above, but showed deregulation of genes involved in TGFβ inhibition, including downregulation of CYP24A1 and SERPINE1 and upregulation of WT1. This study highlights the application of iPSC-derived renal cells for prediction of cellular toxicity and sheds new light on the temporal and early effects of key genes that are involved in cellular stress response pathways.

European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials

Management of nanomaterials and nanosafety data needs to operate under the FAIR (findability, accessibility, interoperability, and reusability) principles and this requires a unique, global identifier for each nanomaterial. Existing identifiers may not always be applicable or sufficient to definitively identify the specific nanomaterial used in a particular study, resulting in the use of textual descriptions in research project communications and reporting. To ensure that internal project documentation can later be linked to publicly released data and knowledge for the specific nanomaterials, or even to specific batches and variants of nanomaterials utilised in that project, a new identifier is proposed: the European Registry of Materials Identifier. We here describe the background to this new identifier, including FAIR interoperability as defined by FAIRSharing, identifiers.org, Bioregistry, and the CHEMINF ontology, and show how it complements other identifiers such as CAS numbers and the ongoing efforts to extend the InChI identifier to cover nanomaterials. We provide examples of its use in various H2020-funded nanosafety projects.

Towards a qAOP framework for predictive toxicology - Linking data to decisions

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Chemical toxicity assessment depends on the quantification of kinetics and dynamics.

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Quantitative AOPs (qAOPs) are toxicodynamic models based on Adverse Outcome Pathways.

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Existing e-resources could form the basis of an e-infrastructure for qAOP modelling.

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Best practices for qAOP development, assessment and application are needed.

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Three qAOP case studies are presented to illustrate a modelling workflow.

The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including in silico, in vitro and in vivo assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.

Temporal transcriptomic alterations of cadmium exposed human iPSC-derived renal proximal tubule-like cells

Cadmium is a well-studied environmental pollutant where the kidney and particularly the proximal tubule cells are especially sensitive as they are exposed to higher concentrations of cadmium than other tissues. Here we investigated the temporal transcriptomic alterations (TempO-Seq) of human induced pluripotent stem cell (iPSC)-derived renal proximal tubule-like (PTL) cells exposed to 5 μM cadmium chloride for 1, 2, 4, 8, 12, 16, 20, 24, 72 and 168 h. There was an early activation (within 4 h) of the metal and oxidative stress responses (metal-responsive transcription factor-1 (MTF1) and nuclear factor erythroid-2-related factor 2 (Nrf2) genes). The Nrf2 response returned to baseline within 24 h. The Activator Protein 1 (AP-1) regulated genes HSPA6 and FOSL-1 followed the Nrf2 time course. While the MTF1 genes also spiked at 4 h, they remained strongly elevated over the entire exposure period. The data and cell culture model utilised will be useful in further research aimed at the refinement of safe human exposure limits for cadmium, other metals and their mixtures.

Metadata Stewardship in Nanosafety Research: Community-Driven Organisation of Metadata Schemas to Support FAIR Nanoscience Data

The emergence of nanoinformatics as a key component of nanotechnology and nanosafety assessment for the prediction of engineered nanomaterials (NMs) properties, interactions, and hazards, and for grouping and read-across to reduce reliance on animal testing, has put the spotlight firmly on the need for access to high-quality, curated datasets. To date, the focus has been around what constitutes data quality and completeness, on the development of minimum reporting standards, and on the FAIR (findable, accessible, interoperable, and reusable) data principles. However, moving from the theoretical realm to practical implementation requires human intervention, which will be facilitated by the definition of clear roles and responsibilities across the complete data lifecycle and a deeper appreciation of what metadata is, and how to capture and index it. Here, we demonstrate, using specific worked case studies, how to organise the nano-community efforts to define metadata schemas, by organising the data management cycle as a joint effort of all players (data creators, analysts, curators, managers, and customers) supervised by the newly defined role of data shepherd. We propose that once researchers understand their tasks and responsibilities, they will naturally apply the available tools. Two case studies are presented (modelling of particle agglomeration for dose metrics, and consensus for NM dissolution), along with a survey of the currently implemented metadata schema in existing nanosafety databases. We conclude by offering recommendations on the steps forward and the needed workflows for metadata capture to ensure FAIR nanosafety data.

A semi-automated workflow for adverse outcome pathway hypothesis generation: The use case of non-genotoxic induced hepatocellular carcinoma

The utility of the Adverse Outcome Pathway (AOP) concept has been largely recognized by scientists, however, the AOP generation is still mainly done manually by screening through evidence and extracting probable associations. To accelerate this process and increase the reliability, we have developed an semi-automated workflow for AOP hypothesis generation. In brief, association mining methods were applied to high-throughput screening, gene expression, in vivo and disease data present in ToxCast and Comparative Toxicogenomics Database. This was supplemented by pathway mapping using Reactome to fill in gaps and identify events occurring at the cellular/tissue levels. Furthermore, in vivo data from TG-Gates was integrated to finally derive a gene, pathway, biochemical, histopathological and disease network from which specific disease sub-networks can be queried. To test the workflow, non-genotoxic-induced hepatocellular carcinoma (HCC) was selected as a case study. The implementation resulted in the identification of several non-genotoxic-specific HCC-connected genes belonging to cell proliferation, endoplasmic reticulum stress and early apoptosis. Biochemical findings revealed non-genotoxic-specific alkaline phosphatase increase. The explored non-genotoxic-specific histopathology was associated with early stages of hepatic steatosis, transforming into cirrhosis. This work illustrates the utility of computationally predicted constructs in supporting development by using pre-existing knowledge in a fast and unbiased manner.

Nanodiscs for INPHARMA NMR Characterization of GPCRs: Ligand Binding to the Human A2A Adenosine Receptor

G-protein-coupled-receptors (GPCRs) are of fundamental importance for signal transduction through cell membranes. This makes them important drug targets, but structure-based drug design (SBDD) is still hampered by the limitations for structure determination of unmodified GPCRs. We show that the interligand NOEs for pharmacophore mapping (INPHARMA) method can provide valuable information on ligand poses inside the binding site of the unmodified human A_2A adenosine receptor reconstituted in nanodiscs. By comparing experimental INPHARMA spectra with back-calculated spectra based on ligand poses obtained from molecular dynamics simulations, a complex structure for A_2A R with the low-affinity ligand 3-pyrrolidin-1-ylquinoxalin-2-amine was determined based on the X-ray structure of ligand ZM-241,358 in complex with a modified A_2A R.

Exploiting Transient Protein States for the Design of Small-Molecule Stabilizers of Mutant p53

The destabilizing p53 cancer mutation Y220C creates an extended crevice on the surface of the protein that can be targeted by small-molecule stabilizers. Here, we identify different classes of small molecules that bind to this crevice and determine their binding modes by X-ray crystallography. These structures reveal two major conformational states of the pocket and a cryptic, transiently open hydrophobic subpocket that is modulated by Cys220. In one instance, specifically targeting this transient protein state by a pyrrole moiety resulted in a 40-fold increase in binding affinity. Molecular dynamics simulations showed that both open and closed states of this subsite were populated at comparable frequencies along the trajectories. Our data extend the framework for the design of high-affinity Y220C mutant binders for use in personalized anticancer therapy and, more generally, highlight the importance of implementing protein dynamics and hydration patterns in the drug-discovery process.

Automated assignment of NMR chemical shifts based on a known structure and 4D spectra

Apart from their central role during 3D structure determination of proteins the backbone chemical shift assignment is the basis for a number of applications, like chemical shift perturbation mapping and studies on the dynamics of proteins. This assignment is not a trivial task even if a 3D protein structure is known and needs almost as much effort as the assignment for structure prediction if performed manually. We present here a new algorithm based solely on 4D [(1)H,(15)N]-HSQC-NOESY-[(1)H,(15)N]-HSQC spectra which is able to assign a large percentage of chemical shifts (73-82 %) unambiguously, demonstrated with proteins up to a size of 250 residues. For the remaining residues, a small number of possible assignments is filtered out. This is done by comparing distances in the 3D structure to restraints obtained from the peak volumes in the 4D spectrum. Using dead-end elimination, assignments are removed in which at least one of the restraints is violated. Including additional information from chemical shift predictions, a complete unambiguous assignment was obtained for Ubiquitin and 95 % of the residues were correctly assigned in the 251 residue-long N-terminal domain of enzyme I. The program including source code is available at https://github.com/thomasexner/4Dassign .

On-the-Fly Integration of Data from a Spin-Diffusion-Based NMR Experiment into Protein-Ligand Docking

INPHARMA (interligand nuclear Overhauser enhancement for pharmacophore mapping) determines the relative orientation of two competitive ligands in the protein binding pocket. It is based on the observation of interligand transferred NOEs mediated by spin diffusion through protons of the protein and is, therefore, sensitive to the specific interactions of each of the two ligands with the protein. We show how this information can be directly included into a protein-ligand docking program to guide the prediction of the complex structures. Agreement between the experimental and back-calculated spectra based on the full relaxation matrix approach is translated into a score contribution that is combined with the scoring function ChemPLP of our docking tool PLANTS. This combined score is then used to predict the poses of five weakly bound cAMP-dependent protein kinase (PKA) ligands. After optimizing the setup, which finally also included trNOE data and optimized protonation states, very good success rates were obtained for all combinations of three ligands. For one additional ligand, no conclusive results could be obtained due to the ambiguous electron density of the ligand in the X-ray structure, which does not disprove alternative ligand poses. The failures of the remaining ligand are caused by suboptimal locations of specific protein side chains. Therefore, side-chain flexibility should be included in an improved INPHARMA-PLANTS version. This will reduce the strong dependence on the used protein input structure leading to improved scores overall, not only for this last ligand.

A biophysical study with carbohydrate derivatives explains the molecular basis of monosaccharide selectivity of the Pseudomonas aeruginosa lectin LecB

The rise of resistances against antibiotics in bacteria is a major threat for public health and demands the development of novel antibacterial therapies. Infections with Pseudomonas aeruginosa are a severe problem for hospitalized patients and for patients suffering from cystic fibrosis. These bacteria can form biofilms and thereby increase their resistance towards antibiotics. The bacterial lectin LecB was shown to be necessary for biofilm formation and the inhibition with its carbohydrate ligands resulted in reduced amounts of biofilm. The natural ligands for LecB are glycosides of d-mannose and l-fucose, the latter displaying an unusual strong affinity. Interestingly, although mannosides are much weaker ligands for LecB, they do form an additional hydrogen bond with the protein in the crystal structure. To analyze the individual contributions of the methyl group in fucosides and the hydroxymethyl group in mannosides to the binding, we designed and synthesized derivatives of these saccharides. We report glycomimetic inhibitors that dissect the individual interactions of their saccharide precursors with LecB and give insight into the biophysics of binding by LecB. Furthermore, theoretical calculations supported by experimental thermodynamic data suggest a perturbed hydrogen bonding network for mannose derivatives as molecular basis for the selectivity of LecB for fucosides. Knowledge gained on the mode of interaction of LecB with its ligands at ambient conditions will be useful for future drug design.

Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes

NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3–0.6 ppm and correlation coefficients (r²) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization.

Performance of protein-ligand docking with simulated chemical shift perturbations

Protein chemical shift perturbations (CSPs) that result from the binding of a ligand to the protein contain structural information about the complex. Therefore, the CSP data, typically obtained during library screening from two-dimensional (2D) nuclear magnetic resonance (NMR) spectra, are often available before attempts to solve the experimental structure of the complex are started, and can be used to solve the complex structure with CSP-based docking. Here, we compare the performance of the post-docking filter and the guided-docking approaches using either amide or α-proton CSPs with 10 protein-ligand complexes. We show that the comparison of experimental CSPs with CSPs simulated for virtual ligand positions can be used to evidence protein conformational change upon binding and possibly improve the CSP-based docking.

Discovery of two classes of potent glycomimetic inhibitors of Pseudomonas aeruginosa LecB with distinct binding modes

The treatment of infections due to the opportunistic pathogen Pseudomonas aeruginosa is often difficult, as a consequence of bacterial biofilm formation. Such a protective environment shields the bacterium from host defense and antibiotic treatment and secures its survival. One crucial factor for maintenance of the biofilm architecture is the carbohydrate-binding lectin LecB. Here, we report the identification of potent mannose-based LecB inhibitors from a screening of four series of mannosides in a novel competitive binding assay for LecB. Cinnamide and sulfonamide derivatives are inhibitors of bacterial adhesion with up to a 20-fold increase in affinity to LecB compared to the natural ligand methyl mannoside. Because many lectins of the host require terminal saccharides (e.g., fucosides), such capped structures as reported here may offer a beneficial selectivity profile for the pathogenic lectin. Both classes of compounds show distinct binding modes at the protein, offering the advantage of a simultaneous development of two new lead structures as anti-pseudomonadal drugs with an anti-virulence mode of action.

Conformational Sampling by Ab Initio Molecular Dynamics Simulations Improves NMR Chemical Shift Predictions

Car-Parrinello molecular dynamics simulations were performed for N-methyl acetamide as a small test system for amide groups in protein backbones, and NMR chemical shifts were calculated based on the generated ensemble. If conformational sampling and explicit solvent molecules are taken into account, excellent agreement between the calculated and experimental chemical shifts is obtained. These results represent a landmark improvement over calculations based on classical molecular dynamics (MD) simulations especially for amide protons, which are predicted too high-field shifted based on the latter ensembles. We were able to show that the better results are caused by the solute-solvents interactions forming shorter hydrogen bonds as well as by the internal degrees of freedom of the solute. Inspired by these results, we propose our approach as a new tool for the validation of force fields due to its power of identifying the structural reasons for discrepancies between the experimental and calculated data.

Pronounced effects on switching efficiency of diarylcycloalkenes upon cycloalkene ring contraction

Several difurylperfluorocyclobutenes showing reversible photochromism were synthesized. In comparison to their cyclopentene homologues they show enhanced quantum yields for ring opening but reduced quantum yields for ring closure. X-ray structure analysis and quantum chemical calculations provide a conclusive explanation for such a behaviour.

Toward the Quantum Chemical Calculation of NMR Chemical Shifts of Proteins. 3. Conformational Sampling and Explicit Solvents Model

Fragment-based quantum chemical calculations are able to accurately calculate NMR chemical shifts even for very large molecules like proteins. But even with systematic optimization of the level of theory and basis sets as well as the use of implicit solvents models, some nuclei like polar protons and nitrogens suffer from poor predictions. Two properties of the real system, strongly influencing the experimental chemical shifts but almost always neglected in the calculations, will be discussed here in great detail: (1) conformational averaging and (2) interactions with first-shell solvent molecules. Classical molecular dynamics simulations in explicit water were carried out for obtaining a representative ensemble including the arrangement of neighboring solvent molecules, which was then subjected to quantum chemical calculations. We could demonstrate with the small test system N-methyl acetamide (NMA) that the calculated chemical shifts show immense variations of up to 6 ppm and 50 ppm for protons and nitrogens, respectively, depending on the snapshot taken from a classical molecular dynamics simulation. Applying the same approach to the HA2 domain of the influenza virus glycoprotein hemagglutinin, a 32-amino-acid-long polypeptide, and comparing averaged values to the experiment, chemical shifts of nonpolar protons and carbon atoms in proteins were calculated with unprecedented accuracy. Additionally, the mean absolute error could be reduced by a factor of 2.43 for polar protons, and reasonable correlations were obtained for nitrogen and carbonyl carbon in contrast to all other studies published so far.

Interaction structure of the complex between neuroprotective factor humanin and Alzheimer's β-amyloid peptide revealed by affinity mass spectrometry and molecular modeling

Humanin (HN) is a linear 24-aa peptide recently detected in human Alzheimer's disease (AD) brain. HN specifically inhibits neuronal cell death in vitro induced by ß-amyloid (Aß) peptides and by amyloid precursor protein and its gene mutations in familial AD, thereby representing a potential therapeutic lead structure for AD; however, its molecular mechanism of action is not well understood. We report here the identification of the binding epitopes between HN and Aß(1-40) and characterization of the interaction structure through a molecular modeling study. Wild-type HN and HN-sequence mutations were synthesized by SPPS and the HPLC-purified peptides characterized by MALDI-MS. The interaction epitopes between HN and Aß(1-40) were identified by affinity-MS using proteolytic epitope excision and extraction, followed by elution and mass spectrometric characterization of the affinity-bound peptides. The affinity-MS analyses revealed HN(5-15) as the epitope sequence of HN, whereas Aß(17-28) was identified as the Aß interaction epitope. The epitopes and binding sites were ascertained by ELISA of the complex of HN peptides with immobilized Aß(1-40) and by ELISA with Aß(1-40) and Aß-partial sequences as ligands to immobilized HN. The specificity and affinity of the HN-Aß interaction were characterized by direct ESI-MS of the HN-Aß(1-40) complex and by bioaffinity analysis using a surface acoustic wave biosensor, providing a K(D) of the complex of 610 nm. A molecular dynamics simulation of the HN-Aß(1-40) complex was consistent with the binding specificity and shielding effects of the HN and Aß interaction epitopes. These results indicate a specific strong association of HN and Aß(1-40) polypeptide and provide a molecular basis for understanding the neuroprotective function of HN.

Toward the Quantum Chemical Calculation of NMR Chemical Shifts of Proteins. 2. Level of Theory, Basis Set, and Solvents Model Dependence

It has been demonstrated that the fragmentation scheme of our adjustable density matrix assembler (ADMA) approach for the quantum chemical calculations of very large systems is well-suited to calculate NMR chemical shifts of proteins [ Frank et al. Proteins2011, 79, 2189-2202 ]. The systematic investigation performed here on the influences of the level of theory, basis set size, inclusion or exclusion of an implicit solvent model, and the use of partial charges to describe additional parts of the macromolecule on the accuracy of NMR chemical shifts demonstrates that using a valence triple-ζ basis set leads to large improvement compared to the results given in the previous publication. Additionally, moving from the B3LYP to the mPW1PW91 density functional and including partial charges and implicit solvents gave the best results with mean absolute errors of 0.44 ppm for hydrogen atoms excluding H(N) atoms and between 1.53 and 3.44 ppm for carbon atoms depending on the size and also on the accuracy of the protein structure. Polar hydrogen and nitrogen atoms are more difficult to predict. For the first, explicit hydrogen bonds to the solvents need to be included and, for the latter, going beyond DFT to post-Hartree-Fock methods like MP2 is probably required. Even if empirical methods like SHIFTX+ show similar performance, our calculations give for the first time very reliable chemical shifts that can also be used for complexes of proteins with small-molecule ligands or DNA/RNA. Therefore, taking advantage of its ab initio nature, our approach opens new fields of application that would otherwise be largely inaccessible due to insufficient availability of data for empirical parametrization.

Toward the quantum chemical calculation of nuclear magnetic resonance chemical shifts of proteins

Despite the many protein structures solved successfully by nuclear magnetic resonance (NMR) spectroscopy, quality control of NMR structures is still by far not as well established and standardized as in crystallography. Therefore, there is still the need for new, independent, and unbiased evaluation tools to identify problematic parts and in the best case also to give guidelines that how to fix them. We present here, quantum chemical calculations of NMR chemical shifts for many proteins based on our fragment-based quantum chemical method: the adjustable density matrix assembler (ADMA). These results show that (13)C chemical shifts of reasonable accuracy can be obtained that can already provide a powerful measure for the structure validation. (1)H and even more (15)N chemical shifts deviate more strongly from experiment due to the insufficient treatment of solvent effects and conformational averaging.

Accelerating molecular docking calculations using graphics processing units

The generation of molecular conformations and the evaluation of interaction potentials are common tasks in molecular modeling applications, particularly in protein-ligand or protein-protein docking programs. In this work, we present a GPU-accelerated approach capable of speeding up these tasks considerably. For the evaluation of interaction potentials in the context of rigid protein-protein docking, the GPU-accelerated approach reached speedup factors of up to over 50 compared to an optimized CPU-based implementation. Treating the ligand and donor groups in the protein binding site as flexible, speedup factors of up to 16 can be observed in the evaluation of protein-ligand interaction potentials. Additionally, we introduce a parallel version of our protein-ligand docking algorithm PLANTS that can take advantage of this GPU-accelerated scoring function evaluation. We compared the GPU-accelerated parallel version to the same algorithm running on the CPU and also to the highly optimized sequential CPU-based version. In terms of dependence of the ligand size and the number of rotatable bonds, speedup factors of up to 10 and 7, respectively, can be observed. Finally, a fitness landscape analysis in the context of rigid protein-protein docking was performed. Using a systematic grid-based search methodology, the GPU-accelerated version outperformed the CPU-based version with speedup factors of up to 60.

pharmACOphore: multiple flexible ligand alignment based on ant colony optimization

The flexible superimposition of biologically active ligands is a crucial step in ligand-based drug design. Here we present pharmACOphore, a new approach for pairwise as well as multiple flexible alignment of ligands based on ant colony optimization (ACO; Dorigo, M.; Stützle, T. Ant Colony Optimization; MIT Press: Cambridge, MA, USA, 2004). An empirical scoring function is used, which describes ligand similarity by minimizing the distance of pharmacophoric features. The scoring function was parametrized on pairwise alignments of ligand sets for four proteins from diverse protein families (cyclooxygenase-2, cyclin-dependent kinase 2, factor Xa and peroxisome proliferator-activated receptor γ). The derived parameters were assessed with respect to pose prediction performance on the independent FlexS data set ( Lemmen, C.; Lengauer, T.; Klebe, G. J. Med. Chem. 1998, 41, 4502 - 4520) in exhausting pairwise alignments. Additionally, multiple flexible alignment experiments were carried out for the pharmacologically relevant targets trypsin and poly (ADP-ribose) polymerase (PARP). The results obtained show that the new procedure provides a robust and efficient way for the pairwise as well as multiple flexible alignment of small molecules.

A short note on the analysis of distance measurements by electron paramagnetic resonance

In electron paramagnetic resonance (EPR) distance distributions between site-directedly attached spin labels in soft matter are obtained by measuring their dipole-dipole interaction. The analysis of these distance distributions can be misleading particularly for broad distributions, because the most probable distance deviates from the distance between the most probable label positions. The current manuscript studies this effect using numerically generated spin label positions, molecular dynamics simulations, and experimental data of a model system. An approach involving Rice distributions is proposed to overcome this problem.

NMR-guided molecular docking of a protein-peptide complex based on ant colony optimization

Standard docking approaches used for the prediction of protein-ligand complexes in the drug development process have problems identifying the correct binding mode of large flexible ligands. Herein we show how additional experimental data from NMR experiments can be used to predict the binding mode of a mucin 1 (MUC-1) pentapeptide recognized by the breast-cancer-selective monoclonal antibody SM3. Distance constraints derived from trNOE and saturation transfer difference NMR experiments are combined with the docking approach PLANTS. The resulting complex structures show excellent agreement with the NMR data and with a published X-ray crystal structure. The method was then further tested on two complexes in order to demonstrate its more general applicability: T-antigen disaccharide bound to Maclura pomifera agglutinin, and the inhibitor SBi279 bound to S100B protein. Our new approach has the advantages of being fully automatic, rapid, and unbiased; moreover, it is based on relatively easily obtainable experimental data and can greatly increase the reliability of the generated structures.

Influence of protonation, tautomeric, and stereoisomeric states on protein-ligand docking results

In this work, we present a systematical investigation of the influence of ligand protonation states, stereoisomers, and tautomers on results obtained with the two protein-ligand docking programs GOLD and PLANTS. These different states were generated with a fully automated tool, called SPORES (Structure PrOtonation and Recognition System). First, the most probable protonations, as defined by this rule based system, were compared to the ones stored in the well-known, manually revised CCDC/ASTEX data set. Then, to investigate the influence of the ligand protonation state on the docking results, different protonation states were created. Redocking and virtual screening experiments were conducted demonstrating that both docking programs have problems in identifying the correct protomer for each complex. Therefore, a preselection of plausible protomers or the improvement of the scoring functions concerning their ability to rank different molecules/states is needed. Additionally, ligand stereoisomers were tested for a subset of the CCDC/ASTEX set, showing similar problems regarding the ranking of these stereoisomers as the ranking of the protomers.

Improving the interaction of Myc-interfering peptides with Myc using molecular dynamics simulations

Previously, a Myc-interfering peptide (Mip) was identified for the targeted inactivation of the Myc:Max complex by the combination of rational design and an in vivo protein-fragment complementation assay. In the subsequent work presented here, molecular dynamics simulations and free energy calculations based on the molecular mechanics GBSA method were performed to define the contribution of the different amino acids in the Myc:Mip coiled coil domain, and compared to wild-type Myc:Max. For further optimization of the Myc interference, point mutations were introduced into Mip and analyzed, from which two showed much higher binding affinities in the computational studies in good agreement with the experiment. These mutants with very high potential for inactivation of Myc can now be used as starting point for further optimizations based on the computational as well as experimental protocols presented here.

Insights into the high fidelity of a DNA polymerase I mutant

Mutants of DNA polymerase I from Thermus aquaticus (Taq) with higher fidelity compared to the wild type enzyme were identified in an earlier study by Summerer et al. (Angew Chem Int Ed 44:4712-4715, 2005). Here, one of these mutants, PLQ (consensus residues 879-881), was analysed using molecular dynamics simulations. This was done by calculating the structures of the ternary complex comprising the enzyme, the DNA primer and template as well as the incoming nucleotide before the chemical reaction for the Watson-Crick and different mismatched base pairings. The results show that the high fidelity of the mutant can be explained partly by different specific interactions between the amino acids of the enzyme and the DNA primer end as well as, in some mismatches, a displacement of the primer relative to the incoming deoxyribonucleoside triphosphate and the catalytic magnesium ion. This displacement is facilitated by reduced steric interactions between the enzyme and the DNA.

Empirical scoring functions for advanced protein-ligand docking with PLANTS

In this paper we present two empirical scoring functions, PLANTS(CHEMPLP) and PLANTS(PLP), designed for our docking algorithm PLANTS (Protein-Ligand ANT System), which is based on ant colony optimization (ACO). They are related, regarding their functional form, to parts of already published scoring functions and force fields. The parametrization procedure described here was able to identify several parameter settings showing an excellent performance for the task of pose prediction on two test sets comprising 298 complexes in total. Up to 87% of the complexes of the Astex diverse set and 77% of the CCDC/Astex clean listnc (noncovalently bound complexes of the clean list) could be reproduced with root-mean-square deviations of less than 2 A with respect to the experimentally determined structures. A comparison with the state-of-the-art docking tool GOLD clearly shows that this is, especially for the druglike Astex diverse set, an improvement in pose prediction performance. Additionally, optimized parameter settings for the search algorithm were identified, which can be used to balance pose prediction reliability and search speed.

Molecular visualization in the rational drug design process

The visualization of molecular scenarios on an atomic level can help to interpret experimental and theoretical findings. This is demonstrated in this review article with the specific field of drug design. State-of-the-art visualization techniques are described and applied to the different stages of the rational design process. Numerous examples from the literature, in which visualization was used as a major tool in the data analysis and interpretation, are provided to show that images are not only useful for drawing the attention of the reader to a specific paper in a scientific journal.

Structural Changes and Binding Characteristics of the Tetracycline-Repressor Binding Site on Induction

The binding motif (pharmacophore) for induction and the changes in the structure of the binding site that accompany induction have been determined from molecular-dynamics simulations on the tetracycline-repressor signal-transduction protein. The changes and the induction mechanism are discussed and compared with conclusions drawn from earlier X-ray structures. The differences in inducer strength of tetracycline and 5a,6-anhydrotetracycline are discussed with respect to their interaction in the MD simulations.

Evaluation of the field-adapted ADMA approach: absolute and relative energies of crambin and derivatives

A large number of conformations and chemically modified variants of the protein crambin were used to extensively test the field-adapted adjustable density matrix assembler (FA-ADMA) method developed for ab initio quality quantum chemistry computations of proteins and other macromolecules, introduced in an earlier publication. In this method, the fuzzy density matrix fragmentation scheme of the original adjustable density matrix assembler (ADMA) method has been made more efficient by combining it with an approach of using point charges to approximate the effects of additional, distant parts of a given macromolecule in the quantum chemical calculation of each fragment. In this way, smaller parent molecules can be used for fragment generation, while achieving accuracy that can be obtained only with large parent molecules in the original ADMA method. Whereas in both methods the error relative to the Hartree-Fock result can be reduced below any threshold by choosing large enough parent molecules, this can be done more efficiently with the new method. In order to obtain reliable test results for the accuracy obtainable by the new method when compared to conventional Hartree-Fock calculations, we performed a large number of energy calculations for the protein crambin using various conformations available in the Protein Data Bank, various protonation states, and side chain mutations. Additionally, in order to test the performance of the method for protein-solvent interaction studies, the energy changes due to the formation of complexes with ethanol and single and multiple water molecules were investigated.