Optimized Method of 3D Scaffold Seeding, Cell Cultivation, and Monitoring Cell Status for Bone Tissue Engineering.

The cultivation of cells in 3D systems is commonly regarded to be more physiological than in 2D as it comes much closer to the natural situation in tissues in many different aspects. However, 3D cell culture is much more complex. Cells within the pores of a printed 3D scaffold face a special situation concerning cell-material interaction and cell adhesion, cell proliferation, and supply of medium and oxygen into the core of the scaffolds. Biological assays (for cell proliferation, viability, and activity) have been validated primarily for 2D cell cultures and need to be adapted for 3D cultures. Likewise, in imaging, a number of points need to be taken into account in order to get a clear picture of the cells in 3D scaffolds, preferably with the method of multiphoton microscopy. Here, we describe a method for pretreatment and cell seeding of porous inorganic composite scaffolds (α-TCP/HA) for bone tissue engineering and for cultivation of the cell-scaffold constructs. The analytical methods described are the cell proliferation assay and the ALP activity assay. A step-by-step protocol is provided here that safely tackles typical difficulties that arise with this 3D cell-scaffold setting. In addition, MPM imaging of cells is described both with and without labeling. The combination of biochemical assays and imaging provides valuable insights into the possibilities of analysis with this 3D cell-scaffold system.

Protocol for Cell Colonization and Comprehensive Monitoring of Osteogenic Differentiation in 3D Scaffolds Using Biochemical Assays and Multiphoton Imaging.

The goal of bone tissue engineering is to build artificial bone tissue with properties that closely resemble human bone and thereby support the optimal integration of the constructs (biografts) into the body. The development of tissues in 3D scaffolds includes several complex steps that need to be optimized and monitored. In particular, cell-material interaction during seeding, cell proliferation and cell differentiation within the scaffold pores play a key role. In this work, we seeded two types of 3D-printed scaffolds with pre-osteoblastic MC3T3-E1 cells, proliferated and differentiated the cells, before testing and adapting different assays and imaging methods to monitor these processes. Alpha-TCP/HA (α-TCP with low calcium hydroxyapatite) and baghdadite (Ca3ZrSi2O9) scaffolds were used, which had comparable porosity (~50%) and pore sizes (~300-400 µm). Cell adhesion to both scaffolds showed ~95% seeding efficiency. Cell proliferation tests provided characteristic progression curves over time and increased values for α-TCP/HA. Transmitted light imaging displayed a homogeneous population of scaffold pores and allowed us to track their opening state for the supply of the inner scaffold regions by diffusion. Fluorescence labeling enabled us to image the arrangement and morphology of the cells within the pores. During three weeks of osteogenesis, ALP activity increased sharply in both scaffolds, but was again markedly increased in α-TCP/HA scaffolds. Multiphoton SHG and autofluorescence imaging were used to investigate the distribution, morphology, and arrangement of cells; collagen-I fiber networks; and hydroxyapatite crystals. The collagen-I networks became denser and more structured during osteogenic differentiation and appeared comparable in both scaffolds. However, imaging of the HA crystals showed a different morphology between the two scaffolds and appeared to arrange in the α-TCP/HA scaffolds along collagen-I fibers. ALP activity and SHG imaging indicated a pronounced osteo-inductive effect of baghdadite. This study describes a series of methods, in particular multiphoton imaging and complementary biochemical assays, to validly measure and track the development of bone tissue in 3D scaffolds. The results contribute to the understanding of cell colonization, growth, and differentiation, emphasizing the importance of optimal media supply of the inner scaffold regions.

A Consistent Scheme for Gradient-Based Optimization of Protein-Ligand Poses.

Scoring and numerical optimization of protein-ligand poses is an integral part of docking tools. Although many scoring functions exist, many of them are not continuously differentiable and they are rarely explicitly analyzed with respect to their numerical optimization behavior. Here, we present a consistent scheme for pose scoring and gradient-based pose optimization. It consists of a novel variant of the BFGS algorithm enabling step-length control, named LSL-BFGS (limited step length BFGS), and the empirical JAMDA scoring function designed for pose prediction and good numerical optimizability. The JAMDA scoring function shows a high pose prediction performance in the CASF-2016 docking power benchmark, top-ranking a pose with an RMSD of ≤2 Å in about 89% of the cases. The combination of JAMDA scoring with the LSL-BFGS algorithm shows a significantly higher optimization locality (i.e., no excessive movement of poses) than with the classical BFGS algorithm while retaining the characteristically low number of scoring function evaluations. The JAMDA scoring and optimization scheme is freely available for noncommercial use and academic research.

Pan-cancer image-based detection of clinically actionable genetic alterations.

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer.

Conformator: A Novel Method for the Generation of Conformer Ensembles.

Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.

NAOMInext - Synthetically feasible fragment growing in a structure-based design context.

Since decades de novo design of small molecules is intensively used and fragment-based drug discovery (FBDD) approaches still gain in popularity. Recent publications considering synthetically feasible de novo drug design underline the ongoing need for new methods. Continuous development of algorithms and tools are made, where a combination of intuitive usage, acceptable runtime, and a thoroughly evaluated workflow on large scale data sets is still a curiosity. Here, we present an intuitive approach for constrained synthetically feasible fragment growing. Starting from a fragment within its crystallized structure building blocks are attached via covalent bond formation to build up larger ligands. Iteratively, conformations are generated inside the binding site and scored to find the best suitable one. To cope with the combinatorial explosion of large flexible building blocks a novel dynamic adaptation algorithm is introduced. The technique achieves low runtimes while keeping high accuracies. The developed workflow is evaluated on a large-scale data set of 264 co-crystallized fragments with their corresponding elaborated ligands. Using our approach for fragment-based ligand growing, we were able to generate putative ligands within an RMSD of less than 2 Šcompared to its crystallized structure. Additionally, we were able to show the benefit of a monolithic tethered docking like methodology compared to state of the art docking. We incorporated our method, NAOMInext, in a clearly arranged graphical user interface that assists the user by defining valuable constraints to improve and accelerate the sampling workflow. In combination with predefined synthetic reaction rules NAOMInext efficiently suggests ideas for the next generation of novel lead compounds.

Computational Macrocyclization: From de†novo Macrocycle Generation to Binding Affinity Estimation.

Macrocycles play an increasing role in drug discovery, but their synthesis is often demanding. Computational tools that suggest macrocyclization based on a known binding mode and that estimate the binding affinity of these macrocycles could have a substantial impact on the medicinal chemistry design process. For both tasks, we established a workflow with high practical value. For five diverse pharmaceutical targets we show that the effect of macrocyclization on binding can be calculated robustly and accurately. Applying this method to macrocycles designed by LigMac, a search tool for de novo macrocyclization, our results suggest that we have a robust protocol in hand to design macrocycles and prioritize them prior to synthesis.

Benchmarking Commercial Conformer Ensemble Generators.

We assess and compare the performance of eight commercial conformer ensemble generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE Conformation Import, and OMEGA) and one leading free algorithm, the distance geometry algorithm implemented in RDKit. The comparative study is based on a new version of the Platinum Diverse Dataset, a high-quality benchmarking dataset of 2859 protein-bound ligand conformations extracted from the PDB. Differences in the performance of commercial algorithms are much smaller than those observed for free algorithms in our previous study (J. Chem. Inf. MODEL: 2017, 57, 529-539). For commercial algorithms, the median minimum root-mean-square deviations measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers are between 0.46 and 0.61 Å. Commercial conformer ensemble generators are characterized by their high robustness, with at least 99% of all input molecules successfully processed and few or even no substantial geometrical errors detectable in their output conformations. The RDKit distance geometry algorithm (with minimization enabled) appears to be a good free alternative since its performance is comparable to that of the midranked commercial algorithms. Based on a statistical analysis, we elaborate on which algorithms to use and how to parametrize them for best performance in different application scenarios.

NAOMInova: Interactive Geometric Analysis of Noncovalent Interactions in Macromolecular Structures.

Noncovalent interactions play an important role in macromolecular complexes. The assessment of molecular interactions is often based on knowledge derived from statistics on structural data. Within the last years, the available data in the Brookhaven Protein Data Bank has increased dramatically, quantitatively as well as qualitatively. This development allows the derivation of enhanced interaction models and motivates new ways of data analysis. Here, we present a method to facilitate the analysis of noncovalent interactions enabling detailed insights into the nature of molecular interactions. The method is integrated into a highly variable framework enabling the adaption to user-specific requirements. NAOMInova, the user interface for our method, allows the generation of specific statistics with respect to the chemical environment of substructures. The substructures as well as the analyzed set of protein structures can be chosen arbitrarily. Although NAOMInova was primarily made for data exploration in protein-ligand crystal structures, it can be used in combination with any structure collection, for example, analysis of a carbonyl in the neighborhood of an aromatic ring on a set of structures resulting from a MD simulation. Additionally, a filter for different atom attributes can be applied including the experimental support by electron density for single atoms. In this publication, we present the underlying algorithmic techniques of our method and show application examples that demonstrate NAOMInova's ability to support individual analysis of noncovalent interactions in protein structures. NAOMInova is available at http://www.zbh.uni-hamburg.de/naominova .

From cheminformatics to structure-based design: Web services and desktop applications based on the NAOMI library.

Nowadays, computational approaches are an integral part of life science research. Problems related to interpretation of experimental results, data analysis, or visualization tasks highly benefit from the achievements of the digital era. Simulation methods facilitate predictions of physicochemical properties and can assist in understanding macromolecular phenomena. Here, we will give an overview of the methods developed in our group that aim at supporting researchers from all life science areas. Based on state-of-the-art approaches from structural bioinformatics and cheminformatics, we provide software covering a wide range of research questions. Our all-in-one web service platform ProteinsPlus (http://proteins.plus) offers solutions for pocket and druggability prediction, hydrogen placement, structure quality assessment, ensemble generation, protein-protein interaction classification, and 2D-interaction visualization. Additionally, we provide a software package that contains tools targeting cheminformatics problems like file format conversion, molecule data set processing, SMARTS editing, fragment space enumeration, and ligand-based virtual screening. Furthermore, it also includes structural bioinformatics solutions for inverse screening, binding site alignment, and searching interaction patterns across structure libraries. The software package is available at http://software.zbh.uni-hamburg.de.

High-Quality Dataset of Protein-Bound Ligand Conformations and Its Application to Benchmarking Conformer Ensemble Generators.

We developed a cheminformatics pipeline for the fully automated selection and extraction of high-quality protein-bound ligand conformations from X-ray structural data. The pipeline evaluates the validity and accuracy of the 3D structures of small molecules according to multiple criteria, including their fit to the electron density and their physicochemical and structural properties. Using this approach, we compiled two high-quality datasets from the Protein Data Bank (PDB): a comprehensive dataset and a diversified subset of 4626 and 2912 structures, respectively. The datasets were applied to benchmarking seven freely available conformer ensemble generators: Balloon (two different algorithms), the RDKit standard conformer ensemble generator, the Experimental-Torsion basic Knowledge Distance Geometry (ETKDG) algorithm, Confab, Frog2 and Multiconf-DOCK. Substantial differences in the performance of the individual algorithms were observed, with RDKit and ETKDG generally achieving a favorable balance of accuracy, ensemble size and runtime. The Platinum datasets are available for download from http://www.zbh.uni-hamburg.de/platinum_dataset .

UNICON: A Powerful and Easy-to-Use Compound Library Converter.

The accurate handling of different chemical file formats and the consistent conversion between them play important roles for calculations in complex cheminformatics workflows. Working with different cheminformatic tools often makes the conversion between file formats a mandatory step. Such a conversion might become a difficult task in cases where the information content substantially differs. This paper describes UNICON, an easy-to-use software tool for this task. The functionality of UNICON ranges from file conversion between standard formats SDF, MOL2, SMILES, PDB, and PDBx/mmCIF via the generation of 2D structure coordinates and 3D structures to the enumeration of tautomeric forms, protonation states, and conformer ensembles. For this purpose, UNICON bundles the key elements of the previously described NAOMI library in a single, easy-to-use command line tool.

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds.

Pilot training: What can surgeons learn from it?

OBJECTIVE: To provide healthcare professionals with an insight into training in aviation and its possible transfer into surgery. METHODS: From research online and into company archives, relevant publications and information were identified. RESULTS: Current airline pilot training consists of two categories, basic training and type-rating. Training methods comprise classroom instruction, computer-based training and practical training, in either the aircraft or a flight-training device, which ranges from a fixed-base flight-training device to a full flight simulator. Pilot training not only includes technical and procedural instruction, but also training in non-technical skills like crisis management, decision-making, leadership and communication. Training syllabuses, training devices and instructors are internationally standardized and these standards are legally binding. Re-qualification and recurrent training are mandatory at all stages of a pilot's and instructor's career. CONCLUSION: Surgeons and pilots have much in common, i.e., they work in a 'real-time' three-dimensional environment under high physiological and psychological stress, operating expensive equipment, and the ultimate cost for error is measured in human lives. However, their training differs considerably. Transferring these well-tried aviation methods into healthcare will make surgical training more efficient, more effective and ultimately safer.