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In recent years, engineering biomimetic cellular microenvironments have been a top priority for regenerative medicine. Collagen II, which is arranged in arches, forms the predominant fiber network in articular cartilage. Due to the shortage of suitable microfabrication techniques capable of producing 3D fibrous structures, in vitro replication of the arch-like cartilaginous tissue constitutes one of the major challenges. Hence, in the present study, we report a 3D bioprinting approach for fabricating arch-like constructs using two types of bioinks, gelatin methacryloyl (GelMa) and silk fibroin-gelatin (SF-G). The bioprinted SF-G constructs displayed increased proliferation of the encapsulated human bone marrow-derived mesenchymal stem cells compared to the GelMA constructs. Biochemical assays, gene, and protein expression exhibited the superior role of SF-G in forming the fibrous collagen network and chondrogenesis. Protein-protein interaction study using Metascape evaluated the function of the proteins involved. Further GeneMANIA and STRING analysis using Col 2A1, SOX 9, ACAN, and the genes upregulated on day 21 in RT-PCR, i.e. β-catenin, TGFβR1, Col 1A1 in SF-G and PRG4, Col 10A1, MMP 13 in GelMA validated our in vitro results. These findings emphasized the role of SF-G in regulating the Wnt/β-catenin and TGF-β signaling pathways. Hence, the 3D bioprinted arch-like constructs possess a substantial potential for cartilage regeneration.

The fragment-based drug design approach consists of screening libraries of fragment-like ligands, to identify hits that typically bind the protein target with weak affinity ( $$100\,\upmu \hbox {M}$$ –5 mM). The determination of the protein–fragment complex 3D structure constitutes a crucial step for uncovering the key interactions responsible for the protein–ligand recognition, and for growing the initial fragment into potent active compounds. The vast majority of fragments are aromatic compounds that induce chemical shift perturbations (CSP) on protein NMR spectra. These experimental CSPs can be quantitatively used to guide the ligand docking, through the comparison between experimental CSPs and CSP back-calculation based on the ring current effect. Here we implemented the CSP back-calculation into the scoring function of the program PLANTS. We compare the results obtained with CSPs measured either on amide or aliphatic protons of the human peroxiredoxin 5. We show that the different kinds of protons lead to different results for resolving the 3D structures of protein–fragment complexes, with the best results obtained with the $$\hbox {H}_{\alpha }$$ protons.

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.

International audience; 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

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International audience; Fragment-based drug design is one of the most promising approaches for discovering novel and potent inhibitors against therapeutic targets. The first step of the process consists of identifying fragments that bind the protein target. The determination of the fragment binding mode plays a major role in the selection of the fragment hits that will be processed into drug-like compounds. Comparing the binding modes of analogous fragments is a critical task, not only to identify specific interactions between the protein target and the fragment, but also to verify whether the binding mode is conserved or differs according to the fragment modification. While X-ray crystallography is the technique of choice, NMR methods are helpful when this fails. We show here how the ligand-observed saturation transfer difference (STD) experiment and the protein-observed 15N-HSQC experiment, two popular NMR screening experiments, can be used to compare the binding modes of analogous fragments. We discuss the application and limitations of these approaches based on STD-epitope mapping, chemical shift perturbation (CSP) calculation and comparative CSP sign analysis, using the human peroxiredoxin 5 as a protein model.

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Protein-protein interactions represent difficult but increasingly important targets for the design of therapeutic compounds able to interfere with biological processes. Recently, fragment-based strategies have been proposed as attractive approaches for the elaboration of protein-protein surface inhibitors from fragment-like molecules. One major challenge in targeting protein-protein interactions is related to the structural adaptation of the protein surface upon molecular recognition. Methods capable of identifying subtle conformational changes of proteins upon fragment binding are therefore required at the early steps of the drug design process. In this report we present a fast NMR method able to probe subtle conformational changes upon fragment binding. The approach relies on the comparison of experimental fragment-induced Chemical Shift Perturbation (CSP) of amine protons to CSP simulated for a set of docked fragment poses, considering the ring-current effect from fragment binding. We illustrate the method by the retrospective analysis of the complex between the anti-apoptotic Bcl-xL protein and the fragment 4'-fluoro-[1,1'-biphenyl]-4-carboxylic acid that was previously shown to bind one of the Bcl-xL hot spots. The CSP-based approach shows that the protein undergoes a subtle conformational rearrangement upon interaction, for residues located in helices [Formula: see text]2, [Formula: see text]3 and the very beginning of [Formula: see text]5. Our observations are corroborated by residual dipolar coupling measurements performed on the free and fragment-bound forms of the Bcl-xL protein. These NMR-based results are in total agreement with previous molecular dynamic calculations that evidenced a high flexibility of Bcl-xL around the binding site. Here we show that CSP of protein amine protons are useful and reliable structural probes. Therefore, we propose to use CSP simulation to assess protein conformational changes upon ligand binding in the fragment-based drug design approach.

Due to the large number of different docking programs and scoring functions available, researchers are faced with the problem of selecting the most suitable one when starting a structure-based drug discovery project. To guide the decision process, several studies comparing different docking and scoring approaches have been published. In the context of comparing scoring function performance, it is common practice to use a predefined, computer-generated set of ligand poses (decoys) and to reevaluate their score using the set of scoring functions to be compared. But are predefined decoy sets able to unambiguously evaluate and rank different scoring functions with respect to pose prediction performance? This question arose when the pose prediction performance of our piecewise linear potential derived scoring functions (Korb et al. in J Chem Inf Model 49:84–96, 2009) was assessed on a standard decoy set (Cheng et al. in J Chem Inf Model 49:1079–1093, 2009). While they showed excellent pose identification performance when they were used for rescoring of the predefined decoy conformations, a pronounced degradation in performance could be observed when they were directly applied in docking calculations using the same test set. This implies that on a discrete set of ligand poses only the rescoring performance can be evaluated. For comparing the pose prediction performance in a more rigorous manner, the search space of each scoring function has to be sampled extensively as done in the docking calculations performed here. We were able to identify relative strengths and weaknesses of three scoring functions (ChemPLP, GoldScore, and Astex Statistical Potential) by analyzing the performance for subsets of the complexes grouped by different properties of the active site. However, reasons for the overall poor performance of all three functions on this test set compared to other test sets of similar size could not be identified.

In this paper we present our reworked approach to generate ligand protonation states with our structure preparation tool SPORES (Structure PrOtonation and REcognition System). SPORES can be used for the preprocessing of proteins and protein-ligand complexes as e.g. taken from the Protein Data Bank as well as for the setup of 3D ligand databases. It automatically assigns atom and bond types, generates different protonation, tautomeric states as well as different stereoisomers. In the revised version, pKa calculations with the ChemAxon software MARVIN are used either to determine the likeliness of a combinatorial generated protonation state or to determine the titrable atoms used in the combinatorial approach. Additionally, the MARVIN software is used to predict microspecies distributions of ligand molecules. Docking studies were performed with our recently introduced program PLANTS (Protein-Ligand ANT System) on all protomers resulting from the three different selection methods for the well established CCDC/ASTEX clean data set demonstrating the usefulness of especially the latter approach.

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.

We will summarize the latest developments for our protein-ligand docking software PLANTS (Protein-Ligand ANT System) [1][2][3]. This will include the pre-processing of the ligands and the protein performed by the program SPORES (Structure PrOtonation and REcognition System), parameterization of a new scoring function, as well as the inclusion of additional degrees of freedom into the docking process like flexible side chains and essential water molecules. The use of constraints from experimental data for improving the docking poses will be presented. Finally, an outlook towards the possible usage of the PLANTS approach for the flexible alignment of multiple ligands will be given.

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