Crystallographic fragment screening - improvement of workflow, tools and procedures, and application for the development of enzyme and protein-protein interaction modulators

One of the great societal challenges of today is the fight against diseases which reduce life expectancy and lead to high economic losses. Both the understanding and the addressing of these diseases need research activities at all levels. One aspect of this is the discovery and development of tool compounds and drugs. Tool compounds support disease research and the development of drugs. For about 20 years, the discovery of new compounds has been attempted by screening small organic molecules by high-throughput methods. More recently, X-ray crystallography has emerged as the most promising method to conduct such screening. Crystallographic fragment-screening (CFS) generates binding information as well as 3D-structural information of the target protein in complex with the bound fragment. This doctoral research project is focused primarily on the optimization of the crystallographic fragment screening workflow. Investigated were the requirements for more successful screening campaigns with respect to the crystal system studied, the fragment libraries, the handling of the crystalline samples, as well as the handling of the data associated with a screening campaign. The improved CFS workflow was presented as a detailed protocol and as an accompanying video to train future CFS users in a streamlined and accessible way. Together, these improvements make CFS campaigns a more high-throughput method, offering the ability to screen larger fragment libraries and allowing higher numbers of campaigns performed per year. The protein targets throughout the project were two enzymes and a spliceosomal protein-protein complex. The enzymes comprised the aspartic protease Endothiapepsin and the SARS-Cov-2 main protease. The protein-protein complex was the RNaseH-like domain of Prp8, a vital structural protein in the spliceosome, together with its nuclear shuttling factor Aar2. By performing the CFS campaigns against disease-relevant targets, the resulting fragment hits could be used directly to develop tool compounds or drugs. The first steps of optimization of fragment hits into higher affinity binders were also investigated for improvements. In summary, a plethora of novel starting points for tool compound and drug development was identified

Application of Consensus Scoring and Principal Component Analysis for Virtual Screening against β-Secretase (BACE-1)

BACKGROUND: In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1. METHOD: Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test. RESULTS: For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay. CONCLUSION: The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications

New Monte Carlo Based Technique To Study DNA–Ligand Interactions

We present a new all-atom Monte Carlo technique capable of performing quick and accurate DNA–ligand conformational sampling. In particular, and using the PELE software as a frame, we have introduced an additional force field, an implicit solvent, and an anisotropic network model to effectively map the DNA energy landscape. With these additions, we successfully generated DNA conformations for a test set composed of six DNA fragments of A-DNA and B-DNA. Moreover, trajectories generated for cisplatin and its hydrolysis products identified the best interacting compound and binding site, producing analogous results to microsecond molecular dynamics simulations. Furthermore, a combination of the Monte Carlo trajectories with Markov State Models produced noncovalent binding free energies in good agreement with the published molecular dynamics results, at a significantly lower computational cost. Overall our approach will allow a quick but accurate sampling of DNA–ligand interactions.The authors thank the Barcelona Supercomputing Center for computational resources. This work was supported by grants from the European Research Council—2009-Adg25027-PELE European project and the Spanish Ministry of Economy and Competitiveness CTQ2013-48287 and “Juan de la Cierva” to F.L.Peer ReviewedPostprint (author's final draft

Computational approaches to virtual screening in human central nervous system therapeutic targets

In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways. To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules. The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer. This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process

Systematic studies of hydrogen bonding

This thesis deals with wider application and implications of the hydrogen bond in crystal engineering studies and beyond; in addition, it also highlights the Cambridge Structural Database (CSD) as the potential knowledge-mine for inorganic chemists. The content of this thesis covers mainly three areas, viz, the role of hydrogen bonding in crystal engineering studies, the bridging between mainstream crystal engineering studies and solvates via hydrogen bond, and CSD studies on metal coordination spheres. Chapter 2 deals with crystal structure prediction through understanding the driving forces for forming supramolecular synthons and some rare supramolecular networks (Carbomndum III). With the help of a series of supraminols we attempt to identify the underlying reason for forming P-As networks. Chapter 3 covers the much debated topic of acceptor capabilities of organic halogens and consequently, how the so-called illusory hydrogen bond involving an organic halogen as an acceptor can explain a complex topic like synthon change-over, in a perfectly comprehensible manner. The aim of the Chapters 4 to 6 is to bring two separate fields, "crystal engineering" and "solvates" closer via a common root, like hydrogen bonding. The serendipitous host molecules are part of our crystal engineering studies, yet they form solvates due to less than optimum hydrogen bonding in their respective crystal structures. Alongside some usual solvates, in an unconventional way, different amines with varying steric, strain and donor hydrogen atoms were used. Different geometrical as well as crystallographical aspects and their explicit role in synthon selection has also been discussed. In Chapter 7, geometrical distortions of three-coordinate metal complexes in the crystal structures in the CSD have been analysed using symmetry modified Principal Component Analysis (PCA). Results shows that 90% of three coordinate species are accounted for by the five elements Cu, Ag, Hg, Au and Zn. Among the three major types of geometries, trigonal planar dominates the data sets, with smaller contribution for Y- and T-shaped structure. For Hg complexes, a possible reaction pathway for ligand addition reaction to two-coordinate linear complexes via T-shaped geometries leading to trigonal planar is discussed in detail. The background information and an overview of the experiments are discussed in the Introductory Chapter

Studies of conformation and configuration using crystallographic methods

This Thesis demonstrates the use of the Cambridge Crystallographic Database for structure correlation studies in two very different fields. The first part of the Thesis (Chapters 2 and 3) is concerned with the systematic conformational analysis of medium-sized rings and satisfies the objectives of the study by: (i) applying novel classification techniques to the conformational descriptions of both the seven- and eight-membered rings, (ii) interpreting the results in terms of the relevant conformational hypersurface by locating the highly populated regions of that hypersurface and mapping the interconversion pathways, (iii) studying, modifying and improving the available methodologies for data analysis, and (iv) relating the conformational minima found using these methods to both the chemical environments of the fragments under investigation, and to energetic features of the hypersurface obtained by computational methods. The second major structure correlation experiment involves the analysis and description of 3-coordinated transition metal complexes using both simple geometrical models and group-theoretically based symmetry deformation coordinates. Non-bonded interactions will be seen to play a significant part in the geometry of the 3-coordinated fragment, and extrapolation of these results leads to the rationalisation of an addition/elimination scheme linking 4- and 2-coordinated fragments through the intermediate 3-coordinated species. Chapter 5 describes the crystallographic structure determinations of eight novel compounds: 3,5-cycloheptadienyl-3,5 dinitrobenzoate [C(_14)H(_12)O(_6)N(_2)]; a 34-membered diolide [C(_32)H(_60)O(_4)]; l-iodo-3-tosyloxy-propan-2-ane [C(_10)H(_11)O(_4)IS)]; 1β, 9 β -diacetyl- 7α-chloro-cis-hydrindane [C(_13)H(_19)O(_2)CI]; (R,R)-l,4-bis (2'-chloro-1 '-hydoxyethyl) benzene [C(_10)H(_12)O(_2)CI(_2)]; a fused penta-cyclic ring compound [C(_17)H(_14)]; 1,4 dibenzyl- 1,2,4,5-tetraazacyclohexane [C(_16) H(_20) N(_4)]; 1,5-di (2'-chloroacetoxy)-3,3-dimethyl-2,4- diphenyl-3-silapentane [C(_22)H(_26)O(_4) CI(_2) Si]

Revealing the Mechanism of Thiopeptide Antibiotics at Atomistic Resolution : Implications for Rational Drug Design

For decades drug design has primarily focused on small molecules that bind to well-formed tight binding pockets, such as the catalytic centers of enzymes. Recently, there is increasing interest to design compounds that disrupt or stabilize biomacromolecular interfaces (e.g. protein–protein, protein–DNA, protein–RNA, protein–lipid interfaces). These non-traditional drug targets hold great therapeutic potential as they govern cellular pathways. In contrast to traditional drug targets, where computational methods are now routinely and productively used to complement experiments, the use of computer-based approaches for the study and design of interfacial modulators is still in its infancy. The current thesis is a first detailed study into understanding the effects of modulators of a protein–RNA interface and developing computer-based approaches for their design. This work focuses on the 23S-L11 subunit of the ribosomal GTPase-associated region (GAR), a prototypic protein–RNA interface of high relevance in the development of novel antibacterials. The GAR is the target of naturally occuring thiopeptide antibiotics. These unique molecules are effective inhibitors of bacterial protein synthesis, but are currently unused in human antibacterial therapy due to their low aqueous solubility. Their mechanism of action is explored in the current thesis, enabling the design and proposition of new chemical scaffolds targeting their binding site. The specific challenges associated with the 23-SL11-thiopeptide system, such as the inherent flexibility of the protein–RNA composite environment and the size and structural complexity of the thiopeptide ligands, are addressed by a combination of computational chemistry approaches at different levels of granularity and a steady feedback with experimental data to validate and improve the computational techniques. These approaches range from quantummechanics for deriving optimized intramolecular parameters and partial atomic charges for the thiopeptide compounds, to molecular dynamics simulations accounting for the binding site’s flexibility, to molecular docking studies for predicting the binding modes of different thiopeptides and derivatives. All-atom molecular dynamics simulations were conducted, providing a detailed understanding of the effect of thiopeptide binding at a previously unmet resolution. The findings of this work, coupled with previous experimental knowledge, strongly support the hypothesis that restricting the binding site’s conformational flexibility is an important component of the thiopeptide antibiotics’ mode of action. With the help of an MD-docking-MD workflow and an energy decomposition analysis crucial residues of the binding site and pharmacologically relevant moieties within the ligand structures could be identified. A 4D-pharmacophore model is presented that was derived from a refined 23S-L11-thiopeptide complex and additionally accounts for the dynamic stability of molecular interactions formed between the antibiotic and the ribosomal binding site as the fourth dimension. The results of this thesis revealed, for the first time, a plausable description of the thiopeptide antibiotics’ mode of action, down to the details of their pharmacologically relevant parts and provide a computational framework for the design of new ligands

Entwicklung und Anwendung von Kernspinresonanz-Spektroskopie und Röntgenkristallographie in früher Wirkstoffentwicklung

Challenges in drug discovery including difficulties obtaining structural information on the ligand-protein complex are approached using multiple methods. Crystal structures of IMP-13, an antibiotic resistance protein, along with its natural antibiotic substrates are presented. Paramagnetic methods in drug discovery using NMR are investigated, and a deep learning approach for computational modelling positions of water molecules in protein structures for use in ligand optimisation is introduced.Herausforderungen in der Arzneimittelforschung einschließlich der Schwierigkeiten, strukturelle Informationen über den Ligand-Protein-Komplex zu erhalten, werden mit verschiedenen Methoden adressiert Kristallstrukturen von IMP-13, einem Antibiotikaresistenzprotein, zusammen mit seinen natürlichen Substraten werden vorgestellt. Paramagnetische Methoden in der Wirkstoffentwicklung mittels NMR werden untersucht, und die Anwendung von Deep Learning für die Modellierung der Positionen von Wassermolekülen in Proteinstrukturen für die Optimierung von Liganden wird vorgestell

Computational Approaches for the Characterization of the Structure and Dynamics of G Protein-Coupled Receptors: Applications to Drug Design

G Protein-Coupled Receptors (GPCRs) constitute the most pharmacologically relevant superfamily of proteins. In this thesis, a computational pipeline for modelling the structure and dynamics of GPCRs is presented, properly combined with experimental collaborations for GPCR drug design. These include the discovery of novel scaffolds as potential antipsychotics, and the design of a new series of A3 adenosine receptor antagonists, employing successful combinations of structure- and ligand-based approaches. Additionally, the structure of Adenosine Receptors (ARs) was computationally assessed, with implications in ligand affinity and selectivity. The employed protocol for Molecular Dynamics simulations has allowed the characterization of structural determinants of the activation of ARs, and the evaluation of the stability of GPCR dimers of CXCR4 receptor. Finally, the computational pipeline here developed has been integrated into the web server GPCR-ModSim (http://gpcr.usc.es), contributing to its application in biochemical and pharmacological studies on GPCRs