Structure-based discovery of fiber-binding compounds that reduce the cytotoxicity of amyloid beta.

Amyloid protein aggregates are associated with dozens of devastating diseases including Alzheimer's, Parkinson's, ALS, and diabetes type 2. While structure-based discovery of compounds has been effective in combating numerous infectious and metabolic diseases, ignorance of amyloid structure has hindered similar approaches to amyloid disease. Here we show that knowledge of the atomic structure of one of the adhesive, steric-zipper segments of the amyloid-beta (Aβ) protein of Alzheimer's disease, when coupled with computational methods, identifies eight diverse but mainly flat compounds and three compound derivatives that reduce Aβ cytotoxicity against mammalian cells by up to 90%. Although these compounds bind to Aβ fibers, they do not reduce fiber formation of Aβ. Structure-activity relationship studies of the fiber-binding compounds and their derivatives suggest that compound binding increases fiber stability and decreases fiber toxicity, perhaps by shifting the equilibrium of Aβ from oligomers to fibers. DOI:http://dx.doi.org/10.7554/eLife.00857.001

Solvents to Fragments to Drugs: MD Applications in Drug Design

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates

Solvents to fragments to drugs: MD applications in drug design

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.Fil: Defelipe, Lucas Alfredo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Arcon, Juan Pablo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Modenutti, Carlos Pablo. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Marti, Marcelo Adrian. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Turjanski, Adrian. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Barril, Xavier. Institucio Catalana de Recerca I Estudis Avancats

In Silico and In Vitro Investigation into the Next Generation of New Psychoactive Substances

New Psychoactive Substances (NPS) were designed to be legal alternatives to existing established recreational drugs. They have fast become a very popular and up until 2016, NPS were legal, cheap and freely accessible via the internet and high street “head shops”. The rapid expansion in the number of these drugs has reached epidemic proportions, whereby hundreds of NPS have been developed and sold within the last five-year period. As NPS are synthesized in clandestine laboratories there is little to no control in the manufacture, dosage and packaging of these drugs. The public health risks posed by these drugs are therefore far-reaching. Fatalities and severe adverse reactions associated with these compounds have become an ongoing challenge to healthcare services, primarily because these drugs have not previously been abused and therefore there is little pharmacological information available regarding NPS. There are a number of different biological receptors that are implicated in the effects of NPS and the mechanism of action for the majority of these drugs is still largely unknown. It is of great importance to try and establish an understanding of how various classes of NPS interact on a molecular level. In this thesis, structure-based and ligand-based in Silico methodologies were employed to gain a better understanding of how NPS may interact with monoamine transporters (MAT). Key findings included both molecular docking studies and a number of robust and predictive QSAR models for the dopamine and serotonin transporters provided insight into how promiscuity of NPS between the different MAT isoforms could arise. In addition, pharmacophore models were generated to identify chemical entities that were structurally dissimilar to known existing NPS that had the potential to interact with the cannabinoid 1 receptor (CB1) and hence were hypothesised could elicit similar biological responses to known potent synthetic cannabinoids. Thirteen of these compounds were identified and carried forward for in vitro and ex vivo analyses, where preliminary results have shown that two compounds activate the CB1 receptor. Further optimisation of these compounds could yield a novel SC scaffold that was previously unseen. Additionally, the compounds identified and the methodology employed in the generation of these new chemical scaffolds could be used to guide Early Warning Systems (EWS) and facilitate law enforcement with respect to emergent NPS

Structural characterization and selective drug targeting of higher-order DNA G-quadruplex systems.

There is now substantial evidence that guanine-rich regions of DNA form non-B DNA structures known as G-quadruplexes in cells. G-quadruplexes (G4s) are tetraplex DNA structures that form amid four runs of guanines which are stabilized via Hoogsteen hydrogen bonding to form stacked tetrads. DNA G4s have roles in key genomic functions such as regulating gene expression, replication, and telomere homeostasis. Because of their apparent role in disease, G4s are now viewed as important molecular targets for anticancer therapeutics. To date, the structures of many important G4 systems have been solved by NMR or X-ray crystallographic techniques. Small molecules developed to target these structures have shown promising results in treating cancer in vitro and in vivo, however, these compounds commonly lack the selectivity required for clinical success. There is now evidence that long single-stranded G-rich regions can stack or otherwise interact intramolecularly to form G4-multimers, opening a new avenue for rational drug design. For a variety of reasons, G4 multimers are not amenable to NMR or X-ray crystallography. In the current dissertation, I apply a variety of biophysical techniques in an integrative structural biology (ISB) approach to determine the primary conformation of two disputed higher-order G4 systems: (1) the extended human telomere G-quadruplex and (2) the G4-multimer formed within the human telomerase reverse transcriptase (hTERT) gene core promoter. Using the higher-order human telomere structure in virtual drug discovery approaches I demonstrate that novel small molecule scaffolds can be identified which bind to this sequence in vitro. I subsequently summarize the current state of G-quadruplex focused virtual drug discovery in a review that highlights successes and pitfalls of in silico drug screens. I then present the results of a massive virtual drug discovery campaign targeting the hTERT core promoter G4 multimer and show that discovering selective small molecules that target its loops and grooves is feasible. Lastly, I demonstrate that one of these small molecules is effective in down-regulating hTERT transcription in breast cancer cells. Taken together, I present here a rigorous ISB platform that allows for the characterization of higher-order DNA G-quadruplex structures as unique targets for anticancer therapeutic discovery

Computational methodologies applied to Protein-Protein Interactions for molecular insights in Medicinal Chemistry

In living systems, proteins usually team up into \u201cmolecular machinery\u201d implementing several protein-to-protein physical contacts \u2013 or protein-protein interactions (PPIs) \u2013 to exert biological effects at both cellular and systems levels. Deregulations of protein-protein contacts have been associated with a huge number of diseases in a wide range of medical areas, such as oncology, cancer immunotherapy, infectious diseases, neurological disorders, heart failure, inflammation and oxidative stress. PPIs are very complex and usually characterised by specific shape, size and complementarity. The protein interfaces are generally large, broad and shallow, and frequently protein-protein contacts are established between non-continuous epitopes, that conversely are dislocated across the protein interfaces. For this reason, in the past two decades, PPIs were thought to be \u201cundruggable\u201d targets by the scientific research community with scarce or no chance of success. However, in recent years the Medicinal Chemistry frontiers have been changing and PPIs have gained popularity amongst the research groups due to their key roles in such a huge number of diseases. Until recently, PPIs were determined by experimental evidence through techniques specifically developed to target a small group of interactions. However, these methods present several limitations in terms of high costs and labour- and time-wasting. Nowadays, a large number of computational methods have been successfully applied to evaluate, validate, and deeply analyse the experimentally determined protein interactomes. In this context, a high number of computational tools and techniques have been developed, such as methods designed to construct interaction databases, quantum mechanics and molecular mechanics (QM/MM) to study the electronic properties, simulate chemical reactions, and calculate spectra, and all-atom molecular dynamics simulations to simulate temporal and spatial scales of inter- and intramolecular interactions. These techniques have allowed to explore PPI networks and predict the related functional features. In this PhD work, an extensive use of computational techniques has been reported as valuable tool to explore protein-protein interfaces, identify their hot spot residues, select small molecules and design peptides with the aim of inhibiting six different studied PPIs. Indeed, in this thesis, a success story of in silico approaches to PPI study has been described, where MD simulations, docking and pharmacophore screenings led to the identification of a set of PPI modulators. Among these, two molecules, RIM430 and RIM442, registered good inhibitory activity with IC50 values even within the nanomolar range against the interaction between MUC1 and CIN85 proteins in cancer disease. Furthermore, computational alanine scanning, all-atom molecular dynamics simulations, docking and pharmacophore screening were exploited to (1) rationally predict three potential interaction models of NLRP3PYD-ASCPYD complex involved in inflammatory and autoimmune diseases; (2) identify a potentially druggable region on the surface of SARS-CoV-2 Spike protein interface and select putative inhibitors of the interaction between Spike protein and the host ACE2 receptor against COVID-19 (CoronaVIrus Disease 2019); (3) investigate intramolecular modifications as a consequence of a point mutation on C3b protein (R102G) associated with the age-related macular degeneration (AMD) disease; (4) design non-standard peptides to inhibit transcriptional events associated with HOX-PBX complex involved in cancer diseases; and (5) to optimise a patented peptide sequence by designing helix-shaped peptides embedded with the hydrogen bond surrogate approach to tackle cocaine abuse relapses associated with Ras-RasGRF1 interaction. Although all the herein exploited techniques are based on predictive calculations and need experimental evidence to confirm the findings, the results and molecular insights retrieved and collected show the potential of the computer-aided drug design applied to the Medicinal Chemistry, guaranteeing labour- and time-saving to the research groups. On the other hand, computing ability, improved algorithms and fast-growing data sets are rapidly fostering advances in multiscale molecular modelling, providing a powerful emerging paradigm for drug discovery. It means that more and more research efforts will be done to invest in novel and more precise computational techniques and fine-tune the currently employed methodologies

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