Development and validation of in silico tools for efficient library design and data analysis in high throughput screening campaigns

My PhD project findings have their major application in the early phase of the drug discovery process, in particular we have developed and validated two computational tools (Molecular Assembles and LiGen) to support the hit finding and the hit to lead phases. I have reported here novel methods to first design chemical libraries optimized for HTS and then profile them for a specific target receptor or enzyme. I also analyzed the generated bio-chemical data in order to obtain robust SARs and to select the most promising hits for the follow up. The described methods support the iterative process of validated hit series optimization up to the identification of a lead. In chapter 3, Ligand generator (LiGen), a de novo tool for structure based virtual screening, is presented. The development of LiGen is a project based on a collaboration among Dompé Farmaceutici SpA, CINECA and the University of Parma. In this multidisciplinary group, the integration of different skills has allowed the development, from scratch, of a virtual screening tool, able to compete in terms of performance with long standing, well-established molecular docking tools such as Glide, Autodock and PLANTS. LiGen, using a novel docking algorithm, is able to perform ligand flexible docking without performing a conformational sampling. LiGen also has other distinctive features with respect to other molecular docking programs: • LiGen uses the inverse pharmacophore derived from the binding site to identify the putative bioactive conformation of the molecules, thus avoiding the evaluation of molecular conformations which do not match the key features of the binding site. • LiGen implemenst a de novo molecule builder based on the accurate definition of chemical rules taking account of building block (reagents) reactivity. • LiGen is natively a multi-platform C++ portable code designed for HPC applications and optimized for the most recent hardware architectures like the Xeon Phi Accelerators. Chapter 3 also reports the further development and optimization of the software starting from the results obtained in the first optimization step performed to validate the software and to derive the default parameters. In chapter 4, the application of LiGen in the discovery and optimization of novel inhibitors of the complement factor 5 receptor (C5aR) is reported. Briefly, the C5a anaphylatoxin acting on its cognate G protein-coupled receptor C5aR is a potent pronociceptive mediator in several models of inflammatory and neuropathic pain. Although there has long been interest in the identification of C5aR inhibitors, their development has been complicated, as is the case with many peptidomimetic drugs, mostly due to the poor drug-like properties of these molecules. Herein, we report the de novo design of a potent and selective C5aR noncompetitive allosteric inhibitor, DF2593A. DF2593A design was guided by the hypothesis that an allosteric site, the “minor pocket”, previously characterized in CXCR1 and CXCR2, could be functionally conserved in the GPCR class.DF2593A potently inhibited C5a-induced migration of human and rodent neutrophils in vitro. Moreover, oral administration of DF2593A effectively reduced mechanical hyperalgesia in several models of acute and chronic inflammatory and neuropathic pain in vivo, without any apparent side effects. Chapter 5 describes another tool: Molecular Assemblies (MA), a novel metrics based on a hierarchical representation of the molecule based on different representations of the scaffold of the molecule and pruning rules. The algorithm used by MA, defining a priori a metrics (a set of rules), creates a representation of the chemical structure through hierarchical decomposition of the scaffold in fragments, in a pathway invariant way (this feature is novel with respect to the other algorithms reported in literature). Such structure decomposition is applied to nine hierarchical representation of the scaffold of the reference molecule, differing for the content of structural information: atom typing and bond order (this feature is novel with respect to the other algorithms reported in literature) The algorithm (metrics) generates a multi-dimensional hierarchical representation of the molecule. This descriptor applied to a library of compounds is able to extract structural (molecule having the same scaffold, wireframe or framework) and sub structural (molecule having the same fragments in common) relations among all the molecules. At least, this method generates relations among molecules based on identities (scaffolds or fragments). Such an approach produces a unique representation of the reference chemical space not biased by the threshold used to define the similarity cut-off between two molecules. This is in contrast to other methods which generate representations based in similarities. MA procedure, retrieving all scaffold representation, fragments and fragmentation’s patterns (according to the predefined rules) from a molecule, creates a molecular descriptor useful for several cheminformatics applications: • Visualization of the chemical space. The scaffold relations (Figure 7) and the fragmentation patterns can be plotted using a network representation. The obtained graphs are useful depictions of the chemical space highlighting the relations that occur among the molecule in a two dimensional space. • Clustering of the chemical space. The relations among the molecules are based on identities. This means that the scaffold representations and their fragments can be used as a hierarchical clustering method. This descriptor produces clusters that are independent from the number and similarity among closest neighbors because belonging to a cluster is a property of the single molecule (Figure 8). This intrinsic feature makes the scaffold based clustering much faster than other methods in producing “stable” clusters in fact, adding and removing molecules increases and decreases the number of clusters while adding or removing relations among the clusters. However these changes do not affect the cluster number and the relation of the other molecules in dataset. • Generate scaffold-based fingerprints. The descriptor can be used as a fingerprint of the molecule and to generate a similarity index able to compare single molecules or also to compare the diversity of two libraries as a whole. Chapter 6 reports an application of MA in the design of a diverse drug-like scaffold based library optimized for HTS campaigns. A well designed, sizeable and properly organized chemical library is a fundamental prerequisite for any HTS project. To build a collection of chemical compounds with high chemical diversity was the aim of the Italian Drug Discovery Network (IDDN) initiative. A structurally diverse collection of about 200,000 chemical molecules was designed and built taking into account practical aspects related to experimental HTS procedures. Algorithms and procedures were developed and implemented to address compound filtering, selection, clusterization and plating. Chapter 7 collects concluding remarks and plans for the further development of the tools

TEASPOON: a once in a lifetime opportunity to Sedna

In the challenge of unveiling the enigmas that still surround the origin and early evolution of the Solar System, the study of trans-Neptunian objects plays a crucial role. For this purpose, Sedna is probably the most intriguing candidate for a space mission. A better understanding of its highly elliptical orbit could improve our knowledge of the evolution of the Solar System and could potentially lead to the discovery of an unknown planet. Moreover, the planetoid is expected to host a significant amount of tholins and probably a subsurface ocean of liquid water, making the analysis of its composition extremely interesting. In 2076, Sedna will reach its minimum distance of 76 AU from the Sun. This is a scientific opportunity that will not happen again in the next 11400 years. Exploiting this instance, TransnEptuniAn Sedna PrObe for Outer exploratioN (TEASPOON) is a mission proposal to send a probe to Sedna, featuring a payload suite to perform an optical characterization, study the particle environment and conduct a radio-science experiment. Moreover, the long travel will be an opportunity to explore the Kuiper Belt looking for observations or, hopefully, discover new objects. The harsh environment, characterized by objects with unknown trajectories, requires Collision Avoidance strategies, while long-term radiation exposition demands electronics shielding and the preference for rad-hard components. More generally, the 77 AU distance and 30 years duration of the mission makes the design even more demanding. Therefore, solving those challenges would inaugurate a new generation of space missions to the edges of the Solar System and beyond. This proposal has been developed in the framework of a Space Mission Analysis and Design course by a team of students at the master level in Space Engineering at Politecnico di Milano. A concurrent engineering approach has been followed, leading the study through its phase 0/A. This enabled them to practice in actual working conditions of a space agency’s mission study, and underlined the importance of this kind of experience at a Master’s level course

DHFR Inhibitors Display a Pleiotropic Anti-Viral Activity against SARS-CoV-2: Insights into the Mechanisms of Action

During the COVID-19 pandemic, drug repurposing represented an effective strategy to obtain quick answers to medical emergencies. Based on previous data on methotrexate (MTX), we evaluated the anti-viral activity of several DHFR inhibitors in two cell lines. We observed that this class of compounds showed a significant influence on the virus-induced cytopathic effect (CPE) partly attributed to the intrinsic anti-metabolic activity of these drugs, but also to a specific anti-viral function. To elucidate the molecular mechanisms, we took advantage of our EXSCALATE platform for in-silico molecular modelling and further validated the influence of these inhibitors on nsp13 and viral entry. Interestingly, pralatrexate and trimetrexate showed superior effects in counteracting the viral infection compared to other DHFR inhibitors. Our results indicate that their higher activity is due to their polypharmacological and pleiotropic profile. These compounds can thus potentially give a clinical advantage in the management of SARS-CoV-2 infection in patients already treated with this class of drugs

Comparison of structure and ligand-based classification models for hERG liability profiling

The human ether-à-go-go-related potassium channel (hERG) is a voltage-gated potassium channel involved in the repolarization of the cardiac action potential. The off-target inhibition of hERG is the most frequent cause of drug-induced cardiotoxicity. Therefore, assessing hERG related cardiotoxicity in the early phase of the drug discovery process is crucial to avoid undesired cardiotoxic effects. For this purpose, we developed several machine learning classification models for hERG liability profiling basing on Random Forest algorithm by means of Weka software. The models were trained on a dataset of molecules collected from the public repository ChEMBL (https://www.ebi.ac.uk/chembl/) and the commercial GOSTAR database (https://www.gostardb.com/). The training molecules were encoded by both ligand- and structure-based attributes. The former consist of a set of physicochemical descriptors and fingerprints computed by RDKit node available in KNIME, while the latter comprise different scores obtained by docking and rescoring calculations performed by LiGen and Rescore+ tools, respectively. The following models are made available: hERG_LB, trained on ligand-based descriptors hERG_LiGen_AV, trained on a set of scores computed on the docking poses yielded by LiGen, considering for each score the mean value over all the computed poses. hERG_LiGen_AV-LB, trained on the combination of the descriptors used to build hERG_LB and hERG_LiGen_AV-LB models. The input datasets used for the models training and evaluation are made available too

A Deep Learning Approach to Optimize Recombinant Protein Production in <i>Escherichia coli</i> Fermentations

Fermentation is a widely used process in the biotechnology industry, in which sugar-based substrates are transformed into a new product through chemical reactions carried out by microorganisms. Fermentation yields depend heavily on critical process parameter (CPP) values which need to be finely tuned throughout the process; this is usually performed by a biotech production expert relying on empirical rules and personal experience. Although developing a mathematical model to analytically describe how yields depend on CPP values is too challenging because the process involves living organisms, we demonstrate the benefits that can be reaped by using a black-box machine learning (ML) approach based on recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks to predict real time OD600nm values from fermentation CPP time series. We tested both networks on an E. coli fermentation process (upstream) optimized to obtain inclusion bodies whose purification (downstream) in a later stage will yield a targeted neurotrophin recombinant protein. We achieved root mean squared error (RMSE) and relative error on final yield (REFY) performances which demonstrate that RNN and LSTM are indeed promising approaches for real-time, in-line process yield estimation, paving the way for machine learning-based fermentation process control algorithms

Combining Different Docking Engines and Consensus Strategies to Design and Validate Optimized Virtual Screening Protocols for the SARS-CoV-2 3CL Protease

The 3CL-Protease appears to be a very promising medicinal target to develop anti-SARS-CoV-2 agents. The availability of resolved structures allows structure-based computational approaches to be carried out even though the lack of known inhibitors prevents a proper validation of the performed simulations. The innovative idea of the study is to exploit known inhibitors of SARS-CoV 3CL-Pro as a training set to perform and validate multiple virtual screening campaigns. Docking simulations using four different programs (Fred, Glide, LiGen, and PLANTS) were performed investigating the role of both multiple binding modes (by binding space) and multiple isomers/states (by developing the corresponding isomeric space). The computed docking scores were used to develop consensus models, which allow an in-depth comparison of the resulting performances. On average, the reached performances revealed the different sensitivity to isomeric differences and multiple binding modes between the four docking engines. In detail, Glide and LiGen are the tools that best benefit from isomeric and binding space, respectively, while Fred is the most insensitive program. The obtained results emphasize the fruitful role of combining various docking tools to optimize the predictive performances. Taken together, the performed simulations allowed the rational development of highly performing virtual screening workflows, which could be further optimized by considering different 3CL-Pro structures and, more importantly, by including true SARS-CoV-2 3CL-Pro inhibitors (as learning set) when available

Binding Mode Exploration of B1 Receptor Antagonists’ by the Use of Molecular Dynamics and Docking Simulation—How Different Target Engagement Can Determine Different Biological Effects

The kinin B1 receptor plays a critical role in the chronic phase of pain and inflammation. The development of B1 antagonists peaked in recent years but almost all promising molecules failed in clinical trials. Little is known about these molecules&rsquo; mechanisms of action and additional information will be necessary to exploit the potential of the B1 receptor. With the aim of contributing to the available knowledge of the pharmacology of B1 receptors, we designed and characterized a novel class of allosteric non-peptidic inhibitors with peculiar binding characteristics. Here, we report the binding mode analysis and pharmacological characterization of a new allosteric B1 antagonist, DFL20656. We analyzed the binding of DFL20656 by single point mutagenesis and radioligand binding assays and we further characterized its pharmacology in terms of IC50, B1 receptor internalization and in vivo activity in comparison with different known B1 antagonists. We highlighted how different binding modes of DFL20656 and a Merck compound (compound 14) within the same molecular pocket can affect the biological and pharmacological properties of B1 inhibitors. DFL20656, by its peculiar binding mode, involving tight interactions with N114, efficiently induced B1 receptor internalization and evoked a long-lasting effect in an in vivo model of neuropathic pain. The pharmacological characterization of different B1 antagonists highlighted the effects of their binding modes on activity, receptor occupancy and internalization. Our results suggest that part of the failure of most B1 inhibitors could be ascribed to a lack of knowledge about target function and engagement