Improved pose and affinity predictions using different protocols tailored on the basis of data availability

This is a post-peer-review, pre-copyedit version of an article published in Journal of Computer-Aided Molecular Design. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10822-016-9982-4.Prathipati, P., Nagao, C., Ahmad, S. et al. Improved pose and affinity predictions using different protocols tailored on the basis of data availability. J Comput Aided Mol Des 30, 817–828 (2016). https://doi.org/10.1007/s10822-016-9982-

Molecular docking: Shifting paradigms in drug discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence

Study of macromolecular interactions using computational solvent mapping

The term "binding hot spots" refers to regions of a protein surface with large contributions to the binding free energy. Computational solvent mapping serves as an analog to the major experimental techniques developed for the identification of such hot spots using X-ray and nuclear magnetic resonance (NMR) methods. Applications of the fast Fourier-transform-based mapping algorithm FTMap show that similar binding hot spots also occur in DNA molecules and interact with small molecules that bind to DNA with high affinity. Solvent mapping results on B-DNA, with or without Hoogsteen (HG) base pairing, have revealed the significance of "HG breathing" on the reactivity of DNA with formaldehyde. Extending the method to RNA molecules, I applied the FTMap algorithm to flexible structures of HIV-1 transactivation response element (TAR) RNA and Tau exon 10 RNA. Results show that despite the extremely flexible nature of these small RNA molecules, nucleic acid bases that interact with ligands consistently have high hit rates, and thus binding sites can be successfully identified. Based on this experience as well as the prior work on DNA, I extended the FTMap algorithm to mapping nucleic acids and implemented it in an automated online server available to the research community. FTSite, a related server for finding binding sites of proteins, was also extended to develop PeptiMap, an accurate and robust protocol that can determine peptide binding sites on proteins. Analyses of structural ensembles of ligand-free proteins using solvent mapping have shown that such ensembles contain pre-existing binding hot spots, and that such hot spots can be identified without any a priori knowledge of the ligand-bound structure. Furthermore, the structures in the ensemble having the highest binding-site hit rate are closest to the ligand-bound structure, and a higher hit rate implies improved structural similarity between the unbound protein and its bound state, resulting in high correlation coefficient between the two measures. These advances should greatly enhance researchers' ability to identify functionally important interactions among biomolecules in silico

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions

Metadynamics for perspective drug design: Computationally driven synthesis of new protein-protein interaction inhibitors targeting the EphA2 receptor

Metadynamics (META-D) is emerging as a powerful method for the computation of the multidimensional freeenergy surface (FES) describing the protein-ligand binding process. Herein, the FES of unbinding of the antagonist N-(3α-hydroxy-5β-cholan-24-oyl)-L-β-homotryptophan (UniPR129) from its EphA2 receptor was reconstructed by META-D simulations. The characterization of the free-energy minima identified on this FES proposes a binding mode fully consistent with previously reported and new structure-activity relationship data. To validate this binding mode, new N-(3α-hydroxy-5β-cholan-24-oyl)-L-β-homotryptophan derivatives were designed, synthesized, and tested for their ability to displace ephrin-A1 from the EphA2 receptor. Among them, two antagonists, namely compounds 21 and 22, displayed high affinity versus the EphA2 receptor and resulted endowed with better physicochemical and pharmacokinetic properties than the parent compound. These findings highlight the importance of free-energy calculations in drug design, confirming that META-D simulations can be used to successfully design novel bioactive compounds

Quantifying the Role of Water in Ligand-Protein Binding Processes

The aim of this thesis is to quantify the contributions of water thermodynamics to the binding free energy in protein-ligand complexes. Various computational tools were directly applied, implemented, benchmarked and discussed. An own implementation of the IFST formulation was developed to facilitate easy integration in workflows that are based on Schrödinger software. By applying the tool to a well-defined test set of congeneric ligand pairs, the potential of IFST for quantitative predictions in lead-optimization was assessed. Furthermore, FEP calculations were applied to an extended test set to validate if these simulations can accurately account for solvent displacement in ligand modifications. As a fast tool that has applications in virtual screening problems, we finally developed and validated a new scoring function that incorporates terms for protein and ligand desolvation. This resulted in total in three distinct studies, that all elucidated different aspects of water thermodynamics in CADD. These three studies are presented in the next section. In the conclusion, the results and implications of these studies are discussed jointly, as well with possible future developments. An additional study was focused on virtual screening and toxicity prediction at the androgen receptor, where distinguishing agonists and antagonists poses difficulties. We proposed and validated an approach based on MD simulations and ensemble docking to improve predictions of androgen agonists and antagonists

Characterization of the Hemagglutinin Cleaving Transmembrane Serine Proteases Matriptase and TMPRSS2

Influenza is one of the commonest infectious diseases affecting millions of people every year including 290,000 – 650,000 heavy casualties. Influenza viruses undergo constant genetic changes and every 10 – 50 years new influenza virus strains emerge that potentially cause a severe pandemic. In this modern interconnected world, experts believe the next influenza pandemic will be a “devastating global health event with far-reaching consequences” [1]. Novel effective anti-influenza drugs are in need. One strategy of influenza research is to focus on host-specific proteases that are essential for virus activation and spread. Trypsin-like serine proteases are crucial for influenza activation by mediating the cleavage of the viral surface glycoprotein HA and hence promoting the fusion potential of the virus. Therefore, their inhibition provides a promising therapeutic approach. The present work focused on the characterization of two relevant HA cleaving type-II transmembrane serine proteases matriptase and TMPRSS2. Chapter 3 and chapter 4 of this thesis engaged with the recombinant production of matriptase (chapter 3) in order to obtain a pure functional enzyme of high quality for a SAR study with novel monobasic (hence potentially bioavailable) matriptase inhibitors of the 3-amidinophenylalanine type (chapter 4). Adequate amounts of high-quality matriptase enzymes were isolated using a new expression system and in total 5 matriptase crystals were available at the end of this thesis for structural analysis. The matriptase inhibitor design in this thesis focused on matriptase-affine compounds with a fair selectivity profile against the blood coagulation enzymes thrombin and fXa. In total, 18 new monobasic and potentially bioavailable, as well as four new dibasic compounds of the 3-amidinophenylalanine types were tested. Based on the last published crystal structure of this inhibitor type in complex with matriptase from 2006 (PDB code 2GV6) docking was used as a structure-based virtual screening method for lead optimization of the compounds N-terminus. Selected compounds were suggested to interact with the carbonyl side chain of Gln175 of matriptase to achieve a higher affinity of matriptase compared to fXa. The 4-tert-butylureido-piperidine could be identified as suitable C-terminus in combination with 3-fluoro-4-hydroxymethyl biphenylsulphonyl N-terminally in order to obtain excellent selectivity over thrombin. The binding mode of this compound (compound 55) was crystallographically determined in complex with matriptase as well as trypsin. Trypsin proved as a suitable alternative to matriptase for detailed binding mode analysis of the compounds N-terminus. However, different preferences were detected for the C-terminus. Dibasic compounds showed higher matriptase affinity and selectivity in comparison with the monobasic analogues. However, the tested monobasic compounds were still decent matriptase inhibitors that are additionally suitable for cell culture and animal studies in their benzamidine prodrug forms, which are well established from related inhibitors of thrombin. In addition, selected monobasic as well as dibasic compounds demonstrated strong suppression of the replication of certain H9N2 influenza viruses in a matriptase-expressing MDCK II cell model. These matriptase inhibitors could be potential lead structures for the development of new drugs against H9 strains for influenza. TMPRSS2 is widely discussed for its role in influenza activation. With a TMPRSS2 dependancy of HA-activation of certain subtypes, the characterization of this protease is an important prerequisite for being available as a target for influenza drug design. However, only little is known about the physiological function of TMPRSS2 and no experimental structure data are available at the moment to enable a structure-based drug development. Therefore, chapter 5 of this thesis focused on the characterization of TMPRSS2 in order to develop a strategy for the isolation of proteolytically active TMPRSS2 from cell culture. Even though, no functional TMPRSS2 could be recovered at the end of this work some new structural characteristics of TMPRSS2 were identified as crucial for functionality insight the cell. In general, TMPRSS2 without the cytosolic part, the transmembrane domain and the LDLRA domain is able to undergo autocatalytically activation if an artificial signal peptide was added N-terminal to enable entry into the endoplasmic reticulum. The presence of the cysteine-rich SRCR domain and the presence of the disulfide chain that connects the SPD and the stem region after activation cleavage have been identified as crucial for activity. N-terminal truncation of TMPRSS2 did not result in obvious dislocation within the cell: as the full-length positive control truncated TMPRSS2 was exclusively found in cell compartments surrounding the nucleus in immunofluorescence experiments. However, a reduced proteolytic cleavage activity towards H3-HA in co-expression experiments has been observed and might be a result of dislocation, since truncated TMPRSS2 is not bound to the biomembrane anymore. In addition, TMPRSS2 has been identified as a potential substrate of matriptase in vitro, which suggests possible participation in several zymogen cascades

More is Better: 3D Human Pose Estimation from Complementary Data Sources

Computer Vision (CV) research has been playing a strategic role in many different complex scenarios that are becoming fundamental components in our everyday life. From Augmented/Virtual reality (AR/VR) to Human-Robot interactions, having a visual interpretation of the surrounding world is the first and most important step to develop new advanced systems. As in other research areas, the boost in performance in Computer Vision algorithms has to be mainly attributed to the widespread usage of deep neural networks. Rather than selecting handcrafted features, such approaches identify which are the best features needed to solve a specific task, by learning them from a corpus of carefully annotated data. Such important property of these neural networks comes with a price: they need very large data collections to learn from. Collecting data is a time consuming and expensive operation that varies, being much harder for some tasks than others. In order to limit additional data collection, we therefore need to carefully design models that can extract as much information as possible from already available dataset, even those collected for neighboring domains. In this work I focus on exploring different solutions for and important research problem in Computer Vision, 3D human pose estimation, that is the task of estimating the 3D skeletal representation of a person characterized in an image/s. This has been done for several configurations: monocular camera, multi-view systems and from egocentric perspectives. First, from a single external front facing camera a semi-supervised approach is used to regress the set of 3D joint positions of the represented person. This is done by fully exploiting all of the available information at all the levels of the network, in a novel manner, as well as allowing the model to be trained with partially labelled data. A multi-camera 3D human pose estimation system is introduced by designing a network trainable in a semi-supervised or even unsupervised manner in a multiview system. Unlike standard motion-captures algorithm, demanding a long and time consuming configuration setup at the beginning of each capturing session, this novel approach requires little to none initial system configuration. Finally, a novel architecture is developed to work in a very specific and significantly harder configuration: 3D human pose estimation when using cameras embedded in a head mounted display (HMD). Due to the limited data availability, the model needs to carefully extract information from the data to properly generalize on unseen images. This is particularly useful in AR/VR use case scenarios, demonstrating the versatility of our network to various working conditions

To boardrooms and sustainability: the changing nature of segmentation

Market segmentation is the process by which customers in markets with some heterogeneity are grouped into smaller homogeneous segments of more ‘similar’ customers. A market segment is a group of individuals, groups or organisations sharing similar characteristics and buying behaviour that cause them to have relatively similar needs and purchasing behaviour. Segmentation is not a new concept: for six decades marketers have, in various guises, sought to break-down a market into sub-groups of users, each sharing common needs, buying behavior and marketing requirements. However, this approach to target market strategy development has been rejuvenated in the past few years. Various reasons account for this upsurge in the usage of segmentation, examination of which forms the focus of this white paper. Ready access to data enables faster creation of a segmentation and the testing of propositions to take to market. ‘Big data’ has made the re-thinking of target market segments and value propositions inevitable, desirable, faster and more flexible. The resulting information has presented companies with more topical and consumer-generated insights than ever before. However, many marketers, analytics directors and leadership teams feel over-whelmed by the sheer quantity and immediacy of such data. Analytical prowess in consultants and inside client organisations has benefited from a stepchange, using new heuristics and faster computing power, more topical data and stronger market insights. The approach to segmentation today is much smarter and has stretched well away from the days of limited data explored only with cluster analysis. The coverage and wealth of the solutions are unimaginable when compared to the practices of a few years ago. Then, typically between only six to ten segments were forced into segmentation solutions, so that an organisation could cater for these macro segments operationally as well as understand them intellectually. Now there is the advent of what is commonly recognised as micro segmentation, where the complexity of business operations and customer management requires highly granular thinking. In support of this development, traditional agency/consultancy roles have transitioned into in-house business teams led by data, campaign and business change planners. The challenge has shifted from developing a granular segmentation solution that describes all customers and prospects, into one of enabling an organisation to react to the granularity of the solution, deploying its resources to permit controlled and consistent one-to-one interaction within segments. So whilst the cost of delivering and maintaining the solution has reduced with technology advances, a new set of systems, costs and skills in channel and execution management is required to deliver on this promise. These new capabilities range from rich feature creative and content management solutions, tailored copy design and deployment tools, through to instant messaging middleware solutions that initiate multi-streams of activity in a variety of analytical engines and operational systems. Companies have recruited analytics and insight teams, often headed by senior personnel, such as an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such personnel out-weigh posts for brand and marketing managers. Far more companies possess the in-house expertise necessary to help with segmentation analysis. Some organisations are also seeking to monetise one of the most regularly under-used latent business assets… data. Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a Chief Data Officer. This emerging role has also driven the professionalism of data exploration, using more varied and sophisticated statistical techniques. CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of value propositions and the need to look after core customers; CFOs because segmentation leads to better and more prudent allocation of resources – especially NPD and marketing – around the most important sub-sets of a market; COOs because they need to better look after key customers and improve their satisfaction in service delivery. More and more it is recognised that with a new segmentation comes organisational realignment and change, so most business functions now have an interest in a segmentation project, not only the marketers. Largely as a result of the digital era and the growth of analytics, directors and company leadership teams are becoming used to receiving more extensive market intelligence and quickly updated customer insight, so leading to faster responses to market changes, customer issues, competitor moves and their own performance. This refreshing of insight and a leadership team’s reaction to this intelligence often result in there being more frequent modification of a target market strategy and segmentation decisions. So many projects set up to consider multi-channel strategy and offerings; digital marketing; customer relationship management; brand strategies; new product and service development; the re-thinking of value propositions, and so forth, now routinely commence with a segmentation piece in order to frame the ongoing work. Most organisations have deployed CRM systems and harnessed associated customer data. CRM first requires clarity in segment priorities. The insights from a CRM system help inform the segmentation agenda and steer how they engage with their important customers or prospects. The growth of CRM and its ensuing data have assisted the ongoing deployment of segmentation. One of the biggest changes for segmentation is the extent to which it is now deployed by practitioners in the public and not-for-profit sectors, who are harnessing what is termed social marketing, in order to develop and to execute more shrewdly their targeting, campaigns and messaging. For Marketing per se, the interest in the marketing toolkit from non-profit organisations, has been big news in recent years. At the very heart of the concept of social marketing is the market segmentation process. The extreme rise in the threat to security from global unrest, terrorism and crime has focused the minds of governments, security chiefs and their advisors. As a result, significant resources, intellectual capability, computing and data management have been brought to bear on the problem. The core of this work is the importance of identifying and profiling threats and so mitigating risk. In practice, much of this security and surveillance work harnesses the tools developed for market segmentation and the profiling of different consumer behaviours. This white paper presents the findings from interviews with leading exponents of segmentation and also the insights from a recent study of marketing practitioners relating to their current imperatives and foci. More extensive views of some of these ‘leading lights’ have been sought and are included here in order to showcase the latest developments and to help explain both the ongoing surge of segmentation and the issues under-pinning its practice. The principal trends and developments are thereby presented and discussed in this paper

Identification of Ligands with Tailored Selectivity: Strategies & Application

In the field of computer-aided drug design, docking is a computational tool, often used to evaluate the sterical and chemical complementarity between two molecules. This technique can be used to estimate the binding or non-binding of a small molecule to a protein binding site. The classical application of docking is to find those molecules within a large set of molecules that bind a certain target protein and modulate its biological activity. This setup can be considered as established for a single target protein. In contrast to this, the docking to multiple target structures offers new possible applications. It can be used, for example, to assess the binding profile of a ligand against a number of proteins. In this work, the applicability of docking is assessed in such a scenario where multiple target structures are used. The corresponding proteins mostly belong to the family of G protein-coupled receptors. This protein family is very large and numerous GPCRs have been identified as potential drug targets, explaining the their relevance in pharmaceutical research. The protein structures used herein have different relationships and thus represent different application scenarios. The first case study uses two structures belonging to different proteins. These proteins are CXCR3 and CXCR4, a pair of chemokine GPCRs. In this chapter, new ligands are identified that bind to these proteins and modulate their biological activity. More importantly, for each of these newly identified ligands it could be predicted using docking, whether this ligand binds only to one of the two target proteins or to both. This study proves the applicability of docking to identify ligands with tailored selectivity. In addition, these ligands show excellent binding affinities to their respective target or targets. In the following two studies, the docking to different structures of the same target protein is investigated. The first application aims at identifying ligands selective for either one of two isoforms of the zebrafish CXC receptor 4. Subsequently, multiple conformations of the chemokine receptor CCR5 are used to show that different starting structures can identify different ligands. Next to the plain identification of chemically new ligands, experimental hurdles to prove the biological activity of these molecules in a functional assay is discussed. These difficulties are based on the fact that docking evaluates the structural complementarity between molecules and protein structures rather than predicting the effect of these molecules on the proteins. In addition, GPCRs form a challenging set of target proteins, since their ligands can induce a variety of different effects. Finally, the general applicability of multi-target docking to a very large number of structures is investigated. For this evaluation, kinases are used as protein family since many more structures have been experimentally determined for these proteins compared to GPCRs as membrane proteins. First, using published experimental data, a dataset is created consisting of several hundred kinase structures and a set of small-molecule kinase inhibitors. This dataset is characterised by the availability of experimental binding data for each single kinase-inhibitor combination. These experimental data were subsequently compared to the docking results of each ligand into each single kinase structure. The results indicate that a reliable selectivity prediction for a ligand is highly demanding in such a large-scale setup and beyond current possibilities. However, it can be shown that the prediction accuracy of docking can be improved by normalising the docking scores over multiple ligands and proteins. Based on these findings, the idea of "protein decoys" is developed, which might in the future allow more accurate predictions of selectivity profiles using docking