The conformational landscape of human transthyretin revealed by cryo-EM.

Rational Structure-Based Drug Design

Computational approaches are widely used to help discover and develop new drugs, in particular, to understand how these molecules interact with their biomolecular targets. There are well-optimised and validated force field parameters available to describe interactions of common biomolecules, such as proteins, lipids and nucleotides. However, these force fields are not designed to represent heteromolecular ligands such as substrates, inhibitors, co-factors and potential drug molecules. Errors in parameters may result in incorrect predictions of ligand structure, orientation and conformation, which in turn can lead to the failure of computational drug design efforts. With over 25% of all the structures in the Protein Data Bank (PDB) containing ligands and over a million ligand molecules of potential interest in drug design in other databases, ligand validation and parameterisation represents a significant scientific challenge. While several automated parameterisation protocols have been proposed to generate the parameters, none of the current procedures are properly validated. As a response to the high demand for interaction parameters for ligands compatible with the GROMOS force field, a web accessible Automated force field Topology Builder (ATB; http://atb.uq.edu.au/) and Repository was developed. The ATB and Repository is intended to facilitate the development of molecular force fields and generates parameters that can be used in X-ray refinement, structure-based drug design and study of biomolecule-ligand complexes. In this thesis, molecular dynamics (MD) simulations were used to calculate the thermodynamic properties for the validation and refinement of ATB force fields parameters. A fully automated validation protocol of the ATB force field parameters for small organic molecules based on thermodynamic and structural information was developed and incorporated into the ATB. A novel integration and convergence protocol which increases the efficiency of the TI method for free energy calculations was also proposed. The validation has shown good overall agreement between the experimental and computed values and indicated problematic functional groups with parameters to be refined. The generation of parameters for novel molecules that are compatible with a given biomolecular force field can be tedious, time-consuming and error-prone. Here, a novel method to refine parameters in classical force fields in an automated manner that can be extended to the parameterisation of all other atom types is also presented. Single-step perturbation protocols were developed for small halogenated molecules to establish alternative van der Waals parameters which describe the interactions of these molecules with high precision. Parameters were successfully refined against experimental hydration free energies, densities and heats of vaporisation. As a result, alternative van der Waals interactions parameters for chlorine and bromine were proposed. Properties calculated with these proposed parameters closely matched the experiment data. The quality of the ATB force field parameters in validating the X-ray structures and investigating binding of ligands to the protein endothiapepsin was also investigated. Analysis of the MD trajectories showed that the protein structure remained relatively stable after 10 ns of simulations. The results for the validation of the ligands were mixed. While in some cases the simulations reproduced the binding mode and the interactions between the protein and the ligand with high accuracy suggesting the ATB parameters performed well, in other cases the complexes were unstable. This was despite the fact that the electron density for all ligands was well defined and the parameters for the ligand were generated using the same procedure. Endothiapepsin contains a catalytic dyad consisting of two aspartates that are generally assumed to share a proton. We demonstrated that the protonation of both the ligand and the residues with the binding site were critical to the ability to reproduce the crystal complex. To conclude, the work presented in this thesis provided insight into the development and validation of the force field parameters for drug-like molecules and their applications in structure-based drug design.

INTRODUCTION. Malignant glioma is one of the most lethal diseases in adulthood and remains refractory to the current therapies including chemoradiotherapies. The cancer stem cell theory dictates that brain tumor stem-like cells (BTSC) account for such intractability of malignant glioma. Recently, several molecularly-targeted therapies have demonstrated a varying degree of success in treatment of cancers including leukemia and breast cancer. In this study, we sought to determine the key kinases that regulate survival, self-renewal, and proliferation of BTSC, and to design novel small molecules by using the structure-based computantional modeling system. RESULTS. With the neurosphere cultures derived from our glioma patients, we identified 3 kinases that play vital roles in survival and/or proliferation of BTSC in vitro; Fibroblast Growth Factor Receptor 1, Aurora kinase B, and Maternal Embryonic Leucine-zipper Kinase. Structure-based computational modeling allowed us to perform in silico docking of various small molecule candidates to these kinases. As a result, four novel small molecules demonstrated highly specific targeting of the key structural elements of these kinases. We then synthesized these identified candidate compounds and confirmed their in vitro inhibition of the targeting kinase activities. Finally, we will also present our data about the treatment effect with these compounds on the in vitro BTSC growth and the in vivo tumor growth. CONCLUSION. Structure-based drug design will likely open up a new avenue to discover novel anti-cancer stem cell agents that can selectively target the key kinases in BTSC, leading to the growth arrest of malignant glioma. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3298. doi:10.1158/1538-7445.AM2011-3298

Molecular ConceptorTM (http://www.molecular-conceptor.com) is a drug design e-courseware that has been developed as a reference, a source of inspiration for medicinal chemists and a database of case studies in drug discovery. Synergix has developed Molecular Conceptor, as drug design e-courseware that can be used as a reference by medicinal chemists as a source of inspiration in drug design, and as an expanding compendium of knowledge in drug discovery. Molecular Conceptor includes 40 volumes and chapters that cover a wide range of topics such as structure-based drug design, ligand-based drug design, cheminformatics, molecular similarity, 3D-QSAR (three-dimensional Quantitative Structure-Activity Relationships), peptidomimetics, molecular docking and many others. In this report, we have chosen ‘molecular mimicry’ as a representative example to illustrate how it appears in Molecular Conceptor. This topic appears in many sections of the e-courseware, including: (i) three chapters devoted to pharmacophore-based drug design and (ii) several chapters on molecular similarity, 3D database searching, principles of rational drug design and library design. Two-dimensional (2D) representations are often misleading for understanding SAR, and one of the key features of Molecular Conceptor is its ability to visualize molecules in 3D, which is crucial to grasping the subtle recognition and discrimination processes of molecular machinery. In the example illustrated in Figure 1, moving the nitrogen atom in the pyrimidine heterocycle from A to B turns an inactive molecule into a very potent one. This is extremely difficult to see in the 2D formulas. A cogent explanation is given in Molecular Conceptor that can only be derived by viewing the molecules in 3D. Molecules A and B differ solely in the position of the nitrogen atom in the pyrimidine ring and exhibit very different biological activities (left). A possible explanation for this difference is visualized in three-dimension (right). Molecular mimicry is extremely useful in the early stages of drug discovery. When little knowledge is available, such as for example, one active reference compound, this is enough and the project can start. The aim of many such projects was described by terms such as ‘non-peptides, non-nucleotides, non-steroids, non-tricyclic mimetics’ and so forth. Snapshots of some pages associated with this topic are available in Figure 2 and Supplementary Material to this report. Simple and more complex case studies are presented, decomposed and assessed so that methodological rules can be deduced. The core topic is presented in three successive chapters: the first discusses ways to analyse the project (principles of analyses, control of molecular geometries, misuse of structural information, managing hypotheses); the second addresses the design issue (e.g. the four design methods) and the third discusses success stories from 13 different therapeutic areas. Other chapters such as the ones dealing with 3D-QSAR, molecular similarity, library design and ADME (Absorption, Distribution, Metabolism, Elimination) properties highlight the importance of molecular mimicry in these different areas. When molecular mimicry is applied to peptides, it is referred to as ‘peptidomimetics’. This type of approach is mandatory in drug research when dealing with a peptide molecule that cannot be developed as a drug. This topic is presented in detail in two different chapters: (i) Introduction to Peptidomimetics and (ii) Peptidomimetic Examples. However, many other examples are presented for diverse purposes in other sections of the software. For instance, the successful design of Aliskiren (a renin inhibitor now in late stages of development) is presented in both the chapter on examples of peptidomimicry as well as in the chapter on case histories in drug discovery. Snapshot of a page from Molecular Conceptor. In conclusion, the concept of molecular similarity has nurtured the imagination and creativity of several generations of medicinal chemists. Because of the fuzziness of the concept, medicinal chemists live with the contradiction that similar compounds simultaneously have and do not have similar activities. The key concepts and features of thousands of scientific articles devoted to this topic are summarized in Molecular Conceptor. The essence of millions of hours of scientific work, human creativity, investment in human resources and expenditures in R & D are presented on a friendly and efficient platform. Molecular Conceptor is rapidly becoming a prime reference for medicinal chemists, a source of inspiration in drug design and can also be used to train newly recruited medicinal chemists in the industry (1-4). Figure S1: Snapshots of pages from Molecular Conceptor dealing with molecular mimicry. For clarity, both the navigation tree and the text of the pages have been omitted Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Structure-based drug design is emerging as one of the key components in drug discovery, with many approved drugs tracing, at least part of their origins, to the use of structural information from X-ray, NMR, surface plasmon resonance, differential thermal denaturation, fluorescence polarization and other techniques for analysis of protein targets and their ligand-bound complexes. Furthermore, in silico structure-based drug design approach has enabled millions of possible structures for a given protein sequence to be evaluated rapidly helping to fast forward drug discovery as well as reduce drug discovery costs. Structure-based drug design is now arguably an essential contributor to addressing the need to improve research and development productivity faced by the pharmaceutical industry. The purpose of this meeting is to highlight the impact of the intersection of structural biology with chemistry and biology particularly on how the structures of relevant drug targets can serve as a starting point for drug design and development and provide the maximal synergy between target validation, structure determination, and hit-to-lead development. Some thoughts will be proposed with regard to the future of structure-based drug design and where emphasis could be placed to further increase the utilization of this approach on drug discovery.

Drug design is a long and costly process taking many stages; start with target identification passing through target validation, lead identification, and candidate optimization of pre-clinical and clinical trials. Drug design types depend on screening of a large number of molecules to distinguish and can select the most effective drug with high pharmaceutical effect. Ligand and Structure Based Drug Design are the two types of drug design. The drug takes journey when administered into the body through several ways (oral, inhalation, Intravenous (IV), Intramuscular (IM)) to deliver its target site. Twenty years ago, Computational strategies applied to understand particular target molecules with hits achieving lead target and that helps in lead Identification and Optimization stages of drug design and development. Screening, molecular modification and rational drug design are the three approaches to search for a modern drug. Bioinformatics plays a vital role in the discovery of drug as Bioinformatics includes both the programmed preparing of huge amounts of existing information and the creation of modern sorts of data resource. Both required in case the information is to be changed into data and utilized to assist in drug discovery. This work will discuss what is the meaning of drug design? What are the stages of drug design? What are the drug design types? Computer- Aided Drug Design (CAAD), Approaches in the search for a new drug, What is the journey of drug to deliver its target site? What are the methods of drug delivery? And will discuss the recommended antiviral therapy in the management of COVID-19.

Structure-based design has emerged as a new tool in medicinal chemistry. A prerequisite for this new approach is an understanding of the principles of molecular recognition in protein-ligand complexes. If the three-dimensional structure of a given protein is known, this information can be directly exploited for the retrieval and design of new ligands. Structure-based ligand design is an iterative approach. First of all, it requires the crystal structure or a model derived from the crystal structure of a closely related homolog of the target protein, preferentially complexed with a ligand. This complex unravels the binding mode and conformation of a ligand under investigation and indicates the essential aspects determining its binding affinity. It is then used to generate new ideas about ways of improving an existing ligand or of developing new alternative bonding skeletons. Computational methods supplemented by molecular graphics are applied to assist this step of hypothesis generation. The features of the protein binding pocket can be translated into queries used for virtual computer screening of large compound libraries or to design novel ligands de novo. These initial proposals must be confirmed experimentally. Subsequently they are optimized toward higher affinity and better selectivity. The latter aspect is of utmost importance in defining and controlling the pharmacological profile of a ligand. A prerequisite to tailoring selectivity by rational design is a detailed understanding of molecular parameters determining selectivity. Taking examples from current drug development programs (HIV proteinase, t-RNA transglycosylase, thymidylate synthase, thrombin and, related serine proteinases), we describe recent advances in lead discovery via computer screening, iterative design, and understanding of selectivity discrimination.

Modern drug discovery relies heavily on a detailed understanding of the nature of complexes formed and the thermodynamics associated with the binding of a ligand to a target protein. Despite this, studies have suggested that up to 70% of ligands in the Protein Data Bank were refined using inappropriate geometric restraints and in 25% of cases, the specific interactions claimed to be associated with binding may be misleading. The problem is that current refinement and validation procedures do not properly account for alternative protonation and tautomeric states, nor the uncertainties in the stereochemistry, orientation and/or conformation of the bound ligand. This thesis addresses the question of whether computational approaches can be used to identify and address problems in protein:ligand complexes and the extent to which free energy calculations can be used to validate the parameters used in computational drug design. Using a series of high-quality co-crystal structures of the aspartic protease Endothiapepsin, Chapter 2 examines whether it is possible to discriminate between alternative protonation states of the ligands and the catalytic dyad using all-atom refinement that incorporates the electrostatic terms. In Chapter 3, molecular dynamic simulations are employed using the same system to examine a range of alternative complexed states to determine which of these states were thermodynamically most stable and thus potentially physically the most realistic. The interaction of different tautomeric forms of simple ligands bound to Trypsin is examined in Chapter 4. Ultimately, to predict how a drug binds to a target protein we must be able to determine the binding free energy with high accuracy. Despite a rigorous theoretical framework and the potential for high accuracy calculations, free energy perturbation or integration methods have not found routine use in structure-based drug design. One of the main challenges is transferring methodology that has been optimised for small, generally rigid ligands carrying a single charged group to that of larger (> 40 atoms), flexible ligands containing a range of diverse chemical functionality; the types of molecules increasingly of interest in structure-based drug design. The final chapter (Chapter 5) of my thesis will investigate a range of protocols involving single and dual topologies, various soft-core potentials and restraints. With the desire to develop automated high throughput protocols that can efficiently exploit advances in hardware. A dataset used by OPLS and AMBER is employed to validate the methodology.

The use of structure in drug design has become widespread, mainly thanks to recent advances in crystallography. Nevertheless, biological macromolecules are intrinsically flexible and it is increasingly evident that their function depends critically on both their structure and dynamics. In this review the authors discuss the implications of protein flexibility for drug design and review recent progress in incorporating protein flexibility into docking and structure-based drug design.

Structure-based drug design

Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

In the current era of high-throughput technology, where enormous amounts of biological data are generated day by day via various sequencing projects, thereby the staggering volume of biological targets deciphered. The discovery of new chemical entities and bioisosteres of relatively low molecular weight has been gaining high momentum in the pharmacopoeia, and traditional combinatorial design wherein chemical structure is used as an initial template for enhancing efficacy pharmacokinetic selectivity properties. Once the compound is identified, it undergoes ADMET filtration to ensure whether it has toxic and mutagenic properties or not. If the compound has no toxicity and mutagenicity is either considered a potential lead molecule. Understanding the mechanism of lead molecules with various biological targets is imperative to advance related functions for drug discovery and development. Notwithstanding, a tedious and costly process, taking around 10–15 years and costing around $4 billion, cascaded approached of Bioinformatics and Computational biology viz., structure-based drug design (SBDD) and cognate ligand-based drug design (LBDD) respectively rely on the availability of 3D structure of target biomacromolecules and vice versa has made this process easy and approachable. SBDD encompasses homology modelling, ligand docking, fragment-based drug design and molecular dynamics, while LBDD deals with pharmacophore mapping, QSAR, and similarity search. All the computational methods discussed herein, whether for target identification or novel ligand discovery, continuously evolve and facilitate cost-effective and reliable outcomes in an era of overwhelming data.

Structure-based design and optimization of potent renin inhibitors on 5- or 7-azaindole-scaffolds

Infectious diseases, caused by bacteria, viruses, fungi, protozoa, or parasites are among the leading causes of death worldwide. The efficacy of all current anti-infectives is threatened by the spread of drug resistance factors, with some already made ineffective, and, as a result, there is a pressing need for a new pipeline of robust anti-infective drugs. In silico approaches, such as virtual high throughput screening and de novo structure-based rational drug design, have been established as powerful tools in drug discovery. In this review, we explore the exciting opportunities for antimalarial, antiviral, and antibacterial drug discovery arising from the new paradigm of structure-based drug design.

Chapter Seven - Beyond Small-Molecule SAR: Using the Dopamine D3 Receptor Crystal Structure to Guide Drug Design