X-Ray Crystallography: Structure Preparation, Electron Density and Solvent Analysis
Structure Preparation / Side-Chain Rotamer Exploration / Electron Density Maps / Solvent Analysis with 3D-RISM
The course will cover methods for evaluating, analyzing and refining protein models derived from X-ray
crystallographic data. Topics related to protein structure preparation and side-chain conformational analysis and
placement will be discussed. Visualization and interpretation of electron density maps for assessing protein models
will be described. The 3D-RISM method for predicting and refining the placement of solvent molecules in protein
models will also be presented.
The course covers methods for analyzing and optimizing peptide-protein interactions in the active site. Topics related to peptide-protein structure preparation, peptide sequence optimization using natural and non-natural acids and conformational analysis will be discussed. Peptide-protein docking and protein-ligand interactions to analyze contact points will be described. The course will also cover advanced conformational searching using distance restraints.
Structure-Based Drug Design and Ligand Modification
Molecular Surfaces and Maps / Ligand Interactions / Conformational Searching / Ligand Optimization / Ligand Selectivity / Protein Alignments and Superposition
The course covers MOE applications for interactive structure based design. Examples include active site visualization, protein-ligand contact analysis and ligand modification/optimization in the receptor pocket. Conformational searching and analysis of the ligand to assess ligand flexibility will be discussed. A protocol for aligning and superposing protein complexes in the context of protein selectivity will be studied.
The course will introduce the cheminformatics applications included in MOE, based within its Database Viewer (DBV). Using a dataset of logBB (blood-brain barrier permeation) values, we will import/export data from/to various formats (SDF, SMILES etc.), calculate molecular descriptors, analyse and visualize data, and create QSAR and binary-QSAR models. We will then perform fingerprint-based similarity searching, clustering, diverse subset selection and consensus modeling.
The course describes advanced SBDD workflows in drug discovery projects and encompasses a range of topics from pharmacophore query generation to protein-ligand interaction fingerprints. More specifically, the course will cover the application of pharmacophores in the context of protein-ligand docking, scaffold replacement and R-group screening. A method for querying a 3D project database will also be presented along with the generation and analysis of protein- ligand interaction fingerprints (PLIF).
Protein Engineering / Protein Properties / Developability / Hot Spot Analysis / Antibody Modeling / Humanization / Molecular Surfaces
The course covers approaches for structure-based antibody design and includes protein-protein interactions analysis, in silico protein engineering, affinity modeling and antibody homology modeling. The interaction of a co-crystallized antibody-antigen complex will be studied by generating and examining the molecular surfaces and visualizing protein-protein interactions in 3D and 2D. Antibody properties will be evaluated using specialized calculated protein property descriptors and analyzing protein patches. The application of protein engineering tools for homology modeling and conducting property optimization of antibodies in the context of developability will be studied. Antibody optimization examples will include identification of glycosylation sites and analysis of correlated pairs using a specialized antibody database. An approach for humanizing antibody homology models will be discussed. All the steps necessary for producing and assessing antibody homology models will be described.
Ligand-Based Drug Design and SAR Analysis R-Group Profiles and Analysis / MOEsaic / MMP Analysis / Descriptor Calculations / Conformational Searching / Molecular Alignments / Pharmacophore Modeling and Searching / Diversity Analysis
The course covers essential in silico methods needed for guiding drug discovery projects in the absence of a protein structure. Analysis of SAR through R-group profiling and matched molecular pairs (MMP) analysis using MOEsaic to determine relationships among a chemical series are examined. Molecular descriptor calculations and their application for determining property correlations along with diversity analysis are described. Molecular alignments and conformational analyses of a congeneric series are explored to assess the impact of ligand substituents. An approach for developing pharmacophore queries is discussed. Management and manipulation of MOE databases are also covered.
Sequence and structure alignments / Template selection / Building and refining homology models
The course covers protein alignments and homology modeling in MOE. Alignment examples include multiple sequence and structure alignments, focusing alignments on a structurally conserved core or active site residues, and visualization of alignment properties such as percentage residue identity and RMSD. The homology modeling section covers the complete sequence to structure workflow, including template searching, query alignment, homology model building and refinement, along with applications for assessing model quality.
Biologics: Protein Alignments, Modeling and Docking
Protein Alignments and Superposition / Loop and Linker Modeling / Homology Modeling / Protein- Protein Docking / Protein Solubility Analysis / 2D Hot Spot Mapping / PLIF / Biologics QSAR Modeling
The course covers methods for aligning protein sequences, superposing structures, homology modeling fusion proteins and conducting protein-protein docking. In particular, an approach for aligning and superposing multiple structures will be described for determining structural and surface protein variations in relation to protein property modulation. A method for grafting and refining antibody CDR loops as well as using a knowledge-based approach to scFv fusion protein modeling using the MOE linker application will be described. An approach to generate homology models of a murine antigen structure from a human template as well as protein-protein docking of an antibody to an antigen will be discussed. A QSAR model for predicting and analyzing protein/biologics solubility will be described.
The course covers the suite of MOE applications which can be applied to small-molecule virtual screening. Topics include the preparation of small molecule databases for virtual screening, filtering databases based on substructure matching and property values, building QSAR/QSPR models and fingerprint similarity models as database filters, pharmacophore query creation and searching, and small-molecule docking. These tools are used in conjunction to present a complete virtual screening workflow. The creation of de novo structures using the MOE Scaffold Replacement and MOE Medchem transformation applications is also covered.
Protein Degradation : Hijacking the Ubiquitin-Proteasome System
Ye Che, Senior Principal Scientist & Head of Computational Design Lab, Pfizer
Chimeric protein degraders (ProDegs, PROTACs) exert their efficacy by simultaneously binding an E3 ligase and a protein target of interest. This necessary first step results in protein target poly-ubiquitination and subsequent proteasomemediated degradation. Compound optimization typically involves identifying a target binder and an E3 ligase binder and connecting the two pieces by varying linkers. Potent and selective degradation of various proteins have been reported in the literature. Recent structural studies of ternary complexes attempt to provide a detailed understanding of the molecular mechanism of action. This presentation will introduce this novel modality, identify challenges and opportunities for computational modeling in this space with a specific study on BTK.
Scaling Binding Site Prediction Tools to the Human Proteome using HPC
Christopher MacDermaid, Scientific Investigator, GSK
Recent step changes in the availability of cheap and dense compute power have enabled the routine scale-up of “one-off” descriptor calculations from handfuls at a time to tens of thousands or more. This complements the current explosion in high throughput experimental data collection and analytics techniques. Indeed, when coupled together, high-throughput computation and experimentation can be used to inform this generation of machine learning approaches, pragmatically guide small molecule development, or rapidly assess the tractability of protein targets. Of particular interest to GSK and the broader drug discovery community is the identification of protein cavities that have the potential to bind small molecules. Owing to the importance of this task, numerous cavity detection methods exist employing both static and dynamic representations of the protein system. In this talk, I will discuss the design and implementation of a high throughput computational protocol that employs these various detection methods, as well the challenges associated with scaling the protocol to identify and assess the makeup of cavities present throughout the human proteome.
Molecular visualization with stereoscopic graphics systems has been at the core of structure-based drug design since its inception, because stereo vision on a monitor or projector can enable better understanding of the complex 3D data found in binding sites. The base technology to enable this visualization, however, has not progressed over decades of use. Some niche solutions and improvements have come and gone. Indeed, with the departure of passive and autostereoscopic monitors (monitors that do not require special graphics cards or powered glasses), there is significant risk of regression. In this talk, we will review the role of stereoscopic visualization in drug discovery, discuss what we think are opportunities that are missing in our ability to analyze 3-dimensional structural information, and describe the porting of MOE to a Zvr virtual reality monitor that enables a more enhanced 3D visualization experience.
Matthias Keil, Principal Scientist, Chemical Computing Group
Will AI redefine the role of Structural Analysis in Drug Development?
Rafael Depetris, Principal Scientist I, Kadmon Pharmaceuticals
The talk will show the basic methods to obtain descriptors of protein surfaces which are of general use in AI powered classification methods. We propose that the inherent restrictions on the nature of epitope sequences as well as the discreet architecture usually present in the paratopes provide a fair ground to develop surface descriptors focused on these areas. These epitope-paratope descriptors can be used for prediction of affinity and stability of therapeutic antibodies. We believe that the need for accurate descriptors of protein surfaces is leading the structural analysis to focus on new structural features and patterns and pushing this discipline into a new arena.
Towards the Next Generation of Crystal Structure Database
Jérémy Desaphy, Research Scientist, Eli Lilly
Jeremy DESAPHY, Cen GAO, Jibo WANG
During the drug discovery phase, many crystal structures of a target in complex with several ligands are solved. Depending on the target, its flexibility, the binding site complexity,
it becomes rapidly difficult for one to get a clear and complete picture. When structure-based design projects arise, articulation of molecular hypotheses become more challenging, more
precise, and often requires a manual and time-consuming analysis. With the additional layer of inconsistencies on the protein side – insertion / deletion / mutation /
inconsistent numbering – it is necessary to create a new system that addresses such issues while opening the door to fast querying capabilities.
Over the last few years, technology has enabled a wider range of opportunities, leading to improve performance and ease to use. In this presentation, we will present our strategy to
provide a wider range of querying capabilities to both medicinal chemists and computational chemists. This new system automatizes some daunting tasks that computational chemist is
required to do while giving them the freedom to ask more complex questions. At last, we will present some applied examples of our work and future directions.
Designing Beyond Rule-Of-Five Molecules for Tough Targets
Alan Cheng, Director, Merck
I’ll present three stories on approaches for tough targets. The first is a computational method for rapidly identifying cryptic pockets that have high potential to be druggable. The second is around drug design approaches contributing to the discovery of a protein-protein inhibitor. The third is around design of a hybrid antibody-small molecule tool for cell-surface targets difficult to modulate by either large molecule or small molecule alone.
Automated Pharmacophore Generation and Its Use in Virtual Screening
Miklos Feher, Senior Research Scientist, D.E. Shaw Research
One potential approach for using molecular dynamics (MD) trajectories of proteins or protein–small molecule complexes as starting points for virtual screening and de novo design relies
on the use of pharmacophores. In the case of a single structure, pharmacophore generation is usually performed manually, which is time-consuming and subjective, and therefore not easily
applied to the many structures coming from an MD trajectory. Here we describe a fast and automated procedure that we have developed to generate pharmacophores from either holo- or
apo-protein structures. We demonstrate the usefulness of such automatically generated pharmacophores using the DUD-E virtual screening validation set in combination with our in-house
virtual screening protocol. We found that our pharmacophore-based virtual screening approach not only greatly outperforms other pharmacophore-based methods for which results are available,
but that it also shows performance comparable to the best-available docking methods (judged according to standard metrics). We also attained a significantly higher early enrichment compared
to docking, which should provide a practical advantage in drug discovery applications.
Prediction of CYP Selectivity, Reactivity, and Regioselectivity Incorporating Enzyme Structural Information
Michael Drummond, Scientific Applications Manager, Chemical Computing Group
Cytochrome P450 oxidases (CYPs) are a class of well-known heme-containing enzymes that are primarily responsible for clearing xenobiotics through oxidative metabolism. In humans, the xenobiotics targeted by CYPs include small molecule therapeutics, and thus understanding the interplay between drug molecules and CYPs is critical for evaluating drug efficacy, clearance, toxicity, and drug-drug interactions. About 80% of CYP activity in humans is dominated by five CYP isoforms, which differ in sequence and somewhat in structure (particularly near the binding pocket around the heme). Although dozens of crystal structures of the five primary CYP isoforms have been solved, most modeling tools to predict drug-CYP interactions completely neglect this structural information. Nonetheless, it has long been known that certain isoforms are selective based on general small molecule characteristics, such as planarity, charge, etc. These empirical preferences suggest that understanding the shape, flexibility, and electrostatic nature of the CYP binding pockets can lead to improved modeling of CYP metabolism vis-à-vis approaches that only consider properties of the small molecules themselves.
In this work, both 2D methods and 3D methods are used to predict the isoform selectivity, small molecule reactivity, and regioselectivity of CYPs. The 2D-based methods developed herein are parsimonious yet accurate and can be used to quickly evaluate selectivity and reactivity. The 3D approach is based on pharmacophore modeling, which provides a rapid and flexible way to predict CYP isoform selectivity and regioselectivity. The modular components of the pharmacophore afford a straightforward means to tailor for a particular problem of interest, thereby improving prediction accuracy. Moreover, directly incorporating 3D CYP structures into the models confers unique advantages over 2D-based approaches, such as the ability to distinguish reactivity differences among stereoisomers. Finally, predicted results can readily be visualized, and thus potential CYP liabilities are not merely evaluated or flagged, but can also be designed against – a clear departure from the pass/fail filtering paradigm prevalent in most CYP modeling efforts to date.
While the orally active azoles fluconazole, posaconazole, and isavuconazole are effective antifungal agents, they exhibit a myriad of off-target issues such as drug-drug interactions, anaphylaxis, and liver and reproductive toxicities, mainly due to their inhibition of other P-450 enzymes. We have described the discovery and development of the more selective tetrazole antifungal agents VT-1161 and VT-1598, which exhibit this increase in specificity while maintaining high potency. In an effort to find equally selective and potent compounds with different physical properties, we investigated analogues with alternative backbone scaffolds which would give us an improvement in PK/PD profiles as well as provide possible functionality for creation of prodrugs.
The pregnane X receptor (PXR) is a promiscuous nuclear hormone receptor responsible for many undesirable drug interactions. This study aims to describe the minimum ligand components that are required for PXR interaction and the features that differentiate actives from inactives, especially with regard to ligand geometric shapes, electrostatic distributions, and distances. A novel molecular descriptor, Smallest Maximum Interatomic Distance (SMID), is described, together with strategies for designing analogs with increased SMID. An interaction model is developed and evaluated against a temporal test set.
The Identification of Novel Phosphodiesterase 2 Inhibitors by Fragment-Based Drug Design
Deping Wang, Associate Principal Scientist, Merck
We have identified a novel PDE2 inhibitor series using fragment-based screening. Pyrazolopyrimidine,
while possessing weak potency (Ki = 22.4 μM), exhibited good binding efficiencies (LBE = 0.49, LLE = 4.48)
to serve as a start for structure-based drug design. Using structure-based approach, this fragment was developed into
a series of potent PDE2 inhibitors with good physicochemical properties. A PDE2 selective inhibitor was identified
that exhibited favorable rat pharmacokinetic properties.
Free Ligand Conformational Case Studies: Can NMR Plus Computation Create Low Hanging Fruit for MedChem Design?
Amber Balazs, Analytical & Structural Chemistry, Oncology, IMED Biotech Unit, AstraZeneca, Boston, US
Experimental and computational tools revolutionized drug discovery, as the ability to visualize 3D protein-ligand interactions and protein structural changes upon ligand-binding became available to a wider community of scientists. More recently, routine inclusion of solution free ligand conformational preferences is providing a crucial “missing link” to rational drug design. First, designing maximal potency optimizes for spontaneous ligand preorganization into the bioactive conformation. Next, design will focus on physical chemistry properties (rigidity, exposed polarity, intramolecular hydrogen bonds, pKa, etc.). The current challenge is to work within timeframes of quickly moving design-make-test-analyze cycles. While computation is fast, experimentally measured solution free ligand conformations often differ from computed lowest energy or highest probability conformations. Together with that, extensive experimental characterization of a free ligand conformation is time consuming.
At AstraZeneca, we’ve been strategically working to identify and quickly extract experimental details relevant to medicinal chemistry design. We have developed a unique platform, aimed at creating maximum information in minimum time, by integrating experimental NMR and computational approaches into a single workflow. Structure based design from Oncology Chemistry, including MCL1 inhibitors and the discovery of
AZD5991, will be described to demonstrate how free ligand conformation analysis can be leveraged within a drug design milieu.
A Kinase Platform for the Discovery of Reversible and Covalent Kinase Inhibitors
Igor Mochalkin, Associate Director, Medicinal Chemistry, EMD Serono
Protein kinases play an important role in a variety of signaling pathways that control cell growth, metabolism, proliferation, differentiation and apoptosis, and, therefore, it is not surprising that a dysregulation of kinase functions can fuel cancers and other diseases.
In our efforts to identify novel, potent, selective and chemically-attractive kinase inhibitors for the treatment of oncological and immunological disorders, we established a Kinase Platform project team to lead de-novo design, kinase target profiling, fragment screening and covalent approaches tailored to individual protein kinases and kinase mini-panels. In this presentation, we highlight our implementation of kinase technologies that led to the identification and development of two clinical candidates, evobrutinib and M2698.
Uncoupling the Structure-Activity Relationships of β2 Adrenergic Receptor Ligands from Membrane Binding
Viktor Hornak, Senior Investigator I, Novartis
Ligand binding to membrane proteins may be significantly influenced by the interaction of ligands with the membrane. The apparent activity of ligands may therefore be composed of
1) ligand association with the membrane and 2) intrinsic binding of ligand to the membrane protein. Using published data for a set of small molecules with measured β2 adrenergic
receptor binding, we demonstrate how correcting for membrane binding provides a path to derive meaningful structure activity relationships, which could then be used for traditional
structure based drug design. Molecular dynamics simulations of ligand-membrane protein complexes was used to validate binding poses, allowing analysis of key interactions and binding
site solvation to develop a structure activity relationship of β2 ligand binding. The successful structure based design of ligands targeting membrane proteins may require an assessment
of membrane affinity to uncouple protein binding from membrane interactions.
Multiple Mechanisms of Ligand Blocking by Antibodies in a Single Target Fernando Garcês, Senior Scientist, Amgen
Structure-Based Design of Broadly Protective Group A Streptococcal M Protein-Based Vaccines
Jerome Baudry, Professor of Biological Sciences, University of Alabama
We present our results on design of novel strep A vaccines based on the structure of peptides present on the surface of the bacteria. We use MOE to characterize the peptides’ property space based on their structures and physicochemical properties. We also use MOE to select a small number of vaccine peptides that are capable of eliciting an immune response against other peptides than themselves. Cross-reaction immunization results validate the computational predictions.
Computational Approaches for Optimizing the Developability of Biotherapeutics
Nels Thorsteinson, Scientific Services Manager, Biologics, Chemical Computing Group
mAb candidates identified from high-throughput screening or binding affinity optimization often present liabilities for developability, such as aggregation-prone regions or poor solution behavior. In this work, we optimized an integrin α11 binding mAb for developability using homology modeling and rational design where reducing hydrophobic surface patches improved HIC behavior. A retrospective data analysis demonstrates that 3D descriptors and multi-parameter models can screen candidates and enrich libraries with favorable developability properties for a range of biotherapeutics.
Group additivity is a concept that has been successfully applied to a variety of thermochemical and kinetic properties in chemistry. This includes drug discovery where functional group additivity is often assumed in ligand binding. Analysis of a large dataset of protein-ligand binding affinities (Ki) for diverse targets shows that in general ligand binding is distinctly non-linear. It is possible, however, to create a group equivalent scheme for ligand binding in the context of closely related proteins, at least with regard to size. This finding has broad implications for drug design from both experimental and computational points of view.
Fluorine Multipolar Interaction: How Much is it Worth in Binding Free Energy?
Li Xing, Senior Director, Computational Chemistry, WuXi AppTec
Multipolar interactions have gained attention due to their roles in molecular recognition events of chemical and biological systems. Structural evidence suggests
that the fluorine multipolar interaction is a favorable molecular interaction in the paradigm of protein-ligand recognition. With an aim to understand its propensity
and energetics in ligand-protein assembly, we mined the protein-ligand X-ray structure database with three-dimensional constraints for the presence of such interactions.
A set of transformation rules were applied to generate their corresponding matched molecular pairs (MMPs) that bear hydrogen(s) in place of the interacting fluorine(s).
Biological activities were retrieved from our internal data warehouse for the MMPs with and without the ability to form the multipolar interaction. On the basis of the
observed potency differences we determined the free energy gain associated with the fluorine multipolar interaction, and estimated its net impact on lipophilic efficiency (LipE).
As a results a general guideline for medicinal chemists was proposed, enabling a more rational employment of this type of interaction in molecular design.
Designing MOE Workflows in KNIME for Automated Drug Discovery
George Nicola, Senior Vice President, Computational Pharmacology, Afecta Pharmaceuticals
We have built an automated, workflow-based system that predicts mechanism of action for new indications of safe, off-patent drugs. The platform technology can also design new molecules for a known target or an active drug program. We do this through a combination of enumerating derivatives from a patent, generating a combinatorial library of analogues around a Markush scaffold, chemical fingerprint searches, 3D similarity (shape, pharmacophores, electrostatics), ADMET descriptor matching, gene expression profiling, and protein docking.
The platform is built in the KNIME workflow environment, and uses open source as well as proprietary software, including MOE. The prediction algorithm is custom designed using machine learning models that have been trained on large data sets. We connect and make use of multiple web-accessible databases including those for binding activity, chemical and protein structures, biological pathways, and gene expression.
To feed compounds into the workflow, we have also built a comprehensive compound registration system that analyses, isomerizes, de-duplicates, and uploads compounds to a database server. Our internal library consists of 10,000 commercially available drug compounds, as well as several hundred hand-picked compounds with known activities.
Our workflow-based platform has proven especially useful when partnering with small and mid-size pharmaceutical companies seeking to address an unmet medical need by redesigning an existing product, and where regulatory approval is likely to be achieved rapidly. We provide examples of this platform being used to repurpose molecules into drug candidates.