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.

MOE Databases / Descriptors / Fingerprints / QSPR Modeling / Pharmacophore Modeling / Template-Forced Docking / Scaffold Replacement / MedChem Transformations

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 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.

Pharmacophore Modeling / Docking / Fragment-based Design / Scaffold Replacement / R-Group Screening / Project Search / Protein-Ligand Interaction Fingerprints

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).

Molecular Surfaces and Maps / Ligand Interactions / Docking / 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. Use of the docking module and its application to assess ligand flexibility will be discussed. A protocol for aligning and superposing protein complexes in the context of protein selectivity will be studied.

Cytochrome P450 (CYP) / Oxidative Metabolism / Small Molecule Liabilities / Pharmacophore Modeling

Cytochrome P450 oxidases (CYPs) are heme-containing enzymes responsible for clearing xenobiotics, including drug molecules, from the human body via oxidative metabolism. Understanding and predicting how CYPs might modify or otherwise interact with a potential drug is an important step in drug design efforts. Although there are dozens of CYP crystal structures, most existing modeling tools addressing potential CYP liabilities completely neglect this structural information. In this Webinar, we’ll present our methodology that predicts: a) which CYP isoforms are most likely to interact with a user-provided small molecule, b) whether the molecule is predicted to be a CYP inhibitor or a substrate, and c) which part of the small molecule is most likely to be oxidated, i.e., the sites of metabolism (SOM). Because our approach for predicting SOMs directly incorporates CYP structural information, these predictions can be visualized in the context of a CYP pocket, thereby affording the ability to proactively address predicted liabilities in a familiar structure-based design context – a clear improvement over the simple pass/fail metric that many existing methods use.

Scaffold Replacement / Fragment Linking / Ligand Growing / R-Group Screening / Medicinal Chemistry Transformations / Combinatorial Fragment Libraries / Pharmacophore Models / Fragment Databases

Fragment-based drug design (FBDD) is a key approach in the discovery of high-quality drug candidates. As a compliment to biophysical FBDD methods, insilico FBDD uses structure-based approaches to rapidly design and screen large libraries of virtual compounds, allowing for the exploration of a much larger chemical space. The webinar will describe insilico FBDD methods ranging from combinatorial fragment design to scaffold-hopping in the receptor active site, along with approaches for fragment linking and growing. A method for generating a series of closely related derivatives through the reaction-based Combinatorial Library and Medicinal Chemistry Transformations applications in the MOE software will be presented. The use of pharmacophore models and 2D/3D descriptors to guide insilico FBDD processes will also be discussed.

PROTAC / Targeted Protein Degradation / Ternary Complexes / Protein-Protein Docking

Bifunctional small molecules known as PROTACs (Proteolysis-Targeting Chimeras) have recently shown great potential in drug discovery. Their mechanism of action involves degradation of a target protein rather than its inhibition, and thus computational modeling of PROTAC-mediated systems requires consideration of not only the traditional issue of how a small molecule interacts with its target, but also considerations of interactions with a second protein, an E3 ligase. In this work, we will discuss our recent work in generating in silico ensembles of possible ternary complexes, followed by identification of those that are more likely to be experimentally relevant. The utility of these modeling approaches will be demonstrated by comparing the relative degradation tendencies of known systems across different targets, different ligases, and with different PROTACs.

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.

MOE Databases / Descriptors / Fingerprints / QSPR Modeling / Pharmacophore Modeling / Template-Forced Docking / Scaffold Replacement / MedChem Transformations

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 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.

Pharmacophore Modeling / Docking / Fragment-based Design / Scaffold Replacement / R-Group Screening / Project Search / Protein-Ligand Interaction Fingerprints

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).

Molecular Surfaces and Maps / Ligand Interactions / Docking / 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. Use of the docking module and its application to assess ligand flexibility will be discussed. A protocol for aligning and superposing protein complexes in the context of protein selectivity will be studied.

Cytochrome P450 (CYP) / Oxidative Metabolism / Small Molecule Liabilities / Pharmacophore Modeling

Cytochrome P450 oxidases (CYPs) are heme-containing enzymes responsible for clearing xenobiotics, including drug molecules, from the human body via oxidative metabolism. Understanding and predicting how CYPs might modify or otherwise interact with a potential drug is an important step in drug design efforts. Although there are dozens of CYP crystal structures, most existing modeling tools addressing potential CYP liabilities completely neglect this structural information. In this Webinar, we’ll present our methodology that predicts: a) which CYP isoforms are most likely to interact with a user-provided small molecule, b) whether the molecule is predicted to be a CYP inhibitor or a substrate, and c) which part of the small molecule is most likely to be oxidated, i.e., the sites of metabolism (SOM). Because our approach for predicting SOMs directly incorporates CYP structural information, these predictions can be visualized in the context of a CYP pocket, thereby affording the ability to proactively address predicted liabilities in a familiar structure-based design context – a clear improvement over the simple pass/fail metric that many existing methods use.

Scaffold Replacement / Fragment Linking / Ligand Growing / R-Group Screening / Medicinal Chemistry Transformations / Combinatorial Fragment Libraries / Pharmacophore Models / Fragment Databases

Fragment-based drug design (FBDD) is a key approach in the discovery of high-quality drug candidates. As a compliment to biophysical FBDD methods, insilico FBDD uses structure-based approaches to rapidly design and screen large libraries of virtual compounds, allowing for the exploration of a much larger chemical space. The webinar will describe insilico FBDD methods ranging from combinatorial fragment design to scaffold-hopping in the receptor active site, along with approaches for fragment linking and growing. A method for generating a series of closely related derivatives through the reaction-based Combinatorial Library and Medicinal Chemistry Transformations applications in the MOE software will be presented. The use of pharmacophore models and 2D/3D descriptors to guide insilico FBDD processes will also be discussed.

PROTAC / Targeted Protein Degradation / Ternary Complexes / Protein-Protein Docking

Bifunctional small molecules known as PROTACs (Proteolysis-Targeting Chimeras) have recently shown great potential in drug discovery. Their mechanism of action involves degradation of a target protein rather than its inhibition, and thus computational modeling of PROTAC-mediated systems requires consideration of not only the traditional issue of how a small molecule interacts with its target, but also considerations of interactions with a second protein, an E3 ligase. In this work, we will discuss our recent work in generating in silico ensembles of possible ternary complexes, followed by identification of those that are more likely to be experimentally relevant. The utility of these modeling approaches will be demonstrated by comparing the relative degradation tendencies of known systems across different targets, different ligases, and with different PROTACs.