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.

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.

MOEsaic / R-Group Profiles and Analysis / MMP Analysis / Similarity and Substructure Searching / Descriptor Calculations / Conformational Searching / Molecular Alignments / Pharmacophore Modeling and Searching

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 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.

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).

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

The course describes 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).

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.

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.

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.

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.

Structure-based drug design, aided by computational methods, is now well established as a key discipline in the discovery of new medicines. The workshop will illustrate a range of computational techniques for SBDD, covering some related areas such as docking, and analysis of SAR data in case protein-ligand crystal structural information is not available, through demonstration using the MOE software.

Molecular Surfaces and Maps / Ligand Interactions / Conformational Searching / Ligand Optimization / Ligand Selectivity / Protein Alignments and Superposition

Docking / Fragment-based Design / Scaffold Replacement / R-Group Screening / Project Search / Protein-Ligand Interaction Fingerprints / MOEsaic / MMP Analysis

Thermodynamic Integration / Free Energy Calculation / FEP / AMBER / Binding Affinity

Until the advent of GPU accelerated molecular dynamics, alchemical free energy simulations had not been routinely applied in early stage drug discovery. While this GPU acceleration made such calculations more feasible, the complexity of the protocols and uncertainty regarding optimal parameters are a lingering problem. In the recently released MOE 2019 a streamlined interface was added to set up free alchemical energy calculations dynamics simulations based on the Thermodynamic Integration (TI) method in AMBER 18. The workflow includes structure preparation, ligand parameterization, simulation planning and analysis. Free energy calculations are known to be extremely sensitive and have a high failure rate. We present an optimal simulation planning methodology that minimizes the expected error for a given system based on the available simulation resources (number of GPUs, nodes, etc.). Another major source of error is the simulation instability due to an imbalance between the electrostatic and van der Waals forces. Optimization of the AMBER soft-core potential resulted in much higher simulation stability and hence lower curvature of the potential function. In conjunction with Fejér numerical integration the evaluation of the alchemical integral can be achieved with spectral accuracy. The methodology was validated through the calculation of relative binding energies of 34 ligands of the p38 MAP Kinase and compared to experimental data.

Antibody Modeling / Developability Assessment / Solubility and Aggregation Prediction / Titration, pI, and pKa Calculation

This scientific talk was presented recently at PEGS Boston, the MOE European User Group Meeting and at the Antibody Engineering & Therapeutics Europe. 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, conformational sampling, stochastic titration, and multi-parameter models can screen candidates and enrich libraries with favorable developability properties for a range of biotherapeutics.

Ensemble protein properties / bispecific antibodies / CDR grafting / high-throughput homology modeling

We will present and demonstrate the new biologics features in MOE 2019.01. These include pH dependent ensemble protein property calculations, bispecific antibody modeling and advanced CDR grafting as well as high-throughput protein homology modeling. Various other related new features will also be discussed.

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.

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.

MOEsaic / R-Group Profiles and Analysis / MMP Analysis / Similarity and Substructure Searching / Descriptor Calculations / Conformational Searching / Molecular Alignments / Pharmacophore Modeling and Searching

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 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.

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).

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

The course describes 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).

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.

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.

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.

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.

Structure-based drug design, aided by computational methods, is now well established as a key discipline in the discovery of new medicines. The workshop will illustrate a range of computational techniques for SBDD, covering some related areas such as docking, and analysis of SAR data in case protein-ligand crystal structural information is not available, through demonstration using the MOE software.

Molecular Surfaces and Maps / Ligand Interactions / Conformational Searching / Ligand Optimization / Ligand Selectivity / Protein Alignments and Superposition

Docking / Fragment-based Design / Scaffold Replacement / R-Group Screening / Project Search / Protein-Ligand Interaction Fingerprints / MOEsaic / MMP Analysis

Thermodynamic Integration / Free Energy Calculation / FEP / AMBER / Binding Affinity

Until the advent of GPU accelerated molecular dynamics, alchemical free energy simulations had not been routinely applied in early stage drug discovery. While this GPU acceleration made such calculations more feasible, the complexity of the protocols and uncertainty regarding optimal parameters are a lingering problem. In the recently released MOE 2019 a streamlined interface was added to set up free alchemical energy calculations dynamics simulations based on the Thermodynamic Integration (TI) method in AMBER 18. The workflow includes structure preparation, ligand parameterization, simulation planning and analysis. Free energy calculations are known to be extremely sensitive and have a high failure rate. We present an optimal simulation planning methodology that minimizes the expected error for a given system based on the available simulation resources (number of GPUs, nodes, etc.). Another major source of error is the simulation instability due to an imbalance between the electrostatic and van der Waals forces. Optimization of the AMBER soft-core potential resulted in much higher simulation stability and hence lower curvature of the potential function. In conjunction with Fejér numerical integration the evaluation of the alchemical integral can be achieved with spectral accuracy. The methodology was validated through the calculation of relative binding energies of 34 ligands of the p38 MAP Kinase and compared to experimental data.

Antibody Modeling / Developability Assessment / Solubility and Aggregation Prediction / Titration, pI, and pKa Calculation

This scientific talk was presented recently at PEGS Boston, the MOE European User Group Meeting and at the Antibody Engineering & Therapeutics Europe. 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, conformational sampling, stochastic titration, and multi-parameter models can screen candidates and enrich libraries with favorable developability properties for a range of biotherapeutics.

Ensemble protein properties / bispecific antibodies / CDR grafting / high-throughput homology modeling

We will present and demonstrate the new biologics features in MOE 2019.01. These include pH dependent ensemble protein property calculations, bispecific antibody modeling and advanced CDR grafting as well as high-throughput protein homology modeling. Various other related new features will also be discussed.