This workshop focuses on methods for analyzing and optimizing peptide-protein interactions in the binding site. Participants will learn peptide-protein structure preparation, peptide sequence optimization using both natural and non-natural amino acids, and perform conformational analysis. Peptide-protein docking will be demonstrated, along with tools for analyzing protein-ligand interactions to assess contact points. Advanced conformational searching techniques using distance restraints will also be described.

This workshop explores some essential ligand-based methods for guiding drug discovery projects. Participants will learn how to efficiently use MOEsaic to perform SAR analyses of compound datasets, using approaches such as R-group profiling and matched molecular pairs (MMP) analysis, to identify relationships within chemical series. The session highlights computational strategies for extracting meaningful insights from structure-activity data to support informed decision-making in drug design. The workshop also covers molecular alignments and methods for generating pharmacophore queries in the absence of a protein-ligand complex, to help understand the requirements for activity and search for novel compounds.

This workshop focuses on structure-based antibody design. Topics such as protein-protein interaction analysis, in silico protein engineering, affinity modeling, and antibody homology modeling will be covered. Participants will explore an antibody-antigen co-crystallized complex by generating and examining molecular surfaces and visualizing protein-protein contacts in 3D. Antibody properties will be evaluated using specialized protein property descriptors and protein patch analysis. The session will also cover the application of protein engineering tools for affinity and property optimization in the context of antibody developability. Additionally, participants will learn to optimize antibody homology models, including identifying glycosylation sites and their selective modification using a specialized MOE Project antibody database. The full workflow for high-throughput antibody homology modeling, from sequence to structure to property calculations for developability analysis, will be demonstrated.

This workshop describes cheminformatics methods for managing and analyzing datasets of small molecules, as well as generating quantitative structure-activity relationship (QSAR) models. Participants will learn how to import data, prepare MOE databases, calculate molecular descriptors, and analyze trends using sorting, coloring, and plotting tools. Advanced database filtering, data clustering, and diverse subset selection techniques will also be explored. The workshop will include sessions on developing both linear regression and binary QSAR models. Finally, data mining of large compound libraries will be demonstrated using substructure and molecular fingerprint similarity searching.

This workshop provides an overview of SVL capabilities and its application within MOE. Participants will be introduced to the essentials of the SVL scripting language, including its use in automating tasks, customizing workflows, and enhancing MOE functionalities for specific research needs.

This workshop covers essential methods for aligning protein sequences, superposing structures, loop modeling, building fusion protein models, and conducting protein-protein docking. Participants will learn techniques for grafting and refining antibody CDR loops, as well as using a knowledge-based approach to scFv fusion protein modeling with the Linker Modeler application. The session will also cover protein-protein docking of an antibody to an antigen and epitope mapping. Finally, the workshop will guide participants through a complete workflow for generating a QSAR model to predict and analyze protein/biologics solubility.

This workshop presents common molecular modeling and design techniques for identifying, optimizing, and prioritizing small-molecule drug candidates. The workshop begins by describing structure preparation and pocket analysis to understand key protein-ligand interactions. Participants will explore template-based docking of congeneric series and pharmacophore-guide docking (to preserve key interactions) to predict how small molecules bind to a protein target. This workshop also presents a series of de novo fragment-based drug design applications, focusing on scaffold hopping and fragment growing methods to generate novel compound ideas, optimize structures in the pocket, and score them. It also discusses the use of molecular descriptors, protein-ligand interaction fingerprints (PLIFs), and pharmacophore models to guide the drug design process. Finally, a method for generating closely related analogs through bioisosteric replacements is presented.

This workshop covers searching and analysis of structural data in PSILO®, and organization of structural families in MOE-Project. Participants will learn to consolidate macromolecular and protein-ligand structural data, generate and navigate advanced queries, and streamline analysis with 3D contact and protein pocket similarity searches. The session also covers organizing structure-based drug design projects, aligning protein families, configuring MOE-Project databases, and using Protein-Ligand Interaction Fingerprints (PLIF) analysis to compare ligand binding modes. Attendees will gain practical skills for efficient structural data mining and project management.

Finding selective small molecule modulators against members of closely related enzyme families can be challenging, especially if high degrees of selectivity are required. Molecular glues can uniquely address this challenge by leveraging the binding surface of a partner protein to effectively discriminate between enzymatic family members. Using structure-guided tools, we designed a novel series of molecular glues that stabilize a weak, endogenous interaction of a protein of interest with an E3 ligase complex component, thereby inducing its rapid degradation. The unique interface between the protein pair enables designs that are active only in the presence of both partners, with limited interactions in the absence of ternary complex formation, thereby avoiding liabilities associated with targeting the partner protein.

Structural information provides critical insights to accelerate antibody discovery and development. Structure analysis helps us select better antibody clones by evaluating binder diversity while predicting functional and developability profiles. I will also highlight structure-guided engineering tools using a combination of with physics-based and machine learning methods for affinity maturation, cross-reactivity optimization, liability mitigation, and humanization. This should provide a comprehensive overview for leveraging structural biology to reduce costs, compress timelines, and ultimately improve success rates in biologics discovery and optimization.

Over the last few years, numerous computational tools have appeared in the literature to model bifunctional degraders. Generally, these studies focus on predicting the structures of degrader-containing ternary complexes, with only occasional investigations into predicting the efficacy of potential degrader designs. In this talk, we will discuss recent results with our well-established ternary complex modeling tool, Method 4B, focused entirely on its ability to score potential degrader designs. This talk will establish best-practices for using Method 4B across diverse systems, focusing on the level of precision that can be expected for prospective degrader screening campaigns.

The developability evaluation for antibody-related modalities includes a wide range of factors, such as non-specific binding, self-interaction, hydrophobicity, electrostatic properties, and structural stability. This evaluation is crucial in biologics drug discovery research for the early identification of candidates with high manufacturability as well as favorable behavior in human, and it is providing numerous benefits like improvements in drug discovery speed and reductions in duration and costs associated with bioprocess development.

In particular, the in-silico approach to predict developability from sequence information has grown exponentially alongside advancements in antibody-related technology platforms and is actively researched in both academia and the pharmaceutical industry. Recently, we established a wet evaluation system for high-throughput analysis of multiple developability parameters and created in-silico workflow for predicting developability by leveraging accumulated wet data and machine learning techniques. This machine learning utilizes protein feature quantities generated by MOE. The details and accuracy of this in-silico developability workflow will be presented.

Authors: Lindsay Hammack, Arlinda Rezhdo, Miracle Ozonma, Ridley Hutton, Ariel Tocher, Nestor Alonso Gutierrez, Morgan Walker, Katie Wright, Neelam Chavan, Neelakantan Thiyyadi Vasudenvan, Shihao Xu, Charles Rosadini, Hye-lin Ha, Valeria Busygina
Affiliation: Merck & Co., Inc., Rahway, NJ, USA

Therapeutic antibody discovery has traditionally relied on mouse immunizations, an approach that has delivered numerous clinical successes. However, for certain targets, mouse campaigns can yield low affinity binders with limited epitope diversity. To address these limitations, we implemented a rabbit immunization as a complementary, cost-effective strategy to expand both affinity and epitope coverage for a high priority pipeline program. Here, we compare outcomes from parallel mouse and rabbit campaigns against the same target. The mouse immunization generated primarily low affinity monoclonal antibodies (mAbs) recognizing six distinct epitopes. In contrast, our rabbit immunization campaign produced 63 high affinity mAbs that encompassed those six epitopes and revealed two additional epitopes. While rabbit-derived mAbs are well recognized for tool applications, their progression to therapeutics has been underwhelming with only three FDA-approved rabbit antibody-based molecules available to date. A key challenge is that rabbit antibodies often carry developability liabilities unique from those commonly identified in murine derived antibodies that necessitate targeted engineering. Further structural differences in rabbit mAbs require prudent humanization to preserve potency and epitope engagement. We detail our engineering and display workflows for optimizing rabbit hits into program leads, including preemptive developability optimizations, humanization strategies tailored to rabbit mAbs, and rationally engineered, structure-guided display libraries to enhance affinity and pH sensitivity. Our findings demonstrate that rabbit immunizations can improve discovery outcomes for difficult targets, delivering high affinity mAbs with broad epitope coverage. With an integrated engineering and humanization toolkit, rabbit-derived binders can be efficiently advanced into therapeutic leads. This work establishes rabbit immunization as a practical, cost- effective strategy that bolsters therapeutic antibody discovery.

In recent years, there has been significant growth in the field of protein therapeutics, encompassing various complex modalities such as multi-specifics, fusion proteins, and protein conjugates. However, the expansion into these diverse modalities poses several challenges for purification process development, which stems from the unique biophysical properties of these novel modalities relative to monoclonal antibodies (mAbs) as well as distinct impurities profiles. Given these challenges, it is imperative to rapidly develop non-platform polishing chromatography processes that can effectively isolate the desired product with adequate yield and purity.

Experimental high throughput screening (HTS) capabilities such as slurry plate screens and robocolumn chromatography have become important tools to rapidly assess operating conditions for polishing chromatography. Predictive modeling enhances HTS capabilities for identifying operating conditions by enabling exploration and prioritization of large design spaces in silico and therefore picking promising conditions to screen experimentally. This talk presents the development and implementation of a quantitative structure activity relationship (QSAR) model that leverages over 10 years of internal Kp screening data and can predict the partition coefficient as a function of protein, resin, and mobile phase conditions. The model contains ~10,000 screened conditions on more than 40 resins. A diverse set of >40 proteins are represented in the data, including mAbs, Fc-fusion proteins, host cell proteins, viruses, and other modalities. Overall, the model has a test set R2=0.91 and can predict elution and strong binding conditions across the protein property and resin space with 95% classification accuracy, enabling extensive design space reduction for HTS experiments. Intentional diversification of the training data has expanded the applicability window of the model and enabled the prediction of both product and impurity partitioning, including aggregates, low molecular weight fragments, and HCPs. The prediction of both product and impurity partitioning facilitates computational assessment of other metrics describing the purification process, including separability and orthogonality between multiple steps. Overall, this work outlines how predictive modeling can be used in conjunction with HTS experimentation to guide the development of polishing chromatography steps. These models can be applied to a diverse set of proteins and across the development lifecycle, either by proposing novel approaches for a given separation or supporting process changes later in development.

Subcutaneous (SC) delivery of therapeutic antibodies can offer multiple benefits to patients and healthcare providers, including convenience, time savings, and cost reduction. To improve the SC injection experience, drug developers may seek a low injection volume (1-2 mL), which for some antibody drugs necessitates a high concentration solution (≥100 mg/mL) to meet dosage requirements. Several molecular-level challenges hinder the development of high concentration antibody drug products, including high viscosity caused by reversible self-association (RSA). Here, we take an enhanced rational design approach to reduce RSA via protein engineering. Using hydrogen-deuterium exchange mass spectrometry (HDX-MS) and in silico modeling in Molecular Operating Environment (MOE), we identified potential self-interaction hotspots on the surface of an in-house IgG1 which has known viscosity issues at high concentration. With this information, several variants were rationally designed which vastly improved on the parent antibody’s viscosity issues and offer insight into the mechanism of self-association. This workflow and strategy will be discussed alongside results and implications for future molecules.