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 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 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 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 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.

Davide Bianchi¹, Simona Saporiti², Omar Ben Mariem¹, Fabio Centola² and Ivano Eberini<sup>1

1</sup> Dipartimento di Scienze Farmacologiche e Biomolecolari “Rodolfo Paoletti”, Università degli Studi di Milano, via Giuseppe Balzaretti 9, 2013, Milan, Italy
² Analytical Excellence and Program Management, Merck Serono S.p.A., Rome, Italy

The activity of monoclonal antibodies (mAbs) emerges from a finely tuned interplay between structural dynamics, post-translational modifications, and immune receptor recognition. In collaboration with Merck Serono S.p.A., we have employed advanced molecular dynamics (MD) simulations to unravel how glycosylation patterns, light- chain (LC) isotype, and antigen binding collectively shape the conformational landscape and effector function of therapeutic IgG1 antibodies.

In our initial study (Biophysical Journal, 2021 https://doi.org/10.1016/j.bpj.2021.10.026), we explored the structural basis by which Fc N-glycans modulate antibody flexibility. Classical MD simulations revealed that core fucosylation not only influences local Fc dynamics but also governs large-scale antibody motions, thereby modulating Fc accessibility to effector receptors. This work offered one of the first atomistic descriptions of glycan-mediated conformational regulation.

Building on these findings, we integrated classical and accelerated molecular dynamics (cMD + aMD) to examine the interplay between glycosylation and light-chain isotype (κ vs λ) in shaping IgG1 flexibility. Our goal was to identify the molecular determinants in FcγR engagement across antibody subclasses (Communications Biology, 2023, 10.1038/s42003-023-04622-7). The simulations uncovered distinct hinge dynamics and Fab-Fc orientations depending on both glycan status and isotypes, suggesting a structural rationale for differential receptor binding. This study marked one of the first applications of enhanced-sampling MD to probe the conformational diversity of full-length antibodies.

Expanding this approach, we investigated the IgG1::FcγRIIIa complex to directly assess how fucosylation and light chain isotype influence receptor recognition (Computational and Structural Biotechnology Journal, 2025, DOI: 10.1016/j.csbr.2025.100034). Our analysis identified novel interfacial residues contributing to complex stability and provided a detailed molecular explanation for the reduced FcγRIIIa affinity observed in afucosylated mAbs.

Most recently, we turned our attention to antigen engagement as a potential allosteric modulator of mAb conformation. Using commercial antibodies in complex with their respective antigens, and comparing G0 and G0F glycoforms via accelerated MD, we uncovered a consistent antigen-induced allosteric network linking Fab and Fc domains. Antigen binding shifted the conformational equilibrium toward more open, Y-shaped Fc states, enhancing receptor accessibility, particularly in afucosylated κ-LC antibodies. This effect was attenuated in λ-LC systems, where stronger CH1-CL contacts constrained Fab motion and limited long-range allosteric propagation. Collectively, these studies establish a detailed mechanistic framework for IgG1 function: from intrinsic Fc dynamics to receptor recognition and antigen-triggered allosteric regulation. This evolving paradigm suggests that Fab engineering, alongside Fc and glycan modifications, may represent a promising frontier for optimizing antibody effector functions.

RNA has rapidly emerged as a promising therapeutic modality, yet reliably identifying true RNA binders remains a major bottleneck in large-scale virtual screening. Most existing docking and scoring approaches, optimized for protein-small molecule systems, fail to accurately rank protein-RNA complexes, resulting in high false-positive rates and costly downstream validation. We present PANTHER Score (Protein-Affinity for Nucleic Target-binding, Hybridization, and Energy Regression), a physics-based, machine learning-driven scoring framework specifically designed for protein-RNA virtual screening. PANTHER Score integrates molecular dynamics derived interaction knowledge with a scalable local-to-global energy strategy, enabling fast and reliable RNA ranking without requiring MD simulations during screening. The method decomposes binding into residue-nucleotide pairwise interactions and employs machine learning models trained on >87,000 curated local interaction energies to predict these contributions directly from structural descriptors. Predicted local energies are then integrated into a single affinity score suitable for high-throughput prioritization. On test set, PANTHER Score achieved a Pearson correlation of r = 0.80 with experimental binding free energies and a mean absolute error of 1.79 kcal/mol. Importantly, on a challenging stress set of 110 heterogeneous and untreated complexes, it retained strong predictive power (r = 0.64, MAE = 1.63 kcal/mol), substantially outperforming existing protein-RNA scoring tools. By combining physical interpretability with industrial scalability, PANTHER Score enables early and accurate discrimination of true RNA binders, reducing false positives and accelerating hit identification. This positions PANTHER Score as a practical and immediately applicable solution for RNA-targeted drug discovery in pharma and biotech pipelines.

Modeling nucleic acids, particularly RNA, presents challenges due to their dynamic nature, with biologically relevant conformations often differing significantly between unbound and bound states. This variability complicates predictions in silico, making efficient conformational sampling crucial. In this presentation, we investigate the efficacy of the LowModeMD method for enhancing conformational sampling in DNA and RNA systems compared to traditional molecular dynamics approaches.By focusing on low-frequency vibrational modes, LowModeMD enables rapid exploration of conformational space, facilitating the identification of biologically relevant structures. We demonstrate the utility of LowModeMD in analyzing bulged residues, sampling terminal and internal loops, cryptic pockets and large conformational changes caused by complexation.