A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

The First CACHE Challenge – Testing Diverse Virtual Screening Scoring Methods to Identify Potential LRRK2 Binders

In December 2021, Molecular Forecaster (MFI) applied to participate in the inaugural CACHE Challenge. Organized by the Structural Genomics Consortium (SGC), CACHE (Critical Assessment of Computational Hit-finding Experiments) is a public–private partnership benchmarking initiative to enable the development of computational methods “to compare and improve small-molecule hit-finding algorithms through cycles of prediction and experimental testing.” The MFI team has decided to take multiple research-focused approaches to our predictions in this first CACHE challenge, aiming to learn from our successes and failures. We are putting MFI’s team, expertise, and algorithms to the test, using them as a foundation to push the boundaries beyond our scientific and application successes to-date. We’ve also decided to double-down and share the details of our work with the community. The experimental results are now in and we have conducted a retrospective analysis in the second half of this manuscript

Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking

Molecular docking programs are primarily designed to align rigid, drug-like fragments into the binding sites of macromolecules and frequently display poor performance when applied to flexible carbohydrate molecules. A critical source of flexibility within an oligosaccharide is the glycosidic linkages. Recently, Carbohydrate Intrinsic (CHI) energy functions were reported that attempt to quantify the glycosidic torsion angle preferences. In the present work, the CHI-energy functions have been incorporated into the AutoDock Vina (ADV) scoring function, subsequently termed Vina-Carb (VC). Two user-adjustable parameters have been introduced, namely, a CHI- energy weight term (<i>chi_coeff</i>) that affects the magnitude of the CHI-energy penalty and a CHI-cutoff term (<i>chi_cutoff</i>) that negates CHI-energy penalties below a specified value. A data set consisting of 101 protein–carbohydrate complexes and 29 apoprotein structures was used in the development and testing of VC, including antibodies, lectins, and carbohydrate binding modules. Accounting for the intramolecular energies of the glycosidic linkages in the oligosaccharides during docking led VC to produce acceptable structures within the top five ranked poses in 74% of the systems tested, compared to a success rate of 55% for ADV. An enzyme system was employed in order to illustrate the potential application of VC to proteins that may distort glycosidic linkages of carbohydrate ligands upon binding. VC represents a significant step toward accurately predicting the structures of protein–carbohydrate complexes. Furthermore, the described approach is conceptually applicable to any class of ligands that populate well-defined conformational states

The Second CACHE Challenge - Targeting the RNA-Binding Pocket of the SARS-CoV2 Nonstructural Protein 13 via a consensus-scoring method and FITTED templated docking.

Disrupting the Nonstructural Protein 13 (NSP13) in SARS-CoV2 could provide a great avenue for the treatment of COVID-19 and help reduce its enormous health burden. As part of the second CACHE challenge, we targeted each of two sub-pockets of the NSP13 RNA-binding site via a multi-pronged virtual screening (VS) campaign, using the latest functionality in FITTED, our docking program, part of the FORECASTER drug discovery suite. After extensive structure preparation and docking (rigid, flexible), we evaluated predicted poses from the VS using four approaches: docking score, machine learning (graph neural network), quantum-mechanics, and visualization, with the final selection being based on the consensus of all four approaches. Additionally, we implemented templated docking within FITTED to take advantage of fragments co-crystallized with NSP13, which supplemented our consensus selection. We now await the experimental testing of our predictions by the Structural Genomics Consortium, and once available, we will update this manuscript accordingly. In sharing our approach and findings, we hope to continue contributing to open science, and engaging in the ongoing effort of the scientific community towards ending COVID-19