Evaluation of Selected Classical Force Fields for Alchemical Binding Free Energy Calculations of Protein-Carbohydrate Complexes

Protein–carbohydrate recognition is crucial in many vital biological processes including host–pathogen recognition, cell-signaling, and catalysis. Accordingly, computational prediction of protein–carbohydrate binding free energies is of enormous interest for drug design. However, the accuracy of current force fields (FFs) for predicting binding free energies of protein–carbohydrate complexes is not well understood owing to technical challenges such as the highly polar nature of the complexes, anomerization, and conformational flexibility of carbohydrates. The present study evaluated the performance of alchemical predictions of binding free energies with the GAFF1.7/AM1-BCC and GLYCAM06j force fields for modeling protein–carbohydrate complexes. Mean unsigned errors of 1.1 ± 0.06 (GLYCAM06j) and 2.6 ± 0.08 (GAFF1.7/AM1-BCC) kcal·mol^–1 are achieved for a large data set of monosaccharide ligands for <i>Ralstonia solanacearum</i> lectin (RSL). The level of accuracy provided by GLYCAM06j is sufficient to discriminate potent, moderate, and weak binders, a goal that has been difficult to achieve through other scoring approaches. Accordingly, the protocols presented here could find useful applications in carbohydrate-based drug and vaccine developments

MODELING PROTEIN–CARBOHYDRATE COMPLEXES IN ROSETTA

Carbohydrates are of fundamental importance in biology. These molecules are essential to life, serving to mediate diverse biological functions. Unraveling the biophysical mechanisms by which carbohydrates operate not only helps complete our understanding of life on Earth but enables rational engineering to fine-tune or even modify their roles in biology. However, carbohydrate molecules are complex, with their conformational diversity and chemical heterogeneity making it notoriously difficult to elucidate their structures experimentally. Yet these models are vital to our mechanistic understanding of carbohydrate-mediated biological functions, making scientific advancements challenging to achieve. Computational methods serve to fill this gap by generating native-like models of protein–carbohydrate systems. In this dissertation, I describe my advancements to the field of computational modeling with the development of GlycanDock, a protein–carbohydrate docking refinement method in Rosetta. I detail the extensive benchmark I developed to evaluate the effectiveness of the GlycanDock protocol to generate native-like protein–carbohydrate models. Further, I provide residue-level analyses of these models to demonstrate the utility of the protocol toward developing a biophysical understanding of protein–carbohydrate complexes. Finally, I describe an approach utilizing GlycanDock and other computational tools to address the more realistic “blind” docking scenarios. The development of GlycanDock enabled my work computationally modeling the structures of FpGalNAcDeAc and FpGalNase, two enzymes that together convert A-type blood to the universal O-type. I identified FpGalNAcDeAc residues likely to govern the binding of terminal LacNAc motifs present on the surface of red blood cells, offering mutational sites to modify targeting to the cell surface. Additionally, I identified the FpGalNAcDeAc binding site residues most important for A-antigen recognition, providing a guide to understanding and controlling its enzymatic activity. For FpGalNase, I proposed a sequence- and structure-driven hypothesis regarding its active-site and unique specificity to the terminal α-GalN carbohydrate. My work serves as a blueprint for future experimental studies, including rational engineering toward modifying FpGalNase’s specificity to the B-antigen, which, if achieved, would mean complete conversion of all A, B, and AB blood types to the universal O-type. In sum, my work advanced our ability to model and dissect protein–carbohydrate systems

Molecular modelling of platelet endothelial cell adhesion molecule 1 and its interaction with glycosaminoglycans

The Platelet Endothelial Cell Adhesion Molecule 1 (PECAM-1) has many functions including its roles in leukocyte extravasation as part of the inflammatory response, and in the maintenance of vascular integrity through its contribution to endothelial cell-cell adhesion. Various heterophilic ligands of PECAM-1 have been proposed. The possible interaction of PECAM-1 with glycosaminoglycans (GAGs) is the focus of this thesis. The three dimensional structure of the extracellular immunoglobulin (Ig)-domains of PECAM-1 was constructed using homology modelling and threading methods. Potential heparin/heparan sulfate binding sites were predicted on the basis of their amino acid consensus sequences and a comparison with known structures of sulfate binding proteins. Heparin and other GAG fragments have been docked to investigate the structural determinants of their protein binding specificity and selectivity. It is predicted that two regions in PECAM-1 appear to bind heparin oligosaccharides. A high affinity binding region was located in Ig-domains 2 and 3 and a low affinity region was located in Ig-domains 5 and 6.These GAG binding regions are distinct from regions involved in PECAM-1 homophilic interactions. Docking of heparin fragments of different size revealed that fragments as small as a pentasaccharide appear to be able to bind to domains 2 and 3 with high affinity. Binding of longer heparin fragments suggests that key interactions can occur between six sulfates in a hexasaccharide with no further increase in binding affinity for longer fragments. Molecular dynamics simulations were also used to characterise and quantify the interactions of heparin fragments with PECAM-1. These simulations confirmed the existence of regions of high and low affinity for GAG binding and revealed that both electrostatic and van der Waals interactions determine the specificity and binding affinity of GAG fragments to PECAM-1. The simulations also suggested the existence of ‘open’ and ‘closed’ conformations of PECAM-1 around domains 2 and 3

Correlation induced electrostatic effects in biomolecular systems

An understanding of electrostatic interactions in biomolecular systems is crucial for many applications in molecular biology. This thesis focuses on the theoretical modeling of two effects: first, the change in the dielectric properties of water due to hydrogen bond formation and second, the reentrant condensation of proteins induced by protein-metal ion complexation. A nonlocal response theory is necessary to describe the dielectric effects of hydrogen bond formation. Correctly formulating this theory for a solvated biomolecule is challenging, because the biomolecule\u27s cavity poses an obstacle for the water network. We develop a theory explicitly incorporating boundary conditions to describe the water network on the molecular surface. We implement an accurate and efficient finite difference solver, which offers the possibility to easily investigate different physically motivated boundary effects. A detailed analysis of different nonlocal models reveals that, for the macroscopic behavior, the boundary conditions are of minor importance, while for a detailed understanding of the electrostatics near the molecular surface the correct modeling of the hydrogen bond formation is crucial. Recent experimental findings describe a reentrant condensation of proteins in solutions of varying metal ion concentration. We present a heuristic model to account for the metal ion binding on the molecular surface which qualitatively and quantitatively explains the phase diagram of this condensation effect.In der vorliegenden Arbeit konzentrieren wir uns auf die Beschreibung elektrostatischer Phänomene in biomolekularen Systemen. Zuerst untersuchen wir den Einfluss von Wasserstoffbrückenbindungen auf die dielektrischen Eigenschaften von Wasser. Dafür ist die Einführung eines nichtlokalen dielektrischen Operators notwendig. Die nichtlokale Reaktion des Wassers wird durch das gelöste Protein und der damit entstandenen Kavität maßgeblich beeinflusst.Wir entwickeln ein Differentialgleichungssystem, welches Veränderungen der dielektrischen Eigenschaften an der Moleküloberfläche explizit berücksichtigt. Um diese Randeffekte genauer zu analysieren und um unsere Modellgleichungen auf ionische Lösungen zu erweitern, implementieren wir ein modifiziertes Finite-Differenzen-Verfahren, welches sich, neben Effizienz, durch hohe Genauigkeit auszeichnet. Mit diesem Lösungsverfahren untersuchen wir erstmals verschiedene Wassermodelle. Die Analyse zeigt, dass die Veränderungen der Randbedingung an der Moleküloberfläche auf makroskopische Größen von untergeordneter Bedeutung sind, jedoch einen signifikanten Einfluss auf das elektrostatische Potential in der Nähe des Moleküls hat. Des Weiteren betrachten wir einen kürzlich entdeckten Effekt in Proteinlösungen: die Bindungsaffinität von gelösten Metallionen induziert die Bildung von Protein-Metallionen-Komplexen. Diese können in Abhängigkeit der gelösten Ionenkonzentration kondensieren und wieder in Lösung gehen. In Analogie zu Protonierungsmodellen entwickeln wir eine Theorie zur Beschreibung der Komplexbildung. Erste Vergleiche mit Experimenten zeigen, dass das vorgeschlagene Modell den Kondensationseffekt qualitativ und quantitativ erklären kann