Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactions

The sheer size of the protein sequence space is massive: a protein of 100 residues can have 20^100 possible sequence combinations; and knowing that this exceeds the number of atoms in the universe, the chance of randomly discovering a stable new sequence with the desired characteristics is infinitesimally small. Therefore, computational methodologies that can search through the sequence space and expand beyond naturally occurring functional protein sequence variants hold enormous potential in biomedicine and nanotechnology.My thesis work leverages machine learning, physics-based, and data-driven techniques to design new protein molecules with distinct shapes (folds) so that they can precisely interact with other molecules to perform biological functions.The first part of my thesis is dedicated to the design of functional proteins. The re-designed of an anti-CRISPR protein that can be controlled via blue light (optogenetic control) to regulate the genome editing activity of the enzyme CRISPRâCas9 is presented. A surface-based design of a broad-spectrum inhibitory Acr towards another natural target SauCas9) exemplifies the re-purposing of existing inhibitory molecules against other related targets. This ultimately led to the development of a general surface-centric design method for generating specific protein-protein interactions from scratch and exemplified by the successful design of novel PD-L1 inhibitors, an immune checkpoint that can halt the immune system from attacking the cancer cells.The successful design of protein-protein interactions heavily relies on the underlying protein fold and structure stabilizing the functional motif in a protein. Because nature has only evolved a small set of protein folds, generated protein-interaction motifs can rarely be incorporated into existing protein structures. To address this problem, the second part of my thesis is dedicated to the development of computational de novo protein design methods for the crafting of proteins with customized folds. To this end, the TopoBuilder framework utilizes a large collection of native proteins to transform a literal description of a protein fold into a physically-realistic protein. Finally, Genesis, a deep neural networks approach for the tailored de novo protein design is presented. Employing both, the TopoBuilder and Genesis, proteins completely absent from the natural repertoire were designed and experimentally validated.My thesis sets the path to explore possibilities of jointly optimizing the protein's shape and its surface geometry to master biological functions. We are now at entering a new era where newly designed protein-based drugs and materials with the potential to solve a vast array of technical challenges and open new avenues for next-generation precision drugs and advanced nanomaterials.

Small Molecule Based Approaches to Inhibit Macrophage Migration Inhibitory Factor (MIF) Activities and Elucidate its Role in Health and Disease

Hajer Ouertatani-Sakouhi

Macrophage migration inhibitory factor (MIF) is a major mediator in innate immunity and inflammation and a potential therapeutic target in multiple inflammatory, infectious and autoimmune diseases including cancer. Current therapeutic strategies for targeting MIF focus on modulating its biological activities using anti-MIF neutralizing antibodies or developing inhibitors of its tautomerase activity. Although the identity of its natural substrate remains unknown, several small molecule inhibitors have been reported to be effective inhibitors of MIF tautomerase activity in vitro. All these inhibitors were identified by rational design and structure-activity studies based on the structure of the catalytic site and/or non physiologic substrates of MIF. The lack of suitable high-throughput screening (HTS) assays has hindered the screening of chemical libraries and discovery of more diverse inhibitors. Motivated by the goal of discovering diverse classes of MIF inhibitors that modulate MIF activity specific targeting of its biochemical, conformation and quaternary structure properties, both activity based endpoint and kinetic HTS assays were developed, optimized and performed on diverse set of libraries. Using the endpoint assay, out of 15440 compounds screened, twelve novel inhibitors of MIF's catalytic activity (∼ 0.1% hit rate) with IC50s in the range of 1.5 to 15.5 µM were identified and validated. The interaction site and mechanism of action of all these inhibitors were defined using structure activity studies and a battery of biochemical and biophysical methods, including mass spectrometry, light scattering, and NMR. The effect on MIF biological activities was also examined on MIF-mediated glucocorticoid overriding and MIF-induced Akt phosphorylation. Using the kinetic-based activity assay, we screened 80,000 small molecules and identified and validated thirteen novel inhibitors of MIF catalytic activity with inhibition constant (Ki,app) values ranging from 0.5 to 13 µM. According to the structure and potency some of these molecules could be of great therapeutic potential. The MIF inhibitors emerging from these studies could be divided into four classes: 1) molecules that covalently modify the catalytic site at the N-terminal proline residue, Pro1 which could be classified into 5 groups; 2) a novel class of catalytic site inhibitors, 3) a class of trimer destabilizing inhibitors. These findings demonstrate the potential of HTS assays as attractive means to identify novel classes of MIF inhibitors as lead drug candidates and/or chemical tools for elucidating the biochemical and structural bases underlying the multifunctionality of MIF's in health and disease. Having these molecules in hand, will advance our understanding of protein-protein interactions mechanism for MIF oligomerization and may help to elucidate the role of MIF enzymatic activity in health and disease.

EPFL2010

Explicit solvent effects on protein physics - modeling lattice proteins in aqueous environments

Protein folding stands as one of the major interdisciplinary challenges of the last ten years, involving biology, chemistry, medicine and physics. In this PhD thesis, we investigate the solvent effects and the resulting hydrophobic effect on proteins. In particular, we focus on a simple lattice model of proteins (the HPW model), where the solvent is (semi)explicitly taken into account (by means of the model of Muller, Lee and Graziano (MLG)), investigating thermodynamical quantities of the modeled proteins and comparing the results with those of real proteins. We approach the protein design problem using the HPW model and we investigate some statistical and thermodynamic properties of the designed sequences. In particular we show that the HPW model is able to capture, within a single framework, both warm and cold denaturation of proteins. We further adapt the MLG model model to include chaotrope cosolvents in the solution. We show that the alteration of the network of hydrogen bonds between water molecules due to the presence of chaotropic agents induces destabilization of proteins. Moreover, we find that the presence of chaotropes originates an effective interaction between the chaotropes and the protein. In addition, we use a common method to extract effective amino-acid interactions on real proteins: a perceptron algorithm. The method applied to proteins designed with the HPW model reveals that the solvent effects can not be reproduced by effective residue interactions. Indeed, we show that it is not possible to determine a set of global interaction values able to stabilize all proteins at once.

EPFL2004

Spatially Controlled Activation of Toll-like Receptor 9 with DNA-Based Nanomaterials

Maartje Martina Cornelia Bastings, Alice Comberlato, Marianna Mainardi Koga

First evidence of geometrical patterns and defined distances of biomolecules as fundamental parameters to regulate receptor binding and cell signaling have emerged recently. Here, we demonstrate the importance of controlled nanospacing of immunostimulatory agents for the activation of immune cells by exploiting DNA-based nanomaterials and pre-existing crystallography data. We created DNA origami nanoparticles that present CpG-motifs in rationally designed spatial patterns to activate Toll-like Receptor 9 in RAW 264.7 macrophages. We demonstrated that stronger immune activation is achieved when active molecules are positioned at the distance of 7 nm, matching the active dimer structure of the receptor. Moreover, we show how the introduction of linkers between particle and ligand can influence the spatial tolerance of binding. These findings are fundamental for a fine-tuned manipulation of the immune system, considering the importance of spatially controlled presentation of therapeutics to increase efficacy and specificity of immune-modulating nanomaterials where multivalent binding is involved.

AMER CHEMICAL SOC2022

Small Molecule Based Approaches to Inhibit Macrophage Migration Inhibitory Factor (MIF) Activities and Elucidate its Role in Health and Disease

Hajer Ouertatani-Sakouhi

EPFL2010