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.

Computational methods and modeling of cellular function for the optimization of protein production and the study of cellular disease states

Joana Raquel Pinto Vieira

Systems biology is a multidisciplinary field that weaves together all the basic sciences through the use of computational and bioinformatics tools, to provide a more integrative view of the complex molecular interactions taking place within and among cells. The successes in the development and improvement of techniques for high throughput âomics has rapidly increased the amount of available data. The complexity of the underlying biological system it describes requires the development of tools to properly process it and analyze it. Computational models mathematically describe the systems interactions, allowing intrinsic properties of the data to emerge that would otherwise be overlooked. These models provide context to the data and are used to make predictions about the behavior of the system and to simulate a broader landscape of hypothesis, saving the time and cost of performing numerous experiments. However, the number of required parameters to mathematically formulate the system increases with the model complexity to integrate the available data. Thus, the development of estimation procedures and workflows to retrieve these values from literature and databases become crucial. In this work, we developed a set of computational models, analysis tools, and pipelines to support the study of two biological systems crucial to the cell survival: metabolism and protein synthesis. Metabolism is responsible for the production of most cell biomass, including proteins. Together, these two systems balance the renewal of protein in the cell, where metabolism provides the amino acids obtained from protein breakdown to the mRNA translation machinery. Deregulation of these systems is known to cause multiple disorders, such as neurodegenerative diseases and cancer. In the study of protein synthesis, we employ a combination of deterministic and stochastic modeling approaches to understand its intrinsic mechanistic properties and its rate-limiting steps. A better understanding of the system properties can have a profound impact on the development of drug targets and, in particular, in the optimization of heterologous protein production. Our studies revealed that more than one factor plays a role in the speed of translation: competition for tRNA resources and the type of cognate binding interaction between tRNA and the mRNA-ribosome complex. We also derived an equation that, given the knowledge about certain intracellular parameters pertaining to the host organism of interest, can assist in the design of transcripts for optimizing heterologous protein production. For the study of human metabolism, we established a pipeline to generate tissue-specific reduced metabolic models that can be used to study the metabolic reprogramming of different cancers and compare it with the metabolic phenotype of a healthy cell type. Despite being presented herein for human models, this pipeline is general and can be applied to the models of any organism. Starting from a human genome scale metabolic model, the pipeline improves compound annotation, identification, thermodynamics parameter retrieval, and facilitates data integration through the connection of several compound databases in a semi-automatized fashion. This work sets a standard for metabolic model assessment and curation and improves on existing tools to generate the first thermodynamically feasible reduced model of human metabolism, which is specifically tailored to the physiology and conditions under study.

EPFL2017

Reviewing Challenges of Predicting Protein Melting Temperature Change Upon Mutation Through the Full Analysis of a Highly Detailed Dataset with High-Resolution Structures

Luciano Andres Abriata

Predicting the effects of mutations on protein stability is a key problem in fundamental and applied biology, still unsolved even for the relatively simple case of small, soluble, globular, monomeric, two-state-folder proteins. Many articles discuss the limitations of prediction methods and of the datasets used to train them, which result in low reliability for actual applications despite globally capturing trends. Here, we review these and other issues by analyzing one of the most detailed, carefully curated datasets of melting temperature change (ΔTm) upon mutation for proteins with high-resolution structures. After examining the composition of this dataset to discuss imbalances and biases, we inspect several of its entries assisted by an online app for data navigation and structure display and aided by a neural network that predicts ΔTm with accuracy close to that of programs available to this end. We pose that the ΔTm predictions of our network, and also likely those of other programs, account only for a baseline-like general effect of each type of amino acid substitution which then requires substantial corrections to reproduce the actual stability changes. The corrections are very different for each specific case and arise from fine structural details which are not well represented in the dataset and which, despite appearing reasonable upon visual inspection of the structures, are hard to encode and parametrize. Based on these observations, additional analyses, and a review of recent literature, we propose recommendations for developers of stability prediction methods and for efforts aimed at improving the datasets used for training. We leave our interactive interface for analysis available online at http://lucianoabriata.altervista.org/papersdata/proteinstability2021/s1626navigation.html so that users can further explore the dataset and baseline predictions, possibly serving as a tool useful in the context of structural biology and protein biotechnology research and as material for education in protein biophysics.

2021

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

Zander Harteveld

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.

EPFL2022