Arabidopsis thaliana dehydroascorbate reductase 2 : conformational flexibility during catalysis

Dehydroascorbate reductase (DHAR) catalyzes the glutathione (GSH)-dependent reduction of dehydroascorbate and plays a direct role in regenerating ascorbic acid, an essential plant antioxidant vital for defense against oxidative stress. DHAR enzymes bear close structural homology to the glutathione transferase (GST) superfamily of enzymes and contain the same active site motif, but most GSTs do not exhibit DHAR activity. The presence of a cysteine at the active site is essential for the catalytic functioning of DHAR, as mutation of this cysteine abolishes the activity. Here we present the crystal structure of DHAR2 from Arabidopsis thaliana with GSH bound to the catalytic cysteine. This structure reveals localized conformational differences around the active site which distinguishes the GSH-bound DHAR2 structure from that of DHAR1. We also unraveled the enzymatic step in which DHAR releases oxidized glutathione (GSSG). To consolidate our structural and kinetic findings, we investigated potential conformational flexibility in DHAR2 by normal mode analysis and found that subdomain mobility could be linked to GSH binding or GSSG release

LightDock: a new multi-scale approach to protein–protein docking

Computational prediction of protein–protein complex structure by docking can provide structural and mechanistic insights for protein interactions of biomedical interest. However, current methods struggle with difficult cases, such as those involving flexible proteins, low-affinity complexes or transient interactions. A major challenge is how to efficiently sample the structural and energetic landscape of the association at different resolution levels, given that each scoring function is often highly coupled to a specific type of search method. Thus, new methodologies capable of accommodating multi-scale conformational flexibility and scoring are strongly needed. We describe here a new multi-scale protein–protein docking methodology, LightDock, capable of accommodating conformational flexibility and a variety of scoring functions at different resolution levels. Implicit use of normal modes during the search and atomic/coarse-grained combined scoring functions yielded improved predictive results with respect to state-of-the-art rigid-body docking, especially in flexible cases.B.J-G was supported by a FPI fellowship from the Spanish Ministry of Economy and Competitiveness. This work was supported by I+D+I Research Project grants BIO2013-48213-R and BIO2016-79930-R from the Spanish Ministry of Economy and Competitiveness. This work is partially supported by the European Union H2020 program through HiPEAC (GA 687698), by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology (TIN2015-65316-P) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Programaciói Entorns d’Execució Paral·lels (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Numerical evaluation of protein global vibrations at terahertz frequencies by means of elastic lattice models

Proteins represent one of the most important building blocks for most biological processes. Their biological mechanisms have been found to correlate significantly with their dynamics, which is commonly investigated through molecular dynamics (MD) simulations. However, important insights on protein dynamics and biological mechanisms have also been obtained via much simpler and computationally efficient calculations based on elastic lattice models (ELMs). The application of structural mechanics approaches, such as modal analysis, to the protein ELMs has allowed to find impressive results in terms of protein dynamics and vibrations. The low-frequency vibrations extracted from the protein ELM are usually found to occur within the terahertz (THz) frequency range and correlate fairly accurately with the observed functional motions. In this contribution, the global vibrations of lysozyme will be investigated by means of a finite element (FE) truss model, and we will show that there exists complete consistency between the proposed FE approach and one of the more well-known ELMs for protein dynamics, the anisotropic network model (ANM). The proposed truss model can consequently be seen as a simple method, easily accessible to the structural mechanics community members, to analyze protein vibrations and their connections with the biological activity

Monte Carlo Techniques for Drug Design: The Success Case of PELE

This chapter summarizes the most representative software packages that readily allow running Monte Carlo (MC) simulations in relevant scenarios for drug design. It explores in detail the Protein Energy Landscape Exploration (PELE) program, providing first the main characteristics of the technique, followed by an analysis of the different application studies in mapping protein‐ligand interactions. The ligand, formed by a rigid core and a set of rotatable side chains, is perturbed by translating and rotating it. PELE creates a list of perturbation poses, and then chooses the one with the lowest system energy. PELE was originally designed to map ligand migration pathways: its first applications aimed at finding exit pathways starting from ligand‐bound crystallographic structures. Additional applied studies have centered on modeling enzymatic mechanisms and engineering; the same techniques applied in mapping protein‐drug interactions can be used in the study of substrate recognition by enzymes.Along the development of PELE in the last years, we gratefully acknowledge financial support from the European Union (in particular from the ERC program) and from the Catalan and Spanish Governments. In addition we want to thank all present and past members from the EAPM lab. at BSC for their dedication and work.Peer ReviewedPostprint (author's final draft

New Monte Carlo Based Technique To Study DNA–Ligand Interactions

We present a new all-atom Monte Carlo technique capable of performing quick and accurate DNA–ligand conformational sampling. In particular, and using the PELE software as a frame, we have introduced an additional force field, an implicit solvent, and an anisotropic network model to effectively map the DNA energy landscape. With these additions, we successfully generated DNA conformations for a test set composed of six DNA fragments of A-DNA and B-DNA. Moreover, trajectories generated for cisplatin and its hydrolysis products identified the best interacting compound and binding site, producing analogous results to microsecond molecular dynamics simulations. Furthermore, a combination of the Monte Carlo trajectories with Markov State Models produced noncovalent binding free energies in good agreement with the published molecular dynamics results, at a significantly lower computational cost. Overall our approach will allow a quick but accurate sampling of DNA–ligand interactions.The authors thank the Barcelona Supercomputing Center for computational resources. This work was supported by grants from the European Research Council—2009-Adg25027-PELE European project and the Spanish Ministry of Economy and Competitiveness CTQ2013-48287 and “Juan de la Cierva” to F.L.Peer ReviewedPostprint (author's final draft

Développement, validation et nouvelles applications d’un modèle d’analyse des modes normaux basé sur la séquence et la structure de protéines

Les protéines existent sous différents états fonctionnels régulés de façon précise par leur environnement afin de maintenir l‘homéostasie de la cellule et de l‘organisme vivant. La prévalence de ces états protéiques est dictée par leur énergie libre de Gibbs alors que la vitesse de transition entre ces états biologiquement pertinents est déterminée par le paysage d‘énergie libre. Ces paramètres sont particulièrement intéressants dans un contexte thérapeutique et biotechnologique, où leur perturbation par la modulation de la séquence protéique par des mutations affecte leur fonction. Bien que des nouvelles approches expérimentales permettent d‘étudier l‘effet de mutations en haut débit pour une protéine, ces méthodes sont laborieuses et ne couvrent qu‘une fraction de l‘ensemble des structures primaires d‘intérêt. L‘utilisation de modèles bio-informatiques permet de tester et générer in silico différentes hypothèses afin d‘orienter les approches expérimentales. Cependant, ces méthodes basées sur la structure se concentrent principalement sur la prédiction de l‘enthalpie d‘un état, alors que plusieurs évidences expérimentales ont démontré l‘importance de la contribution de l‘entropie. De plus, ces approches ignorent l‘importance de l‘espace conformationnel protéique dicté par le paysage énergétique cruciale à son fonctionnement. Une analyse des modes normaux peut être effectuée afin d‘explorer cet espace par l‘approximation que la protéine est dans une conformation d‘équilibre où chaque acide aminé est représenté par une masse régie par un potentiel harmonique. Les approches actuelles ignorent l‘identité des résidus et ne peuvent prédire l‘effet de mutations sur les propriétés dynamiques. Nous avons développé un nouveau modèle appelé ENCoM qui pallie à cette lacune en intégrant de l‘information physique et spécifique sur les contacts entre les atomes des chaînes latérales. Cet ajout permet une meilleure description de changements conformationnels d‘enzymes, la prédiction de l‘effet d‘une mutation allostérique dans la protéine DHFR et également la prédiction de l‘effet de mutations sur la stabilité protéique par une valeur entropique. Comparativement à des approches spécifiquement développées pour cette application, ENCoM est plus constant et prédit mieux l‘effet de mutations stabilisantes. Notre approche a également été en mesure de capturer la pression évolutive qui confère aux protéines d‘organismes thermophiles une thermorésistance accrue

신뢰도가 낮은 구조 환경에서의 단백질 모델 정밀화 방법 개발

학위논문 (박사)-- 서울대학교 대학원 : 화학부 물리화학 전공, 2017. 2. 석차옥.The number of experimentally determined protein structures is increasing exponentially. Based on this abundant structural information, homology modeling is now the most popular method for protein structure prediction. Still however, knowledge of high resolution structures is critical for applications using the protein structure such as drug discovery and protein design. By realizing this, protein structure refinement methods have been developed to improve the structure quality of low resolution experimental structures or model structures. Another realm of protein structure refinement is to predict the protein structure in the environment of interest, such as binding to a specific partner, when only structures resolved in different conformational states or model structures are provided. In this thesis, four modeling methods (GalaxyLoop-PS2, GalaxyRefine2, GalaxyVoyage, and Galaxy7TM) developed in the scope of refining predicted protein structures are introduced. The methods were evolved by either extending the range of structure targeted for refinement or considering the interaction with a particular binding partner. The shared problem of these methods was that the environment of modeling was unreliable due to errors embedded in model structures. Commonly, two approaches were taken to tackle this problem. These were initially searching the conformational space in low resolution and developing a hybrid energy function less sensitive to environmental error. The development and application results of the approaches taken for each modeling method will be addressed in detail.1. Introduction 1 2. Protein loop modeling in unreliable structural environments 4 2.1. Introduction 4 2.2. Methods 5 2.2.1. Development of a new hybrid energy function 5 2.2.2. Initial loop conformation sampling 6 2.2.3. Global optimization using conformational space annealing 7 2.2.4. Generation of test sets with various range of structural error 9 2.3. Results and discussion 10 2.3.1. Energy function for protein loop modeling 10 2.3.2. Environmental error of the test set 14 2.3.3. Loop reconstruction in the crystal structure framework 16 2.3.4. Loop modeling in sidechain perturbed environment 19 2.3.5. Loop modeling in backbone perturbed environment 20 2.3.6. Loop modeling on template-based models 23 2.3.7. Comparison of using hybrid energy, physics-based energy, and knowledge-based energy for loop modeling 26 2.4. Conclusion 29 3. Global refinement of protein model structures 30 3.1. Introduction 30 3.1.1. Extension of the range of structure targeted for refinement 30 3.1.2. Conformational search methods and energy functions for global refinement 32 3.2. Global refinement based on loop modeling and overall relaxation 34 3.2.1. Methods 34 3.2.2. Results and discussion 44 3.3. Global refinement with diverse modeling methods applied on unreliable local regions 63 3.3.1. Methods 63 3.3.2. Results and discussion 68 3.4. Conclusion 82 4. Flexible docking of ligands to G-protein-coupled receptors based on structure refinement 83 4.1. Introduction 83 4.1.1. Ligand docking to model structures 83 4.1.2. Predicting the ligand-bound G-protein-coupled receptor structure 84 4.2. Methods 86 4.2.1. Initial docking of ligand to receptor conformations generated by ANM 86 4.2.2. Energy function for complex structure refinement 87 4.2.3. Complex structure refinement and final model selection 89 4.2.4. Benchmark test set construction 90 4.3. Results and discussion 92 4.3.1. Energy function for complex structure refinement 92 4.3.2. Overall performance of Galaxy7TM 96 4.3.3. The relation between the docking accuracy of Galaxy7TM predictions and the receptor model quality 103 4.4. Conclusion 109 5. Conclusion 110 Bibliography 111 국문초록 121Docto

Creation, refinement, and evaluation of conformational ensembles of proteins using the Torsional Network Model

Máster Universitario en Bioinformática y Biología ComputacionalOne of the main limitations of structural bioinformatics lies in the difficulty of properly accounting for the dynamical aspects of proteins, which are often critical to their functional mechanisms. Among the tools developed to deal with this issue, the Torsional Network Model (TNM) relies on internal degrees of freedom (torsion angles of the protein backbone), and can give a description of the thermal fluctuations of a protein structure, as well as generate structural ensembles. However, the TNM is a coarse-grained model that cannot ensure that the newly created conformations are exempt from any structural defects. Therefore, the main hypothesis of this project is that TNM assembly process can be improved. The ability to generate high-quality structural ensembles describing the dynamical properties of a protein would indeed be highly valuable in various applications. In this thesis, we create, evaluate and refine TNM ensembles from a set of reference protein structures defined experimentally (Levin et al., 2007). An approximation used in Bastolla and Dehouck, 2019, is developed: the evaluation is performed by Molprobity analysis, and the refinement is done by SIDEpro. Furthermore, a new approach is taken when refining the ensembles by Energy Minimization (EM). The results show a potential improvement of the TNM ensembles when adjusting the target RMSD to the protein studied; point to a enhancement when using side-chain reconstructions , and to its combination with Energy Minimization as a way to optimize the structure quality. On the other hand, the pros and cons of the followed methodology are discussed, because the use of the available static-protein oriented measures and methods makes specially important to beware of their limitations when applied to the protein-dynamic oriented TNM. Exploring further target RMSD values, adjusting them to specific protein dynamic simulations or replicating the same pipe-line in different data-sets are some of the proposals for future work. Furthermore, taking into account variables like the temperature, the flexibility of the protein, and the estimated optimal RMSD would be interesting for the next studies

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Elastic Network Models in Biology: From Protein Mode Spectra to Chromatin Dynamics

Biomacromolecules perform their functions by accessing conformations energetically favored by their structure-encoded equilibrium dynamics. Elastic network model (ENM) analysis has been widely used to decompose the equilibrium dynamics of a given molecule into a spectrum of modes of motions, which separates robust, global motions from local fluctuations. The scalability and flexibility of the ENMs permit us to efficiently analyze the spectral dynamics of large systems or perform comparative analysis for large datasets of structures. I showed in this thesis how ENMs can be adapted (1) to analyze protein superfamilies that share similar tertiary structures but may differ in their sequence and functional dynamics, and (2) to analyze chromatin dynamics using contact data from Hi-C experiments, and (3) to perform a comparative analysis of genome topology across different types of cell lines. The first study showed that protein family members share conserved, highly cooperative (global) modes of motion. A low-to-intermediate frequency spectral regime was shown to have a maximal impact on the functional differentiation of families into subfamilies. The second study demonstrated the Gaussian Network Model (GNM) can accurately model chromosomal mobility and couplings between genomic loci at multiple scales: it can quantify the spatial fluctuations in the positions of gene loci, detect large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. The third study revealed close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations in the spatial fluctuations of genomic loci. It also called attention to the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation. Together, these studies provide a comprehensive view of the versatility and utility of the ENMs in analyzing spatial dynamics of biomolecules, from individual proteins to the entire chromatin