Structural characterization of intrinsically disordered proteins by NMR spectroscopy.

Recent advances in NMR methodology and techniques allow the structural investigation of biomolecules of increasing size with atomic resolution. NMR spectroscopy is especially well-suited for the study of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) which are in general highly flexible and do not have a well-defined secondary or tertiary structure under functional conditions. In the last decade, the important role of IDPs in many essential cellular processes has become more evident as the lack of a stable tertiary structure of many protagonists in signal transduction, transcription regulation and cell-cycle regulation has been discovered. The growing demand for structural data of IDPs required the development and adaption of methods such as 13C-direct detected experiments, paramagnetic relaxation enhancements (PREs) or residual dipolar couplings (RDCs) for the study of 'unstructured' molecules in vitro and in-cell. The information obtained by NMR can be processed with novel computational tools to generate conformational ensembles that visualize the conformations IDPs sample under functional conditions. Here, we address NMR experiments and strategies that enable the generation of detailed structural models of IDPs

Validação de heterogeneidade estrutural em dados de Crio-ME por comitês de agrupadores

Orientadores: Fernando José Von Zuben, Rodrigo Villares PortugalDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Análise de Partículas Isoladas é uma técnica que permite o estudo da estrutura tridimensional de proteínas e outros complexos macromoleculares de interesse biológico. Seus dados primários consistem em imagens de microscopia eletrônica de transmissão de múltiplas cópias da molécula em orientações aleatórias. Tais imagens são bastante ruidosas devido à baixa dose de elétrons utilizada. Reconstruções 3D podem ser obtidas combinando-se muitas imagens de partículas em orientações similares e estimando seus ângulos relativos. Entretanto, estados conformacionais heterogêneos frequentemente coexistem na amostra, porque os complexos moleculares podem ser flexíveis e também interagir com outras partículas. Heterogeneidade representa um desafio na reconstrução de modelos 3D confiáveis e degrada a resolução dos mesmos. Entre os algoritmos mais populares usados para classificação estrutural estão o agrupamento por k-médias, agrupamento hierárquico, mapas autoorganizáveis e estimadores de máxima verossimilhança. Tais abordagens estão geralmente entrelaçadas à reconstrução dos modelos 3D. No entanto, trabalhos recentes indicam ser possível inferir informações a respeito da estrutura das moléculas diretamente do conjunto de projeções 2D. Dentre estas descobertas, está a relação entre a variabilidade estrutural e manifolds em um espaço de atributos multidimensional. Esta dissertação investiga se um comitê de algoritmos de não-supervisionados é capaz de separar tais "manifolds conformacionais". Métodos de "consenso" tendem a fornecer classificação mais precisa e podem alcançar performance satisfatória em uma ampla gama de conjuntos de dados, se comparados a algoritmos individuais. Nós investigamos o comportamento de seis algoritmos de agrupamento, tanto individualmente quanto combinados em comitês, para a tarefa de classificação de heterogeneidade conformacional. A abordagem proposta foi testada em conjuntos sintéticos e reais contendo misturas de imagens de projeção da proteína Mm-cpn nos estados "aberto" e "fechado". Demonstra-se que comitês de agrupadores podem fornecer informações úteis na validação de particionamentos estruturais independetemente de algoritmos de reconstrução 3DAbstract: Single Particle Analysis is a technique that allows the study of the three-dimensional structure of proteins and other macromolecular assemblies of biological interest. Its primary data consists of transmission electron microscopy images from multiple copies of the molecule in random orientations. Such images are very noisy due to the low electron dose employed. Reconstruction of the macromolecule can be obtained by averaging many images of particles in similar orientations and estimating their relative angles. However, heterogeneous conformational states often co-exist in the sample, because the molecular complexes can be flexible and may also interact with other particles. Heterogeneity poses a challenge to the reconstruction of reliable 3D models and degrades their resolution. Among the most popular algorithms used for structural classification are k-means clustering, hierarchical clustering, self-organizing maps and maximum-likelihood estimators. Such approaches are usually interlaced with the reconstructions of the 3D models. Nevertheless, recent works indicate that it is possible to infer information about the structure of the molecules directly from the dataset of 2D projections. Among these findings is the relationship between structural variability and manifolds in a multidimensional feature space. This dissertation investigates whether an ensemble of unsupervised classification algorithms is able to separate these "conformational manifolds". Ensemble or "consensus" methods tend to provide more accurate classification and may achieve satisfactory performance across a wide range of datasets, when compared with individual algorithms. We investigate the behavior of six clustering algorithms both individually and combined in ensembles for the task of structural heterogeneity classification. The approach was tested on synthetic and real datasets containing a mixture of images from the Mm-cpn chaperonin in the "open" and "closed" states. It is shown that cluster ensembles can provide useful information in validating the structural partitionings independently of 3D reconstruction methodsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétric

Computational Investigations of Biomolecular Mechanisms in Genomic Replication, Repair and Transcription

High fidelity maintenance of the genome is imperative to ensuring stability and proliferation of cells. The genetic material (DNA) of a cell faces a constant barrage of metabolic and environmental assaults throughout the its lifetime, ultimately leading to DNA damage. Left unchecked, DNA damage can result in genomic instability, inviting a cascade of mutations that initiate cancer and other aging disorders. Thus, a large area of focus has been dedicated to understanding how DNA is damaged, repaired, expressed and replicated. At the heart of these processes lie complex macromolecular dynamics coupled with intricate protein-DNA interactions. Through advanced computational techniques it has become possible to probe these mechanisms at the atomic level, providing a physical basis to describe biomolecular phenomena. To this end, we have performed studies aimed at elucidating the dynamics and interactions intrinsic to the functionality of biomolecules critical to maintaining genomic integrity: modeling the DNA editing mechanism of DNA polymerase III, uncovering the DNA damage recognition/repair mechanism of thymine DNA glycosylase and linking genetic disease to the functional dynamics of the pre-initiation complex transcription machinery. Collectively, our results elucidate the dynamic interplay between proteins and DNA, further broadening our understanding of these complex processes involved with genomic maintenance

Computational Studies of Molecular Mechanisms Mediating Protein Adsorption on Material Surfaces

Protein adsorption at material surfaces is a fundamental concept in many scientific applications ranging from the biocompatibility of implant materials in bioengineering to cleaning environmental material surfaces from toxic proteins in the area of biodefense. Understanding the molecular-level details of protein-surface interactions is crucial for controlling protein adsorption. While a range of experimental techniques has been developed to study protein adsorption, these techniques cannot produce the fundamental molecular-level information of protein adsorption. All-atom empirical force field molecular dynamics (MD) simulations hold great promise as a valuable tool for elucidating and predicting the mechanisms governing protein adsorption. However, current MD simulation methods have not been validated for this application. This research addresses three limitations of the standard MD when applied to the simulations of the protein-surface interactions: (1) representation of the force field parameters governing the interactions of protein amino acids with the material surface; (2) cluster analysis of ensembles of adsorbed protein states obtained in protein-adsorption simulations, in which in addition to the conformation the orientation of the sampled states is also important; and (3) simulation time to ensure a significant level of conformational sampling to cover the entire rough energy landscape of such a large molecular system as protein adsorption. This study, thus, attempted to further advance protein-adsorption simulation methods using high-density polyethylene as a model materials surface

Intrinsically Disordered Proteins within the Genome

The hundreds of millions of DNA base pairs within eukaryotic cells are not found free but packed inside the micrometre-sized nuclei through the formation of a macromolecular structure known as chromatin. Chromatin consists of a chain of nucleosomes – nucleoprotein complexes where the DNA makes ∼1.75 turns around a protein octamer core composed of two copies each of H2A, H2B, H3 and H4 histones. A fifth histone H1 binds on the nucleosomal surface close to the entry/exit site of DNA, interacts with linker DNA and aids in chromatin compaction. Enabling the condensation of DNA to fit into the nucleus is however only one-half of chromatin’s role. The three-dimensional spatial organization of chromatin serves a second important role in allowing the capability to exert control over gene expression. The chromatin structure thus serves as an additional layer of complexity above the genome code and permits the transcription of different proteins varying with cell lineages/cycles. The proteins that makeup, modify and read the chromatin structure are particularly enriched in `Intrinsic Disorder’ – a class of proteins lacking a well-defined structure but existing as a dynamic ensemble of rapidly interchanging states. While folded proteins with well-defined structures are amenable to be characterized through standard methods of protein structure determination, the `plasticity’ of the disordered proteins challenges the use of such ensemble averaged techniques. In this thesis, Molecular Dynamics simulations are used to characterize the disordered regions of three proteins that form the core of chromatin structure: histones, linker histones (H1) and heterochromatin protein (HP1). The carboxy-terminal domain of H1 when within the nucleosome, adopts a compact but unstructured conformation that allows its positioning between the two linker DNA strands. In contrast, the amino-terminal domain of H1 undergoes a disorder-to-order transition to an amphiphilic helical conformation. The transition to the amphiphilic helix is however subtype-dependant with the degree of condensation varying with the subtypes' nucleosomal affinity. Finally, the simulations demonstrate that the affinity of HP1 subtypes for the H3 histone is caused by the synergetic effects of both the proteins' unstructured amino-terminal domain and the structured chromodomain

Towards a Benchmark for Markov State Models: The Folding of HP35

Adopting a $300 \, \mu$s-long molecular dynamics (MD) trajectory of the reversible folding of villin headpiece (HP35) published by D. E. Shaw Research, we recently constructed a Markov state model (MSM) of the folding process based on interresidue contacts [J. Chem. Theory Comput. 2023, ${\bf {19}}$, 3391]. The model reproduces the MD folding times of the system and predicts that both the native basin and the unfolded region of the free energy landscape are partitioned into several metastable substates that are structurally well characterized. Recognizing the need to establish well-defined but nontrivial benchmark problems, in this Perspective we study to what extent and in what sense this MSM may be employed as a reference model. To this end, we test the robustness of the MSM by comparing it to models that use alternative combinations of features, dimensionality reduction methods and clustering schemes. The study suggests some main characteristics of the folding of HP35, which should be reproduced by any other competitive model of the system. Moreover, the discussion reveals which parts of the MSM workflow matter most for the considered problem, and illustrates the promises and possible pitfalls of state-based models for the interpretation of biomolecular simulations.Comment: arXiv admin note: text overlap with arXiv:2303.0381

Simultaneous use of solution, solid-state NMR and X-ray crystallography to study the conformational landscape of the Crh protein during oligomerization and crystallization

We explore, using the Crh protein dimer as a model, how information from solution NMR, solid-state NMR and X-ray crystallography can be combined using structural bioinformatics methods, in order to get insights into the transition from solution to crystal. Using solid-state NMR chemical shifts, we filtered intra-monomer NMR distance restraints in order to keep only the restraints valid in the solid state. These filtered restraints were added to solid-state NMR restraints recorded on the dimer state to sample the conformational landscape explored during the oligomerization process. The use of non-crystallographic symmetries then permitted the extraction of converged conformers subsets. Ensembles of NMR and crystallographic conformers calculated independently display similar variability in monomer orientation, which supports a funnel shape for the conformational space explored during the solution-crystal transition. Insights into alternative conformations possibly sampled during oligomerization were obtained by analyzing the relative orientation of the two monomers, according to the restraint precision. Molecular dynamics simulations of Crh confirmed the tendencies observed in NMR conformers, as a paradoxical increase of the distance between the two β1a strands, when the structure gets closer to the crystallographic structure, and the role of water bridges in this context

Intrinsically Disordered Proteins and Chronic Diseases

This book is an embodiment of a series of articles that were published as part of a Special Issue of Biomolecules. It is dedicated to exploring the role of intrinsically disordered proteins (IDPs) in various chronic diseases. The main goal of the articles is to describe recent progress in elucidating the mechanisms by which IDPs cause various human diseases, such as cancer, cardiovascular disease, amyloidosis, neurodegenerative diseases, diabetes, and genetic diseases, to name a few. Contributed by leading investigators in the field, this compendium serves as a valuable resource for researchers, clinicians as well as postdoctoral fellows and graduate student

Characterizing the Diversity of the CDR-H3 Loop Conformational Ensembles in Relationship to Antibody Binding Properties

We present an approach to assess antibody CDR-H3 loops according to their dynamic properties using molecular dynamics simulations. We selected six antibodies in three pairs differing substantially in their individual promiscuity respectively specificity. For two pairs of antibodies crystal structures are available in different states of maturation and used as starting structures for the analyses. For a third pair we chose two antibody CDR sequences obtained from a synthetic library and predicted the respective structures. For all three pairs of antibodies we performed metadynamics simulations to overcome the limitations in conformational sampling imposed by high energy barriers. Additionally, we used classic molecular dynamics simulations to describe nano- to microsecond flexibility and to estimate up to millisecond kinetics of captured conformational transitions. The methodology represents the antibodies as conformational ensembles and allows comprehensive analysis of structural diversity, thermodynamics of conformations and kinetics of structural transitions. Referring to the concept of conformational selection we investigated the link between promiscuity and flexibility of the antibodies' binding interfaces. The obtained detailed characterization of the binding interface clearly indicates a link between structural flexibility and binding promiscuity for this set of antibodies