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

BIOMOLECULAR FUNCTION FROM STRUCTURAL SNAPSHOTS

Biological molecules can assume a continuous range of conformations during function. Near equilibrium, the Boltzmann relation connects a particular conformation\u27s free energy to the conformation\u27s occupation probability, thus giving rise to one or more energy landscapes. Biomolecular function proceeds along minimum-energy pathways on such landscapes. Consequently, a comprehensive understanding of biomolecular function often involves the determination of the free-energy landscapes and the identification of functionally relevant minimum-energy conformational paths on these landscapes. Specific techniques are necessary to determine continuous conformational spectra and identify functionally relevant conformational trajectories from a collection of raw single-particle snapshots from, e.g. cryogenic electron microscopy (cryo-EM) or X-ray diffraction. To assess the capability of different algorithms to recover conformational landscapes, we:• Measure, compare, and benchmark the performance of four leading data-analytical approaches to determine the accuracy with which energy landscapes are recovered from simulated cryo-EM data. Our simulated data are derived from projection directions along the great circle, emanating from a known energy landscape. • Demonstrate the ability to recover a biomolecule\u27s energy landscapes and functional pathways of biomolecules extracted from collections of cryo-EM snapshots. Structural biology applications in drug discovery and molecular medicine highlight the importance of the free-energy landscapes of the biomolecules more crucial than ever. Recently several data-driven machine learning algorithms have emerged to extract energy landscapes and functionally relevant continuous conformational pathways from single-particle data (Dashti et al., 2014; Dashti et al., 2020; Mashayekhi,et al., 2022). In a benchmarking study, the performance of several advanced data-analytical algorithms was critically assessed (Dsouza et al., 2023). In this dissertation, we have benchmarked the performance of four leading algorithms in extracting energy landscapes and functional pathways from single-particle cryo-EM snapshots. In addition, we have significantly improved the performance of the ManifoldEM algorithm, which has demonstrated the highest performance. Our contributions can be summarized as follows.: • Expert user supervision is required in one of the main steps of the ManifoldEM framework wherein the algorithm needs to propagate the conformational information through all angular space. We have succeeded in introducing an automated approach, which eliminates the need for user involvement. • The quality of the energy landscapes extracted by ManifoldEM from cryo-EM data has been improved, as the accuracy scores demonstrate this improvement. These measures have substantially enhanced ManifoldEM’s ability to recover the conformational motions of biomolecules by extracting the energy landscape from cryo-EM data.In line with the primary goal of our research, we aimed to extend the automated method across the entire angular sphere rather than a great circle. During this endeavor, we encountered challenges, particularly with some projection directions not following the proposed model. Through methodological adjustments and sampling optimization, we improved the projection direction\u27s conformity to the model. However, a small subset of Projection directions (5 %) remained challenging. We also recommended the use of specific methodologies, namely feature extraction and edge detection algorithms, to enhance the precision in quantifying image differentiation, a crucial component of our automated model. we also suggested that integrating different techniques might potentially resolve challenges associated with certain projection directions. We also applied ManifoldEM to experimental cryo-EM images of the SARS-CoV-2 spike protein in complex with the ACE2 receptor. By introducing several improvements, such as the incorporation of an adaptive mask and cosine curve fitting, we enhanced the framework\u27s output quality. This enhancement can be quantified by observing the removal of the artifact from the energy landscape, especially if the post-enhancement landscape differs from the artifact-affected one. These modifications, specifically aimed at addressing challenges from Nonlinear Laplacian Spectral Analysis (NLSA) (Giannakis et al., 2012), are intended for application in upcoming cryo-EM studies utilizing ManifoldEM. In the closing sections of this dissertation, a summary and a projection of future research directions are provided. While initial automated methods have been explored, there remains room for refinement. We have offered numerous methodological suggestions oriented toward addressing solutions to the challenge of conformational information propagation. Key methodologies discussed include Manifold Alignment, Canonical Correlation Analysis, and Multi-View Diffusion Maps. These recommendations are aimed to inform and guide subsequent developments in the ManifoldEM suite

Current state-of-the-art of the research conducted in mapping protein cavities – binding sites of bioactive compounds, peptides or other proteins

Ο σκοπός της διπλωματικής εργασίας είναι η διερεύνηση και αποτύπωση των ερευνητικών μελετών που αφορούν στον χαρακτηρισμό μιας πρωτεϊνικής κοιλότητας – κέντρου πρόσδεσης βιοδραστικών ενώσεων, πεπτιδίων ή άλλων πρωτεϊνών. Στην παρούσα εργασία χρησιμοποιήθηκε η μέθοδος της βιβλιογραφικής επισκόπησης. Παρουσιάζονται τα κυριότερα ευρήματα προηγούμενων ερευνών που σχετίζονται με τη διαδικασία σχεδιασμού φαρμάκων και τον εντοπισμό φαρμακοφόρων με βάση ένα σύνολο προσδετών. Στη συνέχεια συγκρίνονται διαδικασίες επεξεργασίας και ανάλυσης της πρωτεϊνικής κοιλότητας προγενέστερων ερευνών με τη προσέγγιση που προτάθηκε από τους Παπαθανασίου και Φωτόπουλου το 2015. Αναδεικνύονται βασικά πλεονεκτήματα της προσέγγισης αυτής, όπως η εφαρμογή του αλγορίθμου πολυδιάστατη k-means ομαδοποίηση (multidimensional k-means clustering). Η εύρεση βιβλιογραφίας βασίστηκε σε αναζήτηση επιστημονικών άρθρων σε ξενόγλωσσα επιστημονικά περιοδικά, σε κεφάλαια βιβλίων και σε διάφορα άρθρα σε ηλεκτρονικούς ιστότοπους σχετικά με τον σχεδιασμό φαρμάκων και τις κοιλότητες που απαντώνται στις πρωτεΐνες. Στην παρούσα εργασία παρουσιάζονται εν συντομία εργαλεία που εντοπίστηκαν χρησιμοποιώντας λέξεις κλειδιά όπως για παράδειγμα δυναμική πρωτεϊνικής κοιλότητας, καταλυτικό κέντρο ενός ενζύμου, πρόσδεση, πρωτεϊνική θήκη κλπ. Στη συνέχεια συγκροτήθηκε κατάλογος με τα εργαλεία βιοπληροφορικής ανάλυσης που βρέθηκαν και ακολούθησε εκτενής αναφορά επιλεκτικά σε κάποια από αυτά. Κριτήριο επιλογής αυτών των εργαλείων αποτέλεσε η ημερομηνία δημοσίευσής τους, οι αλγόριθμοι και η μεθοδολογία που χρησιμοποιούν. Τα εργαλεία αυτά κατηγοριοποιήθηκαν με βάση τις λέξεις κλειδιά που χρησιμοποιήθηκαν για την εξόρυξη των δεδομένων από την βιβλιογραφία. Τέλος πραγματοποιήθηκε συγκριτική μελέτη αυτών αναδεικνύοντας τα πλεονεκτήματα και εστιάζοντας στην περαιτέρω αξιοποίησή τους.The aim of this thesis was to report on the current state-of-the-art of the research conducted concerning mapping of protein cavities with a potential function role as binding sites of bioactive compounds, peptides or other proteins. A literature review was performed with emphasis on the relevant tools developed during the last decade. In addition, the main research findings regarding drug design and druggable targets based on binding sites are presented. Processes performed in protein cavity detection and analysis, of previous research articles, are compared with the approach described by Anaxagoras Fotopoulos and Athanasios Papathanasiou (2015). The results showed that a competitive advantage of their approach is the multidimensional k-means algorithm for clustering. For the bibliographic review the scientific knowledgebase has been used, which includes international articles and journals, book chapters, as well as online articles regarding drug design and protein cavity. Search keywords such as protein cavity dynamics, catalytic sites of enzymes, protein pocket etc. were used to identify bioinformatics tools with text mining. A catalogue of the most recently developed tools is presented followed by a brief description of selected tools. The selection criteria imposed for preparing the catalogue and the detailed description included the publication date, as well as the algorithms and the methods they use. The tools were then classified according to the search keywords. The findings of this research are discussed, and the algorithms and methods they use are compared, highlighting the advantages of protein cavity detection

Bioinformatics: Basics, Development, and Future

Bioinformatics is an interdisciplinary scientific field of life sciences. Bioinformatics research and application include the analysis of molecular sequence and genomics data; genome annotation, gene/protein prediction, and expression profiling; molecular folding, modeling, and design; building biological networks; development of databases and data management systems; development of software and analysis tools; bioinformatics services and workflow; mining of biomedical literature and text; and bioinformatics education and training. Astronomical accumulation of genomics, proteomics, and metabolomics data as well as a need for their storage, analysis, annotation, organization, systematization, and integration into biological networks and database systems were the main driving forces for the emergence and development of bioinformatics. Current critical needs for bioinformatics among others highlighted in this chapter, however, are to understand basics and specifics of bioinformatics as well as to prepare new generation scientists and specialists with integrated, interdisciplinary, and multilingual knowledge who can use modern bioinformatics resources powered with sophisticated operating systems, software, and database/networking technologies. In this introductory chapter, I aim to give an overall picture on basics and developments of the bioinformatics field for readers with some future perspectives, highlighting chapters published in this book

A Geometric Approach for Deciphering Protein Structure from Cryo-EM Volumes

Electron Cryo-Microscopy or cryo-EM is an area that has received much attention in the recent past. Compared to the traditional methods of X-Ray Crystallography and NMR Spectroscopy, cryo-EM can be used to image much larger complexes, in many different conformations, and under a wide range of biochemical conditions. This is because it does not require the complex to be crystallisable. However, cryo-EM reconstructions are limited to intermediate resolutions, with the state-of-the-art being 3.6A, where secondary structure elements can be visually identified but not individual amino acid residues. This lack of atomic level resolution creates new computational challenges for protein structure identification. In this dissertation, we present a suite of geometric algorithms to address several aspects of protein modeling using cryo-EM density maps. Specifically, we develop novel methods to capture the shape of density volumes as geometric skeletons. We then use these skeletons to find secondary structure elements: SSEs) of a given protein, to identify the correspondence between these SSEs and those predicted from the primary sequence, and to register high-resolution protein structures onto the density volume. In addition, we designed and developed Gorgon, an interactive molecular modeling system, that integrates the above methods with other interactive routines to generate reliable and accurate protein backbone models

Structural insights into phosphoprotein chaperoning of nucleoprotein in measles virus

Instruct Biennial Structural Biology Conference Abstract BookletMeasles virus is an important, highly contagious, human pathogen. The nucleoprotein N binds only to viral genomic RNA and forms the helical ribonucleocapsid that serves as a template for viral replication. We address how N is regulated by another protein, the phosphoprotein, P, to prevent newly synthesized N from binding to cellular RNA. Here, we pulled down an N01-408 fragment lacking most of its C-terminal tail domain by several affinity-tagged, N-terminal, P fragments to map the N0-binding region of P to the first 48 amino acids. We showed biochemically and using P mutants the importance of the hydrophobic interactions for the binding. We fused an N0 binding peptide, P1-48, to the C-terminus of an N021-408 fragment lacking both the N-terminal peptide and the C-terminal tail of N protein to reconstitute and crystallize the N0-P complex. We solved the X-ray structure of the resulting N0-P chimeric protein at 2.7 Å resolution. The structure reveals the molecular details of the conserved N0-P interface and explains how P chaperones N0 preventing both self-assembly of N0 and its binding to RNA. We compare the structure of an N0-P complex to atomic model of helical ribonucleocapsid. We thus propose a model how P may help to start viral RNA synthesis. Our results provide a new insight into mechanisms of paramyxovirus replication. New data on the mechanisms of phosphoprotein chaperone action allows better understanding of the virus genome replication and nucleocapsid assembly. We describe a conserved structural interface for the N-P interaction which could be a target for drug development not only to treat measles but also potentially other paramyxovirus diseases.Non peer reviewe

Prediction of protein-ligand binding affinity using neural networks

Master'sMASTER OF SCIENC