The Use of Experimental Structures to Model Protein Dynamics

The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high—for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods—Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step and show how to use these programs and tools. We also include computer programs to generate movies based on PCs and ENM modes and describe how to visualize them

p53 Plays a Role in Mesenchymal Differentiation Programs, in a Cell Fate Dependent Manner

Background: The tumor suppressor p53 is an important regulator that controls various cellular networks, including cell differentiation. Interestingly, some studies suggest that p53 facilitates cell differentiation, whereas others claim that it suppresses differentiation. Therefore, it is critical to evaluate whether this inconsistency represents an authentic differential p53 activity manifested in the various differentiation programs. Methodology/Principal Findings: To clarify this important issue, we conducted a comparative study of several mesenchymal differentiation programs. The effects of p53 knockdown or enhanced activity were analyzed in mouse and human mesenchymal cells, representing various stages of several differentiation programs. We found that p53 downregulated the expression of master differentiation-inducing transcription factors, thereby inhibiting osteogenic, adipogenic and smooth muscle differentiation of multiple mesenchymal cell types. In contrast, p53 is essential for skeletal muscle differentiation and osteogenic re-programming of skeletal muscle committed cells. Conclusions: These comparative studies suggest that, depending on the specific cell type and the specific differentiatio

Defining the Molecular Basis of Tumor Metabolism: a Continuing Challenge Since Warburg's Discovery

Cancer cells are the product of genetic disorders that alter crucial intracellular signaling pathways associated with the regulation of cell survival, proliferation, differentiation and death mechanisms. the role of oncogene activation and tumor suppressor inhibition in the onset of cancer is well established. Traditional antitumor therapies target specific molecules, the action/expression of which is altered in cancer cells. However, since the physiology of normal cells involves the same signaling pathways that are disturbed in cancer cells, targeted therapies have to deal with side effects and multidrug resistance, the main causes of therapy failure. Since the pioneering work of Otto Warburg, over 80 years ago, the subversion of normal metabolism displayed by cancer cells has been highlighted by many studies. Recently, the study of tumor metabolism has received much attention because metabolic transformation is a crucial cancer hallmark and a direct consequence of disturbances in the activities of oncogenes and tumor suppressors. in this review we discuss tumor metabolism from the molecular perspective of oncogenes, tumor suppressors and protein signaling pathways relevant to metabolic transformation and tumorigenesis. We also identify the principal unanswered questions surrounding this issue and the attempts to relate these to their potential for future cancer treatment. As will be made clear, tumor metabolism is still only partly understood and the metabolic aspects of transformation constitute a major challenge for science. Nevertheless, cancer metabolism can be exploited to devise novel avenues for the rational treatment of this disease. Copyright (C) 2011 S. Karger AG, BaselFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Fed ABC UFABC, CCNH, Santo Andre, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Ciencias Biol, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Bioquim, São Paulo, BrazilUniv Fed Sao Carlos UFSCar, DFQM, Sorocaba, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Ciencias Biol, São Paulo, BrazilUniversidade Federal de São Paulo UNIFESP, Dept Bioquim, São Paulo, BrazilFAPESP: 10/16050-9FAPESP: 10/11475-1FAPESP: 08/51116-0Web of Scienc

Gaia Early Data Release 3: Summary of the contents and survey properties

ABSTRACT: Context. We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities from Gaia DR2. Aims. A summary of the contents of Gaia EDR3 is presented, accompanied by a discussion on the differences with respect to Gaia DR2 and an overview of the main limitations which are present in the survey. Recommendations are made on the responsible use of Gaia EDR3 results. Methods. The raw data collected with the Gaia instruments during the first 34 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium and turned into this early third data release, which represents a major advance with respect to Gaia DR2 in terms of astrometric and photometric precision, accuracy, and homogeneity. Results. Gaia EDR3 contains celestial positions and the apparent brightness in G for approximately 1.8 billion sources. For 1.5 billion of those sources, parallaxes, proper motions, and the (GBP ? GRP) colour are also available. The passbands for G, GBP, and GRP are provided as part of the release. For ease of use, the 7 million radial velocities from Gaia DR2 are included in this release, after the removal of a small number of spurious values. New radial velocities will appear as part of Gaia DR3. Finally, Gaia EDR3 represents an updated materialisation of the celestial reference frame (CRF) in the optical, the Gaia-CRF3, which is based solely on extragalactic sources. The creation of the source list for Gaia EDR3 includes enhancements that make it more robust with respect to high proper motion stars, and the disturbing effects of spurious and partially resolved sources. The source list is largely the same as that for Gaia DR2, but it does feature new sources and there are some notable changes. The source list will not change for Gaia DR3. Conclusions. Gaia EDR3 represents a significant advance over Gaia DR2, with parallax precisions increased by 30 per cent, proper motion precisions increased by a factor of 2, and the systematic errors in the astrometry suppressed by 30-40% for the parallaxes and by a factor ~2.5 for the proper motions. The photometry also features increased precision, but above all much better homogeneity across colour, magnitude, and celestial position. A single passband for G, GBP, and GRP is valid over the entire magnitude and colour range, with no systematics above the 1% levelThe Gaia mission and data processing have financially been supported by ; the Spanish Ministry of Economy (MINECO/FEDER, UE) through grants ESP2016-80079-C2-1-R, ESP2016-80079-C2-2-R, RTI2018-095076-B-C21, RTI2018-095076-B-C22, BES-2016-078499, and BES-2017-083126 and the Juan de la Cierva formación 2015 grant FJCI-2015-2671, the Spanish Ministry of Education, Culture, and Sports through grant FPU16/03827, the Spanish Ministry of Science and Innovation (MICINN) through grant AYA2017-89841P for project “Estudio de las propiedades de los fósiles estelares en el entorno del Grupo Local” and through grant TIN2015-65316-P for project “Computación de Altas Prestaciones VII