• 2018-07
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • VMD v Humphrey et al was used to


    VMD v1.9.2 (Humphrey et al., 1996) was used to analyze the MD trajectories, salt bridge interactions and secondary structure information from each frame. Once the model was optimized, the topological diagram and area and volume of protein cavities were analyzed using PDBsum (Laskowski, 2009) and CASTp (Dundas et al., 2006) respectively. The model quality was assessed with several web services: Procheck (Laskowski et al., 1993), QMEAN (Benkert et al., 2008), ProQ (Wallner and Elofsson, 2003), ProSA-Web (Wiederstein and Sippl, 2007) and VERIFY 3D (Eisenberg et al., 1997). The visualization and comparison of the models were performed using Discovery Studio Client 4.1 freeware (Accelrys) and Swiss PDB Viewer (SPDBV) (Kaplan and Littlejohn, 2001) package programs.
    Results and discussion
    Conclusion The structural features of Sm32 suggest a caspase-like fold with a minimal deviation from the crystal structure of the enzymes of M. musculus and human. However, significant differences were found. The volume of active-site in the catalytic domain is bigger than that observed in mouse and human. Additionally, some conformational changes are observed in the amino cellular metabolism definition residues of the catalytic dyad and amino acid mutations in the βIV strand. The electrostatic potential surface reveals a different charge distribution of the core and in the prodomain of the Sm32 model, features that may produce important physicochemical changes. The activation peptide showed significant changes in the amino acid composition between human, mouse and S. mansoni. A synthetic peptide of this region was previously found highly immunogenic by our group. These differences could be exploited, together with recently developed complementary bioinformatic tools, for the development of diagnostic methods, prevention and treatment of schistosomiasis.
    Acknowledgments This research was supported by grant 2012001644 from Fondo Nacional para la Ciencia, Tecnología e Innovación (FONACIT), Venezuela. The authors thank Dr. Johan Hoebeke, of the French National Centre for Scientific Research, CNRS·UPR9021, Immunologie Chimie Thérapeutiques, Strasbourg, France, for his critical comments on the paper and Andrés Picón for his kind review of the manuscript.
    Introduction Adipose is a complex tissue with an important role in energy homeostasis, endocrine function, and the regulation of immune response. It has a high degree of plasticity and is capable of expanding, contracting, and remodeling to meet a wide range of metabolic challenges [1]. Adipose tissue is primarily composed of adipocytes and adipocyte progenitors but also contains perivascular cells, endothelial cells, and myriad immune cells. Adipocytes are the main adipose constituent by mass and serve the primary role for energy storage and endocrine function. Adipocyte progenitor cells, including mesenchymal stem cells and committed preadipocytes, represent a small mass of tissue but can comprise as much as 50% of adipose tissue by cell number and are the primary source for new adipocytes [2]. Adipose tissue can be divided into at least two metabolically-distinct types: brown adipose tissue (BAT) and white adipose tissue (WAT). BAT is primarily an energy expending tissue, whose human relevance has recently become an active area of research [3], [4]. White adipose tissue, in contrast, is primarily an energy storing tissue. White adipose tissue has been shown to have regional variation based on anatomical location in humans [5], [6]. Some of the most striking differences in WAT are observed between (abdominal) subcutaneous and visceral white adipose tissue. Accumulation of visceral adipose tissue, i.e. central obesity, has been associated with insulin resistance, metabolic syndrome, and diabetes mellitus [6], [7], [8]. By contrast, accumulation of subcutaneous adipose tissue has been associated with metabolically beneficial characteristics, including increased insulin sensitivity and decreased inflammation [7], [9]. White adipose tissue from other regions, such as gluteal adipose tissue, perirenal fat, and bone marrow, also have different properties, including differences in cytokine response and proliferation rates [10], [11]. This regional variation within white adipose tissue stresses the need to understand the underlying mechanisms accounting for differences in white adipose depots in order to develop targeted therapies for diabetes, lipodystrophy, and related metabolic complications.