China
Africa
Explore chapters and articles related to this topic
Infrared Spectroscopy
Published in Adorjan Aszalos, Modern Analysis of Antibiotics, 2020
Neocarzinostatin exhibited a strong bond at 1636 cm−1 and a weak bond at 1685 cm−1 due to amide I, in addition to weak and very weak bands at 1655 and 1671 cm−1. Comparison with known and calculated literature results support antiparallel β structure, α-Helix conformation should result in amide I abosprtion at ˜1646 cm−1. a peak at 1655 cm−1 may indicate random coil formation. This is supported by the spectrum of S-carboxymethylneocarzinostatin in which intrachain disulfide bands were broken, which should result in a more random conformation. This substance shows a slightly stronger absorption band at 1656 cm−1. This frequency had previously been assigned as a random coil conformation.
Preliminary Phytochemical Screening and Identification of Bioactive Compounds from Banana Inflorescence and to Find the Interactions on Molecular Docking for PCOS
Published in Parimelazhagan Thangaraj, Phytomedicine, 2020
M. C. Kamaraj, Suman Thamburaj, R. Akshaya, V. Bhanu Deepthi
The secondary structure of CYP17A (Table 9.3) was predicted using SOPMA and SOPM (Table 9.4). The protein was α helix with other structures, such as an extended stand, β turn, and random coil present in the comparative analysis of SOPMA and SOPM. From which it is clear, that the random coil is predominantly present when the structure was predicted both by SOPMA and SOPM, followed by an extended strand and alpha helix. So this protein is stable in nature.
The High Mobility Group (HMG) Proteins
Published in Lubomir S. Hnilica, Chromosomal Nonhistone Proteins, 2018
Mammalian tissues have been found to have two major proteins in this group, HMG14 and HMG17.5,6 Originally isolated from calf and pig thymus,10,12,69 these two small basic proteins have subsequently been shown to be present in many other calf tissues16 and to be present in rabbits, rats, and mice.33,34,68,70–72 The amino acid compositions and molecular weights of calf thymus HMG14 and 17 are given in Table 3. In addition to the high contents of lysine, aspartic acid, and glutamic acid they have high contents of glycine, alanine, and proline. They have few hydrophobic amino acids, which explains why these proteins do not give tertiary-folded structures in solution and in fact are completely random-coil proteins. Two similar proteins have been found in avian cells17,75 (and have been termed HMG14 and 17), and just recently a third, smaller protein, HMGY, has been described which has the same N-terminal amino acid (proline) and similar composition to the avian HMG14 and 1776 (In fact HMGY runs as a doublet on polyacrylamide electrophoretic gels.)
Self-assembling peptides-based nano-cargos for targeted chemotherapy and immunotherapy of tumors: recent developments, challenges, and future perspectives
Published in Drug Delivery, 2022
Xue-Jun Wang, Jian Cheng, Le-Yi Zhang, Jun-Gang Zhang
According to the Structural Classification of Proteins (SCOP) database, natural proteins now have 1393 distinct folds (Lo Conte et al., 2000). When it comes to the construction of artificial peptides, β-sheets and helices are among the most commonly used secondary structural elements. Another secondary structure, random coil, indicates that the peptide lacks any hydrogen-bonding driven intramolecular structure. In nature, elastin-like peptides (ELPs) are a significant class of these peptides that are made from tropoelastin, the natural elastin precursor. Five amino acids are found in ELPs (i.e. pentad) repeating unit of Val-Pro-Gly-X-Gly sequence, where X is a guest residue (other than Pro) affecting the physical features of the assemblies of peptide, including lower critical solution temperature (LCST) and flexibility. Thermally responsive hydrogels containing elastin-mimetic or ELPs have been synthesized for tissue engineering and stimuli-responsive gene delivery (Meco & Lampe, 2019; Shmidov et al., 2019).
Surveying over 100 predictors of intrinsic disorder in proteins
Published in Expert Review of Proteomics, 2021
Previous surveys define three distinct periods in the development of the disorder predictors [23,25]. We expand this timeline to four periods to recognize recent advances that resulted in the development of the deep learners. The first-generation methods were released between 1979 and 2001. Only a few methods were developed during that time. The first method, which was published in 1979 targets prediction of random coil conformations in protein sequences [98]. However, due to limited availability of data on IDRs at that time, this method could not be sufficiently tested at the time of publication. Later evaluations show that its predictive performance is relatively poor [25]. The first machine learning-based method was proposed by Romero, Obradovic, and Dunker in 1997 [99]. It uses a shallow neural network model that relies on physical and chemical properties of protein sequences. Interestingly, some of these early methods are still available online as webservers and/or standalone code (Table 1 and Supplementary Table S2).
Tracing protein and proteome history with chronologies and networks: folding recapitulates evolution
Published in Expert Review of Proteomics, 2021
Gustavo Caetano-Anollés, M. Fayez Aziz, Fizza Mughal, Derek Caetano-Anollés
Most proteins are both structured and intrinsically flexible. Their polypeptide chains fold into compact atomic three-dimensional (3D) arrangements that are organized around structural, functional and evolutionary modules. These modules are recurrently present in various molecular contexts. The human genome, for example, embodies ~1,500 distinct combinations of folded structures [5]. Conversely, a substantial number of proteins lack typical structure. They represent intrinsically disordered proteins, molecules that lack significant constraints on internal degrees of freedom of the polypeptide chain. Intrinsic disorder is also present in structured proteins in the form of intrinsically disordered regions [6]. These regions exhibit highly dynamic conformations that resemble either random-coils, molten globules or flexible linkers. Disordered regions are often evolutionarily conserved and needed for molecular recognition, regulation and signaling (e.g [7]). Significant surveys of protein modules and intrinsic disorder with advanced computational methods have for example generated protein taxonomies for the classification of the protein world. Similarly, the distribution of protein structure and intrinsic disorder in organisms provide the necessary tools to understand the origin and evolution of proteomes.