An amino acid property-based method for identifying solenoid proteins

2020 ◽  
Vol 24 (3) ◽  
pp. 269
Author(s):  
Arunachalam Jothi ◽  
Senthilnathan Rajendran
2007 ◽  
Vol 23 (20) ◽  
pp. 2795-2796 ◽  
Author(s):  
M. Ganapathiraju ◽  
C. J. Jursa ◽  
H. A. Karimi ◽  
J. Klein-Seetharaman

2009 ◽  
Vol 30 (1) ◽  
pp. 33-39 ◽  
Author(s):  
Bing Niu ◽  
Lin Lu ◽  
Liang Liu ◽  
Tian Hong Gu ◽  
Kai-Yan Feng ◽  
...  

2008 ◽  
Vol 4 (9) ◽  
pp. e1000181 ◽  
Author(s):  
Kai Wang ◽  
Jeremy A. Horst ◽  
Gong Cheng ◽  
David C. Nickle ◽  
Ram Samudrala

Author(s):  
David Cavanaugh ◽  
Krishnan Chittur

In a previous paper we have introduced a new hydrophobicity proclivity scale and justified its superior performance characteristics, particularly in the context of a scale for protein alignments, but also for its strong correlation with many other amino-acid physico-chemical properties. Within that paper, we calculated a corrected free energy of residue burial of each amino-acid in folded proteins from a linear regression of amino-acid free energy of transfer from water to n-Octanol (F&P octanol scale dGow, Y axis) and our Hydrophobicity Proclivity Scale<br>(HPS, X axis). In this present paper we pursue the latter general findings in more detail by considering the relationship of hydrophobicity and other physico-amino-<br>acid scales with the molecular geometry of amino-acids and secondary group structure/surface chemistry, with a concommitant discussion of the dimensions/geometry<br>of the caveties that amino-acids make in water. We identify a series of molecular physico-chemical properties that uniquely define the natural selection and geometry of the 20 natural amino-acids. We use the corrected free energy of amino-acid burials in proteins (Y axis) and a multiple linear regression to identify the AA molecular physico-chemical properties (X1, X2, ...) that explain the energetics of amino-<br>acid water contacts in an unfolded protein state to that of the folded protein state by modeling these two states as a solvent-solvent transfer, thus, providing a thermodynamical model for the initial stages of protein folding. Between our previous paper and the current paper we can greatly simplify and reduce the very large number of amino-acid scales in the literature to a small number of amino-acid property scales. Finally, we explore the numerical relationship between the structure of the genetic code and molecular physico-chemical properties of AA’s that in turn can be related directly to hydrophobicity. We validate and explain our novel models we describe herein with extensive data from the literature.<br>


2020 ◽  
Author(s):  
David Cavanaugh ◽  
Krishnan Chittur

In a previous paper we have introduced a new hydrophobicity proclivity scale and justified its superior performance characteristics, particularly in the context of a scale for protein alignments, but also for its strong correlation with many other amino-acid physico-chemical properties. Within that paper, we calculated a corrected free energy of residue burial of each amino-acid in folded proteins from a linear regression of amino-acid free energy of transfer from water to n-Octanol (F&P octanol scale dGow, Y axis) and our Hydrophobicity Proclivity Scale<br>(HPS, X axis). In this present paper we pursue the latter general findings in more detail by considering the relationship of hydrophobicity and other physico-amino-<br>acid scales with the molecular geometry of amino-acids and secondary group structure/surface chemistry, with a concommitant discussion of the dimensions/geometry<br>of the caveties that amino-acids make in water. We identify a series of molecular physico-chemical properties that uniquely define the natural selection and geometry of the 20 natural amino-acids. We use the corrected free energy of amino-acid burials in proteins (Y axis) and a multiple linear regression to identify the AA molecular physico-chemical properties (X1, X2, ...) that explain the energetics of amino-<br>acid water contacts in an unfolded protein state to that of the folded protein state by modeling these two states as a solvent-solvent transfer, thus, providing a thermodynamical model for the initial stages of protein folding. Between our previous paper and the current paper we can greatly simplify and reduce the very large number of amino-acid scales in the literature to a small number of amino-acid property scales. Finally, we explore the numerical relationship between the structure of the genetic code and molecular physico-chemical properties of AA’s that in turn can be related directly to hydrophobicity. We validate and explain our novel models we describe herein with extensive data from the literature.<br>


2021 ◽  
Author(s):  
Massimo Di Giulio ◽  
Franco Caldararo

We used the Moran's I index of global spatial autocorrelation with the aim of studying the distribution of the physicochemical or biological properties of amino acids within the genetic code table. First, using this index we are able to identify the amino acid property - among the 530 analyzed - that best correlates with the organization of the genetic code in the set of amino acid permutation codes. Considering, then, a model suggested by the coevolution theory of the genetic code origin - which in addition to the biosynthetic relationships between amino acids took into account also their physicochemical properties - we investigated the level of optimization achieved by these properties either on the entire genetic code table, or only on its columns or only on its rows. Specifically, we estimated the optimization achieved in the restricted set of amino acid permutation codes subject to the constraints derived from the biosynthetic classes of amino acids, in which we identify the most optimized amino acid property among all those present in the database. Unlike what has been claimed in the literature, it would appear that it was not the polarity of amino acids that structured the genetic code, but that it could have been their partition energy instead. In actual fact, it would seem to reach an optimization level of about 96% on the whole table of the genetic code and 98% on its columns. Given that this result has been obtained for amino acid permutation codes subject to biosynthetic constraints, that is to say, for a model of the genetic code consistent with the coevolution theory, we should consider the following conclusions reasonable. (i) The coevolution theory might be corroborated by these observations because the model used referred to the biosynthetic relationships between amino acids, which are suggested by this theory as having been fundamental in structuring the genetic code. (ii) The very high optimization on the columns of the genetic code would not only be compatible but would further corroborate the coevolution theory because this suggests that, as the genetic code was structured along its rows by the biosynthetic relationships of amino acids, on its columns strong selective pressure might have been put in place to minimize, for example, the deleterious effects of translation errors. (iii) The finding that partition energy could be the most optimized property of amino acids in the genetic code would in turn be consistent with one of the main predictions of the coevolution theory. In other words, since the partition energy is reflective of the protein structure and therefore of the enzymatic catalysis, the latter might really have been the main selective pressure that would have promoted the origin of the genetic code. Indeed, we observe that the beta-strands show an optimization percentage of 94.45%, so it is possible to hypothesize that they might have become the object of selection during the origin of the genetic code, conditioning the choice of biosynthetic relationships between amino acids. (iv) The finding that the polarity of amino acids is less optimized than their partition energy in the genetic code table might be interpreted against the physicochemical theories of the origin of the genetic code because these would suggest, for example, that a very high optimization of the polarity of amino acids in the code could be an expression of interactions between amino acids and codons or anticodons, which would have promoted their origin. This might now become less sustainable, given the very high optimization that is instead observed in favor of partition energy but not polarity. Finally, (v) the very high optimization of the partition energy of amino acids would seem to make a neutral origin of the ability of the genetic code to buffer, for example, the deleterious effects of translation errors very unlikely. Indeed, an optimization of about 100% would seem that it might not have been achieved by a simple neutral process, but this ability should probably have been generated instead by the intervention of natural selection. In actual fact, we show that the neutral hypothesis of the origin of error minimization has been falsified for the model analyzed here. Therefore, we will discuss our observations within the theories proposed to explain the origin of the organization of the genetic code, reaching the conclusion that the coevolution theory is the most strongly corroborated theory.


Author(s):  
M.K. Lamvik ◽  
L.L. Klatt

Tropomyosin paracrystals have been used extensively as test specimens and magnification standards due to their clear periodic banding patterns. The paracrystal type discovered by Ohtsuki1 has been of particular interest as a test of unstained specimens because of alternating bands that differ by 50% in mass thickness. While producing specimens of this type, we came across a new paracrystal form. Since this new form displays aligned tropomyosin molecules without the overlaps that are characteristic of the Ohtsuki-type paracrystal, it presents a staining pattern that corresponds to the amino acid sequence of the molecule.


Sign in / Sign up

Export Citation Format

Share Document