PREDICTION OF THE THREE-DIMENSIONAL STRUCTURE OF THE ENZYMATIC PART OF T-PA

1987 ◽  
Author(s):  
A Heckel ◽  
K M Hasselbach

Up to now the three-dimensional structure of t-PA or parts of this enzyme is unknown. Using computer graphical methods the spatial structure of the enzymatic part of t-PA is predicted on the hypothesis, the three-dimensional backbone structure of t-PA being similar to that of other serine proteases. The t-PA model was built up in three steps:1) Alignment of the t-PA sequence with other serine proteases. Comparison of enzyme structures available from Brookhaven Protein Data Bank proved elastase as a basis for modeling.2) Exchange of amino acids of elastase differing from the t-PA sequence. The replacement of amino acids was performed such that backbone atoms overlapp completely and side chains superpose as far as possible.3) Modeling of insertions and deletions. To determine the spatial arrangement of insertions and deletions parts of related enzymes such as chymotrypsin or trypsin were used whenever possible. Otherwise additional amino acid sequences were folded to a B-turn at the surface of the proteine, where all insertions or deletions are located. Finally the side chain torsion angles of amino acids were optimised to prevent close contacts of neigh bouring atoms and to improve hydrogen bonds and salt bridges.The resulting model was used to explain binding of arginine 560 of plasminogen to the active site of t-PA. Arginine 560 interacts with Asp 189, Gly 19 3, Ser 19 5 and Ser 214 of t-PA (chymotrypsin numbering). Furthermore interaction of chromo-genic substrate S 2288 with the active site of t-PA was studied. The need for D-configuration of the hydrophobic amino acid at the N-terminus of this tripeptide derivative could be easily explained.

2010 ◽  
Vol 426 (3) ◽  
pp. 337-344 ◽  
Author(s):  
Sayaka Kitamura ◽  
Kosuke Fujishima ◽  
Asako Sato ◽  
Daisuke Tsuchiya ◽  
Masaru Tomita ◽  
...  

RNase H (ribonuclease H) is an endonuclease that cleaves the RNA strand of RNA–DNA duplexes. It has been reported that the three-dimensional structure of RNase H is similar to that of the PIWI domain of the Pyrococcus furiosus Ago (argonaute) protein, although the two enzymes share almost no similarity in their amino acid sequences. Eukaryotic Ago proteins are key components of the RNA-induced silencing complex and are involved in microRNA or siRNA (small interfering RNA) recognition. In contrast, prokaryotic Ago proteins show greater affinity for RNA–DNA hybrids than for RNA–RNA hybrids. Interestingly, we found that wild-type Pf-RNase HII (P. furiosus, RNase HII) digests RNA–RNA duplexes in the presence of Mn2+ ions. To characterize the substrate specificity of Pf-RNase HII, we aligned the amino acid sequences of Pf-RNase HII and Pf-Ago, based on their protein secondary structures. We found that one of the conserved secondary structural regions (the fourth β-sheet and the fifth α-helix of Pf-RNase HII) contains family-specific amino acid residues. Using a series of Pf-RNase HII–Pf-Ago chimaeric mutants of the region, we discovered that residues Asp110, Arg113 and Phe114 are responsible for the dsRNA (double-stranded RNA) digestion activity of Pf-RNase HII. On the basis of the reported three-dimensional structure of Ph-RNase HII from Pyrococcus horikoshii, we built a three-dimensional structural model of RNase HII complexed with its substrate, which suggests that these amino acids are located in the region that discriminates DNA from RNA in the non-substrate strand of the duplexes.


Functional studies on interferon would be helped by a three-dimensional structure for the molecule. However, it may be several years before the structure of the protein is determined by X-ray crystallography. We have therefore used available methods for predicting the secondary - and the tertiary - structure of a protein from its amino acid sequence to propose a tertiary model involving the packing of four a-helices. Details of this work have been published elsewhere (Sternberg & Cohen 1982).


2019 ◽  
Author(s):  
Kai Shimagaki ◽  
Martin Weigt

Statistical models for families of evolutionary related proteins have recently gained interest: in particular pairwise Potts models, as those inferred by the Direct-Coupling Analysis, have been able to extract information about the three-dimensional structure of folded proteins, and about the effect of amino-acid substitutions in proteins. These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in a protein family, i.e. to capture the so-called residue conservation and covariation statistics of proteins of common evolutionary origin. Pairwise Potts models are the maximum-entropy models achieving this. While being successful, these models depend on huge numbers of ad hoc introduced parameters, which have to be estimated from finite amount of data and whose biophysical interpretation remains unclear. Here we propose an approach to parameter reduction, which is based on selecting collective sequence motifs. It naturally leads to the formulation of statistical sequence models in terms of Hopfield-Potts models. These models can be accurately inferred using a mapping to restricted Boltzmann machines and persistent contrastive divergence. We show that, when applied to protein data, even 20-40 patterns are sufficient to obtain statistically close-to-generative models. The Hopfield patterns form interpretable sequence motifs and may be used to clusterize amino-acid sequences into functional sub-families. However, the distributed collective nature of these motifs intrinsically limits the ability of Hopfield-Potts models in predicting contact maps, showing the necessity of developing models going beyond the Hopfield-Potts models discussed here.


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