Sodium‐site in serine protease domain of human coagulation factor IXa: evidence from the crystal structure and molecular dynamics simulations study

Vadivel, Kanagasabai; Schreuder, Herman; Liesum, A.; Schmidt, Amy E.; Goldsmith, Gunaseelan; Bajaj, S. Paul

doi:10.1111/jth.14401

Cited by 16 publications

(13 citation statements)

References 69 publications

(169 reference statements)

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“…All modeling attempts resulted in a single structure, with C-scores > 1.4 and TM-scores > 0.9, so, according to I-TRASSER criteria, these are well-known and very reliable models [32]. The structure of the complex between the EGF domains and the catalytic domain was obtained by superposition of the modeled EGF-2 domains with that of the human EGF-2 domain in the most recent high resolution structure of a fragment of human F9 (PDB ID 6MV4 [33]. All models were inspected in VMD [34].…”

Section: Methodsmentioning

confidence: 99%

In silico analysis of missense mutations in exons 1–5 of the F9 gene that cause hemophilia B

et al. 2019

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Background Missense mutations in the first five exons of F9 , which encodes factor FIX, represent 40% of all mutations that cause hemophilia B. To address the ongoing debate regarding in silico identification of disease-causing mutations at these exons, we analyzed 215 missense mutations from www.factorix.org using six in silico prediction tools, which are the most common used programs for analysis prediction of impact of mutations on the protein structure and function, with further advantage of using similar approaches. We developed different algorithms to integrate multiple predictions from such tools. In order to approach a structural analysis on FIX we performed a modeling of five selected pathogenic mutations. Results SIFT, PolyPhen-2 HumDiv, SNAP2, and MutationAssessor were the most successful in identifying true non-causative and causative mutations. A proposed function integrating these algorithms ( wgP4 ) was the most sensitive (90.1%), specific (22.6%), and accurate (87%) than similar functions, and identified 187 variants as deleterious. Clinical phenotype was significantly associated with predicted causative mutations at all five exons. However, PolyPhen-2 HumDiv was more successful in linking clinical severity to specific exons, while functions that integrate 4–6 predictions were more successful in linking phenotype to genotypes at the light chain (exons 3–5). The most important value of integrating multiple predictions is the inclusion of scores derived from different approaches. Modeling of protein structure showed the effects of pathogenic nsSNPs on structure and function of FIX. Conclusions A simple function that integrates information from different in silico programs yields the best prediction of mutated phenotypes. However, the specificity, sensitivity, and accuracy of genotype-phenotype predictions depend on specific characteristics of the protein domain and the disease of interest as we validated by the structural analysis of selected pathogenic F9 mutations. The proposed function integrating algorithm ( wgP4 ) might be useful for the analysis of nsSNPs impact on other genes. Electronic supplementary material The online version of this article (10.1186/s12859-019-2919-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

In silico analysis of missense mutations in exons 1–5 of the F9 gene that cause hemophilia B

et al. 2019

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“…The Na + ion at this site in FXa is coordinated to the main-chain carbonyl O atoms of residues 184A, 185, 221A and 224 and two water molecules (Zhang & Tulinsky, 1997). Later, Schmidt et al (2002), Bajaj et al (2006) and Vadivel et al (2019) reported that Na + at a similar site in APC, FVIIa and FIXa, respectively, is coordinated to four carbonyl O atoms (Fig. 7b) and two water molecules.…”

Section: Discussionmentioning

confidence: 86%

“…In addition to a Ca 2+ -binding site, the protease domain also contains an Na + -binding site (Bajaj et al, 2006). Sequence similarity (Dang & Di Cera, 1996) predicts that the Na +binding site in FVIIa is similar to those in FXa (Zhang & Tulinsky, 1997), activated protein C (APC; Schmidt et al, 2002) and FIXa (Vadivel et al, 2019) but not to that in thrombin (Di Cera et al, 1995;Zhang & Tulinsky, 1997).…”

Section: Introductionmentioning

confidence: 99%

Structure of human factor VIIa–soluble tissue factor with calcium, magnesium and rubidium

Vadivel

Schmidt

Cascio

et al. 2021

Acta Crystallogr D Struct Biol

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Coagulation factor VIIa (FVIIa) consists of a γ-carboxyglutamic acid (GLA) domain, two epidermal growth factor-like (EGF) domains and a protease domain. FVIIa binds three Mg2+ ions and four Ca2+ ions in the GLA domain, one Ca2+ ion in the EGF1 domain and one Ca2+ ion in the protease domain. Further, FVIIa contains an Na+ site in the protease domain. Since Na+ and water share the same number of electrons, Na+ sites in proteins are difficult to distinguish from waters in X-ray structures. Here, to verify the Na+ site in FVIIa, the structure of the FVIIa–soluble tissue factor (TF) complex was solved at 1.8 Å resolution containing Mg2+, Ca2+ and Rb+ ions. In this structure, Rb+ replaced two Ca2+ sites in the GLA domain and occupied three non-metal sites in the protease domain. However, Rb+ was not detected at the expected Na+ site. In kinetic experiments, Na+ increased the amidolytic activity of FVIIa towards the synthetic substrate S-2288 (H-D-Ile-Pro-Arg-p-nitroanilide) by ∼20-fold; however, in the presence of Ca2+, Na+ had a negligible effect. Ca2+ increased the hydrolytic activity of FVIIa towards S-2288 by ∼60-fold in the absence of Na+ and by ∼82-fold in the presence of Na+. In molecular-dynamics simulations, Na+ stabilized the two Na+-binding loops (the 184-loop and 220-loop) and the TF-binding region spanning residues 163–180. Ca2+ stabilized the Ca2+-binding loop (the 70-loop) and Na+-binding loops but not the TF-binding region. Na+ and Ca2+ together stabilized both the Na+-binding and Ca2+-binding loops and the TF-binding region. Previously, Rb+ has been used to define the Na+ site in thrombin; however, it was unsuccessful in detecting the Na+ site in FVIIa. A conceivable explanation for this observation is provided.

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“…43 Together with the 180-loop CT , the 220-loop CT comprises a binding site for Na + , which is coordinated to four backbone carbonyl groups, including the one of Lys394{224 CT } substituted in our study. 15,44 We considered the possibility that disruption of the Hbond network might affect Na + -binding and as such could explain reduced FIXa activity. However, the amidolytic activity of neither wild-type FIXa, nor that of the three FIXa mutants displayed any appreciable Na + -dependence under the conditions tested (Figure S4).…”

Section: Discussionmentioning

confidence: 99%

Probing activation‐driven changes in coagulation factor IX by mass spectrometry

Freato

Alphen

Boon-Spijker

et al. 2021

Journal of Thrombosis and Haemostasis

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Sodium‐site in serine protease domain of human coagulation factor IXa: evidence from the crystal structure and molecular dynamics simulations study

Cited by 16 publications

References 69 publications

In silico analysis of missense mutations in exons 1–5 of the F9 gene that cause hemophilia B

In silico analysis of missense mutations in exons 1–5 of the F9 gene that cause hemophilia B

Structure of human factor VIIa–soluble tissue factor with calcium, magnesium and rubidium

Probing activation‐driven changes in coagulation factor IX by mass spectrometry

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