Modulation of P2X4 pore closure by magnesium, potassium, and ATP

Immadisetty, Kalyan; Alencicks, Josh; Kekenes‐Huskey, Peter M.

doi:10.1101/2021.05.16.444323

Cited by 3 publications

(2 citation statements)

References 59 publications

(89 reference statements)

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“…MD is a powerful computational tool that is used, among other things, to study various therapeutically important targets at an atomic level [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] . Several MD studies using coarse-grained MD [41] , [42] , [43] or biased MD [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , have been conducted thus far for studying the activation of wild-type (WT) MscL.…”

Section: Introductionmentioning

confidence: 99%

Elucidating the molecular basis of spontaneous activation in an engineered mechanosensitive channel

Immadisetty

Polasa

Shelton

et al. 2022

Computational and Structural Biotechnology Journal

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Section: Introductionmentioning

confidence: 99%

Elucidating the molecular basis of spontaneous activation in an engineered mechanosensitive channel

Immadisetty

Polasa

Shelton

et al. 2022

Computational and Structural Biotechnology Journal

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“…MD simulations [24] have facilitated detailed analyses of Ca 2+ binding in intact proteins. More recent applications include refinement of force field parameters for Ca 2+ [25, 26], Ca 2+ binding energetics in EF-hand containing proteins [27, 28, 29, 30], effects of Ca 2+ binding on protein function [31, 32, 33], mutations that disrupt the binding of Ca 2+ [34, 30] and differential modulation of purinergic receptorsn by Mg 2+ and potassium (K + ) [35].…”

Section: Introductionmentioning

confidence: 99%

Importance of AB domain in parvalbumins’ calcium binding affinity

Immadisetty¹,

Jacob-Dolan²

2022

Preprint

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1AbstractMembers of the parvalbumin (PV) family of calcium binding proteins are found in a variety of vertebrates, where can they influence neural functions, muscle contraction and immune responses. It was reported that the α-parvalbumin (αPV)s AB domain comprising two α-helices, dramatically increases the proteins calcium (Ca2+) affinity by ≈10 kcal/mol. To understand the structural basis of this effect, we conducted all-atom molecular dynamics (MD) simulations of WT αPV and truncated α-parvalbumin (ΔαPV) constructs. Additionally, we also examined the binding of magnesium (Mg2+) to these isoforms, which is much weaker than Ca2+ (Mg2+ actually does not bind to the ΔαPV). Our key finding is that ‘reorganization energies (RE)’ assessed using molecular mechanics generalized Born approximation (MM/GBSA) correctly rank-order the variants according to their published Ca2+ and Mg2+ affinities. The of the ΔαPV compared to the wild-type (WT) is 415.57±0.55 kcal/mol, indicating that forming a holo state of ΔαPV in the presence of Ca2+ incurs a greater reorganization penalty than the WT. This is consistent with the ΔαPV exhibiting lesser Ca2+ affinity than the WT (≈9.5 kcal/mol). Similar trend was observed for Mg2+ bound variants as well. Further, we screened for metrics such as oxygen coordination of EF hand residues with ions and found that the total oxygen coordination number (16 vs. 12 in WT:Ca2+ and ΔαPV:Ca2+) correlate with the reported ion affinities (−22 vs. −12.6 kcal/mol in WT:Ca2+ and ΔαPV:Ca2+), which indicates that AB domain is required for the protein to coordinate with maximal efficiency with the binding ions. To our surprise, no significant differences were observed between the Mg2+ bound WT and ΔαPV isoforms. Additionally, we have screened for factors such as total number of waters, hydrogen bonds, protein helicity and β-content for the entire protein, which enables us to understand the impact of lack of AB domain on the entire structure and not just binding sites. Our data indicate that AB improves the overall helicity (≈5%) in apo as well as holo forms. Particularly, AB increases α-helicity in the D-helix residues (i.e., 60–65) upon ion binding by ≈35% (90% vs. 55% in the Ca2+ bound WT and ΔαPV, 60% vs. 20% in the Mg2+ bound WT and ΔαPV), which likely contributes to high Ca2+ binding affinity. On the contrary, no significant effect on the overall β-content was observed. Similarly, increased dehydration (≈50) and increase in total number of hydrogen bonds (≈7) were observed upon ion binding in both the WT and ΔαPV systems, however, no significant differences were observed between the WT and ΔαPV variants and also between Ca2+ and Mg2+ isoforms. We speculate that this is due to the partially folded apo state that was captured in our MD simulations, which might not be physiologically relevant as suggested by NMR experiments [1]. Also, we have identified seven different interactions that might play a key role in binding the AB domain with the CDEF helices, particularly the D22(AB)–S78(CDEF) hydrogen bond. Overall, this study indicates that local (i.e., the EF hands) as well as global factors play a role in improved ion binding due to AB domain.

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Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

Immadisetty¹,

Kekenes‐Huskey²

2021

Preprint

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1AbstractLong QT syndrome (LQTS) is a cardiac condition characterized by a prolonged QT-interval. This prolongation can contribute to fatal arrhythmias despite otherwise normal metrics of cardiac function. Hence, genetic screening of individuals remains a standard for initial identification of individuals susceptible to LQTS. Several genes, including KCNH2 that encodes for the Kv11.1 channel are known to cause LQTS2, however, only a small subset of variants found in the human population are established as pathogenic. Hence, the majority of its missense mutations are known to be benign or are variant of unknown significance (VUS). Here we use molecular dynamics (MD) simulations and machine learning (ML) to determine the propensity for Kv11.1 channel variants to present loss-of-function behavior. Specifically, we use these computational techniques to correlate structural and dynamic changes in an important Kv11.1 subdomain, the PAS-domain (PASD), with the channel’s ability to traffic normally to the plasma membrane. With these techniques, we have identified several molecular features, namely, number of hydrating waters and intra-PASD hydrogen bonds, as moderately predictive of trafficking. Together with bioinformatics data including sequence conservation and folding energies, we are able to predict with reasonable accuracy (≈75%) the ability of VUS’ to traffic. Additionally, we compared two ML algorithms i.e., Decision tree (DT)s and Random forest (RF) for their robustness, and we report that RF performed moderately better than DTs’. Features derived from MD trajectories particularly help improve the prediction of trafficking deficient variants by both ML techniques.

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Modulation of P2X4 pore closure by magnesium, potassium, and ATP

Cited by 3 publications

References 59 publications

Elucidating the molecular basis of spontaneous activation in an engineered mechanosensitive channel

Elucidating the molecular basis of spontaneous activation in an engineered mechanosensitive channel

Importance of AB domain in parvalbumins’ calcium binding affinity

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

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