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Record W2080286416 · doi:10.1017/s0033583501003729

Simulation approaches to ion channel structure–function relationships

2001· review· en· W2080286416 on OpenAlexaff
D. Peter Tieleman, Philip C. Biggin, Graham R. Smith, Mark S.P. Sansom

Bibliographic record

VenueQuarterly Reviews of Biophysics · 2001
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKcsA potassium channelHelix bundleMolecular dynamicsIon channelChemistryIonNicotinic acetylcholine receptorSolvationHelix (gastropod)CrystallographyNicotinic agonistComputational chemistryProtein structureReceptor

Abstract

fetched live from OpenAlex

1. Introduction 475 1.1 Ion channels 475 1.1.1 Gramicidin 476 1.1.2 Helix bundle channels 477 1.1.3 K channels 480 1.1.4 Porins 483 1.1.5 Nicotinic acetylcholine receptor 483 1.1.6 Physiological properties 483 1.2 Simulations 484 1.2.1 Atomistic versus mean-field simulations 484 2. Atomistic simulations 485 2.1 Modelling of ion-interaction parameters 485 2.1.1 Interatomic distances and the problem of ionic radii 486 2.1.2 Solvation energy 487 2.1.3 Hydration shells and coordination numbers 489 2.1.4 Parameters in common use and transferability 491 2.1.5 Summary 491 2.2 Water in pores versus bulk 491 2.2.1 Simple pore models 494 2.2.2 gA 495 2.2.3 Alm 496 2.2.4 LS36 (and LS24) 496 2.2.5 Nicotinic receptor M2δ5 497 2.2.6 Influenza A M2 497 2.2.7 K channels 497 2.2.8 nAChR 498 2.2.9 Porins 498 2.2.10 Relevance 499 2.2.11 Problems with simulations 501 2.3 Dynamics of ions in pores 503 2.3.1 Simple pore models 503 2.3.2 Helix bundles 504 2.3.3 gA and KcsA 505 2.4 Energetics of permeation and ion selectivity 509 2.4.1 Potential and free energy profiles 509 2.4.2 gA 510 2.4.3 α-Helix bundles 511 2.4.4 KcsA 512 2.4.5 Ion selectivity 514 2.4.6 Problems of estimating energetic profiles 515 2.5 Conformational changes 516 2.5.1 gA 516 2.5.2 Alm and LS3 516 2.5.3 KcsA 517 2.6 Protonation states 523 3. Coarse-grained simulations 524 3.1 Introduction 524 3.1.1 Predicting conductance magnitudes 525 3.2 Electro-diffusion: the Nernst–Planck approach 526 3.2.1 Calculating the potential profile from Poisson and PB theory 528 3.2.2 Calculating the potential profile from BD simulations 530 3.2.3 Combining Nernst–Planck and Poisson: PNP 530 3.3 Beyond PNP 532 3.4 BD simulations 532 3.4.1 Basic theory in ion channels 532 3.4.2 Incorporating the environment 533 3.5 Applications 535 3.5.1 Model systems 535 3.5.1.1 Solving the Poisson and PB equation for channel-like geometries 535 3.5.1.2 Comparing PB, PNP and BD 536 3.5.2 Applications to known structures 537 3.5.2.1 gA 537 3.5.2.2 Porin 539 3.5.2.3 LS3 540 3.5.2.4 Alm 542 3.5.2.5 nAChR 542 3.5.2.6 KcsA 543 3.6 pKa calculations 543 3.7 Selectivity 544 3.7.1 Anion/cation selectivity 545 3.7.2 Monovalent/divalent ion selectivity 545 4. Problems 546 4.1 Atomistic simulations 546 4.1.1 Problems 546 4.1.2 Parameters 548 4.2 BD 549 4.3 Mean-field simulations 549 5. Conclusions 550 5.1 Progress 550 5.2 The future 550 6. Acknowledgements 551 7. References 551 Ion channels are proteins that form ‘holes’ in membranes through which selected ions move passively down their electrochemical gradients. The ions move quickly, at (nearly) diffusion limited rates (ca. 107 ions s−1 per channel). Ion channels are central to many properties of cell membranes. Traditionally they have been the concern of neuroscientists, as they control the electrical properties of the membranes of excitable cells (neurones, muscle; Hille, 1992). However, it is evident that ion channels are present in many types of cell, not all of which are electrically excitable, from diverse organisms, including plants, bacteria and viruses (where they are involved in functions such as cell homeostasis) in addition to animals. Thus ion channels are of general cell biological importance. They are also of biomedical interest, as several dizeases (‘channelopathies’) have been described which are caused by changes in properties of a specific ion channel (Ashcroft, 2000). Moreover, passive diffusion channels for substances other than ions are common (porins, aquaporins), as are active membrane transport processes coupled to ion gradients or ATP hydrolysis. An understanding of ion channels may also provide a gateway to understanding these processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0150.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.184
GPT teacher head0.312
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations199
Published2001
Admission routes1
Has abstractyes

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