What is he best known for?
The fields of study he is best known for:
- Gene
- Amino acid
- Biochemistry
Steven M. Sine mainly focuses on Acetylcholine receptor, Receptor, Biophysics, CHRNE and Agonist.
His Acetylcholine receptor research is multidisciplinary, incorporating perspectives in Endocrinology, Acetylcholine and Protein subunit.
The Receptor study which covers Neuroscience that intersects with Signal transduction.
The concepts of his Biophysics study are interwoven with issues in Muscarinic acetylcholine receptor M5, Nicotinic agonist, Stereochemistry and Ion channel.
His studies in Stereochemistry integrate themes in fields like Binding site and Dissociation constant.
His CHRNE study integrates concerns from other disciplines, such as HEK 293 cells, Molecular biology, Cell biology and RAPSN.
His most cited work include:
- Data transformations for improved display and fitting of single-channel dwell time histograms (691 citations)
- Recent advances in Cys-loop receptor structure and function. (419 citations)
- Congenital myasthenic syndromes: pathogenesis, diagnosis, and treatment. (258 citations)
What are the main themes of his work throughout his whole career to date?
Steven M. Sine mostly deals with Acetylcholine receptor, Receptor, Biophysics, Protein subunit and Stereochemistry.
His biological study spans a wide range of topics, including Agonist, Mutation, Acetylcholine, Nicotinic agonist and Neuroscience.
His work on Dissociation constant, Transmembrane protein and Neurotransmitter binding as part of general Receptor study is frequently linked to Cysteine, bridging the gap between disciplines.
Steven M. Sine interconnects Biochemistry, Ion channel and Molecular dynamics in the investigation of issues within Biophysics.
His Protein subunit research includes themes of Mutant, Point mutation and Cell biology.
His Stereochemistry research is multidisciplinary, incorporating elements of Acetylcholine binding, Nicotinic acetylcholine receptor, Binding site and Ligand.
He most often published in these fields:
- Acetylcholine receptor (63.03%)
- Receptor (44.85%)
- Biophysics (44.85%)
What were the highlights of his more recent work (between 2012-2021)?
- Biophysics (44.85%)
- Acetylcholine receptor (63.03%)
- Receptor (44.85%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Biophysics, Acetylcholine receptor, Receptor, Nicotinic agonist and Protein subunit.
His Biophysics study combines topics from a wide range of disciplines, such as Agonist, Ethanol, Extracellular and Acetylcholine.
His Acetylcholine receptor research integrates issues from Gating, Mutant and Cholinergic.
His work in Receptor addresses issues such as Binding site, which are connected to fields such as Protein structure and Bungarotoxin.
The Nicotinic agonist study combines topics in areas such as Long-term potentiation and Patch clamp.
His Protein subunit research incorporates elements of Allosteric regulation and Cell biology.
Between 2012 and 2021, his most popular works were:
- Congenital myasthenic syndromes: pathogenesis, diagnosis, and treatment. (258 citations)
- Complex between α-bungarotoxin and an α7 nicotinic receptor ligand-binding domain chimaera. (53 citations)
- Stoichiometry for Activation of Neuronal α7 Nicotinic Receptors (49 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Amino acid
- Biochemistry
Acetylcholine receptor, Nicotinic agonist, Receptor, Biophysics and Agonist are his primary areas of study.
His Acetylcholine receptor research is multidisciplinary, incorporating elements of Protein subunit, Bioinformatics and Pathology.
His studies examine the connections between Protein subunit and genetics, as well as such issues in Protein structure, with regards to Binding site, Epibatidine and Plasma protein binding.
His studies in Nicotinic agonist integrate themes in fields like Patch clamp, Stereochemistry and Ion channel.
Steven M. Sine specializes in Receptor, namely Cys-loop receptors.
Particularly relevant to Gating is his body of work in Biophysics.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
