Table 2.

Results from ten-temperature melting analysis of hENT1 variants.

Module	Variant	$T_m$ (${}^{\circ }$C)${}^{\mathrm{a}}$	$T_m$ error (${\pm }^{\circ }$C)${}^{\mathrm{b}}$	$\mathrm{\Delta }{T}_m$ (${}^{\circ }$C)	$\mathrm{\Delta }{T}_m$ error (${\pm }^{\circ }$C)${}^{\mathrm{c}}$	Repeats (n)	Status
n/a	Wild-type	44.6	0.7	0.0	0.9	12	n/a
Deep-sequence	I380V	42.0	0.1	− 2.5	0.6	2	Destabilisng
Deep-sequence	M306T	41.3	0.1	0.4	1.8	2	Neutral
Deep-sequence	G225V	42.1	0.1	− 1.0	0.9	2	Destabilisng
Deep-sequence	S321T	44.9	1.7	− 2.5	0.6	2	Destabilisng
Model-based	R233L	37.1	1.6	− 3.8	0.6	2	Destabilisng
Model-based	S152L	42.7	1.3	− 1.9	1.4	5	Destabilisng
Model-based	E247M	45.8	0.6	1.2	0.8	4	Stabilising
Model-based	N30F	46.8	0.4	2.2	0.8	5	Stabilising
Model-based	L27E	45.5	0.7	0.9	0.9	5	Stabilising
Data-driven	A401L	46.3	0.7	1.7	1.0	2	Stabilising
Data-driven	T336A	43.9	0.7	− 0.7	0.9	5	Neutral
Data-driven	Q246A	47.6	1.1	0.2	0.9	5	Neutral
Data-driven	F153A	40.8	0.1	− 7.5	1.7	2	Destabilisng
Data-driven	G207A	43.6	0.7	− 3.3	0.6	2	Destabilisng
Data-driven	A88L	45.2	0.7	0.8	0.9	5	Neutral
Data-driven	K263A	47.2	1.1	2.6	1.3	5	Stabilising

${}^{\mathrm{a}}$Average $T_m$ was calculated from individual $T_m$ estimated for each individual repeat by fitting with a four-parameter dose-response curve (variable slope) by non-linear least-squares fitting in the python package scipy.stats.

${}^{\mathrm{b}}$ For n > 1: standard error of the mean (SEM) shown.

${}^{\mathrm{c}}$ For n > 1: error calculated and propagated as detailed in “Methods and materials” section.