Table 1 Results from ten-temperature melting analysis of ClPPase variants.

From: IMPROvER: the Integral Membrane Protein Stability Selector

Module

Variant

\(T_m\) (\(^{\circ }\)C)\(^{\mathrm{a}}\)

\(T_m\) error (\(\pm ^{\circ }\)C)\(^{\mathrm{b}}\)

\(\Delta T_m\) (\(^{\circ }\)C)

\(\Delta T_m\) error (\(\pm ^{\circ }\)C)\(^{\mathrm{c}}\)

Repeats (n)

Status

n/a

Wild-type

49.3

0.9

0.0

1.2

8

n/a

Deep-sequence

S113A

53.6

2.8

4.3

3.0

3

Stabilising

Deep-sequence

S273V

48.8

− 0.5

1

Neutral

Deep-sequence

G130S

57.4

8.1

1

Stabilising

Deep-sequence

S371K

50.6

1.3

1

Neutral

Deep-sequence

F20Y

58.4

1.2

9.1

1.5

3

Stabilising

Model-based

V81W

50.5

1.2

1

Stabilising

Model-based

R463W

51.1

1.3

1.8

1.6

3

Neutral

Model-based

L142P

44.0

0.5

− 5.3

1.0

3

Destabilising

Model-based

L151I

49.7

1.2

0.4

1.5

2

Neutral

Model-based

V693Y

51.4

2.1

1

Stabilising

Model-based

R109W

52.7

1.7

3.4

1.9

3

Stabilising

Model-based

R290F

50.5

1.2

3

Stabilising

Model-based

D468F

52.0

0.6

2.7

1.1

1

Stabilising

Model-based

I501L

50.6

1.3

1

Neutral

Model-based

G179A

52.9

1.5

3.6

1.7

3

Stabilising

Data-driven

A492L

47.7

− 1.6

1

Destabilising

Data-driven

A14L

48.4

− 0.9

1

Neutral

Data-driven

G31A

51.0

1.7

1

Stabilising

Data-driven

A319L

51.5

2.2

1

Stabilising

Data-driven

G130A

62.3

1.1

13.0

1.4

3

Stabilising

Data-driven

A114L

50.2

0.9

1

Neutral

Combined

G130A+F20Y

59.6

1.8

10.3

2.0

3

Stabilising

  1. \(^{\mathrm{a}}\)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.
  2. \(^{\mathrm{b}}\)For n > 1: standard error of the mean (SEM) shown.
  3. \(^{\mathrm{c}}\)For n > 1: error calculated and propagated as detailed in “Methods and materials” section.