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Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Mechanistic and structural studies of membrane proteins require their stabilization in specific conformations. Single domain antibodies are potent reagents for this purpose, but their generation relies on immunizations, which impedes selections in the presence of ligands typically needed to populate defined conformational states. To overcome this key limitation, we developed an in vitro selection platform based on synthetic single domain antibodies named sybodies. To target the limited hydrophilic surfaces of membrane proteins, we designed three sybody libraries that exhibit different shapes and moderate hydrophobicity of the randomized surface. A robust binder selection cascade combining ribosome and phage display enabled the generation of conformation-selective, high affinity sybodies against an ABC transporter and two previously intractable human SLC transporters, GlyT1 and ENT1. The platform does not require access to animal facilities and builds exclusively on commercially available reagents, thus enabling every lab to rapidly generate binders against challenging membrane proteins.