Protein stability by number: high-throughput and statistical approaches to one of protein science’s most difficult problems

By Thomas J. Magliery, Jason J. Lavinder, B. Sullivan. 2011

56 citations recorded by the source corpus.

Most proteins are only barely stable, which impedes research, complicates therapeutic applications, makes proteins susceptible to pathologically destabilizing mutations. Our ability to predict the thermodynamic consequences of even single point mutations is still surprisingly limited, and established methods of measuring stability are slow. Recent advances are bringing protein stability studies into the high-throughput realm. Some methods are based on inferential read-outs such as activity, proteolytic resistance or split-protein fragment reassembly. Other methods use miniaturization of direct measurements, such as intrinsic fluorescence, H/D exchange, cysteine reactivity, aggregation and hydrophobic dye binding (DSF). Protein engineering based on statistical analysis (consensus and correlated occurrences of amino acids) is promising, but much work remains to understand and implement these methods.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Consensus protein design (2016, background): “…the stabilities of functional and non-functional proteins, for example increasing melting temperatures by 10–32°C (Wirtz and Steipe, 1999; Lehmann and Wyss, 2001; Lehmann et al., 2002; Dai et al., 2007; Lutz, 2010; Magliery et al., 2011; Magliery, 2015; Porebski et al., 2015; Paatero et al., 2016).”
SDM: a server for predicting effects of mutations on protein stability (2017, methodology): “While experimental techniques to measure changes in stability between wild-type and mutant proteins are the most accurate, they are time consuming and costly (3).”