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Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data.

ABSTRACT: Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the biophysical properties that control protein crystallization. This analysis leads to the conclusion that crystallization propensity depends primarily on the prevalence of well-ordered surface epitopes capable of mediating interprotein interactions and is not strongly influenced by overall thermodynamic stability. We identify specific sequence features that correlate with crystallization propensity and that can be used to estimate the crystallization probability of a given construct. Analyses of entire predicted proteomes demonstrate substantial differences in the amino acid-sequence properties of human versus eubacterial proteins, which likely reflect differences in biophysical properties, including crystallization propensity. Our thermodynamic measurements do not generally support previous claims regarding correlations between sequence properties and protein stability.

SUBMITTER: Price WN

PROVIDER: S-EPMC2746436 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature

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Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data.

Price W Nicholson WN Chen Yang Y Handelman Samuel K SK Neely Helen H Manor Philip P Karlin Richard R Nair Rajesh R Liu Jinfeng J Baran Michael M Everett John J Tong Saichiu N SN Forouhar Farhad F Swaminathan Swarup S SS Acton Thomas T Xiao Rong R Luft Joseph R JR Lauricella Angela A DeTitta George T GT Rost Burkhard B Montelione Gaetano T GT Hunt John F JF

Nature biotechnology 20090101 1

Crystallization is the most serious bottleneck in high-throughput protein-structure determination by diffraction methods. We have used data mining of the large-scale experimental results of the Northeast Structural Genomics Consortium and experimental folding studies to characterize the biophysical properties that control protein crystallization. This analysis leads to the conclusion that crystallization propensity depends primarily on the prevalence of well-ordered surface epitopes capable of m ...[more]

PMID: 19079241

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Project description:BackgroundAntidepressants are known to show heterogeneous effects across individuals and conditions, posing challenges to understanding their efficacy in mental health treatment. Social media platforms enable individuals to share their day-to-day concerns with others and thereby can function as unobtrusive, large-scale, and naturalistic data sources to study the longitudinal behavior of individuals taking antidepressants.ObjectiveWe aim to understand the side effects of antidepressants from naturalistic expressions of individuals on social media.MethodsOn a large-scale Twitter data set of individuals who self-reported using antidepressants, a quasi-experimental study using unsupervised language analysis was conducted to extract keywords that distinguish individuals who improved and who did not improve following the use of antidepressants. The net data set consists of over 8 million Twitter posts made by over 300,000 users in a 4-year period between January 1, 2014, and February 15, 2018.ResultsFive major side effects of antidepressants were studied: sleep, weight, eating, pain, and sexual issues. Social media language revealed keywords related to these side effects. In particular, antidepressants were found to show a spectrum of effects from decrease to increase in each of these side effects.ConclusionsThis work enhances the understanding of the side effects of antidepressants by identifying distinct linguistic markers in the longitudinal social media data of individuals showing the most and least improvement following the self-reported intake of antidepressants. One implication of this work concerns the potential of social media data as an effective means to support digital pharmacovigilance and digital therapeutics. These results can inform clinicians in tailoring their discussion and assessment of side effects and inform patients about what to potentially expect and what may or may not be within the realm of normal aftereffects of antidepressants.

Dataset Information

Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data.

Publications

Understanding the physical properties that control protein crystallization by analysis of large-scale experimental data.

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