Protein Folding and Recognition in the Cell -- an in Silico Approach

The Aβ42 amyloid is the causative factor behind various neurodegenerative processes. It forms elongated fibrils which cause structural devastation in brain tissue. The structure of an amyloid seems to be a contradiction of protein folding principles. Our work focuses on the Aβ(15-40) amyloid containing the D23N mutation (also known as the “Iowa mutation”), upon which an in silico experiment is based. Models generated using I-Tasser software as well as the fuzzy oil drop model – regarded as alternatives to the amyloid conformation – are compared in terms of their respective distributions of hydrophobicity (i.e. the existence of a hydrophobic core). In this process, fuzzy oil drop model parameters are applied in assessing the propensity of selected fragments for undergoing amyloid transformation.

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Protein folding in silico

10.1533/9781908818256 ◽

2012 ◽

Cited By ~ 1

Author(s):

Irena Roterman-Konieczna

Keyword(s):

Protein Folding ◽

In Silico

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Understanding protein folding: Small proteins in silico

Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics ◽

10.1016/j.bbapap.2007.10.010 ◽

2008 ◽

Vol 1784 (1) ◽

pp. 252-258 ◽

Cited By ~ 19

Author(s):

Olav Zimmermann ◽

Ulrich H.E. Hansmann

Keyword(s):

Protein Folding ◽

In Silico ◽

Small Proteins

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Convergent evolution and structural adaptation to the deep ocean in the protein folding chaperonin CCTα

10.1101/775585 ◽

2019 ◽

Author(s):

Alexandra A.-T. Weber ◽

Andrew F. Hugall ◽

Timothy D. O’Hara

Keyword(s):

Protein Folding ◽

Protein Stability ◽

Deep Sea ◽

In Silico ◽

Molecular Mechanisms ◽

Amino Acid Level ◽

Deep Ocean ◽

Cold Shock Protein ◽

Protein Biogenesis ◽

Molecular Convergence

AbstractThe deep ocean is the largest biome on Earth and yet it is among the least studied environments of our planet. Life at great depths requires several specific adaptations, however their molecular mechanisms remain understudied. We examined patterns of positive selection in 416 genes from four brittle star (Ophiuroidea) families displaying replicated events of deep-sea colonization (288 individuals from 216 species). We found consistent signatures of molecular convergence in functions related to protein biogenesis, including protein folding and translation. Five genes were recurrently positively selected, including CCTα (Chaperonin Containing TCP-1 subunit α), which is essential for protein folding. Molecular convergence was detected at the functional and gene levels but not at the amino-acid level. Pressure-adapted proteins are expected to display higher stability to counteract the effects of denaturation. We thus examined in silico local protein stability of CCTα across the ophiuroid tree of life (967 individuals from 725 species) in a phylogenetically-corrected context and found that deep sea-adapted proteins display higher stability within and next to the substrate-binding region, which was confirmed by in silico global protein stability analyses. This suggests that CCTα not only displays structural but also functional adaptations to deep water conditions. The CCT complex is involved in the folding of ∼10% of newly synthesized proteins and has previously been categorized as ‘cold-shock’ protein in numerous eukaryotes. We thus propose that adaptation mechanisms to cold and deep-sea environments may be linked and highlight that efficient protein biogenesis, including protein folding and translation, are key metabolic deep-sea adaptations.

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Sibe: a computation tool to apply protein sequence statistics to folding and design

10.1101/380576 ◽

2018 ◽

Author(s):

Ngaam J. Cheung ◽

Wookyung Yu

Keyword(s):

Protein Folding ◽

Sequence Analysis ◽

Protein Design ◽

Protein Sequence ◽

In Silico ◽

Rational Design ◽

Sequence Data ◽

Protein Sequence Analysis ◽

Rational Protein Design ◽

Interface Modules

ABSTRACTStatistical analysis plays a significant role in both protein sequences and structures, expanding in recent years from the studies of co-evolution guided single-site mutations to protein folding in silico. Here we describe a computational tool, termed Sibe, with a particular focus on protein sequence analysis, folding and design. Since Sibe has various easy-interface modules, expressive architecture and extensible codes, it is powerful in statistically analyzing sequence data and building energetic potentials in boosting both protein folding and design. In this study, Sibe is used to capture positionally conserved couplings between pairwise amino acids and help rational protein design, in which the pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several ‘blocks’. A human β2-adrenergic receptor (β2AR) was used to demonstrated that those ‘blocks’ could contribute rational design at functional residues. In addition, Sibe provides protein folding modules based on both the positionally conserved couplings and well-established statistical potentials. Sibe provides various easy to use command-line interfaces in C++ and/or Python. Sibe was developed for compatibility with the ‘big data’ era, and it primarily focuses on protein sequence analysis, in silico folding and design, but it is also applicable to extend for other modeling and predictions of experimental measurements.

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