Tri-peptide reference structures for the calculation of relative solvent accessible surface area in protein amino acid residues

Topham, Christopher M.; Smith, Jeremy C.

doi:10.1016/j.compbiolchem.2014.11.007

Cited by 9 publications

(12 citation statements)

References 75 publications

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“…Transient β-content was occasionally observed in N-terminal area of M128V-I, and to a greater extent in the areas of the insert and loop S2-H2 in HRdup-I and HRdup-II ( S4 Table ). To quantify structural differences between the alleles we averaged the per-residue solvent-accessible areas (SASA) [ 43 ] over the last 2 ns from the four MD trajectories ( Fig 5B and S5 and S6 Tables). Two trajectories for HRdup and those for M128V exhibit close total SASAs for various groups of residues, as well as for the entire protein ( S5 Table ), and average per-residue SASAs were also close in HRdup and in M128V.…”

Section: Resultsmentioning

confidence: 99%

“…To analyze the PrP constructs from MD trajectories, their secondary structure content, numbers of hydrogen bonds and salt bridges, contact maps, and solvent accessible areas (SASA) have been calculated using scripts implemented in Gromacs [ 112 , 113 ] and VMD [ 114 ] packages. Final SASA analysis was made according to solvent exposure level defined in [ 115 ]. Snapshots from trajectories and graphical representation of models was done with VMD or Accelrys VS [ 115 ].…”

Section: Methodsmentioning

confidence: 99%

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A novel Gerstmann-Sträussler-Scheinker disease mutation defines a precursor for amyloidogenic 8 kDa PrP fragments and reveals N-terminal structural changes shared by other GSS alleles

et al. 2018

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To explore pathogenesis in a young Gerstmann-Sträussler-Scheinker Disease (GSS) patient, the corresponding mutation, an eight-residue duplication in the hydrophobic region (HR), was inserted into the wild type mouse PrP gene. Transgenic (Tg) mouse lines expressing this mutation (Tg.HRdup) developed spontaneous neurologic syndromes and brain extracts hastened disease in low-expressor Tg.HRdup mice, suggesting de novo formation of prions. While Tg.HRdup mice exhibited spongiform change, PrP aggregates and the anticipated GSS hallmark of a proteinase K (PK)-resistant 8 kDa fragment deriving from the center of PrP, the LGGLGGYV insertion also imparted alterations in PrP's unstructured N-terminus, resulting in a 16 kDa species following thermolysin exposure. This species comprises a plausible precursor to the 8 kDa PK-resistant fragment and its detection in adolescent Tg.HRdup mice suggests that an early start to accumulation could account for early disease of the index case. A 16 kDa thermolysin-resistant signature was also found in GSS patients with P102L, A117V, H187R and F198S alleles and has coordinates similar to GSS stop codon mutations. Our data suggest a novel shared pathway of GSS pathogenesis that is fundamentally distinct from that producing structural alterations in the C-terminus of PrP, as observed in other prion diseases such as Creutzfeldt-Jakob Disease and scrapie.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A novel Gerstmann-Sträussler-Scheinker disease mutation defines a precursor for amyloidogenic 8 kDa PrP fragments and reveals N-terminal structural changes shared by other GSS alleles

et al. 2018

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“…In this work, we use the CSU program to calculate the ASA for each residue based on the starting BLIP protein structure (PDB entry 3 gmu) . The theoretical normalization values are based on recent theoretical calculations that provide reference values for each residue . Residues with RSA values >20% are considered to be exposed to the solvent …”

Section: Methodsmentioning

confidence: 99%

“…40 The theoretical normalization values are based on recent theoretical calculations that provide reference values for each residue. 48 Residues with RSA values >20% are considered to be exposed to the solvent. 49…”

Section: Rsa Calculationmentioning

confidence: 99%

The adaptive nature of protein residue networks

Karain

Qaraeen

2017

Proteins

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Protein residue networks PRNs are used to describe proteins. These networks are usually based on an average structure for the protein. However, proteins are dynamic entities that are affected by their surroundings. In this work, we study the effect of temperatures above and below the protein dynamical transition temperature(≈200 K), on three important network parameters gleaned from weighted PRNs for the solvated β-lactamase inhibitory protein BLIP: the betweenness centrality B, the closeness centrality C, and the clustering coefficient CC. The B and C values will be extracted for each node from PRNs at six different temperatures: 150 K, 180 K, 200 K, 220 K, 250 K, and 310 K respectively. The average value for the CC for each PRN will also be calculated at each temperature, respectively. We find that at temperatures ≤200 K, the network nodes with the most significant B and C values tend to have lower relative solvent accessibility RSA values, and to fall within the protein secondary structure elements (α helices and β sheets). At temperatures >200 K, the significant nodes in terms of B and C tend to have larger RSA values, and to fall on the connecting loops in the protein. The average CC decreases in value for the PRNs up to 200 K, and then remains basically constant above 200 K. This clearly shows that any conclusions based on static PRNs should be handled with care. The dynamic nature of proteins and its coupling to the surrounding environment should be taken into consideration when using the PRN paradigm. Proteins 2017; 85:917-923. © 2016 Wiley Periodicals, Inc.

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“…The degree of exposure of each residue to the surrounding water solvent is given by the RSA value for that residue. The RSA values are calculated by dividing the residue’s accessible surface area ASA as given by the CSU program [ 49 ], by the maximum solvent-accessible surface area of the corresponding standard amino acid residue as calculated recently [ 61 ]. Residue with RSA <5 %, RSA ≥ 5 % and < 20 %, and RSA ≥20 %, are considered buried, partially buried, and exposed, respectively [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Weighted protein residue networks based on joint recurrences between residues

Karain

Qaraeen

2015

BMC Bioinformatics

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BackgroundWeighted and un-weighted protein residue networks can predict key functional residues in proteins based on the closeness centrality C and betweenness centrality B values for each residue. A static snapshot of the protein structure, and a cutoff distance, are used to define edges between the network nodes. In this work we apply the weighted network approach to study the β-Lactamase Inhibitory Protein (BLIP). Joint recurrences extracted from molecular dynamics MD trajectory positions of the protein residue carbon alpha atoms are used to define edge weights between nodes, and no cutoff distance is used. The results for B and C from our approach are compared with those extracted from an un-weighted network, and a weighted network that uses interatomic contacts to define edge weights between nodes, respectively.ResultsThe joint recurrence weighted network approach performs well in pointing out key protein residues. Furthermore, it seems to emphasize residues with medium to high relative solvent accessibility that lie in loop regions between secondary structure elements of the protein.ConclusionsProtein residue networks that use joint recurrences extracted from molecular dynamics simulations of a solvated protein perform well in pointing to hotspot residues and hotspot clusters. This approach uses no distance cutoff threshold, and does not exclude any interactions between the residues, including water-mediated interactions.

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Tri-peptide reference structures for the calculation of relative solvent accessible surface area in protein amino acid residues

Cited by 9 publications

References 75 publications

A novel Gerstmann-Sträussler-Scheinker disease mutation defines a precursor for amyloidogenic 8 kDa PrP fragments and reveals N-terminal structural changes shared by other GSS alleles

A novel Gerstmann-Sträussler-Scheinker disease mutation defines a precursor for amyloidogenic 8 kDa PrP fragments and reveals N-terminal structural changes shared by other GSS alleles

The adaptive nature of protein residue networks

Weighted protein residue networks based on joint recurrences between residues

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