scholarly journals From systems to structure — using genetic data to model protein structures

2022 ◽  
Author(s):  
Hannes Braberg ◽  
Ignacia Echeverria ◽  
Robyn M. Kaake ◽  
Andrej Sali ◽  
Nevan J. Krogan
Keyword(s):  
Protein Structures ◽  
Genetic Data ◽  
Model Protein

scholarly journals Protein Structure Idealization: How accurately is it possible to model protein structures with dihedral angles?

2013 ◽  
Vol 8 (1) ◽  
pp. 5 ◽  
Author(s):  
Xuefeng Cui ◽  
Shuai Cheng Li ◽  
Dongbo Bu ◽  
Babak Alipanahi ◽  
Ming Li
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Dihedral Angles ◽  
Model Protein

scholarly journals Exploring Protein Fold Space

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 193 ◽  
Author(s):  
William R. Taylor
Keyword(s):  
Protein Structures ◽  
Pairwise Comparison ◽  
Secondary Structures ◽  
Beta Sheets ◽  
Protein Fold ◽  
Helical Packing ◽  
Model Protein ◽  
Protein Fold Space ◽  
Open Question ◽  
Generate Model

The model of protein folding proposed by Ptitsyn and colleagues involves the accretion of secondary structures around a nucleus. As developed by Efimov, this model also provides a useful way to view the relationships among structures. Although somewhat eclipsed by later databases based on the pairwise comparison of structures, Efimov’s approach provides a guide for the more automatic comparison of proteins based on an encoding of their topology as a string. Being restricted to layers of secondary structures based on beta sheets, this too has limitations which are partly overcome by moving to a more generalised secondary structure lattice that can encompass both open and closed (barrel) sheets as well as helical packing of the type encoded by Murzin and Finkelstein on small polyhedra. Regular (crystalline) lattices, such as close-packed hexagonals, were found to be too limited so pseudo-latticses were investigated including those found in quasicrystals and the Bernal tetrahedron-based lattice that he used to represent liquid water. The Bernal lattice was considered best and used to generate model protein structures. These were much more numerous than those seen in Nature, posing the open question of why this might be.

scholarly journals How are model protein structures distributed in sequence space?

1997 ◽  
Vol 73 (5) ◽  
pp. 2393-2403 ◽  
Author(s):  
E. Bornberg-Bauer
Keyword(s):  
Sequence Space ◽  
Protein Structures ◽  
Model Protein

How Accurately Can We Model Protein Structures with Dihedral Angles?

2012 ◽  
pp. 274-287
Author(s):  
Xuefeng Cui ◽  
Shuai Cheng Li ◽  
Dongbo Bu ◽  
Babak Alipanahi Ramandi ◽  
Ming Li
Keyword(s):  
Protein Structures ◽  
Dihedral Angles ◽  
Model Protein

Refined template selection and combination algorithm significantly improves template-based modeling accuracy

2019 ◽  
Vol 17 (02) ◽  
pp. 1950006 ◽  
Author(s):  
Ashish Runthala ◽  
Shibasish Chowdhury
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Comparative Modeling ◽  
Target Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Modeling Accuracy ◽  
Model Protein ◽  
Rank And Select

In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.

High accuracy protein structures from minimal sparse paramagnetic solid-state NMR restraints

2018 ◽  
Author(s):  
Alberto Perez ◽  
Kari Gaalswyk ◽  
Christopher P. Jaroniec ◽  
Justin L. MacCallum
Keyword(s):  
Solid State ◽  
Solid State Nmr ◽  
Protein Structures ◽  
High Accuracy ◽  
Atomistic Simulations ◽  
Quantitative Mapping ◽  
Model Protein ◽  
Experimental Approaches ◽  
Nmr Restraints ◽  
Protein Gb1

AbstractThere is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid-state NMR restraints with physics-based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi-quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations. We apply our approach to solid-state NMR data for the model protein GB1 labeled with Cu2+-EDTA at six different sites. We are able to determine the structure to ca. 1 Å accuracy within a single day of computation on a modest GPU cluster. We further show that in 4 of 6 cases, the data from only a single paramagnetic tag are sufficient to fold the protein to high accuracy.

scholarly journals Network analysis of synonymous codon usage

2020 ◽  
Vol 36 (19) ◽  
pp. 4876-4884
Author(s):  
Khalique Newaz ◽  
Gabriel Wright ◽  
Jacob Piland ◽  
Jun Li ◽  
Patricia L Clark ◽  
...  
Keyword(s):  
Amino Acids ◽  
Protein Folding ◽  
Codon Usage ◽  
Synonymous Codon ◽  
Protein Structures ◽  
Synonymous Codon Usage ◽  
Supplementary Information ◽  
Rare Codons ◽  
Model Protein ◽  
Structural Positions

Abstract Motivation Most amino acids are encoded by multiple synonymous codons, some of which are used more rarely than others. Analyses of positions of such rare codons in protein sequences revealed that rare codons can impact co-translational protein folding and that positions of some rare codons are evolutionarily conserved. Analyses of their positions in protein 3-dimensional structures, which are richer in biochemical information than sequences alone, might further explain the role of rare codons in protein folding. Results We model protein structures as networks and use network centrality to measure the structural position of an amino acid. We first validate that amino acids buried within the structural core are network-central, and those on the surface are not. Then, we study potential differences between network centralities and thus structural positions of amino acids encoded by conserved rare, non-conserved rare and commonly used codons. We find that in 84% of proteins, the three codon categories occupy significantly different structural positions. We examine protein groups showing different codon centrality trends, i.e. different relationships between structural positions of the three codon categories. We see several cases of all proteins from our data with some structural or functional property being in the same group. Also, we see a case of all proteins in some group having the same property. Our work shows that codon usage is linked to the final protein structure and thus possibly to co-translational protein folding. Availability and implementation https://nd.edu/∼cone/CodonUsage/. Supplementary information Supplementary data are available at Bioinformatics online.

Insights into the biochemical and functional characterization of sortase E transpeptidase of Corynebacterium glutamicum

2019 ◽  
Vol 476 (24) ◽  
pp. 3835-3847 ◽  
Author(s):  
Aliyath Susmitha ◽  
Kesavan Madhavan Nampoothiri ◽  
Harsha Bajaj
Keyword(s):  
Protein Engineering ◽  
Cell Attachment ◽  
Functional Characterization ◽  
Protein Structures ◽  
Structural Similarity ◽  
Surface Proteins ◽  
Sortase A ◽  
Peptide Cleavage ◽  
New Findings

Most Gram-positive bacteria contain a membrane-bound transpeptidase known as sortase which covalently incorporates the surface proteins on to the cell wall. The sortase-displayed protein structures are involved in cell attachment, nutrient uptake and aerial hyphae formation. Among the six classes of sortase (A–F), sortase A of S. aureus is the well-characterized housekeeping enzyme considered as an ideal drug target and a valuable biochemical reagent for protein engineering. Similar to SrtA, class E sortase in GC rich bacteria plays a housekeeping role which is not studied extensively. However, C. glutamicum ATCC 13032, an industrially important organism known for amino acid production, carries a single putative sortase (NCgl2838) gene but neither in vitro peptide cleavage activity nor biochemical characterizations have been investigated. Here, we identified that the gene is having a sortase activity and analyzed its structural similarity with Cd-SrtF. The purified enzyme showed a greater affinity toward LAXTG substrate with a calculated KM of 12 ± 1 µM, one of the highest affinities reported for this class of enzyme. Moreover, site-directed mutation studies were carried to ascertain the structure functional relationship of Cg-SrtE and all these are new findings which will enable us to perceive exciting protein engineering applications with this class of enzyme from a non-pathogenic microbe.

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