scholarly journals Hierarchical Reduction Method of Protein Structures for Understanding Protein Dynamics

2009 ◽  
Vol 96 (3) ◽  
pp. 404a
Author(s):  
Jae In Kim ◽  
Gwonchan Yoon ◽  
Sungsoo Na ◽  
Kilho Eom
2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.


2018 ◽  
Vol 19 (11) ◽  
pp. 3401 ◽  
Author(s):  
Ashutosh Srivastava ◽  
Tetsuro Nagai ◽  
Arpita Srivastava ◽  
Osamu Miyashita ◽  
Florence Tama

Protein structural biology came a long way since the determination of the first three-dimensional structure of myoglobin about six decades ago. Across this period, X-ray crystallography was the most important experimental method for gaining atomic-resolution insight into protein structures. However, as the role of dynamics gained importance in the function of proteins, the limitations of X-ray crystallography in not being able to capture dynamics came to the forefront. Computational methods proved to be immensely successful in understanding protein dynamics in solution, and they continue to improve in terms of both the scale and the types of systems that can be studied. In this review, we briefly discuss the limitations of X-ray crystallography in studying protein dynamics, and then provide an overview of different computational methods that are instrumental in understanding the dynamics of proteins and biomacromolecular complexes.


2014 ◽  
Vol 13 (06) ◽  
pp. 1450053 ◽  
Author(s):  
Meng Zhan ◽  
Suhong Li ◽  
Fan Li

Accurate prediction of the Debye–Waller temperature factor of proteins is of significant importance in the study of protein dynamics and function. This work explores the utility of wavelets for improving the performance of Gaussian network model (GNM). We propose two wavelet transformed Gaussian network models (wtGNM), namely a scale-one wtGNM and a scale-two wtGNM. Based on a set of 113 protein structures, it shows that the mean correlation with experimental results for the scale-one wtGNM is 0.714 and that for the scale-two wtGNM is 0.738. In contrast, the mean correlation for the original GNM is 0.594. Therefore, the wtGNM is a potential algorithm for improving the GNM prediction of protein B-factors.


2020 ◽  
Author(s):  
João Henriques ◽  
Kresten Lindorff-Larsen

AbstractProteins carry out a wide range of functions that are tightly regulated in space and time. Protein phosphorylation is the most common post-translation modification of proteins and plays key roles in the regulation of many biological processes. The finding that many phosphorylated residues are not solvent exposed in the unphosphorylated state opens several questions for understanding the mechanism that underlies phosphorylation and how phosphorylation may affect protein structures. First, since kinases need access to the phosphorylated residue, how do such buried residues become modified? Second, once phosphorylated, what are the structural effects of phosphorylation of buried residues and do they lead to changed conformational dynamics. We have used the ternary complex between p27, Cdk2 and Cyclin A to study these questions using enhanced sampling molecular dynamics simulations. In line with previous NMR and single-molecule fluorescence experiments we observe transient exposure of Tyr88 in p27, even in its unphosphorylated state. Once Tyr88 is phosphorylated, we observe a coupling to a second site, thus making Tyr74 more easily exposed, and thereby the target for a second phosphorylation step. Our observations provide atomic details on how protein dynamics plays a role in modulating multi-site phosphorylation in p27, thus supplementing previous experimental observations. More generally, we discuss how the observed phenomenon of transient exposure of buried residues may play a more general role in regulating protein function.Significance StatementProtein phosphorylation is a common post-translation modification and is carried out by kinases. While many phosphorylation sites are located in disordered regions of proteins or in loops, a surprisingly large number of modification sites are buried inside folded domains. This observation led us to ask the question of how kinases gain access to such buried residues. We used the complex between p27, a regulator of cell cycle progression, and Cyclin-dependent kinase 2/Cyclin A to study this problem. We hypothesized that transient exposure of buried tyrosines in p27 to the solvent would make them accessible to kinases, explaining how buried residues get modified. We provide an atomic-level description of these dynamic processes revealing how protein dynamics plays a role in regulation.


2019 ◽  
Vol 21 (2) ◽  
pp. 780-788 ◽  
Author(s):  
Sashary Ramos ◽  
Rachel E. Horness ◽  
Jessica A. Collins ◽  
David Haak ◽  
Megan C. Thielges

The conformational heterogeneity and dynamics of protein side chains contribute to function, but investigating exactly how is hindered by experimental challenges arising from the fast timescales involved and the spatial heterogeneity of protein structures.


2018 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein

AbstractLarge-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence and clustering-based approaches. Some of these methods also employ three-dimensional protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite the essential role of dynamics in protein functionality. In this work, we present a framework to identify driver genes using a dynamics-based search of mutational hotspot communities. After partitioning 3D structures into distinct communities of residues using anisotropic network models, we map variants onto the partitioned structures. We then search for signals of positive selection among these residue communities to identify putative drivers. We applied our method using the TCGA pan-cancer atlas missense mutation catalog. Overall, our analyses predict one or more mutational hotspots within the resolved structures of 434 genes. Ontological and pathway enrichment analyses implicate genes with predicted hotspots to be enriched in biological processes associated with tumor progression. Additionally, a comparison between our approach and existing hotspot detection methods that use structural data suggests that the inclusion of dynamics significantly increases the sensitivity of driver detection.


2019 ◽  
Vol 476 (24) ◽  
pp. 3835-3847 ◽  
Author(s):  
Aliyath Susmitha ◽  
Kesavan Madhavan Nampoothiri ◽  
Harsha Bajaj

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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