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Relevant bibliographies by topics / Protein Side-chain Networks (PScN) / Journal articles

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Journal articles on the topic 'Protein Side-chain Networks (PScN)'

Author: Grafiati

Published: 6 September 2023

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1

Hwang, Jenn-Kang, and Wen-Fa Liao. "Side-chain prediction by neural networks and simulated annealing optimization." "Protein Engineering, Design and Selection" 8, no. 4 (1995): 363–70. http://dx.doi.org/10.1093/protein/8.4.363.

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2

IRWIN, J., H. BOHR, K. MOCHIZUKI, and P. G. WOLYNES. "CLASSIFICATION AND PREDICTION OF PROTEIN SIDE-CHAINS BY NEURAL NETWORK TECHNIQUES." International Journal of Neural Systems 03, supp01 (1992): 177–82. http://dx.doi.org/10.1142/s0129065792000504.

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Neural Network methodology is used to classify and predict side-chain configurations in proteins on the basis of their sequence and in some cases also Cα-atomic distance information. In some of these methods, where Potts Associative Memories are employed, a mixed set of Potts systems each describe the various orientational states of a particular side-chain. The methods can find the correct side-chain orientations in proteins reasonably well after being trained on a data set of other proteins of known 3-dimensional structure.

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3

Xu, Gang, Qinghua Wang, and Jianpeng Ma. "OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods." Journal of Chemical Information and Modeling 60, no. 12 (2020): 6691–97. http://dx.doi.org/10.1021/acs.jcim.0c00951.

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4

Bond, Paul S., Keith S. Wilson, and Kevin D. Cowtan. "Predicting protein model correctness in Coot using machine learning." Acta Crystallographica Section D Structural Biology 76, no. 8 (2020): 713–23. http://dx.doi.org/10.1107/s2059798320009080.

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Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that is produced by combining multiple sources of information using a neural network. The residues in 639 automatically built models were marked as correct or incorrect by comparing them with the coordinates deposited in the PDB. A number of features were also calculated for each residue using Coot, including map-to-model correlation, density values, B

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5

Conover, Matthew, Max Staples, Dong Si, Miao Sun, and Renzhi Cao. "AngularQA: Protein Model Quality Assessment with LSTM Networks." Computational and Mathematical Biophysics 7, no. 1 (2019): 1–9. http://dx.doi.org/10.1515/cmb-2019-0001.

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AbstractQuality Assessment (QA) plays an important role in protein structure prediction. Traditional multimodel QA method usually suffer from searching databases or comparing with other models for making predictions, which usually fail when the poor quality models dominate the model pool. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quali

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6

Petrovskiy, Denis V., Kirill S. Nikolsky, Vladimir R. Rudnev, et al. "Modeling Side Chains in the Three-Dimensional Structure of Proteins for Post-Translational Modifications." International Journal of Molecular Sciences 24, no. 17 (2023): 13431. http://dx.doi.org/10.3390/ijms241713431.

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Amino acid substitutions and post-translational modifications (PTMs) play a crucial role in many cellular processes by directly affecting the structural and dynamic features of protein interaction. Despite their importance, the understanding of protein PTMs at the structural level is still largely incomplete. The Protein Data Bank contains a relatively small number of 3D structures having post-translational modifications. Although recent years have witnessed significant progress in three-dimensional modeling (3D) of proteins using neural networks, the problem related to predicting accurate PTM

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7

WANG, LIANGJIANG, and SUSAN J. BROWN. "PREDICTION OF DNA-BINDING RESIDUES FROM SEQUENCE FEATURES." Journal of Bioinformatics and Computational Biology 04, no. 06 (2006): 1141–58. http://dx.doi.org/10.1142/s0219720006002387.

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Protein–DNA interaction plays a pivotal role in transcriptional regulation, DNA metabolism and chromatin formation. Although structural data are available for a few hundreds of protein–DNA complexes, the molecular recognition pattern is still poorly understood. With the rapid accumulation of sequence data from many genomes, it is important to develop predictive methods for identification of potential DNA-binding residues in proteins. In this study, neural networks have been trained using five sequence-derived features for prediction of DNA-binding residues. These features include the molecular

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8

Steiner, Thomas, Antoine M. M. Schreurs, Jan A. Kanters, and Jan Kroon. "Water Molecules Hydrogen Bonding to Aromatic Acceptors of Amino Acids: the Structure of Tyr-Tyr-Phe Dihydrate and a Crystallographic Database Study on Peptides." Acta Crystallographica Section D Biological Crystallography 54, no. 1 (1998): 25–31. http://dx.doi.org/10.1107/s0907444997007981.

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The crystal structure of Tyr-Tyr-Phe dihydrate contains a hydrogen bond formed between a water molecule and the Phe side chain. The geometry is centered with a distance of 3.26 Å between the water O atom and the aromatic centroid. In a database study on hydrated peptides, four related examples are found which exhibit a wide variability of hydrogen-bond geometries. The intermolecular surroundings of the water molecules are inspected, showing that they are typically involved in complex networks of conventional and non-conventional hydrogen bonds. Possible relevance for protein hydration is given

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9

Santana, Roberto, Pedro Larrañaga, and José A. Lozano. "Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem." Journal of Heuristics 14, no. 5 (2007): 519–47. http://dx.doi.org/10.1007/s10732-007-9049-8.

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10

Mahatabuddin, Sheikh, Daichi Fukami, Tatsuya Arai, et al. "Polypentagonal ice-like water networks emerge solely in an activity-improved variant of ice-binding protein." Proceedings of the National Academy of Sciences 115, no. 21 (2018): 5456–61. http://dx.doi.org/10.1073/pnas.1800635115.

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Polypentagonal water networks were recently observed in a protein capable of binding to ice crystals, or ice-binding protein (IBP). To examine such water networks and clarify their role in ice-binding, we determined X-ray crystal structures of a 65-residue defective isoform of a Zoarcidae-derived IBP (wild type, WT) and its five single mutants (A20L, A20G, A20T, A20V, and A20I). Polypentagonal water networks composed of ∼50 semiclathrate waters were observed solely on the strongest A20I mutant, which appeared to include a tetrahedral water cluster exhibiting a perfect position match to the (10

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11

Mortenson, David E., Jay D. Steinkruger, Dale F. Kreitler, et al. "High-resolution structures of a heterochiral coiled coil." Proceedings of the National Academy of Sciences 112, no. 43 (2015): 13144–49. http://dx.doi.org/10.1073/pnas.1507918112.

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Interactions between polypeptide chains containing amino acid residues with opposite absolute configurations have long been a source of interest and speculation, but there is very little structural information for such heterochiral associations. The need to address this lacuna has grown in recent years because of increasing interest in the use of peptides generated from d amino acids (d peptides) as specific ligands for natural proteins, e.g., to inhibit deleterious protein–protein interactions. Coiled–coil interactions, between or among α-helices, represent the most common tertiary and quater

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12

Mueller, Benjamin K., Sabareesh Subramaniam та Alessandro Senes. "A frequent, GxxxG-mediated, transmembrane association motif is optimized for the formation of interhelical Cα–H hydrogen bonds". Proceedings of the National Academy of Sciences 111, № 10 (2014): E888—E895. http://dx.doi.org/10.1073/pnas.1319944111.

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Carbon hydrogen bonds between Cα–H donors and carbonyl acceptors are frequently observed between transmembrane helices (Cα–H···O=C). Networks of these interactions occur often at helix−helix interfaces mediated by GxxxG and similar patterns. Cα–H hydrogen bonds have been hypothesized to be important in membrane protein folding and association, but evidence that they are major determinants of helix association is still lacking. Here we present a comprehensive geometric analysis of homodimeric helices that demonstrates the existence of a single region in conformational space with high propensity

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13

Peng, Jiangling, Mingjie Fan, Kelly X. Huang, et al. "Design, Synthesis, Computational and Biological Evaluation of Novel Structure Fragments Based on Lithocholic Acid (LCA)." Molecules 28, no. 14 (2023): 5332. http://dx.doi.org/10.3390/molecules28145332.

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The regulation of bile acid pathways has become a particularly promising therapeutic strategy for a variety of metabolic disorders, cancers, and diseases. However, the hydrophobicity of bile acids has been an obstacle to clinical efficacy due to off-target effects from rapid drug absorption. In this report, we explored a novel strategy to design new structure fragments based on lithocholic acid (LCA) with improved hydrophilicity by introducing a polar “oxygen atom” into the side chain of LCA, then (i) either retaining the carboxylic acid group or replacing the carboxylic acid group with (ii) a

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14

Valverde, P., T. Kawai, and M. A. Taubman. "Potassium Channel-blockers as Therapeutic Agents to Interfere with Bone Resorption of Periodontal Disease." Journal of Dental Research 84, no. 6 (2005): 488–99. http://dx.doi.org/10.1177/154405910508400603.

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Inflammatory lesions of periodontal disease contain all the cellular components, including abundant activated/memory T- and B-cells, necessary to control immunological interactive networks and to accelerate bone resorption by RANKL-dependent and -independent mechanisms. Blockade of RANKL function has been shown to ameliorate periodontal bone resorption and other osteopenic disorders without affecting inflammation. Development of therapies aimed at decreasing the expression of RANKL and pro-inflammatory cytokines by T-cells constitutes a promising strategy to ameliorate not only bone resorption

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15

Salamanca Viloria, Juan, Maria Francesca Allega, Matteo Lambrughi, and Elena Papaleo. "An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass." Scientific Reports 7, no. 1 (2017). http://dx.doi.org/10.1038/s41598-017-01498-6.

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16

Halder, Anushka, Arinnia Anto, Varsha Subramanyan, Moitrayee Bhattacharyya, Smitha Vishveshwara, and Saraswathi Vishveshwara. "Surveying the Side-Chain Network Approach to Protein Structure and Dynamics: The SARS-CoV-2 Spike Protein as an Illustrative Case." Frontiers in Molecular Biosciences 7 (December 18, 2020). http://dx.doi.org/10.3389/fmolb.2020.596945.

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Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using

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17

Konno, Shohei, Takao Namiki, and Koichiro Ishimori. "Quantitative description and classification of protein structures by a novel robust amino acid network: interaction selective network (ISN)." Scientific Reports 9, no. 1 (2019). http://dx.doi.org/10.1038/s41598-019-52766-6.

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Abstract To quantitatively categorize protein structures, we developed a quantitative coarse-grained model of protein structures with a novel amino acid network, the interaction selective network (ISN), characterized by the links based on interactions in both the main and side chains. We found that the ISN is a novel robust network model to show the higher classification probability in the plots of average vertex degree (k) versus average clustering coefficient (C), both of which are typical network parameters for protein structures, and successfully distinguished between “all-α” and “all-β” p

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18

Inada, Yuki, Yuichiro Ono, Kyo Okazaki, et al. "Hydrogen bonds connecting the N-terminal region and the DE loop stabilize the monomeric structure of transthyretin." Journal of Biochemistry, July 3, 2023. http://dx.doi.org/10.1093/jb/mvad049.

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Summary Transthyretin (TTR) is a homo-tetrameric serum protein associated with sporadic and hereditary systemic amyloidosis. TTR amyloid formation proceeds by the dissociation of the TTR tetramer and the subsequent partial unfolding of the TTR monomer into an aggregation-prone conformation. Although TTR kinetic stabilizers suppress tetramer dissociation, a strategy for stabilizing monomers has not yet been developed. Here, we show that an N-terminal C10S mutation increases the thermodynamic stability of the TTR monomer by forming new hydrogen bond networks through the side chain hydroxyl group

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