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

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Academic literature on the topic 'Protein Side-chain Networks (PScN)'

Author: Grafiati
Published: 6 September 2023

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

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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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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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 factors, clashes, Ramachandran scores, rotamer scores and resolution. Two neural networks were created using these features as inputs: one to predict the correctness of main-chain atoms and the other for side chains. The 639 structures were split into 511 that were used to train the neural networks and 128 that were used to test performance. The predicted correctness scores could correctly categorize 92.3% of the main-chain atoms and 87.6% of the side chains. A Coot ML Correctness script was written to display the scores in a graphical user interface as well as for the automatic pruning of chains, residues and side chains with low scores. The automatic pruning function was added to the CCP4i2 Buccaneer automated model-building pipeline, leading to significant improvements, especially for high-resolution structures.
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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 quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure parsed from protein structure at each time-step without using any database, which is different than all existed QA methods. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA
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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 PTMs in proteins has been largely ignored. Predicting accurate 3D PTM models in proteins is closely related to another fundamental problem: predicting the correct side-chain conformations of amino acid residues in proteins. An analysis of publications as well as the paid and free software packages for modeling three-dimensional structures showed that most of them focus on working with unmodified proteins and canonical amino acid residues; the number of articles and software packages placing emphasis on modeling three-dimensional PTM structures is an order of magnitude smaller. This paper focuses on modeling the side-chain conformations of proteins containing PTMs (nonstandard amino acid residues). We collected our own libraries comprising the most frequently observed PTMs from the PDB and implemented a number of algorithms for predicting the side-chain conformation at modification points and in the immediate environment of the protein. A comprehensive analysis of both the algorithms per se and compared to the common Rosetta and FoldX structure modeling packages was also carried out. The proposed algorithmic solutions are comparable in their characteristics to the well-known Rosetta and FoldX packages for the modeling of three-dimensional structures and have great potential for further development and optimization. The source code of algorithmic solutions has been deposited to and is available at the GitHub source.
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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 mass, hydrophobicity index, side chain p K a value, solvent accessible surface area and conservation score of an amino acid. Interestingly, the side chain p K a value appears to be the best feature for prediction, suggesting that the ionization state of amino acid side chains is important for DNA-binding. The predictive performance is enhanced by using multiple features for classifier construction. The classifier that has been constructed using all the five features predicts at 72.71% sensitivity and 67.73% specificity. This is by far the most accurate classifier reported for prediction of DNA-binding residues from sequence data. The classifier has also been evaluated by using the Receiver Operating Characteristic curve and by examining the predictions made for different classes of DNA-binding proteins. Supplementary materials including the datasets are available at .
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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 (101¯0) first prism plane of a single ice crystal. Inclusion of another symmetrical water cluster in the polypentagonal network showed a perfect complementarity to the waters constructing the (202¯1) pyramidal ice plane. The order of ice-binding strength was A20L < A20G < WT < A20T < A20V < A20I, where the top three mutants capable of binding to the first prism and the pyramidal ice planes commonly contained a bifurcated γ-CH3 group. These results suggest that a fine-tuning of the surface of Zoarcidae-derived IBP assisted by a side-chain group regulates the holding property of its polypentagonal water network, the function of which is to freeze the host protein to specific ice planes.
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