Journal articles: '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 (January 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 (November 19, 2020): 6691–97. http://dx.doi.org/10.1021/acs.jcim.0c00951.

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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 (July 27, 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 (May 29, 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, Liudmila I. Kulikova, Tatiana V. Butkova, Kristina A. Malsagova, Arthur T. Kopylov, and Anna L. Kaysheva. "Modeling Side Chains in the Three-Dimensional Structure of Proteins for Post-Translational Modifications." International Journal of Molecular Sciences 24, no. 17 (August 30, 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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WANG, LIANGJIANG, and SUSAN J. BROWN. "PREDICTION OF DNA-BINDING RESIDUES FROM SEQUENCE FEATURES." Journal of Bioinformatics and Computational Biology 04, no. 06 (December 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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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 (January 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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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 (October 23, 2007): 519–47. http://dx.doi.org/10.1007/s10732-007-9049-8.

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Mahatabuddin, Sheikh, Daichi Fukami, Tatsuya Arai, Yoshiyuki Nishimiya, Rumi Shimizu, Chie Shibazaki, Hidemasa Kondo, Motoyasu Adachi, and Sakae Tsuda. "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 (May 7, 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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Mortenson, David E., Jay D. Steinkruger, Dale F. Kreitler, Dominic V. Perroni, Gregory P. Sorenson, Lijun Huang, Ritesh Mittal, et al. "High-resolution structures of a heterochiral coiled coil." Proceedings of the National Academy of Sciences 112, no. 43 (October 12, 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 quaternary packing motif in proteins. Heterochiral coiled–coil interactions were predicted over 50 years ago by Crick, and limited experimental data obtained in solution suggest that such interactions can indeed occur. To address the dearth of atomic-level structural characterization of heterochiral helix pairings, we report two independent crystal structures that elucidate coiled-coil packing between l- and d-peptide helices. Both structures resulted from racemic crystallization of a peptide corresponding to the transmembrane segment of the influenza M2 protein. Networks of canonical knobs-into-holes side-chain packing interactions are observed at each helical interface. However, the underlying patterns for these heterochiral coiled coils seem to deviate from the heptad sequence repeat that is characteristic of most homochiral analogs, with an apparent preference for a hendecad repeat pattern.

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Mueller, Benjamin K., Sabareesh Subramaniam, and 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, no. 10 (February 25, 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 for Cα–H···O=C hydrogen bond formation. This region corresponds to the most frequent motif for parallel dimers, GASright, whose best-known example is glycophorin A. The finding suggests a causal link between the high frequency of occurrence of GASright and its propensity for carbon hydrogen bond formation. Investigation of the sequence dependency of the motif determined that Gly residues are required at specific positions where only Gly can act as a donor with its “side chain” Hα. Gly also reduces the steric barrier for non-Gly amino acids at other positions to act as Cα donors, promoting the formation of cooperative hydrogen bonding networks. These findings offer a structural rationale for the occurrence of GxxxG patterns at the GASright interface. The analysis identified the conformational space and the sequence requirement of Cα–H···O=C mediated motifs; we took advantage of these results to develop a structural prediction method. The resulting program, CATM, predicts ab initio the known high-resolution structures of homodimeric GASright motifs at near-atomic level.

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Peng, Jiangling, Mingjie Fan, Kelly X. Huang, Lina A. Huang, Yangmeng Wang, Runkai Yin, Hanyi Zhao, et al. "Design, Synthesis, Computational and Biological Evaluation of Novel Structure Fragments Based on Lithocholic Acid (LCA)." Molecules 28, no. 14 (July 11, 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 diol group or (iii) a vinyl group. These novel fragments were evaluated using luciferase-based reporter assays and the MTS assay. Compared to LCA, the result revealed that the two lead compounds 1a–1b were well tolerated in vitro, maintaining similar potency and efficacy to LCA. The MTS assay results indicated that cell viability was not affected by dose dependence (under 25 µM). Additionally, computational model analysis demonstrated that compounds 1a–1b formed more extensive hydrogen bond networks with Takeda G protein-coupled receptor 5 (TGR5) than LCA. This strategy displayed a potential approach to explore the development of novel endogenous bile acids fragments. Further evaluation on the biological activities of the two lead compounds is ongoing.

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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 (June 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, but also inflammation. Several reports have demonstrated that the potassium channels Kv1.3 and IKCa1, through the use of selective blockers, play important roles in T-cell-mediated events, including T-cell proliferation and the production of pro-inflammatory cytokines. More recently, a potassium channel-blocker for Kv1.3 has been shown to down-regulate bone resorption by decreasing the ratio of RANKL-to-OPG expression by memory-activated T-cells. In this article, we first summarize the mechanisms by which chronically activated/memory T-cells, in concert with B-cells and macrophages, trigger inflammatory bone resorption. Then, we describe the main structural and functional characteristics of potassium channels Kv1.3 and IKCa1 in some of the cells implicated in periodontal disease progression. Finally, this review elucidates some recent advances in the use of potassium channel-blockers of Kv1.3 and IKCa1 to ameliorate the clinical signs or side-effects of several immunological disorders and to decrease inflammatory bone resorption in periodontal disease. ABBREVIATIONS: AICD, activation-induced cell death; APC, antigen-presenting cells; B(K), large conductance; CRAC, calcium release-activated calcium channels; DC, dendritic cell; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; IFN-γ, interferon-γ; IP3, inositol (1,4,5)-triphosphate; (K)ir, inward rectifier; JNK, c-Jun N-terminal kinase; I(K), intermediate conductance; LPS, lipopolysaccharide; L, ligand; MCSF, macrophage colony-stimulating factor; MHC, major histocompatibility complex; NFAT, nuclear factor of activated T-cells; RANK, receptor activator of nuclear factor-κB; TCM, central memory T-cells; TEM, effector memory T-cells; TNF, tumor necrosis factor; TRAIL, TNF-related apoptosis-inducing ligand; OPG, osteoprotegerin; Omp29, 29-kDa outer membrane protein; PKC, protein kinase C; PLC, phospholipase C; RT-PCR, reverse-transcriptase polymerase chain-reaction; S(K), small conductance; TCR, T-cell receptor; and (K)v, voltage-gated.

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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 (June 6, 2017). http://dx.doi.org/10.1038/s41598-017-01498-6.

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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 side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies.

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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 (November 13, 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” proteins. On the other hand, one of the typical conventional networks, the α-carbon network (CAN), was found to be less robust than the ISN, and another typical network, atomic distance network (ADN), failed to distinguish between these two protein structures. Considering that the links in the CAN and ADN are defined by the interactions only between the main chain atoms and by the distance of the closest atom pair between the two amino acid residues, respectively, we can conclude that reflecting structural information from both secondary and tertiary structures in the network parameters improves the quantitative evaluation and robustness in network models, resulting in a quantitative and more robust description of three-dimensional protein structures in the ISN.

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Inada, Yuki, Yuichiro Ono, Kyo Okazaki, Takuma Yamashita, Tomoyuki Kawaguchi, Shingo Kawano, Yoshihiro Kobashigawa, 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 of Ser10. Nuclear magnetic resonance spectrometry and molecular dynamics simulation revealed that the Ser10 hydroxyl group forms hydrogen bonds with the main chain amide group of either Gly57 or Thr59 on the DE loop. These hydrogen bonds prevent the dissociation of edge strands in the DAGH and CBEF β-sheets during the unfolding of the TTR monomer by stabilizing the interaction between β-strands A and D and the quasi-helical structure in the DE loop. We propose that introducing hydrogen bonds to connect the N-terminal region to the DE loop reduces the amyloidogenic potential of TTR by stabilizing the monomer.

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

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