Effect of initial ion positions on the interactions of monovalent and divalent ions with a DNA duplex as revealed with atomistic molecular dynamics simulations

<div><p>Monovalent (Na⁺) and divalent (Mg²⁺) ion distributions around the Dickerson-Drew dodecamer were studied by atomistic molecular dynamics (MD) simulations with AMBER molecular modeling software. Different initial placements of ions were tried and the resulting effects on the ion distributions around DNA were investigated. For monovalent ions, results were found to be nearly independent of initial cation coordinates. However, Mg²⁺ ions demonstrated a strong initial coordinate dependent behavior. While some divalent ions initially placed near the DNA formed essentially permanent direct coordination complexes with electronegative DNA atoms, Mg²⁺ ions initially placed further away from the duplex formed a full, nonexchanging, octahedral first solvation shell. These fully solvated cations were still capable of binding with DNA with events lasting up to 20 ns, and in comparison were bound much longer than Na⁺ ions. Force field parameters were also investigated with modest and little differences arising from ion (ions94 and ions08) and nucleic acid description (ff99, ff99bsc0, and ff10), respectively. Based on known Mg²⁺ ion solvation structure, we conclude that in most cases Mg²⁺ ions retain their first solvation shell, making only solvent-mediated contacts with DNA duplex. The proper way to simulate Mg²⁺ ions around DNA duplex, therefore, should begin with ions placed in the bulk water.</p> </div

Acid hydrolysis of crude tannins from infructescence of <i>Platycarya strobilacea</i> Sieb. et Zucc to produce ellagic acid

<div><p>The infructescence of <i>Platycarya strobilacea</i> Sieb. et Zucc is a well-known traditional medicine in China, Japan and Korea. The infructescence of <i>P. strobilacea</i> Sieb. et Zucc is a rich source of ellagitannins that are composed of ellagic acid (EA) and gallic acid, linked to a sugar moiety. The aim of this study was to prepare EA by acid hydrolysis of crude tannins from the infructescence of <i>P. strobilacea</i> Sieb. et Zucc, and establish a new technological processing method for EA. The natural antioxidant EA was prepared by using the water extraction of infructescence of <i>P. strobilacea</i> Sieb. et Zucc, evaporation, condensation, acid hydrolysis and prepared by the process of crystallisation. The yield percentage of EA from crude EA was more than 20% and the purity of the product was more than 98%, as identified by using HPLC. The structure was identified on the basis of spectroscopic analysis and comparison with authentic compound.</p></div

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein–Protein Binding Affinity

Protein–protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein–protein complex formation and defining the structure–function relationship. Despite advancements in understanding protein–protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein–protein interface. We found that the numbers of specific amino acids at the protein–protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein–Protein Binding Affinity

Protein–protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein–protein complex formation and defining the structure–function relationship. Despite advancements in understanding protein–protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein–protein interface. We found that the numbers of specific amino acids at the protein–protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry

S20 structural variations during MD simulation.

<p>Backbone snapshots of both proteins are shown in shades of red (<i>E. coli</i> light red; <i>T. thermophilus</i> dark red). Backbone starting structures are in yellow.</p

Structural Comparisons of PEI/DNA and PEI/siRNA Complexes Revealed with Molecular Dynamics Simulations

Polyplexes composed of polyethyleneimine (PEI) and DNA or siRNA have attracted great attention for their use in gene therapy. Although many physicochemical characteristics of these polyplexes remain unknown, PEI/DNA complexes have been repeatedly shown to be more stable than their PEI/siRNA counterparts. Here, we examine potential causes for this difference using atomistic molecular dynamics simulations of complexation between linear PEI and DNA or siRNA duplexes containing the same number of bases. The two types of polyplexes are stabilized by similar interactions, as PEI amines primarily interact with nucleic acid phosphate groups but also occasionally interact with groove atoms of both nucleic acids. However, the number of interactions in PEI/DNA complexes is greater than in comparable PEI/siRNA complexes, with interactions between protonated PEI amines and DNA being particularly enhanced. These results indicate that structural differences between DNA and siRNA may play a role in the increased stability of PEI/DNA complexes. In addition, we investigate the binding of PEI chains to polyplexes that have a net positive charge. The binding of PEI to these overcharged complexes involves interactions between PEI and areas on the nucleic acid surface that have maintained a negative electrostatic potential and is facilitated by the release of water from the nucleic acid

Comparisons of S15, S17, and S20 proteins from two different species.

<p><i>E. coli</i> proteins are shown in the lighter shade and <i>T. thermophilus</i> in the darker shade. Contact residues are shown as stick representations and some important parts of the proteins, discussed in the text, are labeled.</p

S15 structural variations during MD simulation.

<p>Backbone snapshots of both proteins are in shades of blue (<i>E. coli</i> light blue; <i>T. thermophilus</i> dark blue). Backbone starting structures are shown in yellow.</p

Contacts between r-proteins and r-RNA in total and for charged residues.

<p>Note: The total number of protein contacts for S15, S17, and S20 above differs from the total number of contact residues presented in Supplementary <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002530#pcbi.1002530.s001" target="_blank">Tables S1</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002530#pcbi.1002530.s002" target="_blank">S2</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002530#pcbi.1002530.s003" target="_blank">S3</a> because some protein residues are in contact with more than one nucleotide, which are presented here as multiple contacts.</p

ANM enrichment factors and significance for 30S proteins.

<p>Note: EF is the enrichment factor, defined as the ratio of root mean square fluctuations for contacting over non-contacting residues. The P-value is the statistical significance computed with a permutation test. See text for details.</p