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Amino Acid Sequence of Oligopeptide Causes Marked Difference in DNA Compaction and Transcription.

ABSTRACT: Compaction of T4 phage DNA (166 kbp) by short oligopeptide octamers composed of two types of amino acids, four cationic lysine (K), and four polar nonionic serine (S) having different sequence order was studied by single-molecule fluorescent microscopy. We found that efficient DNA compaction by oligopeptide octamers depends on the geometrical match between phosphate groups of DNA and cationic amines. The amino acid sequence order in octamers dramatically affects the mechanism of DNA compaction, which changes from a discrete all-or-nothing coil-globule transition induced by a less efficient (K₄S₄) octamer to a continuous compaction transition induced by a (KS)₄ octamer with a stronger DNA-binding character. This difference in the DNA compaction mechanism dramatically changes the packaging density, and the morphology of T4 DNA condensates: DNA is folded into ordered toroidal or rod morphologies during all-or-nothing compaction, whereas disordered DNA condensates are formed as a result of the continuous DNA compaction. Furthermore, the difference in DNA compaction mechanism has a certain effect on the inhibition scenario of the DNA transcription activity, which is gradual for the continuous DNA compaction and abrupt for the all-or-nothing DNA collapse.

SUBMITTER: Zinchenko A

PROVIDER: S-EPMC6531782 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions.Naïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs.

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Amino Acid Sequence of Oligopeptide Causes Marked Difference in DNA Compaction and Transcription.

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