Dataset Information

Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using a Convolutional Neural Network.

ABSTRACT: Heme proteins serve diverse and pivotal biological functions. Therefore, clarifying the mechanisms of these diverse functions of heme is a crucial scientific topic. Distortion of heme porphyrin is one of the key factors regulating the chemical properties of heme. Here, we constructed convolutional neural network models for predicting heme distortion from the tertiary structure of the heme-binding pocket to examine their correlation. For saddling, ruffling, doming, and waving distortions, the experimental structure and predicted values were closely correlated. Furthermore, we assessed the correlation between the cavity shape and molecular structure of heme and demonstrated that hemes in protein pockets with similar structures exhibit near-identical structures, indicating the regulation of heme distortion through the protein environment. These findings indicate that the tertiary structure of the heme-binding pocket is one of the factors regulating the distortion of heme porphyrin, thereby controlling the chemical properties of heme relevant to the protein function; this implies a structure-function correlation in heme proteins.

SUBMITTER: Kondo HX

PROVIDER: S-EPMC9496533 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using a Convolutional Neural Network.

Kondo Hiroko X HX Iizuka Hiroyuki H Masumoto Gen G Kabaya Yuichi Y Kanematsu Yusuke Y Takano Yu Y

Biomolecules 20220824 9

Heme proteins serve diverse and pivotal biological functions. Therefore, clarifying the mechanisms of these diverse functions of heme is a crucial scientific topic. Distortion of heme porphyrin is one of the key factors regulating the chemical properties of heme. Here, we constructed convolutional neural network models for predicting heme distortion from the tertiary structure of the heme-binding pocket to examine their correlation. For saddling, ruffling, doming, and waving distortions, the exp ...[more]

PMID: 36139011

Similar Datasets

Project description:BackgroundTranscription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent years, convolutional neural networks (CNNs) have succeeded in TF-DNA binding prediction, but existing DL methods' accuracy needs to be improved and convolution function in TF-DNA binding prediction should be further explored.ResultsWe develop a cascaded convolutional neural network model named CacPred to predict TF-DNA binding on 790 Chromatin immunoprecipitation-sequencing (ChIP-seq) datasets and seven ChIP-nexus (chromatin immunoprecipitation experiments with nucleotide resolution through exonuclease, unique barcode, and single ligation) datasets. We compare CacPred to six existing DL models across nine standard evaluation metrics. Our results indicate that CacPred outperforms all comparison models for TF-DNA binding prediction, and the average accuracy (ACC), matthews correlation coefficient (MCC), and the area of eight metrics radar (AEMR) are improved by 3.3%, 9.2%, and 6.4% on 790 ChIP-seq datasets. Meanwhile, CacPred improves the average ACC, MCC, and AEMR of 5.5%, 16.8%, and 12.9% on seven ChIP-nexus datasets. To explain the proposed method, motifs are used to show features CacPred learned. In light of the results, CacPred can find some significant motifs from input sequences.ConclusionsThis paper indicates that CacPred performs better than existing models on ChIP-seq data. Seven ChIP-nexus datasets are also analyzed, and they coincide with results that our proposed method performs the best on ChIP-seq data. CacPred only is equipped with the convolutional algorithm, demonstrating that pooling processing of the existing models leads to losing some sequence information. Some significant motifs are found, showing that CacPred can learn features from input sequences. In this study, we demonstrate that CacPred is an effective and feasible model for predicting TF-DNA binding. CacPred is freely available at https://github.com/zhangsq06/CacPred .

Dataset Information

Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using a Convolutional Neural Network.

Publications

Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using a Convolutional Neural Network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets