Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 30 July 2022 / Approved: 2 August 2022 / Online: 2 August 2022 (03:20:13 CEST)

How to cite: Kondo, H.X.; Iizuka, H.; Masumoto, G.; Kabaya, Y.; Kanematsu, Y.; Takano, Y. Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using A Convolutional Neural Network . Preprints 2022, 2022080029 (doi: 10.20944/preprints202208.0029.v1). Kondo, H.X.; Iizuka, H.; Masumoto, G.; Kabaya, Y.; Kanematsu, Y.; Takano, Y. Elucidation of the Correlation between Heme Distortion and Tertiary Structure of the Heme-Binding Pocket Using A Convolutional Neural Network . Preprints 2022, 2022080029 (doi: 10.20944/preprints202208.0029.v1).

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 regulates 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.

Keywords

heme distortion; pocket conformation; convolutional neural network; machine learning

Subject

LIFE SCIENCES, Biochemistry

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