In silico prediction of prolactin molecules as a tool for equine genomics reproduction

Abstract

The prolactin hormone is involved in several biological functions, although its main role resides on reproduction. As it interferes on fertility changes, studies focused on human health have established a linkage of this hormone to fertility losses. Regarding animal research, there is still a lack of information about the structure of prolactin. In case of horse breeding, prolactin has a particular influence; once there is an individualization of these animals and equines are known for presenting several reproductive disorders. As there is no molecular structure available for the prolactin hormone and receptor, we performed several bioinformatics analyses through prediction and refinement softwares, as well as manual modifications. Aiming to elucidate the first computational structure of both molecules and analyse structural and functional aspects related to these proteins, here we provide the first known equine model for prolactin and prolactin receptor, which obtained high global quality scores in diverse software’s for quality assessment. QMEAN overall score obtained for ePrl was (− 4.09) and QMEANbrane for ePrlr was (− 8.45), which proves the structures’ reliability. This study will implement another tool in equine genomics in order to give light to interactions of these molecules, structural and functional alterations and therefore help diagnosing fertility problems, contributing in the selection of a high genetic herd.

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Acknowledgements

The authors declare there was no conflict of interest. This project was developed by a Biotechnology undergraduate student through supporting scholarship from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). FSK is student of the Graduate Program in Biotechnology at Universidade Federal de Pelotas also supported by CNPQ.

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Correspondence to P. M. M. Leon.

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Online Resource 1 QMEAN graphical result for ePrl local quality estimation. The predicted model is closer to score 1, the greater similarity to the database and correctly positioned residues (TIFF 125 kb)

Online Resource 2 QMEANbrane result for ePrlr. The structure is within the expected range for TM proteins (TIFF 199 kb)

Online Resource 3 Graphical results for quality estimate of the predicted models obtained in ProQ2 for equine prolactin (ePrl) global quality. (A) Plot representation before refinement tools were implied and (B), after. Scores closer to 1 represent an ideal model (TIFF 364 kb)

Online Resource 4 Graphical results for quality estimate of the predicted models obtained in ProQ2 for equine prolactin receptor (ePrlr) global quality. (A) Plot representation before refinement tools were implied and (B), after. Scores closer to 1 represent an ideal model (TIFF 780 kb)

Online Resource 5 PsiPred specific residue quality for ePrl. Blue bars shows the confidence of the secondary structure prediction (TIFF 1194 kb)

Online Resource 6 PsiPred confidence of secondary structure prediction for ePrlr. Blue bars shows the confidence of the secondary structure prediction (PDB 263 kb)

Online Resource 7 Secondary structure comparison in PyMol Molecular Graphics with PDB-available models for (A,B) Prl: Equine Prl (red); 1RW5: human prolactin (blue); 3NPZ: rat prolactin (green). (C) Extracellular and transmembrane domains of different Prlr. Equine Prlr (red); 3NPZ: human prolactin receptor (green); 1BP3: human prolactin receptor (magenta); 3MGZ: human prolactin receptor (dark gray); 2NTI: human transmembrane domain (yellow); 3EW3: rat prolactin receptor (blue) (PDB 379 kb)

Online Resource 8 PDB structure for ePRL (PDB 263 kb)

Online Resource 9 PDB structure for ePRLR (PDB 379 kb)

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Neis, A., Kremer, F.S., Pinto, L.S. et al. In silico prediction of prolactin molecules as a tool for equine genomics reproduction. Mol Divers 23, 1019–1028 (2019). https://doi.org/10.1007/s11030-018-09914-3

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Keywords

  • Mares
  • Infertility
  • PRL
  • PRLR
  • Bioinformatics