About Revenant

Protein resurrection:

Revenant is a database of resurrected proteins. These proteins have been obtained using ancestral sequence reconstruction (ASR) techniques (Joy et al. 2016) which uses computational methods to predict ancestral sequences. ASR uses sequence alignments of present-day organisms and phylogenetic inferences to predict sequences in given internal nodes of the phylogenetic tree. These predicted sequences could be synthesized using molecular biology techniques to be further expressed and purified as nowadays proteins. These purified proteins could be experimentally characterized as any regular protein and it is possible to obtain their structure by X-Ray Crystallography or Nuclear Magnetic Resonance-NMR. These ancestral proteins are therefore resurrected and the serie of methods by which are obtained are called as Ancestral Protein Resurrection (APR). As a time machine, APR represents a powerful strategy for testing different biological hypotheses about the structure, function, dynamics (Zou et al. 2015), specificity and biological activity (Eick et al. 2012), stability of proteins coming from ancient organism who lived millions of years ago.

Database construction:

Scientific publications related with ancestral protein resurrection were identified through the implementation of web-scraping and text mining techniques using standard Python libraries. We manually inspect each publication in order to identify and characterize ancestral proteins which have been resurrected as we explained above.

The reference publication where the resurrected protein was first described is used to annotate relevant information as: estimated chronological time of the ancestral protein, organism, reconstruction method, extant sequences used, among others.

Additionally, those resurrected proteins that have been crystallized are linked with Protein Data Bank, GO terms and Ligand information. It is also possible to visualize the structure and its features in the Revenant web server.

Our Team:

Revenant is created and maintained in a joint effort by the Structural Bioinformatics Group - National University of Quilmes (SBG-UNQ) and the Group of Artificial Intelligence - Pontifical Catholic University of Peru (IA-PUCP).

SBG-UNQ:

The structural Bioinformatics Group at National University of Quilmes is focused in the use and development of molecular evolutionary models to understand structure-function-dynamics of proteins.

Gustavo Parisi

SBG Director

Alexander Monzón

Postdoctoral Fellow

Matias Carletti

PhD Student

Guillermo Benitez

PhD Student

Silvina Fornasari

Adjunct Researcher

Learn more about SBG-UNQ

IA-PUCP:

The Group of Artificial Intelligence IA-PUCP is one of the most groundbreaking research groups at the Pontifical Catholic University of Peru, as well as the most important Peruvian center in Artificial Intelligence

Layla Hirsh

Principal Researcher

Emilio Garcia-Rios

Researcher

Learn more about IA-PUCP

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Eick, G.N., Colucci, J.K., Harms, M.J., Ortlund, E.A. and Thornton, J.W. 2012.
Evolution of minimal specificity and promiscuity in steroid hormone receptors. PLoS Genetics 8(11), p. e1003072.

Gumulya, Y. and Gillam, E.M.J. 2017.
Exploring the past and the future of protein evolution with ancestral sequence reconstruction: the “retro” approach to protein engineering. The Biochemical Journal 474(1), pp. 1–19.

Huelsenbeck, J.P., Joyce, P., Lakner, C. and Ronquist, F. 2008.
Bayesian analysis of amino acid substitution models. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 363(1512), pp. 3941–3953.

Joy, J.B., Liang, R.H., McCloskey, R.M., Nguyen, T. and Poon, A.F.Y. 2016.
Ancestral Reconstruction. PLoS Computational Biology 12(7), p. e1004763.

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Zou, T., Risso, V.A., Gavira, J.A., Sanchez-Ruiz, J.M. and Ozkan, S.B. 2015.
Evolution of conformational dynamics determines the conversion of a promiscuous generalist into a specialist enzyme. Molecular Biology and Evolution 32(1), pp. 132–143.