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Assessing amino acid racemization variability in coral intra-crystalline protein for geochronological applications.

ABSTRACT: Over 500 Free Amino Acid (FAA) and corresponding Total Hydrolysed Amino Acid (THAA) analyses were completed from eight independently-dated, multi-century coral cores of massive Porites sp. colonies. This dataset allows us to re-evaluate the application of amino acid racemization (AAR) for dating late Holocene coral material, 20 years after Goodfriend et al. (GCA56 (1992), 3847) first showed AAR had promise for developing chronologies in coral cores. This re-assessment incorporates recent method improvements, including measurement by RP-HPLC, new quality control approaches (e.g. sampling and sub-sampling protocols, statistically-based data screening criteria), and cleaning steps to isolate the intra-crystalline skeletal protein. We show that the removal of the extra-crystalline contaminants and matrix protein is the most critical step for reproducible results and recommend a protocol of bleaching samples in NaOCl for 48 h to maximise removal of open system proteins while minimising the induced racemization. We demonstrate that AAR follows closed system behaviour in the intra-crystalline fraction of the coral skeletal proteins. Our study is the first to assess the natural variability in intra-crystalline AAR between colonies, and we use coral cores taken from the Great Barrier Reef, Australia, and Jarvis Island in the equatorial Pacific to explore variability associated with different environmental conditions and thermal histories. Chronologies were developed from THAA Asx D/L, Ala D/L, Glx D/L and FAA Asx D/L for each core and least squares Monte Carlo modelling applied in order to quantify uncertainty of AAR age determinations and assess the level of dating resolution possible over the last 5 centuries. AAR within colonies follow consistent stratigraphic aging. However, there are systematic differences in rates between the colonies, which would preclude direct comparison from one colony to another for accurate age estimation. When AAR age models are developed from a combined dataset to include this natural inter-colony variability THAA Asx D/L, Glx D/L and Ala D/L give a 2? age uncertainty of ±19, ±38 and ±29 year, for the 20th C respectively; in comparison 2? age uncertainties from a single colony are ±12, ±12 and ±14 year. This is the first demonstration of FAA D/L for dating coral and following strict protocols 2? precisions of ±24 years can be achieved across different colonies in samples from the last 150 years, and can be ±10 years within a core from a single colony. Despite these relatively large error estimates, AAR would be a valuable tool in situations where a large number of samples need to be screened rapidly and cheaply (e.g. identifying material from mixed populations in beach or uplift deposits), prior to and complementing the more time-consuming geochronological tools of U/Th or seasonal isotopic timeseries.

SUBMITTER: Hendy EJ

PROVIDER: S-EPMC3617617 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Assessing amino acid racemization variability in coral intra-crystalline protein for geochronological applications.

Hendy Erica J EJ Tomiak Peter J PJ Collins Matthew J MJ Hellstrom John J Tudhope Alexander W AW Lough Janice M JM Penkman Kirsty E H KE

Geochimica et cosmochimica acta 20120601 9-2

Over 500 Free Amino Acid (FAA) and corresponding Total Hydrolysed Amino Acid (THAA) analyses were completed from eight independently-dated, multi-century coral cores of massive Porites sp. colonies. This dataset allows us to re-evaluate the application of amino acid racemization (AAR) for dating late Holocene coral material, 20 years after Goodfriend et al. (GCA56 (1992), 3847) first showed AAR had promise for developing chronologies in coral cores. This re-assessment incorp ...[more]

PMID: 23564968

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Project description:BackgroundThe number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction - a process called 'end-to-end learning' - has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN.ResultsBy using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension.ConclusionOur study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Dataset Information

Assessing amino acid racemization variability in coral intra-crystalline protein for geochronological applications.

Publications

Assessing amino acid racemization variability in coral intra-crystalline protein for geochronological applications.

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