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Thermodynamic prediction of protein neutrality.

ABSTRACT: We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustness to the first few substitutions by increasing its thermodynamic stability. We validate our theory with simulations on lattice protein models and by showing that it quantitatively predicts previously published experimental measurements on subtilisin and our own measurements on variants of TEM1 beta-lactamase. Our work unifies observations about the clustering of functional proteins in sequence space, and provides a basis for interpreting the response of proteins to substitutions in protein engineering applications.

SUBMITTER: Bloom JD

PROVIDER: S-EPMC545518 | biostudies-literature | 2005 Jan

REPOSITORIES: biostudies-literature

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Thermodynamic prediction of protein neutrality.

Bloom Jesse D JD Silberg Jonathan J JJ Wilke Claus O CO Drummond D Allan DA Adami Christoph C Arnold Frances H FH

Proceedings of the National Academy of Sciences of the United States of America 20050111 3

We present a simple theory that uses thermodynamic parameters to predict the probability that a protein retains the wild-type structure after one or more random amino acid substitutions. Our theory predicts that for large numbers of substitutions the probability that a protein retains its structure will decline exponentially with the number of substitutions, with the severity of this decline determined by properties of the structure. Our theory also predicts that a protein can gain extra robustn ...[more]

PMID: 15644440

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Project description:BackgroundMicroRNAs (miRNAs) are small regulatory RNA that mediate RNA interference by binding to various mRNA target regions. There have been several computational methods for the identification of target mRNAs for miRNAs. However, these have considered all contributory features as scalar representations, primarily, as thermodynamic or sequence-based features. Further, a majority of these methods solely target canonical sites, which are sites with "seed" complementarity. Here, we present a machine-learning classification scheme, titled Avishkar, which captures the spatial profile of miRNA-mRNA interactions via smooth B-spline curves, separately for various input features, such as thermodynamic and sequence features. Further, we use a principled approach to uniformly model canonical and non-canonical seed matches, using a novel seed enrichment metric.ResultsWe demonstrate that large number of seed-match patterns have high enrichment values, conserved across species, and that majority of miRNA binding sites involve non-canonical matches, corroborating recent findings. Using spatial curves and popular categorical features, such as target site length and location, we train a linear SVM model, utilizing experimental CLIP-seq data. Our model significantly outperforms all established methods, for both canonical and non-canonical sites. We achieve this while using a much larger candidate miRNA-mRNA interaction set than prior work.ConclusionsWe have developed an efficient SVM-based model for miRNA target prediction using recent CLIP-seq data, demonstrating superior performance, evaluated using ROC curves, specifically about 20% better than the state-of-the-art, for different species (human or mouse), or different target types (canonical or non-canonical). To the best of our knowledge we provide the first distributed framework for microRNA target prediction based on Apache Hadoop and Spark.AvailabilityAll source code and data is publicly available at https://bitbucket.org/cellsandmachines/avishkar.

Project description:Proteins from thermophiles are generally more thermostable than their mesophilic homologs, but little is known about the evolutionary process driving these differences. Here we attempt to understand how the diverse thermostabilities of bacterial ribonuclease H1 (RNH) proteins evolved. RNH proteins from Thermus thermophilus (ttRNH) and Escherichia coli (ecRNH) share similar structures but differ in melting temperature (T(m)) by 20 °C. ttRNH's greater stability is caused in part by the presence of residual structure in the unfolded state, which results in a low heat capacity of unfolding (ΔCp) relative to ecRNH. We first characterized RNH proteins from a variety of extant bacteria and found that Tm correlates with the species' growth temperatures, consistent with environmental selection for stability. We then used ancestral sequence reconstruction to statistically infer evolutionary intermediates along lineages leading to ecRNH and ttRNH from their common ancestor, which existed approximately 3 billion years ago. Finally, we synthesized and experimentally characterized these intermediates. The shared ancestor has a melting temperature between those of ttRNH and ecRNH; the T(m)s of intermediate ancestors along the ttRNH lineage increased gradually over time, while the ecRNH lineage exhibited an abrupt drop in Tm followed by relatively little change. To determine whether the underlying mechanisms for thermostability correlate with the changes in T(m), we measured the thermodynamic basis for stabilization--ΔCp and other thermodynamic parameters--for each of the ancestors. We observed that, while the T(m) changes smoothly, the mechanistic basis for stability fluctuates over evolutionary time. Thus, even while overall stability appears to be strongly driven by selection, the proteins explored a wide variety of mechanisms of stabilization, a phenomenon we call "thermodynamic system drift." This suggests that even on lineages with strong selection to increase stability, proteins have wide latitude to explore sequence space, generating biophysical diversity and potentially opening new evolutionary pathways.

Dataset Information

Thermodynamic prediction of protein neutrality.

Publications

Thermodynamic prediction of protein neutrality.

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