Structural genomics projects as well as ab initio protein structure prediction methods provide structures of proteins with no sequence or fold similarity to proteins with known functions. These are often low-resolution structures that may only include the positions of C a atoms. We present a fast and efficient method to predict DNA-binding proteins from just the amino acid sequences and low-resolution, C a -only protein models. The method uses the relative proportions of certain amino acids in the protein sequence, the asymmetry of the spatial distribution of certain other amino acids as well as the dipole moment of the molecule. These quantities are used in a linear formula, with coefficients derived from logistic regression performed on a training set, and DNA-binding is predicted based on whether the result is above a certain threshold. We show that the method is insensitive to errors in the atomic coordinates and provides correct predictions even on inaccurate protein models. We demonstrate that the method is capable of predicting proteins with novel binding site motifs and structures solved in an unbound state. The accuracy of our method is close to another, published method that uses all-atom structures, time-consuming calculations and information on conserved residues.
Average and extreme temperatures will increase in the near future, but how such shifts will affect mortality in natural populations is still unclear. We used a dynamic model to predict mortality under variable temperatures on the basis of heat tolerance laboratory measurements. Theoretical lethal temperatures for 11 Drosophila species under different warming conditions were virtually indistinguishable from empirical results. For Drosophila in the field, daily mortality predicted from ambient temperature records accumulate over weeks or months, consistent with observed seasonal fluctuations and population collapse in nature. Our model quantifies temperature-induced mortality in nature, which is crucial to study the effects of global warming on natural populations, and analyses highlight that critical temperatures are unreliable predictors of mortality.
The appearance of molecular replicators (molecules that can be copied) was probably a critical step in the origin of life. However, parasitic replicators take over, and would have prevented life from taking off, unless the replicators were compartmentalized in reproducing protocells. Paradoxically, control of protocell reproduction would seem to require evolved replicators. We show here that a simpler population structure, based on cycles of transient compartmentalization (TC) and mixing of RNA replicators, is sufficient to prevent takeover by parasitic mutants. TC tends to select for ensembles of replicators that replicate at a similar rate, including a diversity of parasites that could serve as a source of opportunistic functionality. Thus TC in natural, non-biological compartments could have allowed life to take hold. Main text:The earliest molecular replicators (1, 2) must have been plagued by freeloading parasitic replicators (3-6).For example, when the RNA genome of the Q virus was replicated in vitro using the viral replicase, 83% of the genome was deleted due to selection for RNAs with the fastest replication rate (7). Eventually, reproducing compartments (protocells) must have arisen, taming parasites by spatially limiting their propagation and allowing group selection at the compartment level, preventing functional collapse (5,(8)(9)(10). Indeed, serial fusion-division cycles of water-in-oil emulsion
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