Year of seagrass loss for a range of interaction strengths, when the seagrass model parameters are varied.

<p>For each scenario, one parameter was varied while other parameters were held constant. Model scenarios when the local stressor is not managed are indicated with solid lines and scenarios when the local stressor is improved are indicated with dashed lines. Each colour indicates a different parameter value. (A) Varying warming effect sizes (parameter <i>a₁</i>, black <i>a₁</i> = 0.021, blue <i>a₁</i> = 0.023, red <i>a₁</i> = 0.025). (B) Varying recruitment rates (parameter <i>R</i>, black <i>R</i> = 0.04, blue <i>R</i> = 0.05, red <i>R</i> = 0.06). (C) Varying base mortality rates (Mortality in year 2010, <i>M₂₀₁₀</i>, black <i>M₂₀₁₀</i> = 0.076, blue <i>M₂₀₁₀</i> = 0.096, red <i>M₂₀₁₀</i> = 0.116). (D) Varying the effect of the local stressor on mortality rate (parameter <i>a₂</i>, black <i>a₂</i> = 0.02, blue <i>a₂</i> = 0.03, red <i>a₁</i> = 0.04). In (D), the simulations without the local stressor overlay each other.</p

Co-tolerance of species to both climate and local stressors for three types of interactions.

<p>Species tolerances were generated randomly (nominal scales) for an additive interaction (random co-tolerance, ρ = 0), a synergistic interaction (negative co-tolerance, ρ = −0.8), and an antagonistic interaction (positive co-tolerance, ρ = 0.8). Each point represents the tolerances of a single species to the two stressors. Species in the dark grey region will be threatened by climate change stress, the local stressor will additionally affect species in the light grey region. Species in the white region will be unaffected by either stressor. The most species will be lost with a synergism and the least with an antagonism <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065765#pone.0065765-Vinebrooke1" target="_blank">[3]</a>.</p

Predicting species loss with co-tolerance relationships.

<p>(A) The proportion of species remaining out of 10 000 for different magnitudes of warming temperature. Dashed lines show species remaining with local and global stressors for different interactions. The local stressor was assumed to affect half the species in the absence of the climate stressor. Increasing magnitudes of the climate stressor reduce the proportion of species remaining. The solid line shows the species remaining without the local stress (same for all interaction types). (B) Species gained by reducing the local stressor for different cotolerance strengths (x-axis is the correlation coefficient for stressor responses, negative is synergistic and positive is antagonistic). Management will have the greatest benefit at low climate impact sites (dotted line) and little benefit at high climate impact sites (dashed line), regardless of the interaction type. At moderate impact sites however, there are greatest management gains when there is negative co-tolerance.</p

Appendix A. Nine figures showing the sensitivity to key parameters of long term harvest and probability of collapse.

Nine figures showing the sensitivity to key parameters of long term harvest and probability of collapse

Empirical example of management effectiveness for negative co-tolerance.

<p>Proportion of coral reef fish species remaining out of the 134 observed for different magnitudes of climate change impacts (data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065765#pone.0065765-Graham1" target="_blank">[12]</a>). The solid line is for a fishing stressor affecting 50% of species and the dashed line without the fishing impact.</p

Mortality rate of seagrass for different interaction types.

<p>Mortality rate is high with both warming and the local stressor (water quality, dark grey bar). If the local stressor is improved (light grey bars), mortality rate is reduced by 0.02 per year for an additive interaction. Management with a synergistic interaction between warming and local stressor gains a greater reduction in mortality rate, whereas the reduction is small with a dominance antagonism. If there is a mitigative antagonism, mortality rate increases if the local stressor is improved.</p

Gating by Tryptophan 73 Exposes a Cryptic Pocket at the Protein-Binding Interface of the Oncogenic eIF4E Protein

Targeting protein–protein interacting sites for potential therapeutic applications is a challenge in the development of inhibitors, and this becomes more difficult when these interfaces are relatively planar, as in the eukaryotic translation initiation factor 4E (eIF4E) protein. eIF4E is an oncogene that is overexpressed in numerous forms of cancer, making it a prime target as a therapeutic molecule. We report here the presence of a cryptic pocket at the protein-binding interface of eIF4E, which opens transiently during molecular dynamics simulations of the protein in solvent water and is observed to be stable when solvent water is mixed with benzene molecules. This pocket can also be seen in the ensemble of structures available from the solution-state conformations of eIF4E. The accessibility of the pocket is gated by the side-chain transitions of an evolutionarily conserved tryptophan residue. It is found to be feasible for accommodating clusters of benzene molecules, which signify the plasticity and ligandability of the pocket. We also observe that the newly formed cavity provides a favorable binding environment for interaction of a well-recognized small molecule inhibitor of eIF4E. The occurrence of this transiently accessible cavity highlights the existence of a more pronounced binding groove in a region that has traditionally been considered to be planar. Together, the data suggest that an alternate binding cavity exists on eIF4E and could be exploited for the rational design and development of a new class of lead compounds against the protein

Seagrass density for different interactions with and without management of the local stressor.

<p>(A) Decline in seagrass density with and without improving the local stressor (water quality) for additive, synergistic (5% of temperature effect size), and antagonistic interactions (−2.5% of temperature effect size). The grey line represents the 10% seagrass density threshold where seagrass loss is believed to be irreversible <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0065765#pone.0065765-Jorda1" target="_blank">[9]</a>. The interaction scenarios with the local stressor almost perfectly overlay each other. (B) Year of seagrass loss for a range of interaction strengths (positive is synergistic, negative is antagonistic) when water quality is not managed (solid line) and when water quality is improved (dashed line).</p

Comparison of 4EBP1 and eIF4G1 peptides suggests that eIF4E interacting peptides can form an ensemble of conformations when in complex with eIF4E.

<p><b>A)</b> An overlay of two eIF4E crystal structures complexed with either a 4EBP1 (1EJ4) or eIF4G1 (2W97) derived peptide demonstrating the deviation in their C-terminal structural conformations. 4EBP1 is shown in salmon and eIF4G1 in cyan. <b>B)</b> A plot of the φ and ψ angle distribution, derived from the 50 ns simulations of the peptides eIF4G1 and 4EBP bound to eIF4E, for the residues L10 and M10 respectively. <b>C)</b> A plot of the φ and ψ angle distributions, derived from the 50 ns simulations of peptides eIF4G1 and 4EBP bound to eIF4E, for the residues G11 and E11 respectively. <b>D)</b> A plot showing the distribution of distances, for the peptides eIF4G1 and 4E-BP1 when bound to eIF4E, between the Cα atoms of residues 6 and 10 versus the distance between the Cα atoms of residues 8 and 12. The distances were calculated from their respective 50 ns simulations for both peptides. <b>E)</b> A histogram of the angular distribution between the Cα atoms of positions 6, 8 and 10 of the eIF4G1 and 4EBP1 peptides from the 50ns simulations respectively.</p

Comparison of the structural dynamics of N-Capping motifs, in peptides bound to eIF4E, containing either S or T at position 5.

<p>The S5 and T5 N-Capping residues show distinctly different behaviors with respect to each other in terms of the frequency of rotation of their side chains in their respective simulations when bound to eIF4E. <b>A)</b> The S5 side chain can form an interaction with the K1 side chain of the bound peptide and point away from the helix leaving the amides solvated. <b>B)</b> The S5 side chain can also form hydrogen bond interactions with the free amide groups of the first turn of the peptide’s helix. <b>C)</b> The T5 side chain can also make these interactions, however if the hydroxyl of the T5 interacts with K1, its methyl group will disrupt solvation of the free amide groups. Thus it is energetically more favourable for T5 to align its hydroxyl group towards the helix whist S5, which lacks the methyl, has more freedom to rotate, and engages in one of the two hydrogen bonds. Deviations in the planarity of the tyrosine and phenylalanine ring systems are within the tolerances of the torsional restraints of the MD simulations. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047235#pone.0047235-Macias1" target="_blank">[31]</a>.</p