TY - JOUR T1 - Predictability of Evolutionary Trajectories in Fitness Landscapes A1 - Lobkovsky, Alexander E. A1 - Wolf, Yuri I. A1 - Koonin, Eugene V. Y1 - 2011/12/15 N2 - Author Summary Is evolution deterministic, hence predictable, or stochastic, that is unpredictable? What would happen if one could “replay the tape of evolution”: will the outcomes of evolution be completely different or is evolution so constrained that history will be repeated? Arguably, these questions are among the most intriguing and most difficult in evolutionary biology. In other words, the predictability of evolution depends on the fraction of the trajectories on fitness landscapes that are accessible for evolutionary exploration. Because direct experimental investigation of fitness landscapes is technically challenging, the available studies only explore a minuscule portion of the landscape for individual enzymes. We therefore sought to investigate the topography of fitness landscapes within the framework of a previously developed model of protein folding and evolution where fitness is equated with robustness to misfolding. We show that model-derived and experimental landscapes are significantly smoother than random landscapes and resemble moderately perturbed additive landscapes; thus, these landscapes are substantially robust to mutation. The model landscapes show a deficit of suboptimal peaks even compared with noisy additive landscapes with similar overall roughness. Thus, the smoothness and substantial deficit of peaks in fitness landscapes of protein evolution could be fundamental consequences of the physics of protein folding. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 12 UR - https://doi.org/10.1371/journal.pcbi.1002302 SP - e1002302 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002302 ER -