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1. CLADE 2.0: Evolution-Driven Cluster Learning-Assisted Directed Evolution

   Yuchi Qiu; Guo-Wei Wei (May 2022, Journal of chemical information and modeling)
2. Modeling the Evolution of Rates of Continuous Trait Evolution

   https://doi.org/10.1093/sysbio/syac068  

   Martin, Bruce S.; Bradburd, Gideon S.; Harmon, Luke J.; Weber, Marjorie G.; López-Fernández, ed., Hernán (November 2022, Systematic Biology)

   Abstract Rates of phenotypic evolution vary markedly across the tree of life, from the accelerated evolution apparent in adaptive radiations to the remarkable evolutionary stasis exhibited by so-called “living fossils.” Such rate variation has important consequences for large-scale evolutionary dynamics, generating vast disparities in phenotypic diversity across space, time, and taxa. Despite this, most methods for estimating trait evolution rates assume rates vary deterministically with respect to some variable of interest or change infrequently during a clade’s history. These assumptions may cause underfitting of trait evolution models and mislead hypothesis testing. Here, we develop a new trait evolution model that allows rates to vary gradually and stochastically across a clade. Further, we extend this model to accommodate generally decreasing or increasing rates over time, allowing for flexible modeling of “early/late bursts” of trait evolution. We implement a Bayesian method, termed “evolving rates” (evorates for short), to efficiently fit this model to comparative data. Through simulation, we demonstrate that evorates can reliably infer both how and in which lineages trait evolution rates varied during a clade’s history. We apply this method to body size evolution in cetaceans, recovering substantial support for an overall slowdown in body size evolution over time with recent bursts among some oceanic dolphins and relative stasis among beaked whales of the genus Mesoplodon. These results unify and expand on previous research, demonstrating the empirical utility of evorates. [cetacea; macroevolution; comparative methods; phenotypic diversity; disparity; early burst; late burst] 

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3. Evolution in Cities

   https://doi.org/10.1146/annurev-ecolsys-012021-021402  

   Diamond, Sarah E.; Martin, Ryan A. (November 2021, Annual Review of Ecology, Evolution, and Systematics)

   Although research performed in cities will not uncover new evolutionary mechanisms, it could provide unprecedented opportunities to examine the interplay of evolutionary forces in new ways and new avenues to address classic questions. However, while the variation within and among cities affords many opportunities to advance evolutionary biology research, careful alignment between how cities are used and the research questions being asked is necessary to maximize the insights that can be gained. In this review, we develop a framework to help guide alignment between urban evolution research approaches and questions. Using this framework, we highlight what has been accomplished to date in the field of urban evolution and identify several up-and-coming research directions for further expansion. We conclude that urban environments can be used as evolutionary test beds to tackle both new and long-standing questions in evolutionary biology. 

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4. Microbes: Social Evolution

   https://doi.org/10.1016/B978-0-12-809633-8.90165-1  

   Larsen, T.J. (January 2019, Elsevier)
5. Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

   https://doi.org/10.1073/pnas.2207904119  

   Xie, Wen Jun; Warshel, Arieh (August 2022, Proceedings of the National Academy of Sciences)

   Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ( E MaxEnt ) inferred from their natural homologous sequences using the maximum entropy model. The E MaxEnt of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the E MaxEnt for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the E MaxEnt landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design. 

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