All domains of life are affected by temperature. Environmental temperature affects both macro- and molecular features, from species’ distribution and life cycle, to biochemistry. Temperature has a particularly important effect on protein function because it influences protein folding and structure as well as catalytic activity. Because high temperatures can denature key proteins needed for survival, elevated temperatures reduce fitness and lead to death if the organism cannot compensate for the protein loss. Plants are no exception to this rule. Although they have developed specific response pathways to deal with heat stress, elevated temperatures substantially reduce crop yields. Heat-related yield loss is particularly concerning given the increases in global temperature projected to occur in the next century. Adapting crops to maintain high yields in a warming world will require better understanding of molecular heat tolerance in plants, faster breeding strategies for a range of traits, and (likely) targeted genome editing approaches. This dissertation begins by discussing the Practical Haplotype Graph (PHG), a new imputation tool that allows breeders to genotype a breeding population with less input data. Reduced data requirements can lower sequencing costs and may make fast recurrent genomic selection more feasible for small breeding programs. Such tools will be needed to increase the rate of genetic gain and breed for heat-tolerant varieties. The remainder of the dissertation focuses on interactions between temperature and protein stability. It is still difficult to measure protein stability across the proteome, so the work here focuses on using evolutionary conservation to model protein stability across many species. An initial model built to predict optimal growth temperature (OGT) in prokaryotic species demonstrates that tRNA sequence is an excellent predictor of optimal temperature. The resulting OGT predictions are used to determine which elements of protein sequence are most predictive of temperature sensitivity. Site-specific linear regressions based on amino acid chemical properties make it possible to associate residues with temperature sensitivity across both prokaryotes and eukaryotes. These models are applied to plants to investigate how protein stability is distributed across tissues and organelles, and how mutations affect protein stability across populations.