One of the critical variables that determine the rate of any reaction is temperature. For biological systems, the effects of temperature are convoluted with myriad (and often opposing) contributions from enzyme catalysis, protein stability, and temperature-dependent regulation, for example. We have coined the phrase “macromolecular rate theory (MMRT)” to describe the temperature dependence of enzyme-catalyzed rates independent of stability or regulatory processes. Central to MMRT is the observation that enzyme-catalyzed reactions occur with significant values of ΔC_p^‡ that are in general negative. That is, the heat capacity (C_p) for the enzyme–substrate complex is generally larger than the C_p for the enzyme-transition state complex. Consistent with a classical description of enzyme catalysis, a negative value for ΔC_p^‡ is the result of the enzyme binding relatively weakly to the substrate and very tightly to the transition state. This observation of negative ΔC_p^‡ has important implications for the temperature dependence of enzyme-catalyzed rates. Here, we lay out the fundamentals of MMRT. We present a number of hypotheses that arise directly from MMRT including a theoretical justification for the large size of enzymes and the basis for their optimum temperatures. We rationalize the behavior of psychrophilic enzymes and describe a “psychrophilic trap” which places limits on the evolution of enzymes in low temperature environments. One of the defining characteristics of biology is catalysis of chemical reactions by enzymes, and enzymes drive much of metabolism. Therefore, we also expect to see characteristics of MMRT at the level of cells, whole organisms, and even ecosystems.

Biomolecular simulation is increasingly central to understanding and designing biological molecules and their interactions. Detailed, physics‐based simulation methods are demonstrating rapidly growing impact in areas as diverse as biocatalysis, drug delivery, biomaterials, biotechnology, and drug design. Simulations offer the potential of uniquely detailed, atomic‐level insight into mechanisms, dynamics, and processes, as well as increasingly accurate predictions of molecular properties. Simulations can now be used as computational assays of biological activity, for example, in predictions of drug resistance. Methodological and algorithmic developments, combined with advances in computational hardware, are transforming the scope and range of calculations. Different types of methods are required for different types of problem. Accurate methods and extensive simulations promise quantitative comparison with experiments across biochemistry. Atomistic simulations can now access experimentally relevant timescales for large systems, leading to a fertile interplay of experiment and theory and offering unprecedented opportunities for validating and developing models. Coarse‐grained methods allow studies on larger length‐ and timescales, and theoretical developments are bringing electronic structure calculations into new regimes. Multiscale methods are another key focus for development, combining different levels of theory to increase accuracy, aiming to connect chemical and molecular changes to macroscopic observables. In this review, we outline biomolecular simulation methods and highlight examples of its application to investigate questions in biology.This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Free Energy Methods