Search for: All records

Billions of years ago, the Earth’s atmosphere had very little oxygen. It was only after some bacteria and early plants evolved to harness energy from sunlight that oxygen began to fill the Earth’s environment. Oxygen is highly reactive and can interfere with enzymes and other molecules that are essential to life. Organisms living at this point in history therefore had to adapt to survive in this new oxygen-rich world. An ancient family of enzymes known as ribonucleotide reductases are used by all free-living organisms and many viruses to repair and replicate their DNA. Because of their essential role in managing DNA, these enzymes have been around on Earth for billions of years. Understanding how they evolved could therefore shed light on how nature adapted to increasing oxygen levels and other environmental changes at the molecular level. One approach to study how proteins evolved is to use computational analysis to construct a phylogenetic tree. This reveals how existing members of a family are related to one another based on the chain of molecules (known as amino acids) that make up each protein. Despite having similar structures and all having the same function, ribonucleotide reductases have remarkably diverse sequences of amino acids. This makes it computationally very demanding to build a phylogenetic tree. To overcome this, Burnim, Spence, Xu et al. created a phylogenetic tree using structural information from a part of the enzyme that is relatively similar in many modern-day ribonucleotide reductases. The final result took seven continuous months on a supercomputer to generate, and includes over 6,000 members of the enzyme family. The phylogenetic tree revealed a new distinct group of ribonucleotide reductases that may explain how one adaptation to increasing levels of oxygen emerged in some family members, while another adaptation emerged in others. The approach used in this work also opens up a new way to study how other highly diverse enzymes and other protein families evolved, potentially revealing new insights about our planet’s past. 

Mixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori . To address this issue, the REGALS method (regularized alternating least squares) is introduced, which incorporates simple expectations about the data as prior knowledge, and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, making it well suited for exploring datasets with unknown species. Here, REGALS is applied to the analysis of experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing and time-resolved temperature jump. Based on its performance with these challenging datasets, it is anticipated that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and Python and is available freely as an open-source software package.