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ENIGMA: Evolution of Nanomachines in Geospheres and Microbial Ancestors

The research program of the NAI Rutgers University team is focused on a single, compelling question in astrobiology: How did proteins evolve to become the catalysts of life on Earth? Proteins are nanomachines that enable cells to perform complex biochemical tasks, including energy transduction and self-replication. The evolution of these nanomachines allowed early life to convert chemical energy in the environment into useful biological energy. In all extant organisms, electron transfer processes executed by these nanomachines constitute the core of cellular metabolism and catalyze fundamental redox reactions including photosynthesis, respiration and nitrogen fixation. Thus, life’s complexity and its evolution are intimately linked to the evolution of redox proteins. Yet, the origin of such protein nanomachines on Earth and their evolution in microbial ancestors remain an enigma.

To fill this knowledge gap in astrobiological sciences, the ENIGMA research program is focused on understanding the evolution of protein nanomachines, particularly those that are involved in electron transfer and redox processes. It seeks to understand the origin of catalysis, the evolution of protein structures in microbial ancestors, and the co-evolution of proteins and the geosphere through geologic time. ENIGMA has three integrated research themes:

• Theme 1: Synthesis and Function of Nanomachines in the Origin of Life
• Theme 2: Increasing Complexity of Nanomachines in Microbial Ancestors
• Theme 3. Co-Evolution of Nanomachines and the Geosphere

THEME 1: Synthesis and Function of Nanomachines in the Origin of Life
Goal: To understand protein self-assembly, stability, and function in prebiotic systems.
Key Questions:
Can small peptides catalyze reactions of importance to early biochemical evolution?
What are the simplest structures that can perform a biological function?

The Rutgers University team will use protein design to examine the concept that small, low-complexity peptides assembled into minimal functional proteins that subsequently served as modules in the evolution of extant protein nanomachines. Based on analysis of natural proteins (top down approach) or through computational protein design (bottom up approach), they will develop small peptides and characterize their functional potential.

Team members have previously performed extensive structural analysis of metalloproteins in the Protein Data Bank (PDB) and found that the complex structures of nearly all oxidoreductases can be decomposed into a surprisingly limited set of modular metallofold units corresponding to ferredoxin, rubredoxin, cytochrome C, plastocyanin and symmetrin. Each of these units exhibit local pseudosymmetry, indicating they can be constructed from small peptides 10-15 amino acids in length. They now plan to synthesize libraries of such peptides in the presence of relevant metal cofactors and examine their electrochemical and catalytic activity. In addition, the team will also examine these ancient peptide catalysts for structure & effectiveness as electron carriers through novel biological and in vitro assays which have already been developed. Although structural analysis of extant proteins provides clues to their peptide precursors, it has been shown that natural proteins have only explored a minute fraction of possible structure-function space. Using de novo computational protein design, the Rutgers team will develop novel symmetric metallopeptides that have no known natural counterpart. These will be synthesized and characterized for redox and catalytic activity. Such molecules are expected to provide insight into the larger functional potential of prebiotic peptides and may indicate the start of alternative evolutionary paths not traveled by life on Earth.

THEME 2: Increasing Complexity of Nanomachines in Microbial Ancestors
Goal: To understand the evolution of protein structures in early life.
Key Questions:
What are the oldest/most common structures found in proteins?
Which protein structures had evolved at the root of the evolutionary tree?
Can we date important structural innovations that led to new metabolic functions?

To address these issues, the Rutgers team has developed a new method (Sahle – Structural Alignment-based Homology, Ligand-Extended) to estimate functional similarity of microspheres using structural alignments instead of sequence alignments.

They propose using their functional similarity metric to establish relationships of all known metal-ligand binding microspheres. A substantial advantage of their approach is that it does not require extant ligand binding – folds that may have been ligand binding in the past, but then duplicated and re-used in another fashion can still be identified using Sahle scoring. Similarly, the difference in ligands themselves is not important beyond driving fold structure. Thus, they expect to be able to use all known metal-binding folds in the PDB to “fish” for possible relatives, indicative of evolutionary steps, in all structures. The all-to-all Sahle scores can then be used to construct trees, reflecting the likely evolution of biological electron transfer. They will also evaluate tree configurations for effects of missing data and for effects of differential rooting of the tree according to the likely most ancient folds, likely most ancient ligands, and likely most ancient catalytic reactions. To ground their theories in reality, the team will then ask the question – “What would be the metabolites produced by the reactions, which are catalyzed by the proteins carrying our microspheres? If these were executed on a large scale, can we see their traces in the geological record?” By working with Theme 3 (below) the Rutgers team will add available geological time markers to refine the tree architecture and date specific positions in the tree.

Theme 3: Co-Evolution of Nanomachines and the Geosphere
Goal: To understand the co-evolution proteins and minerals through geologic time
Key Questions:
How did proteins and minerals co-evolve?
How can biological data and geologic data be integrated through evolutionary time?
Can we identify and explore protein biosignatures in the geologic record?

The Rutgers team proposes to integrate deep-time data on the nature and distribution of redox-sensitive elements, including their roles in rocks, minerals, and proteins – integrating deep-time data resources with protein structure data in the Protein Data Bank. Their approach involves using the mineral databases that contain mineral age data (e.g. rruff.info) into geochemical models to constrain the bioavailability of metals over geologic time, and then using metal bioavailability as a time marker for protein cofactor evolution (Theme 2). For example, mineral reactions with atmospheric O2 and seawater water sulfide will be used to calculate solubility of Cu, Mo, Mn, Ni, Co, & Fe during the Archean and Proterozoic eras, and then major changes in transition metal chemistry will be used to correlate protein structure and geosphere co-evolution. The aim of their work is to establish a plausible chronology that can be further explored with detailed studies of the geologic record. Finally, they propose to “ground truth” the inferred chronology by applying their expertise in organic and isotope geochemistry to identify protein biosignatures in geologic samples. They hypothesize that characteristic biosignatures at each stage of protein cofactor evolution are preserved in Archean and Proterozoic rocks. This team will also develop analytical methods to examine a variety of protein biosignatures, including amino acid and isotope signatures and cofactor biomarkers (e.g. porphyrins, flavins, pterins).