Biology subjects

Goldford, J. E.

Publications and source records attributed to Goldford, J. E..

9 recordsLinked to original sources

bioRxiv2025

Dual-encoder contrastive learning accelerates enzyme discovery

The ability to engineer enzymes for desired reactions is a cornerstone of modern biotechnology, yet identifying suitable starting proteins remains a critical bottleneck. Although contrastive learning offers a compelling computational approach for enzyme discovery, these models have yet to be implemented at scale or proven effective in real-world experimental settings. Here, we present Horizyn-1, a computationally efficient deep learning framework that enables large-scale reaction-to-enzyme recommendation validated through comprehensive experimental testing. Leveraging a combination of reaction fingerprints and protein language models, we trained Horizyn-1 on millions of reaction-enzyme pairs to achieve state-of-the-art performance, recovering an enzyme with correct activity within the top 100 hits for over 75% of test reactions. We experimentally validate Horizyn-1 across three enzyme discovery scenarios: identifying enzymes for orphan reactions, predicting enzyme promiscuity for both characterized and uncharacterized enzymes, and discovering enzymes for non-natural biochemical reactions including lysine-driven transaminations that enable efficient synthesis of non-canonical amino acids. On underrepresented reaction classes, we find that fine-tuning with fewer than 10 additional reactions can dramatically improve performance. Furthermore, a logarithmic scaling of model performance with training dataset size suggests continued improvement with larger and more diverse reaction datasets. Horizyn-1 addresses the critical bottleneck of sourcing initial enzymes for optimization campaigns, enabling efficient and scalable in silico screening for enzymes with desired activities and promising to accelerate future efforts in biocatalysis and metabolic engineering.

biochemistry↗

bioRxiv2024

Annotation-free prediction of microbial dioxygen utilization

Aerobes require dioxygen (O2) to grow; anaerobes do not. But nearly all microbes -- aerobes, anaerobes, and facultative organisms alike -- express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes, encode more O2-utilizing enzymes, and tend to use different amino acids in their proteins. Here we show that these effects permit high-quality prediction of O2 utilization from genome sequences, with several models displaying >70% balanced accuracy on a ternary classification task wherein blind guessing is only 33.3% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genome sequence content -- e.g. triplets of amino acids -- perform about as well as intensive annotation-based algorithms, enabling the rapid processing of global-scale sequence data to predict aerobic physiology. To demonstrate the utility of efficient physiological predictions we estimated the prevalence of aerobes and anaerobes along a well-studied O2 gradient in the Black Sea, finding strong quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We therefore suggest that statistical methods like ours can be used to estimate, or "sense," pivotal features of the environment from DNA sequencing data. ImportanceWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology -- an organisms capacity to engage metabolically with its environment -- may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without first annotating genes, e.g. by considering only the amino acids in protein sequences. Annotation-free algorithms enabled rapid characterization of natural samples, demonstrating a quantitative correspondence between sequences and local O2 levels. These results suggest that DNA sequencing can be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.

microbiology↗

bioRxiv2023

Metabolic evolution of pyranopterin-dependent biochemistry

Molybdenum (Mo)-dependent biochemistry is essential for many key metabolic pathways. However, theory and geological evidence suggests that its solubility during long intervals with low dioxygen would have limited its availability on early Earth. We developed models of metabolic evolution and found that reactions employing tungsten (W)-dependent biochemistry likely preceded Mo-dependent reactions, where Mo-usage increased dramatically after the production of dioxygen. Consistent with this finding, we analyzed genomes from over 65,000 phylogenetically diverse microbes and metagenomes from an environmental dataset, and we observed that dioxygen-utilizing prokaryotes living in aerobic niches are enriched with Mo-dependent enzymes as compared to anaerobic microbes. As an independent evaluation of this hypothesis, we combined protein language models, machine learning, and phylogenomic analysis to build a classifier for W- or Mo-pterin dependence in the DMSO reductase superfamily, and we found that W-pterin-dependent enzymes cluster near the root of the tree and that a subset of late-evolving aldehyde oxidoreductases (AORs) from aerobes are predicted to rely on Mo instead of W. Overall, our combination of metabolic modeling, phenotypic analysis, machine learning, and phylogenomic analysis suggest that Mo-pterin-dependent biochemistry likely derived from W-pterin-dependent biochemistry, and that Mo-usage increased drastically after the rise of oxygen.

evolutionary biology↗

bioRxiv2022

Continuity between ancient geochemistry and modern metabolism enabled by non-autocatalytic purine biosynthesis

A major unresolved question in the origin and evolution of life is whether a continuous path from geochemical precursors to the majority of molecules in the biosphere can be reconstructed from modern day biochemistry. Here we simulated the emergence of ancient metabolic networks and identified a feasible path from simple geochemically plausible precursors (e.g., phosphate, sulfide, ammonia, simple carboxylic acids, and metals) using only known biochemical reactions and models of primitive coenzymes. We find that purine synthesis constitutes a bottleneck for metabolic expansion, and that non-autocatalytic phosphoryl coupling agents are necessary to enable expansion from geochemistry to modern metabolic networks. Our model predicts punctuated phases of metabolic evolution characterized by the emergence of small molecule coenzymes (e.g., ATP, NAD+, FAD). Early phases in the resulting expansion are associated with enzymes that are metal dependent and structurally symmetric, supporting models of early biochemical evolution. This expansion trajectory produces distinct hypotheses regarding the timing and mode of metabolic pathway evolution, including a late appearance of methane metabolisms and oxygenic photosynthesis consistent with the geochemical record. The concordance between biological and geological analysis suggests that this trajectory provides a plausible evolutionary history for the vast majority of core biochemistry.

systems biology↗

bioRxiv2021

Thermodynamics drives coenzyme redundancy in metabolism

Coenzymes distribute a variety of chemical moieties throughout cellular metabolism, participating in group (e.g., phosphate, acyl) and electron transfer. For a variety of reactions requiring acceptors or donors of specific resources, there often exist degenerate sets of molecules (e.g., NAD(H) and NADP(H)) that carry out similar functions. Although the physiological roles of various coenzyme systems are well established, it is unclear what selective pressures may have driven the emergence of coenzyme redundancy. Here we use genome-wide metabolic modeling approaches to decompose the selective pressures driving enzymatic specificity for either NAD(H) or NADP(H) in the metabolic network of Escherichia coli. We found that few enzymes are thermodynamically constrained to using a single coenzyme, and in principle, a metabolic network relying on only NAD(H) is feasible. However, structural and sequence analyses revealed widespread conservation of residues that retain selectivity for either NAD(H) or NADP(H), suggesting that additional forces may shape specificity. Using a model accounting for the cost of oxidoreductase enzyme expression, we found that coenzyme redundancy universally reduces the minimal amount of protein required to catalyze coenzyme-coupled reactions, inducing individual reactions to strongly prefer one coenzyme over another when reactions are near thermodynamic equilibrium. We propose that protein minimization generically promotes coenzyme redundancy, and that coenzymes typically thought to exist in a single pool (e.g., CoA) may exist in more than one form (e.g., dephospho-CoA). Significance statementMetabolism relies on a small class of molecules (coenzymes) that serve as universal donors and acceptors of key chemical groups and electrons. Although metabolic networks crucially depend on structurally redundant coenzymes (e.g., NAD(H) and NADP(H)) associated with different enzymes, the criteria that led to the emergence of this redundancy remain poorly understood. Our combination of modeling, and structural and sequence analysis indicates that coenzyme redundancy is not essential for metabolism, but rather an evolved strategy promoting efficient usage of enzymes when biochemical reactions are near equilibrium. Our work suggests that early metabolism may have operated with fewer coenzymes, and that adaptation for metabolic efficiency may have driven the rise of coenzyme diversity in living systems.

systems biology↗

bioRxiv2018

Boundary conditions for early life converge to an organo-sulfur metabolism

It has been suggested that a deep memory of early life is hidden in the architecture of metabolic networks, whose reactions could have been catalyzed by small molecules or minerals prior to genetically encoded enzymes (1-6). A major challenge in unraveling these early steps is assessing the plausibility of a connected, thermodynamically consistent proto-metabolism under different geochemical conditions, which are still surrounded by high uncertainty. Here we combine network-based algorithms (9, 10) with physicochemical constraints on chemical reaction networks to systematically show how different combinations of parameters (temperature, pH, redox potential and availability of molecular precursors) could have affected the evolution of a proto-metabolism. Our analysis of possible trajectories indicates that a subset of boundary conditions converges to an organo-sulfur-based proto-metabolic network fueled by a thioester- and redox-driven variant of the reductive TCA cycle, capable of producing lipids and keto acids. Surprisingly, environmental sources of fixed nitrogen and low-potential electron donors seem not to be necessary for the earliest phases of biochemical evolution. We use one of these networks to build a steady-state dynamical metabolic model of a proto-cell, and find that different combinations of carbon sources and electron acceptors can support the continuous production of a minimal ancient \"biomass\" composed of putative early biopolymers and fatty acids.

systems biology↗

bioRxiv2018

Available energy fluxes drive a phase transition in the diversity, stability, and functional structure of microbial communities

A fundamental goal of microbial ecology is to understand what determines the diversity, stability, and structure of microbial ecosystems. The microbial context poses special conceptual challenges because of the strong mutual influences between the microbes and their chemical environment through the consumption and production of metabolites. By analyzing a generalized consumer resource model that explicitly includes cross-feeding, stochastic colonization, and thermodynamics, we show that complex microbial communities generically exhibit a transition as a function of available energy fluxes from a "resource-limited" regime where community structure and stability is shaped by energetic and metabolic considerations to a diverse regime where the dominant force shaping microbial communities is the overlap between species consumption preferences. These two regimes have distinct species abundance patterns, different functional profiles, and respond differently to environmental perturbations. Our model reproduces large-scale ecological patterns observed across multiple experimental settings such as nestedness and differential beta diversity patterns along energy gradients. We discuss the experimental implications of our results and possible connections with disorder-induced phase transitions in statistical physics.

biophysics↗

bioRxiv2018

The thermodynamic landscape of carbon redox biochemistry

Redox biochemistry plays a key role in the transduction of chemical energy in living systems. However, the compounds observed in metabolic redox reactions are a minuscule fraction of chemical space. It is not clear whether compounds that ended up being selected as metabolites display specific properties that distinguish them from non-biological compounds. Here we introduce a systematic approach for comparing the chemical space of all possible redox states of linear-chain carbon molecules to the corresponding metabolites that appear in biology. Using cheminformatics and quantum chemistry, we analyze the physicochemical and thermodynamic properties of the biological and non-biological compounds. We find that, among all compounds, aldose sugars have the highest possible number of redox connections to other molecules. Metabolites are enriched in carboxylic acid functional groups and depleted of carbonyls, and have higher solubility than non-biological compounds. Upon constructing the energy landscape for the full chemical space as a function of pH and electron donor potential, we find that over a large range of conditions metabolites tend to have lower Gibbs energies than non-biological molecules. Finally, we generate Pourbaix phase diagrams that serve as a thermodynamic atlas to indicate which compounds are local and global energy minima in redox chemical space across a set of pH values and electron donor potentials. Our work yields insight into the physicochemical principles governing redox metabolism, and suggests that thermodynamic stability in aqueous environments may have played an important role in early metabolic processes.

systems biology↗

bioRxiv2017

Emergent Simplicity in Microbial Community Assembly

Microbes assemble into complex, dynamic, and species-rich communities that play critical roles in human health and in the environment. The complexity of natural environments and the large number of niches present in most habitats are often invoked to explain the maintenance of microbial diversity in the presence of competitive exclusion. Here we show that soil and plant-associated microbiota, cultivated ex situ in minimal synthetic environments with a single supplied source of carbon, universally re-assemble into large and dynamically stable communities with strikingly predictable coarse-grained taxonomic and functional compositions. We find that generic, non-specific metabolic cross-feeding leads to the assembly of dense facilitation networks that enable the coexistence of multiple competitors for the supplied carbon source. The inclusion of universal and non-specific cross-feeding in ecological consumer-resource models is sufficient to explain our observations, and predicts a simple determinism in community structure, a property reflected in our experiments.

ecology↗