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DOE OSTI2020

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism’s fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase’s fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Predicting variable gene content in Escherichia coli using conserved genes

Having the ability to predict the protein-encoding gene content of an incomplete genome or metagenome-assembled genome is important for a variety of bioinformatic tasks. In this study, as a proof of concept, we built machine learning classifiers for predicting variable gene content in Escherichia coli genomes using only the nucleotide k-mers from a set of 100 conserved genes as features. Protein families were used to define orthologs, and a single classifier was built for predicting the presence or absence of each protein family occurring in 10%–90% of all E. coli genomes. The resulting set of 3,259 extreme gradient boosting classifiers had a per-genome average macro F1 score of 0.944 [0.943–0.945, 95% CI]. We show that the F1 scores are stable across multi-locus sequence types and that the trend can be recapitulated by sampling a smaller number of core genes or diverse input genomes. Surprisingly, the presence or absence of poorly annotated proteins, including “hypothetical proteins” was accurately predicted (F1 = 0.902 [0.898–0.906, 95% CI]). Models for proteins with horizontal gene transfer-related functions had slightly lower F1 scores but were still accurate (F1s = 0.895, 0.872, 0.824, and 0.841 for transposon, phage, plasmid, and antimicrobial resistance-related functions, respectively). Finally, using a holdout set of 419 diverse E. coli genomes that were isolated from freshwater environmental sources, we observed an average per-genome F1 score of 0.880 [0.876–0.883, 95% CI], demonstrating the extensibility of the models. Overall, this study provides a framework for predicting variable gene content using a limited amount of input sequence data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Binding Affinity Prediction by Pairwise Function Based on Neural Network

In this paper, we present a new approach to estimate the binding affinity from given three-dimensional poses of protein–ligand complexes. In this scheme, every protein–ligand atom pair makes an additive free-energy contribution. The sum of these pairwise contributions then gives the total binding free energy or the logarithm of the dissociation constant. The pairwise contribution is calculated by a function implemented via a neural network that takes the properties of the two atoms and their distance as input. The pairwise function is trained using a portion of the PDBbind 2018 data set. The model achieves good accuracy for affinity predictions when evaluated with PDBbind 2018 and with the CASF-2016 benchmark, comparing favorably to many scoring functions such as that of AutoDock Vina. The framework here may be extended to incorporate other factors to further improve its accuracy and power.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting

Background Although pathogenic Gram-negative bacteria lack their own ubiquitination machinery, they have evolved or acquired virulence effectors that can manipulate the host ubiquitination process through structural and/or functional mimicry of host machinery. Many such effectors have been identified in a wide variety of bacterial pathogens that share little sequence similarity amongst themselves or with eukaryotic ubiquitin E3 ligases. Methods To allow identification of novel bacterial E3 ubiquitin ligase effectors from protein sequences we have developed a machine learning approach, the SVM-based Identification and Evaluation of Virulence Effector Ubiquitin ligases (SIEVE-Ub). We extend the string kernel approach used previously to sequence classification by introducing reduced amino acid (RED) alphabet encoding for protein sequences. Results We found that 14mer peptides with amino acids represented as simply either hydrophobic or hydrophilic provided the best models for discrimination of E3 ligases from other effector proteins with a receiver-operator characteristic area under the curve (AUC) of 0.90. When considering a subset of E3 ubiquitin ligase effectors that do not fall into known sequence based families we found that the AUC was 0.82, demonstrating the effectiveness of our method at identifying novel functional family members. Feature selection was used to identify a parsimonious set of 10 RED peptides that provided good discrimination, and these peptides were found to be located in functionally important regions of the proteins involved in E2 and host target protein binding. Our general approach enables construction of models based on other effector functions. We used SIEVE-Ub to predict nine potential novel E3 ligases from a large set of bacterial genomes. SIEVE-Ub is available for download at https://doi.org/10.6084/m9.figshare.7766984.v1 or https://github.com/biodataganache/SIEVE-Ub for the most current version.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Structural Insights into Separase Architecture and Substrate Recognition through Computational Modelling of Caspase-Like and Death Domains

Separases are large proteins that mediate sister chromatid disjunction in all eukaryotes. They belong to clan CD of cysteine peptidases and contain a well-conserved C-terminal catalytic protease domain similar to caspases and gingipains. However, unlike other well-characterized groups of clan CD peptidases, there are no high-resolution structures of separases and the details of their regulation and substrate recognition are poorly understood. Here we undertook an in-depth bioinformatical analysis of separases from different species with respect to their similarity in amino acid sequence and protein fold in comparison to caspases, MALT-1 proteins (mucosa-associated lymphoidtissue lymphoma translocation protein 1) and gingipain-R. A comparative model of the single C-terminal caspase-like domain in separase from C. elegans suggests similar binding modes of substrate peptides between these protein subfamilies, and enables differences in substrate specificity of separase proteins to be rationalised. We also modelled a newly identified putative death domain, located N-terminal to the caspase-like domain. The surface features of this domain identify potential sites of protein-protein interactions. Notably, we identified a novel conserved region with the consensus sequence WWxxRxxLD predicted to be exposed on the surface of the death domain, which we termed the WR motif. We envisage that findings from our study will guide structural and functional studies of this important protein family.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Mouse IgG2c Fc loop residues promote greater receptor-binding affinity than mouse IgG2b or human IgG1

The structures of non-human antibodies are largely unstudied despite the potential for the identification of alternative structural motifs and physical properties that will benefit a basic understanding of protein and immune system evolution as well as highlight unexplored motifs to improve therapeutic monoclonal antibody. Here we probe the structure and receptor-binding properties of the mouse IgG2c crystallizable fragment (Fc) to compare to mouse IgG2b and human IgG1 Fcs. Models of mIgG2c Fc determined by x-ray crystallography with a complex-type biantennary (to 2.05 Å) or a truncated (1)GlcNAc asparagine-linked (N)-glycan attached (to 2.04 Å) show differences in key regions related to mouse Fc γ receptor IV (mFcγRIV) binding. Mouse IgG2c forms different non-bonded interactions between the BC, DE and FG loops than the highly-conserved mIgG2b and binds to FcγRIV with 4.7-fold greater affinity in the complex-type glycoform. Secondary structural elements surrounding the Asn297 site of glycosylation form longer beta strands in the truncated mIgG2c Fc glycoform when compared to mIgG2c with the complex-type N-glycan. Solution NMR spectroscopy of the N-linked (1)GlcNAc residues show differences between mIgG2b, 2c and hIgG1 Fc that correlate to receptor binding affinity. Mutations targeting differences in mIgG2 DE and FG loops decreased affinity of mIgG2c for FcγRIV and increased affinity of mIgG2b. Changes in NMR spectra of the mutant Fc proteins mirrored these changes in affinity. Our studies identified structural and functional differences in highly conserved molecules that were not predicted from primary sequence comparison.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Live Cell Discovery of Microbial Vitamin Transport and Enzyme-Cofactor Interactions

The rapid completion of microbial genomes is inducing a conundrum in functional gene discovery. Novel methods are critically needed to shorten the gap between characterizing a microbial genome and experimentally validating bioinformatically-predicted functions. Of particular importance are transport mechanisms, used to shuttle nutrients and metabolites across cell mem-branes, such as B vitamins, which are indispensable to metabolic reactions crucial to the survival of diverse microbes ranging from members of environmental microbial communities to human pathogens. Methods to accurately assign function and specificity for a wide range of experimentally unidentified and/or predicted membrane-embedded transport proteins, and characterization of intra-cellular enzyme-cofactor/nutrient associations are needed to enable a significantly improved understanding of microbial biochemis-try and physiology, how microbes associate with others, and how they sense and respond to environmental perturbations. Chemical probes derived from B vitamins B1, B2, and B7 have allowed us to experimentally address the aforementioned needs by identifying B vitamin transporters and intracellular protein-cofactor associations through live cell labeling of the filamentous anoxygenic pho-toheterotroph, Chloroflexus aurantiacus J-10-fl, known for both B vitamin biosynthesis and environmental salvage. Our probes provide a unique opportunity to directly link cellular activity and protein function back to ecosystem and/or host dynamics by iden-tifying B vitamin transport and disposition mechanisms required for survival.

Environmental Molecular Sciences Laboratory↗

DOE OSTI2023

Unraveling the functional dark matter through global metagenomics

Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Bioinformatic and Mechanistic Analysis of the Palmerolide PKS-NRPS Biosynthetic Pathway From the Microbiome of an Antarctic Ascidian

Complex interactions exist between microbiomes and their hosts. Increasingly, defensive metabolites that have been attributed to host biosynthetic capability are now being recognized as products of host-associated microbes. These unique metabolites often have bioactivity targets in human disease and can be purposed as pharmaceuticals. Polyketides are a complex family of natural products that often serve as defensive metabolites for competitive or pro-survival purposes for the producing organism, while demonstrating bioactivity in human diseases as cholesterol lowering agents, anti-infectives, and anti-tumor agents. Marine invertebrates and microbes are a rich source of polyketides. Palmerolide A, a polyketide isolated from the Antarctic ascidian Synoicum adareanum, is a vacuolar-ATPase inhibitor with potent bioactivity against melanoma cell lines. The biosynthetic gene clusters (BGCs) responsible for production of secondary metabolites are encoded in the genomes of the producers as discrete genomic elements. A candidate palmerolide BGC was identified from a S. adareanum microbiome-metagenome based on a high degree of congruence with a chemical structure-based retrobiosynthetic prediction. Protein family homology analysis, conserved domain searches, active site and motif identification were used to identify and propose the function of the ~75 kbp trans-acyltransferase (AT) polyketide synthase-non-ribosomal synthase (PKS-NRPS) domains responsible for the stepwise synthesis of palmerolide A. Though PKS systems often act in a predictable co-linear sequence, this BGC includes multiple trans-acting enzymatic domains, a non-canonical condensation termination domain, a bacterial luciferase-like monooxygenase (LLM), and is found in multiple copies within the metagenome-assembled genome (MAG). Detailed inspection of the five highly similar pal BGC copies suggests the potential for biosynthesis of other members of the palmerolide chemical family. This is the first delineation of a biosynthetic gene cluster from an Antarctic microbial species, recently proposed as Candidatus Synoicihabitans palmerolidicus. These findings have relevance for fundamental knowledge of PKS combinatorial biosynthesis and could enhance drug development efforts of palmerolide A through heterologous gene expression.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Insight into different environmental niches adaptation and allergenicity from the Cladosporium sphaerospermum genome, a common human allergy-eliciting Dothideomycetes

Cladosporium sphaerospermum, a dematiaceous saprophytic fungus commonly found in diverse environments, has been reported to cause allergy and other occasional diseases in humans. However, its basic biology and genetic information are largely unexplored. A clinical isolate C. sphaerospermum genome, UM 843, was re-sequenced and combined with previously generated sequences to form a model 26.89 Mb genome containing 9,652 predicted genes. Functional annotation on predicted genes suggests the ability of this fungus to degrade carbohydrate and protein complexes. Several putative peptidases responsible for lung tissue hydrolysis were identified. These genes shared high similarity with the Aspergillus peptidases. The UM 843 genome encodes a wide array of proteins involved in the biosynthesis of melanin, siderophores, cladosins and survival in high salinity environment. In addition, a total of 28 genes were predicted to be associated with allergy. Orthologous gene analysis together with 22 other Dothideomycetes showed genes uniquely present in UM 843 that encode four class 1 hydrophobins which may be allergens specific to Cladosporium. The mRNA of these hydrophobins were detected by RT-PCR. The genomic analysis of UM 843 contributes to the understanding of the biology and allergenicity of this widely-prevalent species.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

Systems biology defines the biological significance of redox‐active proteins during cellulose degradation in an aerobic bacterium

Summary Microbial depolymerization of plant cell walls contributes to global carbon balance and is a critical component of renewable energy. The genomes of lignocellulose degrading microorganisms encode diverse classes of carbohydrate modifying enzymes, although currently there is a paucity of knowledge on the role of these proteins in vivo . We report the comprehensive analysis of the cellulose degradation system in the saprophytic bacterium C ellvibrio japonicus . Gene expression profiling of C . japonicus demonstrated that three of the 12 predicted β‐1,4 endoglucanases ( cel5A , cel5B , and cel45A ) and the sole predicted cellobiohydrolase ( cel6A ) showed elevated expression during growth on cellulose. Targeted gene disruptions of all 13 predicted cellulase genes showed that only cel5B and cel6A were required for optimal growth on cellulose. Our analysis also identified three additional genes required for cellulose degradation: lpmo10B encodes a lytic polysaccharide monooxygenase ( LPMO ), while cbp2D and cbp2E encode proteins containing carbohydrate binding modules and predicted cytochrome domains for electron transfer. Cj LPMO10B oxidized cellulose and C bp2 D demonstrated spectral properties consistent with redox function. Collectively, this report provides insight into the biological role of LPMOs and redox proteins in cellulose utilization and suggests that C . japonicus utilizes a combination of hydrolytic and oxidative cleavage mechanisms to degrade cellulose.

Gardner, Jeffrey G.↗

DOE OSTI2017

A novel signal transduction protein: Combination of solute binding and tandem PAS-like sensor domains in one polypeptide chain: Periplasmic Ligand Binding Protein Dret_0059

We report the structural and biochemical characterization of a novel periplasmic ligand-binding protein, Dret_0059, from Desulfohalobium retbaense DSM 5692, an organism isolated from the Salt Lake Retba in Senegal. The structure of the protein consists of a unique combination of a periplasmic solute binding protein (SBP) domain at the N-terminal and a tandem PAS-like sensor domain at the C-terminal region. SBP domains are found ubiquitously and their best known function is in solute transport across membranes. PAS-like sensor domains are commonly found in signal transduction proteins. These domains are widely observed as parts of many protein architectures and complexes but have not been observed previously within the same polypeptide chain. In the structure of Dret_0059, a ketoleucine moiety is bound to the SBP, whereas a cytosine molecule is bound in the distal PAS-like domain of the tandem PAS-like domain. Differential scanning flourimetry support the binding of ligands observed in the crystal structure. There is significant interaction between the SBP and tandem PAS-like domains, and it is possible that the binding of one ligand could have an effect on the binding of the other. We uncovered three other proteins with this structural architecture in the non-redundant sequence data base, and predict that they too bind the same substrates. The genomic context of this protein did not offer any clues for its function. We did not find any biological process in which the two observed ligands are coupled. The protein Dret_0059 could be involved in either signal transduction or solute transport.

Wu, R.↗

DOE OSTI2025

A non-canonical fungal peroxisome PTS-1 signal, SYM, and its evolutionary aspects

Abstract Proteins localized to peroxisomes, particularly those expressed under specific conditions or in low abundance, are often undetected by routine proteomics methods due to detection sensitivity limits. In silico identification and experimental validation of peroxisomal targeting signals (PTSs) offer a reliable alternative. We demonstrate that SYM, a non-canonical plant PTS-1 signal, functions similarly inAspergillus nidulans, as GFP tagged with a SYM C-terminal tripeptide localizes to peroxisomes. One of two nativeA. nidulansproteins with C-terminal SYM tripeptide shows weak peroxisomal localization alongside cytoplasmic presence, indicating that only a subset of proteins with non-canonical signals access peroxisomes.In silicoanalysis of 1,010 fungal genomes identified diverse SYM-proteins with variable functions, suggesting that non-canonical PTS-1 signals may evolve spontaneously. Two-thirds of SYM-proteins are predicted to localize to specific intracellular compartments other than the peroxisome. We propose that despite their predicted localization, these proteins possessing SYM as a non-canonical peroxisomal signal might also have peroxisomal presence. Among SYM-proteins, pectinesterases, known plant pathogen virulence factors, were frequent. Notably, 25% of fungal pectinesterases harbor non-canonical PTS-1 signals, suggesting that partial peroxisomal localization of pectinesterases has evolved convergently. This suggests that partial peroxisomal localization may enhance protein functional flexibility, contributing to the organism’s adaptability.

Science & Technology - Other Topics↗

DOE OSTI2017

Chlamydomonas reinhardtii LFO1 Is an IsdG Family Heme Oxygenase

Heme is essential for respiration across all domains of life. However, heme accumulation can lead to toxicity if cells are unable to either degrade or export heme or its toxic by-products. Under aerobic conditions, heme degradation is performed by heme oxygenases, enzymes which utilize oxygen to cleave the tetrapyrrole ring of heme. The HO-1 family of heme oxygenases has been identified in both bacterial and eukaryotic cells, whereas the IsdG family has thus far been described only in bacteria. We identified a hypothetical protein in the eukaryotic green alga Chlamydomonas reinhardtii, which encodes a protein containing an antibiotic biosynthesis monooxygenase (ABM) domain consistent with those associated with IsdG family members. This protein, which we have named LFO1, degrades heme, contains similarities in predicted secondary structures to IsdG family members, and retains the functionally conserved catalytic residues found in all IsdG family heme oxygenases. These data establish LFO1 as an IsdG family member and extend our knowledge of the distribution of IsdG family members beyond bacteria. To gain further insight into the distribution of the IsdG family, we used the LFO1 sequence to identify 866 IsdG family members, including representatives from all domains of life. These results indicate that the distribution of IsdG family heme oxygenases is more expansive than previously appreciated, underscoring the broad relevance of this enzyme family. This work establishes a protein in the freshwater alga Chlamydomonas reinhardtii as an IsdG family heme oxygenase. This protein, LFO1, exhibits predicted secondary structure and catalytic residues conserved in IsdG family members, in addition to a chloroplast localization sequence. Additionally, the catabolite that results from the degradation of heme by LFO1 is distinct from that of other heme degradation products. Using LFO1 as a seed, we performed phylogenetic analysis, revealing that the IsdG family is conserved in all domains of life. Also, C. reinhardtii contains two previously identified HO-1 family heme oxygenases, making C. reinhardtii the first organism shown to contain two families of heme oxygenases. These data indicate that C. reinhardtii may have unique mechanisms for regulating iron homeostasis within the chloroplast.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

The total microbiome functions in bacteria and fungi

Unveiling the relationship between phylogeny and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial phylogenetic diversity, our understanding of its relationship to functional diversity is still scarce. Here we predicted the total microbiome functions of bacteria and fungi on Earth using the total known functions from level 3 of KEGG Orthology by modelling the increase of functions with increasing diversity of bacteria or fungi. For bacteria and fungi, the unsaturated model described the data significantly better (for both P?<2.2e-16), suggesting the presence of two types of functions. Widespread functions ubiquitous in every living organism that make up two thirds of our current knowledge of microbiome functions are separated from rare functions from specialised enzymes present in only a few species. Given previous estimates on species richness, we predicted a global total of 35.5 million functions in bacteria and 3.2 million in fungi; of which only 0.02% and 0.14% are known today. Our approach highlights the necessity of novel and more sophisticated methods to unveil the entirety of rare functions to fully understand the involvement of the microbiome in ecosystem functioning. SIGNIFICANCE: The functionality of and within a microbial community is generally inferred based on the taxonomic annotation of the organism. However, our understanding of functional diversity and how it relates to taxonomy is still limited. Here we predict the total microbiome functionality in bacteria and fungi on Earth using known and annotated protein-coding sequences in species accumulation curves. Our estimates reveal that the majority of functionality (>99%) could be assigned to yet unknown and rare functions, highlighting that our current knowledge is incomplete and functional inference is thus lackluster.

Bacteria↗

DOE OSTI2019

Modeling the Cooperative Adsorption of Solid-Binding Proteins on Silica: Molecular Insights from Surface Plasmon Resonance Measurements

Combinatorially selected solid-binding peptides (SBPs) provide a versatile route for synthesizing advanced materials and devices, especially when they are installed within structurally or functionally useful protein scaffolds. However, their promise has not been fully realized because we lack a predictive understanding of SBP-material interactions. Thermodynamic and kinetic binding parameters obtained by fitting quartz crystal microbalance and surface plasmon resonance (SPR) data with the Langmuir model whose assumptions are rarely satisfied provide limited information on underpinning molecular interactions. Using SPR, we show here that a technologically useful SBP called Car9 confers proteins to which is fused a sigmoidal adsorption behavior modulated by partner identity, quaternary structure, and ionic strength. We develop a two-step cooperative model that accurately captures the kinetics of silica binding and provides insights into how SBP–SBP interactions, fused scaffold, and solution conditions modulate adsorption. Because cooperative binding can be converted to Langmuir adhesion by mutagenesis, our approach offers a path to identify and to better understand and design practically useful SBPs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Genetic Transfer in Action: Uncovering DNA Flow in an Extremophilic Microbial Community

ABSTRACT Horizontal genetic transfer (HGT) is a significant driver of genomic novelty in all domains of life. HGT has been investigated in many studies however, the focus has been on conspicuous protein‐coding DNA transfers that often prove to be adaptive in recipient organisms and are therefore fixed longer‐term in lineages. These results comprise a subclass of HGTs and do not represent exhaustive (coding and non‐coding) DNA transfer and its impact on ecology. Uncovering exhaustive HGT can provide key insights into the connectivity of genomes in communities and how these transfers may occur. In this study, we use the term frequency‐inverse document frequency (TF‐IDF) technique, that has been used successfully to mine DNA transfers within real and simulated high‐quality prokaryote genomes, to search for exhaustive HGTs within an extremophilic microbial community. We establish a pipeline for validating transfers identified using this approach. We find that most DNA transfers are within‐domain and involve non‐coding DNA. A relatively high proportion of the predicted protein‐coding HGTs appear to encode transposase activity, restriction‐modification system components, and biofilm formation functions. Our study demonstrates the utility of the TF‐IDF approach for HGT detection and provides insights into the mechanisms of recent DNA transfer.

Microbiology↗