Amino Acid Usage Research Articles

Differential amino acid usage leads to ubiquitous edge effect in proteomes across domains of life that can be explained by amino acid secondary structure propensities

Amino acids are the building blocks of proteins and enzymes which are essential for life. Understanding amino acid usage offers insights into protein function and molecular mechanisms underlying life histories. However, genome-wide patterns of amino acid usage across domains of life remain poorly understood. Here, we analysed the proteomes of 5590 species across four domains and found that only a few amino acids are consistently the most and least used. This differential usage results in lower amino acid usage diversity at the most and least frequent ranks, creating a ubiquitous inverted U-shape pattern of amino acid diversity and rank which we call an ‘edge effect’ across proteomes and domains of life. This effect likely stems from protein secondary structural constraints, not the evolutionary chronology of amino acid incorporation into the genetic code, highlighting the functional rather than evolutionary influences on amino acid usage. We also tested other contemporary hypotheses regarding amino acid usage in proteomes and found that amino acid usage varies across life’s domains and is only weakly influenced by growth temperature. Our findings reveal a novel and pervasive amino acid usage pattern across genomes with the potential to help us probe deep evolutionary relationships and advance synthetic biology.

Decoding Multidrug Resistance: Genetic Architecture and Codon Usage Patterns in ESKAPE Pathogens

Introduction: The escalating challenge of multidrug resistance among ESKAPE pathogens has become a prominent concern for global healthcare providers, leading to amplified morbidity and mortality rates. Method: Therefore, we conducted this study to elucidate the genetic architecture of ESKAPE constituents with the intent of ameliorating pathogenicity and facilitating drug development efforts. A comprehensive array of computational tools and statistical methodologies were employed to scrutinize the genomes of ESKAPE pathogens, revealing that translational selection profoundly influences the codon usage bias within this pathogenic cohort. Notably, leucine emerged as the predominantly amino acid, except in the case of Acinetobacter baumannii, where arginine exhibited preeminence. There was a universal preference for at least one histidine codon across all ESKAPE pathogens. GpC emerged as the most prominently overrepresented dinucleotide at the codon pair junction in all ESKAPE pathogens. Result: Furthermore, a comparison of gyrB gene sequences and phylogenic tree construction showed a distinct evolutionary relationship between AT-rich and GC-rich ESKAPE pathogens. Additionally, identification, characterization, and phylogenetic analysis of multiple antibiotic resistance genes revealed distinct evolutionary relationships. Conclusion: It was discerned that despite substantial variability amongst antibioticresistance genes, leucine emerged as the predominant amino acid. conclusion: Ultimately, we can say that codon and amino acid usage bias in ESKAPE pathogens and their ARGs genes has contributed the evolution of antibiotic resistance nature if the pathogens. Therefore, our study underscores the importance of continued surveillance and research efforts to address the growing threat of antibiotic resistance in ESKAPE pathogens .

Investigating the evolution of green algae with a large transcriptomic data set.

The colonization of land by plants approximately 450-500 million years ago (Mya) is one of the most important events in the history of life on Earth. Land plants, hereafter referred to as "embryophytes," comprise the foundation of every terrestrial biome, making them an essential lineage for the origin and maintenance of biodiversity. The embryophytes form a monophyletic clade within one of the two major phyla of the green algae (Viridiplantae), the Streptophyta. Estimates from fossil data and molecular clock analyses suggest the Streptophyte algae (Charophytes) diverged from the other main phylum of green algae, the Chlorophyta, as much as 1500 Mya. Here we present a phylogenetic analysis using transcriptomic and genomic data of 62 green algae and embryophyte operational taxonomic units, 31 of which were assembled de novo for this project. We have focused on identifying the charophyte lineage that is sister to embryophytes, and show that the Zygnematophyceae have the strongest support, followed by the Charophyceae. Furthermore, we have examined amino acid and codon usage across the tree and determined these data broadly follow the phylogenetic tree. We concluded by searching the data set for protein domains and gene families known to be important in embryophytes. Many of these domains and genes have homologous sequences in the charophyte lineages, giving insight into the processes that underlay the colonization of the land by plants. This provides new insights into green algal diversification, identifies previously unknown attributes of genome evolution within the group, and shows how functional mechanisms have evolved over time.

TRNA regulation and amino acid usage bias reflect a coordinated metabolic adaptation in Plasmodium falciparum

tRNA regulation and amino acid usage bias reflect a coordinated metabolic adaptation in Plasmodium falciparum

Genomic insights into codon usage bias in Cannabis sativa and pathogenic interactions

This study delves into codon usage bias (CUB) across Cannabis sativa and its associated pathogens (Meloidogyne incognita, Alternaria alternata, Aspergillus flavus, Aspergillus niger, and Fusarium oxysporum). It finds an AT-bias in C. sativa and M. incognita, favouring AT ending codons, while fungal pathogens lean towards GC-ending codons. Analysis suggests weak codon bias overall, with host and nematode showing similar preferences, and fungal pathogens preferring GC-ending codons. In ENC/GC3 plot, parity plot analyses both natural selection and mutational pressure influence CUB, slightly favouring T-ending codons.Neutrality plot analysis demonstrated varying degrees of mutational pressure and selection across the organisms. Correspondence analysis revealed indistinguishable CUB effective in all pathogens, despite differences in evolutionary forces. Correlation analysis illustrated the impact of nucleotide composition, mutational pressure, and natural selection on CUB. Additionally, amino acid composition analysis indicated differential usage of amino acids across organisms. Codon context analysis unveiled common trends in codon pairing, emphasizing the prevalence of preferred codons in C. sativa and M. incognita. This comprehensive analysis provides insights into the factors shaping CUB and the evolutionary forces influencing the genomes of the host plant and its associated pathogens, contributing to a deeper understanding of molecular interactions in host-pathogen systems. This data informs conservation for Cannabis sativa by identifying pathogen-resistant genetic traits, guiding breeding and management for biodiversity preservation.

Impact of phylogeny on the inference of functional sectors from protein sequence data.

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information.

Comparative genome wise analysis of codon usage of Staphylococcus Genus.

The genus Staphylococcus encompasses a diverse array of bacteria with significant implications for human health, including disreputable pathogens such as Staphylococcus aureus and Staphylococcus epidermidis. Understanding the genetic composition and codon usage patterns of Staphylococcus species is crucial for unraveling their evolutionary dynamics, adaptive strategies, and pathogenic potential. In this study, we conducted a comprehensive analysis of codon usage patterns across 48 species within the Staphylococcus genus. Our findings uncovered variations in genomic G-C content across Staphylococcus species, impacting codon usage preferences, with a notable preference for A/T-rich codons observed in pathogenic strains. This preference for A/T-rich codons suggests an energy-saving strategy in pathogenic organisms. Analysis of dinucleotide pair expression patterns unveiled insights into genomic dynamics, with overrepresented codon pairs reflecting trends in dinucleotide expression across genomes. Additionally, a significant correlation between CAI and genomic G-C content underscored the intricate relationship between codon usage patterns and gene expression strategies. Amino acid usage analysis highlighted preferences for energetically cheaper amino acids, suggesting adaptive strategies promoting energy efficiency. This comprehensive analysis sheds light on the evolutionary dynamics and adaptive mechanisms employed by Staphylococcus species, providing valuable insights into their pathogenic potential and clinical implications. Understanding these genomic features is crucial for devising strategies to combat staphylococcal infections and improve public health outcomes.

Order of amino acid recruitment into the genetic code resolved by Last Universal Common Ancestor's protein domains.

The current "consensus" order in which amino acids were added to the genetic code is based on potentially biased criteria such as absence of sulfur-containing amino acids from the Urey-Miller experiment which lacked sulfur. Even if inferred perfectly, abiotic abundance might not reflect abundance in the organisms in which the genetic code evolved. Here, we instead exploit the fact that proteins that emerged prior to the genetic code's completion are likely enriched in early amino acids and depleted in late amino acids. We identify the most ancient protein-coding sequences born prior to the archaeal-bacterial split. Amino acid usage in protein sequences whose ancestors date back to a single homolog in the Last Universal Common Ancestor (LUCA) largely matches the consensus order. However, our findings indicate that metal-binding (cysteine and histidine) and sulfur-containing (cysteine and methionine) amino acids were added to the genetic code much earlier than previously thought. Surprisingly, even more ancient protein sequences - those that had already diversified into multiple distinct copies in LUCA - show a different pattern to single copy LUCA sequences: significantly less depleted in the late amino acids tryptophan and tyrosine, and enriched rather than depleted in phenylalanine. This is compatible with at least some of these sequences predating the current genetic code. Their distinct enrichment patterns thus provide hints about earlier, alternative genetic codes.

Evolutionary divergence of TLR9 through ancestral sequence reconstruction.

The transmembrane pattern recognition receptor, Toll-like receptor (TLR), are best known for their roles in innate immunity via recognition of pathogen and initiation of signaling response. Mammalian TLRs recognize molecular patterns associated with pathogens and initiate innate immune response. We have studied the evolutionary diversity of mammalian TLR genes for differences in immunological response. Reconstruction of ancestral sequences is a key aspect of the molecular evolution of TLR to track changes across the TLR genes. The comprehensive analysis of mammalian TLRs revealed a distinct pattern of evolution of TLR9. Various sequence-based features such as amino acid usage, hydrophobicity, GC content, and evolutionary constraints are found to influence the divergence of TLR9 from other TLRs. Ancestral sequence reconstruction analysis also revealed that the gradual evolution of TLR genes in several ancestral lineages leads to the distinct pattern of TLR9. It demonstrates evolutionary divergence with the progressive accumulation of mutations results in the distinct patternof TLR9.

Effect of BAFF blockade on the B cell receptor repertoire and transcriptome in a mouse model of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Anti-B-cell-activating factor (BAFF) therapy effectively depletes B cells and reduces SLE disease activity. This research aimed to evaluate the effect of BAFF blockade on B cell receptor (BCR) repertoire and gene expression. Through next-generation sequencing, we analyzed gene expression and BCR repertoire in MRL/lpr mice that received long-term anti-BAFF therapy. Based on gene expression profiles, we predicted the relative proportion of immune cells using ImmuCellAI-mouse, validating our predictions via flow cytometry and FluoroSpot. The loss of BCR repertoire diversity and richness, along with increased clonality and differential frequency distribution of the immunoglobulin heavy chain variable (IGHV) segment gene usage, were observed in BAFF-blockade mice. Meanwhile, the distribution of complementarity-determining region 3 (CDR3) length and CDR3 amino acid usage remained unaffected. BAFF blockade resulted in extensive changes in gene expression, particularly that of genes related to B cells and immunoglobulins. Besides, the tumor necrosis factor (TNF)-α responses and interferon (IFN)-α/γ were downregulated, consistent with the decrease in IFN-γ and TNF-α serum levels following anti-BAFF therapy. In addition, BAFF blockade significantly reduced B cell subpopulations and plasmacytoid dendritic cells, and caused the depletion of antibody-secreting cells. Our comparative BCR repertoire and transcriptome analyses of MRL/lpr mice subjected to BAFF blockade provide innovative insights into the molecular pathophysiology of SLE.

In vitro B cell experiments explore the role of CD24, CD38, and energy metabolism in ME/CFS.

Disturbances of energy metabolism contribute to the clinical manifestations of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Previously, we found that B cells from ME/CFS patients have an increased expression of CD24, a modulator of many cellular functions including those of cell stress. The relative ability of B cells from ME/CFS patients and healthy controls (HC) to respond to rapid changes in energy demand was compared. CD24, the ectonucleotidases CD39 and CD73, the NAD-degrading enzyme CD38, and mitochondrial mass (MM) were measured following cross-linking of the B cell receptor and costimulation with either T-cell-dependent or Toll-like-receptor-9-dependent agonists. The levels of metabolites consumed/produced were measured using 1H-NMR spectroscopy and analyzed in relation to cell growth and immunophenotype. Proliferating B cells from patients with ME/CFS showed a lower mitochondrial mass and a significantly increased usage of essential amino acids compared with those from HC, with a significantly delayed loss of CD24 and an increased expression of CD38 following stimulation. The immunophenotype results suggested the triggering of a stress response in ME/CFS B cells associated with the increased usage of additional substrates to maintain necessary ATP levels. Disturbances in energy metabolism in ME/CFS B cells were thus confirmed in a dynamic in vitro model, providing the basis for further mechanistic investigations.

Integrating pastured meat chickens into organic vegetable production increased nitrogen and microbial biomass with variability in presence of E. coli and Salmonella spp

Abstract Integrating animals into a farm supports a closed or semi-closed production system where nutrients are recycled and off-farm inputs are reduced. In comparison to other livestock, chickens can be a low-investment option for animal-crop integration of small-scale, diversified, vegetable farms. Although crop-animal integration poses many potential benefits to farms, soils, and the environment, there are significant food safety risks when considering the production of vegetables in close proximity to raw manure. The objectives of this study were to examine the effects of poultry integration with meat chickens (broilers) in two different seasons on soil health, food safety, vegetable yield, and poultry feed efficiency in organic vegetable cropping systems. We explored these effects in an open field study with three rotation treatments (two that integrated chickens and a no-chicken control): vegetables-cover crop (V-CC; control treatment), vegetables-cover crop-poultry (V-CC-P), and vegetables-poultry-cover crop (V-P-CC). In response to crop rotation, over three years, we monitored soil nutrient status, soil microbial biomass carbon (MBC), permanganate oxidizable carbon (POXC), and microbial catabolic potential and diversity using Biolog® microplates. The presence or absence of foodborne pathogens in soil and vegetables was also measured. Nitrate–nitrogen (NO3–N) was higher in V-P-CC in year 2 as compared to both V-CC and V-CC-P (P = 0.001 and <0.001, respectively). After poultry removal in the summer of year 2 and year 3 V-P-CC was on average two times higher in NO3–N as compared to V-CC and V-CC-P, respectively. After chicken removal in the autumn of year 3 V-CC-P was 2.1 and 1.8 times higher in NO3–N as compared to V-CC and V-P-CC, respectively. On average phosphorus (P) increased by 45% in year 2 and by 13.2% in year 3. Microbial biomass carbon (MBC) increased from after harvest (summer) in year 1 to the end of the season (autumn) of year 2 from 219.75 to 303.23 mg carbon (C) kg−1. Integrating poultry increased MBC by 25%, on average between both treatments across all sampling dates, compared to the V-CC (P = 0.042). The vegetable-cover crop control (V-CC) preferentially used carbohydrates, compared to V-P-CC which corresponded to greater amino acid usage. Escherichia coli O157:H7 was detected in all plots in the spring of year 3 and select replications of plots in the autumn of year 3. Salmonella spp. was found in one plot in year 2. No pathogens were detected on the spinach crop when leaf surfaces were tested. Integrating chickens into organic vegetable crop rotations increases NO3–N and has the potential for off-farm fertilizer reductions if time and stocking density are further examined. However, poultry feed is often an off-farm input and should be considered when determining the true N input of this system. Soil health may be improved, but MBC and other soil health indicators should be monitored with longer-term rotations. There are food safety risks that come with the integration of chickens into vegetable production and fields should be treated as if raw manure has been applied. Despite the potential benefits of integrating poultry into vegetable crop rotations, more research on these systems is required to determine optimum integration strategies that provide maximum benefit to the producer, the animals, and the environment.

Plasmodium, the Apicomplexa Outlier When It Comes to Protein Synthesis.

Plasmodium is an obligate intracellular parasite that has numerous interactions with different hosts during its elaborate life cycle. This is also the case for the other parasites belonging to the same phylum Apicomplexa. In this study, we bioinformatically identified the components of the multi-synthetase complexes (MSCs) of several Apicomplexa parasites and modelled their assembly using AlphaFold2. It appears that none of these MSCs resemble the two MSCs that we have identified and characterized in Plasmodium. Indeed, tRip, the central protein involved in the association of the two Plasmodium MSCs is different from its homologues, suggesting also that the tRip-dependent import of exogenous tRNAs is not conserved in other apicomplexan parasites. Based on this observation, we searched for obvious differences that could explain the singularity of Plasmodium protein synthesis by comparing tRNA genes and amino acid usage in the different genomes. We noted a contradiction between the large number of asparagine residues used in Plasmodium proteomes and the single gene encoding the tRNA that inserts them into proteins. This observation remains true for all the Plasmodia strains studied, even those that do not contain long asparagine homorepeats.

Mitogenome of the stink bug Aelia fieberi (Hemiptera: Pentatomidae) and a comparative genomic analysis between phytophagous and predatory members of Pentatomidae.

Aelia fieberi Scott, 1874 is a pest of crops. The mitogenome of A. fieberi (OL631608) was decoded by next-generation sequencing. The mitogenome, with 41.89% A, 31.70% T, 15.44% C and 10.97% G, is 15,471 bp in size. The phylogenetic tree showed that Asopinae and Phyllocephalinae were monophyletic; however, Pentatominae and Podopinae were not monophyletic, suggesting that the phylogenetic relationships of Pentatomoidae are complex and need revaluation and revision. Phytophagous bugs had a ~20-nucleotide longer in nad2 than predatory bugs. There were differences in amino acid sequence at six sites between phytophagous bugs and predatory bugs. The codon usage analysis indicated that frequently used codons used either A or T at the third position of the codon. The analysis of amino acid usage showed that leucine, isoleucine, serine, methionine, and phenylalanine were the most abundant in 53 species of Pentatomoidae. Thirteen protein-coding genes were evolving under purifying selection, cox1, and atp8 had the strongest and weakest purifying selection stress, respectively. Phytophagous bugs and predatory bugs had different evolutionary rates for eight genes. The mitogenomic information of A. fieberi could fill the knowledge gap for this important crop pest. The differences between phytophagous bugs and predatory bugs deepen our understanding of the effect of feeding habit on mitogenome.

SARS-CoV-2 antibodies recognize 23 distinct epitopic sites on the receptor binding domain

The COVID-19 pandemic and SARS-CoV-2 variants have dramatically illustrated the need for a better understanding of antigen (epitope)-antibody (paratope) interactions. To gain insight into the immunogenic characteristics of epitopic sites (ES), we systematically investigated the structures of 340 Abs and 83 nanobodies (Nbs) complexed with the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein. We identified 23 distinct ES on the RBD surface and determined the frequencies of amino acid usage in the corresponding CDR paratopes. We describe a clustering method for analysis of ES similarities that reveals binding motifs of the paratopes and that provides insights for vaccine design and therapies for SARS-CoV-2, as well as a broader understanding of the structural basis of Ab-protein antigen (Ag) interactions.

PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts

The “Protein Abundances Across Organisms” database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in “parts per million” in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.

Integrated evolutionary pattern analyses reveal multiple origins of steroidal saponins in plants.

Steroidal saponins are a class of specialized metabolites essential for plant's response to biotic and abiotic stresses. They are also important raw materials for the industrial production of steroid drugs. Steroidal saponins are present in some monocots, such as Dioscorea and Paris, but their distribution, origin, and evolution in plants remain poorly understood. By reconstructing the evolutionary history of the steroidal saponin-associated module (SSAM) in plants, we reveal that the steroidal saponin pathway has its origin in Asparagus and Dioscorea. Through evaluating the distribution and evolutionary pattern of steroidal saponins in angiosperms, we further show that steroidal saponins originated multiple times in angiosperms, and exist in early diverged lineages of certain monocot lineages including Asparagales, Dioscoreales, and Liliales. In these lineages, steroidal saponins are synthesized through the high copy and/or high expression mechanisms of key genes in SSAM. Together with shifts in gene evolutionary rates and amino acid usage, these molecular mechanisms shape the current distribution and diversity of steroidal saponins in plants. Consequently, our results provide new insights into the distribution, diversity and evolutionary history of steroidal saponins in plants, and enhance our understanding of plants' resistance to abiotic and biotic stresses. Additionally, fundamental understanding of the steroidal saponin biosynthesis will facilitate their industrial production and pharmacological applications.

DEVELOPMENT OF A SELECTIVE CD16A-BASED NK CELL ENGAGER UTILIZING ANTIBODIES TARGETING A SINGLE AMINO ACID VARIATION

Abstract Background and Significance Natural killer (NK) cells play a vital role in the human innate immune system and are being explored as a promising approach for cancer immunotherapy. Of particular interest are NK cell engagers that can target and activate NK cells to attack cancer cells. In this study, we developed novel NK cell engagers by targeting the NK cell activating receptor CD16a using antibodies that selectively distinguish between CD16a on NK cells and CD16b on granulocytes, which are highly homologous to each other. Methods and Results To generate antibodies with high developability, we employed a rational design approach to construct large yeast display libraries of human antibodies. This approach was based on the analysis of a deep sequencing dataset of human antibodies from over 500 individuals, which allowed us to determine the natural amino acid usage patterns of human antibody CDRs and mimic human antibody repertoires. Through screening these libraries, we discovered two classes of antibody clones that selectively recognize CD16a without cross-reactivity to CD16b. Epitope mapping revealed that a single amino acid difference confers over 10,000-fold selectivity for one class of antibody clones, while for the other class a second unique epitope on CD16a was identified. To evaluate the activity of these antibody clones, we produced bispecific antibody clones with one arm targeting CD16a and the other arm targeting a tumor-associated antigen (TAA). Our results demonstrated potent tumor cell-dependent activation of NK cells and effective killing of tumor cells. Several of these antibodies had greatly enhanced resistance to human IgG inhibition in killing target cells. Significantly, our anti-CD16a antibody clones exhibited superior performance compared to leading reference anti-CD16a clones in two distinct NK cell engager formats. This included higher affinity for CD16a, higher thermostability, and more potent killing activity both in the absence and presence of 10 mg/mL human IgGs as competitors. Conclusion Our findings indicate that anti-CD16a antibody-based NK cell engagers have significant potential for cancer immunotherapies.

Amino acid usage and protein expression levels in the flatworm Schistosoma mansoni

Schistosoma mansoni is a parasitic flatworm that causes a human disease called schistosomiasis, or bilharzia. At the genomic level, S. mansoni is AT-rich, but has some compositional heterogeneity. Indeed, some regions of its genome are GC-rich, mainly in the regions located near the extreme ends of the chromosomes. Recently, we showed that, despite the strong bias towards A/T ending codons, highly expressed genes tend to use GC-rich codons. Here, we address the following question: are highly expressed sequences biased in their amino acid frequencies? Our analyses show that these sequences in S. mansoni, as in species ranging from bacteria to human, are strongly biased in nucleotide composition. Highly expressed genes tend to use GC-rich codons (in the first and second codon positions), which code the energetically cheapest amino acids. Therefore, we conclude that amino acid usage, at least in highly expressed genes, is strongly shaped by natural selection to avoid energetically expensive residues. Whether this is an adaptation to the parasitic way of life of S. mansoni, is unclear since the same pattern occurs in free-living species.

Mega-scale experimental analysis of protein folding stability in biology and design.

Advances in DNA sequencing and machine learning are providing insights into protein sequences and structures on an enormous scale1. However, the energetics driving folding are invisible in these structures and remain largely unknown2. The hidden thermodynamics of folding can drive disease3,4, shape protein evolution5-7 and guide protein engineering8-10, and new approaches are needed to reveal these thermodynamics for every sequence and structure. Here we present cDNA display proteolysis, a method for measuring thermodynamic folding stability for up to 900,000 protein domains in a one-week experiment. From 1.8 million measurements in total, we curated a set of around 776,000 high-quality folding stabilities covering all single amino acid variants and selected double mutants of 331 natural and 148 de novo designed protein domains 40-72 amino acids in length. Using this extensive dataset, we quantified (1) environmental factors influencing amino acid fitness, (2) thermodynamic couplings (including unexpected interactions) between protein sites, and (3) the global divergence between evolutionary amino acid usage and protein folding stability. We also examined how our approach could identify stability determinants in designed proteins and evaluate design methods. The cDNA display proteolysis method is fast, accurate and uniquely scalable, and promises to reveal the quantitative rules for how amino acid sequences encode folding stability.