Protein Evolution Research Articles

Protocols for Building and Producing High Diversity Peptide Phage Display Libraries.

Phage display is an important technology to study protein-protein interaction and protein evolution, with applications in basic science and applied biotechnology, such as drug discovery and the development of targeted therapies. However, in order to be successful during a phage display screening, it is paramount to have good phage libraries. Here, we described detailed procedures to generate peptide phage display libraries with high diversity and billions of transformants.

Nematode histone H2A variant evolution reveals diverse histories of retention and loss and evidence for conserved core-like variant histone genes.

Histone variants are paralogs that replace canonical histones in nucleosomes, often imparting novel functions. However, how histone variants arise and evolve is poorly understood. Reconstruction of histone protein evolution is challenging due to large differences in evolutionary rates across gene lineages and sites. Here we used intron position data from 108 nematode genomes in combination with amino acid sequence data to find disparate evolutionary histories of the three H2A variants found in Caenorhabditis elegans: the ancient H2A.ZHTZ-1, the sperm-specific HTAS-1, and HIS-35, which differs from the canonical S-phase H2A by a single glycine-to-alanine C-terminal change. Although the H2A.ZHTZ-1 protein sequence is highly conserved, its gene exhibits recurrent intron gain and loss. This pattern suggests that specific intron sequences or positions may not be important to H2A.Z functionality. For HTAS-1 and HIS-35, we find variant-specific intron positions that are conserved across species. Patterns of intron position conservation indicate that the sperm-specific variant HTAS-1 arose more recently in the ancestor of a subset of Caenorhabditis species, while HIS-35 arose in the ancestor of Caenorhabditis and its sister group, including the genus Diploscapter. HIS-35 exhibits gene retention in some descendent lineages but gene loss in others, suggesting that histone variant use or functionality can be highly flexible. Surprisingly, we find the single amino acid differentiating HIS-35 from core H2A is ancestral and common across canonical Caenorhabditis H2A sequences. Thus, we speculate that the role of HIS-35 lies not in encoding a functionally distinct protein, but instead in enabling H2A expression across the cell cycle or in distinct tissues. This work illustrates how genes encoding such partially-redundant functions may be advantageous yet relatively replaceable over evolutionary timescales, consistent with the patchwork pattern of retention and loss of both genes. Our study shows the utility of intron positions for reconstructing evolutionary histories of gene families, particularly those undergoing idiosyncratic sequence evolution.

Sequence-dependent and -independent information in a combined random energy model for protein folding and coding.

Random energy models (REMs) provide a simple description of the energy landscapes that guide protein folding and evolution. The requirement of a large energy gap between the native structure and unfolded conformations, considered necessary for cooperative, protein-like, folding behavior, indicates that proteins differ markedly from random heteropolymers. It has been suggested, therefore, that natural selection might have acted to choose nonrandom amino acid sequences satisfying this particular condition, implying that a large fraction of possible, unselected random sequences, would not fold to any structure. From an informational perspective, however, this scenario could indicate that protein structures, regarded as messages to be transmitted through a communication channel, would not be efficiently encoded in amino acid sequences, regarded as the communication channel for this transmission, since a large fraction of possible channel states would not be used. Here, we use a combined REM for conformations and sequences, with previously estimated parameters for natural proteins, to explore an alternative possibility in which the appropriate shape of the landscape results mainly from the deviation from randomness of possible native structures instead of sequences. We observe that this situation emerges naturally if the distribution of conformational energies happens to arise from two independent contributions corresponding to sequence-dependent and -independent terms. This construction is consistent with the hypothesis of a protein burial folding code, with native structures being determined by a modest amount of sequence-dependent atomic burial information with sequence-independent constraints imposed by unspecific hydrogen bond formation. More generally, an appropriate combination of sequence-dependent and -independent information accommodates the possibility of an efficient structural encoding with the main physical requirement for folding, providing possible insight not only on the folding process but also on several aspects sequence evolution such as neutral networks, conformational coverage, and de novo gene emergence.

Assessing computational tools for predicting protein stability changes upon missense mutations using a new dataset.

Insight into how mutations affect protein stability is crucial for protein engineering, understanding genetic diseases, and exploring protein evolution. Numerous computational methods have been developed to predict the impact of amino acid substitutions on protein stability. Nevertheless, comparing these methods poses challenges due to variations in their training data. Moreover, it is observed that they tend to perform better at predicting destabilizing mutations than stabilizing ones. Here, we meticulously compiled a new dataset from three recently published databases: ThermoMutDB, FireProtDB, and ProThermDB. This dataset, which does not overlap with the well-established S2648 dataset, consists of 4038 single-point mutations, including over 1000 stabilizing mutations. We assessed these mutations using 27 computational methods, including the latest ones utilizing mega-scale stability datasets and transfer learning. We excluded entries with overlap or similarity to training datasets to ensure fairness. Pearson correlation coefficients for the tested tools ranged from 0.20 to 0.53 on unseen data, and none of the methods could accurately predict stabilizing mutations, even those performing well in anti-symmetric property analysis. While most methods present consistent trends for predicting destabilizing mutations across various properties such as solvent exposure and secondary conformation, stabilizing mutations do not exhibit a clear pattern. Our study also suggests that solely addressing training dataset bias may not significantly enhance accuracy of predicting stabilizing mutations. These findings emphasize the importance of developing precise predictive methods for stabilizing mutations.

No Evidence that Selection on Synonymous Codon Usage Affects Patterns of Protein Evolution in Bacteria.

Bias in synonymous codon usage has been reported across all kingdoms of life. Evidence suggests that codon usage bias is often driven by selective pressures, typically for translational efficiency. These selective pressures have been shown to depress the rate at which synonymous sites evolve. We hypothesize that selection on synonymous codon use could also slow the rate of protein evolution if a non-synonymous mutation changes the codon from being preferred to unpreferred. We test this hypothesis by looking at patterns of protein evolution using polymorphism and substitution data in two bacterial species, Escherichia coli and Streptococcus pneumoniae. We find no evidence that non-synonymous mutations that change a codon from being unpreferred to preferred are more common than the opposite. Overall, selection on codon bias seems to have little influence over non-synonymous polymorphism or substitution patterns.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes.

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Comparative analysis of codon usage patterns of Plasmodium helical interspersed subtelomeric (PHIST) proteins.

Plasmodium falciparum is a protozoan parasite that causes the most severe form of malaria in humans worldwide, which is predominantly found in sub-Saharan Africa, where it is responsible for the majority of malaria-related deaths. Plasmodium helical interspersed subtelomeric (PHIST) proteins are a family of proteins, with a conserved PHIST domain, which are typically located at the subtelomeric regions of the Plasmodium falciparum chromosomes and play crucial roles in the interaction between the parasite and its human host, such as cytoadherence, immune evasion, and host cell remodeling. However, the specific utilization of synonymous codons by PHIST proteins in Plasmodium falciparum is still unknown. Codon usage bias (CUB) refers to the unequal usage of synonymous codons during translation, resulting in over- or underrepresentation of certain nucleotide patterns. This imbalance in CUB can impact various cellular processes, including protein expression levels and genetic variation. To investigate this, the CUB of 88 PHIST protein coding sequences (CDSs) from 5 subgroups were analyzed in this study. The results showed that both codon base composition and relative synonymous codon usage (RSCU) analysis identified a higher occurrence of AT-ended codons (AGA and UUA) in PHIST proteins of Plasmodium falciparum. The average effective number of codons (ENC) for these PHIST proteins was 36.69, indicating a weak codon preference among them, as it was greater than 35. Additionally, the correlation analysis among codon base composition (GC1, GC2, GC3, GCs), codon adaptation index (CAI), codon bias index (CBI), frequency of optimal codons (FOP), ENC, general average hydropathicity (GRAVY), aromaticity (AROMO), length of synonymous codons (L_sym), and length of amino acids (L_aa) revealed the influence of base composition and codon usage indices on codon usage bias, with GC1 having a significant impact in this study. Furthermore, the neutrality plot analysis, PR2-bias plot analysis, and ENC-GC3 plot analysis provided additional evidence that natural selection plays a crucial role in determining codon bias in PHIST proteins. In conclusion, this study has enhanced our understanding of the characteristics of codon usage and genetic evolution in PHIST proteins, thereby providing data foundation for further research on antimalarial drugs or vaccines.

Genome-Wide Identification of Callose Synthase Family Genes and Their Expression Analysis in Floral Bud Development and Hormonal Responses in Prunus mume.

Callose is an important polysaccharide composed of beta-1,3-glucans and is widely implicated in plant development and defense responses. Callose synthesis is mainly catalyzed by a family of callose synthases, also known as glucan synthase-like (GSL) enzymes. Despite the fact that GSL family genes were studied in a few plant species, their functional roles have not been fully understood in woody perennials. In this study, we identified total of 84 GSL genes in seven plant species and classified them into six phylogenetic clades. An evolutionary analysis revealed different modes of duplication driving the expansion of GSL family genes in monocot and dicot species, with strong purifying selection constraining the protein evolution. We further examined the gene structure, protein sequences, and physiochemical properties of 11 GSL enzymes in Prunus mume and observed strong sequence conservation within the functional domain of PmGSL proteins. However, the exon-intron distribution and protein motif composition are less conservative among PmGSL genes. With a promoter analysis, we detected abundant hormonal responsive cis-acting elements and we inferred the putative transcription factors regulating PmGSLs. To further understand the function of GSL family genes, we analyzed their expression patterns across different tissues, and during the process of floral bud development, pathogen infection, and hormonal responses in Prunus species and identified multiple GSL gene members possibly implicated in the callose deposition associated with bud dormancy cycling, pathogen infection, and hormone signaling. In summary, our study provides a comprehensive understanding of GSL family genes in Prunus species and has laid the foundation for future functional research of callose synthase genes in perennial trees.

Evolution towards simplicity in bacterial small heat shock protein system

Evolution can tinker with multi-protein machines and replace them with simpler single-protein systems performing equivalent functions in an equally efficient manner. It is unclear how, on a molecular level, such simplification can arise. With ancestral reconstruction and biochemical analysis, we have traced the evolution of bacterial small heat shock proteins (sHsp), which help to refold proteins from aggregates using either two proteins with different functions (IbpA and IbpB) or a secondarily single sHsp that performs both functions in an equally efficient way. Secondarily single sHsp evolved from IbpA, an ancestor specialized in strong substrate binding. Evolution of an intermolecular binding site drove the alteration of substrate binding properties, as well as the formation of higher-order oligomers. Upon two mutations in the α-crystallin domain, secondarily single sHsp interacts with aggregated substrates less tightly. Paradoxically, less efficient binding positively influences the ability of sHsp to stimulate substrate refolding, since the dissociation of sHps from aggregates is required to initiate Hsp70-Hsp100-dependent substrate refolding. After the loss of a partner, IbpA took over its role in facilitating the sHsp dissociation from an aggregate by weakening the interaction with the substrate, which became beneficial for the refolding process. We show that the same two amino acids introduced in modern-day systems define whether the IbpA acts as a single sHsp or obligatorily cooperates with an IbpB partner. Our discoveries illuminate how one sequence has evolved to encode functions previously performed by two distinct proteins.

Evolution towards simplicity in bacterial small heat shock protein system

Evolution can tinker with multi-protein machines and replace them with simpler single-protein systems performing equivalent functions in an equally efficient manner. It is unclear how, on a molecular level, such simplification can arise. With ancestral reconstruction and biochemical analysis, we have traced the evolution of bacterial small heat shock proteins (sHsp), which help to refold proteins from aggregates using either two proteins with different functions (IbpA and IbpB) or a secondarily single sHsp that performs both functions in an equally efficient way. Secondarily single sHsp evolved from IbpA, an ancestor specialized in strong substrate binding. Evolution of an intermolecular binding site drove the alteration of substrate binding properties, as well as the formation of higher-order oligomers. Upon two mutations in the α-crystallin domain, secondarily single sHsp interacts with aggregated substrates less tightly. Paradoxically, less efficient binding positively influences the ability of sHsp to stimulate substrate refolding, since the dissociation of sHps from aggregates is required to initiate Hsp70-Hsp100-dependent substrate refolding. After the loss of a partner, IbpA took over its role in facilitating the sHsp dissociation from an aggregate by weakening the interaction with the substrate, which became beneficial for the refolding process. We show that the same two amino acids introduced in modern-day systems define whether the IbpA acts as a single sHsp or obligatorily cooperates with an IbpB partner. Our discoveries illuminate how one sequence has evolved to encode functions previously performed by two distinct proteins.

Microbial sensor variation across biogeochemical conditions in the terrestrial deep subsurface.

Microbes can be found in abundance many kilometers underground. While microbial metabolic capabilities have been examined across different geochemical settings, it remains unclear how changes in subsurface niches affect microbial needs to sense and respond to their environment. To address this question, we examined how microbial extracellular sensor systems vary with environmental conditions across metagenomes at different Deep Mine Microbial Observatory (DeMMO) subsurface sites. Because two-component systems (TCSs) directly sense extracellular conditions and convert this information into intracellular biochemical responses, we expected that this sensor family would vary across isolated oligotrophic subterranean environments that differ in abiotic and biotic conditions. TCSs were found at all six subsurface sites, the service water control, and the surface site, with an average of 0.88 sensor histidine kinases (HKs) per 100 genes across all sites. Abundance was greater in subsurface fracture fluids compared with surface-derived fluids, and candidate phyla radiation (CPR) bacteria presented the lowest HK frequencies. Measures of microbial diversity, such as the Shannon diversity index, revealed that HK abundance is inversely correlated with microbial diversity (r2 = 0.81). Among the geochemical parameters measured, HK frequency correlated most strongly with variance in dissolved organic carbon (r2 = 0.82). Taken together, these results implicate the abiotic and biotic properties of an ecological niche as drivers of sensor needs, and they suggest that microbes in environments with large fluctuations in organic nutrients (e.g., lacustrine, terrestrial, and coastal ecosystems) may require greater TCS diversity than ecosystems with low nutrients (e.g., open ocean).IMPORTANCEThe ability to detect extracellular environmental conditions is a fundamental property of all life forms. Because microbial two-component sensor systems convert information about extracellular conditions into biochemical information that controls their behaviors, we evaluated how two-component sensor systems evolved within the deep Earth across multiple sites where abiotic and biotic properties vary. We show that these sensor systems remain abundant in microbial consortia at all subterranean sampling sites and observe correlations between sensor system abundances and abiotic (dissolved organic carbon variation) and biotic (consortia diversity) properties. These results suggest that multiple environmental properties may drive sensor protein evolution and highlight the need for further studies of metagenomic and geochemical data in parallel to understand the drivers of microbial sensor evolution.

25 Years Responding to Respiratory and Other Viruses with Mass Spectrometry.

This review article presents the development and application of mass spectrometry (MS) approaches, developed in the author's laboratory over the past 25 years, to detect; characterise, type and subtype; and distinguish major variants and subvariants of respiratory viruses such as influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). All features make use of matrix-assisted laser desorption ionisation (MALDI) mass maps, recorded for individual viral proteins or whole virus digests. A MALDI-based immunoassay in which antibody-peptide complexes were preserved on conventional MALDI targets without their immobilisation led to an approach that enabled their indirect detection. The site of binding, and thus the molecular antigenicity of viruses, could be determined. The same approach was employed to study antivirals bound to their target viral protein, the nature of the binding residues, and relative binding affinities. The benefits of high-resolution MS were exploited to detect sequence-conserved signature peptides of unique mass within whole virus and single protein digests. These enabled viruses to be typed, subtyped, their lineage determined, and variants and subvariants to be distinguished. Their detection using selected ion monitoring improved analytical sensitivity limits to aid the identification of viruses in clinical specimens. The same high-resolution mass map data, for a wide range of viral strains, were input into a purpose-built algorithm (MassTree) in order to both chart and interrogate viral evolution. Without the need for gene or protein sequences, or any sequence alignment, this phylonumerics approach also determines and displays single-point mutations associated with viral protein evolution in a single-tree building step.

Evolution of the coronavirus spike protein in the full-length genome and defective viral genome under diverse selection pressures.

How coronaviruses evolve by altering the structures of their full-length genome and defective viral genome (DVG) under dynamic selection pressures has not been studied. In this study, we aimed to experimentally identify the dynamic evolutionary patterns of the S protein sequence in the full-length genome and DVG under diverse selection pressures, including persistence, innate immunity and antiviral drugs. The evolutionary features of the S protein sequence in the full-length genome and in the DVG under diverse selection pressures are as follows: (i) the number of nucleotide (nt) mutations does not necessarily increase with the number of selection pressures; (ii) certain types of selection pressure(s) can lead to specific nt mutations; (iii) the mutated nt sequence can be reverted to the wild-type nt sequence under the certain type of selection pressure(s); (iv) the DVG can also undergo mutations and evolve independently of the full-length genome; and (v) DVG species are regulated during evolution under diverse selection pressures. The various evolutionary patterns of the S protein sequence in the full-length genome and DVG identified in this study may contribute to coronaviral fitness under diverse selection pressures.

Cross-species investigation into the requirement of XPA for nucleotide excision repair.

After reconstitution of nucleotide excision repair (excision repair) with XPA, RPA, XPC, TFIIH, XPF-ERCC1and XPG, it was concluded that these six factors are the minimal essential components of the excision repair machinery. All six factors are highly conserved across diverse organisms spanning yeast to humans, yet no identifiable homolog of the XPA gene exists in many eukaryotes including green plants. Nevertheless, excision repair is reported to be robust in the XPA-lacking organism, Arabidopsis thaliana, which raises a fundamental question of whether excision repair could occur without XPA in other organisms. Here, we performed a phylogenetic analysis of XPA across all species with annotated genomes and then quantitatively measured excision repair in the absence of XPA using the sensitive whole-genome qXR-Seq method in human cell lines and two model organisms, Caenorhabditis elegans and Drosophila melanogaster. We find that although the absence of XPA results in inefficient excision repair and UV-sensitivity in humans, flies, and worms, excision repair of UV-induced DNA damage is detectable over background. These studies have yielded a significant discovery regarding the evolution of XPA protein and its mechanistic role in nucleotide excision repair.

A Conserved Ribosomal Protein Has Entirely Dissimilar Structures in Different Organisms.

Ribosomes from different species can markedly differ in their composition by including dozens of ribosomal proteins that are unique to specific lineages but absent in others. However, it remains unknown how ribosomes acquire new proteins throughout evolution. Here, to help answer this question, we describe the evolution of the ribosomal protein msL1/msL2 that was recently found in ribosomes from the parasitic microorganism clade, microsporidia. We show that this protein has a conserved location in the ribosome but entirely dissimilar structures in different organisms: in each of the analyzed species, msL1/msL2 exhibits an altered secondary structure, an inverted orientation of the N-termini and C-termini on the ribosomal binding surface, and a completely transformed 3D fold. We then show that this fold switching is likely caused by changes in the ribosomal msL1/msL2-binding site, specifically, by variations in rRNA. These observations allow us to infer an evolutionary scenario in which a small, positively charged, de novo-born unfolded protein was first captured by rRNA to become part of the ribosome and subsequently underwent complete fold switching to optimize its binding to its evolving ribosomal binding site. Overall, our work provides a striking example of how a protein can switch its fold in the context of a complex biological assembly, while retaining its specificity for its molecular partner. This finding will help us better understand the origin and evolution of new protein components of complex molecular assemblies-thereby enhancing our ability to engineer biological molecules, identify protein homologs, and peer into the history of life on Earth.

Deep-branching evolutionary intermediates reveal structural origins of form I rubisco

The enzyme rubisco (ribulose-1,5-bisphosphate carboxylase/oxygenase) catalyzes the majority of biological carbon fixation on Earth. Although the vast majority of rubiscos across the tree of life assemble as homo-oligomers, the globally predominant form I enzyme-found in plants, algae, and cyanobacteria-forms a unique hetero-oligomeric complex. The recent discovery of a homo-oligomeric sister group to form I rubisco (named form I') has filled a key gap in our understanding of the enigmatic origins of the form I clade. However, to elucidate the series of molecular events leading to the evolution of form I rubisco, we must examine more distantly related sibling clades to contextualize the molecular features distinguishing form I and form I' rubiscos. Here, we present a comparative structural study retracing the evolutionary history of rubisco that reveals a complex structural trajectory leading to the ultimate hetero-oligomerization of the form I clade. We structurally characterize the oligomeric states of deep-branching form Iα and I'' rubiscos recently discovered from metagenomes, which represent key evolutionary intermediates preceding the form I clade. We further solve the structure of form I'' rubisco, revealing the molecular determinants that likely primed the enzyme core for the transition from a homo-oligomer to a hetero-oligomer. Our findings yield new insight into the evolutionary trajectory underpinning the adoption and entrenchment of the prevalent assembly of form I rubisco, providing additional context when viewing the enzyme family through the broader lens of protein evolution.

STAR: A Web Server for Assisting Directed Protein Evolution with Machine Learning.

Protein engineering has made significant contributions to industries such as agriculture, food, and pharmaceuticals. In recent years, directed evolution combined with artificial intelligence has emerged as a cutting-edge R&D approach. However, the application of machine learning techniques can be challenging for those without relevant experience and coding skills. To address this issue, we have developed a web-based protein sequence recommendation system: STAR (Sequence recommendaTion via ARtificial intelligence). Our system utilizes Bayesian optimization as its backbone and includes a filtering step using a regression model to enhance the success rate of recommended sequences. Additionally, we have incorporated an in silico-directed evolution approach to expand the exploration of the protein space. The Web site can be accessed at https://www.FindProteinStar.com/.

The ortholog of human REEP1-4 is required for autophagosomal enclosure of ER-phagy/nucleophagy cargos in fission yeast.

Selective macroautophagy of the endoplasmic reticulum (ER) and the nucleus, known as ER-phagy and nucleophagy, respectively, are processes whose mechanisms remain inadequately understood. Through an imaging-based screen, we find that in the fission yeast Schizosaccharomyces pombe, Yep1 (also known as Hva22 or Rop1), the ortholog of human REEP1-4, is essential for ER-phagy and nucleophagy but not for bulk autophagy. In the absence of Yep1, the initial phase of ER-phagy and nucleophagy proceeds normally, with the ER-phagy/nucleophagy receptor Epr1 coassembling with Atg8. However, ER-phagy/nucleophagy cargos fail to reach the vacuole. Instead, nucleus- and cortical-ER-derived membrane structures not enclosed within autophagosomes accumulate in the cytoplasm. Intriguingly, the outer membranes of nucleus-derived structures remain continuous with the nuclear envelope-ER network, suggesting a possible outer membrane fission defect during cargo separation from source compartments. We find that the ER-phagy role of Yep1 relies on its abilities to self-interact and shape membranes and requires its C-terminal amphipathic helices. Moreover, we show that human REEP1-4 and budding yeast Atg40 can functionally substitute for Yep1 in ER-phagy, and Atg40 is a divergent ortholog of Yep1 and REEP1-4. Our findings uncover an unexpected mechanism governing the autophagosomal enclosure of ER-phagy/nucleophagy cargos and shed new light on the functions and evolution of REEP family proteins.

Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation.

Solubility is one of the most important properties of protein. Protein solubility can be greatly changed by single amino acid mutations and the reduced protein solubility could lead to diseases. Since experimental methods to determine solubility are time-consuming and expensive, in-silico methods have been developed to predict the protein solubility changes caused by mutations mostly through protein evolution information. However, these methods are slow since it takes long time to obtain evolution information through multiple sequence alignment. In addition, these methods are of low performance because they do not fully utilize protein 3D structures due to a lack of experimental structures for most proteins. Here, we proposed a sequence-based method DeepMutSol to predict solubility change from residual mutations based on the Graph Convolutional Neural Network (GCN), where the protein graph was initiated according to predicted protein structure from Alphafold2, and the nodes (residues) were represented by protein language embeddings. To circumvent the small data of solubility changes, we further pretrained the model over absolute protein solubility. DeepMutSol was shown to outperform state-of-the-art methods in benchmark tests. In addition, we applied the method to clinically relevant genes from the ClinVar database and the predicted solubility changes were shown able to separate pathogenic mutations. All of the data sets and the source code are available at https://github.com/biomed-AI/DeepMutSol.

Comparison of Evolutionary Relationships between Branchiostoma floridae, Ciona intestinalis, and Homo sapiens Globins Provide Evidence of Gene Co-Option and Convergent Evolution.

Globins have been studied as model proteins to elucidate the principles of protein evolution. This was achieved by understanding the relationship between amino acid sequence, three-dimensional structure, physicochemical properties, and physiological function. Previous molecular phylogenies of chordate globin genes revealed the monophyletic evolution of urochordate globins and suggested convergent evolution. However, to provide evidence of convergent evolution, it is necessary to determine the physicochemical and functional similarities between vertebrates and urochordate globins. In this study, we determined the expression patterns of Ciona globin genes using real-time RT-PCR. Two genes (Gb-1 and Gb-2) were predominantly expressed in the branchial sac, heart, and hemocytes and were induced under hypoxia. Combined with the sequence analysis, our findings suggest that Gb-1/-2 correspond to vertebrate hemoglobin-α/-β. However, we did not find a robust similarity between Gb-3, Gb-4, and vertebrate globins. These results suggested that, even though Ciona globins obtained their unique functions differently from vertebrate globins, the two of them shared some physicochemical features and physiological functions. Our findings offer a good example for understanding the molecular mechanisms underlying gene co-option and convergence, which could lead to evolutionary innovations.