The sequence-structure relationship and protein function prediction

Salmonella Typhi serine threonine kinase T4519 induces lysosomal membrane permeabilization by manipulating Toll-like receptor 2-Cystatin B-Cathepsin B-NF-κB-reactive oxygen species pathway and promotes survival within human macrophages

Intracellular pathogens of Salmonella spp. survive and replicate within the phagosomes, called Salmonella-containing vacuoles (SCVs) inside macrophages by manipulating phagosomal maturation and phagolysosome formation. While controversies exist about the phagosomal traffic of Salmonella Typhimurium, little studies were carried out with the intracellular survival mechanisms of Salmonella Typhi (S. Typhi). We had previously reported that a eukaryote-like serine/threonine kinase of S. Typhi (T4519) contributes to survival within macrophages and activates host pro-inflammatory signaling pathways regulated by NF-κB. However, neither the mechanisms underlying NF-κB activation nor how it contributes to intracellular survival of S. Typhi were studied. Here we show, by using antibody-mediated blocking and gene knockdown studies that T4519 activates Toll-like receptor 2 (TLR2) signals in the human monocyte-derived macrophages. We computationally predicted the NH2-terminal glycine rich repeat domain of T4519 as the TLR2-binding moiety and confirmed the interaction by co-immunoprecipitation experiment. TLR2-T4519 interaction transcriptionally repressed cystatin B, a cathepsin B inhibitor, leading to the activation of cytosolic cathepsin B, leaked from the lysosomes of the infected cells. Through a series of RT-qPCR, western blotting, gene knockdown, flow cytometry and confocal microscopy experiments, we have shown that active cytosolic cathepsin B cleaves IKB-α, resulting in nuclear translocation of NF-κB and transactivation of its target genes, including reactive oxygen species (ROS), which in turn induces lysosomal membrane permeabilization (LMP). TLR2-dependent targeting of the cystatin B-cathepsin B-NF-κB-ROS pathways by T4519, leading to LMP promotes phagosomal survival of S. Typhi. This study describes a unique mechanism of the exploitation of host NF-κB signaling pathways by bacterial pathogens to promote its own persistence within macrophage cells.

Throw out an oligopeptide to catch a protein: Deep learning and natural language processing-screened tripeptide PSP promotes Osteolectin-mediated vascularized bone regeneration

Angiogenesis is imperative for bone regeneration, yet the conventional cytokine therapies have been constrained by prohibitive costs and safety apprehensions. It is urgent to develop a safer and more efficient therapeutic alternative. Herein, utilizing the methodologies of Deep Learning (DL) and Natural Language Processing (NLP), we proposed a paradigm algorithm that amalgamates Word2vec with a TF-IDF variant, TF-IIDF, to deftly discern potential pro-angiogenic peptides from intrinsically disordered regions (IDRs) of 262 related proteins, where are fertile grounds for developing safer and highly promising bioactive peptides. After the evaluation of the candidate oligopeptides, one tripeptide, PSP, emerged as particularly notable for its exceptional ability to stimulate the vascularization of endothelial cells (ECs), enhance vascular-osteo communication, and then boost the osteogenic differentiation of bone marrow stem cells (BMSCs), evidenced in mouse critical-sized cranial model. Moreover, we found that PSP serves as a 'priming' agent, activating the body's innate ability to produce Osteolectin (Oln) - prompting ECs to release small extracellular vesicles (sEVs) enriched with Oln to facilitate bone formation. In summary, our study established a precise and efficient composite model of DL and NLP to screen bioactive peptides, opening an avenue for the development of various peptide-based therapeutic strategies applicable to a broader range of diseases.

Protein structure accuracy estimation using geometry-complete perceptron networks

Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally-predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the geometry-complete perceptron network for protein structure EMA (GCPNet-EMA), where we demonstrate through rigorous computational benchmarks that GCPNet-EMA's accuracy estimations are 47% faster and more than 10% (6%) more correlated with ground-truth measures of per-residue (per-target) structural accuracy compared to baseline state-of-the-art methods for tertiary (multimer) structure EMA including AlphaFold 2. The source code and data for GCPNet-EMA are available on GitHub, and a public web server implementation is freely available.

GPCR-BERT: Interpreting Sequential Design of G Protein-Coupled Receptors Using Protein Language Models

With the rise of transformers and large language models (LLMs) in chemistry and biology, new avenues for the design and understanding of therapeutics have been opened up to the scientific community. Protein sequences can be modeled as language and can take advantage of recent advances in LLMs, specifically with the abundance of our access to the protein sequence data sets. In this letter, we developed the GPCR-BERT model for understanding the sequential design of G protein-coupled receptors (GPCRs). GPCRs are the target of over one-third of Food and Drug Administration-approved pharmaceuticals. However, there is a lack of comprehensive understanding regarding the relationship among amino acid sequence, ligand selectivity, and conformational motifs (such as NPxxY, CWxP, and E/DRY). By utilizing the pretrained protein model (Prot-Bert) and fine-tuning with prediction tasks of variations in the motifs, we were able to shed light on several relationships between residues in the binding pocket and some of the conserved motifs. To achieve this, we took advantage of attention weights and hidden states of the model that are interpreted to extract the extent of contributions of amino acids in dictating the type of masked ones. The fine-tuned models demonstrated high accuracy in predicting hidden residues within the motifs. In addition, the analysis of embedding was performed over 3D structures to elucidate the higher-order interactions within the conformations of the receptors.

Evolution shapes interaction patterns for epistasis and specific protein binding in a two-component signaling system

The elegant design of protein sequence/structure/function relationships arises from the interaction patterns between amino acid positions. A central question is how evolutionary forces shape the interaction patterns that encode long-range epistasis and binding specificity. Here, we combined family-wide evolutionary analysis of natural homologous sequences and structure-oriented evolution simulation for two-component signaling (TCS) system. The magnitude-frequency relationship of coupling conservation between positions manifests a power-law-like distribution and the positions with highly coupling conservation are sparse but distributed intensely on the binding surfaces and hydrophobic core. The structure-specific interaction pattern involves further optimization of local frustrations at or near the binding surface to adapt the binding partner. The construction of family-wide conserved interaction patterns and structure-specific ones demonstrates that binding specificity is modulated by both direct intermolecular interactions and long-range epistasis across the binding complex. Evolution sculpts the interaction patterns via sequence variations at both family-wide and structure-specific levels for TCS system.

Quantifying microbial guilds

The ecological role of microorganisms is of utmost importance due to their multiple interactions with the environment. However, assessing the contribution of individual taxonomic groups has proven difficult despite the availability of high throughput data, hindering our understanding of such complex systems. Here, we propose a quantitative definition of guild that is readily applicable to metagenomic data. Our framework focuses on the functional character of protein sequences, as well as their diversifying nature. First, we discriminate functional sequences from the whole sequence space corresponding to a gene annotation to then quantify their contribution to the guild composition across environments. In addition, we identify and distinguish functional implementations, which are sequence spaces that have different ways of carrying out the function. In contrast, we found that orthology delineation did not consistently align with ecologically (or functionally) distinct implementations of the function. We demonstrate the value of our approach with two case studies: the ammonia oxidation and polyamine uptake guilds from the Malaspina circumnavigation cruise, revealing novel ecological dynamics of the latter in marine ecosystems. Thus, the quantification of guilds helps us to assess the functional role of different taxonomic groups with profound implications on the study of microbial communities.

Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution

In flowering plants, C4 photosynthesis is superior to C3 type in carbon fixation efficiency and adaptation to extreme environmental conditions, but the mechanisms behind the assembly of C4 machinery remain elusive. This study attempts to dissect the evolutionary divergence from C3 to C4 photosynthesis in five photosynthetic model plants from the grass family, using a combined comparative transcriptomics and deep learning technology. By examining and comparing gene expression levels in bundle sheath and mesophyll cells of five model plants, we identified 16 differentially expressed signature genes showing cell-specific expression patterns in C3 and C4 plants. Among them, two showed distinctively opposite cell-specific expression patterns in C3 vs. C4 plants (named as FOGs). The in silico physicochemical analysis of the two FOGs illustrated that C3 homologous proteins of LHCA6 had low and stable pI values of ~6, while the pI values of LHCA6 homologs increased drastically in C4 plants Setaria viridis (7), Zea mays (8), and Sorghum bicolor (over 9), suggesting this protein may have different functions in C3 and C4 plants. Interestingly, based on pairwise protein sequence/structure similarities between each homologous FOG protein, one FOG PGRL1A showed local inconsistency between sequence similarity and structure similarity. To find more examples of the evolutionary characteristics of FOG proteins, we investigated the protein sequence/structure similarities of other FOGs (transcription factors) and found that FOG proteins have diversified incompatibility between sequence and structure similarities during grass family evolution. This raised an interesting question as to whether the sequence similarity is related to structure similarity during C4 photosynthesis evolution.

A Systematic Compilation of Human SH3 Domains: A Versatile Superfamily in Cellular Signaling

SRC homology 3 (SH3) domains are fundamental modules that enable the assembly of protein complexes through physical interactions with a pool of proline-rich/noncanonical motifs from partner proteins. They are widely studied modular building blocks across all five kingdoms of life and viruses, mediating various biological processes. The SH3 domains are also implicated in the development of human diseases, such as cancer, leukemia, osteoporosis, Alzheimer's disease, and various infections. A database search of the human proteome reveals the existence of 298 SH3 domains in 221 SH3 domain-containing proteins (SH3DCPs), ranging from 13 to 720 kilodaltons. A phylogenetic analysis of human SH3DCPs based on their multi-domain architecture seems to be the most practical way to classify them functionally, with regard to various physiological pathways. This review further summarizes the achievements made in the classification of SH3 domain functions, their binding specificity, and their significance for various diseases when exploiting SH3 protein modular interactions as drug targets.

Protein-Inspired Control over Synthetic Polymer Folding for Structured Functional Nanoparticles in Water

The folding of proteins into functional nanoparticles with defined 3D structures has inspired chemists to create simple synthetic systems mimicking protein properties. The folding of polymers into nanoparticles in water proceeds via different strategies, resulting in the global compaction of the polymer chain. Herein, we review the different methods available to control the conformation of synthetic polymers and collapse/fold them into structured, functional nanoparticles, such as hydrophobic collapse, supramolecular self-assembly, and covalent cross-linking. A comparison is made between the design principles of protein folding to synthetic polymer folding and the formation of structured nanocompartments in water, highlighting similarities and differences in design and function. We also focus on the importance of structure for functional stability and diverse applications in complex media and cellular environments.

Biological soft matter: intrinsically disordered proteins in liquid-liquid phase separation and biomolecular condensates

The facts that many proteins with crucial biological functions do not have unique structures and that many biological processes are compartmentalized into the liquid-like biomolecular condensates, which are formed via liquid-liquid phase separation (LLPS) and are not surrounded by the membrane, are revolutionizing the modern biology. These phenomena are interlinked, as the presence of intrinsic disorder represents an important requirement for a protein to undergo LLPS that drives biogenesis of numerous membrane-less organelles (MLOs). Therefore, one can consider these phenomena as crucial constituents of a new IDP-LLPS-MLO field. Furthermore, intrinsically disordered proteins (IDPs), LLPS, and MLOs represent a clear link between molecular and cellular biology and soft matter and condensed soft matter physics. Both IDP and LLPS/MLO fields are undergoing explosive development and generate the ever-increasing mountain of crucial data. These new data provide answers to so many long-standing questions that it is difficult to imagine that in the very recent past, protein scientists and cellular biologists operated without taking these revolutionary concepts into account. The goal of this essay is not to deliver a comprehensive review of the IDP-LLPS-MLO field but to provide a brief and rather subjective outline of some of the recent developments in these exciting fields.

Enhancing the Inhibition Potential of AHL Acylase PF2571 against Food Spoilage by Remodeling Its Substrate Scope via a Computationally Driven Protein Design

The N-acyl homoserine lactone (AHL) acylases are widely used as quorum sensing (QS) blockers to inhibit bacterial food spoilage. However, their substrate specificity for long-chain substrates weakens their efficiency. In this study, a computer-assisted design of AHL acylase PF2571 was performed to modify its substrate scope. The results showed that the variant PF2571^{H194Y, L221R} could effectively quench N-hexanoyl-l-homoserine lactone and N-octanoyl-l-homoserine lactone without impairing its activity against long-chain AHLs. Kinetic analysis of the enzymatic activities further corroborated the observed substrate expansion. The inhibitory activities of this variant were significantly enhanced against the QS phenotype of Aeromonas veronii BY-8, with inhibition rates of 45.67, 78.25, 54.21, and 54.65% against proteases, motility, biofilms, and extracellular polysaccharides, respectively. Results for molecular dynamics simulation showed that the steric hindrance, induced by residue substitution, could have been responsible for the change in substrate scope. This study dramatically improves the practicability of AHL acylase in controlling food spoilage.

Generalized Property-Based Encoders and Digital Signal Processing Facilitate Predictive Tasks in Protein Engineering

Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, where we replace each amino acid by its value for a given property. However, what property (or group thereof) is best for a given predictive task remains an open problem. In this work, we generalize property-based encoding strategies to maximize the performance of predictive models in protein engineering. First, combining text mining and unsupervised learning, we partitioned the AAIndex database into eight semantically-consistent groups of properties. We then applied a non-linear PCA within each group to define a single encoder to represent it. Then, in several case studies, we assess the performance of predictive models for protein and peptide function, folding, and biological activity, trained using the proposed encoders and classical methods (One Hot Encoder and TAPE embeddings). Models trained on datasets encoded with our encoders and converted to signals through the Fast Fourier Transform (FFT) increased their precision and reduced their overfitting substantially, outperforming classical approaches in most cases. Finally, we propose a preliminary methodology to create de novo sequences with desired properties. All these results offer simple ways to increase the performance of general and complex predictive tasks in protein engineering without increasing their complexity.

Molecular characterization and determination of the biochemical properties of cathepsin L of Trichinella spiralis

Cathepsin L is an important cysteine protease, but its function in T. spiralis remains unclear. The aim of this research was to explore the biological characteristics of T. spiralis cathepsin L (TsCatL) and its role in T. spiralis-host interactions. Bioinformatic analysis revealed the presence of the cysteine protease active site residues Gln, Cys, His and Asn in mature TsCatL, as well as specific motifs of cathepsin L similar to ERFNIN and GYLND in the prepeptide of TsCatL. Molecular docking of mature TsCatL and E64 revealed hydrophobic effects and hydrogen bonding interactions. Two domains of TsCatL (TsCatL2) were cloned and expressed, and recombinant TsCatL2 (rTsCatL2) was autocatalytically cleaved under acidic conditions to form mature TsCatL. TsCatL was transcribed and expressed in larvae and adults and located in the stichosome, gut and embryo. Enzyme kinetic tests showed that rTsCatL2 degraded the substrate Z-Phe-Arg-AMC under acidic conditions, which was inhibited by E64 and PMSF and enhanced by EDTA, L-cysteine and DTT. The kinetic parameters of rTsCatL2 were a Km value of 48.82 μM and Vmax of 374.4 nM/min at pH 4.5, 37 °C and 5 mM DTT. In addition, it was shown that rTsCatL2 degraded haemoglobin, serum albumin, immunoglobulins (mouse IgG, human IgG and IgM) and extracellular matrix components (fibronectin, collagen I and laminin). The proteolytic activity of rTsCatL2 was host specific and significantly inhibited by E64. rTsCatL2 possesses the natural activity of a sulfhydryl-containing cysteine protease, and TsCatL is an important digestive enzyme that seems to be important for the nutrient acquisition, immune evasion and invasion of Trichinella in the host.

Comprehensive analysis of 1R- and 2R-MYBs reveals novel genic and protein features, complex organisation, selective expansion and insights into evolutionary tendencies

Myeloblastosis (MYB) family, the largest plant transcription factor family, has been subcategorised based on the number and type of repeats in the MYB domain. In spite of several reports, evolution of MYB genes and repeats remains enigmatic. Brassicaceae members are endowed with complex genomes, including dysploidy because of its unique history with multiple rounds of polyploidisation, genomic fractionations and rearrangements. The present study is an attempt to gain insights into the complexities of MYB family diversity, understand impacts of genome evolution on gene families and develop an evolutionary framework to understand the origin of various subcategories of MYB gene family. We identified and analysed 1129 MYBs that included 1R-, 2R-, 3R- and atypical-MYBs across sixteen species representing protists, fungi, animals and plants and exclude MYB identified from Brassicaceae except Arabidopsis thaliana; in addition, a total of 1137 2R-MYB genes from six Brassicaceae species were also analysed. Comparative analysis revealed predominance of 1R-MYBs in protists, fungi, animals and lower plants. Phylogenetic reconstruction and analysis of selection pressure suggested ancestral nature of R1-type repeat containing 1R-MYBs that might have undergone intragenic duplication to form multi-repeat MYBs. Distinct differences in gene structure between 1R-MYB and 2R-MYBs were observed regarding intron number, the ratio of gene length to coding DNA sequence (CDS) length and the length of exons encoding the MYB domain. Conserved as well as novel and lineage-specific intron phases were identified. Analyses of physicochemical properties revealed drastic differences indicating functional diversification in MYBs. Phylogenetic reconstruction of 1R- and 2R-MYB genes revealed a shared structure-function relationship in clades which was supported when transcriptome data was analysed in silico. Comparative genomics to study distribution pattern and mapping of 2R-MYBs revealed congruency and greater degree of synteny and collinearity among closely related species. Micro-synteny analysis of genomic segments revealed high conservation of genes that are immediately flanking the surrounding tandemly organised 2R-MYBs along with instances of local duplication, reorganisations and genome fractionation. In summary, polyploidy, dysploidy, reshuffling and genome fractionation were found to cause loss or gain of 2R-MYB genes. The findings need to be supported with functional validation to understand gene structure-function relationship along the evolutionary lineage and adaptive strategies based on comparative functional genomics in plants.

The Structural Rule Distinguishing a Superfold: A Case Study of Ferredoxin Fold and the Reverse Ferredoxin Fold

Superfolds are folds commonly observed among evolutionarily unrelated multiple superfamilies of proteins. Since discovering superfolds almost two decades ago, structural rules distinguishing superfolds from the other ordinary folds have been explored but remained elusive. Here, we analyzed a typical superfold, the ferredoxin fold, and the fold which reverses the N to C terminus direction from the ferredoxin fold as a case study to find the rule to distinguish superfolds from the other folds. Though all the known structural characteristics for superfolds apply to both the ferredoxin fold and the reverse ferredoxin fold, the reverse fold has been found only in a single superfamily. The database analyses in the present study revealed the structural preferences of αβ- and βα-units; the preferences separate two α-helices in the ferredoxin fold, preventing their collision and stabilizing the fold. In contrast, in the reverse ferredoxin fold, the preferences bring two helices near each other, inducing structural conflict. The Rosetta folding simulations suggested that the ferredoxin fold is physically much more realizable than the reverse ferredoxin fold. Therefore, we propose that minimal structural conflict or minimal frustration among secondary structures is the rule to distinguish a superfold from ordinary folds. Intriguingly, the database analyses revealed that a most stringent structural rule in proteins, the right-handedness of the βαβ-unit, is broken in a set of structures to prevent the frustration, suggesting the proposed rule of minimum frustration among secondary structural units is comparably strong as the right-handedness rule of the βαβ-unit.

Linking protein structural and functional change to mutation using amino acid networks

The function of a protein is strongly dependent on its structure. During evolution, proteins acquire new functions through mutations in the amino-acid sequence. Given the advance in deep mutational scanning, recent findings have found functional change to be position dependent, notwithstanding the chemical properties of mutant and mutated amino acids. This could indicate that structural properties of a given position are potentially responsible for the functional relevance of a mutation. Here, we looked at the relation between structure and function of positions using five proteins with experimental data of functional change available. In order to measure structural change, we modeled mutated proteins via amino-acid networks and quantified the perturbation of each mutation. We found that structural change is position dependent, and strongly related to functional change. Strong changes in protein structure correlate with functional loss, and positions with functional gain due to mutations tend to be structurally robust. Finally, we constructed a computational method to predict functionally sensitive positions to mutations using structural change that performs well on all five proteins with a mean precision of 74.7% and recall of 69.3% of all functional positions.

Degeneracy measures in biologically plausible random Boolean networks

Background

Degeneracy—the ability of structurally different elements to perform similar functions—is a property of many biological systems. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands. Although degeneracy is a feature of network topologies and seems to be implicated in a wide variety of biological processes, research on degeneracy in biological networks is mostly limited to weighted networks. In this study, we test an information theoretic definition of degeneracy on random Boolean networks, frequently used to model gene regulatory networks. Random Boolean networks are discrete dynamical systems with binary connectivity and thus, these networks are well-suited for tracing information flow and the causal effects. By generating networks with random binary wiring diagrams, we test the effects of systematic lesioning of connections and perturbations of the network nodes on the degeneracy measure.

Results

Our analysis shows that degeneracy, on average, is the highest in networks in which ~ 20% of the connections are lesioned while 50% of the nodes are perturbed. Moreover, our results for the networks with no lesions and the fully-lesioned networks are comparable to the degeneracy measures from weighted networks, thus we show that the degeneracy measure is applicable to different networks.

Conclusions

Such a generalized applicability implies that degeneracy measures may be a useful tool for investigating a wide range of biological networks and, therefore, can be used to make predictions about the variety of systems’ ability to recover function.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04601-5.

Microsecond Active-Site Dynamics Primarily control Proteolytic Activity of Bromelain: A Single Molecular Level Study with a Denaturant, a Stabilizer and a Macromolecular Crowder

Proteins are dynamic entity with various molecular motions at different timescale and length scale. Molecular motions are crucial for the optimal function of an enzyme. It seems intuitive that these motions are crucial for optimal enzyme activity. However, it is not easy to directly correlate an enzyme's dynamics and activity due to biosystems' enormous complexity. amongst many factors, structure and dynamics are two prime aspects that combinedly control the activity. Therefore, having a direct correlation between protein dynamics and activity is not straightforward. Herein, we observed and correlated the structural, functional, and dynamical responses of an industrially crucial proteolytic enzyme, bromelain with three versatile classes of chemicals: GnHCl (protein denaturant), sucrose (protein stabilizer), and Ficoll-70 (macromolecular crowder). The only free cysteine (Cys-25 at the active-site) of bromelain has been tagged with a cysteine-specific dye to unveil the structural and dynamical changes through various spectroscopic studies both at bulk and at the single molecular level. Proteolytic activity is carried out using casein as the substrate. GnHCl and sucrose shows remarkable structure-dynamics-activity relationships. Interestingly, with Ficoll-70, structure and activity are not correlated. However, microsecond dynamics and activity are beautifully correlated in this case also. Overall, our result demonstrates that bromelain dynamics in the microsecond timescale around the active-site is probably a key factor in controlling its proteolytic activity.

Integrating single-molecule spectroscopy and simulations for the study of intrinsically disordered proteins

Over the last two decades, intrinsically disordered proteins and protein regions (IDRs) have emerged from a niche corner of biophysics to be recognized as essential drivers of cellular function. Various techniques have provided fundamental insight into the function and dysfunction of IDRs. Among these techniques, single-molecule fluorescence spectroscopy and molecular simulations have played a major role in shaping our modern understanding of the sequence-encoded conformational behavior of disordered proteins. While both techniques are frequently used in isolation, when combined they offer synergistic and complementary information that can help uncover complex molecular details. Here we offer an overview of single-molecule fluorescence spectroscopy and molecular simulations in the context of studying disordered proteins. We discuss the various means in which simulations and single-molecule spectroscopy can be integrated, and consider a number of studies in which this integration has uncovered biological and biophysical mechanisms.

Identifying genomic regions targeted during eggplant domestication using transcriptome data

Identifying genes and traits that have diverged during domestication provides key information of importance for maintaining and even increasing yield and nutrients in existing crops. A “bottom-up” population genetics approach was used to identify signatures of selection across the eggplant genome, to better understand the process of domestication. RNA-seq data were obtained for 4 wild eggplants (Solanum insanum L.) and 16 domesticated eggplants (S. melongena L.) and mapped to the eggplant genome. Single-nucleotide polymorphism (SNPs) exhibiting signatures of selection in domesticates were identified as those exhibiting high F_ST between the 2 populations (evidence of significant divergence) and low π for the domesticated population (indicative of a selective sweep). Some of these regions appear to overlap with previously identified quantitative trait loci for domestication traits. Genes in regions of linkage disequilibrium surrounding these SNPs were searched against the Arabidopsis thaliana and tomato genomes to find orthologs. Subsequent gene ontology (GO) enrichment analysis identified over-representation of GO terms related to photosynthesis and response to the environment. This work reveals genomic changes involved in eggplant domestication and improvement, and how this compares to observed changes in the tomato genome, revealing shared chromosomal regions involved in the domestication of both species.

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

Exploring Cyclic Sulfamidate Building Blocks for the Synthesis of Sequence-Defined Macromolecules

The preparation of sequence-defined macromolecules using cyclic sulfamidates on solid-phase is outlined. The challenges surrounding an AB+CD approach are described with focus on understanding the formation of ring-opened side products when using amide coupling reagents. To avoid undesired side product formation, a strategy of iterative ring-openings of cyclic sulfamidates on solid-phase is explored. Ring-opening on primary and secondary amines is successfully reported, generating both linear and branched chain growth. However, attempts to selectively cleave N-sulfate bearing sp³ -hybridized groups cannot be demonstrated, limiting the overall building block scope for this methodology. Consequently, the active ring-opening of cyclic sulfamidates on amine-functionalized oligo(amidoamine) backbones is successfully applied to produce sequence-defined, N-sulfated macromolecules.

A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study

Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time-effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence-based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non-thermophilic, thermophilic, and hyper-thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross-validations as well as holdout tests, and it is freely accessible as a web-service at arimees.com.

Current Understanding of the Structure and Function of Fungal Immunomodulatory Proteins

Fungal immunomodulatory proteins (FIPs) are a group of proteins found in fungi, which are extensively studied for their immunomodulatory activity. Currently, more than 38 types of FIPs have been described. Based on their conserved structure and protein identity, FIPs can be classified into five subgroups: Fve-type FIPs (Pfam PF09259), Cerato-type FIPs (Pfam PF07249), PCP-like FIPs, TFP-like FIPs, and unclassified FIPs. Among the five subgroups, Fve-type FIPs are the most studied for their hemagglutinating, immunomodulating, and anti-cancer properties. In general, these small proteins consist of 110–125 amino acids, with a molecular weight of ~13 kDa. The other four subgroups are relatively less studied, but also show a noticeable influence on immune cells. In this review, we summarized the protein modifications, 3-dimensional structures and bioactivities of all types of FIPs. Moreover, structure-function relationship of FIPs has been discussed, including relationship between carbohydrate binding module and hemagglutination, correlation of oligomerization and cytokine induction, relevance of glycosylation and lymphocyte activation. This summary and discussion may help gain comprehensive understanding of FIPs' working mechanisms and scope future studies.

On the evolution of protein-adenine binding

Proteins' interactions with ancient ligands may reveal how molecular recognition emerged and evolved. We explore how proteins recognize adenine: a planar rigid fragment found in the most common and ancient ligands. We have developed a computational pipeline that extracts protein-adenine complexes from the Protein Data Bank, structurally superimposes their adenine fragments, and detects the hydrogen bonds mediating the interaction. Our analysis extends the known motifs of protein-adenine interactions in the Watson-Crick edge of adenine and shows that all of adenine's edges may contribute to molecular recognition. We further show that, on the proteins' side, binding is often mediated by specific amino acid segments ("themes") that recur across different proteins, such that different proteins use the same themes when binding the same adenine-containing ligands. We identify numerous proteins that feature these themes and are thus likely to bind adenine-containing ligands. Our analysis suggests that adenine binding has emerged multiple times in evolution.

Analyzing the Sequences of Intrinsically Disordered Regions with CIDER and localCIDER

Intrinsically disordered proteins and protein regions are ubiquitous across eukaryotic proteomes where they play a range of functional roles. Unlike folded proteins, IDRs lack a well-defined native state but exist in heterogeneous ensembles of conformations. In the absence of a defined native state, structure-guided mutations to test specific mechanistic hypotheses are generally not possible. Despite this, the use of mutations to alter sequence properties has become a relatively common approach for teasing out the relationship between sequence, ensemble, and function. A key step in designing informative mutants is the ability to identify specific sequence features that may reveal an interpretable response if perturbed. Here, we provide guidance on using the CIDER and localCIDER tools for amino acid sequence analysis, with a focus on building intuition with respect to the most commonly described features.

Evolutionary couplings detect side-chain interactions

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.

Effect of Correlated Pair Mutations in Protein Misfolding

A Monte Carlo simulation based sequence design method is proposed to explore the effect of correlated pair mutations in proteins. In the designed sequences, the most correlated residue pairs are identified and mutated with all possible amino acid pairs except those already present. The cumulative correlated pair mutations generated an array of mutated sequences. Results show a significant increase in the probability of misfolding for correlated pair mutations as compared to that of the random pair mutations. The pair mutations of correlated residues that are in contact record a higher probability of misfolding as compared to the correlated residues that are not in contact. The probability of misfolding increases on pair mutation of nonlocally correlated residue pairs as compared to that of the locally correlated residue pairs. The choice of a compact or expanded conformation does not depend on the type of correlated pair mutations. Pair mutation of the most correlated residue pairs at the surface with hydrophobic amino acids results in higher misfolding probability as compared to that in the core. An exactly opposite behavior is observed on pair mutation with hydrophilic and charged amino acid pairs. The neutral amino acid pairs do not differentiate between core and surface sites. This study may be used for targeted mutation experiments to predict complex mutation patterns, reengineer the existing proteins, and design new proteins with reduced misfolding propensity.

Navigating within thiamine diphosphate-dependent decarboxylases: Sequences, structures, functional positions, and binding sites

Thiamine diphosphate-dependent decarboxylases catalyze both cleavage and formation of CC bonds in various reactions, which have been assigned to different homologous sequence families. This work compares 53 ThDP-dependent decarboxylases with known crystal structures. Both sequence and structural information were analyzed synergistically and data were analyzed for global and local properties by means of statistical approaches (principle component analysis and principal coordinate analysis) enabling complexity reduction. The different results obtained both locally and globally, that is, individual positions compared with the overall protein sequence or structure, revealed challenges in the assignment of separated homologous families. The methods applied herein support the comparison of enzyme families and the identification of functionally relevant positions. The findings for the family of ThDP-dependent decarboxylases underline that global sequence identity alone is not sufficient to distinguish enzyme function. Instead, local sequence similarity, defined by comparisons of structurally equivalent positions, allows for a better navigation within several groups of homologous enzymes. The differentiation between homologous sequences is further enhanced by taking structural information into account, such as BioGPS analysis of the active site properties or pairwise structural superimpositions. The methods applied herein are expected to be transferrable to other enzyme families, to facilitate family assignments for homologous protein sequences.

ProBAPred: Inferring protein–protein binding affinity by incorporating protein sequence and structural features

Protein-protein binding interaction is the most prevalent biological activity that mediates a great variety of biological processes. The increasing availability of experimental data of protein–protein interaction allows a systematic construction of protein–protein interaction networks, significantly contributing to a better understanding of protein functions and their roles in cellular pathways and human diseases. Compared to well-established classification for protein–protein interactions (PPIs), limited work has been conducted for estimating protein–protein binding free energy, which can provide informative real-value regression models for characterizing the protein–protein binding affinity. In this study, we propose a novel ensemble computational framework, termed ProBAPred (Protein–protein Binding Affinity Predictor), for quantitative estimation of protein–protein binding affinity. A large number of sequence and structural features, including physical–chemical properties, binding energy and conformation annotations, were collected and calculated from currently available protein binding complex datasets and the literature. Feature selection based on the WEKA package was performed to identify and characterize the most informative and contributing feature subsets. Experiments on the independent test showed that our ensemble method achieved the lowest Mean Absolute Error (MAE; 1.657[Formula: see text]kcal/mol) and the second highest correlation coefficient ([Formula: see text]), compared with the existing methods. The datasets and source codes of ProBAPred, and the supplementary materials in this study can be downloaded at http://lightning.med.monash.edu/probapred/ for academic use. We anticipate that the developed ProBAPred regression models can facilitate computational characterization and experimental studies of protein–protein binding affinity.

Fibrous Protein Self-Assembly in Biomimetic Materials

Protein self-assembly processes, by which polypeptides interact and independently form multimeric structures, lead to a wide array of different endpoints. Structures formed range from highly ordered molecular crystals to amorphous aggregates. Order arises in the system from a balance between many low-energy processes occurring due to a set of interactions between residues in a chain, between residues in different chains, and between solute and solvent. In Nature, self-assembling protein systems have evolved over millions of years to organize into supramolecular structures, optimized for specific functions, with this propensity determined by the sequence of their constituent amino acids, of which only 20 are encoded in DNA. The structural materials that arise from biological self-assembly can display remarkable mechanical properties, often as a result of hierarchical structure on the nano- and microscales, and much research has been devoted to mimicking and exploiting these properties for a variety of end uses. This work presents a review of a range of studies in which biological functions are effectively reproduced through the design of self-assembling fibrous protein systems.

The Immune Protection Induced by a Serine Protease Inhibitor From the Foodborne Parasite Trichinella spiralis

Serine protease inhibitors (SPI) are a superfamily of the proteins able to suppress serine protease activity, and may exert the major biological function in complement activation, inflammation, and fibrinolysis. A SPI was identified from Trichinella spiralis adult worms (AW) by immunoproteomics with early infection sera. The aim of this study was to investigate the protective immune elicited by TsSPI. The complete TsSPI cDNA sequence was cloned into pQE-80 L and then expressed in Escherichia coli BL21. The rTsSPI was purified and its antigenicity was determined by Western blotting analysis. By using anti-rTsSPI serum the native TsSPI was identified in somatic and ES proteins from muscle larvae (ML). The results of qPCR and immunofluorescence assay (IFA) revealed that the expression of the TsSPI gene was observed throughout all developmental stages of T. spiralis (ML, intestinal infective larvale, 3- and 6-days AW, and newborn larvae, NBL), located principally in cuticles, stichosome, and embryos of this parasitic nematode. Vaccination of mice with rTsSPI triggered high level of anti-TsSPI IgG response, and showed a 62.2 and 57.25% worm burden reduction in the recovery of intestinal AW at 6 days post-infection (dpi) and ML at 35 dpi, respectively. The TsSPI might be a novel potential target for anti-Trichinella vaccine.

Functional and Structural Diversity of Acyl-coA Binding Proteins in Oil Crops

Diversities in structure and function of ACBP were discussed in this review. ACBP are important proteins that could transport newly synthesized fatty acid, activated into -coA, from plastid to endoplasmic reticulum, where oil in the form of triacylglycerol occurs. ACBP were detected in various animal and plants species, which indicated their importance in biological function. In fact, involvement of ACBP in important process such as lipid metabolism, regulation of enzyme and gene expression, and in response to plant stresses has been proven in several studies. In this review, findings on ACBP of 11 well-known oil crops were reviewed to comprehend diversity, comparative analyses on ACBP structure were made, and link between structure and function, tissue expression and subcellular location of ACBP were also observed. Incomplete reports in some species were mentioned, which might be encouraging to start or to perform deeper studies. Similar characteristics were found in paralogs ACBP, and orthologs ACBP had different functions, despite the high identity in amino acid sequence. At the end, it is confirmed that ortholog proteins could not necessarily display the same function, even from closely related species.

Use of designed sequences in protein structure recognition

Background

Knowledge of the protein structure is a pre-requisite for improved understanding of molecular function. The gap in the sequence-structure space has increased in the post-genomic era. Grouping related protein sequences into families can aid in narrowing the gap. In the Pfam database, structure description is provided for part or full-length proteins of 7726 families. For the remaining 52% of the families, information on 3-D structure is not yet available. We use the computationally designed sequences that are intermediately related to two protein domain families, which are already known to share the same fold. These strategically designed sequences enable detection of distant relationships and here, we have employed them for the purpose of structure recognition of protein families of yet unknown structure.

Results

We first measured the success rate of our approach using a dataset of protein families of known fold and achieved a success rate of 88%. Next, for 1392 families of yet unknown structure, we made structural assignments for part/full length of the proteins. Fold association for 423 domains of unknown function (DUFs) are provided as a step towards functional annotation.

Conclusion

The results indicate that knowledge-based filling of gaps in protein sequence space is a lucrative approach for structure recognition. Such sequences assist in traversal through protein sequence space and effectively function as ‘linkers’, where natural linkers between distant proteins are unavailable.

Reviewers

This article was reviewed by Oliviero Carugo, Christine Orengo and Srikrishna Subramanian.

Electronic supplementary material

The online version of this article (10.1186/s13062-018-0209-6) contains supplementary material, which is available to authorized users.

Composition-related structural transition of random peptides: insight into the boundary between intrinsically disordered proteins and folded proteins

Previous studies based on bioinformatics showed that there is a sharp distinction of structural features and residue composition between the intrinsically disordered proteins and the folded proteins. What induces such a composition-related structural transition? How do various kinds of interactions work in such processes? In this work, we investigate these problems based on a survey on peptides randomly composed of charged residues (including glutamic acids and lysines) and the residues with different hydrophobicity, such as alanines, glycines, or phenylalanines. Based on simulations using all-atom model and replica-exchange Monte Carlo method, a coil-globule transition is observed for each peptide. The corresponding transition temperature is found to be dependent on the contents of the hydrophobic and charged residues. For several cases, when the mean hydrophobicity is larger than a certain threshold, the transition temperature is higher than the room temperature, and vise versa. These thresholds of hydrophobicity and net charge are quantitatively consistent with the border line observed from the study of bioinformatics. These results outline the basic physical reasons for the compositional distinction between the intrinsically disordered proteins and the folded proteins. Furthermore, the contributions of various interactions to the structural variation of peptides are analyzed based on the contact statistics and the charge-pattern dependence of the gyration radii of the peptides. Our observations imply that the hydrophobicity contributes essentially to such composition-related transitions. Thus, we achieve a better understanding on composition-structure relation of the natural proteins and the underlying physics.

Improving the Folding of Supramolecular Copolymers by Controlling the Assembly Pathway Complexity

A family of amphiphilic, heterograft copolymers containing hydrophilic, hydrophobic, and supramolecular units based on Jeffamine M-1000, dodecylamine, and benzene-1,3,5-tricarboxamide (BTA) motifs, respectively, was prepared via a postfunctionalization approach. The folding of the copolymers in water into nanometer-sized particles was analyzed by a combination of dynamic and static light scattering, circular dichroism spectroscopy, and small-angle neutron scattering. The sample preparation protocol was crucial for obtaining reproducible and consistent results, showing that only full control over the structure and pathway complexity will afford the desired folded structure, a phenomenon similar to protein folding. The results revealed that relatively small changes in the polymer’s graft composition strongly affected the intra- versus intermolecular assembly processes. Depending on the amount of the hydrophobic grafts based on either dodecyl or BTA groups, pronounced behavioral differences were observed for copolymers that comprise similar degrees of hydrophobic content. A high number of BTA grafts (>10%) resulted in the formation of multichain aggregates comprising around six polymer chains. In contrast, for copolymers comprising up to 10% BTA grafts the folding results in nanoparticles that adopt open, sparse conformations and comprise one to two polymer chains. Interestingly, predominantly single-chain polymeric nanoparticles were formed when the copolymer comprised only Jeffamine or Jeffamine and dodecyl grafts. In addition, replacing part of the BTA grafts by hydrophobic dodecyl grafts while keeping the hydrophobic content constant promoted single-chain folding and resulted in the formation of a compact, globular nanoparticle with a more structured interior. Thus, the intra- and intermolecular self-assembly pathways can be directed by carefully tuning the polymer’s hydrophilic–hydrophobic balance in combination with the number of supramolecular grafts.

Homology modeling in a dynamical world

A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.

BnSIP1-1, a Trihelix Family Gene, Mediates Abiotic Stress Tolerance and ABA Signaling in Brassica napus

The trihelix family genes have important functions in light-relevant and other developmental processes, but their roles in response to adverse environment are largely unclear. In this study, we identified a new gene, BnSIP1-1, which fell in the SIP1 (6b INTERACTING PROTEIN1) clade of the trihelix family with two trihelix DNA binding domains and a fourth amphipathic α-helix. BnSIP1-1 protein specifically targeted to the nucleus, and its expression can be induced by abscisic acid (ABA) and different stresses. Overexpression of BnSIP1-1 improved seed germination under osmotic pressure, salt, and ABA treatments. Moreover, BnSIP1-1 decreased the susceptibility of transgenic seedlings to osmotic pressure and ABA treatments, whereas there was no difference under salt stress between the transgenic and wild-type seedlings. ABA level in the transgenic seedlings leaves was higher than those in the control plants under normal condition. Under exogenous ABA treatment and mannitol stress, the accumulation of ABA in the transgenic plants was higher than that in the control plants; while under salt stress, the difference of ABA content before treatment was gradually smaller with the prolongation of salt treatment time, then after 24 h of treatment the ABA level was similar in transgenic and wild-type plants. The transcription levels of several general stress marker genes (BnRD29A, BnERD15, and BnLEA1) were higher in the transgenic plants than the wild-type plants, whereas salt-responsive genes (BnSOS1, BnNHX1, and BnHKT) were not significantly different or even reduced compared with the wild-type plants, which indicated that BnSIP1-1 specifically exerted different regulatory mechanisms on the osmotic- and salt-response pathways in seedling period. Overall, these findings suggested that BnSIP1-1 played roles in ABA synthesis and signaling, salt and osmotic stress response. To date, information about the involvement of the Brassica napus trihelix gene in abiotic response is scarce. Here, we firstly reported abiotic stress response and possible function mechanisms of a new trihelix gene in B. napus.

Conservation of Three-Dimensional Helix-Loop-Helix Structure through the Vertebrate Lineage Reopens the Cold Case of Gonadotropin-Releasing Hormone-Associated Peptide

GnRH-associated peptide (GAP) is the C-terminal portion of the gonadotropin-releasing hormone (GnRH) preprohormone. Although it was reported in mammals that GAP may act as a prolactin-inhibiting factor and can be co-secreted with GnRH into the hypophyseal portal blood, GAP has been practically out of the research circuit for about 20 years. Comparative studies highlighted the low conservation of GAP primary amino acid sequences among vertebrates, contributing to consider that this peptide only participates in the folding or carrying process of GnRH. Considering that the three-dimensional (3D) structure of a protein may define its function, the aim of this study was to evaluate if GAP sequences and 3D structures are conserved in the vertebrate lineage. GAP sequences from various vertebrates were retrieved from databases. Analysis of primary amino acid sequence identity and similarity, molecular phylogeny, and prediction of 3D structures were performed. Amino acid sequence comparison and phylogeny analyses confirmed the large variation of GAP sequences throughout vertebrate radiation. In contrast, prediction of the 3D structure revealed a striking conservation of the 3D structure of GAP1 (GAP associated with the hypophysiotropic type 1 GnRH), despite low amino acid sequence conservation. This GAP1 peptide presented a typical helix-loop-helix (HLH) structure in all the vertebrate species analyzed. This HLH structure could also be predicted for GAP2 in some but not all vertebrate species and in none of the GAP3 analyzed. These results allowed us to infer that selective pressures have maintained GAP1 HLH structure throughout the vertebrate lineage. The conservation of the HLH motif, known to confer biological activity to various proteins, suggests that GAP1 peptides may exert some hypophysiotropic biological functions across vertebrate radiation.

Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions

Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences.

Characterizing monoclonal antibody structure by carboxyl group footprinting

Structural characterization of proteins and their antigen complexes is essential to the development of new biologic-based medicines. Amino acid-specific covalent labeling (CL) is well suited to probe such structures, especially for cases that are difficult to examine by alternative means due to size, complexity, or instability. We present here a detailed account of carboxyl group labeling (with glycine ethyl ester (GEE) tagging) applied to a glycosylated monoclonal antibody therapeutic (mAb). The experiments were optimized to preserve the structural integrity of the mAb, and experimental conditions were varied and replicated to establish the reproducibility of the technique. Homology-based models were generated and used to compare the solvent accessibility of the labeled residues, which include aspartic acid (D), glutamic acid (E), and the C-terminus (i.e., the target probes), with the experimental data in order to understand the accuracy of the approach. Data from the mAb were compared to reactivity measures of several model peptides to explain observed variations in reactivity. Attenuation of reactivity in otherwise solvent accessible probes is documented as arising from the effects of positive charge or bond formation between adjacent amine and carboxyl groups, the latter accompanied by observed water loss. A comparison of results with previously published data by Deperalta et al using hydroxyl radical footprinting showed that 55% (32/58) of target residues were GEE labeled in this study whereas the previous study reported 21% of the targets were labeled. Although the number of target residues in GEE labeling is fewer, the two approaches provide complementary information. The results highlight advantages of this approach, such as the ease of use at the bench top, the linearity of the dose response plots at high levels of labeling, reproducibility of replicate experiments (<2% variation in modification extent), the similar reactivity of the three target probes, and significant correlation of reactivity and solvent accessible surface area.

Rational Protein Engineering Guided by Deep Mutational Scanning

Sequence-function relationship in a protein is commonly determined by the three-dimensional protein structure followed by various biochemical experiments. However, with the explosive increase in the number of genome sequences, facilitated by recent advances in sequencing technology, the gap between protein sequences available and three-dimensional structures is rapidly widening. A recently developed method termed deep mutational scanning explores the functional phenotype of thousands of mutants via massive sequencing. Coupled with a highly efficient screening system, this approach assesses the phenotypic changes made by the substitution of each amino acid sequence that constitutes a protein. Such an informational resource provides the functional role of each amino acid sequence, thereby providing sufficient rationale for selecting target residues for protein engineering. Here, we discuss the current applications of deep mutational scanning and consider experimental design.

Characterizing monoclonal antibody structure by carbodiimide/GEE footprinting

Amino acid-specific covalent labeling is well suited to probe protein structure and macromolecular interactions, especially for macromolecules and their complexes that are difficult to examine by alternative means, due to size, complexity, or instability. Here we present a detailed account of carbodiimide-based covalent labeling (with GEE tagging) applied to a glycosylated monoclonal antibody therapeutic, which represents an important class of biologic drugs. Characterization of such proteins and their antigen complexes is essential to development of new biologic-based medicines. In this study, the experiments were optimized to preserve the structural integrity of the protein, and experimental conditions were varied and replicated to establish the reproducibility and precision of the technique. Homology-based models were generated and used to compare the solvent accessibility of the labeled residues, which include D, E, and the C-terminus, against the experimental surface accessibility data in order to understand the accuracy of the approach in providing an unbiased assessment of structure. Data from the protein were also compared to reactivity measures of several model peptides to explain sequence or structure-based variations in reactivity. The results highlight several advantages of this approach. These include: the ease of use at the bench top, the linearity of the dose response plots at high levels of labeling (indicating that the label does not significantly perturb the structure of the protein), the high reproducibility of replicate experiments (<2 % variation in modification extent), the similar reactivity of the 3 target probe residues (as suggested by analysis of model peptides), and the overall positive and significant correlation of reactivity and solvent accessible surface area (the latter values predicted by the homology modeling). Attenuation of reactivity, in otherwise solvent accessible probes, is documented as arising from the effects of positive charge or bond formation between adjacent amine and carboxyl groups, the latter accompanied by observed water loss. The results are also compared with data from hydroxyl radical-mediated oxidative footprinting on the same protein, showing that complementary information is gained from the 2 approaches, although the number of target residues in carbodiimide/GEE labeling is fewer. Overall, this approach is an accurate and precise method for assessing protein structure of biologic drugs.

De-DUFing the DUFs: Deciphering distant evolutionary relationships of Domains of Unknown Function using sensitive homology detection methods

Background

In the post-genomic era where sequences are being determined at a rapid rate, we are highly reliant on computational methods for their tentative biochemical characterization. The Pfam database currently contains 3,786 families corresponding to “Domains of Unknown Function” (DUF) or “Uncharacterized Protein Family” (UPF), of which 3,087 families have no reported three-dimensional structure, constituting almost one-fourth of the known protein families in search for both structure and function.

Results

We applied a ‘computational structural genomics’ approach using five state-of-the-art remote similarity detection methods to detect the relationship between uncharacterized DUFs and domain families of known structures. The association with a structural domain family could serve as a start point in elucidating the function of a DUF. Amongst these five methods, searches in SCOP-NrichD database have been applied for the first time. Predictions were classified into high, medium and low- confidence based on the consensus of results from various approaches and also annotated with enzyme and Gene ontology terms. 614 uncharacterized DUFs could be associated with a known structural domain, of which high confidence predictions, involving at least four methods, were made for 54 families. These structure-function relationships for the 614 DUF families can be accessed on-line at http://proline.biochem.iisc.ernet.in/RHD_DUFS/. For potential enzymes in this set, we assessed their compatibility with the associated fold and performed detailed structural and functional annotation by examining alignments and extent of conservation of functional residues. Detailed discussion is provided for interesting assignments for DUF3050, DUF1636, DUF1572, DUF2092 and DUF659.

Conclusions

This study provides insights into the structure and potential function for nearly 20 % of the DUFs. Use of different computational approaches enables us to reliably recognize distant relationships, especially when they converge to a common assignment because the methods are often complementary. We observe that while pointers to the structural domain can offer the right clues to the function of a protein, recognition of its precise functional role is still ‘non-trivial’ with many DUF domains conserving only some of the critical residues. It is not clear whether these are functional vestiges or instances involving alternate substrates and interacting partners.

Reviewers

This article was reviewed by Drs Eugene Koonin, Frank Eisenhaber and Srikrishna Subramanian.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0069-2) contains supplementary material, which is available to authorized users.

Characterization of a Trichinella spiralis 31 kDa protein and its potential application for the serodiagnosis of trichinellosis

The Trichinella spiralis 31 kDa protein (Ts31) was screened from the excretory-secretory (ES) proteins of muscle larvae (ML) by immunoproteomics using serum from mice infected with T. spiralis at 18 days post infection (dpi). The aim of this study was to characterize the Ts31 protein and to evaluate the potential of the recombinant Ts31 protein (rTs31) for serodiagnosis of human trichinellosis. Ts31 gene was cloned and rTs31 was produced in an E. coli expression system. An anti-rTs31serum recognized the native protein migrating in a 25-55 kDa range by Western blotting of ML crude or ES antigens. Expression of Ts31 gene was observed at all developmental stages of T. spiralis (adult worms, newborn larvae, pre-encapsulated larvae and ML). An immunolocalization analysis identified Ts31 in the cuticle and stichocytes of the parasite. The sensitivity of rTs31-ELISA and ES antigen ELISA for detecting anti-Trichinella IgG antibodies in sera of patients with trichinellosis was 97.83% (45/46) and 86.78% (39/46), respectively (P>0.05); The specificity of rTs31-ELISA was 99.13% (114/115), which was significantly higher than 85.22% (98/115) of ES antigen ELISA (P<0.01). The rTs31 protein of T. spiralis could be considered as a potential diagnostic antigen for trichinellosis.

Identification of functional candidates amongst hypothetical proteins of Mycobacterium leprae Br4923, a causative agent of leprosy

Mycobacterium leprae is an intracellular obligate parasite that causes leprosy in humans, and it leads to the destruction of peripheral nerves and skin deformation. Here, we report an extensive analysis of the hypothetical proteins (HPs) from M. leprae strain Br4923, assigning their functions to better understand the mechanism of pathogenesis and to search for potential therapeutic interventions. The genome of M. leprae encodes 1604 proteins, of which the functions of 632 are not known (HPs). In this paper, we predicted the probable functions of 312 HPs. First, we classified all HPs into families and subfamilies on the basis of sequence similarity, followed by domain assignment, which provides many clues for their possible function. However, the functions of 320 proteins were not predicted because of low sequence similarity with proteins of known function. Annotated HPs were categorized into enzymes, binding proteins, transporters, and proteins involved in cellular processes. We found several novel proteins whose functions were unknown for M. leprae. These proteins have a requisite association with bacterial virulence and pathogenicity. Finally, our sequence-based analysis will be helpful for further validation and the search for potential drug targets while developing effective drugs to cure leprosy.

Analysis of structures, functions, and epitopes of cysteine protease from Spirometra erinaceieuropaei Spargana

Spirometra erinaceieuropaei cysteine protease (SeCP) in sparganum ES proteins recognized by early infection sera was identified by MALDI-TOF/TOF-MS. The aim of this study was to predict the structures and functions of SeCP protein by using the full length cDNA sequence of SeCP gene with online sites and software programs. The SeCP gene sequence was of 1 053 bp length with a 1011 bp biggest ORF encoding 336-amino acid protein with a complete cathepsin propeptide inhibitor domain and a peptidase C1A conserved domain. The predicted molecular weight and isoelectric point of SeCP were 37.87 kDa and 6.47, respectively. The SeCP has a signal peptide site and no transmembrane domain, located outside the membrane. The secondary structure of SeCP contained 8 α -helixes, 7 β -strands, and 20 coils. The SeCP had 15 potential antigenic epitopes and 19 HLA-I restricted epitopes. Based on the phylogenetic analysis of SeCP, S. erinaceieuropaei has the closest evolutionary status with S. mansonoides. SeCP was a kind of proteolytic enzyme with a variety of biological functions and its antigenic epitopes could provide important insights on the diagnostic antigens and target molecular of antisparganum drugs.