Classification schemes for protein structure and function

Metal-Protein Hybrid Materials: Unlocking New Frontiers in Biomedical Applications

Metal-protein hybrid materials represent a novel class of functional materials that exhibit exceptional physicochemical properties and tunable structures, rendering them remarkable applications in diverse fields, including materials engineering, biocatalysis, biosensing, and biomedicine. The design and development of multifunctional and biocompatible metal-protein hybrid materials have been the subject of extensive research and a key aspiration for practical applications in clinical settings. This review provides a comprehensive analysis of the design strategies, intrinsic properties, and biomedical applications of these hybrid materials, with a specific emphasis on their potential in cancer therapy, drug and vaccine delivery, antibacterial treatments, and tissue regeneration. Through rational design, stable metal-protein hybrid materials can be synthesized using straightforward methods, enabling them with therapeutic, delivery, immunomodulatory, and other desired functionalities. Finally, the review outlines the existing limitations and challenges associated with metal-protein hybrid materials and evaluates their potential for clinical translation, providing insights into their practical implementation within biomedical applications.

DPFunc: accurately predicting protein function via deep learning with domain-guided structure information

Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, making it difficult to understand the relations between protein structures and functions. In this study, we propose a deep learning-based solution, named DPFunc, for accurate protein function prediction with domain-guided structure information. DPFunc can detect significant regions in protein structures and accurately predict corresponding functions under the guidance of domain information. It outperforms current state-of-the-art methods and achieves a significant improvement over existing structure-based methods. Detailed analyses demonstrate that the guidance of domain information contributes to DPFunc for protein function prediction, enabling our method to detect key residues or regions in protein structures, which are closely related to their functions. In summary, DPFunc serves as an effective tool for large-scale protein function prediction, which pushes the border of protein understanding in biological systems.

A comparative study of two α-L-rhamnosidases with high sequence identity

The GH78 α-L-rhamnosidase from Aspergillus tubingensis (AT-Rha) was proved to be a new clade of Aspergillus α-L-rhamnosidases in the previous study. A putative α-L-rhamnosidase from A. kawachii IFO 4308 (AK-Rha) has 92 % identity in amino acid sequence with AT-Rha. In this study, AK-Rha was expressed in P. pastoris and characterized. Similar to AT-rRha, the recombinant AK-Rha (AK-rRha) showed a narrow substrate specificity to naringin. Interestingly, the enzyme activity of AK-rRha was 0.816 U/mg toward naringin, significantly lower than 125.142 U/mg of AT-rRha. Their large differences in catalytic efficiency was mainly due to their differences in kcat values between AK-rRha (0.67 s-1) and AT-rRha (4.89 × 104 s-1). The molecular dynamics simulation exhibited that the overall conformation of AK-Rha was rigid and that of AT-Rha was flexible; the Loop Y-L located above the catalytic domain formed different steric hindrances to naringin, and interacted with the flavonoid matrices at different strengths. The polar solvation energy analysis implied that the glycosidic bond was more easily hydrolysed in AT-Rha. The comparative study verified that the main feature of AK-Rha and AT-Rha represented Aspergillus α-L-rhamnosidase was the narrow substrate specificity toward naringin, and provided an insight of the relationships between their catalytic abilities and structures.

Cellular and Biophysical Applications of Genetic Code Expansion

Despite their diverse functions, proteins are inherently constructed from a limited set of building blocks. These compositional constraints pose significant challenges to protein research and its practical applications. Strategically manipulating the cellular protein synthesis system to incorporate novel building blocks has emerged as a critical approach for overcoming these constraints in protein research and application. In the past two decades, the field of genetic code expansion (GCE) has achieved significant advancements, enabling the integration of numerous novel functionalities into proteins across a variety of organisms. This technological evolution has paved the way for the extensive application of genetic code expansion across multiple domains, including protein imaging, the introduction of probes for protein research, analysis of protein-protein interactions, spatiotemporal control of protein function, exploration of proteome changes induced by external stimuli, and the synthesis of proteins endowed with novel functions. In this comprehensive Review, we aim to provide an overview of cellular and biophysical applications that have employed GCE technology over the past two decades.

An NAD+-dependent group Ⅲ alcohol dehydrogenase involved in long-chain alkane degradation in Acinetobacter venetianus RAG-1

Alcohol dehydrogenases (ADHs) are a class of key enzymes responsible for the oxidation of alkyl alcohols in the aerobic alkane metabolic pathway. Currently, the degradation mechanisms of short- and medium-chain alkanes are commonly reported, while those of long-chain alkanes have received less attention. In this work, a putative long-chain ADH was screened from Acinetobacter venetianus RAG-1 via RNA-seq with n-octacosane (C28) as the sole carbon source. Conserved sequence analysis revealed that it is a group III (Fe-containing/activated) ADH, which is widespread in the genus Acinetobacter. The deletion of adhA led to a significant reduction in the degradation of C28. AdhA exhibited optimal oxidative activity at pH 8.0 and 50 °C with NAD+ as coenzyme, while showing better tolerability to chemical reagents. Enzyme activity assay showed that AdhA owed the oxidative activity to a wide range of substrates including alkyl alcohols (C1-C32) and some isomeric alcohols, such as isopropanol, isobutanol, isoamyl alcohol, and propanetriol, and could reduce the alkyl aldehyde (C1-C12). Meanwhile, the binding of AdhA to different alkyl alcohols was mediated by different amino acids. AdhA is an ADH with an extremely broad substrate utilization range and excellent biochemical characteristics. These results provided important insights in the subsequent investigation of long-chain alkane degradation and petroleum pollution bioremediation.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

CGG toolkit: Software components for computational genomics

Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.

Molecular insights into complexation between protein and silica: Spectroscopic and simulation investigations

The synergistic effect of protein-silica complexation leads to exacerbated membrane fouling in the membrane desalination process, exceeding the individual impacts of silica scaling or protein fouling. However, the molecular-level dynamics of silica binding to proteins and the resulting structural changes in both proteins and silica remain poorly understood. This study investigates the complexation process between silica and proteins-negatively charged bovine serum albumin (BSA) and positively charged lysozyme (LYZ) at neutral pH-using infrared spectroscopy (IR), in situ attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), and multiple computational simulations. The findings reveal that both protein and silica structures undergo changes during the complexation process, with calcium ions in the solution significantly exacerbating these alterations. In particular, in situ ATR-FTIR combined with two-dimensional correlation spectroscopy analysis shows that BSA experiences more pronounced unfolding, providing additional binding sites for silica adsorption compared to LYZ. The adsorbed proteins promote silica polymerization from lower-polymerized to higher-polymerized species. Furthermore, molecular dynamics simulations demonstrate greater conformational variation in BSA through root-mean-square-deviation analysis and the bridging role of calcium ions via mean square displacement analysis. Molecular docking and density functional theory calculations identify the binding sites and energy of silica on proteins. In summary, this research offers a comprehensive understanding of the protein-silica complexation process, contributing to the knowledge of synergistic behaviors of inorganic scaling and organic fouling on membrane surfaces. The integrated approach used here may also be applicable for exploring other complex complexation processes in various environments.

Role of distal sites in enzyme engineering

The limitations associated with natural enzyme catalysis have triggered the rise of the field of protein engineering. Traditional rational design was based on the analysis of protein structural information and catalytic mechanisms to identify key active sites or ligand binding sites to reshape the substrate pocket. The role and significance of functional sites in the active center have been studied extensively. With a deeper understanding of the structure-catalysis relationship map, the entire protein molecule can be filled with residues that play a substantial role in its structure and function. However, the catalytic mechanism underlying distal mutations remains unclear. The aim of this review was to highlight the criticality of the distal site in enzyme engineering based on the following three aspects: What can distal mutations exert on function from mutability landscape? How do distal sites influence enzyme function? How to predict and design distal mutations? This review provides insights into the catalytic mechanism of enzymes from the global interaction network, knowledge from sequence-structure-dynamics-function relationships, and strategies for distal mutation-based protein engineering.

Disease association and comparative genomics of compositional bias in human proteins

Background: The evolutionary rate of disordered proteins varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of intrinsically disordered regions (IDRs) across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards low complexity regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, low complexity proteins across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of low complexity, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Disease association and comparative genomics of compositional bias in human proteins

Background: The evolutionary rate of disordered protein regions varies greatly due to the lack of structural constraints. So far, few studies have investigated the presence/absence patterns of compositional bias, indicative of disorder, across phylogenies in conjunction with human disease. In this study, we report a genome-wide analysis of compositional bias association with disease in human proteins and their taxonomic distribution. Methods: The human genome protein set provided by the Ensembl database was annotated and analysed with respect to both disease associations and the detection of compositional bias. The Uniprot Reference Proteome dataset, containing 11297 proteomes was used as target dataset for the comparative genomics of a well-defined subset of the Human Genome, including 100 characteristic, compositionally biased proteins, some linked to disease. Results: Cross-evaluation of compositional bias and disease-association in the human genome reveals a significant bias towards biased regions in disease-associated genes, with charged, hydrophilic amino acids appearing as over-represented. The phylogenetic profiling of 17 disease-associated, proteins with compositional bias across 11297 proteomes captures characteristic taxonomic distribution patterns. Conclusions: This is the first time that a combined genome-wide analysis of compositional bias, disease-association and taxonomic distribution of human proteins is reported, covering structural, functional, and evolutionary properties. The reported framework can form the basis for large-scale, follow-up projects, encompassing the entire human genome and all known gene-disease associations.

Impact of Peptide Sequences on Their Structure and Function: Mimicking of Virus-Like Nanoparticles for Nucleic Acid Delivery

We rationally designed a series of amphiphilic hepta-peptides enriched with a chemically conjugated guanidiniocarbonylpyrrole (GCP) unit at the lysine side chain. All peptides are composed of polar (GCP) and non-polar (cyclohexyl alanine) residues but differ in their sequence periodicity, resulting in different secondary as well as supramolecular structures. CD spectra revealed the assembly of β-sheet-, α-helical and random structures for peptides 1, 2 and 3, respectively. Consequently, this enabled the formation of distinct supramolecular assemblies such as fibres, nanorod-like or spherical aggregates. Notably, all three cationic peptides are equipped with the anion-binding GCP unit and thus possess a nucleic acid-binding centre. However, only the helical (2) and the unstructured (3) peptide were able to assemble into small virus-like DNA-polyplexes and effectively deliver DNA into cells. Notably, as both peptides (2 and 3) were also capable of siRNA-delivery, they could be utilized to downregulate expression of the caner-relevant protein Survivin.

Novel Algorithm for Improved Protein Classification Using Graph Similarity

Considerable sequence data are produced in genome annotation projects that relate to molecular levels, structural similarities, and molecular and biological functions. In structural genomics, the most essential task involves resolving protein structures efficiently with hardware or software, understanding these structures, and assigning their biological functions. Understanding the characteristics and functions of proteins enables the exploration of the molecular mechanisms of life. In this paper, we examine the problems of protein classification. Because they perform similar biological functions, proteins in the same family usually share similar structural characteristics. We employed this premise in designing a classification algorithm. In this algorithm, auxiliary graphs are used to represent proteins, with every amino acid in a protein to a vertex in a graph. Moreover, the links between amino acids correspond to the edges between the vertices. The proposed algorithm classifies proteins according to the similarities in their graphical structures. The proposed algorithm is efficient and accurate in distinguishing proteins from different families and outperformed related algorithms experimentally.

Propagation, detection and correction of errors using the sequence database network

Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.

Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space

The recognition of protein structural folds is the starting point for protein function inference and for many structural prediction tools. We previously introduced the idea of using empirical comparisons to create a data-augmented feature space called PESS (Protein Empirical Structure Space)¹ as a novel approach for protein structure prediction. Here, we extend the previous approach by generating the PESS feature space over fixed-length subsequences of query peptides, and applying a sequential neural network model, with one long short-term memory cell layer followed by a fully connected layer. Using this approach, we show that only a small group of domains as a training set is needed to achieve near state-of-the-art accuracy on fold recognition. Our method improves on the previous approach by reducing the training set required and improving the model's ability to generalize across species, which will help fold prediction for newly discovered proteins.

Hydrothermal Degradation of Amino Acids

Within the past years, hydrothermal processes have gathered much attention as promising conversion technologies for especially wet biomass. Amino acids are an integral component of biomass, zoo biomass in particular. However, what happens to them during hydrothermal treatment? Reviewing the available literature going back to the mid of the 20th century revealed an astonishing, but still fragmentary view. In fact, two universal degradation reactions could be identified (i. e., deamination and decarboxylation), competing with each other. Thereby, small structural differences may obviously have huge impacts on the fate of individual amino acids. Nevertheless, the amount of available experimental data is relatively scarce in many cases. In this work, the available knowledge about the degradation of 20 proteinogenic amino acids under hydrothermal conditions was presented and discussed critically. The hydrothermal conversion of proteinaceus biomass as well as the Maillard reaction, both extensively reviewed elsewhere, were only touched on.

Surface functionalization strategies of extracellular vesicles

Extracellular vesicles (EVs) are lipid-protein bilayer vesicular constructs secreted to the extracellular spaces by cells. All cells secrete EVs as a regular biological process that appears to be conserved throughout the evolution. Owing to the rich molecular cargo of EVs with specific lipid and protein content and documented role in cellular communication, EVs have been exploited as a versatile agent in the biomedical arena, including as diagnostic, drug delivery, immunomodulatory, and therapeutic agents. With these multifaceted applications in the biomedical field, the functionalization of EVs to add diverse functionality has garnered rapid attention. EVs can be functionalized with an exogenous imaging and targeting moiety that allows for the target specificity and the real-time tracking of EVs for diagnostic and therapeutic applications. Importantly, such added functionalities can be used to explore EVs' biogenesis pathway and their role in cellular communication, which can lead to a better understanding of EVs' cellular mechanisms and processes. In this report, we have reviewed the existing surface functionalization strategies of EVs and broadly classified them into three major approaches: physical, biological, and chemical approaches. The physical approach of EV functionalization includes methods like sonication, extrusion, and freeze-thaw that can change the surface properties of EVs via membrane rearrangements. The biological approach includes genetically and metabolically engineering cells to express protein or cargo molecules of interest in secreted EVs. The chemical approach includes different facile click type chemistries that can be used to covalently conjugate the EV lipid or protein construct with different linker groups for diverse functionality. Different chemistries like thiol-maleimide, EDC/NHS, azide-alkyne cycloaddition, and amidation chemistry have been discussed to functionalize EVs. Finally, a comparative discussion of all approaches has been done focusing on the significance of each approach. The collective knowledge of the major approach of surface functionalization can be used to improve the limitation of one technique by combining it with another. An optimized surface functionalization approach developed accordingly can efficiently add required functionality to EVs while maintaining their natural integrity.

Peptide-Based Functional Biomaterials for Soft-Tissue Repair

Synthetically derived peptide-based biomaterials are in many instances capable of mimicking the structure and function of their full-length endogenous counterparts. Combine this with the fact that short mimetic peptides are easier to produce when compared to full length proteins, show enhanced processability and ease of modification, and have the ability to be prepared under well-defined and controlled conditions; it becomes obvious why there has been a recent push to develop regenerative biomaterials from these molecules. There is increasing evidence that the incorporation of peptides within regenerative scaffolds can result in the generation of structural recognition motifs that can enhance cell attachment or induce cell signaling pathways, improving cell infiltration or promote a variety of other modulatory biochemical responses. By highlighting the current approaches in the design and application of short mimetic peptides, we hope to demonstrate their potential in soft-tissue healing while at the same time drawing attention to the advances made to date and the problems which need to be overcome to advance these materials to the clinic for applications in heart, skin, and cornea repair.

HipMCL: a high-performance parallel implementation of the Markov clustering algorithm for large-scale networks

Biological networks capture structural or functional properties of relevant entities such as molecules, proteins or genes. Characteristic examples are gene expression networks or protein–protein interaction networks, which hold information about functional affinities or structural similarities. Such networks have been expanding in size due to increasing scale and abundance of biological data. While various clustering algorithms have been proposed to find highly connected regions, Markov Clustering (MCL) has been one of the most successful approaches to cluster sequence similarity or expression networks. Despite its popularity, MCL’s scalability to cluster large datasets still remains a bottleneck due to high running times and memory demands. Here, we present High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that can run on distributed-memory computers. We show that HipMCL can efficiently utilize 2000 compute nodes and cluster a network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. By exploiting distributed-memory environments, HipMCL clusters large-scale networks several orders of magnitude faster than MCL and enables clustering of even bigger networks. HipMCL is based on MPI and OpenMP and is freely available under a modified BSD license.

PIRSitePredict for protein functional site prediction using position-specific rules

Methods focused on predicting 'global' annotations for proteins (such as molecular function, biological process and presence of domains or membership in a family) have reached a relatively mature stage. Methods to provide fine-grained 'local' annotation of functional sites (at the level of individual amino acid) are now coming to the forefront, especially in light of the rapid accumulation of genetic variant data. We have developed a computational method and workflow that predicts functional sites within proteins using position-specific conditional template annotation rules (namely PIR Site Rules or PIRSRs for short). Such rules are curated through review of known protein structural and other experimental data by structural biologists and are used to generate high-quality annotations for the UniProt Knowledgebase (UniProtKB) unreviewed section. To share the PIRSR functional site prediction method with the broader scientific community, we have streamlined our workflow and developed a stand-alone Java software package named PIRSitePredict. We demonstrate the use of PIRSitePredict for functional annotation of de novo assembled genome/transcriptome by annotating uncharacterized proteins from Trinity RNA-seq assembly of embryonic transcriptomes of the following three cartilaginous fishes: Leucoraja erinacea (Little Skate), Scyliorhinus canicula (Small-spotted Catshark) and Callorhinchus milii (Elephant Shark). On average about 1200 lines of annotations were predicted for each species.

Construction of a Sequenceable Protein Mimetic Peptide Library with a True 3D Diversifiable Chemical Space

We present here a library of protein mimetic bicyclic peptides. These nanosized structures exhibit rigid backbones and spatially diversifiable side chains. They present modular amino acids on all three linkages, providing access to a true 3D diversifiable chemical space. These peptides are synthesized through a Cu-catalyzed click reaction and a Ru-catalyzed ring-closing metathesis reaction. Their bicyclic topology can be reduced to a linear one, using Edman degradation and Pd-catalyzed deallylation reactions. The linearization approaches allow de novo sequencing through mass spectrometry methods. We demonstrate the function of a particular peptide that was identified through a high throughput screening against the E₃₆₃-R₃₇₈ epitope on the intrinsically disordered c-Myc oncoprotein. Intracellular delivery of this peptide could interfere with the c-Myc-mediated transcription and inhibit proliferation in a human glioblastoma cell line.

Systematic Identification and Classification of β-Lactamases Based on Sequence Similarity Criteria: β-Lactamase Annotation

β-lactamases, the enzymes responsible for resistance to β-lactam antibiotics, are widespread among prokaryotic genera. However, current β-lactamase classification schemes do not represent their present diversity. Here, we propose a workflow to identify and classify β-lactamases. Initially, a set of curated sequences was used as a model for the construction of profiles Hidden Markov Models (HMM), specific for each β-lactamase class. An extensive, nonredundant set of β-lactamase sequences was constructed from 7 different resistance proteins databases to test the methodology. The profiles HMM were improved for their specificity and sensitivity and then applied to fully assembled genomes. Five hierarchical classification levels are described, and a new class of β-lactamases with fused domains is proposed. Our profiles HMM provide a better annotation of β-lactamases, with classes and subclasses defined by objective criteria such as sequence similarity. This classification offers a solid base to the elaboration of studies on the diversity, dispersion, prevalence, and evolution of the different classes and subclasses of this critical enzymatic activity.

No wisdom in the crowd: genome annotation in the era of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from 'the sequence tells the structure tells the function' fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader.

Linking in domain-swapped protein dimers

The presence of knots has been observed in a small fraction of single-domain proteins and related to their thermodynamic and kinetic properties. The exchanging of identical structural elements, typical of domain-swapped proteins, makes such dimers suitable candidates to validate the possibility that mutual entanglement between chains may play a similar role for protein complexes. We suggest that such entanglement is captured by the linking number. This represents, for two closed curves, the number of times that each curve winds around the other. We show that closing the curves is not necessary, as a novel parameter G', termed Gaussian entanglement, is strongly correlated with the linking number. Based on 110 non redundant domain-swapped dimers, our analysis evidences a high fraction of chains with a significant intertwining, that is with |G'| > 1. We report that Nature promotes configurations with negative mutual entanglement and surprisingly, it seems to suppress intertwining in long protein dimers. Supported by numerical simulations of dimer dissociation, our results provide a novel topology-based classification of protein-swapped dimers together with some preliminary evidence of its impact on their physical and biological properties.

Substrate tunnels in enzymes: structure-function relationships and computational methodology

In enzymes, the active site is the location where incoming substrates are chemically converted to products. In some enzymes, this site is deeply buried within the core of the protein, and, in order to access the active site, substrates must pass through the body of the protein via a tunnel. In many systems, these tunnels act as filters and have been found to influence both substrate specificity and catalytic mechanism. Identifying and understanding how these tunnels exert such control has been of growing interest over the past several years because of implications in fields such as protein engineering and drug design. This growing interest has spurred the development of several computational methods to identify and analyze tunnels and how ligands migrate through these tunnels. The goal of this review is to outline how tunnels influence substrate specificity and catalytic efficiency in enzymes with buried active sites and to provide a brief summary of the computational tools used to identify and evaluate these tunnels.

STRING v10: protein-protein interaction networks, integrated over the tree of life

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

Functional genomics evidence unearths new moonlighting roles of outer ring coat nucleoporins

There is growing evidence for the involvement of Y-complex nucleoporins (Y-Nups) in cellular processes beyond the inner core of nuclear pores of eukaryotes. To comprehensively assess the range of possible functions of Y-Nups, we delimit their structural and functional properties by high-specificity sequence profiles and tissue-specific expression patterns. Our analysis establishes the presence of Y-Nups across eukaryotes with novel composite domain architectures, supporting new moonlighting functions in DNA repair, RNA processing, signaling and mitotic control. Y-Nups associated with a select subset of the discovered domains are found to be under tight coordinated regulation across diverse human and mouse cell types and tissues, strongly implying that they function in conjunction with the nuclear pore. Collectively, our results unearth an expanded network of Y-Nup interactions, thus supporting the emerging view of the Y-complex as a dynamic protein assembly with diverse functional roles in the cell.

Detection of genomic idiosyncrasies using fuzzy phylogenetic profiles

Phylogenetic profiles express the presence or absence of genes and their homologs across a number of reference genomes. They have emerged as an elegant representation framework for comparative genomics and have been used for the genome-wide inference and discovery of functionally linked genes or metabolic pathways. As the number of reference genomes grows, there is an acute need for faster and more accurate methods for phylogenetic profile analysis with increased performance in speed and quality. We propose a novel, efficient method for the detection of genomic idiosyncrasies, i.e. sets of genes found in a specific genome with peculiar phylogenetic properties, such as intra-genome correlations or inter-genome relationships. Our algorithm is a four-step process where genome profiles are first defined as fuzzy vectors, then discretized to binary vectors, followed by a de-noising step, and finally a comparison step to generate intra- and inter-genome distances for each gene profile. The method is validated with a carefully selected benchmark set of five reference genomes, using a range of approaches regarding similarity metrics and pre-processing stages for noise reduction. We demonstrate that the fuzzy profile method consistently identifies the actual phylogenetic relationship and origin of the genes under consideration for the majority of the cases, while the detected outliers are found to be particular genes with peculiar phylogenetic patterns. The proposed method provides a time-efficient and highly scalable approach for phylogenetic stratification, with the detected groups of genes being either similar to their own genome profile or different from it, thus revealing atypical evolutionary histories.

Association of Putative Members to Family of Mosquito Odorant Binding Proteins: Scoring Scheme Using Fuzzy Functional Templates and Cys Residue Positions

Proteins may be related to each other very specifically as homologous subfamilies. Proteins can also be related to diverse proteins at the super family level. It has become highly important to characterize the existing sequence databases by their signatures to facilitate the function annotation of newly added sequences. The algorithm described here uses a scheme for the classification of odorant binding proteins on the basis of functional residues and Cys-pairing. The cysteine-based scoring scheme not only helps in unambiguously identifying families like odorant binding proteins (OBPs), but also aids in their classification at the subfamily level with reliable accuracy. The algorithm was also applied to yet another cysteine-rich family, where similar accuracy was observed that ensures the application of the protocol to other families.

Mining tertiary structural motifs for assessment of designability

The observation of a limited secondary-structural alphabet in native proteins, with significant sequence preferences, has profoundly influenced the fields of protein design and structure prediction (Simons, Kooperberg, Huang, & Baker, 1997; Verschueren et al., 2011). In the era of structural genomics, as the size of the structural dataset continues to grow rapidly, it is becoming possible to extend this analysis to tertiary structural motifs and their sequences. For a hypothetical tertiary motif, the rate of its utilization in natural proteins may be used to assess its designability-the ease with which the motif can be realized with natural amino acids. This requires a structural similarity search methodology, which rather than looking for global topological agreement (more appropriate for categorization of full proteins or domains), identifies detailed geometric matches. In this chapter, we introduce such a method, called MaDCaT, and demonstrate its use by assessing the designability landscapes of two tertiary structural motifs. We also show that such analysis can establish structure/sequence links by providing the sequence constraints necessary to encode designable motifs. As logical extension of their secondary-structure counterparts, tertiary structural preferences will likely prove extremely useful in de novo protein design and structure prediction.

MESSA: MEta-Server for protein Sequence Analysis

Background

Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study. Since reliable prediction is usually based on the consensus of many computer programs, meta-severs have been developed to fit such needs. Most meta-servers focus on one aspect of sequence analysis, while others incorporate more information, such as PredictProtein for local sequence feature predictions, SMART for domain architecture and sequence motif annotation, and GeneSilico for secondary and spatial structure prediction. However, as predictions of local sequence properties, three-dimensional structure and function are usually intertwined, it is beneficial to address them together.

Results

We developed a MEta-Server for protein Sequence Analysis (MESSA) to facilitate comprehensive protein sequence analysis and gather structural and functional predictions for a protein of interest. For an input sequence, the server exploits a number of select tools to predict local sequence properties, such as secondary structure, structurally disordered regions, coiled coils, signal peptides and transmembrane helices; detect homologous proteins and assign the query to a protein family; identify three-dimensional structure templates and generate structure models; and provide predictive statements about the protein's function, including functional annotations, Gene Ontology terms, enzyme classification and possible functionally associated proteins. We tested MESSA on the proteome of Candidatus Liberibacter asiaticus. Manual curation shows that three-dimensional structure models generated by MESSA covered around 75% of all the residues in this proteome and the function of 80% of all proteins could be predicted.

Availability

MESSA is free for non-commercial use at http://prodata.swmed.edu/MESSA/

Self consistency grouping: a stringent clustering method

Background

Numerous types of clustering like single linkage and K-means have been widely studied and applied to a variety of scientific problems. However, the existing methods are not readily applicable for the problems that demand high stringency.

Methods

Our method, self consistency grouping, i.e. SCG, yields clusters whose members are closer in rank to each other than to any member outside the cluster. We do not define a distance metric; we use the best known distance metric and presume that it measures the correct distance. SCG does not impose any restriction on the size or the number of the clusters that it finds. The boundaries of clusters are determined by the inconsistencies in the ranks. In addition to the direct implementation that finds the complete structure of the (sub)clusters we implemented two faster versions. The fastest version is guaranteed to find only the clusters that are not subclusters of any other clusters and the other version yields the same output as the direct implementation but does so more efficiently.

Results

Our tests have demonstrated that SCG yields very few false positives. This was accomplished by introducing errors in the distance measurement. Clustering of protein domain representatives by structural similarity showed that SCG could recover homologous groups with high precision.

Conclusions

SCG has potential for finding biological relationships under stringent conditions.

3DSwap: curated knowledgebase of proteins involved in 3D domain swapping

Three-dimensional domain swapping is a unique protein structural phenomenon where two or more protein chains in a protein oligomer share a common structural segment between individual chains. This phenomenon is observed in an array of protein structures in oligomeric conformation. Protein structures in swapped conformations perform diverse functional roles and are also associated with deposition diseases in humans. We have performed in-depth literature curation and structural bioinformatics analyses to develop an integrated knowledgebase of proteins involved in 3D domain swapping. The hallmark of 3D domain swapping is the presence of distinct structural segments such as the hinge and swapped regions. We have curated the literature to delineate the boundaries of these regions. In addition, we have defined several new concepts like ‘secondary major interface’ to represent the interface properties arising as a result of 3D domain swapping, and a new quantitative measure for the ‘extent of swapping’ in structures. The catalog of proteins reported in 3DSwap knowledgebase has been generated using an integrated structural bioinformatics workflow of database searches, literature curation, by structure visualization and sequence–structure–function analyses. The current version of the 3DSwap knowledgebase reports 293 protein structures, the analysis of such a compendium of protein structures will further the understanding molecular factors driving 3D domain swapping.

Database URL:http://caps.ncbs.res.in/3dswap

Specific and nonhomologous isofunctional enzymes of the genetic information processing pathways as potential therapeutical targets for tritryps

Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi (Tritryps) are unicellular protozoa that cause leishmaniasis, sleeping sickness and Chagas' disease, respectively. Most drugs against them were discovered through the screening of large numbers of compounds against whole parasites. Nonhomologous isofunctional enzymes (NISEs) may present good opportunities for the identification of new putative drug targets because, though sharing the same enzymatic activity, they possess different three-dimensional structures thus allowing the development of molecules against one or other isoform. From public data of the Tritryps' genomes, we reconstructed the Genetic Information Processing Pathways (GIPPs). We then used AnEnPi to look for the presence of these enzymes between Homo sapiens and Tritryps, as well as specific enzymes of the parasites. We identified three candidates (ECs 3.1.11.2 and 6.1.1.-) in these pathways that may be further studied as new therapeutic targets for drug development against these parasites.

Bacterial protein structures reveal phylum dependent divergence

Protein sequence space is vast compared to protein fold space. This raises important questions about how structures adapt to evolutionary changes in protein sequences. A growing trend is to regard protein fold space as a continuum rather than a series of discrete structures. From this perspective, homologous protein structures within the same functional classification should reveal a constant rate of structural drift relative to sequence changes. The clusters of orthologous groups (COG) classification system was used to annotate homologous bacterial protein structures in the Protein Data Bank (PDB). The structures and sequences of proteins within each COG were compared against each other to establish their relatedness. As expected, the analysis demonstrates a sharp structural divergence between the bacterial phyla Firmicutes and Proteobacteria. Additionally, each COG had a distinct sequence/structure relationship, indicating that different evolutionary pressures affect the degree of structural divergence. However, our analysis also shows the relative drift rate between sequence identity and structure divergence remains constant.

Exploring protein structural dissimilarity to facilitate structure classification

BACKGROUND

Classification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs).

RESULTS

We compute a coefficient of dissimilarity (Omega) between proteins, based on structural and sequence-based descriptors characterising the respective constituent SSEs. For a set of 1,661 pairs of proteins with sequence identity up to 35%, the performance of Omega in predicting shared Class, Fold and Super-family levels is comparable to that of DaliLite Z score and shows a greater than four-fold increase in the true positive rate (TPR) for proteins sharing the Family level. On a larger set of 600 domains representing 200 families, the performance of Z score improves in predicting a shared Family, but still only achieves about half of the TPR of Omega. The TPR for structures sharing a Super-family is lower than in the first dataset, but Omega performs slightly better than Z score. Overall, the sensitivity of Omega in predicting common Fold level is higher than that of the DaliLite Z score.

CONCLUSION

Classification to a deeper level in the hierarchy is specific and difficult. So the efficiency of Omega may be attractive to the curators and the end-users of SCOP. We suggest Omega may be a better measure for structure classification than the DaliLite Z score, with the caveat that currently we are restricted to comparing structures with equal number of SSEs.

A comprehensive analysis of the structure-function relationship in proteins based on local structure similarity

Background

Sequence similarity to characterized proteins provides testable functional hypotheses for less than 50% of the proteins identified by genome sequencing projects. With structural genomics it is believed that structural similarities may give functional hypotheses for many of the remaining proteins.

Methodology/Principal Findings

We provide a systematic analysis of the structure-function relationship in proteins using the novel concept of local descriptors of protein structure. A local descriptor is a small substructure of a protein which includes both short- and long-range interactions. We employ a library of commonly reoccurring local descriptors general enough to assemble most existing protein structures. We then model the relationship between these local shapes and Gene Ontology using rule-based learning. Our IF-THEN rule model offers legible, high resolution descriptions that combine local substructures and is able to discriminate functions even for functionally versatile folds such as the frequently occurring TIM barrel and Rossmann fold. By evaluating the predictive performance of the model, we provide a comprehensive quantification of the structure-function relationship based only on local structure similarity. Our findings are, among others, that conserved structure is a stronger prerequisite for enzymatic activity than for binding specificity, and that structure-based predictions complement sequence-based predictions. The model is capable of generating correct hypotheses, as confirmed by a literature study, even when no significant sequence similarity to characterized proteins exists.

Conclusions/Significance

Our approach offers a new and complete description and quantification of the structure-function relationship in proteins. By demonstrating how our predictions offer higher sensitivity than using global structure, and complement the use of sequence, we show that the presented ideas could advance the development of meta-servers in function prediction.

The life and death of gene families

One of the unique insights provided by the growing number of fully sequenced genomes is the pervasiveness of gene duplication and gene loss. Indeed, several metrics now suggest that rates of gene birth and death per gene are only 10-40% lower than nucleotide substitutions per site, and that per nucleotide, the consequent lineage-specific expansion and contraction of gene families may play at least as large a role in adaptation as changes in orthologous sequences. While gene family evolution is pervasive, it may be especially important in our own evolution since it appears that the "revolving door" of gene duplication and loss has undergone multiple accelerations in the lineage leading to humans. In this paper, we review current understanding of gene family evolution including: methods for inferring copy number change, evidence for adaptive expansion and adaptive contraction of gene families, the origins of new families and deaths of previously established ones, and finally we conclude with a perspective on challenges and promising directions for future research.

A portrait of the "SCP/TAPS" proteins of eukaryotes--developing a framework for fundamental research and biotechnological outcomes

A wide range of proteins belonging to the SCP/TAPS "family" has been described for various eukaryotic organisms, including plants and animals (vertebrates and invertebrates, such as helminths). Although SCP/TAPS proteins have been proposed to play key roles in a number of fundamental biological processes, such as host-pathogen interactions and defence mechanisms, there is a paucity of information on their genetic relationships, structures and functions, and there is no standardised nomenclature for these proteins. A detailed analysis of the relationships of members of the SCP/TAPS family of proteins, based on key protein signatures, could provide a foundation for investigating these areas. In this article, we review the current state of knowledge of key SCP/TAPS proteins of eukaryotes, with an emphasis on those from parasitic helminths, and undertake a comprehensive, systematic phylogenetic analysis of currently available full-length protein sequence data (considering characteristic protein signatures or motifs) to infer relationships and provide a framework (based on statistical support) for the naming of these proteins. This framework is intended to guide genomic and molecular biological explorations of key SCP/TAPS molecules associated with infectious diseases of plants and animals. In particular, fundamental investigations of these molecules in parasites and the integration of structural and functional data could lead to new and innovative approaches for the control of parasitic diseases, with important biotechnological outcomes.

Sequence-based feature prediction and annotation of proteins

The combination of prediction tools in complex workflows and pipelines facilitates prediction of protein features from sequence.

A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome.

Proteomics in gram-negative bacterial outer membrane vesicles

Gram-negative bacteria constitutively secrete outer membrane vesicles (OMVs) into the extracellular milieu. Recent research in this area has revealed that OMVs may act as intercellular communicasomes in polyspecies communities by enhancing bacterial survival and pathogenesis in hosts. However, the mechanisms of vesicle formation and the pathophysiological roles of OMVs have not been clearly defined. While it is obvious that mass spectrometry-based proteomics offers great opportunities for improving our knowledge of bacterial OMVs, limited proteomic data are available for OMVs. The present review aims to give an overview of the previous biochemical, biological, and proteomic studies in the emerging field of bacterial OMVs, and to give future directions for high-throughput and comparative proteomic studies of OMVs that originate from diverse Gram-negative bacteria under various environmental conditions. This article will hopefully stimulate further efforts to construct a comprehensive proteome database of bacterial OMVs that will help us not only to elucidate the biogenesis and functions of OMVs but also to develop diagnostic tools, vaccines, and antibiotics effective against pathogenic bacteria.