Bioinformatics pipeline for functional identification and characterization of proteins

Automatically annotating functions of proteins is a key task in bioinformatics. Functional annotations of proteins are collected from multiple sources; thus, noisy annotations are inevitably introduced. However, the current research in protein function prediction almost always focuses on predicting functions for completely unannotated (or incompletely annotated) proteins, and seldom identifies the noisy annotations of proteins. In this paper, we propose a method called identifying noisy functional annotations (NFAs) of proteins using sparse semantic similarity. NFA first utilizes a protein-function association matrix to store the functional annotations of proteins, differentially weighs the annotations using the evidence codes attached with these annotations, and subsequently upward propagates the weights to the expanded annotations via the hierarchical structure among the functional labels. Next, NFA measures the semantic similarity between proteins by the $l_1$-norm regularized sparse representation on the weighted protein-function association matrix. Finally, it identifies the noisy functions of a protein based on the functions annotated to its semantic neighborhood proteins. The experimental results on two model species (A. thaliana and S. cerevisiae) show that the NFA more accurately identifies noisy annotations than other related methods. Additionally, removing the identified noisy annotations improves the accuracy of the current function prediction model.

Annotation of prokaryotic sequences can be separated into structural and functional annotation. Structural annotation is dependent on algorithmic interrogation of experimental evidence to discover the physical characteristics of a gene. This is done in an effort to construct accurate gene models, so understanding function or evolution of genes among organisms is not impeded. Functional annotation is dependent on sequence similarity to other known genes or proteins in an effort to assess the function of the gene. Combining structural and functional annotation across genomes in a comparative manner promotes higher levels of accurate annotation as well as an advanced understanding of genome evolution. As the availability of bacterial sequences increases and annotation methods improve, the value of comparative annotation will increase.

BackgroundHigh-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user.ResultsHere, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator.ConclusionsWe demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots.

Protein classification by machine learning algorithms is now widely used in structural and functional annotation of proteins. The Protein Classification Benchmark collection () was created in order to provide standard datasets on which the performance of machine learning methods can be compared. It is primarily meant for method developers and users interested in comparing methods under standardized conditions. The collection contains datasets of sequences and structures, and each set is subdivided into positive/negative, training/test sets in several ways. There is a total of 6405 classification tasks, 3297 on protein sequences, 3095 on protein structures and 10 on protein coding regions in DNA. Typical tasks include the classification of structural domains in the SCOP and CATH databases based on their sequences or structures, as well as various functional and taxonomic classification problems. In the case of hierarchical classification schemes, the classification tasks can be defined at various levels of the hierarchy (such as classes, folds, superfamilies, etc.). For each dataset there are distance matrices available that contain all vs. all comparison of the data, based on various sequence or structure comparison methods, as well as a set of classification performance measures computed with various classifier algorithms.

Protein sequences are symbols generally different characters representing the 20 amino acids used in human proteins those sequences can range from the very sort to the very long. There are many proteins database for the sequences are known but the function and functional annotation is not. Protein function prediction (PFP) as well as functional annotation (FA) from its structure or sequence is a major field of bioinformatics at the same time how to judge how well perform these algorithms. We proposed the novel method that converts the protein function problem into a language translation problem by a new proposed protein sequence language encoded to the protein function language decoded and build a recurrent neural machine encoding decoding translator (RNNEDT) based on the recurrent neural networks model. The excellent acting on training, testing datasets exhibits the proposed system as an improving direction for PFP. The proposed system alters the PFP matter to a language translation issue as well as applies a recurrent neural network machine version model for PFP, and visualizes the annotation of biological process (BP), molecular function (MF), as well as cellular component (CP).

Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.

Simple SummaryDue to the development of high-throughput sequencing technologies, computational genome annotation of sequences has become one of the principal research area in computational biology. First, we reviewed comparative annotation tools and pipelines for both annotations of structures and functions, which enable us to comprehend gene functions and their genome evolution. Second, we compared genome annotation tools that utilize homology-based and ab initio methods depending on the similarity of sequences or the lack of evidences. Third, we explored visualization tools that aid the annotation process and stressed the need for the quality control of annotations and re-annotations, because misannotations may happen due to experimental errors or missed genes by preceding technologies. Finally, we highlighted how emerging technologies can be used in future annotations.Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation.

The Genomics Annotation Platform (GAP) was designed to develop new tools for high throughput functional annotation and characterization of protein sequences and structures resulting from genomics and structural proteomics, benchmarking and application of those tools. Furthermore, this platform integrated the genomic scale sequence and structural analysis and prediction tools with the advanced structure prediction and bioinformatics environment of ICM. The development of GAP was primarily oriented towards the annotation of new biomolecular structures using both structural and sequence data. Even though the amount of protein X-ray crystal data is growing exponentially, the volume of sequence data is growing even more rapidly. This trend was exploited by leveraging the wealth of sequence data to provide functional annotation for protein structures. The additional information provided by GAP is expected to assist the majority of the commercial users of ICM, who are involved in drug discovery, in identifying promising drug targets as well in devising strategies for the rational design of therapeutics directed at the protein of interest. The GAP also provided valuable tools for biochemistry education, and structural genomics centers. In addition, GAP incorporates many novel prediction and analysis methods not available in other molecular modeling packages. This development led to signing the first Molsoft agreement in the structural genomics annotation area with the University of oxford Structural Genomics Center. This commercial agreement validated the Molsoft efforts under the GAP project and provided the basis for further development of the large scale functional annotation platform.

Bacteriophages are viruses that target bacteria, playing a crucial role in microbial ecology. Phage proteins are important in understanding phage biology, such as virus infection, replication, and evolution. Although a large number of new phages have been identified via metagenomic sequencing, many of them have limited protein function annotation. Accurate function annotation of phage proteins presents several challenges, including their inherent diversity and the scarcity of annotated ones. Existing tools have yet to fully leverage the unique properties of phages in annotating protein functions. In this work, we propose a new protein function annotation tool for phages by leveraging the modular genomic structure of phage genomes. By employing embeddings from the latest protein foundation models and Transformer to capture contextual information between proteins in phage genomes, GOPhage surpasses state-of-the-art methods in annotating diverged proteins and proteins with uncommon functions by 6.78% and 13.05% improvement, respectively. GOPhage can annotate proteins lacking homology search results, which is critical for characterizing the rapidly accumulating phage genomes. We demonstrate the utility of GOPhage by identifying 688 potential holins in phages, which exhibit high structural conservation with known holins. The results show the potential of GOPhage to extend our understanding of newly discovered phages.

The genomes of many organisms have been sequenced in the last 5 years. Typically about 30% of predicted genes from a newly sequenced genome cannot be given functional assignments using sequence comparison methods. In these situations three-dimensional structural predictions combined with a suite of computational tools can suggest possible functions for these hypothetical proteins. Suggesting functions may allow better interpretation of experimental data (e.g., microarray data and mass spectroscopy data) and help experimentalists design new experiments. In this paper, we focus on three hypothetical proteins of Shewanella oneidensis MR-1 that are potentially related to iron transport/metabolism based on microarray experiments. The threading program PROSPECT was used for protein structural predictions and functional annotation, in conjunction with literature search and other computational tools. Computational tools were used to perform transmembrane domain predictions, coiled coil predictions, signal peptide predictions, sub-cellular localization predictions, motif prediction, and operon structure evaluations. Combined computational results from all tools were used to predict roles for the hypothetical proteins. This method, which uses a suite of computational tools that are freely available to academic users, can be used to annotate hypothetical proteins in general.

The goal of this project is to redesign and repurpose existing high-flux ion soft-landing (SL) instrumentation developed at PNNL for new structural biology applications by integrating SL, mass-selection/separation of high mass-to-charge species, native mass spectrometry (MS), and cryo-electron microscopy (EM) to generate a workflow enabling structural and functional annotation of proteins and other complex biological samples. Ultimately, this work will result in a new instrument for mass-selection and SL based cryo-EM sample preparation and potentially increase the impact of PNNL and the broader microscopy community through commercial licensing.

Measuring the altered gene expression level and identifying differentially expressed genes/proteins during HIV infection, replication and latency is fundamental for broadening our understanding of the mechanisms of HIV infection and T-cell dysfunction. Such studies are crucial for developing effective strategies for virus eradication from the body. Inspired by the availability and enrichment of gene expression data during HIV infection, replication and latency, in this study, we proposed a novel compendium termed HIVed (HIV expression database; http://hivlatency.erc.monash.edu/) that harbours comprehensive functional annotations of proteins, whose genes have been shown to be dysregulated during HIV infection, replication and latency using different experimental designs and measurements. We manually curated a variety of third-party databases for structural and functional annotations of the protein entries in HIVed. With the goal of benefiting HIV related research, we collected a number of biological annotations for all the entries in HIVed besides their expression profile, including basic protein information, Gene Ontology terms, secondary structure, HIV-1 interaction and pathway information. We hope this comprehensive protein-centric knowledgebase can bridge the gap between the understanding of differentially expressed genes and the functions of their protein products, facilitating the generation of novel hypotheses and treatment strategies to fight against the HIV pandemic.

Alternative splicing is an important means of generating the protein diversity necessary for cellular functions. Hence, there is a growing interest in assessing the structural and functional impact of alternative protein isoforms. Typically, experimental studies are used to determine the structures of the canonical proteins ignoring the other isoforms. Therefore, there is still a large gap between abundant sequence information and meager structural data on these isoforms. During the last decade, significant progress has been achieved in the development of bioinformatics tools for structural and functional annotations of proteins. Moreover, the appearance of the AlphaFold program opened up the possibility to model a large number of high-confidence structures of the isoforms. In this study, using state-of-the-art tools, we performed in silico analysis of 58 eukaryotic proteomes. The evaluated structural states included structured domains, intrinsically disordered regions, aggregation-prone regions, and tandem repeats. Among other things, we found that the isoforms have fewer signal peptides, transmembrane regions, or tandem repeat regions in comparison with their canonical counterparts. This could change protein function and/or cellular localization. The AlphaFold modeling demonstrated that frequently isoforms, having differences with the canonical sequences, still can fold in similar structures though with significant structural rearrangements which can lead to changes of their functions. Based on the modeling, we suggested classification of the structural differences between canonical proteins and isoforms. Altogether, we can conclude that a majority of isoforms, similarly to the canonical proteins are under selective pressure for the functional roles.

The Universal Protein Resource (UniProt) provides a central resource on protein sequences and functional annotation with three database components, each addressing a key need in protein bioinformatics. The UniProt Knowledgebase (UniProtKB), comprising the manually annotated UniProtKB/Swiss-Prot section and the automatically annotated UniProtKB/TrEMBL section, is the preeminent storehouse of protein annotation. The extensive cross-references, functional and feature annotations and literature-based evidence attribution enable scientists to analyse proteins and query across databases. The UniProt Reference Clusters (UniRef) speed similarity searches via sequence space compression by merging sequences that are 100% (UniRef100), 90% (UniRef90) or 50% (UniRef50) identical. Finally, the UniProt Archive (UniParc) stores all publicly available protein sequences, containing the history of sequence data with links to the source databases. UniProt databases continue to grow in size and in availability of information. Recent and upcoming changes to database contents, formats, controlled vocabularies and services are described. New download availability includes all major releases of UniProtKB, sequence collections by taxonomic division and complete proteomes. A bibliography mapping service has been added, and an ID mapping service will be available soon. UniProt databases can be accessed online at or downloaded at .

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