Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.

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).

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.

This protocol presents methods for isolating and identifying novel nucleic acid-binding proteins. We focus on bacterial DNA-binding proteins, although the methods can be readily adapted to identify nucleic acid-binding proteins of eukaryotes or archaea, and proteins that bind to RNAs. Briefly, the DNA sequence of interest is affixed to beads and then incubated with bacterial cytoplasmic extract. Washes with buffers containing nonspecific DNA and low salt concentrations will remove non-adhering and low-specificity DNA-binding proteins, while subsequent washes with higher salt concentrations will elute DNA-binding proteins that have greater specificity. Eluted proteins are then identified by standard proteomic techniques, such as mass spectrometry. © 2025 Wiley Periodicals LLC. Basic Protocol 1: Lysis procedure Basic Protocol 2: DNA affinity chromatography: Borrelia burgdorferi and Leptospira interrogans Basic Protocol 3: Visualization and identification of DNA-binding proteins.

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.

Biological process annotation of proteins across the plant kingdom

Motivation: Computational approaches that can predict protein functions are essential to bridge the widening function annotation gap especially since <1.0% of all proteins in UniProtKB have been experimentally characterized. We present a domain-based method for protein function classification and prediction of functional sites that exploits functional sub-classification of CATH superfamilies. The superfamilies are sub-classified into functional families (FunFams) using a hierarchical clustering algorithm supervised by a new classification method, FunFHMMer.Results: FunFHMMer generates more functionally coherent groupings of protein sequences than other domain-based protein classifications. This has been validated using known functional information. The conserved positions predicted by the FunFams are also found to be enriched in known functional residues. Moreover, the functional annotations provided by the FunFams are found to be more precise than other domain-based resources. FunFHMMer currently identifies 110 439 FunFams in 2735 superfamilies which can be used to functionally annotate > 16 million domain sequences.Availability and implementation: All FunFam annotation data are made available through the CATH webpages (http://www.cathdb.info). The FunFHMMer webserver (http://www.cathdb.info/search/by_funfhmmer) allows users to submit query sequences for assignment to a CATH FunFam.Contact:sayoni.das.12@ucl.ac.ukSupplementary information:Supplementary data are available at Bioinformatics online.

The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023-2024. GORetriever is publicly available at https://github.com/ZhuLab-Fudan/GORetriever.

The new sequencing methods, called Next Generation Sequencing gives an opportunity to possess a vast amount of data in short time. This data requires structural and functional annotation. Functional identification and characterization of predicted proteins could be done by in silico approches, thanks to a numerous computational tools available nowadays. However, there is a need to confirm the results of proteins function prediction using different programs and comparing the results or confirm experimentally. Here we present a bioinformatics pipeline for structural and functional annotation of proteins.

The definition of the automatic protein function means designating the function with the automation by utilizing the data that already revealed unknown protein function. The demand for analysis on the sequencing technology such as the next generation genome analysis (NGS) and the subsequent genome are on the rise; thus, the need for the method of predicting the protein function automatically has been more and more highlighted. As for the existing methods, the studies on the definition of function between the similar species based on the similarities of sequence have been primarily conducted. However, this paper aims to designate by automatically predicting the function of genome by utilizing InterPro (IPR) that can represent the properties of the protein family, which similarly groups the protein function. Moreover, the gene ontology (GO), which is the controlled vocabulary to describe the protein function comprehensively, is to be used. As for the data used in the experiment, the analysis on properties was conducted in the sparse state that is deflected to one side. Thus, this paper aims to analyze the prediction method for protein function automatically through selecting the features, assigning the data processing and weights and applying a variety of classification methods to overcome that property.

BackgroundBiological data has grown explosively with the advance of next-generation sequencing. However, annotating protein function with wet lab experiments is time-consuming. Fortunately, computational function prediction can help wet labs formulate biological hypotheses and prioritize experiments. Gene Ontology (GO) is a framework for unifying the representation of protein function in a hierarchical tree composed of GO terms.ResultsWe propose GODoc, a general protein GO prediction framework based on sequence information which combines feature engineering, feature reduction, and a novel ​k​-nearest-neighbor algorithm to resolve the multiple GO prediction problem. Comprehensive evaluation on CAFA2 shows that GODoc performs better than two baseline models. In the CAFA3 competition (68 teams), GODoc ranks 10th in Cellular Component Ontology. Regarding the species-specific task, the proposed method ranks 10th and 8th in the eukaryotic Cellular Component Ontology and the prokaryotic Molecular Function Ontology, respectively. In the term-centric task, GODoc performs third and is tied for first for the biofilm formation of Pseudomonas aeruginosa and the long-term memory of Drosophila melanogaster, respectively.ConclusionsWe have developed a novel and effective strategy to incorporate a training procedure into the k-nearest neighbor algorithm (instance-based learning) which is capable of solving the Gene Ontology multiple-label prediction problem, which is especially notable given the thousands of Gene Ontology terms.

DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

Ecotoxicoproteomics: A decade of progress in our understanding of anthropogenic impact on the environment

The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the confidence score of the modeling. This protocol provides new insights and guidelines for designing of online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.

With few exceptions protein functions depend sensitively upon their interactions with other biomolecules. Thus, the surface of a protein is of particular interest for function annotation: definition of the protein surface in experimental or modelled protein structure enables the application of a wide range of structural bioinformatic tools for function prediction. The development of such tools has been significantly accelerated in recent years as a response to the flux of information from Structural Genomics programs which, at least in part, have deliberately targeted mysterious protein families of unknown function about which conventional homology-based protein function annotation can say little or nothing (Bateman et al. in Acta Crystallographica Section F: Structural Biology and Crystallization Communications 66:1148–1152, 2010). As this chapter will illustrate, the underlying principles behind the resulting toolset vary impressively but, ultimately, most are based upon discovering putative interaction sites through detecting ways in which they differ somehow from protein surface in general. These differences may be physicochemical, electrostatic or steric in nature, or be of evolutionary origin. Predictions can be strengthened by observing concordant results from orthogonal methods. Indeed, many programs now improve performance by combining multiple factors in their calculations. Some methods find functional sites in general, others provide direct evidence supporting specific biochemical functions. This chapter will not attempt a comprehensive historical overview of the area, rather aiming to guide the user to the current state of the art while acknowledging key methodology papers. Methods that are readily available will be favoured, particularly those implemented at servers and those for which plug-ins for popular molecular visualisation tools exist.

Many sequenced genes are mainly annotated through automatic transfer of annotation from similar sequences. Manual comparison of results or intermediate results from different tools can help avoid wrong annotations and give hints to the function of a gene even if none of the automated tools could return any result. AFAWE simplifies the task of manual functional annotation by running different tools and workflows for automatic function prediction and displaying the results in a way that facilitates comparison. Because all programs are executed as web services, AFAWE is easily extensible and can directly query primary databases, thereby always using the most up-to-date data sources. Visual filters help to distinguish trustworthy results from non-significant results. Furthermore, an interface to add detailed manual annotation to each gene is provided, which can be displayed to other users.