Recurrent Neural Network Encoding Decoding Translator based Prediction Protein Function and Functional Annotation

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.

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.

Co-occurrence of different phenotypes hampers the understanding of the molecular mechanisms characterizing diseases. While many resources focus on the relationship among phenotypes, diseases, and genes, little is known about the relevance of molecular functions and functional processes underlying the occurrence of phenotypes. To this aim, here we describe a new resource called PhenPath (phenpath.biocomp.unibo.it), recently published. PhenPath allows phenotype functional annotation, after an enrichment procedure of the functional annotation of the different phenotype/disease-associated genes. Functional annotations consider Gene Ontology (Molecular Function, Biological Process and Cellular Component), KEGG and Reactome pathways. PhenPath can be adopted to endow a disease (described with a set of phenotypes) with novel links to genes and functional terms, retrieved by intersecting the sets of genes and functional terms associated with the single phenotypes in PhenPath, as we proved in many study cases (e.g.: Rett syndrome, Tourette syndrome). We participated in the Critical Assessment of protein Function Annotation algorithms (CAFA) 4 th edition using a method based on PhenPath, and the preliminary results of the experiment show that PhenPath is among the top-scoring methods. We propose our resource for directing scientific efforts, helping the diagnosis and retrieving new possible associations among biological processes and diseases.

Protein function, which is determined by sequence, structure, and other characteristics, plays a crucial role in an organism’s performance. Existing protein function prediction methods mainly rely on sequence data and often ignore structural properties that are crucial for accurate prediction. Protein structure provides richer spatial and functional insights, which can significantly improve prediction accuracy. In this work, we propose a multi-modal protein function prediction model (MMPFP) that integrates protein sequence and structure information through the use of GCN, CNN, and Transformer models. We validate the model using the PDBest dataset, demonstrating that MMPFP outperforms traditional single-modal models in the molecular function (MF), biological process (BP), and cellular component (CC) prediction tasks. Specifically, MMPFP achieved AUPR scores of 0.693, 0.355, and 0.478; scores of 0.752, 0.629, and 0.691; and scores of 0.336, 0.488, and 0.459, showing a 3–5% improvement over single-modal models. Additionally, ablation studies confirm the effectiveness of the Transformer module within the GCN branch, further validating MMPFP’s superior performance over existing methods. This multi-modal approach offers a more accurate and comprehensive framework for protein function prediction, addressing key limitations of current models.

NCBI's Conserved Domain Database (CDD, http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) is a protein classification and annotation resource. It comprises a collection of annotated multiple sequence alignment (MSA) models representing ancient conserved protein domains, basic units of protein function and protein evolution. These MSAs are available as position specific score matrices (PSSMs) for the rapid identification of conserved domains in protein sequences via Reverse Position Specific -BLAST (RPS-BLAST), a variation of the commonly used PSI-BLAST method. CDD includes manually curated domain models that are organized into family hierarchies, which use protein 3D structure information explicitly to refine domain models and provide insights into the relationship between sequence conservation and molecular function. The domain models can be visualized and studied interactively using NCBI's CDTree/Cn3D software applications. Conserved functional annotations such as ligand-binding sites and/or catalytic active sites are highlighted on protein structures sharing significant homology to the query, thereby pointing out or inferring the underlying mechanism(s) of the protein's function. Here, we present the functionally annotated, evolutionary hierarchical classification of the seven-transmembrane G-protein coupled receptors (7TM GPCRs), which represent the largest family of membrane-bound proteins across the three kingdoms of life. GPCRs are involved in a wide range of physiological roles including tumor growth and metastasis. With the increasing availability of high-resolution crystal structures of 7TM receptors, we recently undertook a systematic comparative analysis of the highly divergent GPCRs. Orphan GPCR subfamilies, which only contain uncharacterized protein sequences, have putative functions and the location of 7TM helices annotated by inference from the molecular and cellular functions of known related proteins with available 3D structure, and/or based on the available literature. We demonstrate the utility of CDTree/Cn3D in studying the molecular evolution of protein domain families, and in accelerating the pace of GPCR drug discovery by enabling researchers to obtain valuable clues regarding as-yet-unidentified molecular functions and mechanisms. The CDTree/Cn3D software is freely available for download via the CDD website. Acknowledgement: This research was supported by the Intramural Research Program of the National Library of Medicine, NIH. Citation Format: James S. Song, Noreen R. Gonzales, Roxanne A. Yamashita, Aron Marchler-Bauer, Stephen H. Bryant. Evolutionary, structural, and functional insights into the seven-transmembrane GPCR superfamily through NCBI's Conserved Domain Database. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1090. doi:10.1158/1538-7445.AM2015-1090

Indonesia consists of various tribes with their respective regional languages, one of which is the Besemah tribe in South Sumatra Province with its language culture, namely the Besemah Language. Until now, the Besemah Language is still used by the Besemah tribe, but over time the number of Besemah Language speakers has decreased, plus most of the wider community does not know what the Besemah Language is. Machine Learning is a part of artificial intelligence that is often used to solve various problems. Machine Learning involves the use of computers and mathematical algorithms that use data to make predictions in the future. Machine translation is a tool that can convert one language to another. This study aims to collect datasets in the form of sentences and words from the Besemah Language, then create a Besemah Language translation machine to Indonesian and vice versa. In the research conducted, the approach used is Experimental Research in Machine Learning. Experimental research in machine learning for language translation is a research approach that involves designing and implementing a series of experiments to test and validate the performance of the language translation model. In this study, Neural Machine Translation (NMT) technology was applied with the Recurrent Neural Network (RNN) approach. The results of the study showed that the val_accuracy value for the Besemah-Indonesian translation was 0.8469 and for Indonesia-Besemah was 0.8492, in the translation trial conducted using the RNN model, 100 epochs, batch size 64, and validation split of 0.2.

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.

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 .

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried &amp;amp; which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

Network-based function prediction and interactomics: The case for metabolic enzymes

Proteins are the main undertakers of life activities, and accurately predicting their biological functions can help human better understand life mechanism and promote the development of themselves. With the rapid development of high-throughput technologies, an abundance of proteins are discovered. However, the gap between proteins and function annotations is still huge. To accelerate the process of protein function prediction, some computational methods taking advantage of multiple data have been proposed. Among these methods, the deep-learning-based methods are currently the most popular for their capability of learning information automatically from raw data. However, due to the diversity and scale difference between data, it is challenging for existing deep learning methods to capture related information from different data effectively. In this paper, we introduce a deep learning method that can adaptively learn information from protein sequences and biomedical literature, namely DeepAF. DeepAF first extracts the two kinds of information by using different extractors, which are built based on pre-trained language models and can capture rudimentary biological knowledge. Then, to integrate those information, it performs an adaptive fusion layer based on a Cross-attention mechanism that considers the knowledge of mutual interactions between two information. Finally, based on the mixed information, DeepAF utilizes logistic regression to obtain prediction scores. The experimental results on the datasets of two species (i.e., Human and Yeast) show that DeepAF outperforms other state-of-the-art approaches.

Query auto-completion (QAC) is one of the most visible features in modern search engines. It helps users complete their queries by presenting a list of possible completions while they are typing in the search box. Existing works on QAC focus on employing learning-to-rank algorithms over handcrafted features. However, those manually designed features are unable to capture non-linear relationships between users and their submitted queries. Meanwhile, although Recurrent Neural Networks (RNNs) show significant advances in various areas, little attention is paid to its application to QAC. To bridge this gap, we propose three RNN-based models for QAC ranking: a simple session-based RNN model, a personalized RNN model and an attentive RNN model. Extensive experiments are conducted on a real-world query log. The significant improvement over the compared baseline verifies the effectiveness of the personalized RNN model and the attentive RNN model.

A recurrent neural network (RNN) model is developed for simulating and predicting shear behavior of both a fine-grained residual soil and a dune sand. The RNN model with one hidden layer of 20 nodes appears very effective in modeling complex soil behavior, due to its feedback connections from a hidden layer to an input layer. A dynamic gradient descent learning algorithm is used to train the network. By training part of the experimental data, which include strain-controlled undrained tests and stress-controlled drained tests performed on a residual Hawaiian volcanic soil, the network is able to capture significant variability of shear behavior existing in the residual soil. The unusual characteristics that the denser soil samples dilate under a higher stress level and the looser soil samples contract under a lower stress level are well represented by the RNN model. The RNN model also shows encouraging results in simulation and prediction of behavior of a dune sand which experienced loading-unloading-reloading conditions. Excellent agreements between the measured data and the modeling results are observed in both stress-strain behavior and volumetric-change characteristics. As compared with a traditional model, the RNN model shows more effectiveness and less effort.Key words: neural network, modeling, soil behavior, shear tests, simulation, prediction.

Accurate production forecasting is crucial for effective reservoir management and field development planning. Traditional methods; such as numerical simulation, material balance equations coupled with well models, and advanced decline curve analysis (DCA) require extensive data and involve tradeoffs between accuracy and complexity. Recently, deep learning techniques, specifically artificial neural network (ANN) and recurrent neural network (RNN) models have demonstrated promising performance in this domain. This study applies ANN and RNN for production forecasting and compares their effectiveness with traditional DCA models.An actual production dataset for a gas field containing 13 wells is used to compare ANN and RNN models to traditional decline curve analysis models. After thorough data cleaning and preparation, the dataset was divided into training (historical) and test data sets. The training data was used to build ANN and RNN models in addition to estimating the model parameters for traditional DCA models. Traditional DCA models that were investigated include Arps (exponential, hyperbolic, and harmonic), Duong, Power Law Exponential Decline (PLE), and Stretched Exponential Decline (SEPD). Finally, the prediction of different models was evaluated and compared using the testing dataset. The comparison also included long, medium, and short-term prediction.Based on the comparison of daily gas production rate forecast, the ANN model demonstrated notable improvements over Arps models for the majority of wells, with a mean absolute percent error (MAPE) of 3.9% compared to 9.1%. However, across all wells, the average MAPE was slightly lower at 6.3% versus 8.1%. Conversely, the RNN model exhibited varied performance compared to Arps models. It showed poorer long-term performance (13% versus 4.5%), similar performance in the medium term (4.4% versus 4.2%), and superior performance in the short term (2.7% versus 4.2%). Furthermore, among DCA models specifically developed for unconventional wells, SEPD exhibited nearly identical performance to Arps (8.3% compared to 8.1%). In contrast, Duong model demonstrated the poorest performance with MAPE of 21.9%.This study provides insights into model analysis and introduces guidelines in using artificial and recurrent neural networks for predicting production rates. The ANN and RNN models were developed through careful testing with different designs and techniques. All the models in this research were created using Python scripts and powerful open-source libraries like SciPy and TensorFlow.

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.