Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.

Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.

High-throughput prediction of enzyme promiscuity based on substrate-product pairs.

The screening of enzymes for catalyzing specific substrate-product pairs is often constrained in the realms of metabolic engineering and synthetic biology. Existing tools based on substrate and reaction similarity predominantly rely on prior knowledge, demonstrating limited extrapolative capabilities and an inability to incorporate custom candidate-enzyme libraries. Addressing these limitations, we have developed the Substrate-product Pair-based Enzyme Promiscuity Prediction (SPEPP) model. This innovative approach utilizes transfer learning and transformer architecture to predict enzyme promiscuity, thereby elucidating the intricate interplay between enzymes and substrate-product pairs. SPEPP exhibited robust predictive ability, eliminating the need for prior knowledge of reactions and allowing users to define their own candidate-enzyme libraries. It can be seamlessly integrated into various applications, including metabolic engineering, de novo pathway design, and hazardous material degradation. To better assist metabolic engineers in designing and refining biochemical pathways, particularly those without programming skills, we also designed EnzyPick, an easy-to-use web server for enzyme screening based on SPEPP. EnzyPick is accessible at http://www.biosynther.com/enzypick/.

iProPhos: A Web-Based Interactive Platform for Integrated Proteome and Phosphoproteome Analysis.

Large-scale omics studies have generated a wealth of mass spectrometry-based proteomics data, which provide additional insights into disease biology spanning genomic boundaries. However, there is a notable lack of web-based analysis and visualization tools that facilitate the reutilization of these data. Given this challenge, we present iProPhos, a user-friendly web server to deliver interactive and customizable functionalities. iProPhos incorporates a large number of samples, including 1444 tumor samples and 746 normal samples across 12 cancer types, sourced from the Clinical Proteomic Tumor Analysis Consortium. Additionally, users can also upload their own proteomics/phosphoproteomics data for analysis and visualization. In iProPhos, users can perform profiling plotting and differential expression, patient survival, clinical feature-related, and correlation analyses, including protein-protein, mRNA-protein, and kinase-substrate correlations. Furthermore, functional enrichment, protein-protein interaction network, and kinase-substrate enrichment analyses are accessible. iProPhos displays the analytical results in interactive figures and tables with various selectable parameters. It is freely accessible at http://longlab-zju.cn/iProPhos without login requirement. We present two case studies to demonstrate that iProPhos can identify potential drug targets and upstream kinases contributing to site-specific phosphorylation. Ultimately, iProPhos allows end-users to leverage the value of big data in cancer proteomics more effectively and accelerates the discovery of novel therapeutic targets.

Design and off-target prediction for antisense oligomers targeting bacterial mRNAs with the MASON web server.

Antisense oligomers (ASOs), such as peptide nucleic acids (PNAs), designed to inhibit the translation of essential bacterial genes, have emerged as attractive sequence- and species-specific programmable RNA antibiotics. Yet, potential drawbacks include unwanted side effects caused by their binding to transcripts other than the intended target. To facilitate the design of PNAs with minimal off-target effects, we developed MASON (make antisense oligomers now), a web server for the design of PNAs that target bacterial mRNAs. MASON generates PNA sequences complementary to the translational start site of a bacterial gene of interest and reports critical sequence attributes and potential off-target sites. We based MASON's off-target predictions on experiments in which we treated Salmonella enterica serovar Typhimurium with a series of 10-mer PNAs derived from a PNA targeting the essential gene acpP but carrying two serial mismatches. Growth inhibition and RNA-sequencing (RNA-seq) data revealed that PNAs with terminal mismatches are still able to target acpP, suggesting wider off-target effects than anticipated. Comparison of these results to an RNA-seq data set from uropathogenic Escherichia coli (UPEC) treated with eleven different PNAs confirmed that our findings are not unique to Salmonella We believe that MASON's off-target assessment will improve the design of specific PNAs and other ASOs.

Auto-Kla: a novel web server to discriminate lysine lactylation sites using automated machine learning.

Recently, lysine lactylation (Kla), a novel post-translational modification (PTM), which can be stimulated by lactate, has been found to regulate gene expression and life activities. Therefore, it is imperative to accurately identify Kla sites. Currently, mass spectrometry is the fundamental method for identifying PTM sites. However, it is expensive and time-consuming to achieve this through experiments alone. Herein, we proposed a novel computational model, Auto-Kla, to quickly and accurately predict Kla sites in gastric cancer cells based on automated machine learning (AutoML). With stable and reliable performance, our model outperforms the recently published model in the 10-fold cross-validation. To investigate the generalizability and transferability of our approach, we evaluated the performance of our models trained on two other widely studied types of PTM, including phosphorylation sites in host cells infected with SARS-CoV-2 and lysine crotonylation sites in HeLa cells. The results show that our models achieve comparable or better performance than current outstanding models. We believe that this method will become a useful analytical tool for PTM prediction and provide a reference for the future development of related models. The web server and source code are available at http://tubic.org/Kla and https://github.com/tubic/Auto-Kla, respectively.

GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

Drug-drug interaction prediction: databases, web servers and computational models.

In clinical treatment, two or more drugs (i.e. drug combination) are simultaneously or successively used for therapy with the purpose of primarily enhancing the therapeutic efficacy or reducing drug side effects. However, inappropriate drug combination may not only fail to improve efficacy, but even lead to adverse reactions. Therefore, according to the basic principle of improving the efficacy and/or reducing adverse reactions, we should study drug-drug interactions (DDIs) comprehensively and thoroughly so as to reasonably use drug combination. In this review, we first introduced the basic conception and classification of DDIs. Further, some important publicly available databases and web servers about experimentally verified or predicted DDIs were briefly described. As an effective auxiliary tool, computational models for predicting DDIs can not only save the cost of biological experiments, but also provide relevant guidance for combination therapy to some extent. Therefore, we summarized three types of prediction models (including traditional machine learning-based models, deep learning-based models and score function-based models) proposed during recent years and discussed the advantages as well as limitations of them. Besides, we pointed out the problems that need to be solved in the future research of DDIs prediction and provided corresponding suggestions.

Bioinformatics toolbox for exploring target mutation-induced drug resistance.

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus.

There are a number of antigens that induce autoimmune response against ß-cells, leading to type 1 diabetes mellitus (T1DM). Recently, several antigen-specific immunotherapies have been developed to treat T1DM. Thus, identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g. immunotherapeutic). In this study, for the first time, an attempt has been made to develop a method for predicting, designing, and scanning of T1DM associated peptides with high precision. We analysed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. First, we developed a similarity/alignment based method using Basic Local Alignment Search Tool and achieved a high probability of correct hits with poor coverage. Second, we developed an alignment-free method using machine learning techniques and got a maximum AUROC of 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment-based methods and achieves maximum area under the receiver operating characteristic of 0.95 with Matthew's correlation coefficient of 0.81 on an independent dataset. We developed a web server 'DMPPred' and stand-alone server for predicting, designing and scanning T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/).

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. Addressing this biological complexity requires innovative and accurate tools for trait prediction. In response to this challenge, we have developed SoyDNGP, a deep learning-based model that offers significant advancements in the field of soybean trait prediction. Compared to existing methods, such as DeepGS and DNNGP, SoyDNGP boasts a distinct advantage due to its minimal increase in parameter volume and superior predictive accuracy. Through rigorous performance comparison, including prediction accuracy and model complexity, SoyDNGP represents improved performance to its counterparts. Furthermore, it effectively predicted complex traits with remarkable precision, demonstrating robust performance across different sample sizes and trait complexities. We also tested the versatility of SoyDNGP across multiple crop species, including cotton, maize, rice and tomato. Our results showed its consistent and comparable performance, emphasizing SoyDNGP's potential as a versatile tool for genomic prediction across a broad range of crops. To enhance its accessibility to users without extensive programming experience, we designed a user-friendly web server, available at http://xtlab.hzau.edu.cn/SoyDNGP. The server provides two features: 'Trait Lookup', offering users the ability to access pre-existing trait predictions for over 500 soybean accessions, and 'Trait Prediction', allowing for the upload of VCF files for trait estimation. By providing a high-performing, accessible tool for trait prediction, SoyDNGP opens up new possibilities in the quest for optimized soybean breeding.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

DREAMweb: An online tool for graph-based modeling of NMR protein structure.

The value of accurate protein structural models closely conforming to the experimental data is indisputable. DREAMweb deploys an improved DREAM algorithm, DREAMv2, that incorporates a tighter bound in the constraint set of the underlying optimization approach. This reduces the artifacts while modeling the protein structure by solving the distance-geometry problem. DREAMv2 follows a bottom-up strategy of building smaller substructures for regions with a larger concentration of experimental bounds and consolidating them before modeling the rest of the protein structure. This improves secondary structure conformance in the final models consistent with experimental data. The proposed method efficiently models regions with sparse coverage of experimental data by reducing the possibility of artifacts compared to DREAM. To balance performance and accuracy, smaller substructures ( ∼ 200 $\sim 200$ atoms) are solved in this regime, allowing faster builds for the other parts under relaxed conditions. DREAMweb is accessible as an internet resource. The improvements in results are showcased through benchmarks on 10 structures. DREAMv2 can be used in tandem with any NMR-based protein structure determination workflow, including an iterative framework where the NMR assignment for the NOESY spectra is incomplete or ambiguous. DREAMweb is freely available for public use at http://pallab.cds.iisc.ac.in/DREAM/ and downloadable at https://github.com/niladriranjandas/DREAMv2.git.

m6A-TCPred: a web server to predict tissue-conserved human m6A sites using machine learning approach.

BACKGROUND: N6-methyladenosine (m6A) is the most prevalent post-transcriptional modification in eukaryotic cells that plays a crucial role in regulating various biological processes, and dysregulation of m6A status is involved in multiple human diseases including cancer contexts. A number of prediction frameworks have been proposed for high-accuracy identification of putative m6A sites, however, none have targeted for direct prediction of tissue-conserved m6A modified residues from non-conserved ones at base-resolution level. RESULTS: We report here m6A-TCPred, a computational tool for predicting tissue-conserved m6A residues using m6A profiling data from 23 human tissues. By taking advantage of the traditional sequence-based characteristics and additional genome-derived information, m6A-TCPred successfully captured distinct patterns between potentially tissue-conserved m6A modifications and non-conserved ones, with an average AUROC of 0.871 and 0.879 tested on cross-validation and independent datasets, respectively. CONCLUSION: Our results have been integrated into an online platform: a database holding 268,115 high confidence m6A sites with their conserved information across 23 human tissues; and a web server to predict the conserved status of user-provided m6A collections. The web interface of m6A-TCPred is freely accessible at: www.rnamd.org/m6ATCPred .

Passionfruit Genomic Database (PGD): a comprehensive resource for passionfruit genomics.

Passionfruit (Passiflora edulis) is a significant fruit crop in the commercial sector, owing to its high nutritional and medicinal value. The advent of high-throughput genomics sequencing technology has led to the publication of a vast amount of passionfruit omics data, encompassing complete genome sequences and transcriptome data under diverse stress conditions. To facilitate the efficient integration, storage, and analysis of these large-scale datasets, and to enable researchers to effectively utilize these omics data, we developed the first passionfruit genome database (PGD). The PGD platform comprises a diverse range of functional modules, including a genome browser, search function, heatmap, gene expression patterns, various tools, sequence alignment, and batch download, thereby providing a user-friendly interface. Additionally, supplementary practical tools have been developed for the PGD, such as gene family analysis tools, gene ontology (GO) terms, a pathway enrichment analysis, and other data analysis and mining tools, which enhance the data's utilization value. By leveraging the database's robust scalability, the intention is to continue to collect and integrate passionfruit omics data in the PGD, providing comprehensive and in-depth support for passionfruit research. The PGD is freely accessible via http://passionfruit.com.cn .

iRice-MS: An integrated XGBoost model for detecting multitype post-translational modification sites in rice.

Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins after protein biosynthesis, which orchestrates a variety of biological processes. Detecting PTM sites in proteome scale is one of the key steps to in-depth understanding their regulation mechanisms. In this study, we presented an integrated method based on eXtreme Gradient Boosting (XGBoost), called iRice-MS, to identify 2-hydroxyisobutyrylation, crotonylation, malonylation, ubiquitination, succinylation and acetylation in rice. For each PTM-specific model, we adopted eight feature encoding schemes, including sequence-based features, physicochemical property-based features and spatial mapping information-based features. The optimal feature set was identified from each encoding, and their respective models were established. Extensive experimental results show that iRice-MS always display excellent performance on 5-fold cross-validation and independent dataset test. In addition, our novel approach provides the superiority to other existing tools in terms of AUC value. Based on the proposed model, a web server named iRice-MS was established and is freely accessible at http://lin-group.cn/server/iRice-MS.

HLAncPred: a method for predicting promiscuous non-classical HLA binding sites.

Human leukocyte antigens (HLA) regulate various innate and adaptive immune responses and play a crucial immunomodulatory role. Recent studies revealed that non-classical HLA-(HLA-E & HLA-G) based immunotherapies have many advantages over traditional HLA-based immunotherapy, particularly against cancer and COVID-19 infection. In the last two decades, several methods have been developed to predict the binders of classical HLA alleles. In contrast, limited attempts have been made to develop methods for predicting non-classical HLA binding peptides, due to the scarcity of sufficient experimental data. Of note, in order to facilitate the scientific community, we have developed an artificial intelligence-based method for predicting binders of class-Ib HLA alleles. All the models were trained and tested on experimentally validated data obtained from the recent release of IEDB. The machine learning models achieved more than 0.98 AUC for HLA-G alleles on validation dataset. Similarly, our models achieved the highest AUC of 0.96 and 0.94 on the validation dataset for HLA-E*01:01 and HLA-E*01:03, respectively. We have summarized the models developed in the past for non-classical HLA and validated the performance with the models developed in this study. Moreover, to facilitate the community, we have utilized our tool for predicting the potential non-classical HLA binding peptides in the spike protein of different variants of virus causing COVID-19, including Omicron (B.1.1.529). One of the major challenges in the field of immunotherapy is to identify the promiscuous binders or antigenic regions that can bind to a large number of HLA alleles. To predict the promiscuous binders for the non-classical HLA alleles, we developed a web server HLAncPred (https://webs.iiitd.edu.in/raghava/hlancpred) and standalone package.

MetaLogo: a heterogeneity-aware sequence logo generator and aligner.

Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will lead to an overlook of the heterogeneity among the sequences during the sequence logo making process. Heterogeneous groups of sequences may represent clades of different evolutionary origins, or genes families with different functions. Therefore, it is essential to divide the sequences into different phylogenetic or functional groups to reveal their specific sequence motifs and conservation patterns. To solve these problems, we developed MetaLogo, which can automatically cluster the input sequences after multiple sequence alignment and phylogenetic tree construction, and then output sequence logos for multiple groups and aligned them in one figure. User-defined grouping is also supported by MetaLogo to allow users to investigate functional motifs in a more delicate and dynamic perspective. MetaLogo can highlight both the homologous and nonhomologous sites among sequences. MetaLogo can also be used to annotate the evolutionary positions and gene functions of unknown sequences, together with their local sequence characteristics. We provide users a public MetaLogo web server (http://metalogo.omicsnet.org), a standalone Python package (https://github.com/labomics/MetaLogo), and also a built-in web server available for local deployment. Using MetaLogo, users can draw informative, customized and publishable sequence logos without any programming experience to present and investigate new knowledge on specific sequence sets.

Development of interactive biological web applications with R/Shiny.

Development of interactive web applications to deposit, visualize and analyze biological datasets is a major subject of bioinformatics. R is a programming language for data science, which is also one of the most popular languages used in biological data analysis and bioinformatics. However, building interactive web applications was a great challenge for R users before the Shiny package was developed by the RStudio company in 2012. By compiling R code into HTML, CSS and JavaScript code, Shiny has made it incredibly easy to build web applications for the large R community in bioinformatics and for even non-programmers. Over 470 biological web applications have been developed with R/Shiny up to now. To further promote the utilization of R/Shiny, we reviewed the development of biological web applications with R/Shiny, including eminent biological web applications built with R/Shiny, basic steps to build an R/Shiny application, commonly used R packages to build the interface and server of R/Shiny applications, deployment of R/Shiny applications in the cloud and online resources for R/Shiny.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism.

The location of microRNAs (miRNAs) in cells determines their function in regulation activity. Studies have shown that miRNAs are stable in the extracellular environment that mediates cell-to-cell communication and are located in the intracellular region that responds to cellular stress and environmental stimuli. Though in situ detection techniques of miRNAs have made great contributions to the study of the localization and distribution of miRNAs, miRNA subcellular localization and their role are still in progress. Recently, some machine learning-based algorithms have been designed for miRNA subcellular location prediction, but their performance is still far from satisfactory. Here, we present a new data partitioning strategy that categorizes functionally similar locations for the precise and instructive prediction of miRNA subcellular location in Homo sapiens. To characterize the localization signals, we adopted one-hot encoding with post padding to represent the whole miRNA sequences, and proposed a deep bidirectional long short-term memory with the multi-head self-attention algorithm to model. The algorithm showed high selectivity in distinguishing extracellular miRNAs from intracellular miRNAs. Moreover, a series of motif analyses were performed to explore the mechanism of miRNA subcellular localization. To improve the convenience of the model, a user-friendly web server named iLoc-miRNA was established (http://iLoc-miRNA.lin-group.cn/).