Computational Biology Research Articles

Target Discovery Driven by Chemical Biology and Computational Biology.

Target identification is crucial for drug screening and development because it can reveal the mechanism of drug action and ensure the reliability and accuracy of the results. Chemical biology, an interdisciplinary field combining chemistry and biology, can assist in this process by studying the interactions between active molecular compounds and proteins and their physiological effects. It can also help predict potential drug targets or candidates, develop new biomarker assays and diagnostic reagents, and evaluate the selectivity and range of active compounds to reduce the risk of off-target effects. Chemical biology can achieve these goals using techniques such as changing protein thermal stability, enzyme sensitivity, and molecular structure and applying probes, isotope labeling and mass spectrometry. Concurrently, computational biology employs a diverse array of computational models to predict drug targets. This approach also offers innovative avenues for repurposing existing drugs. In this paper, we review the reported chemical biology and computational biology techniques for identifying different types of targets that can provide valuable insights for drug target discovery.

TransConv: convolution-infused transformer for protein secondary structure prediction.

Protein secondary structure prediction is essential for understanding protein function and characteristics and can also facilitate drug discovery. Traditional methods for experimentally determining protein structures are both time-consuming and costly. Computational biology offers a viable alternative by predicting protein structures from their sequences. Protein secondary structure is defined by the local spatial arrangement of the protein backbone, resulting from hydrogen bonds between amino acids. In this study, we introduce TransConv, a model that combines transformer models with convolutional blocks to predict protein secondary structures from amino acid sequences. Transformer models are effective at capturing long-range dependencies through self-attention mechanisms. We integrate convolutional blocks into the transformer architecture to improve the detection of important local features. This hybrid model captures both long-range interactions and local features, leading to more accurate predictions of protein secondary structures, thus offering an efficient solution for this critical task. The experimental outcomes on the benchmark datasets depict the superiority of the proposed approach over the state-of-the-art (SOTA) models in the literature.

CMCS-PVA@CA hydrogel dressing: A promoter of wound healing with MRSA virulence attenuation function.

Traditional wound dressings, primarily centered on antimicrobial or bactericidal strategies, have inadvertently contributed to the rise of drug-resistant bacterial colonies at wound sites, thus prolonging the healing process. In this study, we developed an innovative hydrogel dressing, CMCS-PVA@CA, incorporating carboxymethyl chitosan (CMCS), polyvinyl alcohol (PVA), and cichoric acid (CA), specifically designed to treat skin wounds infected with methicillin-resistant Staphylococcus aureus (MRSA). Computational biology analyses reveal that CA exerts substantial anti-virulence activity by targeting serine/threonine phosphatase (Stp1), achieving an IC50 of 3.912μM, thereby mitigating MRSA pathogenicity. Notably, CA lacks intrinsic antibacterial properties, minimizing the risk of fostering drug resistance. Furthermore, CMCS-PVA@CA demonstrates effective wound healing acceleration and meets clinical application standards, with its robust mechanical properties enhancing patient comfort. In essence, this study presents CMCS-PVA@CA as a promising hydrogel dressing offering a viable solution for treating drug-resistant bacterial infections in skin wounds.

Identifying Biomarkers for Atherosclerosis via Gene Expression and Biological Networking.

Atherosclerosis is a chronic disease caused by the accumulation of lipids, inflammatory cells, and fibrous elements in arterial walls, leading to plaque formation and cardiovascular conditions like coronary artery disease, stroke, and peripheral arterial disease. Factors like hyperlipidemia, hypertension, smoking, and diabetes contribute to its development. Diagnosis relies on imaging and biomarkers, while management includes lifestyle modifications, pharmacotherapy, and surgical interventions. Computational biology is transforming biological knowledge into clinical practice by identifying biomarkers that can predict clinical outcomes. This involves omics data, predictive modeling, and data integration. Statistical analysis-based methods are also being developed to develop and integrate methods for screening, diagnosing, and prognosing atherosclerosis. The present work aimed to uncover critical genes and pathways to enhance the understanding of the mechanism of atherosclerosis. GSE23746 was analyzed to find differentially expressed genes (DEGs) using 19 control samples and 76 atherosclerotic samples. A total of 76 DEGs were identified. Analysed DEGs using Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) to generate enrichment datasets. A Protein- Protein Interaction (PPI) network of DEGs was created utilizing the Search Tool for the Retrieval of Interacting Genes (STRING). Ten hub genes, namely EGR1, PTGS2, TNF, NFKBIA, CXCL8, TNFAIP3, CCL3, IL1B, PTPRC, and CD83, were found to be significantly linked to atherosclerosis. Furthermore, the metabolic pathway analysis through KEGG and STRING provides potential targets for therapeutic interventions through HUB genes to diagnose the illness at an early stage, which aids in the reduction of cardiovascular risk. From risk factor profiling to the discovery of novel biomarkers, several components such as phospholipids, ANGPTL3, LCAT, and the proteinencoded OCT-1 gene, play a vital role in crucial processes. These compounds are potential therapeutic targets for early diagnosis of atherosclerotic lesions and future novel biomarkers.

EMSPLA for accurate feature molecular extraction from protein-ligand interactions

Protein-ligand interactions are fundamental in various biological and medical fields, influencing drug discovery and therapeutic development. In recent years, deep learning (DL) has revolutionized the study of these interactions, but significant challenges remain in accurately representing molecular structures for DL models. Traditional featurization techniques often depend on handcrafted features, requiring expert knowledge and potentially missing crucial molecular aspects. This work addresses these challenges by developing and evaluating a novel protein-ligand feature extraction system using an enhanced molecular similarity protein-ligand aligner (EMSPLA). The primary objective is to leverage EMSPLA for similarity matching in protein-ligand interactions, improving predictive model accuracy. The methodology combines convolutional neural networks (CNN) for local feature extraction with an attention module to capture long-distance dependencies, enhancing binding site predictions. Using the PDBbind v.2020 dataset, the EMSPLA model demonstrated superior performance with a root mean square error (RMSE) of 0.67, surpassing current state-of-the-art models. These findings highlight the system’s potential for efficient deployment and scalability, positioning it as a powerful tool in computational biology and drug discovery, ultimately advancing our understanding of protein-ligand interactions.

Critical considerations and computational tools in plant genome editing.

Recent advances in genome editing tools and CRISPR-Cas technologies have enabled plant genome engineering reach new heights. The current regulatory exemptions for certain categories of genome edited products, such as those derived from SDN-1 and SDN-2, which are free of any transgene, have significantly accelerated genome editing research in a number of agricultural crop plants in different countries. Although CRISPR-Cas technology is becoming increasingly popular, it is still important to carefully consider a number of factors before planning and carrying conducting CRISPR-Cas studies. To attempt genome editing in a plant, a high-quality genome sequence and a repeatable tissue culture protocol for in vitro regeneration are essential. One of the most important steps in plant genome editing is the designing of a CRISPR construct, which involves selecting the appropriate Cas protein, sgRNA sequence, and appropriate regulatory sequence to trigger expression. Computational tools and algorithms play a crucial role in construct design and gRNA selection to minimize off-target effects and also to optimize their delivery techniques. Researchers may need to select appropriate software tools capable of analyzing post-editing detection of mutation events and other DNA sequence abnormalities to identify off-target effects. To fully fulfill the potential of plant genome editing, continued advances in computational biology are essential to meet the challenges it faces today.

Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning.

Elucidating how protein sequence determines the properties of disordered proteins and their phase-separated condensates is a great challenge in computational chemistry, biology, and biophysics. Quantitative molecular dynamics simulations and derived free energy values can in principle capture how a sequence encodes the chemical and biological properties of a protein. These calculations are, however, computationally demanding, even after reducing the representation by coarse-graining; exploring the large spaces of potentially relevant sequences remains a formidable task. We employ an "active learning" scheme introduced by Yang et al. (bioRxiv, 2022, https://doi.org/10.1101/2022.08.05.502972) to reduce the number of labelled examples needed from simulations, where a neural network-based model suggests the most useful examples for the next training cycle. Applying this Bayesian optimisation framework, we determine properties of protein sequences with coarse-grained molecular dynamics, which enables the network to establish sequence-property relationships for disordered proteins and their self-interactions and their interactions in phase-separated condensates. We show how iterative training with second virial coefficients derived from the simulations of disordered protein sequences leads to a rapid improvement in predicting peptide self-interactions. We employ this Bayesian approach to efficiently search for new sequences that bind to condensates of the disordered C-terminal domain (CTD) of RNA Polymerase II, by simulating molecular recognition of peptides to phase-separated condensates in coarse-grained molecular dynamics. By searching for protein sequences which prefer to self-interact rather than interact with another protein sequence we are able to shape the morphology of protein condensates and design multiphasic protein condensates.

Exploring novel inhibitors for Babesia bigemina lactate dehydrogenase: a computational structural biology perspective.

Babesia bigemina is an apicomplexan parasite responsible for causing "Texas fever" in bovines. Current treatments for bovine babesiosis are hindered by several limitations, including toxicity, insufficient efficacy in eliminating the parasite, and the potential for resistance development. A promising approach to overcome these challenges is the identification of compounds that specifically target essential metabolic pathways unique to the parasite. One such target is lactate dehydrogenase (LDH), a critical enzyme involved in the regulation of anaerobic glycolysis. Notably, Babesia bigemina LDH (BbigLDH) exhibits a five-amino acid insertion in the active site, a feature that differentiates it from the host's LDH. This structural divergence makes apicomplexan LDH an attractive and potentially selective drug target for therapeutic intervention. In this study, a structure-based drug discovery approach was implemented to find novel inhibitor candidates. Potential candidates were identified using a virtual screening workflow. The compounds with favorable docking scores were filtered using the QM-polarized ligand docking and induced fit docking methods. As a result, 20 novel compounds were identified that bind to the active site of BbigLDH but show low affinity to the host LDHs. Molecular dynamics simulations of the complexes (8.8µs in total) were performed, and binding free energies were calculated. As a result, protein structures containing compounds C9, C16 and C18 maintained their stability throughout 1µs simulations with low binding free energies and conserved interactions with known catalytic residues. Therefore, these three compounds deserve further investigation to better understand their mode of action and therapeutic potential for babesiosis. The results of this study elucidate the structural features of the BbigLDH enzyme and provide novel LDH binders that may pave the way for further research into the development of parasite-specific LDH inhibitors.

Analysis of Teacher Readiness to Implement Bioinformatics to Biology Learning in Senior High School

This study aimed to analyze teachers' readiness in applying bioinformatics to biology learning in senior high schools. Bioinformatics is a combination of computer science, statistics, and biology to analyze and interpret complex biological data. The application of bioinformatics into learning can foster students' realization of the real-life commitment of bioinformatics, enhance their understanding, and expand their curiosity about bioinformatics. The application of bioinformatics into biology learning needs to be supported by teacher readiness in terms of understanding, skills and factors that influence teacher readiness in applying bioinformatics. This research used a case study method with a qualitative descriptive approach to analyze the readiness of biology teachers from several senior high schools in Banten province in implementing bioinformatics into biology learning. The results of this study show that most teachers understand and realize the importance of bioinformatics in learning, on the other hand some teachers also feel not fully confident and able to apply bioinformatics into learning effectively. Although most teachers have adequate access to technology, teachers are not familiar with bioinformatics concepts and software such as BLAST and NCBI. Therefore, to fulfill the aspect of teachers' understanding and skills in applying bioinformatics to learning, focused training is needed that discusses the basic concepts of bioinformatics, the use of bioinformatics software, and its application in learning to improve teacher competence and create interactive and relevant biology learning.

Integrating taxonomy and drug discovery: Liliopsida flora of Rajbari, Bangladesh targeting Amorphophallus paeoniifolius for colorectal cancer therapy

The present study explores the angiosperm flora belonging to the class Liliopsida in Rajbari district, seamlessly integrating taxonomy with phytocompound-based drug discovery through advanced computational biology approaches. The study covered all five upazilas (sub-districts) of the district. A total of 201 taxa across 118 genera and 24 families of Liliopsida were identified. The flora is predominantly composed of herbs (79.06%), followed by climbers (7.96%), trees (7.46%), shrubs (2.98%), and a minimal occurrence of epiphytes (1.99%). Poaceae emerged as the largest family, comprising 58 taxa across 36 genera, followed by Araceae (26 taxa) and Cyperaceae (17 taxa). Notably, the study identified 25 medicinal plant species under Liliopsida. Some rare species within Liliopsida, such as Coix aquatica, Wolffia arrhiza, Typha domingensis, and Schumannianthus benthamianus were also recorded in the study area. Among the medicinal plants identified, Amorphophallus paeoniifolius (Dennst.) Nicolson was selected for further investigation into colorectal cancer drug discovery. The computational therapeutics design endeavor unveiled two lead compounds: Riboflavin (-7.9 kcal/mol) and Lupeol (-6.1 kcal/mol), both of which demonstrated promising favorable drug-likeness properties. Molecular dynamics simulation spanning 100 ns revealed structural stability of the identified leads. PCA and Gibbs free energy landscape study further corroborated the drug-candidacy of the leads. DFT-based molecular reactivity study unveiled Lupeol as the most kinetically stable compound (6.915 eV). The findings highlight the significance of multi-disciplinary approach integrating classical taxonomy with bioinformatics and pave the way for future colorectal cancer therapeutics. Bangladesh J. Plant Taxon. 31(2): 239-264, 2024 (December)

DEVELOPMENT OF HYDROPONICS FOR THE FORMATION OF PRACTICAL SKILLS OF THE STEM EDUCATION SUBJECTS

The article analyzes the use of a hydroponic device as a STEM education tool. STEM education is the basis of practical interest and a high level of motivation of schoolchildren for research activities and obtaining all the necessary competencies. The use of STEM technology will allow you to transfer the educational process to another basis: from observation to hypothesis and experiment, from studying individual subjects to studying phenomena, from obtaining abstract knowledge to solving real life problems. The hydroponic system as a teaching technology can be used in the study of a number of school subjects: computer science, biology, chemistry, physics and natural sciences. First of all, in order to build the design of a hydroponic installation, creative data is needed on the selection of necessary products, the ability to work with tools for connecting plastic pipes, connecting to a pump, etc. In the manufacture of the structure, it is necessary to calculate the length, height of the structure, the number of holes for pots, when connected to the pump, etc. In addition, it is necessary to calculate the amount of nutrients necessary for the growth of plants, which are controlled by changes in the acid-base environment and total salinity. The hydroponic plant can monitor the growth and development of vegetable and green crops and monitor the transport of nutrients. In all courses of such subjects as "Natural Science", "Biology", "Chemistry" and "Physics", students will have the opportunity to gain practical skills when working with hydroponic devices.

CRISPR-Cas9 as a gene editing tool using cardiac glycoside reductase operon for digoxin metabolism

Gene editing technology has gained popularity over the past two decades with the scientific advancements being made in genetics and computational biology. CRISPR-Cas9 technique allows us to target genetic material and perform accurate manipulation of targeted gene function. Cgr operon, commonly found in the gut microbiome bacteria of E. lenta has several attributes which can be modified using the CRISPR-Cas9 methodology. Here, we briefly describe how the Cas9 nuclease can be used to prevent Digoxin from being suppressed by the cgr gene, directly correlating to improved cardiac function. The editing tool is described to assist in mutating the genetic material of cgr gene discussing the inhibition of Na+/K+ ATPase in cardiac myocytes and CG-content. The principle of operation behind CRISPR-Cas9 is described using non-homologous end joining or homology-directed repair cellular mechanisms. Target site selection, designing sgRNAs and introducing various mutations are discussed with limitations of current technology and possible applications in the field of point of care diagnostics and biosensing developing therapeutic interventions using cgr operon.

Domain adaptation in small-scale and heterogeneous biological datasets.

Machine-learning models are key to modern biology, yet models trained on one dataset are often not generalizable to other datasets from different cohorts or laboratories due to both technical and biological differences. Domain adaptation, a type of transfer learning, alleviates this problem by aligning different datasets so that models can be applied across them. However, most state-of-the-art domain adaptation methods were designed for large-scale data such as images, whereas biological datasets are smaller and have more features, and these are also complex and heterogeneous. This Review discusses domain adaptation methods in the context of such biological data to inform biologists and guide future domain adaptation research. We describe the benefits and challenges of domain adaptation in biological research and critically explore some of its objectives, strengths, and weaknesses. We argue for the incorporation of domain adaptation techniques to the computational biologist's toolkit, with further development of customized approaches.

Unveiling the power of high-dimensional cytometry data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

Investigating the anti-obesity potential of Nelumbo nucifera leaf bioactive compounds through machine learning and computational biology methods.

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseases, type 2 diabetes, cancer, and neuropsychiatric disorders. Conventional pharmacological treatments often have significant side effects, highlighting the need for safer alternatives. Traditional Chinese Medicine (TCM) offers potential solutions, with plant extracts like those from Nelumbo nucifera leaves showing promise due to their historical use and minimal side effects. This study employs a comprehensive computational biology approach to explore the anti-obesity effects of Nelumbo nucifera Leaf Bioactive Compounds. Sixteen active compounds from Nelumbo nucifera leaves were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Clustering analysis identified three representative molecules, and network pharmacology pinpointed PPARG as a common target gene. Molecular docking and machine learning models were used for inhibitors screening, and molecular dynamics simulations were futher used to investigate the inhibitory effects and mechanisms of these molecules on PPARG. Subsequent cellular assays confirmed the ability of Sitogluside to reduce lipid accumulation and triglyceride levels in 3T3-L1 cells, underscoring its potential as an effective and safer obesity treatment. Our findings provide a molecular basis for the anti-obesity properties of Nelumbo nucifera Leaf Bioactive Compounds and pave the way for developing new, effective, and safer obesity treatments.

ProtChat: An AI Multi-Agent for Automated Protein Analysis Leveraging GPT-4 and Protein Language Model.

Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use. Researchers have integrated LLMs with PLLMs to develop automated protein analysis tools to address these challenges, simplifying analytical workflows. Existing technologies often require substantial human intervention for specific protein-related tasks, maintaining high barriers to implementing automated protein analysis systems. Here, we propose ProtChat, an AI multiagent system for protein analysis that integrates the inference capabilities of PLLMs with the task-planning abilities of LLMs. ProtChat integrates GPT-4 with multiple PLLMs, like ESM and MASSA, to automate tasks such as protein property prediction and protein-drug interactions without human intervention. This AI agent enables users to input instructions directly, significantly improving efficiency and usability, making it suitable for researchers without a computational background. Experiments demonstrate that ProtChat can automate complex protein tasks accurately, avoiding manual intervention and delivering results rapidly. This advancement opens new research avenues in computational biology and drug discovery. Future applications may extend ProtChat's capabilities to broader biological data analysis. Our code and data are publicly available at github.com/SIAT-code/ProtChat.

Efficient quantum algorithm for lattice protein folding

Abstract Predicting a protein's three-dimensional structure from its primary amino acid sequence constitutes the protein folding problem, a pivotal challenge within computational biology. This task has been identified as a fitting domain for applying quantum annealing, an algorithmic technique posited to be faster than its classical counterparts. Nevertheless, the utility of quantum annealing is intrinsically contingent upon the spectral gap associated with the Hamiltonian of lattice proteins. This critical dependence introduces a limitation to the efficacy of these techniques, particularly in the context of simulating the intricate folding processes of proteins. In this paper, we address lattice protein folding as a polynomial unconstrained binary optimization problem, devising a hybrid quantum-classical algorithm to determine the minimum energy conformation effectively. Our method is distinguished by its logarithmic scaling with the spectral gap, conferring a significant edge over the conventional quantum annealing algorithms. The present findings indicate that the folding of lattice proteins can be achieved with a resource consumption that is polynomial in the lattice protein length, provided an ansatz state that encodes the target conformation is utilized. We also provide a simple and scalable method for preparing such states and further explore the adaptation of our method for extension to off-lattice protein models. This work paves a new avenue for surmounting complex computational biology problems via the utilization of quantum computers.

Subspace learning using low-rank latent representation learning and perturbation theorem: Unsupervised gene selection.

In recent years, gene expression data analysis has gained growing significance in the fields of machine learning and computational biology. Typically, microarray gene datasets exhibit a scenario where the number of features exceeds the number of samples, resulting in an ill-posed and underdetermined equation system. The presence of redundant features in high-dimensional data leads to suboptimal performance and increased computational time for learning algorithms. Although feature extraction and feature selection are two approaches that can be employed to deal with this challenge, feature selection has greater interpretability ability which causes it to receive more attention. In this study, we propose an unsupervised feature selection which is based on pseudo label latent representation learning and perturbation theory. In the first step, pseudo labels are extracted and constructed using latent representation learning. In the second step, the least square problem is solved for original data matrix and perturbed data matrix. Features are clustered based on the similarity between the original data matrix and the perturbed data matrix using k-means. In the last step, features in each subcluster are ranked based on information gain criterion. To showcase the efficacy of the proposed approach, numerical experiments were carried out on six benchmark microarray datasets and two RNA-Sequencing benchmark datasets. The outcomes indicate that the proposed technique surpasses eight state-of-the-art unsupervised feature selection methods in both clustering accuracy and normalized mutual information.

DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores.

Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations in amino acid usage due to the need to maintain complementary interaction interfaces. Thus, the problem of finding interacting pairs can be formulated as searching for a pairing of sequences that maximizes a sequence similarity or a coevolution score. Several methods have been developed to address this problem, applying different approximate optimization methods to different scores. We introduce Differentiable Pairing using Soft Scores (DiffPaSS), a differentiable framework for flexible, fast, and hyperparameter-free optimization for pairing interacting biological sequences, which can be applied to a wide variety of scores. We apply it to a benchmark prokaryotic dataset, using mutual information and neighbor graph alignment scores. DiffPaSS outperforms existing algorithms for optimizing the same scores. We demonstrate the usefulness of our paired alignments for the prediction of protein complex structure. DiffPaSS does not require sequences to be aligned, and we also apply it to nonaligned sequences from T-cell receptors. A PyTorch implementation and installable Python package are available at https://github.com/Bitbol-Lab/DiffPaSS.

Expression of Genes Involved in Banana (Musa spp.) Response to Black Sigatoka.

This work aimed to evaluate the relative gene expression of the candidate genes psI, psII, isr, utp, and prk involved in the defense response to Black Sigatoka in banana cultivars Calcutta-4, Krasan Saichon, Grand Nain, and Akondro Mainty, by a quantitative real-time PCR. Biotic stress was imposed on 6-month-old plants during five sampling intervals under greenhouse conditions. The psII and isr genes were upregulated for the Calcutta-4- and Krasan Saichon-resistant cultivars, and were validated in this study. For Grande Naine, a susceptible cultivar, there was an early downregulation of the psI, psII, and isr genes and a late upregulation of the psII gene. There was no significant expression of any of the genes for the susceptible cultivar Akondro Mainty. Computational biology tools such as ORFFinder and PlantCARE revealed that the utp gene has more introns and exons and that, in general, cis-elements involved in the response to biotic stress, such as as-1, w-box, and STRE, were detected in the promoter region of the genes studied. Data from this work also support the phenotyping studies of banana cultivars affected by Black Sigatoka in the field. Once validated in promising new hybrids, these genes may be used in marker-assisted selection (MAS) and/or gene-editing techniques.