Applications In Computational Biology Research Articles

Computational drug development for membrane protein targets.

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

A New Mixed Fractional Derivative with Applications in Computational Biology

This study develops a new definition of a fractional derivative that mixes the definitions of fractional derivatives with singular and non-singular kernels. This developed definition encompasses many types of fractional derivatives, such as the Riemann–Liouville and Caputo fractional derivatives for singular kernel types, as well as the Caputo–Fabrizio, the Atangana–Baleanu, and the generalized Hattaf fractional derivatives for non-singular kernel types. The associate fractional integral of the new mixed fractional derivative is rigorously introduced. Furthermore, a novel numerical scheme is developed to approximate the solutions of a class of fractional differential equations (FDEs) involving the mixed fractional derivative. Finally, an application in computational biology is presented.

Critical assessment of methods of protein structure prediction (CASP)-Round XV.

Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.

Diffusion models in bioinformatics and computational biology.

Denoising diffusion models embody a type of generative artificial intelligence that can be applied in computer vision, natural language processing and bioinformatics. In this Review, we introduce the key concepts and theoretical foundations of three diffusion modelling frameworks (denoising diffusion probabilistic models, noise-conditioned scoring networks and score stochastic differential equations). We then explore their applications in bioinformatics and computational biology, including protein design and generation, drug and small-molecule design, protein-ligand interaction modelling, cryo-electron microscopy image data analysis and single-cell data analysis. Finally, we highlight open-source diffusion model tools and consider the future applications of diffusion models in bioinformatics.

Recognizing k -Leaf Powers in Polynomial Time, for Constant k

A graph G is a k -leaf power if there exists a tree T whose leaf set is V ( G ), and such that uv ∈ E ( G ) if and only if the distance between u and v in T is at most k (and u ≠ v ). The graph classes of k -leaf powers have several applications in computational biology, but recognizing them has remained a challenging algorithmic problem for the past two decades. The best known result is that 6-leaf powers can be recognized in polynomial time. In this article, we present an algorithm that decides whether a graph G is a k -leaf power in time O ( n f(k) for some function f that depends only on k (but has the growth rate of a power tower function). Our techniques are based on the fact that either a k -leaf power has a corresponding tree of low maximum degree, in which case finding it is easy, or every corresponding tree has large maximum degree. In the latter case, large-degree vertices in the tree imply that G has redundant substructures which can be pruned from the graph. In addition to solving a long-standing open problem, it is our hope that the structural results presented in this work can lead to further results on k -leaf powers and related classes.

Understanding the cross-talk of major abiotic-stress-responsive genes in rice: A computational biology approach

This study aims to identify key abiotic-stress-responsive genes in Oryza sativa and investigate their expression profiles, protein–protein interactions, and co-expression patterns to better understand the molecular mechanisms underlying stress response in rice. The ultimate goal is to employ these findings in the development of gene-based molecular markers to breed rice cultivars with enhanced resistance to challenging environmental conditions. A total of 14 abiotic-stress-responsive genes in O. sativa were identified through literature mining. These genes were analyzed for their chromosomal distribution, transcript length, CDS length, and translated amino acid length. Pairwise similarity matrix, phylogenetic analysis, and protein–protein interaction networks were employed to understand the evolutionary relationships and functional interactions among these genes. Expression profiles of six key genes (AOX1a, AOX1b, ALDH2a, ALDH2b, OsNAC6, OsDHN1) were investigated using the electronic fluorescent pictogram (eFP) program. KEGG pathway analysis and co-expression studies were also conducted to further understand the roles of these genes in abiotic stress response pathways. The 14 abiotic-stress-responsive genes were distributed across different chromosomes of O. sativa, suggesting the presence of interconnected cascades regulating abiotic-stress-response. Phylogenetic analysis revealed four clusters of genes, indicating their potential shared ancestry. Protein-protein interaction analysis identified three prominent clusters of interactions, with the strongest interactions occurring among Aldh2a, Aldh2b, OS07T0188800-01, and OsJ_04113 in one cluster, and between AOX1a and AOX1b in another cluster. Expression profiles of the six key genes varied across different stages of the rice life cycle. KEGG pathway analysis showed that ALDH2a and ALDH2b participated in almost all pathways except propanoate metabolism.The study demonstrated that the six key genes play significant regulatory roles in abiotic stress responses in O. sativa. The expression profiles, phylogenetic analysis, protein–protein interactions, and gene co-expression studies revealed interconnected cascades and cross-talk in response mechanisms of these genes. These findings can be employed to develop gene-based molecular markers for breeding rice cultivars with enhanced resistance to abiotic stresses, and contribute to the successful application of computational biology in plant breeding towards sustainable development goal.

Longest common substring in Longest Common Subsequence’s solution service: A novel hyper-heuristic

The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a new criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer (S2D) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present our proposed hyper-heuristic that exploits the S2D and one of the internal properties of the given strings to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show that our proposed dichotomizer (i.e., S2D) can classify datasets with 98% of accuracy. Also, our proposed hyper-heuristic obtains competitive performance in comparison with the best methods and outperforms best hyper-heuristics for uncorrelated datasets in terms of both quality of solutions and run time factors. All supplementary files, including the source codes and datasets, are publicly available on GitHub.11https://github.com/BioinformaticsIASBS/LCS-DSclassification.

RobustTree: An adaptive, robust PCA algorithm for embedded tree structure recovery from single-cell sequencing data.

Robust Principal Component Analysis (RPCA) offers a powerful tool for recovering a low-rank matrix from highly corrupted data, with growing applications in computational biology. Biological processes commonly form intrinsic hierarchical structures, such as tree structures of cell development trajectories and tumor evolutionary history. The rapid development of single-cell sequencing (SCS) technology calls for the recovery of embedded tree structures from noisy and heterogeneous SCS data. In this study, we propose RobustTree, a unified framework to reconstruct the inherent topological structure underlying high-dimensional data with noise. By extending RPCA to handle tree structure optimization, RobustTree leverages data denoising, clustering, and tree structure reconstruction. It solves the tree optimization problem with an adaptive parameter selection scheme that we proposed. In addition to recovering real datasets, RobustTree can reconstruct continuous topological structure and discrete-state topological structure of underlying SCS data. We apply RobustTree on multiple synthetic and real datasets and demonstrate its high accuracy and robustness when analyzing high-noise SCS data with embedded complex structures. The code is available at https://github.com/ucasdp/RobustTree.

Temporal networks in biology and medicine: a survey on models, algorithms, and tools.

The use of static graphs for modelling and analysis of biological and biomedical data plays a key role in biomedical research. However, many real-world scenarios present dynamic behaviours resulting in both node and edges modification as well as feature evolution. Consequently, ad-hoc models for capturing these evolutions along the time have been introduced, also referred to as dynamic, temporal, time-varying graphs. Here, we focus on temporal graphs, i.e., graphs whose evolution is represented by a sequence of time-ordered snapshots. Each snapshot represents a graph active in a particular timestamp. We survey temporal graph models and related algorithms, presenting fundamentals aspects and the recent advances. We formally define temporal graphs, focusing on the problem setting and we present their main applications in biology and medicine. We also present temporal graph embedding and the application to recent problems such as epidemic modelling. Finally, we further state some promising research directions in the area. Main results of this study include a systematic review of fundamental temporal network problems and their algorithmic solutions considered in the literature, in particular those having application in computational biology and medicine. We also include the main software developed in this context.

Bayesian bi-clustering methods with applications in computational biology

Bi-clustering is a useful approach in analyzing large biological data sets when the observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian approach in tackling bi-clustering problems in moderate to high dimensions and propose three Bayesian bi-clustering models on categorical data which increase in complexities in their modeling of the distributions of features across bi-clusters. Our proposed methods apply to a wide range of scenarios: from situations where data are cluster-distinguishable only among a small subset of features but masked by a large amount of noise to situations where different groups of data are identified by different sets of features or data exhibit hierarchical structures. Through simulation studies we show that our methods outperform existing (bi-)clustering methods in both identifying clusters and recovering feature distributional patterns across bi-clusters. We further apply the developed approaches to a human genetic dataset, a human single-cell genomic dataset, and a collection of 1774 mouse genomic datasets with a focus on 58 genes from two pathways.

Application of Computational Biology and Artificial Intelligence in Drug Design.

Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.

Estimation for dynamical systems using a population-based Kalman filter – Applications in computational biology

Estimating dynamical systems — in particular identifying theirs parameters — involved in computational biology – for instance in pharmacology, in virology or in epidemiology – is fundamental to put in accordance the model trajectory with the measurements at hand. Unfortunately, when the sampling of data is very scarce or the data are corrupted by noise, parameters mean and variance priors must be chosen very adequately to balance our measurement distrust. Otherwise the identification procedure fails. A circumvention consists in using repeated measurements collected in configurations that share common priors – for instance with multiple population subjects in a clinical study or clusters in an epidemiology investigation. This common information is of benefit and is typically modeled in statistics by nonlinear mixed-effect models. In this paper, we introduce a data assimilation methodology compatible with such mixed-effect strategy without being strangled by the potential resulting curse of dimensionality. We define population-based estimators through maximum likelihood estimation. Then, from filtering theory, we set-up an equivalent robust large population sequential estimator that integrates the data as they are collected. Finally, we limit the computational complexity by defining a reduced-order version of this population Kalman filter clustering subpopulations of common observation background. The resulting algorithm performances are evaluated on classical pharmacokinetics benchmark. The versatility of the proposed method is finally fully challenged in a real-data epidemiology study of COVID spread in regions and departments of France.

Plant Melatonin: Regulatory and Protective Role

Melatonin is an antioxidant that is widely distributed in plants and animals. It is a conservative molecule. In early studies, scientists often used isolation and identification techniques to observe whether the endogenous melatonin cycle in plants was related to the external photoperiod, plant growth, and development cycles, including seed germination, plant rooting, and floral induction. With the development of isolation and identification technology, there is ample evidence that plants possess a variety of melatonin-synthesis pathways. The comprehensive application of molecular biology, genomics, and computational biology has also led to a comprehensive understanding of the physiological functions of plant melatonin. In this paper, we not only highlight the candidate genes from Arabidopsis thaliana and Oryza sativa that might be contributing to increasing plant endogenous melatonin but also elucidate and characterize the role of melatonin in plant growth and development in response to biotic and abiotic stresses.

Investigation of the Defective Growth Pattern and Multidrug Resistance in a Clinical Isolate of Candida glabrata Using Whole-Genome Sequencing and Computational Biology Applications.

ABSTRACTCandida glabrata is increasingly isolated from blood cultures, and multidrug-resistant isolates have important implications for therapy. This study describes a cholesterol-dependent clinical C. glabrata isolate (ML72254) that did not grow without blood (containing cholesterol) on routine mycological media and that showed azole and amphotericin B (AmB) resistance. Matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) and whole-genome sequencing (WGS) were used for species identification. A modified Etest method (Mueller-Hinton agar supplemented with 5% sheep blood) was used for antifungal susceptibility testing. WGS data were processed via the Galaxy platform, and the genomic variations of ML72254 were retrieved. A computational biology workflow utilizing web-based applications (PROVEAN, AlphaFold Colab, and Missense3D) was constructed to predict possible deleterious effects of these missense variations on protein functions. The predictive ability of this workflow was tested with previously reported missense variations in ergosterol synthesis genes of C. glabrata. ML72254 was identified as C. glabrata sensu stricto with MALDI-TOF, and WGS confirmed this identification. The MICs of fluconazole, voriconazole, and amphotericin B were >256, >32, and >32 μg/mL, respectively. A novel frameshift mutation in the ERG1 gene (Pro314fs) and many missense variations were detected in the ergosterol synthesis genes. None of the missense variations in the ML72254 ergosterol synthesis genes were deleterious, and the Pro314fs mutation was identified as the causative molecular change for a cholesterol-dependent and multidrug-resistant phenotype. This study verified that web-based computational biology solutions can be powerful tools for examining the possible impacts of missense mutations in C. glabrata.IMPORTANCE In this study, a cholesterol-dependent C. glabrata clinical isolate that confers azole and AmB resistance was investigated using artificial intelligence (AI) technologies and cloud computing applications. This is the first of the known cholesterol-dependent C. glabrata isolate to be found in Turkey. Cholesterol-dependent C. glabrata isolates are rarely isolated in clinical samples; they can easily be overlooked during routine laboratory procedures. Microbiologists therefore need to be alert when discrepancies occur between microscopic examination and growth on routine media. In addition, because these isolates confer antifungal resistance, patient management requires extra care.

Fast and interpretable genomic data analysis using multiple approximate kernel learning.

MotivationDataset sizes in computational biology have been increased drastically with the help of improved data collection tools and increasing size of patient cohorts. Previous kernel-based machine learning algorithms proposed for increased interpretability started to fail with large sample sizes, owing to their lack of scalability. To overcome this problem, we proposed a fast and efficient multiple kernel learning (MKL) algorithm to be particularly used with large-scale data that integrates kernel approximation and group Lasso formulations into a conjoint model. Our method extracts significant and meaningful information from the genomic data while conjointly learning a model for out-of-sample prediction. It is scalable with increasing sample size by approximating instead of calculating distinct kernel matrices.ResultsTo test our computational framework, namely, Multiple Approximate Kernel Learning (MAKL), we demonstrated our experiments on three cancer datasets and showed that MAKL is capable to outperform the baseline algorithm while using only a small fraction of the input features. We also reported selection frequencies of approximated kernel matrices associated with feature subsets (i.e. gene sets/pathways), which helps to see their relevance for the given classification task. Our fast and interpretable MKL algorithm producing sparse solutions is promising for computational biology applications considering its scalability and highly correlated structure of genomic datasets, and it can be used to discover new biomarkers and new therapeutic guidelines.Availability and implementationMAKL is available at https://github.com/begumbektas/makl together with the scripts that replicate the reported experiments. MAKL is also available as an R package at https://cran.r-project.org/web/packages/MAKL.Supplementary information Supplementary data are available at Bioinformatics online.

Special issue on computational biology and bioinformatic applications to the COVID‐19 pandemic

Special issue on computational biology and bioinformatic applications to the COVID‐19 pandemic

[Advances in the microbial production of the compatible solute ectoine: a review].

Ectoine is an amino acid derivative and an important natural product in halophilic microorganisms. It plays an important role in protecting cells and stabilizing biological macromolecules, and can be widely used in biomedical fields such as drug preparation adjuvants, organ transplantation and preservation, skin wound repair and cosmetics. Due to the medical value and commercial market demand of ectoine, this article summarized the recent advances in the microbial production of ectoine, including the mutation and breeding of hyper-producing strains, construction of genetically and metabolically engineered strains, optimization of fermentation processes, and extraction and purification processes. The application of multi-omics technologies and computational biology to develop an ectoine producing cell factory was prospected, with the aim to provide a reference for ectoine overproduction.

Heterogeneity in biomarkers, mitogenome and genetic disorders of the Arab population with special emphasis on large-scale whole-exome sequencing.

More than 25 million DNA variations have been discovered as novel including major alleles from the Arab population. Exome studies on the Saudi genome discovered > 3000 novel nucleotide variants associated with > 1200 rare genetic disorders. Reclassification of many pathogenic variants in the Human Gene Mutation Database and ClinVar Database as benign through the Arab database facilitates building a detailed and comprehensive map of the human morbid genome. Intellectual disability comes first with the combined and observed carrier frequency of 0.06779 among Saudi Arabians; retinal dystrophy is the next highest. Genome studies have discovered interesting novel candidate disease marker variations in many genes from consanguineous families. More than 7 pathogenic variants in the C12orf57 gene are prominently associated with the etiology of developmental delay/intellectual impairment in Arab ancestries. Advances in large-scale genome studies open a new outlook on Mendelian genes and disorders. In the past half-dozen years, candidate genes of intellectual disability, neurogenetic disorders, blood and bleeding disorders and rare genetic diseases have been well documented through genomic medicine studies in combination with advanced computational biology applications. The Arab mitogenome exposed hundreds of variations in the mtDNA genome and ancestral sharing with Africa, the Near East and East Asia and its association with obesity. These recent discoveries in disease markers and molecular genetics of the Arab population will have a positive impact towards supporting genetic counsellors on reaching consanguineous families to manage stress linked to genetics and precision medicine. This narrative review summarizes the advances in molecular medical genetics and recent discoveries on pathogenic variants. Despite the fact that these initiatives are targeting the genetics and genomics of disorders prevalent in Arab populations, a lack of complete cooperation across the projects needed to be revisited to uncover the Arab population's prominent disease markers. This shows that further study is needed in genomics to fully comprehend the molecular abnormalities and associated pathogenesis that cause inherited disorders in Arab ancestries.

Network-constrained regularization in computational biology and medicine

Introduction: Computational biology, diagnostic modalities, clinical patient results often involve working with high-dimensional data (p >> n). Penalized regression methods are often used on such data, as they can perform feature selection effectively. In particular, network penalized approaches also allow to model relationships between predictors, as often occurs when analyzing omics data. Unfortunately, the wide variety of such methods in literature leads to difficulty in choosing the most suitable one for a given dataset. Aim: This paper focuses on a variety of regression methods and potential applications in computational biology and medical research. Materials and Methods: The following basic regression methods are briefly discussed: Ridge, Lasso, Elastic Net, and Grace. Their application in solving problems in medical research as well as in everyday practice of the Modelling and Simulation research group at Medical University of Varna is demonstrated. Results and Conclusion: Regression methods were successfully used to define the characteristics of the available materials with 3D printing technologies. Based on these, novel physical models were manufactured and for x-ray imaging research use. Application of network-based regularization method was reviewed to be suitable for defining the relation between patient’s genotype and drug response.

Big Data and its Future in Computational Biology: A Literature Review

Big data technologies are used majorly for computational biology research nowadays due to the emerge of massive amounts of biological data that have been generated and collected at an unprecedented speed and scale and with different structures. As an example, the processing of billions of DNA sequence data per day represents the new generation of sequencing technologies. It is expected that the cost of acquiring and analyzing biomedical data will decrease with the assistance of technology upgrades in the computational biology field. The application of big data in health care is a fast-growing field, with many new discoveries and methodologies. In this study, an overview of recent developments and technologies in big data in the computational biology field. Moreover, in this study, we summarize and highlight the challenges, gaps and opportunities to improve and advance big data applications in computational biology. Also, this review presents a useful starting point for the application of Big Data in future Bioinformatics research. Finally, some of the Big Data sources in computational biology that help in applying these new emerging technologies are mentioned.