Computational molecular biology approaches to ligand-target interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Lupieri P
PROVIDER: S-EPMC2799985 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.
| S-EPMC8148342 | biostudies-literature
Project description:IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing.MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19.DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.
| S-EPMC10897000 | biostudies-literature
Project description:Motivation:Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. Results:We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. Availability and implementation:The data and code are provided at https://bitbucket.org/RSO24/ddr/. Contact:vladimir.bajic@kaust.edu.sa. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5998943 | biostudies-literature
Project description:RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA-small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA-ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a "meta-predictor" leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http://ligandrna.genesilico.pl.
| S-EPMC3860260 | biostudies-literature
Project description:Enhancers are non-coding regulatory elements that are distant from their target gene. Their characterization still remains elusive especially due to challenges in achieving a comprehensive pairing of enhancers and target genes. A number of computational biology solutions have been proposed to address this problem leveraging the increasing availability of functional genomics data and the improved mechanistic understanding of enhancer action. In this review we focus on computational methods for genome-wide definition of enhancer-target gene pairs. We outline the different classes of methods, as well as their main advantages and limitations. The types of information integrated by each method, along with details on their applicability are presented and discussed. We especially highlight the technical challenges that are still unresolved and hamper the effective achievement of a satisfactory and comprehensive solution. We expect this field will keep evolving in the coming years due to the ever-growing availability of data and increasing insights into enhancers crucial role in regulating genome functionality.
| S-EPMC6611831 | biostudies-literature
Project description:Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for "anonymous" compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound-protein interactions and thus may benefit drug development.
| S-EPMC4376866 | biostudies-literature
Project description:RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein-RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein-RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.
| S-EPMC5430296 | biostudies-literature
Project description:Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.
| S-EPMC10054308 | biostudies-literature
Project description:Cysteine (Cys) residues serve many functions, such as catalysis, stabilization of protein structure through disulfides, metal binding, and regulation of protein function. Cys residues are also subject to numerous post-translational modifications. In recent years, various computational tools aiming at classifying and predicting different functional categories of Cys have been developed, particularly for structural and catalytic Cys. On the other hand, given complexity of the subject, bioinformatics approaches have been less successful for the investigation of regulatory Cys sites. In this review, we introduce different functional categories of Cys residues. For each category, an overview of state-of-the-art bioinformatics methods and tools is provided, along with examples of successful applications and potential limitations associated with each approach. Finally, we discuss Cys-based redox switches, which modify the view of distinct functional categories of Cys in proteins.
| S-EPMC3110093 | biostudies-literature
Project description:The recognition process between a protein and a partner represents a significant theoretical challenge. In silico structure-based drug design carried out with nothing more than the three-dimensional structure of the protein has led to the introduction of many compounds into clinical trials and numerous drug approvals. Central to guiding the discovery process is to recognize active among non-active compounds. While large-scale computer simulations of compounds taken from a library (virtual screening) or designed de novo are highly desirable in the post-genomic area, many technical problems remain to be adequately addressed. This article presents an overview and discusses the limits of current computational methods for predicting the correct binding pose and accurate binding affinity. It also presents the performances of the most popular algorithms for exploring binary and multi-body protein interactions.
| S-EPMC3223636 | biostudies-literature