Comparative modeling of retinol-binding protein-3 and retinal S-antigen in Eales' disease and prediction of their binding sites using computational methods.
Ontology highlight
ABSTRACT:
SUBMITTER: Tiwari A
PROVIDER: S-EPMC3372607 | biostudies-other | 2010 Sep
REPOSITORIES: biostudies-other
Publications
Comparative modeling of retinol-binding protein-3 and retinal S-antigen in Eales' disease and prediction of their binding sites using computational methods.
Tiwari Anshul A Trivedi Ashish Chandra AC Srivastava Prachi P Pant Aditya Bhusan AB Saxena Sandeep S
Journal of ocular biology, diseases, and informatics 20100901 3
Retinal S-antigen and interphotoreceptor retinoid-binding protein-3 play a significant role in the etiopathogenesis of Eales' disease. Protein 3D structures are functionally very important and play a significant role in progression of the disease, hence these 3D structures are better target for further drug designing and relative studies. We developed 3D model structure of retinol-binding protein-3 and retinal S-antigen protein of human involved in Eales' disease. Functional site prediction is a ...[more]
Similar Datasets
Project description:The accurate identification of protein-ligand binding sites is of critical importance in understanding and modulating protein function. Accordingly, ligand binding site prediction has remained a research focus for over three decades with over 50 methods developed and a change of paradigm from geometry-based to machine learning. In this work, we collate 13 ligand binding site predictors, spanning 30 years, focusing on the latest machine learning-based methods such as VN-EGNN, IF-SitePred, GrASP, PUResNet, and DeepPocket and compare them to the established P2Rank, PRANK and fpocket and earlier methods like PocketFinder, Ligsite and Surfnet. We benchmark the methods against the human subset of our new curated reference dataset, LIGYSIS. LIGYSIS is a comprehensive protein-ligand complex dataset comprising 30,000 proteins with bound ligands which aggregates biologically relevant unique protein-ligand interfaces across biological units of multiple structures from the same protein. LIGYSIS is an improvement for testing methods over earlier datasets like sc-PDB, PDBbind, binding MOAD, COACH420 and HOLO4K which either include 1:1 protein-ligand complexes or consider asymmetric units. Re-scoring of fpocket predictions by PRANK and DeepPocket display the highest recall (60%) whilst IF-SitePred presents the lowest recall (39%). We demonstrate the detrimental effect that redundant prediction of binding sites has on performance as well as the beneficial impact of stronger pocket scoring schemes, with improvements up to 14% in recall (IF-SitePred) and 30% in precision (Surfnet). Finally, we propose top-N+2 recall as the universal benchmark metric for ligand binding site prediction and urge authors to share not only the source code of their methods, but also of their benchmark.Scientific contributionsThis study conducts the largest benchmark of ligand binding site prediction methods to date, comparing 13 original methods and 15 variants using 10 informative metrics. The LIGYSIS dataset is introduced, which aggregates biologically relevant protein-ligand interfaces across multiple structures of the same protein. The study highlights the detrimental effect of redundant binding site prediction and demonstrates significant improvement in recall and precision through stronger scoring schemes. Finally, top-N+2 recall is proposed as a universal benchmark metric for ligand binding site prediction, with a recommendation for open-source sharing of both methods and benchmarks.
| S-EPMC11552181 | biostudies-literature
Project description:Paraoxonase (PON) constitutes a family of calcium-dependent mammalian enzymes comprising of PON1, PON2, and PON3. PON family shares ~60% sequence homology. These enzymes exhibit multiple activities like paraoxonase, arylesterase, and lactonase in a substrate dependent manner. Decreased PON activity has been reported in diseases like cardiovascular disease, atherosclerosis, and diabetes. Even though, PON2 is the oldest member of the family, PON1 is the only member studied in silico. In this study, the structure of PON2 was modeled using MODELLER 9v7 and its interactions with relevant ligands and it's physiological substrate homocysteine thiolactone was performed using AutoDock 4.0. The results reveal that PON1 and PON2 share common ligand binding patterns for arylesterase and lactonase activity, whereas in case of paraoxon binding, the residues involved in the interactions were different. Interestingly, the substrate HCTL was found to have the lowest free energy of binding (ΔG) and highest affinity for PON2 than PON1.
| S-EPMC3148138 | biostudies-literature
Project description:Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein-ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein-ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.
| S-EPMC7049599 | biostudies-literature
Project description:Protein binding to small molecules is fundamental to many biological processes, yet it remains challenging to predictively design this functionality de novo. Current state-of-the-art computational design methods typically rely on existing small molecule binding sites or protein scaffolds with existing shape complementarity for a target ligand. Here we introduce new methods that utilize pools of discrete contacts between protein side chains and defined small molecule ligand substructures (ligand fragments) observed in the Protein Data Bank. We use the Rosetta Molecular Modeling Suite to recombine protein side chains in these contact pools to generate hundreds of thousands of energetically favorable binding sites for a target ligand. These composite binding sites are built into existing scaffold proteins matching the intended binding site geometry with high accuracy. In addition, we apply pools of side chain rotamers interacting with the target ligand to augment Rosetta's conventional design machinery and improve key metrics known to be predictive of design success. We demonstrate that our method reliably builds diverse binding sites into different scaffold proteins for a variety of target molecules. Our generalizable de novo ligand binding site design method provides a foundation for versatile design of protein to interface previously unattainable molecules for applications in medical diagnostics and synthetic biology.
| S-EPMC7575090 | biostudies-literature
Project description:N6-methyladenosine (m6A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m6A mapping have accelerated the accumulation of m6A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m6A site prediction. However, it is still a major challenge to precisely predict m6A sites using in silico approaches. To advance the computational support for m6A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m6A modification. We revisited 52 computational approaches published since 2015 for m6A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m6A identification and facilitate more rigorous comparisons of new methods in the future.
| S-EPMC11504118 | biostudies-literature
Project description:BackgroundCyclic AMP receptor protein (CRP), also known as catabolite gene activator protein (CAP), is an important transcriptional regulator widely distributed in many bacteria. The biological processes under the regulation of CRP are highly diverse among different groups of bacterial species. Elucidation of CRP regulons in cyanobacteria will further our understanding of the physiology and ecology of this important group of microorganisms. Previously, CRP has been experimentally studied in only two cyanobacterial strains: Synechocystis sp. PCC 6803 and Anabaena sp. PCC 7120; therefore, a systematic genome-scale study of the potential CRP target genes and binding sites in cyanobacterial genomes is urgently needed.ResultsWe have predicted and analyzed the CRP binding sites and regulons in 12 sequenced cyanobacterial genomes using a highly effective cis-regulatory binding site scanning algorithm. Our results show that cyanobacterial CRP binding sites are very similar to those in E. coli; however, the regulons are very different from that of E. coli. Furthermore, CRP regulons in different cyanobacterial species/ecotypes are also highly diversified, ranging from photosynthesis, carbon fixation and nitrogen assimilation, to chemotaxis and signal transduction. In addition, our prediction indicates that crp genes in modern cyanobacteria are likely inherited from a common ancestral gene in their last common ancestor, and have adapted various cellular functions in different environments, while some cyanobacteria lost their crp genes as well as CRP binding sites during the course of evolution.ConclusionThe CRP regulons in cyanobacteria are highly diversified, probably as a result of divergent evolution to adapt to various ecological niches. Cyanobacterial CRPs may function as lineage-specific regulators participating in various cellular processes, and are important in some lineages. However, they are dispensable in some other lineages. The loss of CRPs in these species leads to the rapid loss of their binding sites in the genomes.
| S-EPMC2633013 | biostudies-literature
Project description:MotivationCell-type-specific gene expression is maintained in large part by transcription factors (TFs) selectively binding to distinct sets of sites in different cell types. Recent research works have provided evidence that such cell-type-specific binding is determined by TF's intrinsic sequence preferences, cooperative interactions with co-factors, cell-type-specific chromatin landscapes and 3D chromatin interactions. However, computational prediction and characterization of cell-type-specific and shared binding sites is rarely studied.ResultsIn this article, we propose two computational approaches for predicting and characterizing cell-type-specific and shared binding sites by integrating multiple types of features, in which one is based on XGBoost and another is based on convolutional neural network (CNN). To validate the performance of our proposed approaches, ChIP-seq datasets of 10 binding factors were collected from the GM12878 (lymphoblastoid) and K562 (erythroleukemic) human hematopoietic cell lines, each of which was further categorized into cell-type-specific (GM12878- and K562-specific) and shared binding sites. Then, multiple types of features for these binding sites were integrated to train the XGBoost- and CNN-based models. Experimental results show that our proposed approaches significantly outperform other competing methods on three classification tasks. Moreover, we identified independent feature contributions for cell-type-specific and shared sites through SHAP values and explored the ability of the CNN-based model to predict cell-type-specific and shared binding sites by excluding or including DNase signals. Furthermore, we investigated the generalization ability of our proposed approaches to different binding factors in the same cellular environment.Availability and implementationThe source code is available at: https://github.com/turningpoint1988/CSSBS.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9825777 | biostudies-literature
Project description:Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information.We report a hybrid approach for recognizing calcium-binding sites in disordered regions. Our approach combines loop modeling with a machine learning method (FEATURE) for structure-based site recognition. For validation, we compared the performance of our method on known calcium-binding sites for which there are both holo and apo structures. When loops in the apo structures are rebuilt using modeling methods, FEATURE identifies 14 out of 20 crystallographically proven calcium-binding sites. It only recognizes 7 out of 20 calcium-binding sites in the initial apo crystal structures.We applied our method to unstructured loops in proteins from SCOP families known to bind calcium in order to discover potential cryptic calcium binding sites. We built 2745 missing loops and evaluated them for potential calcium binding. We made 102 predictions of calcium-binding sites. Ten predictions are consistent with independent experimental verifications. We found indirect experimental evidence for 14 other predictions. The remaining 78 predictions are novel predictions, some with intriguing potential biological significance. In particular, we see an enrichment of beta-sheet folds with predicted calcium binding sites in the connecting loops on the surface that may be important for calcium-mediated function switches.Protein crystal structures are a potentially rich source of functional information. When loops are missing in these structures, we may be losing important information about binding sites and active sites. We have shown that limited loop modeling (e.g. loops less than 17 residues) combined with pattern matching algorithms can recover functions and propose putative conformations associated with these functions.
| S-EPMC2808310 | biostudies-literature
Project description:Phosphopeptide-binding domains, including the FHA, SH2, WW, WD40, MH2, and Polo-box domains, as well as the 14-3-3 proteins, exert control functions in important processes such as cell growth, division, differentiation, and apoptosis. Structures and mechanisms of phosphopeptide binding are generally diverse, revealing few general principles. A computational method for analysis of phosphopeptide-binding domains was therefore developed to elucidate the physical and chemical nature of phosphopeptide binding, given this lack of structural similarity. The surfaces of nine phosphopeptide-binding proteins, representing seven distinct classes of phosphopeptide-binding modules, were discretized, and encoded with information about amino acid identity, surface curvature, and electrostatic potential at every point on the surface in order to identify local surface properties enriched in phosphoresidue contact sites. Cross-validation indicated that propensities corresponding to this enrichment calculated from a subset of the training data could be used to predict the phosphoresidue contact site on proteins not used in training with no false negative results, and with few unconfirmed positive predictions. The locations of phosphoresidue contact sites were then predicted on the surfaces of the checkpoint kinase Chk1 and the BRCA1 BRCT repeat domain, and these predictions are consistent with recent experimental evidence.
| S-EPMC2253325 | biostudies-literature
Project description:Cellular retinol-binding proteins (CRBPs) I and II, which are members of the intracellular lipid-binding protein (iLBP) family, are retinoid chaperones that are responsible for the intracellular transport and delivery of both retinol and retinal. Although structures of retinol-bound CRBPI and CRBPII are known, no structure of a retinal-bound CRBP has been reported. In addition, the retinol-bound human CRBPII (hCRBPII) structure shows partial occupancy of a noncanonical conformation of retinol in the binding pocket. Here, the structure of retinal-bound hCRBPII and the structure of retinol-bound hCRBPII with retinol fully occupying the binding pocket are reported. It is further shown that the retinoid derivative seen in both the zebrafish CRBP and the hCRBPII structures is likely to be the product of flux-dependent and wavelength-dependent X-ray damage during data collection. The structures of retinoid-bound CRBPs are compared and contrasted, and rationales for the differences in binding affinities for retinal and retinol are provided.
| S-EPMC4257620 | biostudies-literature