Dataset Information

GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs.

ABSTRACT: Versatile RNA modifications play important roles in post-transcriptional regulations of gene expression, among which glycosylation modifications on small RNAs emerge as a novel clade whose characteristics need further interrogations. Here, we demonstrated that the sequence pattern around RNA glycosylation sites was not random and could be exploited for glycosylation site prediction. A machine learning predictor, GlyinsRNA, which integrated multiple RNA sequence representation encodings, was established. GlyinsRNA achieved AUROC (area under the receiver operating characteristic curve) of 0.7933 and 0.7979 in five-fold cross-validation and independent tests, respectively. GlyinsRNA was implemented as an online webserver, where both the predicted glycosylation sites and the overrepresented RNA-binding protein (RBP)-related motifs were annotated to facilitate the users. GlyinsRNA webserver is freely available at http://www.rnanut.net/glyinsrna.

SUBMITTER: Cui C

PROVIDER: S-EPMC8782180 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs.

Cui Chunmei C Wu Xiaobin X Zhou Yuan Y

RNA biology 20210924 sup2

Versatile RNA modifications play important roles in post-transcriptional regulations of gene expression, among which glycosylation modifications on small RNAs emerge as a novel clade whose characteristics need further interrogations. Here, we demonstrated that the sequence pattern around RNA glycosylation sites was not random and could be exploited for glycosylation site prediction. A machine learning predictor, GlyinsRNA, which integrated multiple RNA sequence representation encodings, was esta ...[more]

PMID: 34559595

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Project description:MotivationProtein-protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.ResultsWe collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.Availability and implementationProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

Project description:BACKGROUND: Identification of drug-like molecules is one of the major challenges in the field of drug discovery. Existing approach like Lipinski rule of 5 (Ro5), Operea have their own limitations. Thus, there is a need to develop computational method that can predict drug-likeness of a molecule with precision. In addition, there is a need to develop algorithm for screening chemical library for their drug-like properties. RESULTS: In this study, we have used 1347 approved and 3206 experimental drugs for developing a knowledge-based computational model for predicting drug-likeness of a molecule. We have used freely available PaDEL software for computing molecular fingerprints/descriptors of the molecules for developing prediction models. Weka software has been used for feature selection in order to identify the best fingerprints. We have developed various classification models using different types of fingerprints like Estate, PubChem, Extended, FingerPrinter, MACCS keys, GraphsOnlyFP, SubstructureFP, Substructure FPCount, Klekota-RothFP, Klekota-Roth FPCount. It was observed that the models developed using MACCS keys based fingerprints, discriminated approved and experimental drugs with higher precision. Our model based on one hundred fifty nine MACCS keys predicted drug-likeness of the molecules with 89.96% accuracy along with 0.77 MCC. Our analysis indicated that MACCS keys (ISIS keys) 112, 122, 144, and 150 were highly prevalent in the approved drugs. The screening of ZINC (drug-like) and ChEMBL databases showed that around 78.33% and 72.43% of the compounds present in these databases had drug-like potential. CONCLUSION: It was apparent from above study that the binary fingerprints could be used to discriminate approved and experimental drugs with high accuracy. In order to facilitate researchers working in the field of drug discovery, we have developed a webserver for predicting, designing, and screening novel drug-like molecules (http://crdd.osdd.net/oscadd/drugmint/).

Dataset Information

GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs.

Publications

GlyinsRNA: a webserver for predicting glycosylation sites on small RNAs.

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