Biology subjects

R, P.

Publications and source records attributed to R, P..

8 recordsLinked to original sources

bioRxiv2024

Deciphering enzymatic potential in metagenomic reads through DNA language model

Microbial communities drive essential global processes, yet much of their functional potential remains unexplored. Metagenomics stands to elucidate this microbial "dark matter" by directly sequencing the microbial community DNA from environmental samples. However, the exploration of metagenomic sequences is mostly limited to establishing their similarity to curated reference sequences. A paradigm shift - language model (LM) -based methods - offer promising avenues for reference-free analysis of metagenomic reads. Here, we introduce two LMs, a pretrained foundation model REMME, aimed at understanding the DNA context of metagenomic reads, and the fine-tuned REBEAN for predicting the enzymatic potential encoded within the read-corresponding genes. By emphasizing function recognition over gene identification, REBEAN labels gene-encoded molecular functions of previously explored and new (orphan) sequences. Even though it was not trained to do so, REBEAN identifies the genes function-relevant parts. It thus expands enzymatic annotation of unassembled metagenomic reads. Here, we present novel enzymes discovered using our models, highlighting model impact on our understanding of microbial communities.

bioinformatics↗

bioRxiv2022

USAG1 protein: An important drug target in teeth regeneration

The BMP antagonist and Wnt signaling modulator, USAG-1 (uterine sensitization associated gene-1) is mostly expressed in the kidneys and suppresses BMP7 bioactivity, which is crucial for tooth development. The homodimeric 35-kDa protein known as bone morphogenetic protein-7 is essential for the specification and patterning of the early embryo and controls apoptosis in a variety of developmental events. A single-gene knockout (KO) mouse model for Usag-1, also known as Sclerostin domain containing 1 (SOSTDC1), ectodin, Wnt modulator in surface ectoderm (WISE), CCAAT/enhancer-binding protein beta (CEBPB), Sprouty homolog 2 (SPRY2), Sprouty homolog 3 (SPRY3), or Epiprofin (EPFN), has demonstrated that Usag1 inhibition is important for teeth regeneration in mice. Anti Usag1 method was done by other scientists outside India. Tideglusib when placed at damaged teeth, it increases WNT signaling by inhibiting GSK-3.It results into teeth repair .Suginami et al proposed USAG-1 as a promising drug target for teeth regeneration .They proposed anti-USAG-1 approach to induce teeth regeneration but it has been associated with many sideaffects. This study aims to provide a natural lead molecule as a therapeutic drug for teeth regeneration.

bioinformatics↗

bioRxiv2021

Artificial Intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage response pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bona fide (non)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity as well as their potential to induce proliferation, oxidative stress, genomic instability, alterations in the epigenome, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable since it reveals the contribution of the aforementioned biochemical properties in imparting carcinogenicity. Metabokiller outperforms existing best-practice methods for carcinogenicity prediction. We used Metabokiller to unravel cells endogenous metabolic threats by screening a large pool of human metabolites and predicted a subset of these metabolites that could potentially trigger malignancy in normal cells. To cross-validate Metabokiller predictions, we performed a range of functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites namely 4-Nitrocatechol and 3,4-Dihydroxyphenylacetic acid and observed high synergy between Metabokiller predictions and experimental validations.

bioinformatics↗

bioRxiv2021

M-CAMP™: A cloud-based web platform with a novel approach for species-level classification of 16S rRNA microbiome sequences

The M-CAMP (Microbiome Computational Analysis for Multiomic Profiling) Cloud Platform was designed to provide users with an easy-to-use web interface to access best in class microbiome analysis tools. This interface allows bench scientists to conduct bioinformatic analysis on their samples and then download publication-ready graphics and reports. The core pipeline of the platform is the 16S-seq taxonomic classification algorithm which provides species-level classification of Illumina 16s sequencing. This algorithm uses a novel approach combining alignment and kmer based taxonomic classification methodologies to produce a highly accurate and comprehensive profile. Additionally, a comprehensive proprietary database combining reference sequences from multiple sources was curated and contains 18056 unique V3-V4 sequences covering 11527 species. The M-CAMP 16S taxonomic classification algorithm was validated on 52 sequencing samples from both public and in-house standard sample mixtures with known fractions. Compared to current popular public classification algorithms, our classification algorithm provides the most accurate species-level classification of 16S rRNA sequencing data.

bioinformatics↗

bioRxiv2021

In Silico: Mutagenicity and carcinogenicity prediction of Sugar substitutes

Sugar substitutes are mostly artificial, man-made industrial products used as additives in food and beverages. Most of these substances flow through the digestive tract and food chains to become emerging organic contaminants in various abiotic and biotic environmental media. Here, we predict the mutagenicity and carcinogenicity of commonly used sugar substitutes using the in silico based methods. The simplified molecular-input line-entry system (SMILES) of sugar substitutes were obtained from the PubChem database for the toxicity predictions. Here, sixteen sugar substitutes tested out of these four compounds Glucin (GLU), and 5-nitro-2-propoxyaniline (P-4000), SCL, Ace were predicted as mutagens by using in silico tools such as LAZAR, pKCSM, and Toxtree. Based on the predicted results GLU and P-4000 were predicted as carcinogenic sugar substitutes.

bioengineering↗

bioRxiv2021

Raspberry pi: Assessments of emerging organic chemicals by the predictive in silico methods

Phthalic acid esters (PAEs) and bisphenols are used as plasticizers worldwide. During plastic production, use, deposition, and recycling these compounds contaminate the environment and affect environmental health. In this study, we investigated the toxicity of plasticizers by using in silico tools. None of the test compounds were found to be hERG blockers in multiclass predictions as evaluated by the Pred-hERG 4.1 tool. Among all tested compounds in Pred-Skin 2.0, only BBP, BCP, DBP, diethyl phthalate (DEP), DMP, DNHP, DNPP, DPP, DTDP, DUP, and ODP were non-skin sensitizers. Our results demonstrate that in silico tools provide a reliable, fast, and economic way to explore the toxicological effects of EOCs.

pharmacology and toxicology↗

bioRxiv2021

Repurposing of an Antifungal Drug against Gastrointestinal Stromal Tumors

Drug discovery is an important research area to improve human health. Currently, treatment of gastrointestinal stromal tumors (GISTs) is unsuccessful due to drug-resistance, hence, there is a demand for alternatives. Often, there is limited time available for toxicological assessments and a lack of safer drugs. It is possible to identify new drugs from existing approved drugs possessing another purpose in the clinical lines. In this study, virtual screening of some Food and Drug Administration (FDA-USA) approved and available antifungal and antineoplastic drugs were performed against GISTs based on docking affinity of human platelet-derived growth factor receptor alpha (PDGFRA) with these drugs to identify a suitable PDGFRA inhibitor for saving the time required for toxicity screening. The protein and ligand-binding affinity were investigated for five FDA approved antineoplastic and thirty-six antifungal drugs against PDGFRA using the AutoDock (AD) and AutoDock Vina (ADV) software. Based on docking score and inhibition constant (Ki), Itraconazole was predicted as a better PDGFRA inhibitor among all the computationally tested drugs.

cancer biology↗

bioRxiv2020

Rational design of protein-specific folding modifiers

Protein folding can go wrong in vivo and in vitro, with significant consequences for the living cell and the pharmaceutical industry, respectively. Here we propose a general design principle for constructing small peptide-based protein-specific folding modifiers. We construct a xenonucleus, which is a pre-folded peptide that resembles the folding nucleus of a protein, and demonstrate its activity on the folding of ubiquitin. Using stopped-flow kinetics, NMR spectroscopy, Forster Resonance Energy transfer, single-molecule force measurements, and molecular dynamics simulations, we show that the ubiquitin xenonucleus can act as an effective decoy for the native folding nucleus. It can make the refolding faster by 33 {+/-} 5% at 3 M GdnHCl. In principle, our approach provides a general method for constructing specific, genetically encodable, folding modifiers for any protein which has a well-defined contiguous folding nucleus.

biophysics↗