Biology subjects

Anand, N.

Publications and source records attributed to Anand, N..

7 recordsLinked to original sources

bioRxiv2024

PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers

AO_SCPLOWBSTRACTC_SCPLOWPredicting protein-ligand binding affinity is crucial for drug discovery, as it enables efficient identification of drug candidates. We introduce PLAPT, a novel model utilizing transfer learning from pre-trained transformers like ProtBERT and ChemBERTa to predict binding affinities with high accuracy. Our method processes one-dimensional protein and ligand sequences, leveraging a branching neural network architecture for feature integration and affinity estimation. We demonstrate PLAPTs superior performance through validation on multiple datasets, achieving state-of-the-art results while requiring significantly less computational resources for training compared to existing models. Our findings indicate that PLAPT offers a highly effective and accessible approach for accelerating drug discovery efforts.

bioinformatics↗

bioRxiv2023

Haplo-pheno association for OsNRT1.1 paralog in rice reveals superior haplogroup with high nitrate uptake efficiency

Haplotype-based breeding approaches hold promise for enhancing crop improvement strategies, allowing for targeted selection of superior genetic combinations to develop high-yielding and resilient varieties. The current study aimed at identification of NRT1.1 nitrate transporter haplotype that could serve as "donors" in haplotype-based breeding. We phenotyped 272 rice accessions in hydroponics with sufficient and low nitrogen (N) for nitrate uptake efficiency. By employing principal component and hierarchical cluster analysis, the accessions were grouped into N efficient, intermediate, and inefficient clusters. Haplotype analysis unveiled the presence of two haplogroups for OsNRT1.1A, three for OsNRT1.1B, and five for OsNRT1.1C. Through haplo-pheno association, the comparison of mean trait values revealed H2 and H3 as the superior haplotypes (SH) for OsNRT1.1A and OsNRT1.1B, respectively. In the case of OsNRT1.1C, H3 and H1 emerged as SH within the N-efficient cluster. Conversely, the inferior haplotypes (IH) consisted of H1 in OsNRT1.1A, H3 in OsNRT1.1B, and H3 and H2 in OsNRT1.1C within the N-inefficient cluster. However, relative expression of OsNRT1.1 (with specific paralogs) in contrasting rice accessions revealed that a few of the inferior accession exhibited higher expression levels in the root but lower in the shoot, which might have contributed to their N-inefficiency. Furthermore, amino acid change at position 403 (Isoleucine to Valine) in inferior accessions influences the active site OsNRT1.1C protein causing N-inefficiency. Ours is the first report on haplotype analysis of NRT1.1 gene demonstrating its genetic diversity, as well as its association with phenotype will have potential implications for improving nitrate uptake efficiency.

plant biology↗

bioRxiv2022

A cell non-autonomous FOXO/DAF-16-mediated germline quality assurance program that responds to somatic DNA damage

Germline integrity is critical for progeny fitness. Organisms deploy the DNA damage response (DDR) signalling to protect germline from genotoxic stress, facilitating cell-cycle arrest of germ cells and DNA repair or their apoptosis. Cell-autonomous regulation of germline quality is well-studied; however, how quality is enforced cell non-autonomously on sensing somatic DNA damage is less known. Using Caenorhabditis elegans, we show that DDR disruption, only in the uterus, when insulin-IGF-1 signalling (IIS) is low, arrests germline development and induces sterility in a FOXO/DAF-16 transcription factor (TF)-dependent manner. Without FOXO/DAF-16, germ cells of the IIS mutant escape arrest to produce poor quality oocytes, showing that the TF imposes strict quality control during low IIS. In response to low IIS in neurons, FOXO/DAF-16 works cell autonomously as well as non-autonomously to facilitate the arrest. Activated FOXO/DAF-16 promotes transcription of checkpoint and DDR genes, protecting germline integrity. However, on reducing DDR during low IIS, the TF decreases ERK/MPK-1 signaling below a threshold, and transcriptionally downregulates genes involved in spermatogenesis-to-oogenesis switch as well as cdk-1/Cyclin B to promote germline arrest. Altogether, our study reveals how cell non-autonomous function of FOXO/DAF-16 promotes germline quality and progeny fitness in response to somatic DNA damage. Significance StatementReproductive decisions are supervised processes that take into account various inputs like cellular energy availability and status of damage repair in order to ensure healthy progeny. In this study, we show that the absence of optimal DNA damage repair in the somatic uterine tissues prevents oocyte development by the cell-autonomous as well non-autonomous function of activated FOXO transcription factor DAF-16. Thus, this study elucidates a new surveillance role of FOXO/DAF-16 in somatic tissues that ensures progeny fitness.

developmental biology↗

bioRxiv2020

Ig-VAE: Generative Modeling of Immunoglobulin Proteins by Direct 3D Coordinate Generation

AO_SCPLOWBSTRACTC_SCPLOWWhile deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation--an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the models generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

bioinformatics↗

bioRxiv2020

ProGen: Language Modeling for Protein Generation

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. We pose protein engineering as an unsupervised sequence generation problem in order to leverage the exponentially growing set of proteins that lack costly, structural annotations. We train a 1.2B-parameter language model, ProGen, on [~]280M protein sequences conditioned on taxonomic and keyword tags such as molecular function and cellular component. This provides ProGen with an unprecedented range of evolutionary sequence diversity and allows it to generate with fine-grained control as demonstrated by metrics based on primary sequence similarity, secondary structure accuracy, and conformational energy.

synthetic biology↗

bioRxiv2020

Protein sequence design with a learned potential

AO_SCPLOWBSTRACTC_SCPLOWThe task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. We investigate the capability of a deep neural network model to automate design of sequences onto protein backbones, having learned directly from crystal structure data and without any human-specified priors. The model generalizes to native topologies not seen during training, producing experimentally stable designs. We evaluate the generalizability of our method to a de novo TIM-barrel scaffold. The model produces novel sequences, and high-resolution crystal structures of two designs show excellent agreement with the in silico models. Our findings demonstrate the tractability of an entirely learned method for protein sequence design.

bioinformatics↗

bioRxiv2019

Asymmetric Division Promotes Therapeutic Resistance In Glioblastoma Stem Cells

Asymmetric cell division (ACD) enables the maintenance of a stem cell population while simultaneously generating differentiated progeny. Cancer stem cells (CSCs) undergo multiple modes of cell division during tumor expansion and in response to therapy, yet the functional consequences of these division modes remain to be determined. Using a fluorescent reporter for cell surface receptor distribution during mitosis, we found that ACD in glioblastoma CSCs generated a daughter cell with enhanced therapeutic resistance and increased co-inheritance of epidermal growth factor receptor (EGFR) and neurotrophin receptor (p75NTR). Stimulation of both receptors maintained self-renewal under differentiation conditions. While p75NTR knockdown did not compromise CSC maintenance, therapeutic efficacy of EGFR inhibition was enhanced, indicating that co-inheritance of p75NTR and EGFR promotes resistance to EGFR inhibition through a redundant mechanism. These data demonstrate that ACD produces progeny with co-enriched growth factor receptors, which contributes to the generation of a more therapeutically resistant CSC population.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=165 SRC=\"FIGDIR/small/569962_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (41K):\norg.highwire.dtl.DTLVardef@15cc472org.highwire.dtl.DTLVardef@1654633org.highwire.dtl.DTLVardef@1e3ec1forg.highwire.dtl.DTLVardef@a2eb45_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗