Biology subjects

Agrawal, D.

Publications and source records attributed to Agrawal, D..

7 recordsLinked to original sources

bioRxiv2026

snputils: A High-Performance Python Library for Genetic Variation and Population Structure

The increasing size and resolution of genomic and population genetic datasets offer unprecedented opportunities to study population structure and uncover the genetic basis of complex traits and diseases. The collection of existing analytical tools, however, is characterized by format incompatibilities, limited functionality, and computational inefficiencies, forcing researchers to construct fragile pipelines that chain together fragmented command-line utilities and ad hoc scripts. These are difficult to maintain, scale, and reproduce. To address such limitations, we present snputils, a Python library that unifies high-performance I/O, transformation, and analysis of genotype, ancestry, and phenotypic information within a single framework suitable for biobank-scale research. The library provides efficient tools for essential operations, including querying, cleaning, merging, and statistical analysis. In addition, it offers classical population genetic statistics with optional ancestry-specific masking. An identity-by-descent module supports reading of multiple formats, filtering and ancestry-restricted segment trimming for relatedness and demographic inference. snputils also incorporates ancestry-masking and multi-array functionalities for dimensionality reduction methods, as well as efficient implementations of admixture simulation, admixture mapping, and advanced visualization capabilities. With support for the most commonly used file formats, snputils integrates smoothly with existing tools and clinical databases. At the same time, its modular and optimized design reduces technical overhead, facilitating reproducible workflows that accelerate discoveries in population genetics, genomic research, and precision medicine. Benchmarking demonstrates a significant reduction in genotype data loading speed compared to existing Python libraries. The open-source library is available at https://github.com/AI-sandbox/snputils, with full documentation and tutorials at snputils.org.

bioinformatics↗

bioRxiv2024

Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design

Protein function modulation using small molecule binding is an important therapeutic strategy for many diseases. However, many proteins remain undruggable due to lack of suitable binding pockets for small molecule binding. Proximity induced protein degradation using molecular glues has recently been identified as an important strategy to target undruggable proteins. Molecular glues were discovered serendipitously and as such currently lack an established approach for in-silico driven rationale design. In this work, we aim to establish an in-silico method for designing molecular glues. To achieve this, we leverage known molecular glue-mediated ternary complexes and derive a rationale for in-silico design of molecular glues. Establishing an in-silico rationale for molecular glue design would significantly contribute to the literature and accelerate the discovery of molecular glues for targeting previously undruggable proteins. Our work presented here and named as Molecular Glue-Designer-Evaluator (MOLDE) contributes to the growing literature of in-silico approaches to drug design in-silico literature.

bioinformatics↗

bioRxiv2023

An explainable machine learning data analytics method using TIGIT-linked genes for identifying biomarker signatures to clinical outcomes.

In the last decade, immunotherapies targeting immune checkpoint inhibitors have been extremely effective in eliminating subsets of some cancers in some patients. Multi-modal immune and non-immune factors that contribute to clinical outcomes have been utilized for predicting response to therapies and developing diagnostics. However, these data analytic methods involve a combination of complex mathematical data analytics, and even-more complex biological mechanistic pathways. In order to develop a method for data analytics of transcriptomics data sets, we have utilized an explainable machine learning (ML) model to investigate the genes involved in the signaling pathway of T-cell-immunoreceptor with immunoglobulin and ITIM domain (TIGIT). TIGIT is a receptor on T, NK, and T-regulatory cells, that has been classified as a checkpoint inhibitor due to its ability to inhibit innate and adaptive immune responses. We extracted gene whole genome sequencing data of 1029 early breast cancer patient tumors, and adjacent normal tissues from the TCGA and UCSC Xena Data Hub public databases. We followed a workflow which involved the following steps: i) data acquisition, processing, and visualization followed by ii) developed of a predictive prognostic model using input (gene expression data) and output (survival time) parameters iii) model interpretation was performed by calculating SHAP (Shapely-Additive-exPlanations); iv) the application of the model involved a Cox-regression model, trained with L-2 regularization and optimization using 5 fold cross validation. The model identified gene signatures associated with TIGIT that predicted survival outcome with a test set with a score of 0.601. In summary, we have utilized this case study of TIGIT-mediated signaling pathways to develop a roadmap for biologists to harness ML methods effectively.

cancer biology↗

bioRxiv2023

PROTAC-Design-Evaluator (PRODE) -- An Advanced Method for in-silico PROTAC design

PROTAC (proteolysis-targeting chimeras) is a rapidly evolving technology to target undruggable targets. The mechanism by which this happens is when a bifunctional molecule binds to a target protein and also brings in proximity an E3 ubiquitin ligase to trigger ubiquitination and degradation of the target protein. Yet in-silico driven approaches to design these hetero-bifunctional molecules that have the desired functional properties to induce proximity between the target protein and E3 ligase remains to be established. In this paper we present a novel in-silico method for PROTAC design and to demonstrate the validity of our approach. We show that for a BRD4-VHL PROTAC ternary complex known in the literature, we are able to reproduce the PROTAC binding mode, the structure of ternary complex formed therein and the free energy ({Delta}G) thermodynamics favoring ternary complexation through theoretical computational methodologies. Further, we demonstrate the use of Thermal Titration Molecule Dynamics (TTMD) to differentiate the stability of PROTAC mediated ternary complexes. We employ the proposed methodology to design a PROTAC for a new system of FGFR1-MDM2 to degrade the FGFR1 (Fibroblast growth factor receptor 1) which is overexpressed in cancer. Our work presented here and named as PROTAC-Designer-Evaluator (PRODE) contributes to the growing literature of in-silico approaches to PROTAC design and evaluation by incorporating the latest in-silico methods and demonstrates advancement over previously published PROTAC in-silico literature.

bioinformatics↗

bioRxiv2022

An in-silico approach for novel molecular glue design by rationalizing known molecular glue mediated ternary complex formation

Protein function modulation using small molecule binding is an important therapeutic strategy for many diseases. However, many proteins remain undruggable due to lack of suitable binding pockets for small molecule binding. Proximity induced protein degradation using molecular glues has recently been identified as in important strategy to target the undruggable proteins. Molecular glues were discovered serendipitously and as such currently lack an established approach for in-silico design rationale. In this work, we attempt to establish the rationale for a known case and having inferred the rationale, we discuss how the rationale can be applied in-silico to design novel molecular glue through AI powered techniques. We believe the establishing of in-silico rationale for molecular glue design would be a valuable and welcome addition to the literature to further accelerate the discovery of molecular glues to drug undruggable targets.

bioinformatics↗

bioRxiv2022

A New In-Silico Approach for PROTAC Design and Quantitative Rationalization of PROTAC mediated Ternary Complex Formation

Proteolysis-Targeting Chimeric Molecules (PROTAC) is a rapidly emerging technology for drug target protein degradation and drugging undruggable drug targets. There is a growing literature on in-silico approaches to the complex problem of PROTAC design, specifically the advantages of AI/ in-silico methods in the PROTAC Design Make Test Analyze (DMTA) cycle. Our work presented here aims to contribute to the growing literature of in-silico approaches to PROTAC design by incorporating and demonstrating incremental advancement over previously published methods. We use AI based generative methods for PRTOAC design and Molecular Dynamics to evaluate the stability of the ternary complex formed and ability of the PROTAC to hold the target protein and E3 ligase together stably. To quantify the performance of the PROTAC candidate, we also estimate computationally the PROTAC performance metrics routinely measured by the experimentalists in PROTAC assays. We use highly accurate absolute binding free energy calculations used traditionally in protein-ligand space for the PROTAC system. We calculate (Gibbs free energy change) {Delta}G for binary complex formation and ternary complex formation mediated by the PROTAC using Free Energy Perturbation - Thermodynamics Integration (FEP-TI) method which is benchmarked in literature with a root mean square error of 0.8 kcal/mol. We calculate {Delta}G for ternary and binary complexes and estimate whether {Delta}G for ternary is lower than the {Delta}G estimated for binary complexes. When the {Delta}G for ternary is lower than the binary it is inferred that ternary complexation is favoured over binary. Therefore, through these methods we can theoretical estimate {Delta}G measured by experimentalists in PROTAC assays such as Isothermal titration calorimetry (ITC) and Surface plasmon resonance (SPR) which capture the {Delta}G for ternary and binary complex formation mediated by the PROTAC. This method will help reduce time as well as costs of the PROTAC DMTA cycle and will accelerate early stage PROTAC drug discovery. As an illustrative application of our in-silico PROTAC design approach, we chose the target Fibroblast growth factor receptor 1(FGFR-1) which is a target approved drug for colorectal cancer. We report the findings and conclude with future research directions.

bioinformatics↗

bioRxiv2018

Distinct timescales of RNA regulators enable the construction of a genetic pulse generator

To build complex genetic networks with predictable behaviours, synthetic biologists use libraries of modular parts that can be characterized in isolation and assembled together to create programmable higher-order functions. Characterization experiments and computational models for gene regulatory parts operating in isolation are routinely employed to predict the dynamics of interconnected parts and guide the construction of new synthetic devices. Here, we individually characterize two modes of RNA-based transcriptional regulation, using small transcription activating RNAs (STARs) and CRISPR interference (CRISPRi), and show how their distinct regulatory timescales can be used to engineer a composed feedforward loop that creates a pulse of gene expression. We use a cell-free transcription-translation system (TXTL) to rapidly characterize the system, and we apply Bayesian inference to extract kinetic parameters for an ODE-based mechanistic model. We then demonstrate in simulation and verify with TXTL experiments that the simultaneous regulation of a single gene target with STARs and CRISPRi leads to a pulse of gene expression. Our results suggest the modularity of the two regulators in an integrated genetic circuit, and we anticipate that construction and modeling frameworks that can leverage this modularity will become increasingly important as synthetic circuits increase in complexity.

synthetic biology↗