Biology subjects

Geoffrey A S, B.

Publications and source records attributed to Geoffrey A S, B..

5 recordsLinked to original sources

bioRxiv2024

How do we respond to the next SARS CoV epidemic/pandemic? A bioinformatics approach with the promise of preventing or reducing the severity of future SARS CoV related pandemics

In this work, we develop a Bayesian weighted scheme to generate evolutionary lineages of a particular viral protein sequence of interest and through a process of clustering and choosing representative lineages from the different clusters according to an evolutionary fitness objective function, we demonstrate it is possible to have anticipated the emergence of the SARS-CoV 2 (2019) strain from the SARS-CoV 1(2004) strain and having shown this retrospectively, we discuss the possibility of applying this approach along with continuous genomic surveillance of SARS-CoVs to prevent or reduce severity of future SARS-CoV related pandemics by being prepared with broad neutralization strategies for anticipated future lineages of SARS-CoVs identified through bioinformatics approaches such as that reported in this work.

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↗

bioRxiv2021

Protein structure prediction using AI and quantum computers

This work seeks to combine the combined advantage of leveraging these emerging areas of Artificial Intelligence and quantum computing in applying it to solve the specific biological problem of protein structure prediction using Quantum Machine Learning algorithms. The CASP dataset from ProteinNet was downloaded which is a standardized data set for machine learning of protein structure. Its large and standardized dataset of PDB entries contains the coordinates of the backbone atoms, corresponding to the sequential chain of N, C_alpha, and C atoms. This dataset was used to train a quantum-classical hybrid Keras deep neural network model to predict the structure of the proteins. To visually qualify the quality of the predicted versus the actual protein structure, protein contact maps were generated with the experimental and predicted protein structure data and qualified. Therefore this model is recommended for the use of protein structure prediction using AI leveraging the power of quantum computers.

bioinformatics↗

bioRxiv2020

Target2DeNovoDrug : a novel programmatic tool for deep learning based de novo drug design for a target of interest

The past decade has seen a surge in the range of application data science, machine learning, deep learning, and AI methods to drug discovery. The presented work involves an assemblage of a variety of AI methods for drug discovery along with the incorporation of in silico techniques to provide a holistic tool for automated drug discovery. When drug candidates are required to be identified for a particular drug target of interest, the user is required to provide the tool target signatures in the form of an amino acid sequence or its corresponding nucleotide sequence. The tool collects data registered on PubChem required to perform an automated QSAR and with the validated QSAR model, prediction and drug lead generation are carried out. This protocol we call Target2Drug. This is followed by a protocol we call Target2DeNovoDrug wherein novel molecules with likely activity against the target are generated de novo using a generative LSTM model. It is often required in drug discovery that the generated molecules possess certain properties like drug-likeness, and therefore to optimize the generated de novo molecules toward the required drug-like property we use a deep learning model called DeepFMPO, and this protocol we call Target2DeNovoDrugPropMax. This is followed by the fast automated AutoDock-Vina based in silico modeling and profiling of the interaction of optimized drug leads and the drug target. This is followed by an automated execution of the Molecular Dynamics protocol that is also carried out for the complex identified with the best protein-ligand interaction from the AutoDock-Vina based virtual screening. The results are stored in the working folder of the user. The code is maintained, supported, and provide for use in the following GitHub repository https://github.com/bengeof/Target2DeNovoDrugPropMax

bioinformatics↗

bioRxiv2020

A program to automate the discovery of drugs for West Nile and Dengue virus -- programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling

Our work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning based AutoQSAR algorithm to generate drug leads for the flaviviruses - Dengue and West Nile. The drug leads generated by the program are feed as programmatic inputs to AutoDock Vina package for automated In Silico modelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue virus in way which is reflective of the constantly growing PubChem database. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The leads generated by the program are fed as programmatic inputs to an In Silico modelling package. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus, was modelled in an automated way through programmatic commands. Thus our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning based AutoQSAR algorithm and an automated In Silico modelling run through the program to study the interaction between the drug lead compounds and the drug target protein of West Nile and Dengue virus

bioinformatics↗