Biology subjects

Sveshnikova, A.

Publications and source records attributed to Sveshnikova, A..

5 recordsLinked to original sources

bioRxiv2024

Designing pathways for bioproducing complex chemicals by combining tools for pathway extraction and ranking

The synthesis of many important biochemicals involves complex molecules and many reactions. Therefore, the design and optimization of whole-cell biocatalysts to produce these molecules requires the use of metabolic modeling. Such modeling involves the extraction of the production pathways from biochemical databases and their integration into genome-scale metabolic models of the host organism. However, the synthesis of such complex molecules requires reactions from multiple pathways operating in balanced subnetworks that are not assembled in existing databases. Here we present SubNetX, a novel computational algorithm that extracts reactions from a given reaction database and assembles balanced reaction subnetworks to produce a target biochemical from a selected set of precursor metabolites, energy currencies, and cofactors of the host organism. These subnetworks can be directly integrated into whole-cell metabolic models, and using available methods, we can then reconstruct all alternative biosynthetic pathways and rank them according to design criteria such as yield, pathway length, and other optimization goals. We applied SubNetX to eight selected secondary metabolites and one non-natural chemical used as pharmaceuticals to demonstrate the potential of this pipeline.

systems biology↗

bioRxiv2023

Enzyme promiscuous profiles for protein sequence and reaction annotation

1.1Novel sequencing techniques and biochemical pathway prediction resources provide a wealth of data on novel proteins and computationally predicted enzymatic reactions. Accurate matching of protein sequences to enzymatic activities is crucial for advancing synthetic biology and metabolic engineering efforts. Here we present BridgIT+, a computational workflow that accounts for enzyme promiscuity and accurately predicts protein-reaction and reaction-protein associations. BridgIT+ builds upon the promiscuity-based method for annotating orphan and novel reactions with enzymatic activities, BridgIT, and utilizes position-specific scoring matrices (PSSM). The framework uses sequence alignment and enzyme promiscuity predictions to analyze protein sequences, identify sequence patterns, and create promiscuous protein sequence profiles for each reaction. These profiles allow us to predict the protein sequences most likely involved in the reaction. We showcase BridgIT+ by annotating (i) computationally predicted reactions with proteins and (ii) unannotated proteins of E. coli proteome with enzymatic functions. We demonstrated the performance of BridgIT+ on several biochemical assays and compared it to three current state-of-the-art methods for matching proteins and reactions. We anticipate that the proposed conceptual framework will enhance our understanding of gene-protein-reaction relations and advance biological sequence and reaction annotation in biology and synthetic biology studies.

bioinformatics↗

bioRxiv2021

ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope of covered reactions and compounds, as well as in metrics for ranking and evaluation. In this work, we present a new computational resource called ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer. It consists of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 28000 known and 100000 novel reactions predicted with generalized enzymatic reactions rules and over 70000 compounds, of which 22000 are known to biochemical databases and 48000 only to PubChem. Over 1,000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included into the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways to predict valuable compound derivations. In line with the standards of open science, we have made the toolbox freely available to the scientific community at http://lcsb-databases.epfl.ch/arbre/. We envision that ARBRE will provide the community with a new computational toolbox and comprehensive search tool to predict and rank pathways towards industrially important aromatic compounds.

synthetic biology↗

bioRxiv2021

ATLASx: a computational map for the exploration of biochemical space

Metabolic "dark matter" describes currently unknown metabolic processes, which form a blind spot in our general understanding of metabolism and slow down the development of biosynthetic cell factories and naturally derived pharmaceuticals. Mapping the dark matter of metabolism remains an open challenge that can be addressed globally and systematically by existing computational solutions. In this work, we use 490 generalized enzymatic reaction rules to map both known and unknown metabolic processes around a biochemical database of 1.5 million biological compounds. We predict over 5 million reactions and integrate nearly 2 million naturally and synthetically-derived compounds into the global network of biochemical knowledge, named ATLASx. ATLASx is available to researchers as a powerful online platform that supports the prediction and analysis of novel biochemical pathways and evaluates the biochemical vicinity of molecule classes (https://lcsbdatabases.epfl.ch/Atlas2).

biochemistry↗

bioRxiv2019

Drug repositoning or target repositioning: a structural perspective of drug-target-indication relationship for available repurposed drugs.

Drug repositioning aims to find new indications for existing drugs, in order to reduce drug development cost and time. Currently, numerous successful stories of drug repositioning have been reported and many drugs are already available on the market. Although many of those cases are products of serendipitous findings, repositioning opportunities can be uncovered systematically by following either a disease-centric approach, as a result of a close relation between an old and new indication, a target-centric one, which links a known target and its established drug to a new indication, or a drug-centric approach, which connects a known drug to a new target and its associated indication. The three approaches differ in their complexity, potential, and limits, and most important the necessary starting information and computational power. Which one is predominant in current drug repositioning and what does this imply for future developments? To address these questions, we systematically evaluated over 100 drugs, 200 targets structures and over 300 indications from the Drug Repositioning Database. Each of the analysed cases has been classified based on one of the three repositioning approaches, showing that the majority, more than 60%, falls within the disease-centric definition, around 30% within the target-centric, and only less than 10% within the drug-centric. We therefore concluded that so far repositioning has mainly been disease and target repositioning and not, as drug repositioning, as expected by definition. We discuss the reasons and suggest directions to exploit the full potential of techniques useful for drug-centric in order to sustain future rationale repositioning pipelines.

bioinformatics↗