Biology subjects

Rocha, I.

Publications and source records attributed to Rocha, I..

6 recordsLinked to original sources

bioRxiv2025

Computer-guided enzyme engineering of PET hydrolase mutants towards improved PET affinity

Polyethylene Terephthalate (PET) is an extensively used plastic whose durability and resistance to degradation contribute to growing environmental pollution and concerns. Enzymatic PET degradation, particularly via PETase from Ideonella sakaiensis, has emerged as a sustainable approach due to its ability to depolymerize PET under mild conditions. While research has largely focused on enhancing the enzymes thermal stability through distal mutations, less attention has been given to active-site engineering aimed at directly improving catalytic efficiency. Here, we used an automated in silico protein engineering platform called Gene Discovery and Enzyme Engineering (GDEE), designed to systematically explore mutations at the active site. By leveraging FastPETase (FP) as scaffold, we perform a high throughput generation of thousands of variants, evaluated them via docking studies with a PET substrate analogue, and ranked candidates based on binding affinity and catalytic geometry. We identified S238Y as a key mutation that enhanced PET film degrading performance at 40 {degrees}C when inserted in two of the most active PETase variants reported to date: 2.2-fold increase in the FP scaffold and 3.4-increase in the ThermoStable-PETase (TSP) background. Compared to wild type PETase, FP S238Y showed a 14.8-fold increase in bulk activity, translating into 9.4-fold more TPA and 20-fold more MHET by UPLC, while TSP S238Y reached a 25.8-fold increase (14.4-fold more TPA and 42.6-fold more MHET). This mutation also enhanced catalytic efficiency and resistance to enzyme concentration inhibition, especially in the TSP scaffold. Molecular dynamics confirm position 238 as a relevant modulator of ligand stabilisation. These findings underscore the potential of targeted active-site engineering, combined with structure-guided prediction, to accelerate the development of efficient mesophilic biocatalysts for plastic waste remediation.

bioinformatics↗

bioRxiv2025

GDEE: A Structure-Based Platform for Gene Discovery and Enzyme Engineering

Enzymes are extensively utilized for catalyzing the production of value-added compounds in diverse sectors with significant economic impact. However, challenges such as poor protein expression, low catalytic activity, substrate/co-factor limitations, and toxicity of final products limit the efficiency of these biocatalysts. Protein engineering offers a solution by re-designing enzyme catalytic properties to enhance biosynthetic pathways. In this study, we present an automated platform for gene discovery and enzyme engineering, aimed at overcoming these bottlenecks. The GDEE platform finds and optimizes enzymes responsible for rate-limiting steps in biosynthetic pathways, with objectives ranging from improving catalytic efficiency to enabling novel transformations. The platform operates through four key steps: an initial sequence step that either sources natural enzyme sequences or generates mutant variants, followed by an atomistic protein structure prediction step, a docking step, and a binding energy evaluator, obtaining a set of variant enzyme sequences that are prioritized for experimental validation. Additionally, machine learning-based re-scoring of binding poses to improve binding affinities significantly enhances the accuracy of predicting the effect of mutations on binding, highlighting its potential in metabolic engineering applications.

bioinformatics↗

bioRxiv2024

In silico target discovery for aromatic amino acid production in Escherichia coli

Aromatic amino acids (AAA) and derived compounds are rising on global market demand, which leads to the need of better production means. These compounds are usually produced by means of microbial cell factories. However, bacterial metabolism is complex and highly regulated, which complicates the discovery of new higher yield strains, optimized for the production of these compounds. Dynamic modelling has the capability to solve this problem by predicting metabolic behavior and helping to find targets for strain design. In this paper, a new Escherichia coli dynamic model is presented. Evolutionary algorithm processes were used in order to find targets for metabolic engineering strategies for AAA increased production. The solutions obtained are compatible with strategies found in literature, while at the same time presenting new possible targets, not yet reported in literature.

bioinformatics↗

bioRxiv2024

Lead as a toxic environmental toxicant in models of synucleinopathies

Lead, a toxic heavy metal, is prevalent in various industrial applications, contributing to environmental contamination and significant health concerns. Lead affects various body systems, especially the brain, causing long-lasting cognitive and behavioral changes. While most studies have focused on continuous lead exposure, intermittent exposure, such as that caused by migration or relocations, has received less attention. Importantly, lead exposure intensifies the severity of Parkinsons disease (PD) and dementia with Lewy bodies, diseases involving the accumulation of alpha-synuclein (aSyn) in the brain and in the gut. Although, the precise mechanisms underlying these observations remain unclear, oxidative stress and mitochondrial dysfunction likely play a role. Here, we investigated how two different profiles of lead exposure - continuous and intermittent - affect models of synucleinopathies. We found that lead exposure enhances the formation of aSyn inclusions, resulting in an increase in both their number and size in cell models. In addition, we found that animals injected with aSyn pre-formed fibrils display serine 129-phosphorylated aSyn inclusions and a reduction in astrocytes in the substantia nigra. These animals also display neuronal damage and alterations in locomotor activity, exploratory behavior, anxiety, memory impairments and hypertension. Our results suggest a mechanistic link between environmental lead exposure and the onset and progression of diseases associated with aSyn pathology. Understanding the molecular and cellular interactions between lead and aSyn is crucial for shaping public health policies and may provide novel insight into strategies for mitigating the impact of environmental toxins on neurodegenerative processes involved in Parkinsons disease and related synucleinopathies. Key points summaryO_LILead exposure increases the number and size of alpha-synuclein (aSyn) inclusions in cell models.[~] C_LIO_LIAnimals exposed to aSyn fibrils and lead show increased phosphorylation of aSyn, neuronal damage and reduced astrogliosis in the substantia nigra. C_LIO_LILead-exposed animals exhibit impaired locomotion, anxiety, memory deficits, hypertension and chemoreceptor reflex hypersensitivity. C_LIO_LIThe study suggests a mechanistic link between environmental lead exposure and the progression of diseases involving aSyn pathology, such as Parkinsons disease. C_LI

physiology↗

bioRxiv2021

iEC7871 Quercus suber model: the first multi-tissue diel cycle genome-scale metabolic model of a woody tree

In the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behavior at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871), the first of a woody plant. The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights into other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

systems biology↗

bioRxiv2020

Inclusion of maintenance energy improves the intracellular flux predictions of CHO

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by pFBA using the CHO GSMM and a wide range of 13C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function.

systems biology↗