Biology subjects

D. Davari, M.

Publications and source records attributed to D. Davari, M..

6 recordsLinked to original sources

bioRxiv2026

Exploring the substrate specificity and catalytic mechanism in a PAPS independent ASST from Salmonella typhimurium

Sulfation plays an important role in many metabolic processes such as substrate detoxification, solubilization or activation. The enzymes responsible for these reactions, sulfotransferases, occur in all domains of life and can be classified in two major, evolutionary independent groups, PAPS dependent and PAPS independent or arysulfate sulfotransferases (ASST) enzymes. Besides the fact that they play key roles in biological functions, sulfotransferases can be interesting enzymatic tools to selectively sulfate specific positions of various metabolites. To this end we need to understand the relation between sequence, structure and function. Actually, eukaryotic PAPS dependent sulfotransferases have extensively been studied, while we largely lack knowledge about the diversity of bacterial PAPS-independent ASSTs. We have conducted a multidisciplinary study of a bacterial sulfotransferase from Salmonella typhimurium, revealing the importance of active site residues for activity and guiding substrate specificity. Based on the crystal structure in complex with substrate, we used molecular docking combined with Quantum Mechanics/Molecular Mechanics (QM/MM) free energy calculations to investigate the reaction catalytic mechanism with a focus on the role of specific active site residues in substrate specificity. Our results show that the catalytic mechanism involving three conserved histidines is common with previously determined ASSTs, and highlight a specific pair of aromatic residues, Phe174 and Tyr557, that play an important role in defining the substrate specificity in ASST clade 5. These findings enhance our general understanding of how substrate specificity is attained and has the potential to guide further enzyme engineering to expand the acceptor scope of ASSTs.

biochemistry↗

bioRxiv2026

Mechanistic Basis of Sulfo Transfer in a PAPS-Independent ArylSulfate SulfoTransferases Revealed by Crystallography and QM/MM Simulations

Enzymatic sulfation offers a green-chemistry route to the selective sulfation of phenolic and other hydroxylated compounds. Bacterial arylsulfate sulfotransferases (ASSTs) are particularly attractive biocatalysts because they use simple aryl sulfate donors, such as p-nitrophenyl sulfate (pNPS), instead of the complex and labile cofactor PAPS required by conventional sulfotransferases. However, their rational application and engineering remain limited because the molecular principles controlling substrate recognition, catalytically competent donor and acceptor positioning, covalent sulfo-enzyme formation, and sulfo transfer are not fully understood. In particular, it has remained unclear how conserved active-site histidines cooperate during catalysis and how active-site preorganization promotes efficient sulfo transfer. Here, we address these questions for the newly characterized ASST (ASTB) from Desulfitobacterium hafniense clade 6 (Dh_ASST_6) by combining X-ray crystallography, steady-state enzyme kinetics, and quantum mechanics/molecular mechanics (QM/MM) simulations. Crystal structures of Dh_ASST_6 in the apo state and donor-soaked state reveal a monomeric {beta}-propeller enzyme with a histidine-centered active site and directly identify His350 as the transient sulfo carrier. Kinetic analysis shows efficient turnover and supports a ping-pong bi-bi mechanism, consistent with formation of a covalent sulfo-enzyme intermediate. Steered QM/MM simulations of the second catalytic step show that sulfuryl transfer from sulfated His350 to the phenolic acceptor proceeds through a proton-coupled, multistep pathway in which His283 assists acceptor activation by proton transfer. Active-site preorganization precedes the main bond-reorganization event and generates a near-attack geometry in which acceptor alignment, proton transfer, and stabilization of the sulfo group by a conserved hydrogen-bonding network jointly promote sulfo transfer. Free-energy reconstruction gives a low barrier of approximately 9.6 kcal/ mol, in agreement with efficient enzymatic turnover. The structural, kinetic, and computational data support a complete catalytic cycle for Dh_ASST_6 based on a cooperative two-histidine mechanism. This model establishes catalytically competent acceptor positioning and preservation of the histidine-centered hydrogen-bonding network as key design principles for ASST engineering. These features provide concrete targets for expanding substrate scope and tuning donor or acceptor preference while maintaining efficient sulfuryl transfer.

biochemistry↗

bioRxiv2026

Data-Centric Evaluation of Protein Function Prediction Pipelines

Performance estimates in protein function prediction depend not only on model choice but also on upstream decisions that define the learning problem. Using antioxidant protein classification as a controlled case study, we evaluated how dataset harmonisation, protein representation, redundancy control, and partitioning strategy affect protein machine learning pipelines. We integrated 18,804 records from 12 publicly available dataset entries into a curated consensus dataset of 4,193 protein sequences. One-hot encoding and six pretrained protein language model representations were evaluated as model inputs and as similarity spaces for redundancy reduction and distance-aware splitting. Representation choice substantially altered dataset geometry, retained dataset size, class balance, and downstream evaluation. At representation-specific p90 thresholds, one-hot encoding retained the complete dataset, whereas pretrained embeddings retained between 5% and 25% of sequences. Distance-aware partitioning reduced apparent performance relative to random splitting by up to 0.15 MCC before redundancy control, while this difference narrowed after similarity filtering. Selected configurations nevertheless maintained high performance under stricter evaluation, reaching an MCC of 0.84. These findings show that performance estimates should be interpreted as outcomes of complete data-centric workflows rather than isolated properties of predictive models.

bioinformatics↗

bioRxiv2026

Visualize, Explore, and Select: A protein Language Model-based Approach Enabling Navigation of Protein Sequence Space for Enzyme Discovery and Mining

The rapid expansion of protein sequence databases continues to outpace functional characterization, creating a persistent bottleneck in enzyme discovery and mining--particularly in large, heterogeneous, and sparsely annotated sequence spaces. This gap is amplified by visualization challenges and the lack of informed strategies for exploration, selection, and mining across sequence spaces. Here, we present an embedding-based workflow, implemented in a computational platform called SelectZyme, for alignment-free visualization and exploration of protein sequence space that combines protein language model (pLM) representations with dimensionality reduction and hierarchical density-based clustering. The approach links complementary visualizations of protein sequence space as low-dimensional landscapes, connectivity projections (minimum spanning trees), and dendrogram-based organization, enabling coherent interactive exploration and candidate selection across global context and local neighborhoods without relying on sequence-identity thresholds, EC numbers, conserved motifs, or predefined functional annotations. Across distinct case studies, we demonstrate that embedding-defined neighborhoods remain structurally conserved even when sequence identity falls within the twilight zone, and that coherent functional organization emerges also for modular protein segments in a fully unsupervised analysis. We also show how this workflow supports user friendly, interactive and scalable enzyme mining in a sparsely annotated, complex multi-family protein space surpassing > 100,000 sequences, enabling constrained candidate selection around experimentally validated anchors. By enabling interactive exploration across visualizations, supporting informed candidate selection, our workflow streamlines biocatalyst discovery and helps to bridge uncharacterized sequence-space to functional characterization campaigns - thus providing a broad starting point to downstream protein engineering, machine-learning-guided design cycles, and iterative experimental screening campaigns.

bioinformatics↗

bioRxiv2024

Interpretable and explainable predictive machine learning models for data-driven protein engineering

Protein engineering using directed evolution and (semi)rational design has emerged as a powerful strategy for optimizing and enhancing enzymes or proteins with desired properties. Integrating artificial intelligence methods has further enhanced and accelerated protein engineering through predictive models developed in data-driven strategies. However, the lack of explainability and interpretability in these models poses challenges. Explainable Artificial Intelligence addresses the interpretability and explainability of machine learning models, providing transparency and insights into predictive processes. Nonetheless, there is a growing need to incorporate explainable techniques in predicting protein properties in machine learning-assisted protein engineering. This work explores incorporating explainable artificial intelligence in predicting protein properties, emphasizing its role in trustworthiness and interpretability. It assesses different machine learning approaches, introduces diverse explainable methodologies, and proposes strategies for seamless integration, improving trust-worthiness. Practical cases demonstrate the explainable models effectiveness in identifying DNA binding proteins and optimizing Green Fluorescent Protein brightness. The study highlights the utility of explainable artificial intelligence in advancing computationally assisted protein design, fostering confidence in model reliability.

bioinformatics↗

bioRxiv2022

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering

Protein engineering through directed evolution and (semi-)rational approaches has been applied successfully to optimize protein properties for broad applications in molecular biology, biotechnology, and biomedicine. The potential of protein engineering is not yet fully realized due to the limited screening throughput hampering the efficient exploration of the vast protein sequence space. Data-driven strategies have emerged as a powerful tool to leverage protein engineering by providing a model of the sequence-fitness landscape that can exhaustively be explored in silico and capitalize on the high diversity potential offered by nature However, as both the quality and quantity of the inputted data determine the success of such approaches, the applicability of data-driven strategies is often limited due to sparse data. Here, we present a hybrid model that combines direct coupling analysis and machine learning techniques to enable data-driven protein engineering when only few labeled sequences are available. Our method achieves high performance in predicting a proteins fitness based on its sequence regardless of the number of sequences-fitness pairs in the training dataset. Besides reducing the computational effort compared to state-of-the-art methods, it outperforms them for sparse data situations, i.e., 50 - 250 labeled sequences available for training. In essence, the developed method is auspicious for data-driven protein engineering, especially for protein engineers who have only access to a limited amount of data for sequence-fitness landscape modeling.

synthetic biology↗