Biology subjects

Barbe, S.

Publications and source records attributed to Barbe, S..

5 recordsLinked to original sources

bioRxiv2026

A Generative Neuro-Symbolic AI for Protein Sequence Design

Deep learning has revolutionized computational protein design, enabling the generation of sequences that fold onto target backbones with unprecedented accuracy. However, state-of-the-art inverse folding tools largely rely on auto-regressive sampling. While powerful, this paradigm is increasingly recognized for its inability to "think ahead", a crucial capacity to reliably create the complex, long-range inter-residue dependencies essential for most biological functions. To overcome these fundamental limitations, we introduce EffieDes, a generative neuro-symbolic AI framework that synergizes the predictive capabilities of deep learning with the logical precision of automated reasoning. EffieDes leverages deep learning to encode the target backbones fitness landscape into Effie-- a fully decomposable probabilistic graphical model (Potts model). This landscape is then rigorously explored by an automated reasoning prover to identify sequences that simultaneously satisfy complex design constraints and optimize backbone fitness. We validated this neuro-symbolic approach through the design of orthogonal sequence pairs that adopt identical folds but exhibit selective self-assembly, as well as the design of a de novo selective nanobody with nanomolar affinity for an immune-evasive SARS-CoV-2 variant. EffieDes provides a robust architecture for precisely dissecting learned fitness landscapes, offering a new path toward proteins with highly optimized performances and sophisticated functional objectives.

bioengineering↗

bioRxiv2025

Promiscuous structural cross-compatibilities between major shell components of Klebsiella pneumoniae bacterial microcompartments

Bacterial microcompartments (BMC) are submicrometric reactors that encapsulate dedicated metabolic activities. BMC-H hexamers, the most abundant components of BMC shells, play major roles for shell plasticity and permeability. In part, chemical exchanges between the BMC lumen and the cellular cytosol will be defined by the disposition of amino acids lining the central BMC-H pores. Current models attribute to BMC-H a homo-oligomeric nature. The hexagonal symmetry of corresponding pores, however, would break down if hetero-hexamers formed, a possibility suggested by the frequent presence of multiple paralogs within BMC operons. Here, we gauged the degree of structural promiscuity between the 11 BMC-H paralogs from Klebsiella pneumoniae, a potential human pathogen endowed with the capacity to express three different BMC types. Concomitant activation of transcription of several BMC operons was first shown to be possible. By leveraging an adapted tripartite GFP technology, all possible BMC-H pair combinations were screened in E. coli. Multiple structural cross-compatibilities were pinpointed between homologs arising not only from the same BMC operon, but also from different BMC types, results supported by Alphafold and ESMFold predictions. The structural stability and assembly propensity of selected hetero-associations was established by biochemical means. In light of these results, we reinterpreted published lysine cross-linking mass spectrometry data to demonstrate that one of these hetero-hexamers, involving PduA and PduJ, was already detected to form in the shell of a recombinantly-expressed 1,2-propanediol utilization compartment from Salmonella enterica. Altogether, this study points to the need to embrace an augmented structural complexity in BMC shells.

biochemistry↗

bioRxiv2024

Designing symmetrical multi-component proteins using a hybrid generative AI approach

Proteins, the fundamental building blocks of biological function, orchestrate complex cellular processes by assembling into intricate structures through meticulous interactions. The design of specific protein-protein interfaces to create customized protein assemblies holds immense potential for various biotechnological applications. To address the current limitations in designing heteromeric interactions for multi-component assemblies, we developed a hybrid generative AI design approach. This method combines deep learning and automated reasoning, explicitly considering both positive and negative interaction states to favor heteromeric desired over undesired interactions. The approach leverages Effie, a deep-learned pairwise decomposable scoring function, and an advanced reasoning tool extended for multicriteria optimization of this function. Here, we tested the ability of this hybrid AI method to redesign homomeric interfaces of bacterial microcompartment components (BMC-H) into heteromeric assemblies. We benchmarked its performance against ProteinMPNN, a sequence design autoregressive model. Our in silico assessment, complemented by experimental validation, highlights the outperformance of the hybrid AI generative design approach, and its potential to unlock the engineering of complex multi-component self-assembling protein entities.

bioengineering↗

bioRxiv2021

Three ParA dimers cooperatively assemble on type Ia partition promoters

Accurate DNA segregation is essential for faithful inheritance of genetic material. In bacteria, this process is mainly ensured by a partition system (Par) composed of two proteins, ParA and ParB, and a centromere site. The auto-regulation of Par operon expression is important for efficient partitioning, and is primarily mediated by ParA for type Ia plasmid partition systems. For the plasmid F, four ParAF monomers were proposed to bind to four repeated sequences in the promoter region. By contrast, using quantitative surface plasmon resonance, we showed that three ParAF dimers bind to this region. We uncovered that one perfect inverted repeat (IR) motif, consisting of two hexamer sequences spaced by 28-bp, constitutes the primary ParAF DNA binding site. A similar but degenerated motif overlaps the former. ParAF binding to these motifs is well supported by biochemical and modeling analyses. In addition, molecular dynamics simulations predict that the winged-HTH domain displays high flexibility, which may favor the cooperative ParA binding to the promoter region. We propose that three ParAF dimers bind cooperatively to overlapping motifs thus covering the promoter region. A similar organization is found on both closely related and distant plasmid partition systems, suggesting that such promoter organization for auto-regulated Par operons is widespread and may have evolved from a common ancestor.

biochemistry↗

bioRxiv2021

Seven amino acid types suffice to reconstruct the core fold of RNA polymerase

AbstractThe extant complex proteins must have evolved from ancient short and simple ancestors. Nevertheless, how such prototype proteins emerged on the primitive earth remains enigmatic. The double-psi beta-barrel (DPBB) is one of the oldest protein folds and conserved in various fundamental enzymes, such as the core domain of RNA polymerase. Here, by reverse engineering a modern DPBB domain, we reconstructed its evolutionary pathway started by "interlacing homo- dimerization" of a half-size peptide, followed by gene duplication and fusion. Furthermore, by simplifying the amino acid repertoire of the peptide, we successfully created the DPBB fold with only seven amino acid types (Ala, Asp, Glu, Gly, Lys, Arg, and Val), which can be coded by only GNN and ARR (R = A or G) codons in the modern translation system. Thus, the DPBB fold could have been materialized by the early translation system and genetic code.

biochemistry↗