Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions

Structural Biology in the AlphaFold Era: How Far Is Artificial Intelligence from Deciphering the Protein Folding Code?

Proteins are biomolecules characterized by uncommon chemical and physicochemical complexities coupled with extreme responsiveness to even minor chemical modifications or environmental variations. Since the shape that proteins assume is fundamental for their function, understanding the chemical and structural bases that drive their three-dimensional structures represents the central problem for an atomic-level interpretation of biology. Not surprisingly, this question has progressively become the Holy Grail of structural biology (the folding problem). From this perspective, we initially describe and discuss the different formulations of the folding problem. In the present manuscript, the folding problem is framed from a historical perspective, effectively highlighting the progress made in the last lustrum. We chronologically summarize the major contributions that traditional methodologies provide in approaching this multifaceted problem. We then describe the recent advent and evolution of predictive approaches based on machine learning techniques that are revolutionizing the field by pointing out the potentialities and limitations of this approach. In the final part of the perspective, we illustrate the contribution that computational approaches will make in current structural biology to overcome the limitations of the reductionist approach of studying individual molecules to afford the atomic-level characterization of entire cellular compartments.

The aPBP-type cell wall synthase PBP1b plays a specialized role in fortifying the Escherichia coli division site against osmotic rupture

A multi-protein system called the divisome promotes bacterial division. This apparatus synthesizes the peptidoglycan (PG) cell wall layer that forms the daughter cell poles and protects them from osmotic lysis. In the model Gram-negative bacterium Escherichia coli , PG synthases called class A penicillin-binding proteins (aPBPs) have been proposed to play crucial roles in division. However, there is limited experimental support for aPBPs playing a specialized role in division that is distinct from their general function in the expansion and fortification of the PG matrix. Here, we present in situ cryogenic electron tomography data indicating that the aPBP-type enzyme PBP1b is required to produce a wedge-like density of PG at the division site. Furthermore, atomic force and live cell microscopy showed that loss of this structure weakens the division site and renders it susceptible to lysis. Surprisingly, we found that the lipoprotein activator LpoB needed to promote the general function of PBP1b was not required for normal division site architecture or its integrity. Additionally, we show that of the two PBP1b isoforms produced in cells, it is the one with an extended cytoplasmic N-terminus that functions in division, likely via recruitment by the FtsA component of the divisome. Altogether, our results demonstrate that PBP1b plays a specialized, LpoB-independent role in E. coli cell division involving the biogenesis of a PG structure that prevents osmotic rupture. The conservation of aPBPs with extended cytoplasmic N-termini suggests that other Gram-negative bacteria may use similar mechanisms to reinforce their division site.

In silico prediction method for plant Nucleotide-binding leucine-rich repeat- and pathogen effector interactions

Plant Nucleotide-binding leucine-rich repeat (NLR) proteins play a crucial role in effector recognition and activation of Effector triggered immunity following pathogen infection. Genome sequencing advancements have led to the identification of a myriad of NLRs in numerous agriculturally important plant species. However, deciphering which NLRs recognize specific pathogen effectors remains challenging. Predicting NLR-effector interactions in silico will provide a more targeted approach for experimental validation, critical for elucidating function, and advancing our understanding of NLR-triggered immunity. In this study, NLR-effector protein complex structures were predicted using AlphaFold2-Multimer for all experimentally validated NLR-effector interactions reported in literature. Binding affinities- and energies were predicted using 97 machine learning models from Area-Affinity. We show that AlphaFold2-Multimer predicted structures have acceptable accuracy and can be used to investigate NLR-effector interactions in silico. Binding affinities for 58 NLR-effector complexes ranged between -8.5 and -10.6 log(K), and binding energies between -11.8 and -14.4 kcal/mol-1, depending on the Area-Affinity model used. For 2427 "forced" NLR-effector complexes, these estimates showed larger variability, enabling identification of novel NLR-effector interactions with 99% accuracy using an Ensemble machine learning model. The narrow range of binding energies- and affinities for "true" interactions suggest a specific change in Gibbs free energy, and thus conformational change, is required for NLR activation. This is the first study to provide a method for predicting NLR-effector interactions, applicable to all pathosystems. Finally, the NLR-Effector Interaction Classification (NEIC) resource can streamline research efforts by identifying NLRs important for plant-pathogen resistance, advancing our understanding of plant immunity.