Impact of protein conformational diversity on AlphaFold predictions.

MOTIVATION: After the outstanding breakthrough of AlphaFold in predicting protein 3D models, new questions appeared and remain unanswered. The ensemble nature of proteins, for example, challenges the structural prediction methods because the models should represent a set of conformers instead of single structures. The evolutionary and structural features captured by effective deep learning techniques may unveil the information to generate several diverse conformations from a single sequence. Here, we address the performance of AlphaFold2 predictions obtained through ColabFold under this ensemble paradigm. RESULTS: Using a curated collection of apo-holo pairs of conformers, we found that AlphaFold2 predicts the holo form of a protein in ∼70% of the cases, being unable to reproduce the observed conformational diversity with the same error for both conformers. More importantly, we found that AlphaFold2's performance worsens with the increasing conformational diversity of the studied protein. This impairment is related to the heterogeneity in the degree of conformational diversity found between different members of the homologous family of the protein under study. Finally, we found that main-chain flexibility associated with apo-holo pairs of conformers negatively correlates with the predicted local model quality score plDDT, indicating that plDDT values in a single 3D model could be used to infer local conformational changes linked to ligand binding transitions. AVAILABILITY AND IMPLEMENTATION: Data and code used in this manuscript are publicly available at https://gitlab.com/sbgunq/publications/af2confdiv-oct2021. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rationally Designed Pooled CRISPRi-Seq Uncovers an Inhibitor of Bacterial Peptidyl-tRNA Hydrolase.

Pooled knockdown libraries of essential genes are useful tools for elucidating the mechanisms of action of antibacterial compounds, a pivotal step in antibiotic discovery. However, achieving genomic coverage of antibacterial targets poses a challenge due to the uneven proliferation of knockdown mutants during pooled growth, leading to the unintended loss of important targets. To overcome this issue, we describe the construction of CIMPLE ( C RISPR i - m ediated p ooled library of e ssential genes), a rationally designed pooled knockdown library built in a model antibiotic-resistant bacteria, Burkholderia cenocepacia. By analyzing growth parameters of clonal knockdown populations of an arrayed CRISPRi library, we predicted strain depletion levels during pooled growth and adjusted mutant relative abundance, approaching genomic coverage of antibacterial targets during antibiotic exposure. We first benchmarked CIMPLE by chemical-genetic profiling of known antibacterials, then applied it to an uncharacterized bacterial growth inhibitor from a new class. CRISPRi-Seq with CIMPLE, followed by biochemical validation, revealed that the novel compound targets the peptidyl-tRNA hydrolase (Pth). Overall, CIMPLE leverages the advantages of arrayed and pooled CRISPRi libraries to uncover unexplored targets for antibiotic action. Summary: Bacterial mutant libraries in which antibiotic targets are downregulated are useful tools to functionally characterize novel antimicrobials. These libraries are used for chemical-genetic profiling as target-compound interactions can be inferred by differential fitness of mutants during pooled growth. Mutants that are functionally related to the antimicrobial mode of action are usually depleted from the pool upon exposure to the drug. Although powerful, this method can fail when the unequal proliferation of mutant strains before exposure causes mutants to fall below the detection level in the library pool. To address this issue, we constructed an arrayed essential gene mutant library (EGML) in the antibiotic-resistant bacterium Burkholderia cenocepacia using CRISPR interference (CRISPRi) and analyzed the growth parameters of individual mutant strains. We then modelled depletion levels during pooled growth and used the model to rationally design an optimized CRISPR interference-mediated pooled library of essential genes (CIMPLE). By adjusting the initial inoculum of the knockdown mutants, we achieved coverage of the bacterial essential genome with mutant sensitization. We exposed CIMPLE to a recently discovered antimicrobial of a novel class and discovered it inhibits the peptidyl-tRNA hydrolase, an essential bacterial enzyme. In summary, we demonstrate the utility of CIMPLE and CRISPRi-Seq to uncover the mechanism of action of novel antimicrobial compounds.