Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Mapping of Recognition Sites of Monoclonal Antibodies Responsible for the Inhibition of Pneumolysin Functional Activity

The pathogenicity of many bacteria, including Streptococcus pneumoniae, depends on pore-forming toxins (PFTs) that cause host cell lysis by forming large pores in cholesterol-containing cell membranes. Therefore, PFTs-neutralising antibodies may provide useful tools for reducing S. pneumoniae pathogenic effects. This study aimed at the development and characterisation of monoclonal antibodies (MAbs) with neutralising activity to S. pneumoniae PFT pneumolysin (PLY). Five out of 10 produced MAbs were able to neutralise the cytolytic activity of PLY on a lung epithelial cell line. Epitope mapping with a series of recombinant overlapping PLY fragments revealed that neutralising MAbs are directed against PLY loops L1 and L3 within domain 4. The epitopes of MAbs 3A9, 6E5 and 12F11 located at L1 loop (aa 454–471) were crucial for PLY binding to the immobilised cholesterol. In contrast, the MAb 12D10 recognising L3 (aa 403–423) and the MAb 3F3 against the conformational epitope did not interfere with PLY-cholesterol interaction. Due to conformation-dependent binding, the approach to use overlapping peptides for fine epitope mapping of the neutralising MAbs was unsuccessful. Therefore, the epitopes recognised by the MAbs were analysed using computational methods. This study provides new data on PLY sites involved in functional activity.

SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

Motivation

Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.

Results

We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding.

Availability and implementation

The database is available as supplementary data and at https://life.bsc.es/pid/skempi2/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exogenous interleukin-1α signaling negatively impacts acquired chemoresistance and alters cell adhesion molecule expression pattern in colorectal carcinoma cells HCT116

Proinflammatory cytokine and chemokine signaling from the tumor microenvironment is thought to be crucial for developing and sustaining colorectal cancer by regulating a multitude of pathways associated with a variety of cellular mechanisms. Among these pathways there is acquired chemoresistance, which is usually a major obstacle in the way towards successful chemotherapeutic treatment of advanced colorectal cancer cases. Despite of an emerging body of data published on the role of cytokine signaling network in cancer, little is known about the effects of the upstream cytokine interleukin-1α (IL-1α) signaling to the cancer cells. In this study we have shown that the increase in exogenous IL-1α signaling increases chemosensitivity of both chemosensitive and chemoresistant colorectal cancer cell lines, treated with a widely used cytotoxic antimetabolite 5-fluorouracil (5-FU). This was a result of increased cell death but not of the changes in 5-FU-induced cell cycle arrest. Noticeably, combined exogenous IL-1α and 5-FU treatment had significant effects on the expression of cell adhesion molecules, suggesting a decrease in adhesion-dependent chemoresistance and, on the other hand, an increase in metastatic potential of the cells. These results lead to a conclusion that modulation of IL-1 receptor activity could have applications as a part of combination therapy for advanced and highly metastatic colorectal cancers.

The PPI3D web server for searching, analyzing and modeling protein-protein interactions in the context of 3D structures

Summary

The PPI3D web server is focused on searching and analyzing the structural data on protein-protein interactions. Reducing the data redundancy by clustering and analyzing the properties of interaction interfaces using Voronoi tessellation makes this software a highly effective tool for addressing different questions related to protein interactions.

Availability and Implementation

The server is freely accessible at http://bioinformatics.lt/software/ppi3d/ .

Contact

ceslovas.venclovas@bti.vu.lt.

Supplementary information

Supplementary data are available at Bioinformatics online.

Trainable structure-activity relationship model for virtual screening of CYP3A4 inhibition

A new structure-activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.