Recent advances in predicting and modeling protein-protein interactions

Martínez-Castilla L, García-Sandoval R, Ballester P, Ferrándiz C, Sanchez MDLP, García-Ponce B, Garay-Arroyo A, R. Álvarez-Buylla E. HDACs MADS-domain protein interaction: a case study of HDA15 and XAL1 in Arabidopsis thaliana

Cellular behavior, cell differentiation and ontogenetic development in eukaryotes result from complex interactions between epigenetic and classic molecular genetic mechanisms, with many of these interactions still to be elucidated. Histone deacetylase enzymes (HDACs) promote the interaction of histones with DNA by compacting the nucleosome, thus causing transcriptional repression. MADS-domain transcription factors are highly conserved in eukaryotes and participate in controlling diverse developmental processes in animals and plants, as well as regulating stress responses in plants. In this work, we focused on finding out putative interactions of Arabidopsis thaliana HDACs and MADS-domain proteins using an evolutionary perspective combined with bioinformatics analyses and testing the more promising predicted interactions through classic molecular biology tools. Through bioinformatic analyses, we found similarities between HDACs proteins from different organisms, which allowed us to predict a putative protein-protein interaction between the Arabidopsis thaliana deacetylase HDA15 and the MADS-domain protein XAANTAL1 (XAL1). The results of two-hybrid and Bimolecular Fluorescence Complementation analysis demonstrated in vitro and in vivo HDA15-XAL1 interaction in the nucleus. Likely, this interaction might regulate developmental processes in plants as is the case for this type of interaction in animals.

African Swine Fever Virus Protein-Protein Interaction Prediction

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

High throughput methods to study protein-protein interactions during host-pathogen interactions

The ability of a pathogen to survive and cause an infection is often determined by specific interactions between the host and pathogen proteins. Such interactions can be both intra- and extracellular and may define the outcome of an infection. There are a range of innovative biochemical, biophysical and bioinformatic techniques currently available to identify protein-protein interactions (PPI) between the host and the pathogen. However, the complexity and the diversity of host-pathogen PPIs has led to the development of several high throughput (HT) techniques that enable the study of multiple interactions at once and/or screen multiple samples at the same time, in an unbiased manner. We review here the major HT laboratory-based technologies employed for host-bacterial interaction studies.

Deus Ex Machina? The Rise of Artificial Intelligence in Toxicology

Artificial intelligence (AI) is rising rapidly, driven by big data, complex algorithms, and computing resources. Current research presented at the American Chemical Society Fall 2023 Meeting demonstrates AI to be a valuable predictive and supporting tool across all facets of toxicology.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Contribution of Artificial Intelligence to the Identification of Protein-Protein Interactions: A Case Study on PAR-3 and Its Partner Adapter Molecule Crk

Protein-protein interactions (PPIs) are known to be involved in most cellular functions, and a detailed knowledge of such interactions is essential for studying their role in normal and pathological conditions. Significant progress is being made in the identification of PPIs through advances in computational methods. In particular, the AlphaFold2 machine learning-based model has been shown to accelerate drug discovery process by predicting the 3D structure of protein complexes. In this chapter, a straightforward protocol for predicting interprotein interactions between PAR-3 and its protein partner adapter molecule crk is provided. Such artificial intelligence-based and publicly available approaches can provide a resource for further investigation of therapeutic drug targets.