Scoring of protein–protein docking models utilizing predicted interface residues

Microstructure of Sea Cucumber Parastichopus tremulus Peptide Hydrogels and Bioactivity in Caco-2 Cell Culture Model

Wider availability of marine proteins for the development of food and biomedical applications has a high importance. Sea cucumber body wall proteins have specific functional properties that could be very promising for such product development. However, protein extraction from whole animals is costly and complex, whereas peptide hydrogel production using biotechnological methods can be considered an economically viable approach. Body-wall derived peptides from sea cucumber Parastichopus tremulus have been suggested as a nontraditional source of potentially edible hydrocolloids. In the current work, four peptides were produced through custom synthesis. Scanning electron microscopy (SEM) of the combined mix of the four peptides (1:1 ratio; 15 mM concentration) in a calcium ion-containing buffer confirmed untargeted self-assembly with long, thick fibrillar formations at a microscale (measured mean cross-section 2.78 µm and length sizes of 26.95 µm). The antioxidant activity of the peptides separately, and in combination (1:1 molar ratio), was studied in vitro through ORAC (values in the range from 279 to 543 µmol TE/g peptide), ABTS (from 80.4 to 1215 µmol TE/g peptide), and DPPH (from 5.2 to 19.9 µmol TE/g) assays, and confirmed for protection against oxidation in a Caco-2 cell culture model. Angiotensin-I converting enzyme inhibitory activity was also confirmed for two of the four peptides, with the highest IC 50 of 7.11 ± 0.84 mg/mL.

Encoding the space of protein-protein binding interfaces by artificial intelligence

The physical interactions between proteins are largely determined by the structural properties at their binding interfaces. It was found that the binding interfaces in distinctive protein complexes are highly similar. The structural properties underlying different binding interfaces could be further captured by artificial intelligence. In order to test this hypothesis, we broke protein-protein binding interfaces into pairs of interacting fragments. We employed a generative model to encode these interface fragment pairs in a low-dimensional latent space. After training, new conformations of interface fragment pairs were generated. We found that, by only using a small number of interface fragment pairs that were generated by artificial intelligence, we were able to guide the assembly of protein complexes into their native conformations. These results demonstrate that the conformational space of fragment pairs at protein-protein binding interfaces is highly degenerate. Features in this degenerate space can be well characterized by artificial intelligence. In summary, our machine learning method will be potentially useful to search for and predict the conformations of unknown protein-protein interactions.

Software and Databases for Protein-Protein Docking

Protein-protein interactions (PPIs) provide valuable insights for understanding the principles of biological systems and for elucidating causes of incurable diseases. One of the techniques used for computational prediction of PPIs is protein-protein docking calculations, and a variety of software has been developed. This chapter is a summary of software and databases used for protein-protein docking.

Sampling and Scoring in Protein-Protein Docking

Protein-protein docking is considered one of the most important techniques supporting experimental proteomics. Recent developments in the field of computer science helped to improve this computational technique so that it better handles the complexity of protein nature. Sampling algorithms are responsible for the generation of numerous protein-protein ensembles. Unfortunately, a primary docking output comprises a set of both near-native poses and decoys. Application of the efficient scoring function helps to differentiate poses with the most favorable properties from those that are very unlikely to represent a natural state of the complex. This chapter explains the importance of sampling and scoring in the process of protein-protein docking. Moreover, it summarizes advances in the field.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.