PRISM BioLab Blog – PPI Pursuit
Commentary on Protein-Protein and Protein-Peptide Interactions, peptidomimetics and related topics in drug discovery.

Protein-protein interactions (PPIs) library is an indispensable and invaluable tool for PPI drug discovery and development. 2P2Idb v2¹⁾, TIMBAL v2²⁾, iPPI-DB³⁾ have been the libraries focused on PPIs and data availability in the recently elucidated cases were limited for long. It will open up to everyone a great opportunity to initiate another PPIs-targeted drug discovery program if ...

Peptide-based drug design is one of the widely investigated field in pharmaceutical sciences for targeting protein-peptide or protein-protein interaction (PPI) inhibitions. Small molecule-based peptidomimetics like PepMetics^® is a solution but a biologically active peptide itself is also interesting drug motif for designing a target-specific molecule candidate for a particular disease...

A small molecule designed for the mimicry of a part of a biomolecule is playing a key role in drug discovery and development. PepMetic^® molecules are the mimetics of α-helices and β-turns in a general sense, Mimicry of another well-known secondary structure of proteins, β-strands, would also have a possibility of controlling PPIs. Here is a curious series of papers involving synthesis and characterization of stretching peptides...

Affinity of protein-protein/peptide is significantly important to understand the nature of PPI and to find a target of interest. We are always looking at the PPI binding affinity. Lots of information available on the databases now, owing to tremendous efforts of pharmaceutical companies, academia and research centers in this field. However, affinities are still unknown...

Resurgence of machine learning (ML) is becoming a generally usable paradigm shift trigger in a variety of fields. ML has a potential to apply for anything in a sense its artificial neural networks with an appropriate amount of dataset and training mimics human’s way of thinking. In synthetic chemistry, smart and reliable prediction...

In synthetic chemistry, optimization of reaction conditions is a huge task and most of the chemists learn various ways in the university and graduate school as a training. In many cases in the lab synthesis, intuition-based, trial-and-error campaigns are performed when faced with a difficult reaction to maximize the yield, shorten the reaction time, obtain the product with higher purity and so on.

AI and ITs are offering us fabulous opportunities for peptide structure prediction. The impact of AlphaFold2 and RoseTTAFold on protein structure prediction is the typical example that rapidly changes our attitudes on “prediction”. We are also interested because of the expanding potential of computational approaches. AlphaFold is based on a deep learning method and the structural dataset...

It is no doubt that In silico drug design by structure-based drug design (SBDD) has been a powerful methodology for drug development. Structural data of the target protein, desirably of the binding state with hit or lead compound, delivers us a framework for designing more potent organic molecule structures by medicinal chemists. But with the invent of deep learning, it is realistically...

Computational methods have been paving the way for the advent of PPIs prediction. The emergence and technological development of machine learning raise the possibility of more precise prediction of PPIs. But, for the beginners of machine learning or scientists in other fields, it takes hard time to clearly understand the state-of-the-art computational methods. It often prevents positive attitudes toward predictive methodologies...

Drug discovery in peptidomimetics field often requires unnatural. You would need just natural α-amino acids when you synthesize a natural peptide of interest but in the lead finding and optimization stage, a number of unnatural α-amino acid would be used for rapid synthesis and screening. However, it sometimes shows difficulty even though unnatural α-Amino acid synthesis...