Design, synthesis and biological evaluation of PD-1 derived peptides as inhibitors of PD-1/PD-L1 complex formation for cancer therapy.

Over the past few years, many molecules such as monoclonal antibodies, affibodies, nanobodies, and small compounds have been designed and tested as inhibitors of PD-1/PD-L1 complex formation. Some of them have been successfully implemented into clinical oncology practice. However, the majority of these compounds have disadvantages and limitations, such as high production price, potential for immunogenicity and/or prolonged clearance. Thus, new inhibitors of the PD-1/PD-L1 immune checkpoints are needed. Recently, peptides emerged as potential novel approach for blocking receptor/ligand interaction. In the presented studies we have designed, synthesised and tested peptides, which are potential inhibitors of the PD-1/PD-L1 axis. The amino acid sequences of the designed peptides were based on the binding sites of PD-1 to PD-L1, as determined by the crystal structure of the protein complex and also based on MM/GBSA analysis. Interactions of the peptides with PD-L1 protein were confirmed using SPR, while their inhibitory properties were studied using cell-based PD-1/PD-L1 immune checkpoint blockade assays. The characterization of the peptides has shown that the peptides PD-1(119-142)T120C-E141C, PD-1(119-142)C123-S137C and PD-1(122-138)C123-S137C strongly bind to PD-L1 protein and disrupt the interaction of the proteins. PD-1(122-138)C123-S137C peptide was shown to have the best inhibitory potential from the panel of peptides. Its 3D NMR structure was determined and the binding site to PD-L1 was established using molecular modelling methods. Our results indicate that the PD-1 derived peptides are able to mimic the PD-1 protein and inhibit PD-1/PD-L1 complex formation.

BioVDB: biological vector database for high-throughput gene expression meta-analysis.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

Energy landscapes for proteins described by the UNRES coarse-grained potential.

The self-assembly of proteins is encoded in the underlying potential energy surface (PES), from which we can predict structure, dynamics, and thermodynamic properties. However, the corresponding analysis becomes increasingly challenging with larger protein sizes, due to the computational time required, which grows significantly with the number of atoms. Coarse-grained models offer an attractive approach to reduce the computational cost. In this Feature Article, we describe our implementation of the UNited RESidue (UNRES) coarse-grained potential in the Cambridge energy landscapes software. We have applied this framework to explore the energy landscapes of four proteins that exhibit native states involving different secondary structures. Here we have tested the ability of the UNRES potential to represent the global energy landscape of proteins containing up to 100 amino acid residues. The resulting potential energy landscapes exhibit good agreement with experiment, with low-lying minima close to the PDB geometries and to results obtained using the all-atom AMBER force field. The new program interfaces will allow us to investigate larger biomolecules in future work, using the UNRES potential in combination with all the methodology available in the computational energy landscapes framework.