Protein-Protein Interactions

Unnatural amino acids (UAAs), also referred to as non-canonical amino acids (ncAAs) or non-proteinogenic amino acids, are a class of amino acids that are distinct from the 20 standard natural amino acids. They can be obtained through chemical synthesis, biosynthesis, and other approaches, with structural diversity far exceeding that of natural amino acids. UAAs are mainly including naturally occurring non-canonical amino acids, chemically synthesized amino acids, and biosynthetic amino acids, which provide a molecular basis for protein function design.

UAAs exhibit significant value in multiple fields. They can optimize the pharmacokinetic properties of peptide drugs and peptidomimetics, modify enzyme functions and endow them with new biological activities, thereby overcoming the limitations of traditional peptide drugs and expanding the chemical space . Meanwhile, UAAs can serve as molecular probes to analyze protein-protein interactions and investigate the regulatory mechanisms of protein functions.

MCE has compiled a UAAs Fragment Library comprising nearly a thousand unnatural amino acid fragments with extensive coverage of chemical space and enhanced structural diversity. This compound library can be widely applied in peptide synthesis, drug design, and protein engineering.

Cyclic peptide library have advantages such as high affinity, high selectivity, and suitability for targeting protein–protein interactions. Through DEL synthesis technology, the library size can achieve hundreds of millions. DEL cyclic peptide library have advantages like low cost andhigh screeing efficiency, making them valuable for discovering lead compounds against challenging drug targets.

This cyclic peptide library is constructed with unnatural amino acids as building block, synthesized through DNA-compatible chemical reactions. Each cyclic peptide consist of six amino acids and constrained conformations such as side-chain cross-linking, disulfide bonds, and macrocyclization. These cyclic peptides exhibit significantly improved stability and druggability compared with linear peptides, filling the gap between small molecules and macromolecular biologics. Each cyclic peptide is uniquely conjugated to a DNA barcode sequence for molecular identification and sequencing decoding.

MCE’s cyclic peptide library has8 independent sub-libraries, with a total molecular diversity of 1.2 billion. It is constructed via multi-round combinatorial assembly of building blocks and diverse cyclization strategies, facilitating the discovery of cyclic peptide leads for undruggable targets.

Recently, significant advancements in tyrosine-targeting electrophiles have primarily occurred in the field of protein-protein interactions (PPIs), where cysteine residues are often underrepresented and novel chemistries are needed to address these interfaces. In this context, tyrosines are frequently more accessible compared to more buried binding sites. Moreover, they are commonly found at "hot spots," which are functional epitopes of PPIs, with 12.3% of the residues consisting of tyrosines. This prevalence is likely due to the hydrophobic nature of tyrosine, its ability to participate in aromatic π-interactions, and its capacity for hydrogen bonding. Beyond PPIs, some progress has also been made in covalent tyrosine targeting in other areas where more commonly addressed side chains are lacking. Even though tyrosine has a slightly lower pKa value compared to the protonated lysine side chain (approximately 10 vs. 10.5 for the unprotected amino acid side chains), significantly less progress has been made in the development of tyrosine-targeted covalent ligands compared to lysine. This is likely due to the reduced flexibility of the tyrosine side chain and the greater steric hindrance of its hydroxy group, which makes it more challenging to adopt suitable reaction geometries.

Through careful selection, we constructed a structural filter containing over 110 electrophilic groups. By analyzing the electrophilic fragments selected by the structural filter, we removed any molecules with trivial or undesirable structural features. Ultimately, we obtained 124 fragment molecules which can target tyrosine residue and can be used for fragment-based covalent drug discovery.

MCE 18 stands for Medicinal Chemistry Evolution 2018. This metric was established based on structural data of 28,161 patented lead molecules, 1,370 marketed innovative drugs, and nearly 30,000 investigational candidates from preclinical to Phase III stages across 23 major global pharmaceutical companies from 1950 to 2018. After scaffold clustering analysis, a scoring model was constructed by integrating five three dimensional scaffold characteristics, including aromatic rings (AR), non aromatic heterocycles (NAR), chiral centers (CHIRAL), spirocycles (SPIRO), and the sp³ carbon ratio in cyclic and acyclic moieties, enabling quantitative assessment of molecular scaffold novelty and three dimensional complexity.

According to the score distribution of patented molecules, the top 25% of the original patent dataset was defined as the high novelty region. MCE 18 high scoring compounds selected based on this criterion can effectively avoid scaffold patent conflicts and intellectual property risks from the source. Molecules in this range typically feature a high sp³ carbon ratio, abundant chiral centers, spirocycles, and fused heterocycles with prominent three dimensional conformations. Their spatial properties allow precise matching to complex non traditional undruggable target pockets such as PPI interfaces and allosteric sites, making them ideal structural types for early stage screening of First in class drugs.

MCE‑18 Novelty Focused drug‑Like library strictly selects molecules from the aforementioned high scoring range, containing more than 10,000 premium drug like molecules with highly diverse scaffolds and rich 3D diversity. It can be used for high throughput screening of well established targets such as kinases, GPCRs, and proteases, and is especially suitable for hit identification in allosteric modulation, protein–protein interactions, and various undruggable orphan targets, fully supporting early stage drug discovery for cutting edge innovat