Characterization of Ionizable Groups’ Environments in Proteins and Protein–Ligand Complexes through a Statistical Analysis of the Protein Data Bank

Abstract

We conduct a statistical analysis of the molecular environment of common ionizable functional groups in both protein–ligand complexes and inside proteins from the Protein Data Bank (PDB). In particular, we characterize the frequency, type, and density of the interacting atoms as well as the presence of a potential counterion. We found that for ligands, most guanidinium groups, half of primary and secondary amines, and one-fourth of imidazole neighbor a carboxylate group. Tertiary amines bind more rarely near carboxylate groups, which may be explained by a crowded neighborhood and hydrophobic character. In comparison to the environment seen by the ligands, inside proteins, an environment enriched in main-chain atoms is found, and the prevalence of direct charge neutralization by carboxylate groups is different. When the ionizable character of water molecules and phenolic or hydroxyl groups is accounted, considering a high-resolution dataset (less than 1.5 Å), charge neutralization could occur for well above 80% of the ligand functional groups considered, but for tertiary amines.

Exploring cooperative molecular contacts using a PostgreSQL database system

“…As a result, among the 10,627 ligands having at least one carboxylic acid functional group, 4,073 have at least one of potentially positively charged nitrogen as a closest neighbour of a carboxylic oxygen atom (1,087 a N.ar, 2,478 a N.pl3 and 932 a N.3). This is in line with previous work on the topic [1]. Accordingly, these three protein nitrogens are overrepresented in the environment of O.co2 in ligands (Table 2).…”

“…Environments of ionisable groups – We built a second test case of scientific relevance, i. e. analysing the presence of possible counter ions (N.ar, N.pl3 and N.3) near potentially negatively charged carboxylate oxygens (O.co2) [1]. As a result, among the 10,627 ligands having at least one carboxylic acid functional group, 4,073 have at least one of potentially positively charged nitrogen as a closest neighbour of a carboxylic oxygen atom (1,087 a N.ar, 2,478 a N.pl3 and 932 a N.3).…”

“…Analysis of molecular complexes at the atomistic level is key to many drug discovery applications, for example, to understand and score molecular interactions [1], to predict binding affinities [2], to design ligands based on structure [3], to compare binding poses across unrelated binding sites [4], and to study bioisosterism [5]. Such analyses are conducted based on different representations such as the statistical distributions of interacting atom pairs [6], pharmacophore models [7], and molecular interaction fingerprints [8].…”

“…These interactions are common and include coordinated hydrogen bonds in protein-ligand complexes [14], the catalytic triad [15] and the oxyanion hole in serine proteases [11]. Defining dipolar interactions and ionic interactions [1] also requires identifying complex networks of bonded and non-nonded atoms. Consequently, these multi-atom motifs are most often ignored by state-of-the-art scoring functions [16,17].…”

Identification of Isosteric Replacements of Glycosyl Domain of Ligands by Data Mining

“…Besides the classical isosteres of phosphate, such as carboxylate, sulfone or sulfonamide [53], unexpected replacements that do not conserve charge or polarity, such as aryl, aliphatic, or positively charged groups were found. The disclosed results were timely applied for online computational tools development [54], cheminformatics re nement [55], probe ligands [56] and Plasmodium falciparum pyrophosphatase inhibitors [57] synthesis, demonstrated the necessity and demand of phosphate replacement in drug development streamline. The data mining work ow we previously presented could be generalized to exploit structural isosteres of any chemical fragments interested.…”