Molecular lipophilicity potential by CLIP, a reliable tool for the description of the 3D distribution of lipophilicity: application to 3-phenyloxazolidin-2-one, a prototype series of reversible MAOA inhibitors

[Computer modelling of monoaminoxidases]

The article summarized results of studies on active site structures of monoamine oxidases (MAO) performed in the Institute of Biomedical Chemistry (Russia) by computer modelling approaches. MAO, catalyzing the reaction of oxidative deamination of major neurotransmitter monoamines, exists in two highly homologous forms, MAO A and MAO B, distinguished by substrate specificity and inhibitor selectivity. The development of approaches for active site modelling of these enzymes (with unknown three-dimensional structures) started from analysis of relationship between the geometrical sizes of rigid indole and isatin derivatives and their inhibitory activity. These studies resulted in molding of the active site structures of MAO A and MAO B. These molds reflect the sizes and shapes of active sites of these enzymes. These mold models have been used for virtual screening of molecular databases for new inhibitors. The models obtained at different stages of MAO investigations have been compared with recently appeared three-dimensional structures of MAO A and MAO B.

Automatic classification of protein structures using physicochemical parameters

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

A reversible monoamine oxidase A inhibitor, befloxatone: structural approach of its mechanism of action

Experimental and theoretical physico-chemical methods were used to investigate the interaction between several reversible monoamine oxidase A inhibitors in the oxazolidinone series and the active site of the enzyme. Phenyloxazolidinones include toloxatone and analogues, among which befloxatone was selected as drug candidate for the treatment of depression. Identification of the forces responsible for the crystal cohesion of befloxatone reveals functional groups that could interact with monoamine oxidase. Calculation of electronic properties of those compounds using ab initio molecular orbital methods lead to a description of the mode of interaction between befloxatone and the cofactor of the enzyme. Electronic absorption spectroscopy measurements confirm the hypothesis of a privileged interaction of phenyloxazolidinone-type inhibitors with the flavin cofactor of MAO. Additional sites of interaction with the protein core of MAO A are also examined with regard to the primary structure of the enzyme. As a result of this work, a model is proposed for the reversible inhibition of MAO A by befloxatone via long distance, reversible interactions with the flavin adenine dinucleotide (FAD) cofactor of the enzyme and with specific amino acids of the active site. This model is partially corroborated by experimental evidence and should be helpful in designing new potent inhibitors of monoamine oxidase.