In silico Drug Design: some concepts & tools - Protein-Protein interaction
Bioinformatics: Protein-protein and protein-membrane interactions
Your target can be DNA, RNA, or a protein... it can also be a mechanism, for instance, you may want to modulate protein-protein interactions or a transient protein-membrane interaction, to do these, again many approaches have been developed starting around 1990, you may need docking tool, tools to find hotspots etc...
In many cases, one needs to predict interface residues. Several approaches can be used, those investigating specific features of protein sequences and/or structures, they look at amino acid composition, physico-chemical properties and can use machine learning strategies. For example, some tools use evolutionary information to try to predict interface residues that tend to be more conserved that other residues on the rest of the protein surface. Amino acid features and evolutionary information can then be combined to analyze amino acid sequences and perform some predictions. Of course, predictions based only on sequences are limited and it is important when possible to add 3D information. Thus it is possible to map sequence evolution onto the molecular surface. Analysis of the surface in term of hydrophobicity, desolvation energy can also be used. These approaches are said to belong to the mapping approaches, for instance ODA (physics based)... but some others are based on descriptors and machine learning...Because these types of prediction are difficult, meta-predictors have been developed and tend to give better results over individual methods (e.g., meta-PPISP).
More recently, template-based methods have been presented. Because interfaces tend to be conserved in homologous complexes, such data can help to make predictions. Predictions can be done using structural-neighbors, as proteins sharing a similar fold with the query protein, even if not evolutionarily related, can offer similar predictive information to that of homologues. Some other approaches are often called partner-specific methods (e.g., PAIRpred) that are sometimes subdivided into intrinsic-based methods (mentioned above, that is 3D-classifier predictors use 3D structural features possibly combined with sequence features but this time the set of features that is being computed for training and testing is complemented by partner-specific features), docking-based methods and coevolution-based predictors (The co-evolution principle suggests that mutations on one protein in a complex are often compensated for by correlated mutations within the same chain or on a binding partner. Such correlated mutations are assumed to maintain the stability of the protein or protein–protein complex).
Specific methods have been developed for antibody-antigen interactions, in this case one can find paratope predictions and epitope prediction (linear and conformational predictors) methods but above mentioned tools can also be applied. See the Protein-Protein and Antibody-Peptide sections.
If the goal is to modulate protein-protein interactions with small compounds, one usually need to combine many approaches, prediction of hot spot, prediction of 3D complexes (with or without restraints, with different scoring functions..), prediction of druggable pockets, analysis of flexibility with simulations tools, analysis of sequence variations in patients or in related protein family..., one needs to design compound collections, peptides, use biophysical approaches, combine with mutagenesis..., search in numerous databases..., look at interaction networks, disease databases, run text mining and patent search...etc
For additional information, you can for example check our recent review introducing several aspects of PPI and in silico approaches:
Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology (review). Villoutreix BO, Kuenemann MA, Poyet J-L, Bruzzoni-Giovanelli H, Labbe C, Lagorce D, Sperandio O, Miteva MA. Molecular Informatics 2014; 6-7: 414-437. (open)
We were among the first to propose the modulation of transient interactions between a protein and the cell membrane with a small molecule and to observe that next to membrane binding regions, proteins often tend to have a cavity large enough to bind a small ligand. The work was for instance reported with application on blood coagulation cofactors by Segers et al., PNAS 2007, or Nicolaes et al., Blood 2014. This molecular mechanism is still essentially unexplored in 2015 for therapeutic intervention.
A tool to predict this potential membrane binding regions is Membrane Optimal Docking Area (MODA, see Kufareva I et al., 2014). Such information can help to find small molecules acting on such mechanism.
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