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Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

Molecular dynamics simulations, computational alanine scanning and sequence analysis were used to investigate the structural properties of the Galpha(i1)/GoLoco peptide complex. Using these methodologies, binding of the GoLoco motif peptide to the Galpha(i1) subunit was found to restrict the relative movement of the helical and catalytic domains in the Galpha(i1) subunit, which is in agreement with a proposed mechanism of GDP dissociation inhibition by GoLoco motif proteins. In addition, the results provide further insights into the role of the "Switch IV" region located within the helical domain of Galpha, the conformation of which might be important for interactions with various Galpha partners.

Odorant receptors belong to class A of the G protein-coupled receptors (GPCRs) and detect a large number of structurally diverse odorant molecules. A recent structural bioinformatic analysis suggests that structural features are conserved across class A of GPCRs in spite of their low sequence identity. Based on this work, we have aligned the sequences of 29 ORs for which ligand binding data are available. Recent site-directed mutagenesis experiments on one such receptor (MOR174-9) provide information that helped to identify nine amino-acid residues involved in ligand binding. Our modeling provides a rationale for amino acids in equivalent positions in most of the odorant receptors considered and helps to identify other amino acids that could be important for ligand binding. Our findings are consistent with most of the previous models and allow predictions for site-directed mutagenesis experiments, which could also validate our model.