Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predicting distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. Here we examine the potential of these residue-residue distance predictions to predict protein flexibility rather than static structure. We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were considered and classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. The average number of local maxima per residue pair was found to be different between the sets of rigid and flexible residue pairs. Flexible residue pairs more often had multiple local maxima in their predicted distance distribution than rigid residue pairs suggesting that the shape of predicted distance distributions is predictive of rigidity or flexibility of residue pairs.

中文翻译：

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协同进化距离预测用于灵活性预测

协同进化分析可用于从多个序列比对中准确预测残基-残基接触。机器学习技术的引入极大地提高了精度，并从预测二进制接触转变为预测残基对之间的距离。这些发展极大地提高了从头开始的准确性静态蛋白质结构的预测。在这里，我们检查了这些残基-残基距离预测的潜力，以预测蛋白质的柔韧性而不是静态结构。我们使用DMPfold预测一组既显示刚性又显示柔性行为的蛋白质中每个残基对的距离分布。认为在至少一种参考结构中接触的残基对被分类为刚性，柔性或两者都不分类。分析每个残基对的预测距离分布的局部概率最大值，该概率表示一对残基之间最可能的距离。发现每个残基对的局部最大值的平均数目在刚性残基对和柔性残基对的集合之间不同。