The root mean square deviation (RMSD), without the size dependency correction is?(a) \(\sum_{i=1}^N \frac{D_i^2}{N}\)(b) \(\sum_{i=1}^N \frac{D_i^2}{N + 1}\)(c) \(\sum_{i=1}^N \frac{D_i^2}{N x N}\)(d) \(\sum_{i=1}^N \frac{D_i^2}{N – 1}\)The question was posed to me in an internship interview.My question comes from Protein Structure Comparison in chapter Structural Bioinformatics of Bioinformatics

The root mean square deviation (RMSD), without the size dependency cor

1.	The root mean square deviation (RMSD), without the size dependency correction is?(a) \(\sum_{i=1}^N \frac{D_i^2}{N}\)(b) \(\sum_{i=1}^N \frac{D_i^2}{N + 1}\)(c) \(\sum_{i=1}^N \frac{D_i^2}{N x N}\)(d) \(\sum_{i=1}^N \frac{D_i^2}{N – 1}\)The question was posed to me in an internship interview.My question comes from Protein Structure Comparison in chapter Structural Bioinformatics of Bioinformatics
Answer» Correct answer is (a) \(\sum_{i=1}^N \frac{D_i^2}{N}\) For explanation: An important measurement of the structure FIT during superposition is the DISTANCE between equivalent positions on the protein structures. This requires using a LEAST square-fitting function called root mean square deviation (RMSD), which is the square root of the averaged sum of the squared differences of the atomic distances. Here D is the distance between coordinate data points and N is the total number of CORRESPONDING residue pairs.

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