I wish to calculate the energy for a protein where the position of all the C-alpha atoms is known.

One way is to calculate the pairwise distances between the atoms and then look up a probability distribution to determine the likelihood that the distance for that particular pair is the given value, take the negative logarithm of that value, sum this over all pairs and this is a proxy for the molecular energy. This is the approach taken by the machine learning community. (Physics-Based Design of Protein-Ligand Binding)

Can anyone point me to a sample calculation or publication where this is elaborated in some detail?

I have very little background in chemistry beyond the introductory college courses. The question that I am trying to research is this: Given two spatial configurations of a molecule, how does one determine that one configuration has a lower energy than the other? What is the benchmark that is used for measurement? When Ramachandran generated the plots of torsion angles, how were the optimal angles calculated?

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    $\begingroup$ This isn't actual energy, but a scoring function that can be called like that (good to remember that). This one seems kinda simplistic one, not taking actual sequence into account? $\endgroup$ – Mithoron Feb 6 '20 at 22:55
  • $\begingroup$ What is your source for "this is the approach taken by the machine learning community"? Maybe that would be a starting point for finding a related publication. $\endgroup$ – Karsten Theis Feb 7 '20 at 2:02
  • $\begingroup$ There are a wide variety of ways of calculating energy or scoring - but none that I know of would be compatible with this type of descriptor. $\endgroup$ – Geoff Hutchison Feb 24 '20 at 17:57
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    $\begingroup$ You ask about the torsion angles - the first plots were done from experimental crystal structures. So 'ideal' torsions come from finding mean/median torsions across a lot of proteins and domains. $\endgroup$ – Geoff Hutchison Feb 24 '20 at 17:58
  • $\begingroup$ Thank you to all for your assistance. $\endgroup$ – Chris Mar 15 '20 at 14:00

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