[gmx-users] Extreme

[pawan raghav]

Dear tsjerk,

I am very sorry to say that your suggestions does not help but increase
confusions only. I am highly obliged if you give me clear statements about
the scores to understand this concept in depth. I have searched research
article about this concept but failed to find even an single article.
Neither Gromacs tutorial give any description about these scores, nor your
PCA tutorial. I am really interested to understand this concept so help by
giving me answers of my questions.

eigenvector           Minimum           Maximum

                 value       time      value       time

      1      -9.162661       99.0   2.682097     9450.0

      2      -2.728093     4695.0   3.272116      558.0


I got clearcut idea about this line after read your tutorial "*These are the
maximum and minimum projections on the eigenvectors."*

What is the meaning of this line here? *"They are very unlikely to
correspond to energy minima, as minima will be modal."*

If the values in second column not the energy values they are scores only.*
*Are scores directly corresponds to the energy minima for second column and
energy maxima for 4th column on which they are based? I have seen that in
second column there are negative values (Minimum) and in 4th column there
are positive values (Maximum). In above example along the first eigenvector,
Are -9.162661 value at time 99 ps corresponding to the structure at low
energy during the projection and the value 2.682097 score corresponds to the
maximum energy. If I am wrong then tell me, what are the criteria on which
there scores are based?

I am totally agree with your this concept and understand completely in this
line *"Think of a pendulum, projecting the position on the floor."*

But this statement confused me *"The extreme projections actually correspond
to states of higher (potential) energy"*. Which extreme projection
corresponds to state of higher (potential) energy either minimum or maximum
or both of them?

Please reply me line by line so that I can understand this concept.

-- 
Pawan Kumar Raghav
Bioinformatician
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