How to interpret data projected on the sum of first few principal components weighted by eigen values?

I have simulation time series data of a molecule from Molecular dynamics and I want to visualize the very high-dimensional trajectory in two dimensions and also identify some clusters. The problem is that when I do PCA, the first 20 eigenvectors are needed to explain 80% of the variance. Is it possible to add the first 10 eigenvectors and get a single vector V1 and add up the 11th to 20th vector as V2 all components weighted by their eigen value? I can then project the data on the plane defined by v1 and v2. Does this make sense from the point of view of data analysis or there are some pitfalls.