Pyspark select multiple columns from list and filter on different values

Question

I have a table with ~5k columns and ~1 M rows that looks like this:

ID	Col1	Col2	Col3	Col4	Col5	Col6	Col7	Col8	Col9	Col10	Col11
ID1	0	1	0	1	0	2	1	1	2	2	0
ID2	1	0	1	0	1	2	0	0	0	2	1
ID3	1	0	0	0	0	2	1	0	1	1	1
ID4	0	1	1	1	1	1	1	1	1	0	0
ID5	0	1	1	1	1	1	0	1	0	0	0

I want to select different columns matching column names from different lists and subset the rows according to different criteria. For example if my list1 has col1, col3, col4, col11 and list2 has col2, col6, col9, col10. I want to filter rows as list1 == 0 AND list2 == 1. E.g df1 = df.filter((df.col1 == 0) & (df.col3 == 0) & (df.col4 == 0) & (df.col6== 1) & (df.col9 == 1) & (df.col10 == 1)) . Instead of adding column name each time, I want these columns to be selected from two different lists. How can I achieve this using PySpark?