Python Programming for Biology: Bioinformatics and Beyond



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[Tim J. Stevens, Wayne Boucher] Python Programming

Conservation analysis


Once we have some sequences that we are sure are related to one another, we can begin to

look  at  how  the  sequences  differ,  despite  the  common  connection.  Such  sequences  are

often  different  versions  of  a  gene,  which  function  in  the  same  way,  from  different

organisms. The basic principle of this type of analysis is that when the biological role of a

particular  set  of  DNA  sequences  (and  thus  also  any  protein  produced)  is  conserved,  the

residues in the sequence that are important for this function are also conserved, but those

that  are  not  so  important  are  more  free  to  vary.  DNA  sequences  naturally  change  when

cells  divide  (and  the  changes  are  passed  on  to  future  generations  when  organisms

reproduce)  because  of  the  error-prone  nature  of  DNA  replication.  If  a  sequence  change

occurs that is detrimental to the function of the cell or organism, then the change will tend

not  to  be  passed  on;  the  cell  may  die,  offspring  may  not  survive  or  the  descendants  will

not be as successful as those within the population that are unaltered. Conversely, changes

that are of little or no detriment will be tolerated. These could be at unimportant genetic

locations,  for  example,  the  last  position  of  a  codon  is  often  irrelevant  for  determining

which  amino  acid  is  produced;  or  they  could  be  variations  that  do  cause  a  noticeable

change but which function just as well, like when one amino acid changes for another that

can act in the same way.

Simply  by  aligning  sequences  and  discovering  positions  that  significantly  preserve

residue  type  we  can  tell  that  those  positions  are  important,  even  if  we  do  not  yet  know

why  they  are  important.  Also,  if  we  can  classify  sequences  that  we  know  act  differently

despite being similar, then the individual changes in the sequence can often explain why

the  sequences  as  a  whole  act  differently.  To  take  an  example  from  the  study  of  genetic

diseases, if you look at the beta-globin gene in people who have sickle-cell anaemia and

compare  it  to  those  who  do  not  have  the  disease,  it  is  very  easy  to  generate  a  sequence

alignment  to  see  that  there  is  a  change  in  the  DNA,  and  hence  protein  sequence,  of  the

seventh  codon  which  is  only  present  in  those  with  the  disease.

2

 Further  investigation



shows that this change really is the underlying cause of the disease; it causes haemoglobin

to stick together aberrantly.

When you look in detail at positions in a protein sequence and measure how well the

residues  are  preserved,  then  the  reasons  and  effects  are  often  best  understood  by

considering  the  folded  structure  of  the  protein;  i.e.  by  considering  the  three-dimensional

locations  of  the  atoms.  Amino  acid  residues  that  are  involved  in  a  specific  chemical

reaction that is catalysed by the protein, at its active site, are usually very well preserved.

Other  residues,  for  example,  in  the  folded  core  of  the  protein,  may  be  well  conserved

because  of  their  importance  in  determining  the  shape  of  the  protein,  although  some

variation will be tolerated in the amino acids if they are replaced by similar types that fit

together  in  a  similar  way.  Positions  that  are  not  so  important  for  the  shape  of  a  protein,

generally  the  residues  on  the  surface  and  those  in  flexible  regions,  will  tend  to  vary  the

most.  However,  even  in  such  locations  there  are  some  constraints  on  which  amino  acids

are tolerated for normal function; for example, a change could make a necessary flexible

region inflexible.

If  we  step  backwards  from  the  scale  of  an  individual  gene  or  protein  and  look  at  the

context of lots of genes on the chromosomes which make up a whole genome, then we can

observe trends that show how the genome as a whole is evolving. A good example of this

is  that  when  the  human  genome  is  compared  to  the  chimpanzee  genome  it  becomes



apparent  that  the  human  chromosome  2

3

 has  no  single  chimpanzee  equivalent;  indeed



there  are  two  chimp  chromosomes  that  correspond  to  the  human  one.  We  are  certain  of

this  because  the  relative  location  and  identity  of  equivalent  human  genes  is  preserved,

even if the length of chromosome differs. Going on from this, further analysis shows that

the  human  chromosome  has  been  created  from  the  merging  of  two  smaller  ones;  other

monkeys and apes have two rather than one, so we are sure that two chromosomes is the

ancestral situation.




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