An Introduction to Protein Interactions

An Introduction to Protein Interactions


Most people think that computational
biology is nothing but data analysis, but in reality what we do is much more than
that. What we are doing is inventing a new biology. Understanding physical and
functional interactions between proteins and living systems is of crucial
importance in biology. Proteins are complex dynamical objects and can look
like this or like this. Let us call it Alice. At LCQB, we want to understand how Alice behaves with the other proteins living in the cell. We want to understand
what kind of interactions Alice has with other proteins in her network. Alice
displays different binding affinities with her potential partners. These
circles of affinity exist for each protein in the community. This is how a
society of proteins takes shape. In order to reconstruct the whole network, we need
computational power to carry out molecular modeling at a large scale. By
taking a close look at Alice’s surface, we can understand her physical
interactions and identify her binding partners. The interaction sites of
proteins are these colored patches on their surface. When Alice meets Bob,
another protein from her community, Bob will prefer one of these sites and they
both might modify their structure for that. Due to this plasticity, a society of
proteins is not based on a one-to-one relationship, and Alice can have more
than one partner at a time. But how does she select them? By looking at how Alice
behaves with all the proteins in her community, we can identify the ones with
whom she has stronger affinities. This is how we can differentiate between Alice’s
fake partners, with whom she has low affinities, and her true partners.
Identifying the interaction sites of proteins helps us to compose complex
models. These sites can be predicted from the proteins’ amino acid sequences and
from their evolutionary conservation. First of all, we identify all the
organisms where we can find Alice. From human beings to unicellular organisms
like yeast, we construct the protein’s evolutionary tree. We collect Alice’s sequences of residues in all these organisms, align
them together, and search for conserved residues in Alice. When a position is
conserved across the evolutionary tree, almost all species preserve the same
amino acid. Amazing but true, conserved residues grouped together on the protein
surface, and once combined with shape and chemical properties, allow us to identify
binding sites. Let’s now take two sequence alignments–one for Alice, and we
fix a position on it–and the second for Bob–that we want to scan in its entirety.
For each pattern observed for Alice, we look for the same pattern in Bob, and
when we find it we also see the pattern on their trees. This means that Alice and
Bob’s residues co-evolved. These correlated mutations for the two
residues in Alice and Bob are an indicator of a physical contact between
them. Let’s now imagine that Bob lives in a child who is affected by a disease.
This might be because the sequence of Bob is altered and changed, say from H to
K. Running through his evolutionary tree, we can see how Bob’s sequences have
changed through evolution, and we can measure the difference from Bob H to Bob
K present in a different species that displays the mutation. The minimal
distance in the evolutionary tree between H and K is an important
indicator of the deleterious effect of Bob’s mutation from H to K. This way, we
can predict whether this mutation will have a strong impact on the child’s
health or not. A consequence of Bob’s mutation might be that he cannot bind
Alice anymore because their contact position is lost.
Due to the mutation, the network of interactions changes and might be
completely rearranged. At LCQB, we want to study how this network changes on a
large scale and develop computational methods sensitive to single and multiple
mutations. We want to provide our results to
medical doctors, biologists and pharmaceutical companies.

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