And how do you predict it ? What is your input data (sequence of amino acids, temperature, pH, ...) ? Is there a "standardized" input that scientists agree on ?

Moreover, I've read that knowing the structure of a protein helps predicting its function, but is the prediction [Structure -> Function] reliable ? Should't we predict directly the function if that's our interest (I don't mean we should not take into account predicted structure, but I don't understand why the structure IS the purpose instead of the function) I also read the structure helps predicting affinities with other proteins and how it will bind : same question here, is this prediction [Structure -> Affinity] reliable and why don't we predict directly affinities.

To sum up a bit, I have the impression that the structure in itself is not important to know except that it is a good predictor of other protein properties (like function of affinity) and that the structure is kind of an 'intermediate' ? What am I missing ?

  • $\begingroup$ Structure tells you about the "hows" of the function. Just knowing the function will only answer the "what". Regarding affinity: that only works for binding reactions. What about conformational changes? $\endgroup$
    Commented Jul 23, 2019 at 12:08
  • 2
    $\begingroup$ Welcome to SE Biology. New users are expected to complete the Tour to find out how the site works. (We know that you haven't, otherwise you would have a 'badge'.) Please also read the Help on how to ask questions. You will see that you are expected to ask one question at a time and that you are expected to research the topic before asking. It is clear that you haven't tried to find out about methods of protein structure prediction, or much about proteins themselves. Please do that first. $\endgroup$
    – David
    Commented Jul 23, 2019 at 12:26
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    $\begingroup$ There are dozens if not hundreds of textbooks on this question. I do not feel it is suitable for the SE format. Even though some answers are present and have received upvotes, they don't even begin to answer the question(s). $\endgroup$
    – James
    Commented Sep 30, 2019 at 13:26

3 Answers 3


How do you want to predict function and binding partners without knowing how your protein looks like? The sequence itself contains only limited information. Similar sequences might fold into similar structures with similar functions. These motifs can be used to transfer your knowledge from one protein to another, which might have similar e.g. binding abilities. But the motif might be nonfunctional in the second protein, because it is hidden in an inaccessible part of the protein due to its folding structure.

Interactions between proteins are weak compared to intramolecular bonds and dynamic. Different amino acid side chains have different characteristics (like polarity, hydrophobia etc.) which make specific interactions possible. Specific amino acids have to be accessible and while they might be far apart in the sequence, the folding of the protein brings them close together in its final shape.

Even small modifications like phosphorylation can alter the structural conformation significantly and e.g. change the enzymatic activity. Therefore, for analyzing protein function, finding binding partners or designing binding compounds (drug development), we need to know its three-dimensional structure. With the structure you can simulate binding affinity/dynamics. Scientists also try to resolve the protein structure in its different states to clearly see the differences.

Keep in mind that complex diseases can be caused by a single mutation, which exchanges only a single amino acid in the sequence but can have severe implications for protein function. Knowing the structure, the position of the amino acid and how the change affects the characteristics of the protein domain (e.g. charge), we can fully understand what's happening on the molecular level.

Since it is far from trivial to analyze the structure of a protein, predictions bridge the gap for functional predictions until the molecular structure of the protein has been reconstructed. But only with atomic resolution you will be able to properly identify interactions.

The question of how to do structural prediction might be beyond the scope of this answer. Pubmed lists around 400 papers each year on this topic. Depending on the amount of information you have about the protein or its family members (proteins with very similar sequences), you can use other known structures to predict an unknown structure:

https://medium.com/@HeleneOMICtools/a-guide-for-protein-structure-prediction-methods-and-software-916a2f718cfe https://medium.com/@HeleneOMICtools/a-guide-for-protein-structure-prediction-methods-and-software-916a2f718cfe


There are several reasons why understanding protein structure is useful; the most obvious is that drugs that interfere with a specific protein can be deliberately designed based on the protein structure.

Today, even though there is still quite a bit of fine-tuning necessary to perfect the process, structure-based drug design is an integral part of most industrial drug discovery programs [4] and is the major subject of research for many academic laboratories. ... The process of structure-based drug design is an iterative one ... Additional cycles include synthesis of the optimized lead, structure determination of the new target:lead complex, and further optimization of the lead compound. After several cycles of the drug design process, the optimized compounds usually show marked improvement in binding and, often, specificity for the target.

--The Process of Structure-Based Drug Design


There are three broad questions here, together covering much of the field of structural bioinformatics. I will answer each briefly but point you to a textbook for more.

Why is predicting protein structure useful?

This is actually a very good question. The standard answer here is "drug discovery", but as things stand anything other than a high quality homology model isn't particularly useful for drug discovery. I can't think of any examples where de novo structure prediction has directly led to the discovery of a drug, for example by virtual docking in a binding site, though I am willing to be proved wrong. In the future though, as protein structure prediction and virtual screening both improve, this could end up being an important application of structure prediction.

Other current uses that are more developed are: A) protein design, where improvements in structure prediction allow you to find better sequences that form certain structures and carry out certain functions (the inverse folding problem); B) exploring the evolutionary relationships and function of a protein, e.g. if a predicted structure looks like all the other membrane transporters then it probably is one too (see more on that below); and C) running molecular dynamics simulation on the structure to gain biological insights and complement experiments.

On a deeper level, scientists will always seek to answer the question of what structure proteins fold to and how they fold, because it's just such an interesting problem that is central to molecular biology. Solving it will almost certainly lead to useful breakthroughs, even if their exact nature is unclear now.

How do we predict protein structures?

By the original formulation of the problem, protein structure prediction is arguably solved. If you can find a template, i.e. a related protein sequence with an available experimental structure, then you can pretty reliably get a high quality model (less than ~3 Å RMSD). Improving a model beyond this is currently called "refinement" and this will become increasingly important as we look to get ~1 Å RMSD models that can be used in place of experimental data.

If you can't find a template you can still have a decent go at the structure, provided you can find enough related sequences. It turns out that positions in a multiple sequence alignment will covary if the residues are close in space in the structure. Initially statistical techniques were used to extract direct from indirect coupling effects, but now deep residual neural networks are showing state of the art results in this field. These developments are recent and have been the focus of news reports. The explosion of sequence data facilitates this approach, though it is still not "the solution" to those who only want to use a single sequence as input data. For pure physics-based approaches there has been limited success on small proteins, see for example here, but these methods are not in broad use for structure prediction.

Usually the input to these methods is just the protein sequence, though you often bring in other data (templates, related sequences) as part of the pipeline. We generally care about the structure at physiological conditions, which usually corresponds to the structure found in x-ray crystallography or NMR, so predictions under different conditions are not yet routine. For more on protein structure prediction, see the CASP website and have a read of their papers.

How useful is protein structure at predicting function?

Predicted structure can be used to transfer function from related structures with known function - see for example here and here.

It is not currently possible to use a predicted structure to predict the function using chemical arguments, for example by saying "I have predicted a binding site with a certain arrangement of amino acids so this must have function X". However as structure prediction improves and we have more structures and functional annotations, this is an exciting prospect.

With respect to protein-protein affinities, if you have the structure you can start to predict and rationalise the structure of protein complexes. Such predictions from structure alone (i.e. not using homology to known complexes) are not routine yet, though more data and better models will improve this. See for example CAPRI. This is clearly a biologically important area, as most proteins form complexes.


Sequence determines structure determines function (fingers crossed as I'm simplifying quite a bit).

You shouldn't have to know the structure to predict function/binding from sequence, but it helps, and a sufficiently advanced system would learn this anyway in order to make the connection.

Protein structure prediction is a hot research topic that currently has limited applications, but is certain to have more in the future. If anything, it has only got more interesting over the last 50 years.


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