Hi,
How popular are projects in the field of structural bioinformatics (homology modeling performed by human experts)?
As someone that's done a little of both, here's my take, but this is entirely subjective.
Structural bioinformatics seems to be to be seen as a subset of "Bioinformatics". Certainly, I think genomics is the major player in the general field of bioinformatics, but it has to be that way, since all disciplines, including structural biology benefit from improved genome sequences etc. I don't think it is wrong to see structural bioinformatics as a subset though.
I agree with your premise that I think biologists and more 'conventional' bioinformatics projects could by and large benefit from the inclusion of more structural bioinformatics. Part of the problem is that structural bioinformatics, to my mind, cannot really provide many concrete answers, unlike a lot of the other subdisciplines. It is well known that we do not even have MD force fields which can model even just water very well, and this is fundamental. So, consequently, all models are wrong, and one way or another you're going to go and solve the structure of a protein experimentally instead.
Since you're going to go and solve the structure, the models become redundant, and therefore are always going to be relegated to being the 'best of a bad situation'. Something that you can lean on to come up with hypotheses about how some molecule or ligand interacts for instance - but it never 'solves' the question.
'Structural-functional approaches' are non-trivial in terms of the complexity and computation required (simulating protein structures for instance, is still a slow process). Not to mention, there is a significant amount missed by all of these hybrid approaches. A truly combinatorial study would have to include genomics, transcriptomics, epigenomics, proteomics, glycomics and any number of other -omic's, since the reality is that it is that complicated.
For what it's worth, I think structural modelling might be more intensely used in pharma companies, where they have some reliable starting models or data, and want to perform high throughput studies of what effects minor alterations to molecules has on activity etc (thinking along the lines of docking studies and QSAR here really).
Don't know if that answers your question, I'm still not sure what the objective of this question really is.
I definitely agree that operations on genomic sequences data can give answers that can be described in more approachable mathematical terms.
The presented problem of accuracy of structural bioinformatics is more a "mode of thinking" by people that work in the "wetlab" than scientific reality. Idea, that the most of wet experiments can be replaced by simulations seems to be posing many jobs in danger. I can understand that, and the social factors that lead people to think of computational modeling in such terms.
My original question meaning is that: if the only way to get something useful (new designer proteins, drugs, etc.) is by understanding molecular mechanisms, so why people think that analysis of genomes can provide any useful data beyond homology modeling?
The presented problem of accuracy of structural bioinformatics is more a "mode of thinking" by people that work in the "wetlab" than scientific reality.
I disagree with this entirely. Models are hypothetical, even if correct or accurate. You will never supersede the need to resolve structures or do experiments for real. I do wet lab work and computation, and I don't care how good your protein structure looks like it might be, at the end of the day its a stand-in until you get the real deal. Which to me means that this statement:
Idea, that the most of wet experiments can be replaced by simulations seems to be posing many jobs in danger
Simply isn't true. A far more significant threat to a wet lab biologists job security is automation rather than simulation.
In short: you cannot simulate molecular data and call it 'job done'.
so why people think that analysis of genomes can provide any useful data beyond homology modeling?
I think part of the problem with this question is that I don't see any evidence for this premise? Bioinformatics in broader terms encompasses lots of people doing a lot of transcriptomics/proteomics etc - if I'm not misunderstanding, you seem to be saying that people 'stop at the genome', which also - to me - just doesn't seem to match the reality. Not to mention, genomes provide much more information than just the sequence of a gene/protein.
This is going beyond science - into the philosophy of science realm. The main problem is that most of scientists can actually work in science without much thinking about its philosophical underpinnings. Sadly this is undermining the thing that is most important: their results.
Experimental scientists that work with physical equipment can in practice generate useful data, just by using that equipment. This information in turn can be used by others, and can be the basis of verification of theoretical results. This was the process that led to discovery of DNA double helix structure - theoretical exploration with the use of models.
There goes one important notion: theoretical science is just as important even if not more important than working with equipment and turning knobs (which indeed can be more and more automated). One should understand meaning of terms like "model" "hypothesis" and "theory", and the subtle differences that accompany each of those...
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What do you mean?
There’s essentially no way to quantify how popular these tools or approaches are.
As your question can't be answered precisely. This is a nice article (a little bit old 2016), but it may answer your inquiry especially related and external links in the article. Spotlight on Bioinformatics
Thanks. I was technically thinking in terms of "how structural approach to solving problems via application of tools used in computational/theoretical chemistry and molecular simulations" is a part of th thing called "bioinformatics". Sequences (an their comparative/computational analysis), even if they can provide useful information can only take us to the point that demand some hard data on how actually molecular biosystems operate to gain some insight how to use the knowledge in attractive applications. I think that we see some overrepresentation of genomes analysis in bioinformatics at the cost of structural-functional approach.