Yes, it’s that time when we all get together in front of the screen to watch another beautiful game played by that fantastic team contributing to the Carnival of Evolution. This time hosted on the lovely green pitch of Byte Size Biology. So get your popcorn, sunflower-seeds, crisps or any other culturally-appropriate sports-watching food and…… the referee whistles! The game has begun!

Phenotypes! How do they happen?

Kicking off is Grrlsicentist from Punctuated Equilibrium telling us How the Penguin got its Tuxedo. While skillfully dribbling across the field, she tells the story of fossilized feathers from a giant, extinct penguin which contain fossilized melanosomes: intracellular structures whose shape can that tell us of the feather coloration of the bird. No, it was not black and white, but rather brownish and gray. However, melanosomes also strengthen the feathers, and today’s giant melanosomes, giving the familiar black coloration may have evolved as a results of a selection for feather strength, rather than color. Feather-minded (but far from feather brained!) she touches the ball across the defender and reports on how the parrot got its beautiful plumage. (Norwegian Blue?) Would you have thought resistance to bacteria degradation?! A short pass to Jerry A. Coyne who, while on the same topic, explains in Why Evolution is True about the evolution of cat coat-patterns and other issues relating to genetics of the coat in cats.  He makes a quick pass to Bjørn Østman who may have personally discovered the next stage* in feline evolution: the six digit cat! Bjørn toe-punches the ball hard and…

…the ball travels high forward left  to be intercepted by Eric Michael Johnson from The Primate Diaries in Exile. He high-knees the ball twice while asking whether our ancestors were polygamists, monogamists, or happy sluts? All this in his post: “Sex Evolution and the Case of the Missing Polygamists“. Eric launches it off with a strong left kick, the ball arches and jumps once on the ground, only to encounter  Jason Goldman’s knee, bouncing the ball while showing a movie which presents two different hypotheses explaining how wolves were domesticated into dogs. The first: young wolves would be adopted into the camps of early humans. Only those who were most tame would breed with eachother, and over many generations, the domestic dog would emerge. The second: wolves “chose” to be domesticated – they noticed a lot of tasty trash around human encampments, and if they were unafraid enough to hang around, they got to eat lots of leftovers, and those individuals would mate, and over generations, the domestic dog would emerge. His theory-and-ball juggling are interrupted by Kevin Z who takes over smoothly and talks about eyes and sex in lizardfishes posted at Deep Sea News. Kevin now with a square pass to John Wilkins who ponders a rather big question in our understanding of speciation: how many concepts of species are out there? He passes the ball all the way to Hannah Waters in the 16 meter box, who cleanly intercepts the ball while asking a related question: are Eukarya actually part of the Archaea domain, making life a two-domain system, or does the three-domain system still hold? But just as she is about to turn the ball around preparing for a shot at the goal, the referee (whom some say is biased towards the now-defunct 5 domain hypothesis) whistles for an offside violation, prompting loud boos from the crowd! Hannah grudgingly relinquishes the ball, which is given to the other team.

Evolution and Creationism

ProbabilityZero dead-balls a strong and furious kick. Furious over the agenda of the US Tea Party that includes teaching creationism in US public schools. All this in the recurial blog. The ball travels to Jayson D Cooke who is asking in an open letter why the University of Southern Queensland in Australia is hosting a creationist event under a scientific guise, he also defended his opinion on the air. Meanwhile in the stands, Michael D. Barton is selling cartoons of Darwin and evolution (from both sides of the fence, also here) from The Dispersal of Darwin. Some of the football fans accuse Michael of selling products of a man who advocated “Might is Right”. That is patently untrue, for many different reasons, the chief one being a misunderstanding of the word “fittest” in “survival of the fittest”. Fittest does not means “strongest”, but “the best able to reproduce”. However, Michael’s business associate, Eric Johnson decides to talk to the crowd about Darwin as a compassionate person, as manifested in his opposition to vivisection.

Lucas Brouwers from thoughtomics appears from ProbabilityZero’s blind-side, grabs the ball and, considerably faster than plate tectonics, advances up the pitch to the rival penalty box. Although, speaking of plate tectonics, Lucas talks about how freshwater crabs help us map continental drift. He is tackled by a rival player, falls, gets up, picking burrs from his socks, and wondering how they evolved? (The burrs, not the socks.) The answer comes from Melissa who while out walking the dog talks about the burry man, the burry dog and burdock. Why she is walking the dog in the middle of a football game? No idea.

Lucas forward-passes to another player concerned with speciation, Jeremy Yoder at Denim and Tweed talks about the speciation of rockfish: it appears that in many cases depth, not geographic distance, is the allopatric factor in rockfish speciation.  He passes it to DeLene Beeland who takes this question even further: how do we define species in the first place? She turns the ball around, sets for a kick and… goooooaaaaaaal!!!!! Yes! In the stands, Digital Cuttlefish dances with joy.

The referee whistles for halftime, and the players, sweaty and covered with mud and burrs step off the pitch.

Halftime

While we are waiting for the second half to begin, Bjørn Østman tells the viewers at home why intelligent people watch more TV. Or, perhaps not? Read to find out. This public service announcement has been sponsored by Time Tree: just enter the names two species, and find out how long ago they diverged! While the players are resting, they audience watches a beautiful video of the Applied Evolution Summit in Heron Island, courtesy of R. Ford Denison from This Week in Evolution. Also, Bjørn announces the long-awaited results of the Carnival of Evolution Readers Survey. One interesting point that came up is the contentious phrasing of the question: “do you believe in evolution?”

The second half begins. A short pass by Greg Laden explaining what is the most important human adaptation. (Hint: no, not bipedalism.) Zen Faulkes sprints forward – and wonders: did sprinting behavior shape the stings of scorpions, or is this explanation yet another “just so” evolutionary story? Cross-pass to R. Ford Denison who talks about the evolutionary benefits of cooperation and kin selection. Specifically, that Hamilton’s rule still holds, even though it has recently come under fire in a much publicized article in Nature. Competition is also an adaptive force, and Becky Ward tells us about the weird competition between a spider and a plant: both of which are predators! She passes to Lucas Brouwers, who makes small adjustment to the ball’s trajectory before passing it on, noting that evolution also generates novelty through subtle tinkering.

So how does the game end? It doesn’t. Evolution does not end. It just keeps going on and on and on… The next Carnival will be hosted at This Scientific Life. It is never to early to submit.

As part of the process of manufacturing  a new car,  the designers will take the blueprints to the factory floor. There they will set up an experimental assembly line, tinkering with the manufacturing process of the prototype until it is ready for mass-production. Can we do the same with the machinery of life – the assembly of proteins? Can we set up an alternative assembly line for a new protein prototype — and then actually set up a working assembly line for the whole new protein?  A proof-of-concept has been published this week in Nature by Jason Chin’s group at the Medical Research Council Laboratory of Molecular Biology, Cambridge UK.

If there is a single common denominator to all life, it is the genetic code. All life is built around DNA encoding information for proteins  nucleotide triplets or codons. Since there are four types of nucleotides (A,T,G,C)  that are read in words of thee, there are 4³ = 64 possible codons: more than enough to encode for the 22 amino acids that make up proteins. There is nothing more basic and fundamental to life on Earth than the three-letter based genetic code.

Until now.

Chin’s group has created a four-nucleotide codon system.  It is not that the DNA is different: it is the way the cellular machinery decoding  RNA transcripts interprets the nucleotide sequence. Ribosomes –large RNA and protein complexes  which are the platform upon which messenger RNA is read and decoded — are set to serve up messenger RNA three nucleotides at a time. (Messenger RNA or mRNA is a transcript of the DNA which is carried to the ribosome.)  Transfer RNA or tRNA is a short RNA molecule that shuttles the proper amino acid to the ribosome, but will only attach if the proper codon is served up by the ribosome. The whole protein synthesis “assembly line” looks something like this:

Protein synthesis. Credit: Wikimedia Commons.

To change the interpretation of the genetic code from three lettered words  to four, Chin and his colleagues had to make new ribosomes, and new tRNAs.  To create these new ribosomes, they designed orthogonal ribosomes, or o-ribosomes. O-ribosomes are genes inserted to produce extra ribosomes that operate in the cell alongside the regular ribosomes. The cell functions because it has the regular ribosomes to maintain its viability. The ribosomal RNA in the o-ribosomes is free to be mutated to create new unnatural traits: in this case, the ability to serve as a platform read four-letter codons. They selected for Escherichia coli bacterial cells that expressed a o-ribosomes which translated a four-letter codon in a gene, which would otherwise go untranslated by the regular ribosome. The gene gives the bacterial cells resistance to the antibiotic chloramphenicol. So cells that survive a dosage of chloramphenicol are those which have functioning o-ribosomes, as they have the chloramphenicol resistance gene that is being translated by the o-ribosomes.

They also needed to create new tRNAs that have an four-nucleotide anticodon (the part that complementarily binds to the messenger RNA –  see figure above.)  So the surviving E. coli cells have a population of working o-ribosomes, regular ribosomes, modified tRNA (with a  four-letter anticodon) and regular tRNA.

Then they took their work a step further. Each three-letter tRNA carries a specific amino-acid, depending on its anticodon. Thus tRNA_AAG will always have a phenylalanine attached, because CTT (the complement of AAG on the messenger RNA) codes for phenylalanine. If you start messing with that, the translation machinery will produce non-functional proteins, which will probably kill the cells pretty quick. But with the orthogonal 4-letter code machinery, that is not really a problem: the orthogonal machinery operates alongside the normal one. Also, there are no amino acids naturally assigned to any four letter code, because this code does not appear in nature in the first place! So Chin’s lab assigned an unnatural amino acids to a four-letter code. The non-naturally occurring p-azido-l-phenylalanine amino acid was assigned to tRNA_UCCU. They then showed that the whole alternative translational machinery worked by synthesizing a mutant of the protein calmodulin which used this amino-acid in its structure.

Why do it? Well, personally I don’t see the need for justification: just being able to do it is so cool!  But seriously: think of the ability to design proteins from up to 4⁴=256 different amino acids other than the 22 we have now.  The possibilities of tinkering with existing proteins using this orthogonal, four-letter based machinery are huge. The other benefit of this orthogonal synthesis setup is the ability to control this orthogonal translational machinery: because it does not use the three-letter vocabulary, this orthogonal machinery would be much easier to manipulate, tinker with and switch on and off without getting in the way of regular cellular translational machinery. The analogy to a car assembly line breaks here. It is as if two different models are being assembled on the same line just by using different robots. The better analogy is for a program source code to be read by two different compilers, each producing a different program. Awesome.

Another example of polypharmacology is a drug that binds to multiple targets in the human body. This could be used for overcoming drug resistance, a known problem with cancer. Cancer tumors often develop a resistance to anti-cancer drugs by simple natural selection: the protein that the drug binds to mutates, and no longer binds the drug. However, if the drug acts by binding redundantly to several proteins, it would be more effective, since several mutations would be required to effect drug resistance.

Another important polypharmacological consideration  is toxicity. If a drug binds to one protein, its drug target, it may also bind to another one which it should not bind to as it disrupts the normal functions and the health of the patient. If the side effects outweigh the cure, the drug is no good.

How one drug (cyan) can bind to two different proteins with different overall shapes (pink and green), but with similar binding sites

Because it can either increase  profits, or conversely derail a whole process of drug development, predicting polypharmacophoric effects is very much something drug developers want. A study published yesterday in PLoS Computational Biology by Jacob Durrant and his colleagues suggest a way  bioinformatics and theoretical biophysics can help in identifying multiple drug targets. Durrant’s goal was simple: given the molecular structure of a candidate drug, which proteins are expected to bind it? The strategy this group took is a combination of bioinformatic and experimental screening.

Finding additional drug targets in four  steps

Reproduced under CC license from doi:10.1371/journal.pcbi.1000648.g001. Click for original image.

Step 1 (A-C in the figure above): identify the known target protein. Now pick all the protein structures that are not similar to it. Why those that are not similar? Similar proteins could be potential drug targets, since they have a similar shape to the known target protein. But here they are interested in finding targets from proteins that are of a different shape, and have no homology to the known target protein: secondary targets.

Step 2 (D): take this set of dissimilar proteins, and look for binding site similarities. Binding sites are clefts in the protein that may bind drugs. If those clefts are similar in shape to the cleft in the known target proteins, they may bind the drug. Leave only those non-homologous proteins that have similar binding sites (D in the figure)

Step 3 (E): Now add all the proteins that are homologous to the set generated in step 2. This increases the number of possible targets to homologs of the proteins that were initially selected only for binding site similarity.

Step 4 (F): take the drug molecule, and try to dock it to the various protein structures. Rank the druggability of each protein according to the score provided by the drug docking software (Autodock).

Experimental verification

Now for the cool part. Durrant and colleagues tested  the computational prediction in the lab, using the compound NSC-45208. NSC-45208 inhibits a protein responsible for RNA processing in Trypanosoma brucei. So we know it is a potential drug against African sleeping sickness. What else is it good for?

(NSC-45208), 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7 -naphthalenedisulfonic acid, a recently discovered inhibitor of T. brucei RNA editing ligase 1 (TbREL1)

“The predicted secondary targets that gave the best docking scores, H. sapiens mitochondrial 2-enoyl thioester reductase (HsETR1), T. brucei UDP-galactose 4′ epimerase (TbGalE), H. sapiens phosphodiesterase 9A (HsPDE9A2), and Streptococcus pneumoniae teichoic acid phosphorylcholine esterase (SpPce), were subsequently tested experimentally.”

Durrant and his colleagues tested their predictions that NSC-45208 also binds to two human proteins (HsETR1 and HsPDE9A2), one additional Trypansome protein (TbGalE), and a bacterial (Streptococcus pneumoniae) protein (SpPce). Not only binds, but also inhibits their enzymatic activity. Their predictions worked well on the top two predicted targets: NSC-45308 inhibited the enzymatic activity of HsETR1and TbGalE, but HsPDE9A2 and SpPce were not affected by the drug.

At first blush, this does not seem to be much of a batting average: two positives and two false positives. But we have to remember that the experimental verification of these predictions — even if you have a good enzymatic assay to check predictions– can be very time- and resource consuming. An exhaustive test of the predictions for several compounds and many target enzymes is still not possible. As initial proof-of-principle, this work goes much farther than most other joint experimental / computational works I have read. The authors also go into lengthy and detailed discussions on limitations and improvements, as well as on another form of non-specific inhibition of the secondary targets, makes for a really interesting read on polypharmacological considerations in drug screening.

The structures’ PDB codes are:

1CMW, 1DF9/2QID, 1G40, 1G44, 1L6L, 2OU1, 1RID, 1Y8E, 2A01, and 2HR0 Some of them are still in the databank.

The University of Alabama at Birmingham has requested that the Research Collaboratory for Structural Bioinformatics Protein Data Bank remove certain protein structure files deposited by a former UAB employee. UAB also has identified nine publications related to the same protein structures that should be retracted from various scientific journals, and is making those journals aware of this matter.

Allegations of data fabrication and/or falsification were made concerning certain protein structures published by the former UAB employee. In accordance with UAB’s scientific integrity policy, and that of the Office of Research Integrity of the U.S. Department of Health & Human Services, UAB empanelled a committee of experts with no conflicting interests to investigate these allegations. After a thorough examination of the available data, which included a re-analysis of each structure alleged to have been fabricated, the committee found a preponderance of evidence that structures 1BEF, 1CMW, 1DF9/2QID, 1G40, 1G44, 1L6L, 2OU1, 1RID, 1Y8E, 2A01, and 2HR0 were more likely than not falsified and/or fabricated and recommended that they be removed from the public record.

“Scientific misconduct is absolutely unacceptable,” said UAB Scientific Integrity Officer Richard B. Marchase, Ph.D., vice president for Research and Economic Development. “It was important that the files be removed from the database and the articles be retracted to ensure that future research in the areas of macromolecular structure analysis and the function of proteins could continue uncompromised by faulty data.”

Some of these structures date back to 2002; this has been going on for quite a while then.  Apparently the investigation ended May 2009, but UAB only  issued a statrement today. The associated papers are also being retracted.  If anyone has more information on this strange affair, please share here.

Dear friends and colleagues:

It’s now been over a week since Warren has passed away.  We are trying to
move toward a permanent way to honor Warren’s memory and what
he stood for: Open Source Computational Biosciences and molecular
visualization. To do this, Jim Wells and I put together a mission statement
with the approval of Warren’s family:
The Warren L. DeLano Memorial Award for Computational Biosciences

This award shall be given to a top computational bioscientist in
recognition of the contributions made by Warren L. DeLano to creating powerful
visualization tools for three dimensional structures and making them freely accessible.
The award, accompanying lecture, and honorium will be given annually in the context of a
national bioscience meeting or a Bay Area gathering of
computational bioscientists at Stanford, UCSF or UC Berkeley. For the award special emphasis
will be given for Open Source developments and service to the bioscience community.
The award selection committee, consisting of experts in the computational and
biological sciences, will accept nominations from anyone.
To make something like this happen in perpetuity would take about ~100K for
the endowment.

For donations, Warren’s family has set up a tax deductible fund:

Silicon Valley Community Foundation
memo:  Warren L. DeLano Memorial Fund
2440 West El Camino Real, Suite 300
Mountain View, CA 94040
tel: 650.450.5400

We hope that you’ll consider making a contribution (not matter
how small) in Warren’s honor.  Also, please forward this message
to anybody who might be able be willing to contribute.

Best regards,
Axel

Axel T. Brunger
Investigator,  Howard Hughes Medical Institute
Professor of Molecular and Cellular Physiology
Stanford University

Dear CCP4 Community:
I write today with very sad news about Dr. Warren Lyford DeLano.
I was informed by his family today that Warren suddenly passed
away at home on Tuesday morning, November 3rd.
While at Yale, Warren made countless contributions to the computational tools
and methods developed in my laboratory (the X-PLOR and CNS programs),
including the direct rotation function, the first prediction of
helical coiled coil
structures, the scripting and parsing tools that made CNS a universal
computational
crystallography program.
He then joined Dr. Jim Wells laboratory at USCF and Genentech where he pursued
a Ph.D. in biophysics, discovering some of the principles that govern
protein-protein interactions.
Warren then made a fundamental contribution to biological sciences by
creating the
Open Source molecular graphics program PyMOL that is widely used throughout
the world. Nearly all publications that display macromolecular
structures use PyMOL.
Warren was a strong advocate of freely available software and the Open Source
movement.
Warren’s family is planning to announce a memorial service, but
arrangements have
not yet been made. I will send more information as I receive it.
Please join me in extending our condolences to Warren’s family.
Sincerely yours,
Axel Brunger

Axel T. Brunger
Investigator,  Howard Hughes Medical Institute
Professor of Molecular and Cellular Physiology
Stanford University

First, a victory dance:

Next, the scientific background:

And part of Ada Yonath’s model in this clip:

Proteins are the machinery of life, and they facilitate most of life’s functions. Traffic into and out of the cell? Protein pumps, pores and channels. Respiration? Proteins. Metabolism and catabolism? Proteins. Immune system, signaling, development…  all complex networks of interacting proteins. Understanding a protein’s  structure can tell us a lot about how it performs its function. If we know what a protein does, we can look at it’s molecular workings, and generally figure out how it does it. Hemoglobin carries oxygen in most animals, something that has been known since 1840. However, it is only when Max Perutz and John Kendrew solved the structure, that the actual mechanism of oxygen binding and release has been elucidated. Since Perutz’s and Kendrew’s discovery in 1949, the structures of some 35,000 proteins have been solved.

Animation showing binding and release of oxygen molecules to hemoglobin

When we know the protein’s structure we know a lot about how it performs its function.That would be the equivalent of looking at a  diagram of a car engine, and then exclaiming: “oh, so that’s how it works!” But the converse does not hold true. If we have the structure, we may not be able to infer the protein’s function. Imagine having the diagram of a new engine which you have never seen before. It might be a car engine, but which make and model? Or it might not be a car engine at all, but that of a lawnmower, or a boat, an electric generator. The point is, without knowing what the diagram represents, we would only have a general idea that we have a machine that burns some sort of fuel to power something.

[Show as slideshow]

[View with PicLens]

We face the same problem with protein structures. It does happen that we solve the structure of a protein, whose function is unknown. Oh. Kay. What now? We are stuck with a diagram for a machine which we do not know what it does.  Therefore, any kind of method we can devise to predict a protein’s function from its structure would be very helpful. Christine Orengo’s group at University College London, UK has been tackling this problem for quite a while. Her group has recently published a paper in PLoS Computational Biology where they describe an algorithm that can classify engines enzymes: a subgroup of proteins that catalyze chemical reactions. The classification algorithm works as follows:

1) They partitioned all enzymes of known function into functional subgroups, or FSGs. Within an FSG, all proteins have the same function. Two proteins from different FSGs will have different functions.

2) Next, they selected a set of conserved vectors from a given domain in a given FSG which, when compared against relatives of different functions/FSGs, would produce a low score. Conversely, when proteins from the same FSG are compared, they should have a significantly higher score.The vectors are measurements of distance and direction along the side chains of conserved amino acid residues. They found that this differentiating set of vectors is best obtained when the proteins are aligned within and between FSGs, and the vectors are taken from the conserved residues in the FSG alignments.

Graphical outline of FLORAMake algorithm. Click to enlarge. doi:10.1371/journal.pcbi.1000485.g002

3) Once they determined which vectors are more conserved within a given functional sub-group (FSG), they created a library of conserved vectors within FSG, a sort of an FSG bar-code. Although the constriction is technically unsupervised, limiting the vectors to conserved residues within an FSG naturally lands them with lots of active site residues.

Having created the template library, they can now find vectors on test proteins, and scan those against the library of conserved vectors, using a simple similarity function. Although (or because) their method is quite simple, they receive very high sensitivity and precision. The methods they compare against are all global structure aligners (such as CE and CATHEDRAL), and by virtue of simply adding spatial information of the conserved / functional residues they greatly improve the function annotation. The great thing about this work is the jump in improvement by adding this very simple, yet so far mostly neglected, attribute.

Unfortunately, no software yet. Too bad because…..

(Thanks to F.B.  for the inspiration).

Sigh… people don’t seem to learn. It’s been almost 22 years (yikes!) since a distinguished group of scientists published a letter in Cell calling for a responsible use of the word “homology”. If you were born when that letter was published, then in the US you can already drink legally. And you may very well want to, by the time you finish reading this post.

As of today there are one hundred and sixty seven articles listed  in PubMed with the phrases “distant homology” or “remote homology” in either the title or the abstract.

Please: make it stop.

Homology is a qualitative term.  It means having a common evolutionary origin. Two genes / proteins / organs are either homologous, or they are not. They cannot be “somewhat homologous” or “partially homologous” or (a favorite among molecular and structural biologists) “distantly / remotely homologous”.

Homology is inferred from similarity.  Similarity is quantitative. If organs are sufficiently similar, like mammalian forelimbs, then they are considered to be homologous. They maybe more similar (like the hands of humans and chimpanzees), or less similar (like human hand and a bat wing). Nevertheless, once they pass a certain similarity threshold, homology is inferred. The same applies to sequences of proteins and nucleic acids.  Similarity can be measured. Different degrees of similarities can be compared and scaled.

If two protein sequences are aligned, and 40% of the amino acids in the alignment are identical, then the two sequences have a 40% identity. The do not have a 40% homology. They are  homologous, and the homology is inferred from the similarity.  We observe that the two sequences are similar, and then we conclude that they are homologous. We use the sequence similarity, as measured by percent identity, to trace a line of common descent for those proteins we deem homologous.

(As an aside I should say that the percentage of sequence identity, or %ID is not a very good measure for inferring homology, nor is it for measuring similarity. It is an easy one to use: but it is very coarse and prone to errors. There are many better measures out there, including statistical ones like e-values, p-values or information theoretic ones like bit scores. But I digress, and this is a matter for another post.)

But once we confuse observations with conclusions, things quickly become an impossible muddle.

Am I not not just picking nits here? I mean, surely when the term “distant homology” comes up in a paper or in conversation, we all know the meaning. Distant homology means having a common evolutionary origin,  but with a common ancestor that was around a long time ago. “Distant homology” is intuitive, brief yet understandable. it is less cumbersome than: “homologous, with a distant common ancestor, as concluded form a low yet statistically significant similarity” which is what we really should say if we properly separate observations from conclusions, as captain nitpick would have us do.

Allow me to answer with two examples.  First, I have read several papers discussing “structural homology”  in the context of protein structure. Those papers that discuss structural homology were actually using a verbal shortcut for  a homology inferred from structural similarity. That is, they inferred common descent from protein structural similarity. This kind of inference is highly contentious, and while not necessarily wrong, must be done with great care and proper caveats. However, once the researchers rolled up observations with conclusions by using the “structural homology” verbal shortcut, they absolved themselves from convincing the reader that structural similarity is indeed a good measure of homology, and jumped directly to the conclusion that there is indeed an homology here. The framework for inferring homology from sequence similarity is well worked out, but not so for structure, yet.   Therefore, even if we do use the verbal shortcut “distant homology”, we can only use it by virtue of having a certain measure of similarity well-established already, as in sequence based similarity. If it is not well established, and in using structural similarities, we fail to go through the proper scientific channels that consist of providing convincing observations prior to providing conclusions.

Second: even worse is the use of the term “functional homology”. This is a clear case of the word homology used as a drop-in synonym for similarity. The misnomer “functional homology”  is typically used in studies where proteins that are clearly not homologous perform similar functions. Why infer evolutionary descent when clearly that was not intended in the first place? Well, once you start confusing similarity with homology, observations with conclusions, and make them synonymous, this is what happens.

So don’t even start this confusion.  Separate observations from conclusions, and make the former support the latter. Homology is qualitative, similarity is quantitative.  Genes cannot be distantly homologous any more than a woman can be a little pregnant.

Now you can have that drink. Unless you are a little pregnant.

Gerald R. Reeck, Christoph de Haëna, David C. Teller, Russell F. Doolittle, Walter M. Fitch, Richard E. Dickerson, Pierre Chambon, Andrew D. McLachlan, Emanuel Margoliash, Thomas H. Jukes and Emile Zuckerkandl (1987). “Homology” in proteins and nucleic acids: A terminology muddle and a way out of it Cell, 50 (5) DOI: 10.1016/0092-8674(87)90322-9