AI "peer" review - the impact on scientific publishing

It is the first time, in the second half of this year, that I am not trying to urgently deal with something. So, instead of working on some manuscripts from the lab (sorry!), I took some time to look in more detail at the outputs of two recently announced science AI "assistants" dedicated to scientific publishing. The q.e.d. science peer review system and the Nature Research Assistant tool. This was not a very rigorous or quantitative assessment but instead I had a look at the tool's outputs based on 3 manuscripts from our lab - 2 recent preprints and 1 manuscript that we are still working on. 

If you haven't tried it yet, q.e.d. tries to identify and list what are the claims made in a scientific paper and then identify any major or minor gaps in these claims. Visually, it is presented as hierarchical tree with a main message for the whole manuscript, main claims and related (sub) claims. It is refreshing and positive to me that they decided to present this in a way that is different from the standard text peer review format but, in essence, this is very much the type of information obtained in a peer review report. In addition, the section "What's new" also provides a description of what the model believes is the most novel about the work and what might have been done in some way by other studies.

Before getting into more details about the output of q.e.d., I also tried the same 3 manuscripts in the Nature Research Assistant tool. This is clearly more conservative in scope and it provides a series of suggestions, primarily focused on improving the text. The tool does provide a list of identified "overstated claims" which comes closer to the idea of finding gaps in scientific claims/statements as done in q.e.d. science.

How good is the output of these AI assistants

Regarding the output of these tools, I am really impressed by the level of detail of q.e.d. For every gap, it has a written explanation of the identified issues and suggestions for additional work or text changes to mitigate the issue. Many of the identified gaps require quite detailed technical knowledge. In one particular example, the tool found a very non-trivial gap in the null model of a statistical test that required knowledge of proteomics, evolution and bioinformatics. The 3 manuscripts are very computational, which the authors indicate is not an area they have focused during development. One of the manuscripts was flagged as being from a domain knowledge that does not fit their current set of domain areas. Still, I could expect to see many of these comments in a human peer review report. Is there something in these gaps that we never considered before, and that I need to absolutely act on? Not really, but that can be said honestly of a significant fraction of all peer-review comments. I would generally rank these AI generated comments as about average. Not among the most useful peer-review comments but certainly better than many we have received over the years.  

The output of Nature's research assistant is much more what you would expect of a tool dedicated to improving the text of a manuscript. I think it is most useful to find parts of the text that could benefit from improved clarity. In the way the information is presented it also promotes the author's revising the sections, deciding to use or not the suggestions from the tool, instead of simply feeding the whole text through an LLM. It is more of an assistant than a replacement for writing. I don't think I would give money to a tool like this over say a general LLM chat bot.   

For comparison purposes, I tried to recreate the output of q.e.d. using a standard LLM chatbot (Gemini Pro in this case). I took one of the manuscripts and tried to formulate the prompt in the way to get also a list of claims, gaps and suggested changes. The output was not as good as q.e.d. but some of the gaps were the same although it seemed qualitatively a bit more superficial. 

AI "peer" review is here to stay

Whether we want it or not, these tools are now reaching a point where they can be used to identify gaps in a scientific manuscript that could pass as a human (peer) review report. There are many ways these tools can be used and abused. The most positive outcome of this might be that authors take advantage of these as assistants to help improve the clarity of the manuscripts before making them public. The most obvious negative outcome is that these will be used as lazy human reviewing just copy-pasted to satisfy the ever growing need to peer-review our ever growing production of scientific papers every year.  Given that these reports can be generated quickly, potentially as part of the submission process, it could well be that a good way to preempt the use of these by human peer-reviewers might be that the journal already provides them to the peer-reviewers as part of the request for assessment. This would already make clear that the editor/journal is aware of the things that an automated report would bring up and avoid having the reviewers simply trying to fake a report. Finally, there is also a likely scenario that editors of scientific journals start to integrate these reports as part of their initial editorial decisions. In particular, from the editorial perspective, these tools might end up serving as biased and lazy assessments of novelty and impact.  

As a peer-reviewer, I don't think these automated reports would reduce the level of work I need to do. I still would need to spend the time to read through a paper, consider the methods used and try to figure out if there are issues that the authors might have missed and if the claims and interpretation make sense relative to the data. Having such automated reports might be a useful addition as well as having a list of related published papers. 

Perhaps one aspect that is not strongly emphasized in q.e.d. but more obvious in Nature's research assistant and even other tools is the connection of a given manuscript with the broader scientific literature. As scientists, I think it is fair to admit that it is hard to be fully aware of all of the work that has been published in a field. Sometimes the connections between our work and existing literature are less obvious because they can happen through analogy and/or shared methods. Surfacing such connections in the process of writing up a manuscript in an easy way would be particularly useful. 

Science and scientific publishing in the age of AI slop

We were already drowning in scientific papers before ChatGPT and co.  Now there is growing evidence of papers being produced by AI and quite a lot of buzz around the concept of fully automated AI scientists. So it is unfortunately unavoidable that this is going to translate to an even stronger increase in the number of publications and added pressures to the scientific publishing system. One optimistic take on this is that the added publications will be easy to ignore crap that won't affect our productivity but it is at least likely to result in more added wasted money being spent feeding the already rich publishing industry. Unfortunately, I think this will also hurt attempts to move away from our expensive and inefficient traditional publishing system when scientists worry more about "high-quality" science. The current (bad) proxies for quality (i.e. high impact factor journals) can't be easily changed to something else in an environment where many scientists will rightfully be even more worried about scientific rigor. 

  

  

Why do we still publish in scientific journals ?

We publish in scientific journals to disclose our discoveries, such that others can build upon them. But we now have preprint servers and we can quickly make our discoveries available to others. So maybe we publish in scientific journals because we value the peer review that is organized by them. However, we also have now journal independent peer review systems, like Review Commons, which allow us to perform peer review on top of preprints, in a way that does not require subsequent submission to a scientific journal. So why do we still publish in scientific journals ? 

Once in a while someone online complains about the cost of open access publication fees, the so called article processing charge (APC).  Looking at this simplistically, it does seem ridiculous that a journal might ask the authors $5-10k USD to publish a paper when all the work is apparently done by scientists that write and review the articles. Of course, this APC cost is a lot more complicated than this and there is an historical context and background knowledge that is needed to discuss these. In reality, a lot of the cost goes into sustaining the editorial salaries of journals with high rejection rates. I covered this in detail in a previous blog post discussing the costs from EMBO Press. In addition to the editorial salary costs for journals with high rejection rates, we also don't have a free market since we don't pick journals based on price and service quality but on how publishing in certain journals will be perceived by others.  

So, for many reasons, the major costs of scientific publishing are not the act of peer review and making knowledge public. If I had to guess, the actual costs publishing a peer-review article with near 0% rejection rate would be below $500USD per paper if done in high volume. The main costs of publishing are primarily the costs linked to the system of filtering scientific publications into tiers of perceived "impact". It was, for a long time, nearly impossible to evolve scientific publishing and I have argued for almost 20 years that we needed to split the publishing process into modular bits that would allow for much more innovation. With the rise of preprints, social media and dedicated peer-review services, I think we now could work towards getting rid of scientific journals. Or at least, we now have a clear direction of focus on what is missing in this potential alternative system - a new reward infrastructure.

The reward infrastructure in science

So why do we still publish in scientific journals ?  The reality is that people still want to chase high impact journals. Pretending that we don't is not going to change anything. Despite having tenure and secure funding for my group, I feel that I cannot stop trying to publish in some journals because of what it means for the career of my lab members; for how my peers perceive and evaluate our work; for establishing new collaborations and applying for additional funding. So how are we going to change this and what could the consequences be ?

Unfortunately, there is no incentive for any single individual to change the reward system. At least as of now, this would require a large number of labs within a sub-field to jointly commit to a change in practice, perhaps assisted by some external entity.  We could assume that social media, conferences and recommendation engines (Google Scholar) are enough to spread knowledge and that within a specific sub-field it is possible to evaluate each other without the need for journal proxies. I am not sure this is really true but if we accepted this, then a number of labs in a field could commit to no longer publishing in scientific journals. This could be assisted by, at the same time, creating an overlay journal of their field where academic editors would select a subset of peer-reviewed preprints that represent some particularly strong advance in the field. 

Unfortunately, this idea is unlikely to work because it relies on collective action by a majority of groups within a field. I don't have better ideas but this is for me the last barrier remaining. We still need to work out how we would pay for the peer-review service but ideas that would help change the reward system in a way that do not require collective action are now what is needed. 

What could go wrong if it happened

Despite all that we complain about in our current system of tiered journals, they do aim to improve science. They might not work as intended but they aim to filter science by accuracy and perceived value to others. If we managed to get rid of these things, we could have an even worse problem with the sheer number and quality of scientific outputs. As an almost anecdotal evidence, our group has become at lot worst at working through the revisions of our papers in a timely fashion. If our manuscripts were not out as preprints I think we would be much more in a hurry to do the revisions. 

The other important caveat around this is that time and attention is always limiting. There will always be a need to filter and evaluate science by proxies. If we didn't have science journals we might be complaining about how attention in social media is being used a bad proxy for the value of research.

I am truly curious to know how scientific interactions would change without scientific journals. Would people still want to apply to our group, want to collaborate on projects, invite us to conferences if our outputs were essentially peer-reviewed preprints? For my lab members that might read this - don't worry, this is not a declaration of intention. 

State of the lab 12 - Becoming an established scientist

This blog post is part of a (nearly) yearly series on running a research group in academia. This post summarizes year 12, the 3rd year after moving to ETH Zurich. In the last blog post I wrote down some of our overall research directions for the first 5 years of the group at ETH and I will wait another year or two before reflecting back on those commitments. This time, I wanted to try to write down some thoughts I have been having about essentially becoming more established in academia. This includes a longer term perception of group turnover, the time and resources needed to achieve research objectives and some activities that go beyond the management of the research group.

Group member turnover cycles

With 12 years of managing a research group, I have gotten used to some of the broader rhythms of turnover of the lab. Our lab is now almost totally renewed with just 1 lab member that came with the lab from EMBL. While this turnover was somewhat enforced by the move from EMBL to ETH, the turnover of lab members is a constant in academia given the short term nature of the lab members’ positions. In our group PhD students have typically stayed for around 4 years and postdoc have typically stayed for up to 5 years. Since there is some degree of clustering of the hires there tends to be some periods of higher turnover. We have had something like 2 to 3 periods where the lab has seen a large change. In the group, I try to hire from diverse backgrounds (e.g. biology, CS and math) and we work with a range of experimental and computational approaches, including for example yeast genetics, proteomics, structural bioinformatics, machine learning, etc. This creates a nice dynamic of group members building up their projects, while at the same time learning about the capabilities of the rest of the lab. The projects are usually meant to be somewhat synergistic, trying to address bigger goals from the individual problems (see past blog post on this). This means we have had windows of around 3 years when things click together before the turnover starts again. We are just around that exciting stage in the cycle and I am really looking forward to making the best of it. I still don’t enjoy what comes next, when the group will inevitably turnover again. I have accepted that it is an opportunity to steer the ship into new directions but sometimes it is disappointing to change the group just around the time it feels like we can take on almost any challenge.

Longer term view of science

One thing that has been on my mind is that I am sometimes weary about the time it can take to achieve a research goal. I am not talking here about an individual research project which tends to take on the order of 2 to 3 years on average. In our group we have tried to address some bigger research goals, such as trying to understand the evolution of protein phosphorylation or the functional relevance of individual phosphosites. These kinds of challenges take multiple independent projects and over 10 years of time to make a meaningful dent on. These days I will look at a potential long term research goal and I will think about the many different types of methods and steps that will be needed and this can distract me from the excitement of figuring those things out. I should say that I am by no means jaded about doing research. I still get such a thrill discussing the day-to-day results with lab members, being at the frontier and trying to figure things out. It is just when I pause to think about the longer term view, either in the past or trying to project into the future that I sometimes wish things could just move faster. I have taken part in a couple of large multi-PI projects that have moved very quickly and from these I can see the temptation of trying to have large labs. 

From junior to “established” PI

There is no point in time when a switch happens and someone is no longer considered a junior PI but after 12 years I can safely assume that label no longer applies to me. This has brought some relatively small changes in my job, one simple one being that I no longer think about tenure. For most of my career I was on fixed term positions, including my first group leader position at EMBL which had a time limit of 9 years. I joined ETH 3 years ago on a tenured contract and not having to think about my next job has left me with a tiny post-tenure slump - what am I aiming for ? Related to the previous section, I have considered that I could enjoy overseeing science at a higher level than as a group leader. As one example, I organized an application for a National Centre of Competence in Research (NCCRs) with 19 PIs interested in human genetics in Switzerland. While the application failed, I was really keen and excited to co-direct the center if it had been funded. 

Another aspect of my job that has changed somewhat is a higher commitment to activities outside the lab, such as taking part in committees, advisory panels or formal and informal mentorship of junior PIs. I don’t feel particularly overwhelmed by these activities but that might change if I am required to take part in more committees within ETH. Not everything is an additional burden to an already busy job. I have felt that being more visible and connected in international science comes with benefits, including being easier to at least discuss collaborations or having labs interested in joint grant applications.

Scientists that have worked in academia for longer than I have might find some of these things funny and I am certainly curious about what it will feel like reading this 10 years and more from now. In fact, the blog is now a bit over 20 years old with posts starting in my PhD. While I don’t post much these days I aim to continue at least this yearly series while I feel there are some new things to say beyond the progress in our science.

 

State of the lab 10 and 11 - the first years at ETH Zurich

Yet another lake by a mountain in Switzerland

This blog post is part of a (nearly) yearly series on running a research group in academia. This post summarizes years 10 and 11, the first 2 years after moving to ETH Zurich. It also marks the end of the first decade as a research group leader, which is meaningful only because we have ten fingers and use 10 as a base for counting but I digress. There has been a lot to adapt to in moving to a new country including all the basics of moving, re-building the group and starting teaching. It was a lot easier than the first time around since I didn't have to set up the group from zero. Some people came with me, some stayed at EMBL-EBI with funding that couldn't be moved and generally speaking we could continue several computational related projects without much interruption. If we were primarily lab based then I think the interruption would have been more dramatic. Unexpectedly, there were more periods of high stress than I typically have. There was no particular reason for the stress but just a combination of multiple small things and probably due mostly to the adaptation to a new place. I will cover here some of the biggest things I am having to adapt to and also some of the research directions planned for the first 5 years of the group at ETH. One aspect that I will not cover is networking and getting to know the Swiss research landscape, but I will come to it in a later post.

The Swiss style of leadership

The EMBL, where I was before, has a very top-down leadership. EMBL is funded by different counties that are represented in the EMBL council. There is a director general who is appointed by the council and has a lot of control. Of course, there is a hierarchical support structure with a senior management team, heads of research units and a group of "senior scientists" that support the director in decision making. I am still figuring out ETH but there is a very different feel to it, both in size and style of leadership. EMBL employs around 2000 people while ETH has around 12,000. Organizationally, ETH is divided into 16 departments, and each department is further split into different institutes. For example, I am in the Department of Biology, which has 6 institutes, and I am in the Institute of Molecular Systems Biology (IMSB). As leadership, there is an executive board, including the president of ETH, then the Department heads, and in each department there is the meeting of heads of institute and the professorial conferences (i.e. all votes from professors). At least in the Department of Biology the heads of the institutes and the leadership of the Department are meant to rotate every 2 years. At these levels - institute and department - the leadership feels highly representative with lots and lots (!) of voting. This representative rotational leadership feels very different from EMBL and I think mirrors more broadly a Swiss way of doing things. The obvious consequence of this is that any change requires deep consensus and therefore radical change is less likely but it is too early to say much more.  

Teaching at undergraduate level

During 9 years at EMBL I had almost zero teaching duties. I voluntarily taught some classes in the GABBA PhD program in Portugal and not much more. At ETH teaching is now an important part of my job. I am teaching courses in Bioinformatics and Systems Biology, primarily to biology students, which are all very familiar topics and close to my area of research. I don't particularly enjoy the act of teaching, in particular standing in front of 70-100 students and trying to explain things. As an introvert I am more comfortable with 1-on-1 or small group discussions and I get very tired with the interaction of teaching in a classroom setting. I have always said that Biology students should learn more computational skills so at least I have the opportunity now to influence that at ETH. In fact, the biology curriculum was changed right when I was joining to add more bioinformatics and they do have the chance to learn it with multiple lectures that cover bioinformatics and machine learning. Despite it being a mixed bag for me I am privileged in that I have a very low teaching load in topics that I like. Teaching is an area that I feel I could do more for and it could have an impact, in particular if we made it open to anyone. However, it is still something that I find difficult to fully devote to given the research role. 

Our research at ETH during the first 5 years

The start of the research group at ETH has been fantastic. There was another big turnover of the group members during the transition, the second major turnover since the group started 11 years ago. I am really happy with the team we have here and having done this sort of turnover before, I can already see the growing potential of many projects that have started here. So the next 2-3 years is going to be about building up these projects and trying to coordinate them such that they interact and feed off each other. We have very generous stable funding as all other tenured prof positions at ETH  - so called endowed professorships in the US or positions with core funding for the European researchers.  Surprisingly, there is not a lot of oversight on this research funding which is a big difference from EMBL where the units, and their group leaders, are reviewed every 4 years. So I thought I could at least write down our commitment for research over the first 5 years here, in the spirit of disclosing what we are doing with this public research funding.

Human genetics research - mechanisms linking genotype to phenotype

Human genetics is an area that we started working on in the last 3-4 years or so of EMBL. Some of these things are already visible in recently published articles, including some protein-interaction network-based analyses of trait-associated genes. We continue to actively work on this and one direction of focus is to try to build interaction networks that are specific to different tissues or cell types.  We are working on a manuscript on this and it is an area to continue to build upon, to be able to study the differences in cell biology of different cells/tissues and how genetic changes manifest differently in these. A second direction of focus here is to study the relation between common and rare variants linked to related traits using networks.

From cells to proteins - we are finishing a project where we are using protein structures to annotate functional residues in proteins to study mechanisms of pathogenicity.  One aspect of this that will need further development is expanding on the prediction of structural modelling of protein interactions with other proteins and other molecules. Finally, we are interested in how genetic variation controls protein levels and ideally how to build computational models that can integrate the impact of genetic variation through control of protein levels, interactions, organs and organismal traits, ideally without a black-box modelling approach. All of these things are actively ongoing and I expect to have progress to report in the coming years. 

Post-translational regulation - large scale studies of kinase signalling

There are over 100,000 phophosphosites discovered in human proteins and over 20,000 found in budding yeast proteins. We don't have good methods to study the functional role of these phosphosites nor to reconstruct the kinase/phosphatase-substrate signalling network of different cells.  About half of the group is continuing to work on these problems and here at ETH we managed to consolidate the computational and experimental parts of our group which used to run in different locations while I was at EMBL. Because we are doing more of the experimental work now, this part of the group had a slower start but things are now moving along very well. Some of the problems that we are working on include the prediction of the biological process regulated by phosphosites; studying the impact of phosphorylation on protein conformational change; experimental methods to map kinase-substrate interactions and large scale mutational studies of PTMs. The thought has crossed my mind to phased-down a bit this area of research, or at least to move more into mammalian systems in our experimental work to make it more complementary to the human genetics side of the lab. 

Structural bioinformatics, protein evolution and other

We have been having a lot of fun with AlphaFold2 ! With the current fast pace of change in protein related bioinformatics methods I am sure we will continue to play with these methods as they come. It is not likely that we will do a lot of method development ourselves, it is not our way, but I think we are very good partners for method developers to help make the bridge to applications. Protein structures, protein design and evolution models are all things we will likely be playing around with in the coming years. 

20 years of open science or how we haven't radically changed the way we do science online

Around 20 years ago I was a starting PhD student and it was an exciting time for the internet. It was the time of blogs, wikis and a large increase in public participation with more user generated content in what is commonly known as the start of Web 2.0.  These were the times of web based online communities such as the now defunct Kuro5hin or the great survivor slashdot.org. I started this blog 19 years ago and I was also "hanging out" in an online community called Nodalpoint. Nodalpoint no longer exists but it was a discussion forum/wiki for bioinformatics with some of these discussions still preserved thanks to the magic of the way back machine. 

Around the time of 2002-2006 all of the excitement around Web 2.0 was also infecting academia with many discussions around open science. I know that open science is a vague term that can mean many different things including open access, citizen science, open source and many others. One specific aspect that I want to focus on is the idea of organizing research in a way that is not based on local group structures. In 2005 I wrote a Nodalpoint post on "Virtual collaborative research" which is similar in spirit to open source software development but with a focus on discovery not tool development. Part of this would mean surfacing more of our ongoing research and taking part in research projects that are not organized by traditional research group structures. The idea of being extremely open about ongoing research activities was advocated by others under the term of "open notebook science".

Over the following years I made a few attempts at starting such open research projects with blog posts where I tried to set up tools and ideas where others could take part in (see posts from 2007, 2008 and 2010).  The last project idea I tried to propose in such way ended up being one of the major projects from my postdoc and basically one of research lines I am still working on. In the end, none of these attempts really took off as open collaborative research projects. In hindsight, I am not surprised it didn't work. Even within local structures of research institutes and university departments there is so much discussion on incentives for local collaborations. While I think the traditional structures for organizing research do work, as a PhD student and postdoc I was very frustrated by the apparent difficulty of making the most of everyone's expertise. As a group leader I have more capacity to establish collaborations but I still think we aren't using the internet to its full capacity. 

So what happened in the decade from 2010 to 2020 ? Blogs and online communities mostly died out and Web2.0 was swallowed by corporations. One major change was the rise of large social networks and the standardization of the stream as way for people to share information and interact. Academia started participating in social networks around the time of Friendfeed (2007-2015) and such participation become mainstream with the popularization of Twitter. I honestly would never have predicted the rise of academic twitter and it is truly a sign of how the geeks have inherited the earth. 

The reason I am even thinking about open science these days is that over the past couple of years we have been involved in projects that have illustrated this potential of large collaborations empowered by the internet. I wanted to write this down also to have something to come back to in the future. The first project was a study of phosphorylation changes during SARS-CoV-2 infection. Like many others, when the pandemic sent our research group home, I though about what we could do to help and sent emails to a few people that could be working on the topic. Nevan Krogan, my former postdoc supervisor, was very keen to involve us which lead to several projects including this study of protein phosphorylation. This was probably one of the most exciting projects I have been involved with and included a very spontaneous collaboration among a large international team coordinated by a few people through slack. In this case the network of interactions was provided by Nevan and it was possible because everyone was pushing in the same direction triggered by a catastrophe. I wish everyone could feel the sense of power that I think we felt during this project. There was so much scientific capacity at the disposal of this single project and we could iterate through experiments and data analysis at an incredible pace. It is even hard to express how it felt to be able to just get things done when you had the world experts for what was required to do at every step. 

A second even more interesting example was a community effort to study the value of AlphaFold2 in a series of applications. When AlphaFold2 was released, several scientists started sharing their early observations of how AlphaFold2 and predicted structures could be used for different applications. I though all of these examples were really exciting and that we could structure this output into a manuscript. So I just contacted people that were doing this and also asked on social media if anyone else wanted to participate. In the end every contribution to this was quite modular and it was easy to integrate this into a manuscript with a few meetings and a google doc to put things together. Perhaps the less usual thing that happened was receiving actual results through Twitter chat. 

I think both of these examples required a trigger - the pandemic and the release of AlphaFold2 - that led to many scientists moving in the same direction.  In both of these cases I think we achieved in a few months what would take a single group potentially one to several years to do. Yet, these interactions remain difficult to make. Perhaps simply because we are just too busy with our own research questions or more likely because of the importance of credit and evaluation systems in academia.  These days I am actually less in favor of radical sharing of ongoing research, in the spirit of open notebook science.  I don't think we have the attention span for it. It would be too difficult to navigate and may lead to more "group think" instead of divergent thinking and ideas. Maybe the simple existence of social networks like twitter are already a good step forward. I certainly get to know more people and what they may be up to via this. Lets see what the next 20 years bring. 
  

Independent evaluation of AlphaFold-Multimer

AlphaFold2 has been widely reported as a fantastic leap forward in the prediction of protein structures from sequence, when sequence has enough homologs to build a reasonable multiple sequence alignment.  When AlphaFold2 was released (Jumper et al. 2021) there were several independent reports of how it could also be used for the prediction of structures of protein complexes despite the fact that it was not trained to do so (Bryant et al., 2021; Ko and Lee, 2021; Mirdita et al. 2022). Together with the lab of Arne Elofsson, in work led by David Burke in our group and Patrick Bryant in Arne's group, we have shown that it can be applied in reasonably large scale to predict structures of protein complexes for known human interactions (Burke et al. 2021). There is a lot to investigate still but it is clear that this is an extremely exciting direction of research since that lead to a major advances in the structural analysis of cell biology, evolution, biotechnology, etc. 

Soon after these first reports, DeepMind released an AlphaFold version that was re-trained specifically for prediction of structures of protein complex - AlphaFold-Multimer (Evans et al. 2021). Given that they reported an even higher success rate with this specifically trained model we were quite excited to give this a try. David Burke selected a set of 650 pairs of human proteins from the Hu.MAP dataset, known to physically interact and for which the experimental structure has been solved. A structure was predicted using AF v2.1.1 (AF-multimer) using default settings and the model_1_multimer parameter set. A second model was predicted using AF using the model1 monomer parameter set and the FoldDock pipeline. For each model, DockQ scores were produced which reflect the similarity of the predicted structure with the experimental structure with a specific focus on the interaction interface residues. A DockQ score value below 0.23 can be considered essentially an incorrect or random model. 

Below we show a direct comparison between the two AlphaFold2 models with the AF2 Multimer showing a very significant improvement based on DockQ scores. Of all predictions tested, there were 51% above DockQ>0.23 with AF2 Multimer and 40%>0.23 with "standard" AlphaFold2. This improvement (+11%) is not as large as that reported by the DeepMind team (+25%) on their own test set. There could be several reasons for the difference but more importantly this would be more than enough to justify using Multimer for the prediction of protein complexes. 

However, David quickly realised that there were many examples of clashes at the predicted interface with the AF2 Multimer model. In the figure below we show just an example of this which, despite the high DockQ score (0.85) clearly has several overlapping residues. That is, while the interface region is likely to be correct, the model at the interface has serious errors. 

These clashes in predicted structures are quite frequent with 69% of predictions having some clash. The clashes can be quite extreme with several involving a very high fraction of the total length of the protein as shown in the distribution below. Such clashes are essentially not seen in the predictions made with the earlier version of AlphaFold2. 

While there may be some cases where the clashes could be minimised, as it stands the models produced by AF-multimer may not be usable for a large fraction of cases. However, these issues are of course easy to spot. DeepMind is in fact aware of this bug since around November and have said they are working on it. From the point of view of predicting the regions of the proteins where the interaction will occur AF-multimer may still be usable as it is and hopefully DeepMind will find a fix for this problem. 

A closer look at the costs of EMBO publishing

There has been a lot of discussions on social media about the price that some publishers are coming up for publishing a paper in their journals - the so called article processing charges (APC). With some journals asking for values that are on the order of 10k and many scientists finding these values to be outrageous. Given that journals don't work to produce the research articles and get academics to do the evaluation, how can these journals claim the costs of publishing a paper to be anywhere close to 10k ? While I agree that these are outrageous values, I don't really believe that the price is mostly profit. A good source of information for the costs associated with running a publisher are those that have been disclosed by EMBO Publishing. Before we go into these I need to disclose that I serve on the Publications Advisory Board of EMBO publishing. I don't receive anything from EMBO and this is merely an advisory committee but it has given me some insight into what is a very real attempt from non-profit publisher to come up with an APC that is low and what they could compromise on their current set-up to achieve it. 

With that out of the way lets just look at the most recent numbers that EMBO has disclosed which were for 2019 (see here). EMBO has (or had in 2019) 17 professional scientific editors and 6 support staff, that handled a total of 5,766 submissions in 2019. That is on the order of 28 submissions handled per month per editor, 1.3 per working day. I don't know about you but making a call on 1 paper per day plus finding/chasing reviewers is not easy if you try to do it properly, even if you can make some rejections fairly quickly. From these they ended up publishing 472 (8%). This part is not totally transparent, for example maybe some of the submissions included the reviews and news&views articles that were ultimately also published. If that is the case then the total number published would be 681 (12%). It is also not totally clear if the submissions include also revision submissions. Regardless, this shows that the total of EMBO publishing ends up having acceptance rates that are quite low (10-20%). I should stress that I truly don't know the actual number. As we easily see, this rejection rate is really key for the high estimated cost per paper. 

The costs that they have disclosed includes ~2,5 million euro for the EMBO Press office, of which around 2 million is listed as salaries and benefits. The number of staff is there as well so you can guestimate the average salary for the 23 staff and you can also look up EMBO editor salary on Glassdoor to get an idea. I truly don't know what the salary is but I guess on average it could be on the order of 4-6k net per month. The other costs include 1,723,639 euro that EMBO Publishing pays to Wiley which in fact does the actual publishing. The majority of this cost is listed as "Wiley publishing services (incl. production, sales and marketing)" (1,281,552 euro). This is certainly a place where costs are not very transparent, at least to me, and where profit to Wiley is included, likely with a decent margin. I certainly don't know enough about finances to figure out but Wiley is claimed to have around 30% of operating profit margin but for the purposes of some later calculations, lets assume that maybe 50% of these costs are profit that could be magically removed (e.g. EMBO sets up their own publishing infrastructure). Finally, EMBO also lists 1,342,374 euro in "surplus" which is re-invested into some publishing related actives like the EMBO Source Data project, other pilots trying to innovate on the publishing side and back to EMBO itself which further supports EMBO program activities (fellowships, etc). 

With these numbers then the total cost includes the 4,225,920 of actual cost and the 1,342,374 for EMBO activities (5,568,294 euro total). So if you don't take anything out of this, you would need a price of 11797 euro for each of the 472 paper published in 2019 to finance this. If you exclude the EMBO surplus that would be 8953 per paper and excluding 50% of Wiley costs it would get down to 7127 per paper. Even without anything from Wiley you would only get to 5301 per paper. Of course, you can also argue that the salaries costs could be lower but what can't really be argued is that academic editors can do this for "free" since that is time that most likely is even more expensive and less efficient. 

So the 10k APC number certainly contains parts that can be reduced but we are not talking about a 1k per paper cost. For that you would need to change the rejection rates and this is what really starts mattering in the end. If you go to maybe something like 50% acceptance rates which could correspond to something like 2000 papers published in this case, then the APC could be somewhere on the order of 1500-2500 euro. Keep also in mind that submission numbers would tend to decrease over time if the impact factors go down with higher acceptance rates (yes, some people still care about those). Of course, this scales across multiple journals and this is where the big publishers are just taking advantage since the overall acceptance rate across the large portfolio of journals is much higher than 10% and high acceptance rate journals (e.g. Scientific Reports) can cross-subsidise low acceptance rate journals (Nature). 

It is important again to keep in mind that all of these prices per paper have been there for decades but were paid via journals subscription charges instead of APCs and therefore they were not transparent and people were not really paying attention. In the end, the discussion for me is not really around the 30% savings we could have by pushing the publishers to lower their prices, but more about how we go about doing the filtering (i.e. target audience) and subjective evaluation of value to science (i.e. impact). Revolutions are not real solutions in academic publishing. If you propose a solution that requires a majority of people to change their habits in the span of 3 years it is dead on arrival.