Last week my boyfriend Ben Rainthorpe returned from Argentina having successfully climbed Aconcagua – the highest mountain in South America. At a staggering 6963m above sea level it is the highest peak outside of the Himalayas. The climb took 20 days in total with a massive 14 hours of hiking and climbing on summit day.

Aconcagua is part of the mountaineering challenge known as the Seven Summits. This is achieved by summiting the highest mountain in each of the seven continents. This was first successfully completed in 1985 by Richard Bass. In 1992 Junko Tabei became the first woman to complete the challenge. In December Ben quit his job as a primary teacher to follow his dream of achieving this feat. Which mountains constitute the seven summits is debated and there are a number of different lists. In addition the challenge can be extended by including the highest volcano in each continent.

The Peaks:

1.Kilimanjaro – Africa (5895m) 

Kilimanjaro is usually the starting point for the challenge. At 5895 m above sea level and no technical climbing required it is a good introduction to high altitude trekking. However, this often means it is underestimated and the most common cause of death on the mountain is altitude sickness.

2. Aconcagua – South America (6963 m)

The next step up from Kilimanjaro Aconcagua is the second highest of the seven summits. However the lack of technical climbing required make it a good second peak to ascend after Kilimanjaro. For Aconcagua however, crampons and ice axes are required. The trek takes three weeks instead of one.

3. Elbrus – Europe (5,642 m)

Heralded as the Kilimanjaro of Europe, Elbrus even has a chair lift part of the way up! This mountain is regularly underestimated causing a high number of fatalities per year. Due to snowy conditions crampons and ice axes are once again required. Some believe that Elbrus should not count as the European peak and instead Mount Blanc should be summited – a much more technical and dangerous climb.

4. Denali – North America (6190 m).

Denali is a difficult mountain to summit. Although slightly lower than other peaks, the distance from the equator means the effects of altitude are more keenly felt. More technical skills are needed. In addition there are no porters to help carry additional gear so climbers must carry a full pack and drag a sled.

5. Vinson Massif – Antartica (4892 m).

Vinson is difficult because of the location rather than any technical climbing. The costs of going to Antartica are great and the conditions are something to be battled with.

6. Puncak Jaya – Australasia (4884 m) or Kosciuszko – Australia (2228 m)

The original Seven Summits included Mount Kosciuszko of Australia – the shortest and easiest climb on the list. However it is now generally agreed that Puncak Jaya is the offering from the Australasia continent. Despite being smaller than others on the list this is the hardest of the seven to climb with the highest technical rating. It is also located in an area that is highly inaccessible to the public due to a large mine, and is one of the few where a rescue by helicopter is not possible.

7. Everest – Asia (8848 m).

Everest is the highest mountain in the world at 8848 m above sea level. Many regard the trek to Everest Base Camp as challenge enough. Some technical climbing is required as well as bottled oxygen to safely reach altitudes of that level. One of the most dangerous parts is the Khumbu Icefall which must be traversed every time the climbers leave base camp. As of 2017 at least 300 people have died on Everest – most of their bodies still remain on the mountain.

Ben has now climbed two of the Seven Summits. His immediate plans are to tackle Elbrus in July (which I might try and tag along to) and Vinson next January. If you are interested in his progress check out his instagram (@benrainthorpe).

I couldn’t decide whether to write this blog post. However, I sifted through the archives of BLOPIG and found in the original post this excerpt:

“And if your an athlete, like Anna (Dr. Lewis) who crossed the atlantic in a rowing boat or Eleanor who used to row for the blues – what can I say, this is how we roll, or row [feeble attempt at humour] – thats a non-scientific but unique and interesting experience too (Idea #8).  .”

Therefore I’ve decided that it might be an interesting post to look into what life is like when you are studying for a DPhil and also training for the blues. Rowing in particular is a controversial sport – I have heard of many stories advocating that rowing will be the absolute detriment to your DPhil. I’ve never felt pressured as part of OPIG to give up rowing – all of my supervisors have been very fair, in that if I get the work done then they accept this is part of my life. However, I realise all supervisors are not so understanding. I hope this blog post will give some insight into what it is like to trial for a Blues sport (in this case Women’s Lightweight Rowing), whilst studying for a DPhil at Oxford.

4:56 am – Alarm goes off. If its after September it’s dark, cold and likely raining. No breakfast as I will do the first training session fasted – just get dressed and go!

5:15 am – Leave the house with a bag full of kit, food for the day, laptop and papers to cycle to Iffley Sport’s Centre

5:45 am – Lightweight Women’s minibus leaves from Iffley to drive to Wallingford. Some girls try to study in the bus, but to be honest its too dark and we’re all a bit too sleepy.

6:15 am – Arrive at Wallingford. Get onto the water for a session in the boats. Although in the Boat Race we race in an 8 (8 rowers with one oar each, with a cox steering), we spend lots of time in different boats throughout the season. Perhaps unlike our openweight counterparts, we also do a lot of sculling (two oars per rower) as the only Olympic class boat for lightweight women is a sculling boat. We travel to Wallingford for a much longer, emptier stretch of river and normally get to see the sunrise.

8:10 am – We leave Wallingford to head back to Oxford. Start waiting in A LOT of traffic once you hit the ring road, and there’s a lot of panic in the bus about whether 9 am lectures will be made on time!

8:50 am – Arrive back at Iffley Sport’s Centre. Grab bike and cycle to the department.

9:00-9:15 am – Arrive at the Department. Quick shower to thaw frozen fingers and to not repulse my fellow OPIG members. I then get to eat warm porridge (highlight of the day) and go through my emails. I also check whether any of my jobs have finished on the group servers – one of the great perks of being in OPIG is the computational resources available to the group. Check the to-do list from yesterday and write a to-do list for today and get to work (coding, plotting results, reading papers or writing)!

11:00 am (Tuesdays & Thursdays) – Coffee morning! Although if it’s any time close to a race no bourbon biscuits or cake for me. This is a bit of an issue because at OPIG we eat a lot of cake. However, one member can usually be relied upon to eat my portion..

1:00 pm – Lunchtime! As a lightweight rower I am required to weigh-in at 59kg on the day of the Boat Race. If I am over that weight I don’t get to race. Therefore, I spend a portion of the year dieting to make sure I hit that target. The dieting lunch consists of soup and Greek yogurt. The post race non-dieting lunch consists of pasta from Taylors, chocolate and a Coke (yum!). OPIG members generally all have lunch at this time and enjoy solving the Times Cryptic Crossword. I’m not the best at crosswords so I normally chat to Laura and don’t concentrate.

2:00 pm – Back to work. Usually coding whilst listening to music. I normally start rushing to be able to submit some jobs to the group servers before I have to leave the office.

3:00 pm – Go to get a chocomilk with Clare. A chocomilk from the vending machine in our department costs 20p and is only 64 calories!

5:30 pm – Cycle to Iffley Sports Centre for the second training session of the day.

5:45 pm – If it’s light enough we hop in the minibus to go to Wallingford for another outing on the water. However, for most of the season its too dark and we head to the gym. This will either consist of weights to build strength, or we will use the indoor rowing machine (erg) to build fitness. The erg is my nemesis, so this is not a session I look forward to. Staring at a screen that constantly tells you how hard you are pushing, or if you are no longer pushing as hard I find to be psychologically quite tough. I’d much rather be gliding along the river.

8:35 pm – Leave Iffley after a long session to head home. Quickly down a Yazoo (strawberry milk) to boost recovery as I won’t be eating dinner until 45 minutes to an hour after the end of the session.

9:00 pm – Arrive home. I “cook” dinner which when I’m dieting consists of chucking sweet potato and healthy sausages from M&S in the oven while I pack my kit bag for the next day.

9:30 pm – Wolf down dinner and drink about a pint of milk, whilst finally catching up with my boyfriend about both our days.

10:00 pm – Bedtime at the latest.

Repeat!

For Journal Club this week I decided to discuss the following paper by M. Rarey et al., which describes a method of using SMARTS patterns to discriminate between two sets of molecules. Link to paper here.

Given two sets of molecules can one generate a pattern that discriminates between two sets? This relates to a key question in drug design: can we predict whether molecules bind or not given a set of binders and a set of non-binders. The method is of particular interest because it makes use of data available, unlike conventional methods. However, for this technique to work, the correct molecular classification is required to discriminate between the two sets of molecules.

Originally molecules were classified using physiochemical properties for example, molecular weight or log P. However these classifications are too general and do not encompass enough molecular detail for accurate discrimination. An alternative is to using topological fingerprints. These encode a set the presence of a set of topological features using a series of bits. One of the limitations with this classification is that it is restricted by the predefined set of structures and features. This method makes use of chemical patterns which advantageously can can classify a chemical feature that cannot be sufficiently described by molecular substructure.

SMARTS (a molecular description language based on SMILES) allow description of structures with varying levels of specificity. For example one can specify atomic element, whether the atom is a subset of elements, whether it is aliphatic or aromatic, or whether it is in a ring. The method makes use of this description of molecules as the group have already developed some software to visualise SMARTS strings and modify them: the SMARTSeditor.

The method involves combining automatic pattern generation and visualisation to form SMARTSminer. Given two distinct molecule sets, the algorithm derives connected chemical patterns to differentiate both sets by using a sub-graph mining technique: solutions are extended by single elements iteratively.

The SMARTSminer was then used to test a series of test cases using the DUD (Database of Useful Decoys) data set. This seems strange when the data set has been shown to be inaccurate and perhaps there are more accurate test sets available, such as DUDe (Database of Useful Decoys enchanced). Let us look a couple of these case sets in more detail.

1. Discrimination between Active Molecules on Similar Targets

The first case set looks at discriminating between molecules that are active for COX-1 and COX-2. COX proteins are cyclooxygenase that are involved in inflammatory reaction. These proteins are targeted by inhibitors such as aspirin and ibuprofen for the relief of inflammatin and pain. Both COX-1 and COX-2 are similar targets with similar molecular weight and 65% sequence identity. Selective inhibition is only due to a difference in residue at position 523.

Separation of the sets of molecules was possible with a pattern identified that hit 21/25 of the molecules active for COX-1 and 15/348 of molecules of molecules active for COX-2. When the positive and negative set are reversed a pattern is identified that matched 313/348 of COX-2 actives but only 1 of the COX-1 ligands. The group state that perfect separation is not possible as there is an overlap of 2 molecules.

It is interesting that patterns were identified that could discriminate between the two sets. However, there is no discussion of how to use this information. Additionally the pattern determined has not been tested on any molecules outside of the training set – there are no blind tests. This seems strange as a blind test could emphasise the usefulness of this method if it was successful.

2. Discrimination between Active and Inactive Molecules

The second case investigates determining whether a pattern can be generated that discriminates between active and inactive targets. The test case used target SAHH (S-adenosyl-homocysteine hydrolase). A pattern was generated that matched all active molecules and only 1% of inactives. What is particularly exciting is that the pattern found contains part of the interaction network hydrogen bonding partners of the ligand, as shown in the figure below (the pattern identified is highlighted in green).

I find it very surprising that the group did not follow up with blind tests of molecules not used in the training set – especially as the pattern identified a key part of the binding mechanism.

To summarise a new method, SMARTSminer, calculates discriminative patterns between two sets of molecules using the SMARTS language. The authors state that the method has shown applicability in several use cases covering the application of actives vs decoys, kinase classifications, analysis of data sets and characterisation of reaction centers. However, I’m not sure I can agree with that statement. I believe further blind tests would be required to prove the applicability of the method once the pattern has been found. I also believe that an analysis of whether the pattern is over fitted to the training data is also required.

For my second short project I have developed Theox, molecular diversity software, to aid the selection of synthetically viable molecules from a subset of diverse molecules. The selection of molecules for synthesis is currently based on synthetic intuition. The developed software indicates whether the selection is an adequate representation of the initial dataset, or whether molecular diversity has been compromised. Theox plots the distribution of diversity indices for 10,000 randomly generated subsets of the same size as the chosen subset. The diversity index of the chosen subset can then be compared to the distributions, to determine whether the molecular diversity of the chosen subset is sufficient. The figure shows the distribution of the Tanimoto diversity indices with the diversity index of the subset of molecules shown in green.

Efficient design of antibodies as therapeutic agents requires understanding of their structure and behavior in solution. I have recently performed molecular dynamics simulations to investigate the flexibility and solution dynamics of complementarity determining regions (CDRs). Eight structures of the Fv region of antibody SPE7 were found in the Protein Data Bank with identical sequences. Twenty-five replicas of 100 ns simulations were performed on the Fvregion of one of these structures to investigate whether the CDRs adopted the conformation of one of the other X-Ray structures. The simulations showed the H3 and L3 loops start from one conformation and adopt another experimentally determined conformation.

This confirms the potential of molecular dynamics to be used to investigate antibody binding and flexibility. Further investigation would involve simulating different systems, for example using solution NMR resolved structures, and comparing the conformations deduced here to the canonical forms of CDR loops. Looking forward it is hoped molecular dynamics could be used to predict the bound conformation of an antibody from the unbound structure.

Click here for simulation videos.