1 00:00:01,740 --> 00:00:06,360 Okay. Could you just start by saying your name and what your position here is at Oxford at the moment? 2 00:00:06,600 --> 00:00:09,870 Yep. So my name is Henry Chan and I'm an. 3 00:00:12,320 --> 00:00:20,780 There we are. Now, I think I need you to go back to just telling me what you told me about the scent of doctoral training and your supervision. 4 00:00:21,260 --> 00:00:27,620 Yes. So I'm a student at the Synthesis for Biology and Medicine Centre for Doctoral Training. 5 00:00:28,010 --> 00:00:32,900 This is a program here at the Department of Chemistry at the University of Oxford. 6 00:00:33,260 --> 00:00:38,770 And and, yeah, I'm jointly supervised by Professor Fernanda. 7 00:00:38,790 --> 00:00:48,190 Do I tell you on the computational organic chemistry? And Professor Chris Schofield, working more on the biological and organic chemistry thoughts. 8 00:00:48,950 --> 00:00:55,040 And tell me how you first got interested in chemistry when you were at school? Yes, I grew up in Hong Kong and did my high school there. 9 00:00:55,080 --> 00:01:04,310 And yeah, I've always enjoyed doing experiments and chemistry lessons and just seeing how things work in real life. 10 00:01:04,550 --> 00:01:12,560 And I've always been fascinated and sort of fascinated by the different applications of chemistry from textiles, 11 00:01:12,560 --> 00:01:16,700 plastics to the medicines that cure diseases. 12 00:01:17,040 --> 00:01:31,130 And yeah, and I guess as if every young kid back when I was in kindergarten, I experienced the the first source outbreak in 2003. 13 00:01:31,160 --> 00:01:38,569 And that sort of provided me with some motivation to use chemical knowledge 14 00:01:38,570 --> 00:01:46,830 to solve medicinal problems and to help tackle diseases in regard to and to. 15 00:01:46,940 --> 00:01:50,570 Yeah. Yeah. And how did you come to be doing your dphil here in Oxford? 16 00:01:50,840 --> 00:01:55,280 Yeah. So I did my undergraduate. I had Oxford as well. 17 00:01:55,380 --> 00:02:08,390 And, you know, just really enjoyed doing organic chemistry while I was an undergraduate and and I did one year and the final year and can project 18 00:02:08,630 --> 00:02:19,400 with Professor Chris Schofield during which I studied the beta lactam cases which were important enzymes in antibiotic resistance. 19 00:02:19,630 --> 00:02:23,750 And yeah, since then I've really enjoyed the research and. 20 00:02:24,590 --> 00:02:28,610 And George working in the interface between chemistry and biology. 21 00:02:28,750 --> 00:02:40,370 Hmm. Mm hmm. Well, coming from East Asia, as you do, presumably you have pretty a life to what was going on in Wuhan when it first happened. 22 00:02:40,700 --> 00:02:49,630 What were you hearing about from family and friends? Yes, I remember the news probably appeared when I was about two, 23 00:02:49,640 --> 00:02:57,810 and so I was having my Christmas holiday in Hong Kong and that was around the time when I flew back to the UK. 24 00:02:57,830 --> 00:03:04,040 But initially I thought it might be just similar to what we experienced many years ago, 25 00:03:04,160 --> 00:03:09,500 that there will be a short and maybe just local outbreak of coronavirus. 26 00:03:09,680 --> 00:03:12,110 But that turned out to be wrong. 27 00:03:12,110 --> 00:03:21,690 And yeah, and the virus spread to different parts of the world with it was much more contagious and much later than we would have thought. 28 00:03:21,720 --> 00:03:34,340 And yeah, I remember in February 2020, I was still able to go to London for a computational chemistry workshop with some colleagues. 29 00:03:34,610 --> 00:03:45,799 But then and then we were a little bit worried when we were on the flights, but then yes, still managed to travel, but eventually in March, 30 00:03:45,800 --> 00:03:54,410 then things worsened quite quickly and and we had to start working from home and went into the first national lockdown. 31 00:03:54,560 --> 00:03:59,930 Mm hmm. And what stage were you at in your Ph.D. at that point where you're still doing your first year? 32 00:03:59,930 --> 00:04:05,150 Or have you chosen your project to do it for the final part of your dphil? 33 00:04:05,330 --> 00:04:12,860 Yes, I was beginning my first of the third Substantial and Dphil project. 34 00:04:12,950 --> 00:04:24,440 So back then I was doing some computational modelling on metabolic directions and also and on a cancer related protein called citrate dehydrogenase. 35 00:04:26,700 --> 00:04:39,570 And so when the the group the way to group, you know, Fernanda and her colleagues started to meet regularly to talk about working on on COVID. 36 00:04:39,730 --> 00:04:42,960 Did you did you become involved in that straightaway? Yeah. 37 00:04:42,970 --> 00:04:55,290 So, yeah, I was so introduced to this project by both Fernando and Chris and they asked me whether I would be interested in doing some computational 38 00:04:55,290 --> 00:05:06,089 modelling to look at the activity mechanisms and also the binding of inhibitors to the SARS-CoV-2 corona virus main protease. 39 00:05:06,090 --> 00:05:12,450 So mpro and yeah, I've been yeah, of course I'm just asked to take on this project. 40 00:05:13,230 --> 00:05:23,310 So I understand that you and three other graduate students, two from here and one from another university, really quite took the lead in this project. 41 00:05:23,460 --> 00:05:26,550 Tell me about how it developed and how that came about. Yes. 42 00:05:26,550 --> 00:05:32,670 So I guess so as a computational chemist back then then was mostly working from 43 00:05:32,670 --> 00:05:39,780 hype and and then also within the Schofield Group so also supervised by Chris. 44 00:05:39,780 --> 00:05:51,299 And and there's also Teeka Kamala Dphil students are working here and just she came into the lab 45 00:05:51,300 --> 00:05:58,410 to and did some and some work so developing assays and testing different inhibitors against them. 46 00:05:59,380 --> 00:06:13,350 And and but the work also involved collaboration with the group of Professor Garrett Morris at the Department of Statistics here at the University 47 00:06:13,350 --> 00:06:25,889 of Oxford and was yeah initially got into contact with Garrett through I think because I had some questions on using talking to so computational 48 00:06:25,890 --> 00:06:36,360 methods to predict how inhibitors binds through the protein and those initial questions opened up that possibility of starting a collaboration 49 00:06:36,360 --> 00:06:48,929 with him and also with his graduate students and Mark and and eventually that collaboration went further and also to the University of Bristol, 50 00:06:48,930 --> 00:06:54,930 where we collaborated with the group of Professor Adrian Mulholland with a graduate student, 51 00:06:55,170 --> 00:07:06,540 including back from her and also other other academics at Bristol, such as Sophia and Debbie. 52 00:07:06,540 --> 00:07:16,680 And, and yeah. So the, the collaboration just got larger and larger as we, as we developed and did that. 53 00:07:16,920 --> 00:07:19,079 I I'm just wondering if social media was involved in this. 54 00:07:19,080 --> 00:07:26,190 Did you was was there a any kind of sort of call out on social media that let the community know that there was something going on, 55 00:07:26,190 --> 00:07:36,420 that that was that you were open to collaboration? I think it was mostly maybe collaboration and knowledge, but direct to the supervisors. 56 00:07:36,720 --> 00:07:46,980 Yeah. Mm hmm. And so how did this you I think when we I was talking to Fernanda earlier, she talked about the regular Wednesday meeting. 57 00:07:47,760 --> 00:07:53,280 Was that an unusual way of working in science and what what happened at those meetings? 58 00:07:53,760 --> 00:07:55,379 Yeah, I think so. So, yeah, 59 00:07:55,380 --> 00:08:06,570 I think we started off having a6pm Wednesday and meetings where we discuss and exchange ideas and or say computational and experimental results 60 00:08:07,260 --> 00:08:18,030 on the SARS-CoV-2 improved and during which we sort of because we all came from different backgrounds and had different areas of expertise. 61 00:08:18,240 --> 00:08:24,660 So it was we exchange ideas on the methods that we're more familiar with and 62 00:08:24,660 --> 00:08:30,389 also and the results and and that sort of pushed the collaboration forward. 63 00:08:30,390 --> 00:08:39,900 And through that exchange of ideas, we are then able to develop new ideas, design new inhibiteurs that eventually were tested in the lab. 64 00:08:40,770 --> 00:08:46,200 But let's go into that a little bit more. So tell me, first of all, what was your your personal role? 65 00:08:46,200 --> 00:08:52,590 What were you what was the challenge that you with the you set yourself to to solve in this as part of this project? 66 00:08:52,950 --> 00:09:00,059 Yes. So on empathy. Yeah. I've been involved in sort of two smaller projects, one at a time and it's fun. 67 00:09:00,060 --> 00:09:05,280 Yeah. So, so the first one is more about the fundamental question of how it works. 68 00:09:05,280 --> 00:09:16,740 And so I've been modelling the and the main protease complex with its native substrates and modelled us short 69 00:09:17,370 --> 00:09:25,380 poets and then fruit and molecular dynamics simulations and analyse how the protein interacted with those. 70 00:09:25,920 --> 00:09:35,370 Interact with these substrate peptides and see what are the key interactions that we might be able to exploit and design inhibitors. 71 00:09:35,640 --> 00:09:43,110 And the second branch of the project is to look at how inhibitors bind and how to design better inhibitors. 72 00:09:43,440 --> 00:09:48,209 And these inhibitor protease is a big molecule. The inhibitors, they're much smaller. 73 00:09:48,210 --> 00:09:53,850 Is that is that right? Yeah, I just said that just looking to dock in a particular point on the bigger protein. 74 00:09:54,120 --> 00:10:05,969 Yes. So there's inhibitor molecules are more drug like and because what's their interest and helping with the drug repurposing approach. 75 00:10:05,970 --> 00:10:16,540 And and here the experimental colleagues have also tested a range of and different compounds that are already drugs and lots of yeah. 76 00:10:16,950 --> 00:10:27,650 New drugs and and you're just blocking those and seeing how they might bonds to improve and that's that's all done computationally yeah 77 00:10:28,200 --> 00:10:41,760 computational computationally even but also and complemented by different techniques here and then by experiments and also sometimes if, 78 00:10:41,940 --> 00:10:46,890 if we're lucky, a crystal structure will not get into that as well. 79 00:10:47,800 --> 00:10:53,340 And and roughly how many compounds be happy did you look at in the course of the process? 80 00:10:53,340 --> 00:10:58,500 I think there are quite a lot. So hundreds and hundreds. 81 00:10:59,010 --> 00:11:07,829 But don't yeah. We switch our focus to maybe just around ten or so to so do you do. 82 00:11:07,830 --> 00:11:12,299 Sorry to interrupt, but do you mean do you do the one by one or does the computational approach allow 83 00:11:12,300 --> 00:11:15,950 you to screen a whole lot at the same time and then see which ones work best? 84 00:11:16,380 --> 00:11:25,940 Yeah. And we're able to screen them a large batch initially, but then yeah, I guess eventually as the project developed, 85 00:11:26,020 --> 00:11:34,229 a small experimental data came in and we're able to turn our focus to a smaller range of compounds, 86 00:11:34,230 --> 00:11:38,790 so maybe around ten compounds to, to look at to more specifically. 87 00:11:40,180 --> 00:11:44,010 And how well did they seem to be working that's inhibiting the protease? 88 00:11:44,280 --> 00:11:51,509 Yes. So I think there's though, maybe just a feeling like for me that in terms of inhibition. 89 00:11:51,510 --> 00:11:55,220 Affinity. Yeah. So but yeah. 90 00:11:55,290 --> 00:11:58,920 Can you explain that in more everyday life? 91 00:12:00,120 --> 00:12:11,939 Yes, I guess they're able to to binds through the protein and actually because MPRO consists of a nuclear like system residues. 92 00:12:11,940 --> 00:12:23,940 So that's involves a sulphur that's and that attacks the substrates and helps catalyse the whole hydrolysis reaction of the amide bond. 93 00:12:24,300 --> 00:12:32,160 So those inhibitors are able to to target sort nuclear for the existing residue and and 94 00:12:32,340 --> 00:12:41,280 modify it and to form a confidence complex that's unable to perform its native function. 95 00:12:42,390 --> 00:12:49,260 So is it so does that block it completely? I think well it's like it's dependent on concentration, right. 96 00:12:49,370 --> 00:12:54,479 It's a for goes through and the like. Yeah. Under a process of optimisation. 97 00:12:54,480 --> 00:13:02,100 Yes. Yes. And that then so the idea would be then to to take that work further and develop those those compounds, 98 00:13:02,100 --> 00:13:09,060 those blocking compounds to see whether any of them might have promise as as antivirals. 99 00:13:09,390 --> 00:13:21,900 Yes, hopefully. Yeah. And so that rather not your, your, for your dphil program, um, well into a different course let's say. 100 00:13:22,170 --> 00:13:27,930 But I guess in the end it doesn't as long as it is worked as well as so I mean, did you, 101 00:13:27,930 --> 00:13:36,900 have you had to read to rewrite the title of your of your thesis or will it all be included in, in your original program? 102 00:13:38,220 --> 00:13:50,360 Yeah, I, I had to revise and sort of switch my focus from my initial projects to this new project of modelling the SARS-CoV-2 proteases and. 103 00:13:50,790 --> 00:13:57,080 But, um, I think that's yeah, I would have to take on this project. 104 00:13:57,090 --> 00:14:02,460 Absolutely. Yes. It's clearly a very fashionable no, that's the wrong way of putting it. 105 00:14:02,880 --> 00:14:05,930 Important area, area to be in. 106 00:14:06,480 --> 00:14:10,709 Um, and I mean, it sounds terrible in a way, 107 00:14:10,710 --> 00:14:15,960 but in some ways the fact that there has been this terrible thing that's affected the world has presented you with an opportunity 108 00:14:16,680 --> 00:14:24,510 to to really break new ground in a way that might not be open to a lot of people at your early stage in your career. 109 00:14:25,110 --> 00:14:25,680 Yeah, I think. 110 00:14:25,820 --> 00:14:38,180 Is the the free the free exchange of ideas and knowledge and expertise that really helps develop this project and help us achieve some results, 111 00:14:38,180 --> 00:14:41,450 both computationally and in the lab. 112 00:14:41,750 --> 00:14:48,680 So yeah, without you both, there's this, I guess, 113 00:14:48,680 --> 00:14:56,090 Norm of working for soon and having some meetings and being open to online collaborations that 114 00:14:56,360 --> 00:15:02,270 might not have started and maybe I wouldn't have learned that much from my collaborators. 115 00:15:03,620 --> 00:15:08,510 So another thing I've heard of, I just put this in here is the COVID Moonshot Project. 116 00:15:08,840 --> 00:15:12,170 Is that something you've been involved with at all or is that completely separate? 117 00:15:13,070 --> 00:15:17,270 Not really, no. I don't even we need to talk anymore about that. 118 00:15:17,570 --> 00:15:23,660 Yeah, I've just been doing my research online and finding these things slightly overlapping, but not not quite. 119 00:15:24,140 --> 00:15:29,990 Some of the collaborators I think have been available for that. So, yes. 120 00:15:30,290 --> 00:15:39,469 So how again, how was did you find it personally having to suddenly work remotely and lose the kind 121 00:15:39,470 --> 00:15:43,430 of interaction which is the student you would you would normally expect a lot of, 122 00:15:44,000 --> 00:15:48,340 you know, close interaction with colleagues and friends. Yeah. That's a that's. 123 00:15:48,850 --> 00:15:50,550 Dphil student Yeah. 124 00:15:50,740 --> 00:16:00,640 Initially, as I took on this new project, there were still lots of things to learn and a lot of papers to read and a lot of literature to absorb. 125 00:16:00,880 --> 00:16:11,980 So and yet, back at the very beginning of the first lockdown and remember, like working quite hard, just trying to get resources as soon as possible. 126 00:16:12,730 --> 00:16:24,160 And that probably helps. And yeah, it keeps on kept on motivating myself to, to progress on this project. 127 00:16:24,400 --> 00:16:30,160 But I think eventually a start as the lockdown prolonged, 128 00:16:30,360 --> 00:16:42,669 I just started feeling the impact of working alone and being maybe a bit lonely and yet lacking the the support 129 00:16:42,670 --> 00:16:49,240 and the conversation you would usually get when you're working in an office with your fellow colleagues. 130 00:16:49,720 --> 00:16:59,680 So, and yeah, it got a bit tough later and there are many ups and downs during this journey. 131 00:16:59,680 --> 00:17:09,010 But, you know, thanks to the support of the collaborators and my supervisors and friends and family support 132 00:17:09,020 --> 00:17:16,540 to overcome these challenges and continue making progress and continue with my work. 133 00:17:16,750 --> 00:17:22,060 Were you living in college or in a in a in a rented room somewhere? 134 00:17:22,270 --> 00:17:30,400 Yes. So initially was working from home, but then I moved into my college career and from there. 135 00:17:32,230 --> 00:17:37,120 And of course, your family. Your family still in Hong Kong, so. Yes, I was with my family. 136 00:17:37,210 --> 00:17:41,650 And being far away must have been very easy. It was difficult and. 137 00:17:42,250 --> 00:17:45,490 Yeah, yeah. 138 00:17:52,360 --> 00:17:55,600 Yeah. So what's your main focus at the moment? 139 00:17:55,990 --> 00:18:00,310 How are you trying to take this work forward? Yeah, so I'm trying to finish up. 140 00:18:00,340 --> 00:18:08,290 Well, there remain many unanswered questions and about improve and uh, because uh, there, 141 00:18:08,980 --> 00:18:18,700 there have been experimental data on how I'm actually and the activity of empathy towards these 11 different and native subjects. 142 00:18:18,940 --> 00:18:26,120 So I've been trying to understand by a computational chemistry why this difference and its the way it is. 143 00:18:26,670 --> 00:18:36,160 Um, but also um, extending my project a little bit more and I've been working on the computational modelling of the papain like protease, 144 00:18:36,370 --> 00:18:45,459 also known as property, and that's also an important and a protease enzyme in the SARS-CoV-2 Corona virus. 145 00:18:45,460 --> 00:18:50,410 And it helps the virus evade the human immune response. 146 00:18:51,100 --> 00:19:03,130 And, and yeah, so working very closely with some experimental colleagues and on that and, but, and me focusing on the, um, the computational side. 147 00:19:03,160 --> 00:19:07,460 Mm hmm. Mm hmm. So that that would be an alternative route to blocking the. 148 00:19:07,510 --> 00:19:12,160 The virus. Um, as you said, so not so much the replication, but the evading of the. 149 00:19:12,490 --> 00:19:15,790 Of the immune system. Yes, definitely. Mm hmm. Mm hmm. 150 00:19:17,510 --> 00:19:27,350 Very interesting. And. Yes. 151 00:19:27,350 --> 00:19:32,179 I think I think you did answer this really that it was the same question that I put to to Fernando, 152 00:19:32,180 --> 00:19:39,950 whether you felt that having an important job to do helped to support your own wellbeing through the through the pandemic. 153 00:19:40,460 --> 00:19:51,530 Yeah, I think maybe one important, important lesson I learned from working from home is be understanding and caring towards each other because 154 00:19:53,510 --> 00:20:03,230 like I'm sure the lockdowns and like these few years of living under COVID have been really difficult. 155 00:20:03,250 --> 00:20:12,409 And sometimes we have and the circumstances might be different and circumstances about family, 156 00:20:12,410 --> 00:20:17,930 about friends and different worries about the world and the pandemic and just 157 00:20:17,930 --> 00:20:24,230 trying to be and to be caring and reaching out to help others is really important. 158 00:20:24,260 --> 00:20:34,520 Mm hmm. And that's that's interesting because I think I mean, a lot of people find that the early academic careers are pretty tough and competitive. 159 00:20:35,150 --> 00:20:40,460 And actually, you're not the first person who I've interviewed who said that it was such a revelation 160 00:20:40,790 --> 00:20:45,200 when working with the COVID project to to be working in a much more collaborative way. 161 00:20:45,440 --> 00:20:50,659 And that the the competitive side of it was kind of dialled down and the cooperation was dialled up. 162 00:20:50,660 --> 00:20:55,970 And I wonder if that's a lesson for how to manage science better in the future. 163 00:20:56,450 --> 00:21:04,670 Yeah, I think we definitely being open to collaboration to the free exchange of information and expertise. 164 00:21:04,850 --> 00:21:08,630 And though science is a competitive field, but at the same time, 165 00:21:09,530 --> 00:21:19,069 after we have a common goal of making lives better for everyone in the world and solving real, real world problems, 166 00:21:19,070 --> 00:21:29,149 like no matter whether it's the energy challenge, food challenges, or in this case, the current coronavirus pandemic, 167 00:21:29,150 --> 00:21:37,520 and just working together and being able to share that information, that expertise is really important. 168 00:21:37,550 --> 00:21:44,110 Mm hmm. So I must excuse me. I'm assuming you're going to want to stay in this field for the future. 169 00:21:44,120 --> 00:21:58,350 You sound very enthusiastic. Yeah, probably, if it's hard to predict, I suppose, but, yeah, I would like to continue using my chemical knowledge and. 170 00:21:58,360 --> 00:22:08,149 And maybe experience and or computational chemistry to continue working on topics of medicinal relevance and. 171 00:22:08,150 --> 00:22:12,590 And sort of chemistry at the interface of biology and medicine. 172 00:22:12,740 --> 00:22:15,850 Mm hmm. Well, thank you very much for doing.