Antibody Expert, Dr. Andrew Martin, Discusses Antibodies, the Complexity of Antibodies, His work, and Passions
“I am a Bioinformatician / Computational Biologist specializing in antibodies and the effects of mutations on protein structure and function. During my PhD, I co-developed one of the first automated protein modelling programs for modelling antibodies which was commercialized by Oxford Molecular as ‘AbM’. I have maintained my interest in antibodies ever since and have published widely on this topic. We have developed various tools for dealing with antibody sequence and structure including the abYsis web-based analysis platform (www.abysis.org). I have helped a number of labs and companies with antibody humanization and acted as an Expert Witness in numerous patent disputes related to antibodies and general bioinformatics. I am also an adviser to the WHO-INN on the description and annotation of antibody-based drugs and naming of biologics.
My other main interest is in the effects of mutations on protein structure and predicting whether a mutation will be pathogenic using machine learning. We have developed specialized predictors for specific phenotype prediction in individual proteins as well as a general predictor.
We also develop tools for use by the structural bioinformatics community, most of which are available as open source. In the past I have also done contract scientific programming for the National Grid Company and Bradwell Nuclear Power Station.
I teach general biochemistry topics as well as the structure of antibodies and bioinformatics at various levels from introductory first-year undergraduate lectures to masters and specialist training level.”
Here is the first question and answer of the text interview as well (It’s long, which is why I’m not including all of them) Click here to read the rest.
1) “effects of mutations on protein structure and predicting whether a mutation will be pathogenic using machine learning. We have developed specialized predictors for specific phenotype prediction in individual proteins as well as a general predictor.” Question Can you unpack this interest and what about it fascinates you?
My work on mutations really is something completely different from the antibody work but really constitutes the other main area that we work on. I am interested in understanding how a mutation affects the structure of a protein and then how that affects protein function. It’s very difficult to tell what will happen to the overall structure when a mutation occurs. However we can categorise mutations into a number of groups: (i) First, a mutation might be ‘functional’ – in other words it is changing a residue that is intimately involved in the function of a protein, perhaps in an enzyme active site or in a site involved in interacting with another protein or something like a metal ion. (ii) Second, a mutation might prevent the protein from folding up – for example, if you had a small amino acid buried in the middle of the protein and you replaced it with a much larger amino acid, then it will be physically impossible for the protein to fold properly. (iii) Third, and perhaps most interestingly, a mutation may not prevent the protein from folding properly, but somehow destabilizes the correctly folded form compared with misfolded or unfolded forms. We know this is the case because there have been a number of experiments where people have taken proteins that have a mutation and reactivated them simply by lowering the temperature where the destabilizing effects are mitigated. This gives the potential for drug design to rescue mutated proteins.
So what we try to do, is look at the local structural effects of a mutation and from those identify which class the mutation will fall into. For example, a mutation might break a hydrogen bond, a small-to-large mutation might mean that the new residue won’t fit properly, or conversely a large-to-small mutation would lead to the protein having a void in the structure if it maintains its normal conformation, but in reality the protein will change conformation somehow collapsing to fill the void. However, we don’t try to predict that – we simply say that *something* will happen to the overall structure.
Once we have all this structural information we can use that to train an artificial intelligence machine learning method to predict whether the mutation is going to be pathogenic. In other words is the mutation going to have an effect on the structure of the protein that affects its function in a way that is detrimental.
With projects such as the 1000 genomes project, the UK10k genomes project and now the UK100k project as well as similar projects in other countries (such as the Personal Genome Project of Australia) we have huge amounts of information about differences between people’s genomes and we have links between those differences and diseases. Using this sort of information, we can start to try to understand mutations that are likely to be linked to disease and, in my case, try to understand what they are actually doing to proteins. Also in Mendelian inherited diseases we can try to predict whether a novel mutation seen in the clinic is going to be linked to disease.
Over 440,000 Deaths Each Year Due to Preventable Medical Errors and How One Startup, Scalpel, is Fighting to Reduce That Number
About Yesh CEO and Founder:
“I am a generalist who builds technologies that improve healthcare. Trained as a dentist, I have over five years of interdisciplinary experience in healthcare and technology (Virtual Reality, Augmented Reality and Computer Vision). I previously built a startup (Open Simulation) to provide low-cost surgical simulation using Augmented Reality. In my PhD, I designed and evaluated one of the first immersive virtual reality training tools for Oral and Maxillofacial Surgery. I can understand healthcare challenges from a clinical point of view and build tools that address those needs. Currently, I am focused on making surgery safer through Scalpel Ltd.”
“To help hospitals reduce preventable errors and cut down costs in litigation, Scalpel Ltd. is building an end to end patient safety platform. This AI-powered platform checks and verifies the implementation of safety steps during surgery using a combination of computer vision and machine learning technologies. Unlike standard checklists, Scalpel’s solution doesn’t require any human interaction, sitting in the background in any operating room it automatically monitors, it provides real-time feedback to detect and prevent errors.”
Key factors that this startup is working on or with: Surgery, Patient Safety, AI, Machine Learning, Computer Vision, Medical errors, and Human Factors