 Welcome to MOOC Interactomics course. Hello students and participants. This course is essentially going to give you a more detailed understanding of how various type of biomolecular interactions work. While the major focus of the course will be to look at different type of technologies which measures protein-protein and protein other biomolecular interactions. But today what I thought I am going to talk to you about a journey of entire proteomics field and different technologies which are part of the field of proteomics. This is going to give you a much broader overview of how different technologies are playing an important role to study the very complex proteome almost all the proteins which constitutes any organism. And of course to understand this we need much more breadth of the technologies as well as the depth of the technologies. So we cannot just achieve all the questions by looking at single technology platform. So in today's lecture outline will be first I am going to give you an overview of proteomic technologies. And then I am going to talk to you about various opportunities which big data sciences offers to us and which are the challenges which are part of this field. As you are aware the central dogma starts from the basic biomolecular workflow starting from the genes to the transcripts and the protein. The entire field which aims to study all the genes of a given organism is known as genomics and all the genes which constitute the organism is known as genome. And likewise all the transcripts we study with transcriptome in the field of transcriptomics and all the proteins we study in the field of proteomics. As you can see in the slide that genome provides only the snapshot of the total information which is encoded in a cell but the biological processes are very dynamic and very complex and they are governed along with the transcripts and the proteins which makes the changes in response to the dynamic behaviour of the cell. When it comes for the medical field we cannot just simply rely on one set of technologies and looking at only single type of biomolecule of interest because the human physiology or any other organism physiology depends on the intricate relationship of various biomolecules. So, if we are looking at the complex physiological systems we have to look at all the possibilities of different type of biomolecules. For example, if we are trying to capture the actual physiological consequence then we should look at the genome, the transcriptome, the proteome as well as the clinical information and the phenotyping of these individuals. All of this comes under the field of omics. So, for what we are doing in the field of medicine is essentially we are generalizing the medicine and giving the same treatment to all the patients. But the field of personalized medicine aims to look at each individual as a unique entity. So, can we look at a given individual and all of its biomolecule and try to make an educated sense of that what should be the best drug given to this individual? How to monitor the response of a given drug or therapy? What should be the best way of diagnosing the disease for the individual? How multi marker risk assessment could be analyzed? How one could eventually offer the therapies which are very, very effective for a given individual based on our comprehensive understanding of the omics system of that individual. All of these things are revolutionary changes which are happening in the field of biology. I think it is good idea through this course you will be exposed for many of these technologies which will be interesting for all the life systems including human. So, the entire field of omics started from the very ambitious and very comprehensive human genome project along with many other genome projects which actually laid the foundation of the whole field of omics. It was not only that 15 years of effort in a very disciplined manner which laid first time the draft genome map of human, but also it also gives us an ability that we have now gone in the field of life sciences and biology towards paradigm shift where we can start looking at all the possible genes of a given system. So, genomics which was you know the studies published in Nature and Science in 2001-2002 those really laid the foundation for the whole omics field including the field of proteomics which got inspiration from the field of genomics. As soon as people unraveled all the possibilities of the genes which constitutes human and other organism, they realize that biological systems are very complex, but the number of genes which are present they are not that many and they are not very different in different organism. So, the overall gene range was around 20,000 to 30,000 for different model organism including human. So, what makes human different, what makes different organism different and what gives them the unique ability to cope up with the environment to cope up with the especially physiological context and that is where the whole field shifted towards more functional molecules more towards transcriptomics and proteomics. Here I am depicting you the proteomics journey which is started from 1970s and started with simple experiment of how to separate the complex protein mixture using two different properties their molecular weight an isoelectric point which made the two-dimensional electrophoresis. And then along with that there were different developments are happening in the field of mass spectrometry and various type of sequencing projects ESTs and various type of nucleotide sequencing they were all also happening parallel and together all of those made huge progress in the field of omics around 1990 to 2000. When many of these things started getting more mature on the side we see the advent of electro spray ionization and milding as the ionization sources for the mass spectrometers. So, all of them were really giving the foundation for the new field of proteomics to start but only after the completion of the human genome project scientists started moving towards the entire field of proteomics and thought now we can start understanding the functional molecules which are the proteins. Different other technologies including various intractomics based technologies protein microarrays various type of immunoprecipitation followed by mass spectrometers and label free biosensors all of them started coming together and then started making the field of functional proteomics and intractomics. Further there were many constraints were there to validate the biomarkets validate the proteins. So, new field of target proteomics emerged around 2012-2014 and then as we moved along then first time the scientists have started realizing the full potential of mass spectrometers and the first draft of human proteome maps are published. So, what you see here that the cover page images of the nature which first time showed 2014 the human proteome map the draft was actually published while the whole proteome was not unraveled but still majority of the proteins were discovered and then idea was that first time we have now the evidence that the genes make the protein and we are now able to measure those peptides using mass spectrometers. So, two different independent groups published these studies first time and showed the draft human proteome maps. Additionally another group worked on the antibody based approaches and they also showed first time in 2015 a study published in science which was the tissue based map of the human proteome. First time they showed that where the proteins are localized and there is evidence that proteins not only exist but how it is localized and they also provided evidence at the tissue level and looking at the various type of immunohistochemistry and tissue arrays based approaches. So, there are various facets of proteomics one could think about gel based proteomics which essentially looks for variety of you know gels to separate the proteins, gel free proteomics which essentially could be mass spectrometry or even various type of intractomic technologies. Functional proteomics which again involves various technologies which looks at the biomolecule interactions look at the functional consequence of these proteins, how they interact, how they are part of the different networks and pathways and targeted proteomics which essentially looks at validating the protein targets. So, broadly we can divide the whole field into two broad categories one is discovery proteomics and second could be functional proteomics. Different technologies which are part of the gel based proteomics as well as different type of mass spectrometers could be covered under discovery proteomics. Various technologies which are more high throughput in nature essentially protein microarrays label free biosensors which constitute the intractome analysis they are more part of the functional proteomics. So, I am going to give you an overview of different technologies in a very nutshell because the focus of this whole course will be mainly on intractomics. But it is good idea for you to appreciate that where intractomics field fits into the broad field of proteomics and which are other technologies which are all going to help each other to understand the complex biomolecules. So, gel based proteomics as the image conveys that you can separate the proteins based on either only their molecular weight which you do in the standard SDS page electrophoresis or you can also separate them based on the isoelectric point in the isoelectric focusing units or you can combine the two and then separate proteins in the two-dimensional which is known as two-dimensional electrophoresis or you can make it more advanced you can do some quantitative proteomics when you can add the dyes to your control samples i3 dye your treatment samples i5 dyes and then you mix even the various control and treatment samples in one tube label them as i3 dye mix all of them together now separate them in the first dimension with the isoelectric focusing second dimension with the molecular weight SDS page and then now you are able to get not only that you know the protein separation in two-dimension but also the protein expression changes you can start monitoring. So, this whole thing is itself is very vast and heavily used which we will talk in a different course in different platform but I just thought to give you a reminder again that there are many technologies which are equally important which one need to appreciate in the whole field of proteomics then come the field of shotgun proteomics which is essentially driven with the mass spectrometry the complex protein sample you can make the lysates you can digest them using enzyme electrophysin due reduction alkylation and digestion followed by the cleanup of the peptides. So, from the complex proteins now you have made the peptides which could be cleanup and now these peptides can be separated using mass spectrometers for MS or MS-MS analysis which is going to provide as the peptide sequences and the protein information using the different database search. But what if you want to quantify the proteins using shotgun approaches where you do not want to separate them on different gels but directly you want to separate in the mass spectrometers and also you want to look at their expression changes. So, therefore the quantitative proteomics using mass spectrometers have become very attractive way of proteome analysis where all the proteins which are not digested in the peptide forms you want to label those peptides on the N-terminal sequence and the different type of reporter ions there are different type of tags available which could be used to label these peptides and that could be used for the field of quantitative proteomics. I have shown you here one example of using isobaric tags which are known as itrack or isobaric tag for relative and absolute quantification. Let us take a simple situation when we have control samples along with three different grades of disease and we want to analyze that what are the changes happening for each protein or each peptide from the healthy individual to the patients which are having the low grade of any cancer middle grade and the high grade. So, then we are labelling all the four different conditions using four different type of these itrack labels which gives you the numbers which are reporter ions from 114 to 117 and these reporter ions you are going to analyze and look at in the mass spectrometer which will be further used for the peptide quantification and protein quantification. This whole field of mass spectrometry based proteomics is really evolving very rapidly and while we will have much more detail of this whole field in different codes but it is good idea for you to appreciate the lot of advancements are happening in the mass spectrometry based proteomics. For example, I have shown on the slide different approaches both based on the label based and label free analysis using mass spectrometry for looking at quantitative proteome changes. For example, one could use like itrack even the TMT tags which are another isobaric tag known as tandem mass tags and one could do 6 plex 10 plex or even 14 plex analysis now with the recent tandem mass tags which are available or you want to not label your proteins and you still want to look at their quantification in the mass spectrometers that is known as label free quantification and in this middle part which you see what you are looking at you are looking at the spectral count of peptide. So, idea is that if a given let us say you know we are comparing control with a disease for a given protein if that protein expression is very high in the disease sample as compared to the control then many peptides will be formed for that particular protein and then the spectral count will be much more or different numbers will be obtained for the same protein. So, can we measure those spectral count and then that information we can use for the label free quantification. Additional approaches to also look at data independent acquisition where each of the window of your mass to charge separation you want to really obtain a very high resolution image of that and analyze the protein using label free manner. All of these things are very recent advancement of course I am not talking in detail right now because that is not the focus of the whole course right now but you should appreciate lot of advancement which has happened in the field of proteomics is starting from the gel based moving to the mass spectrometers and various type of quantitative proteomics methods. The whole scientific community has been struggling how to validate our targets. We have identified many proteins whether using microarrays or using the mass spectrometers or gels but now if you want to validate your proteins then you are relying simply on the antibody based assays either western blots or immuno assays some way you need some antibody or some biochemical assay to confirm that what you identified is correct. Therefore a new field of mass spectrometry based validation strategy has emerged which is known as target proteomics. Here idea is you have identified set of peptides which are differentially expressed from your control versus disease. Now you want to validate these peptides using triple quadrupole based mass spectrometers you will set up the various parameters so that software and hardware will only recognize the specific set of peptides and their transitions and you are only going to monitor the intensity of these peptides to find out are they really upregulated or downregulated when you compare the expression changes in different samples. So you can now set up this assay in more high throughput manner and if you really want to go even to look at more accurate quantification of the protein so you can synthesize the synthetic peptides which could be heavy labeled and now first you run in your mass spectrometers only synthetic peptides and look at their MS-MS spectrum pattern then you run your unknown sample and if it is matching then you are very confident and you can also compare the intensity with the heavy labels. So this is known as accurate quantification of the protein in this way you are able to validate your targets and also you are able to measure that what will be the concentration of these peptides in the given sample. So while variety of these technologies have been emerging, scientists have been still exploring are there ways more high throughput manner where we can with very small volume of the sample with very you know a lot of clinical samples are precious so whether drug molecules are precious lot of biological samples are very precious your protein and peptides which you extract from the samples are very precious how best we can conserve the sample and still obtain the meaningful information in a very high throughput manner. And also most of the previous technique which I talked are able to give you lot of leads but how to now determine the functional consequence of these proteins how to assign some function to these proteins. As a result different feed has emerged known as intractomics which relies on protein microarrays and different type of label free biosensors. I have shown you here one of the workflows for using protein microarrays for auto antibody screening in the human patient samples. So let us assume that you know the patient's blood sample followed by serum has been separated and now we are looking at which are the auto antibodies produced in the serum and can we detect that at the proteome wide manner. So in this case on the protein ships the very small volume of the patient serum which is diluted with the buffer will be floated and now then with a very small volume you know let us say we are talking about 50 to 100 microlitre of the volume of the sample which is sufficient to cover your glass slide which is already having the entire proteome of a given organism. Let us say all the 20,000 proteins of human you are able to screen with a very very small volume of the given sample. And wherever you see the signal which you can detect using secondary antibodies which is anti-human IgGs labeled with Psi 3 or Psi 5 then you can find out the potential biomarker candidate or you take another context you want to look for the an interactor of a given protein and you have the entire chip which is having all the 20,000 human proteins. So this protein which is your target protein of interest how this protein goes and binds with the many other proteins in the entire chip that will give you idea for the potential interactors of these proteins and likewise there are many applications which can be done using protein microarrays. But what if if you want to determine that you know the not only that the protein X bind to the protein Y but also what is the strength of this binding what is the on rate of binding what is the off rate of binding how strongly these are associated and how strongly or weakly they are dissociated. So then you have to move on to a label free biosensor platform and surface plasma resonance or SPR is one of the very strong platform to offer this kind of approach. So as you can see in the slide here now we have a gold slide on which the antibodies are printed. When you flow through your protein of interest the right antigen will bind to the right antibody pair and likewise you can have even the you know various other type of molecules printed on the gold slide and then if a small molecule is coming then protein is binding to the small molecule of the drug and you are looking at protein drug binding interactions. So many type of interaction studies could be planned and designed using these kind of label free biosensors and what you obtain is essentially the sensorogram which gives an idea for the association and dissociation rate of these binding and eventually you can also calculate the capital K capital D which is the dissociation constant and the kinetics of this binding which is happening in the system. So there are many novel label free methods are currently being used of course I have only showed one of them but many of them are relying on different type of physical principles to measure the proteins and different biomolecules with the physical principles without having any tag which is associated with the these molecules to be detected. So you are not adding any kind of fluorescent tag or radioactive tags to measure the binding interaction but you are simply looking at the change in the reflectance or looking at the conductance change various type of you know even interferometry based change those are all going to help us to determine what is the binding happening in the label free manner. So this is probably most you know natural most closest thing which can happen without giving any artifact signal. So even our own group and others have also started using some of these technologies for even some very interesting applications which are beyond the standard workflow of these you know instruments. For example even SPR we have used to measure the protein concentration from the serum sample if there are some serum biomarkers of interest rather than relying on the ELISA kit can we measure their binding on the gold chip by passing the complex serum samples and looking at how unique this binding interaction is happening and one could actually start developing these assays much faster. And we have also come up with some very innovative ways of measuring these interactions using CFCA which is calibration free concentration analysis of different type of serum protein measurement. As you go along in the course you will be exposed to many technologies but you should also keep in mind how best to integrate different technologies and make best use of each of the technology platform. For example SPR could be used to identify the interactors of interest let us say you have an antibody and you are passing a complex lysate. Now many some many proteins will be there in the complex lysate which will probably bind to the antibody. So antibody will bind to an antigen of interest an antigen may have even the complex which is having many other proteins. So now if you enrich this particular interaction by running the same reaction in multiple wells multiple channels and now you can elute out all the potential interactors which have identified from these reactions followed by if you can digest these proteins make a peptide form and run in the mass spectrometer so you can actually identify now the potential interactors. So in many ways you are now able to look for the new potential interactors which was not known but you have to apply coupling the different technology platform. So we have utilized here surface plasmon resonance with mass spectrometry to identify some new interactors. So in a nutshell if you look at the slide I try to convey you the idea that there are many technologies which are a part of the field of proteomics starting from the gel based proteomics to mass spectrometry based platform target proteomics interactomics. Of course each field itself is very vast and deserve a full course on its own. So we have dedicated this particular course on the field of interactomics which is much more functional understanding functional consequence of the proteins to understand and that is probably much more relevant for lot of you know biologist and people working in the field of you know life sciences. When it comes the looking at any kind of complex biological problem I think you have to be very unbiased and think about any technology which can answer the question in a more efficient manner. So therefore even when people are looking at the biomarkers for a given disease it is good idea to look at various biomolecules and then try to integrate the information. As you can see the complex system biology the complex central dogma of the life is starting from DNA RNA mRNA protein and then followed by metabolites all of this offers you know the various type of novel molecules and you never know that the most of the clues are coming from which type of biomolecule. If our resources our time if our technologies are available and we could apply that it is actually good idea to take the same samples and try to understand different level of information starting from the genome transcriptome proteome and metabolome and then try to integrate the information to find the most meaningful dataset. This is what is conveyed in the slide that the actual physiological context is very complex we have to build the layers of information for the same organism for any organism we have to start looking at the entire cascade of the events happening from the genes to transcripts to the proteins to metabolites and along with their phenotype the environmental effect all of them is only going to give us the complete picture. And if we are looking at more on the human biology or medicine then we have to add the clinical details and clinical information to make it more comprehensive and then only I think we should be able to get some very very right answer for the question which we wanted to address. So, all of this technologies which we talked starting from the genomics and of course genomics have really taken the big leaves with the next generation sequencing technology platforms and a lot of things are still you know very revolutionary happening in the field of genomics and then field also progressed toward transceptomics and proteomics and metabolomics all of them are generating the big dataset along with lot of medical information the radiology immunohistochemistry and different type of other datasets in life sciences. If you think about now the life science field and omics field is generating huge amount of data and that data is going to be very very revolutionary nature very is going to transform our understanding of biology. I showed a slide here for a given disease which we were looking at in the colloquial cancer. How different type of data set when we started analyzing them in the system network level what we identified was that you know set of proteins were quite distinct in the colloquial cancer patients as compared to the you know the air peritimoral regions or the normal regions. And then some of these associated candidate proteins we try to map them as a part of the interaction analysis and finally with the system network analysis we found that most of these are actually mapping towards certain nodes and which are those nodes how they are going to regulate the complex disease this is kind of excitement which one could try to obtain by looking at this is a big data set and then try to understand that there are many changes happening but what can be the root cause of many of the changes which are the major nodes which are governing all of these associated protein changes. So then I think you know you looking at data in much more comprehensive manner and then trying to identify the system level analysis is going to be very powerful. However the big data also offers a lot of challenges. How are we going to link the big data which we obtain from variety of these technologies I discussed to the real life innovation and the new knowledge and ultimately how it can really transform the societal development. These are the questions in front of us and of course the big data which is coming very rapidly also opens many questions what are the guidelines of the good data set which we are obtaining for the big data and of course field is evolving for each of the kind of instrument and technology. Now there are set of guidelines coming forward which are of you should keep in mind and keep an eye on those papers which defines the guideline for what is accepted as the best data set. Next question comes where the data should be stored. So much data terabytes and terabytes of data every day we are generating. How are you going to store the data which can also be retrieved easily from the other users? Data accessibility are we going to just simply share with everybody or we are going to have at least you know some way of coding the data some way of coding the patient information and then start utilizing that and share that information in more meaningful way. What could be the ethical consequence of sharing this data? There are many things which are a part of the regulations and guidelines of the big data set. I must appreciate that Department of Biotechnology Government of India has recently taken up this matter seriously and they have also now defined the certain guidelines and regulations for the big data and likewise the whole omics field is also keep deliberating what should be the best guideline of looking at the big data and sharing the big data sets. All right, just to summarize the lecture, I hope I conveyed you that there are many technologies, many promising technologies and approaches are part of the field of omics and specifically I spoke about the proteomics and variety of platforms are available which you can utilize to address different type of biological questions. Each of these technologies have a lot of potential, but you need not to get influenced with these technologies. Rather you think about what is the biological question you want to address and accordingly you can choose the right type of technology for your application type. I hope this sets up a good platform for you to appreciate the vast field of proteomics and several excitement and challenges which are part of this field and we are going to focus more on specifically on interactomics from the subsequent lectures. But I am going to leave you with this video which you will watch on journey of proteomics field and how the proteomics can translate the code of life. So we made this documentary and please watch this. Thank you very much for attending the lecture and finish this video and then we will be ready to interact with you in the next lecture on interactomics. Thank you. Most important, most wondrous map ever produced by humankind. With genomics setting a foundation in the quest to uncover the mysteries of life, what lies next? I wonder, perhaps time has now come to appreciate the significance of omic technologies such as proteomics. Proteins and proteomics are central to connect genomes with phenotypes with normal biological processes. There is no way to predict from the genome itself the crucial features of proteins. The real key here is that genomics can only tell you of the snapshot of the organism whereas proteomics will enable us to look at the organism dynamically. Proteomics is a science where we want to know about all the proteins that are coded for by the genome. Proteomics addresses whole systems and uses a broad unbiased approach to derive new findings. What are the technologies driving proteomics? Several techniques like mass spectrometry, protein microarrays and label free biosensors have been used in proteomics to elucidate the expression, localization and interaction of proteins. Unraveling the proteome is a very onerous and complex journey that demands the efforts and insights of an interdisciplinary team of scientists. Fourteen years after the release of the first draft of the human genome, scientists are working on the map of the human proteome using high-resolution mass spectrometry combined with high-springency informatics and other methods including antibody-based protein microarrays. In addition to mass spectrometry-based protein maps, scientists used tissue array platforms to spatially locate proteins in human cells and tissues and map their expression levels in various biological tissues. One major effort in this area has resulted in the human protein atlas. Despite this progress, there exist missing proteins which are yet to be annotated. Scientists hypothesize that the missing proteome could be a result of relative low abundance, tissue type, development or stress-specific expression of proteins. With the road ahead requiring comprehensive proteogenomic analysis and consideration of many additional sources of biological information. Researchers are using novel approaches for biomarker discovery by studying the differential expression of alternatively spliced isoforms in cancer, low abundance blood-based biomarkers for disease diagnosis, studying cell secretome, analyzing human immune response to develop vaccines, improving proteomic methods to study the host-pathogen interactions and so on. Hopefully some of the biomarkers that we discover will be useful for identifying disease early for helping doctors identify the best prognosis for patients. Proteomics has the unique ability to analyze protein production, degradation and post-translational modifications which are highly pertinent for biomarker discovery and translation. In glycosylation specific form of protein will give us a more specific and sensitive approach to detect the disease in tissue and the body food. Several novel techniques such as multiplex protein microarrays and beat-based arrays are also contributing to biomarker discovery. Protein microarray contains thousands of proteins in one chip which allow us to explore protein functions with a certain molecular probe. A recent innovation in this field is the development of ultra-high-density peptide arrays comprising of millions of peptides thereby allowing large-scale probing of biological samples. Present diagnostic as well as therapeutic strategies that are existing in clinical scenario clinical setting are all a result of the biology discoveries that have gone on over the last 50 years. The proteomics community has kept up its expectation of being a complementary approach alongside genomics thus contributing to diagnostic assays in clinics. How can proteomics cater to the needs of precision medicine? What if figuring out the right dose of medicine was as simple as taking our temperature and that's the promise of precision medicine? The precision medicine means one patient one drug at a time and therefore if you can detect specific target for any given cancer type we can then develop treatment based on the target information and therefore the precision medicine is going to be the future of oncology. My opinion that to be able to figure out precision medicine both at the diagnostic level as well as to be able to determine the the pathways in which drugs should be targeted to you you have to be able to really quantify well all of the proteome. How was proteomics moving towards targeted validation? One of the challenges facing proteomics is being able to get from the discovery all the way to validated markers that are useful in the clinic. So that's why targeted proteomics is so important by sacrificing a little bit in terms of depth of coverage but selecting analytes that we care very deeply about we can design proteomic assays that will be able to measure analytes whether they're proteins or peptides each and every time that we do an experiment. More specifically this essay have been now widely accepted in the community as an SRM or a performant triple quadruple instrument. And I think it's become very clear that those methods including multiple reaction monitoring and PRM and allied methods are going to have a major impact on the development of clinical diagnostics and then ultimately on patients health. Innovations in technologies like targeted proteomics have allowed researchers to validate interesting targets overcoming the bottleneck of availability of reliable analytical reagents. The transplanting pyrolysis has given us the capability to analyze proteomics in an unprecedented manner but that has also given us the capability to understand which proteins are being expressed which proteins can be analyzed by mass spectrometry and we can utilize that in a targeted approach. I've been developing SCOIN for the past eight years and it's grown incredibly it's in use around the world and it's exciting to see that proteomics researchers really seem to feel that it helps them with their research. SWATMS method was to expand the SRM type approach to much larger number of proteins potentially hundreds to thousands of proteins. The advent of new age techniques like SWAT with ability to identify and quantify proteins or peptides over a dynamic range and powerful tools like Skyline and SRM Atlas have added to the arsenal of targeted proteomics. How is the global proteomics community poised? The human proteome organization is an international scientific organization that promotes international scientific cooperation, educational training and technology breakthroughs in proteomics. HUPO was started in February 2001 in Versailles, France around the time when the first draft of the human genome was released. The human proteome project was launched nearly 10 years later in Sydney at our annual World Congress. HUPO coordinates its flagship scientific project that's called the human proteome project and that is broken down into three main thrusts a chromosomal effort, a biology effort and a disease effort and it has three supporting technology pillars bioinformatics mass spectrometry and antibodies that support those thrusts. It must take a comprehensive proteome wide view of cell functions connect the genome proteome and the phenotypes. Whether you are an early researcher or a seasoned veteran seeking to make important new discoveries I encourage you to engage in this vibrant and really exciting project. The journey of proteomics has been promising. My belief is that the proteomics community can contribute immensely to functional biology and decipher innovative solutions. Persistent efforts from communities like HUPO has helped proteomics establish an undeniable international presence. This has established the broader theme of research directions thus translating the code of life.