ENGINEERING RECOMBINANT PROTEINS: FROM STRUCTURE TO FUNCTION AND BIOLOGICAL ACTIVITY

Green Fluorescent Protein Glows Gold

<sec><p indent="0mm">The 2025 Nobel Prize in Chemistry has been awarded to Susumu Kitagawa from Kyoto University, Richard Robson from the University of Melbourne, and Omar M. Yaghi from the University of California, Berkeley, for the development of metal–organic frameworks (MOFs). The Nobel Committee for Chemistry emphasized that MOFs have enormous potential, bringing previously unforeseen opportunities for custom-made materials with new functions. The groundbreaking significance of MOFs centers on their definition of a unique “new space” in chemistry, which encompasses both the open physical space within their ordered structures and the paradigm-shifting platforms they have unlocked for scientific research. This space spanning structural and academic dimensions has established MOFs as a key engine for developments in chemistry, materials science, and related disciplines, earning them recognition with this year’s Nobel Prize in Chemistry. </sec><sec> As a central science, chemistry has long enabled precise molecular design, but extending atomic-level control to two- and three-dimensional extended structures remained a daunting challenge. Robson’s pioneering concept of predicting coordination network structures based on building units’ geometry laid the important groundwork, while Yaghi and Kitagawa shifted the field from “crystal engineering” to “framework chemistry”. MOFs with high stability, permanent porosity, and tunability were realized through the introduction of key concepts such as secondary building units, isoreticular design, and soft porous crystals. This View article traces the historical development of MOF chemistry, emphasizing key breakthroughs in the structural design and synthesis, adsorption measurements, functionalization, and flexibility and dynamics. </sec><sec> The article further examines the evolving trends in modern MOF synthesis research, distinguished by new topology theories (e.g., merged net, related net, derived net, and net-clipping) and hybrid linkage construction methods. The increasing diversification of building units has spurred exploration of anisotropic materials, interwoven materials, interlocked structures, and frameworks with sequences. Developments in techniques such as <italic>in-situ</italic> electron microscopy and electron crystallography have rendered structural characterization of MOFs more comprehensive and unambiguous, while their integration with artificial intelligence (AI) and machine learning has accelerated material prediction and synthesis. </sec><sec> Porous MOFs not only offer methods to control matter beyond the molecular level, but also provide highly open space for further manipulation, with accessibility, responsiveness, and adaptability as key research focuses. Leveraging ordered active sites on the framework backbones or in the pores, they serve as ideal catalytic centers with unparalleled precision and controllable microenvironments. Electrically conductive, proton-conductive, electrocatalytic, and photocatalytic frameworks have emerged. Additionally, studies exploring the distinct interfaces between MOFs and other chemical or biological entities have garnered substantial research interest. </sec><sec> Highlighting MOFs’ practical value, the article also discusses their role in addressing global challenges: their ultrahigh gas adsorption capacity and selectivity drive applications in carbon capture (including direct air capture), natural gas storage, and water harvesting. MOFs also show promise in pollutant removal, targeted drug delivery, and electronics, with commercialization advancing via leading companies and supported by global research initiatives. </sec><sec> Over a century since Gilbert Lewis’s seminal paper, <italic>The Atom and the Molecule</italic>, human mastery of microscale units has advanced from understanding atomic bonding to designing deliberate spatial architectures. Atoms are fixed in molecules with specific spatial arrangements; similarly, molecules are anchored in frameworks with distinct directional alignments and spatial configurations. Fittingly, the 2025 Nobel Prize in Chemistry honors the pioneering development of MOFs—the epitome of this spatial design philosophy—recognizing their transformative potential to address global challenges in clear energy, clean water, and carbon neutrality. </sec>

The Royal Swedish Academy of Sciences awarded The Nobel Prize in Chemistry for 2003 jointly to Peter Agre and Roderick MacKinnon for their discoveries concerning ‘channels in cell membranes' being of fundamental importance for understanding how water and ions move through these membranes. Agre discovered and characterized the first water channel protein and MacKinnon has elucidated the structural and mechanistic basis for ion channel function. Lipid bilayer membranes are generally impermeable to water, ions and other polar molecules, yet, in many instances, such entities need to be rapidly and selectively transported across a membrane, often in response to an extra- or intracellular signal. Transport along a concentration gradient is mediated by membrane channel proteins, whereas transport against a concentration gradient is mediated by membrane pumps such as the Na+/K+ ATPase (a protein discovered in 1957 by Jens Skou, who received the Nobel Prize in chemistry in 1997). Water channels allow the cell to regulate its volume and internal osmotic pressure and are needed when water must be retrieved from a body fluid, such as when urine is concentrated in the kidney. Water channels are found in all organisms, from bacteria to man, and are crucial for life. The water channels were discovered by chance by Agre in the mid-1980s when he was studying blood group antigens from the red blood cell membrane. Agre's unexpected discovery of the aquaporins revolutionized the study of water transport and laid a firm biochemical foundation for a very important area of physiology and medicine. Aquaporin-like proteins have since been found across taxonomic kingdoms. In humans alone, there are at least 11 different aquaporin-like proteins, many of which have been linked to various diseases, some of them inflammatory and autoimmune in nature (J Physiol 2002;542:3–16). Plants have an even higher number of aquaporins. The physiological importance of the aquaporins is perhaps most conspicuous in the kidney, where 150–200 l of water need to be reabsorbed from the primary urine each day. This is made possible mainly by the AQP1 and AQP2 aquaporins. Diseases that have been linked to changes in levels of these aquaporins are nephrogenic diabetes insipidus, congestive heart failure and Sjögren's syndrome (Trends Endocrinol Metab 2002;13:355–360; Arthritis Rheum 2003;48:1167–1168). Other areas that are topics for current studies are searches for disease phenotypes that may result from mutations or perturbation of specific aquaporins. Additions to this list are loss of major blood group antigens, cataracts, renal tubular acidosis and brain oedema. As early as 1890, Wilhelm Ostwald (Nobel laureate in chemistry 1909) suggested, based on experiments with artificially prepared colloidal membranes, that electrical currents in living tissues might be induced by ions moving across cellular membranes (Z Phys Chem 1890;6:71–82). A breakthrough came in 1998, when MacKinnon succeeded in determining the first high-resolution structure of an ion channel, the KcsA K+ channel from Streptococcus lividans (Science 1998;280:69–77). The design of the selectivity filter was seen to be perfectly adapted to the job of desolvating potassium ions while keeping smaller sodium ions out, thus explaining the high K+ selectivity and the high transport rate. As already shown by Hodgkin and Huxley (Nobel laureates in 1963 in physiology/medicine) in the early 1950s, in excitable cells such as nerve, muscle and endocrine cells, voltage-induced gating of ions channels is the central principle of activation. Very recently, MacKinnon solved the structure of the archaeal voltage-gated K+ channel KvaP in a complex with antibody fragments directed against the voltage sensor domain (Nature 2003;423:33–41). Interestingly, the antibody fragments appear to have pulled the sensor domains away from the ion channel itself. MacKinnon's structural and mechanistic work on K+ channels has unravelled the molecular underpinnings of ion selectivity, gating and inactivation and has uncovered entirely new possibilities for very detailed biochemical, biophysical and theoretical studies of ion channel function. His discoveries also provide a firm basis for a molecular understanding of many neurological, muscular and cardiac diseases, opening up new possibilities for drug design. In conclusion, this year's Nobel Prize in chemistry has been awarded to two scientists involved in fundamental studies of membrane channels. The rapid progress in our understanding of membrane channel function over the past decade is in large part due to the discoveries concerning water and ion channels. Agre's discovery of the aquaporin water channels and MacKinnon's detailed structural and mechanistic studies of K+ channels are singular achievements that have made it possible for us to see these exquisitely designed molecular machines in action at the atomic level. It is clear that the Nobel Assembly has awarded a prize to a research field that is mature and closely related to molecular immunology, inflammation and investigations in some immune-mediated disease mechanisms.

Machine learning-guided protein engineering is a new paradigm that enables the optimization of complex protein functions. Machine-learning methods use data to predict protein function without requiring a detailed model of the underlying physics or biological pathways. They accelerate protein engineering by learning from information contained in all measured variants and using it to select variants that are likely to be improved. We begin with a review of the basics of machine learning with a focus on applications to protein engineering and protein sequence-function datasets (Chapter 1). We used the entire machine-learning guided engineering paradigm to engineer the algal-derived light-gated channel channelrhodopsin (ChR), which can be used to modulate neuronal activity with light. We build models that discover ChRs with strong plasma membrane localization in mammalian cells (Chapter 2) and unprecedented light sensitivity and photocurrents for optogenetic applications (Chapter 3). Machine learning-guided evolution requires a machine-learning model that learns the relationship between sequence and function. For machine-learning models to learn about protein sequences, protein sequences must be represented as vectors or matrices of numbers. How each protein sequence is represented determines what can be learned. We learn continuous vector encodings of sequences from patterns in unlabeled sequences (Chapter 4). Learned encodings are low-dimensional, do not require alignments, and may improve performance by transferring information in unlabeled sequences to specific prediction tasks. Alternately, we demonstrate an interpretable Gaussian process kernel tailored to biological sequences (Chapter 6). In addition to a model to predict function from sequence, engineering requires a method to use the model to choose sequences for the next round of evolution. Most machine-learning guided engineering strategies assume that selected sequences can be queried directly. However, in directed evolution it is common to design a library of sequences and then sample stochastic batches from that library. We propose a batched stochastic Bayesian optimization algorithm for iteratively designing and screening site-saturation mutagenesis libraries (Chapter 5).

Data-driven modeling based on machine learning (ML) isbecominga central component of protein engineering workflows. This perspectivepresents the elements necessary to develop effective, reliable, andreproducible ML models, and a set of guidelines for ML developmentsfor protein engineering. This includes a critical discussion of softwareengineering good practices for the development and evaluation of ML-basedprotein engineering projects, emphasizing supervised learning. Theseguidelines cover all of the necessary steps for ML development, fromdata acquisition to model deployment. Additionally, the present perspectiveprovides practical resources for the implementation of the outlinedguidelines. These recommendations are also intended to support editorsand scientific journals in enforcing good practices in ML-based proteinengineering publications, promoting high standards across the community.With this, the aim is to further contribute to improved ML transparencyand credibility by easing the adoption of software engineering bestpractices into ML development for protein engineering. We envisionthat the wide adoption and continuous update of best practices willencourage informed use of ML on real-world problems related to proteinengineering.

The Nobel Laureate Harry Kroto passed away aged 76 on April 30, 2016. His name will always be associated with the discovery of C60 , for which he was awarded the Nobel Prize in Chemistry 1996 together with Rick Smalley and Robert Curl. Experiments designed to aid the discovery of molecules in space ultimately led to an entirely new branch of condensed-phase physics and chemistry. On April 30, 2016, Sir Harold (Harry) Kroto, Nobel Prize winner and Professor of Chemistry passed away aged 76. Kroto's name will always be associated with C60 , the discovery of which resulted in the award of the Nobel Prize in Chemistry in 1996 to Kroto, Rick Smalley, and Robert Curl. The events surrounding the discovery of C60 must rank as a definitive example of serendipity in research—an experiment designed to aid the discovery of molecules in space that ultimately led to an entirely new branch of condensed-phase physics and chemistry. Kroto was born Harold Walter Krotoschiner in 1939 in Wisbech, Cambridgeshire; his parents were refugees from the National Socialist regime in Germany. The family settled in Bolton after the war, and from there he went to Sheffield University in 1958 to study chemistry. After taking a PhD in molecular spectroscopy with Richard Dixon, Harry went first to the National Research Council Laboratory in Canada to work with Gerhard Herzberg and then spent time at Bell Laboratories in New York. He returned to the UK in 1967 as a tutorial fellow at the University of Sussex and rose through the ranks to become Professor of Chemistry in 1985. In 2004, Harry accepted a position at Florida State University, Tallahassee, but returned to live in Sussex when he retired in 2015. Kroto was elected a fellow of the Royal Society in 1990 and was knighted for services to science in 1996. He was the recipient of many awards in addition to the Nobel Prize, including the Royal Society's Michael Faraday Medal and Lectureship in 2001 and the Copley Medal in 2004. As a young lecturer at the University of Sussex, Kroto began developing experiments to study the rotational spectra of transient species containing main-group-element atoms in multiple bonds with carbon atoms. Selected precursors were thermalized at the entrance to a microwave spectrometer to produce high-resolution spectra of such species as CH2=PH and CH3CH=S. The next step in this work was to lead ultimately to the Nobel Prize. David Walton, a colleague at Sussex, had been synthesizing long-chain molecules of carbon atoms, and to Kroto their spectroscopy seemed ideal as a means of testing how rotational and bending motions couple. However, in association with Takeshi Oka, it was found that the accurate spectroscopic measurements also resulted in HC5N, HC7N, and HC9N, which belong to the heaviest molecules in space. It soon became apparent to Harry that large carbon-containing species could be an integral part of the composition of some interstellar dust clouds. A visit by Robert Curl to Sussex in the early 1980s introduced Harry to the technique of laser vaporisation that Rick Smalley had been using to create transient species from refractory materials—SiC2 being a classic example. Following visits to Smalley's laboratory in 1984 and 1985, Harry became convinced carbon chains might hold the key to identifying the elusive diffuse interstellar bands, and that laser vaporization of solid carbon might simulate conditions found close to giant carbon-rich stars. However, once C60 and C70 had been identified by mass spectrometry, links to space chemistry became secondary to the emergence of what became known as fullerene science. At first the proposal that two peaks in a mass spectrum might represent a whole new branch of chemistry was met with some scepticism from his colleagues. Harry remained convinced of the significance of this discovery; but it was a bittersweet moment when in the summer of 1990 he received a manuscript by Wolfgang Krätschmer and co-workers in which the synthesis of isolable quantities of C60 was described. Sweet, in that the work vindicated Harry's conviction that the fullerene family of molecules was something unique and special, but bitter, in that working parallel to Krätschmer, Harry and a student, Jonathan Hare, were just days away from isolating their own sample of C60. More about the work that led to the Nobel Prize can be found in two Reviews by Harry Kroto in Angewandte Chemie: “C60: Buckminsterfullerene, The Celestial Sphere that Fell to Earth” (Angew. Chem. Int. Ed. Engl. 1992, 31, 111) and “Symmetry, Space, Stars, and C60 (Nobel Lecture)” (Angew. Chem. Int. Ed. 1997, 36, 1578). Once reliable techniques had been developed to synthesise fullerenes on laboratory scales, there followed an explosion of both synthetic chemistry and hyperbole—C60 was going to solve most if not all of humankind's problems. Harry did not subscribe to the latter view. However, the story does go full circle—last year a very elegant experiment by John Maier and co-workers provided conclusive evidence for the existence of C60+ in the interstellar medium. In addition to research, Harry was also passionate about education and the teaching of science to young people. In 1994, he established the Vega Science Trust, which began as a catalogue of inspiring lectures by famous scientists, but went on to become a world-wide teaching resource. The move to Tallahassee in 2004 provided the opportunity to expand his outreach activities to schools and colleges, and up until a year ago Harry was criss-crossing the world to give public lectures and workshops. He was an inspiring teacher and was only too willing to share his passion for science to audiences young and old. I was fortunate to be able to visit Harry just a few weeks before he died. Although quite frail, he still wanted to talk science and was quite critical of recent work on a graphene derivative. He was an original and very creative scientist whose contagious enthusiasm for research and teaching will be sadly missed.

Background Recently, the need for artificial intelligence (AI) and machine learning (ML) methods in drug development and research is gaining high concern and more grounds. Moreover, providing pharmaceutical and related schools with non-commercial, free-to-use programming languages, software and tools is becoming an unavoidable need. The R programming language can be easily used, through the correct and simplified codes and packages, in conducting unsupervised ML methods, such as principal component analysis (PCA) and hierarchical clustering analysis (HCA), after calculating relevant descriptors of drugs and molecules. Objective The objective of this study was to assess the enhancement of non-computer sciences-based students’ perception of the use of machine learning methods such as PCA and HCA using R-programming in drug formulation. Results Undergraduate students were taught to use R program to derive PCA distinguishable plots such as score, loading and scree, in addition to HCA dendrograms, in the context of developing new pharmaceutical formulations. Surveys conducted pre- and post-teaching the course proved that implementation of such ML methods can help in better understanding and exploring the data, in order to derive meaningful conclusions, and make informed decisions that help develop pharmaceutical formulations of premium quality, with minimal resources consumption. Conclusion We hereby report the easy use of R-programming in applications and activities that introduce undergraduate Pharmaceutical Engineering and Biotechnology students to ML methods. Student surveys showed better student satisfaction and understanding of AI applications in solving pharmaceutical problems. We claim that these students and early_career researchers, who are non-specialists in computer science, can utilize R-programming to perform important pharmaceutical applications through the step-by-step guide and codes provided in this article. Graphical Abstract

As people’s pursuit of art gradually increases, the forms and types of music have become increasingly diverse. In the process of exploring different types of music, the demand for music audio analysis has gradually increased. With the continuous development of artificial intelligence technology, machine learning, deep learning and other methods have gradually entered the public eye with their efficient data processing capabilities. Therefore, artificial intelligence methods in music audio analysis have gradually become the focus of research. At present, the processing mode based on traditional features such as Mel frequency cepstral coefficients (MFCC) combined with machine learning methods has been widely adopted, but there are still certain limitations in feature expression ability and classification accuracy. In recent years, endto-end deep learning methods have shown stronger adaptability and accuracy by automatically extracting features for classification and recognition, promoting the advancement of pure music audio analysis technology. This article aims to provide theoretical support and practical guidance for researchers in related fields by organizing the application of artificial intelligence methods in pure music audio analysis, comparing and analyzing the advantages and disadvantages of various methods, and promoting the sustainable development and technological innovation of this field.

Journal Growth and Maturation

A big honor for small objects: The Nobel Prize in Chemistry 2014 was jointly awarded to Eric Betzig, Stefan Hell, and William E. Moerner "for the development of super-resolved fluorescence microscopy". This Highlight describes how the field of super-resolution microscopy developed from the first detection of a single molecule in 1989 to the sophisticated techniques of today.

Alan Graham MacDiarmid, who made important contributions to physics and chemistry, died in his home in Philadelphia on 7 February 2007. Despite being in poor health, he was about to depart on a long and arduous trip to New Zealand, his homeland, to see family and to visit the MacDiarmid Institute for Advanced Materials and Nanotechnology, the scientific organization created in his name. Weakened by myeloplastic syndrome, a leukemia-like disease, Alan fell down a flight of stairs just moments before the planned departure.Born in Masterton, New Zealand, on 14 April 1927, Alan worked part-time and attended classes at Victoria University College, where he received his bachelor's and master's degrees in chemistry. He earned two PhDs in inorganic chemistry, one at the University of Wisconsin in 1953 and the second at Cambridge University in England in 1955. After a brief appointment at the University of St. Andrews in Scotland, he became a chemistry professor at the University of Pennsylvania.Alan, Hideki Shirakawa, and I were awarded the Nobel Prize in Chemistry in 2000 for the discovery of conducting polymers. Three earlier Nobel Prizes in Chemistry had been presented for discoveries in polymer science: to Hermann Staudinger in 1953, to Karl Zeigler and Giulio Natta in 1963, and to Paul Flory in 1974. Their discoveries are associated with three generations of polymers: natural ones, such as leather, spider webs, and silk, that have been used by our ancestors for thousands of years; synthetic fibers; and the structural plastics that are so important in our society today.None of the first three generations, however, is interesting in relation to electronic materials. They are insulators. The materials that Alan, Shirakawa, and I discovered in the late 1970s brought electronic function into the area of polymer science. Conducting polymers are the fourth generation of polymeric materials; they are electronically active and have the properties of semiconductors and metals.Now, three decades later, our discovery is well known and often used as a highly successful example of the importance of interdisciplinary research. When we started this work, however, the basic concepts that define semiconducting and metallic polymers were not understood. Alan and I had previously worked on poly(sulfur-nitride), a metallic polymer. In 1976 the creation of this truly interdisciplinary collaboration between an inorganic chemist (Alan), a physicist (me), and a polymer scientist (Shirakawa) was bold and risky.Alan was well aware of the risks. Before moving from inorganic chemistry into conducting polymers, he had a successful career that focused on the chemistry of silicon. But new directions in interdisciplinary science are where great discoveries can be found. Alan understood that opportunity, and he embraced our effort with enthusiasm, vigor, and dedication.I remember many stories, often told, of those exciting early days. Alan pushed hard. His graduate students would come to me not infrequently and complain of having been subject to a “Big Mac attack”; Alan would have an idea and would not be patient in seeing that idea become experimental fact. On one occasion, he and I were having lunch at a cafe on the University of Pennsylvania campus. I complained that although we had spectacular results on doping polyacetylene with various acceptors, the changes in electrical conductivity—by factors exceeding 109—occurred on a time scale that was too short to enable a detailed study of the insulator-to-metal transition. By the time we had finished lunch, Alan had suggested using electrochemistry to control the doping, that is, using the electrode in an electrochemical cell to oxidize or reduce the semiconducting polymer. We had it all sketched out on a napkin and hurried back to the lab for a Big Mac attack. Later that same day, Paul Nigrey, then a student in Alan's lab, provided the data that confirmed the success of that approach.Courageous as a scientist, Alan was willing to move into an entirely new area, to learn new ideas, and even to learn a little physics. We made a habit of getting together on Saturday mornings, sometimes to work on a manuscript, sometimes to just discuss science and to learn from one another. On one such occasion, I decided to teach him the basic physics of the metal–insulator transition. I went to the blackboard, drew a chain of H–H–H–H–H, and said, “Let's consider a chain of hydrogen atoms.” My motive was good, for a chain of hydrogen atoms can be used as a model to explain the essential physics. Alan responded with a characteristically blunt phrase: “No!” he said. “A chain of hydrogen atoms does not exist.” Fortunately, we met again a week later and actually discussed and understood, together, the physics of the metal–insulator transition in terms of a chain of C–H units, the fundamental repeating unit of polyacetylene.Alan was courageous in many ways. The six years following the Nobel Prize conferment were difficult. He fell and broke a foot, causing him to need a cane for walking; he battled skin cancer and associated surgery; he fell again and broke his hip, which had to be replaced; and he lived for more than three years with the myeloplastic syndrome that required him to get blood transfusions every few weeks. Throughout these difficult times, he “raged against the dying of the light,” to paraphrase the poetry of Dylan Thomas; he traveled incessantly, he continued to do science with laboratories and ongoing projects at Penn and the University of Texas at Dallas, and he exerted his leadership in two institutes named in his honor, the one in New Zealand and another in China.Alan was famous for his rendition of a Maori war dance. As he told the story, he and his sports teammates performed this fierce dance with associated shouting in the Maori language to frighten opponents. And he delighted in performing it at conference banquets. I will always remember his performance in the wee hours after the Nobel award ceremony and the banquet and ball. We had all moved to a new venue where the university students put on a show, full of fun and spiced with sarcasm. At just the right moment, Alan got up, walked onstage, and did his Maori war dance, as shown in the accompanying photo.Alan MacDiarmid had a full life in every respect. Those of us who knew him well will truly miss him.Alan Graham MacDiarmidPPT|High resolution© 2007 American Institute of Physics.

In this work, deoxyribose-5-phosphate aldolase (Ec DERA, EC 4.1.2.4) from Escherichia coli was chosen as the protein engineering target for improving the substrate preference towards smaller, non-phosphorylated aldehyde donor substrates, in particular towards acetaldehyde. The initial broad set of mutations was directed to 24 amino acid positions in the active site or in the close vicinity, based on the 3D complex structure of the E. coli DERA wild-type aldolase. The specific activity of the DERA variants containing one to three amino acid mutations was characterised using three different substrates. A novel machine learning (ML) model utilising Gaussian processes and feature learning was applied for the 3rd mutagenesis round to predict new beneficial mutant combinations. This led to the most clear-cut (two- to threefold) improvement in acetaldehyde (C2) addition capability with the concomitant abolishment of the activity towards the natural donor molecule glyceraldehyde-3-phosphate (C3P) as well as the non-phosphorylated equivalent (C3). The Ec DERA variants were also tested on aldol reaction utilising formaldehyde (C1) as the donor. Ec DERA wild-type was shown to be able to carry out this reaction, and furthermore, some of the improved variants on acetaldehyde addition reaction turned out to have also improved activity on formaldehyde.Key points• DERA aldolases are promiscuous enzymes.• Synthetic utility of DERA aldolase was improved by protein engineering approaches.• Machine learning methods aid the protein engineering of DERA.

The Royal Swedish Academy of Sciences has awarded the 1982 Nobel Prize in Chemistry to Aaron Klug, “for his development of crystallographic electron microscopy and his structural elucidation of biologically important nucleic acid–protein complexes.” Klug's academic degrees are in physics. After taking a master's degree at the University of Capetown in x‐ray crystallography, he received his PhD in solid‐state physics at Cambridge in 1952. Since 1962 he has been at the (British) Medical Research Council's Laboratory of Molecular Biology in Cambridge.

G-protein-Coupled Receptors and Their (Bio) Chemical Significance Win 2012 Nobel Prize in Chemistry

In 2000, the Nobel Prize in Chemistry was awarded to Hideki Shirakawa, Alan G. MacDiarmid, and Alan J. Heeger “for the discovery and development of electrically conductive polymers.” While this award was in reference to their collaborative efforts on conducting polyacetylene in the mid-to-late 1970s, the narrative leading up to these efforts began in 1967 with the production of polyacetylene plastic films via what has been called a "fortuitous error." At the heart of this discovery were Shirakawa and a visiting Korean scientist, Hyung Chick Pyun. The current report provides background on Pyun and, for the first time, presents his version of the events leading to the discovery of polyacetylene films in order to provide new insight into this important historical event.