What? Many places use place branding to attract residents and tourists. Residents vary from those who were born and have lived in the same place all their life, and translocal ones - with connection to multiple places. The project explores the engagement with branding campaign of 'translocal' residents – those who have bonds with multiple locations, and contrasts it to the responses of locals. Why? Success of place branding often rests on residents' support. But how do residents, especially those who feel connected to multiple places, react to such branding campaigns? Place attachment has been suggested as a driver of engagement with place branding campaigns, but past work only looked at locals with a single place attachment, not acknowledging the role multiple place attachment can play. How? The project uses qualitative methodology - photos and interviews - for a case study of Girona, Spain. Different groups of local and translocal residents will be asked to provide pictures of places they feel attached to. Followingly, the pictures will be used to ask for their stories of these places - about their attachment to them, and their reactions to place branding campaigns of Girona.

Life could not be sustained without the presence of enzymes, which are responsible for accelerating the chemical reactions in a biologically compatible timescale. Enzymes present other advantageous features such as high specificity and selectivity, plus they operate under very mild biological conditions. Inspired by these extraordinary characteristics, many scientists wondered about the possibility of designing new enzymes for industrially-relevant targets. Unfortunately, none of the current enzyme design strategies is able to rapidly design tailor-made enzymes at a reduced cost. This is limiting the general routine application of enzyme catalysis in industry, and thus the chemical manufacturing competitiveness. The goal of this project is to develop a fast yet accurate computational enzyme design approach for allowing the routine design of highly efficient enzymes. FASTEN combines computational chemistry, deep learning, graph theory, and computational geometry for controlling the complexity of enzyme catalysis in a new computational protocol that will capture the chemical steps and conformational changes that take place along the catalytic itinerary. Active site and distal activity-enhancing mutations are predicted based on correlation and co-evolutionary-based guidelines, and the catalytic potential of the new designs is estimated by means of geometry-based oracles. This new computational approach will be validated with the design of enzymes presenting complex conformational dynamics and multi-step mechanisms. The experimental evaluation of many of the designs will finally reveal the potential of this new approach for the fast routinely design of industrially-relevant enzymes. FASTEN has the potential of making the routine design of enzymes possible, thus improving our current lives and leading to a more sustainable world for our generations.

Natural enzymes have evolved to perform their functions under complex selective pressures, being capable of accelerating reactions by several orders of magnitude. In particular, heteromeric enzyme complexes catalyze an enormous array of useful reactions that are often allosterically regulated by different protein partners. Unfortunately, the underlying physical principles of this regulation are still under debate, which makes the alteration of enzyme structure towards useful isolated subunits a tremendous challenge for modern chemical biology. Exploitation of isolated enzyme subunits, however, is advantageous for biosynthetic applications as it reduces the metabolic stress on the host cell and greatly simplifies efforts to engineer specific properties of the enzyme. Current approaches to alter natural enzyme complexes are based on the evaluation of thousands of variants, which make them economically unviable and the resulting catalytic efficiencies lag far behind their natural counterparts. The revolutionary nature of EnzVolNet relies on the application of conformational network models (e.g Markov State Models) to extract the essential functional protein dynamics and key conformational states, reducing the complexity of the enzyme design paradigm and completely reformulating previous computational design approaches. Initial mutations are extracted from costly random mutagenesis experiments and chemoinformatic tools are used to identify beneficial mutations leading to more proficient enzymes. This new strategy will be applied to develop stand-alone enzymes from heteromeric protein complexes, with advantageous biosynthetic properties and improve activity and substrate scope. Experimental evaluation of our computational predictions will finally elucidate the potential of the present approach for mimicking Nature’s rules of evolution.