Structure-based neural network protein–carbohydrate interaction predictions at the residue level

Carbohydrates dynamically and transiently interact with proteins for cell–cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate–Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein–carbohydrate structures

Enhancing resiliency of perishable product supply chains in the context of the COVID-19 outbreak

Globally, countries are struggling to fulfil customer demands due to the effects of the COVID-19 pandemic on perishable food supply chains (PFSCs). This study aims to analyse the factors influencing PFSCs during the pandemic and improve their resiliency. This is essential as some factors discourage the productive execution of PFSCs and decrease organizational performance, thus lowering stakeholder satisfaction. This study has been conducted in two phases. The first phase, through extensive review and discussion with experts, identifies the influencing factors related to supply chain (SC) disturbances in PFSCs. In the second phase, a hybrid method i.e. g-DANP, a combination of grey-decision making trial and evaluation laboratory and analytic network process, is employed to develop a hierarchical structure to measure their influence. The proposed framework is validated with a case of the current COVID-19 outbreak. The study revealed that factors, restriction on import-export and fear of violation of social distancing guidelines, are the primary “cause” group factors; whereas, price variation of perishable products and panic buying and stockpiling are the crucial “effect” group factors affecting the PFSCs. The findings also enrich existing literature by providing analytical support to relationships between various factors affecting PFSCs during the pandemic. The results of this study can be utilised by decision-makers to anticipate the operative and long-haul effects of COVID-19 on PFSCs and create plans to deal with the pandemic.N/

Facilitating Minima Search for Large Water Clusters at the MP2 Level via Molecular Tailoring

Water clusters (H₂O)₂₀ and (H₂O)₂₅ are explored at the Møller–Plesset second-order perturbation (MP2) level of theory. Geometry optimization is carried out on favorable structures, initially generated by the temperature basin paving (TBP) method, utilizing the fragment-based molecular tailoring approach (MTA). MTA-based stabilization energies at the complete basis set limit are accurately estimated by grafting the energy correction using a smaller basis set. For prototypical cases, the minima are established via MTA-based vibrational frequency calculations at the MP2/aug-cc-pVDZ level. The potential of MTA in tackling large clusters is further demonstrated by performing geometry optimization at MP2/aug-cc-pVDZ starting with the global minimum of (H₂O)₃₀ reported by Monte Carlo (MC) and molecular dynamics (MD) investigations. The present study brings out the efficacy of MTA in performing computationally expensive ab initio calculations with minimal off-the-shelf hardware without significant loss of accuracy

Agroecological transformation for sustainable food systems : Insight on France-CGIAR research

This 26th dossier d’Agropolis is devoted to research and partnerships in agroecology. The French Commission for International Agricultural Research (CRAI) and Agropolis International, on behalf of CIRAD, INRAE and IRD and in partnership with CGIAR, has produced this new issue in the ‘Les dossiers d’Agropolis international’ series devoted to agroecology. This publication has been produced within the framework of the Action Plan signed by CGIAR and the French government on February 4th 2021 to strengthen French collaboration with CGIAR, where agroecology is highlighted as one of the three key priorities (alongside climate change, nutrition and food systems)

Abstracts of National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020

This book presents the abstracts of the papers presented to the Online National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020 (RDMPMC-2020) held on 26th and 27th August 2020 organized by the Department of Metallurgical and Materials Science in Association with the Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, India. Conference Title: National Conference on Research and Developments in Material Processing, Modelling and Characterization 2020Conference Acronym: RDMPMC-2020Conference Date: 26–27 August 2020Conference Location: Online (Virtual Mode)Conference Organizer: Department of Metallurgical and Materials Engineering, National Institute of Technology JamshedpurCo-organizer: Department of Production and Industrial Engineering, National Institute of Technology Jamshedpur, Jharkhand, IndiaConference Sponsor: TEQIP-