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      Hybrid Micro-/Nanoprotein Platform Provides Endocrine-like and Extracellular Matrix-like Cell Delivery of Growth Factors

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          Abstract

          Protein materials are versatile tools in diverse biomedical fields. Among them, artificial secretory granules (SGs), mimicking those from the endocrine system, act as mechanically stable reservoirs for the sustained release of proteins as oligomeric functional nanoparticles. Only validated in oncology, the physicochemical properties of SGs, along with their combined drug-releasing and scaffolding abilities, make them suitable as smart topographies in regenerative medicine for the prolonged delivery of growth factors (GFs). Thus, considering the need for novel, safe, and cost-effective materials to present GFs, in this study, we aimed to biofabricate a protein platform combining both endocrine-like and extracellular matrix fibronectin-derived (ECM-FN) systems. This approach is based on the sustained delivery of a nanostructured histidine-tagged version of human fibroblast growth factor 2. The GF is presented onto polymeric surfaces, interacting with FN to spontaneously generate nanonetworks that absorb and present the GF in the solid state, to modulate mesenchymal stromal cell (MSC) behavior. The results show that SGs-based topographies trigger high rates of MSCs proliferation while preventing differentiation. While this could be useful in cell therapy manufacture demanding large numbers of unspecialized MSCs, it fully validates the hybrid platform as a convenient setup for the design of biologically active hybrid surfaces and in tissue engineering for the controlled manipulation of mammalian cell growth.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            ColabFold: making protein folding accessible to all

            ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold’s 40−60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/sokrypton/ColabFold and its novel environmental databases are available at https://colabfold.mmseqs.com . ColabFold is a free and accessible platform for protein folding that provides accelerated prediction of protein structures and complexes using AlphaFold2 or RoseTTAFold.
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              Geometric cues for directing the differentiation of mesenchymal stem cells.

              Significant efforts have been directed to understanding the factors that influence the lineage commitment of stem cells. This paper demonstrates that cell shape, independent of soluble factors, has a strong influence on the differentiation of human mesenchymal stem cells (MSCs) from bone marrow. When exposed to competing soluble differentiation signals, cells cultured in rectangles with increasing aspect ratio and in shapes with pentagonal symmetry but with different subcellular curvature-and with each occupying the same area-display different adipogenesis and osteogenesis profiles. The results reveal that geometric features that increase actomyosin contractility promote osteogenesis and are consistent with in vivo characteristics of the microenvironment of the differentiated cells. Cytoskeletal-disrupting pharmacological agents modulate shape-based trends in lineage commitment verifying the critical role of focal adhesion and myosin-generated contractility during differentiation. Microarray analysis and pathway inhibition studies suggest that contractile cells promote osteogenesis by enhancing c-Jun N-terminal kinase (JNK) and extracellular related kinase (ERK1/2) activation in conjunction with elevated wingless-type (Wnt) signaling. Taken together, this work points to the role that geometric shape cues can play in orchestrating the mechanochemical signals and paracrine/autocrine factors that can direct MSCs to appropriate fates.
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                Author and article information

                Journal
                ACS Appl Mater Interfaces
                ACS Appl Mater Interfaces
                am
                aamick
                ACS Applied Materials & Interfaces
                American Chemical Society
                1944-8244
                1944-8252
                18 June 2024
                03 July 2024
                : 16
                : 26
                : 32930-32944
                Affiliations
                []Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona , Barcelona 08193, Spain
                []Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona , Barcelona 08193, Spain
                [§ ]Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III , Barcelona 08193, Spain
                []Centre for the Cellular Microenvironment, School of Molecular Biosciences, College of Medical, Veterinary and Life Sciences, Mazumdar-Shaw Advanced Research Centre, University of Glasgow , Glasgow G11 6EW, U.K.
                []Centre for the Cellular Microenvironment, Division of Biomedical Engineering, James Watt School of Engineering, Mazumdar-Shaw Advanced Research Centre, University of Glasgow , Glasgow G11 6EW, U.K.
                [# ]Institut de Recerca Sant Pau (IR SANT PAU) , Barcelona 08041, Spain
                Author notes
                Author information
                https://orcid.org/0000-0001-5119-2266
                https://orcid.org/0000-0002-8112-2100
                https://orcid.org/0000-0003-1052-0424
                https://orcid.org/0000-0002-0528-3359
                https://orcid.org/0000-0002-2615-4521
                Article
                10.1021/acsami.4c01210
                11231985
                38888932
                ca6ce430-1df4-4514-abb0-15d36bfa32f2
                © 2024 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 22 January 2024
                : 07 June 2024
                : 03 June 2024
                Funding
                Funded by: Agència de Gestió d''Ajuts Universitaris i de Recerca, doi 10.13039/501100003030;
                Award ID: 2019 FI_B 00352
                Funded by: Centre for Digital Entertainment, doi 10.13039/501100014813;
                Award ID: EP/P001114/1
                Funded by: European Social Fund, doi NA;
                Award ID: NA
                Funded by: Agencia Estatal de Investigación, doi 10.13039/501100011033;
                Award ID: PID2022-1368450 OB-10/AEI/10.13039/501100011033
                Funded by: Agencia Estatal de Investigación, doi 10.13039/501100011033;
                Award ID: PID2020-116174RB-I00
                Funded by: Agencia Estatal de Investigación, doi 10.13039/501100011033;
                Award ID: PID2019-105416RB-I00/AEI/10.13039/501100011033
                Funded by: Agencia Estatal de Investigación, doi 10.13039/501100011033;
                Award ID: PDC2022-133858-I00
                Funded by: Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, doi 10.13039/501100005053;
                Award ID: CB06/01/0014
                Funded by: Instituto de Salud Carlos III, doi 10.13039/501100004587;
                Award ID: PI20/00400
                Funded by: Instituto de Salud Carlos III, doi 10.13039/501100004587;
                Award ID: CP19/00028
                Funded by: Institució Catalana de Recerca i Estudis Avançats, doi 10.13039/501100003741;
                Award ID: NA
                Funded by: Agència de Gestió d''Ajuts Universitaris i de Recerca, doi 10.13039/501100003030;
                Award ID: SGR 2021 00092
                Categories
                Research Article
                Custom metadata
                am4c01210
                am4c01210

                Materials technology
                secretory granules,microparticles,drug delivery,growth factors,smart topographies

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