Computational Modelling of Glucose Uptake in the Enterocyte

We describe an implemented model of glucose absorption in the enterocyte, as previously published by Afshar et al. (2019), The model used mechanistic descriptions of all the responsible transporters and was built in the CellML framework. It was validated against published experimental data and implemented in a modular structure which allows each individual transporter to be edited independently from the other transport protein models. The composite model was then used to study the role of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2), along with the requirement for the existence of the apical Glut2 transporter, especially in the presence of high luminal glucose loads, in order to enhance the absorption. Here we demonstrate the reproduction of the figures in the original paper by using the associated model. EDITOR'S NOTE (v3): Instructions within the manuscript changed, in order to properly execute the model files. Spelling of author's name corrected in filenames. (v4): Abstract fixes.

Computational Modelling of Glucose Uptake in the Enterocyte

We describe an implemented model of glucose absorption in the enterocyte, as previously published by Afshar et al. Afshar et al. (2019), The model used mechanistic descriptions of all the responsible transporters and was built in the CellML framework. It was validated against published experimental data and implemented in a modular structure which allows each individual transporter to be edited independently from the other transport protein models. The composite model was then used to study the role of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2), along with the requirement for the existence of the apical Glut2 transporter, especially in the presence of high luminal glucose loads, in order to enhance the absorption. Here we demonstrate the reproduction of the figures in the original paper by using the associated model. EDITOR'S NOTE (v2): Instructions within the manuscript changed, in order to properly execute the model files. Spelling of author's name corrected in filenames.

Improving AlphaFold modeling using implicit information from experimental density maps

Machine learning prediction algorithms such as AlphaFold can create remarkably accurate protein models, but these models usually have some regions that are predicted with low confidence or poor accuracy. We hypothesized that by implicitly including experimental information, a greater portion of a model could be predicted accurately, and that this might synergistically improve parts of the model that were not fully addressed by either machine learning or experiment alone. An iterative procedure was developed in which AlphaFold models are automatically rebuilt based on experimental density maps and the rebuilt models are used as templates in new AlphaFold predictions. We find that including experimental information improves prediction beyond the improvement obtained with simple rebuilding guided by the experimental data. This procedure for AlphaFold modeling with density has been incorporated into an automated procedure for crystallographic and electron cryo-microscopy map interpretation.

Mutations in the Phenicol Exporter Gene fexA Impact Resistance Levels in Three Bacterial Hosts According to Susceptibility Testing and Protein Modeling

The prototype fexA gene confers combined resistance to chloramphenicol and florfenicol. However, fexA variants mediating resistance only to chloramphenicol have been identified, such as in the case of a Staphylococcus aureus isolate recovered from poultry meat illegally imported to Germany. The effects of the individual mutations detected in the fexA sequence of this isolate were investigated in this study. A total of 11 fexA variants, including prototype fexA and variants containing the different previously described mutations either alone or in different combinations, were generated by on-chip gene synthesis and site-directed mutagenesis. The constructs were inserted into a shuttle vector and transformed into three recipient strains (Escherichia coli, Staphylococcus aureus, and Salmonella Typhimurium). Subsequently, minimal inhibitory concentrations (MIC) of florfenicol and chloramphenicol were determined. In addition, protein modeling was used to predict the structural effects of the mutations. The lack of florfenicol-resistance mediating properties of the fexA variants could be attributed to the presence of a C110T and/or G98C mutation. Transformants carrying fexA variants containing either of these mutations, or both, showed a reduction of florfenicol MICs compared to those transformants carrying prototype fexA or any of the other variants. The significance of these mutations was supported by the generated protein models, indicating a substitution toward more voluminous amino-acids in the substrate-binding site of FexA. The remaining mutations, A391G and C961A, did not result in lower florfenicol-resistance compared to prototype fexA.

Sequence assignment validation in cryo-EM models with checkMySequence

The availability of new AI-based protein structure prediction tools radically changed the way cryo-EM maps are interpreted, but it has not eliminated the challenges of map interpretation faced by a microscopist. Models will continue to be locally rebuilt and refined using interactive tools. This inevitably results in occasional errors, among which register-shifts remain one of the most difficult to identify and correct. Here we introduce checkMySequence; a fast, fully automated and parameter-free method for detecting register-shifts in protein models built into cryo-EM maps. We show that the method can assist model building in cases where poorer map resolution hinders visual interpretation. We also show that checkMySequence could have helped avoid a widely discussed sequence register error in a model of SARS-CoV-2 RNA-dependent RNA polymerase that was originally detected thanks to a visual residue-by-residue inspection by members of the structural biology community.

AlphaFill: enriching the AlphaFold models with ligands and co-factors

Artificial intelligence (AI) methods for constructing structural models of proteins on the basis of their sequence are having a transformative effect in biomolecular sciences. The AlphaFold protein structure database makes available hundreds of thousands of protein structures. However, all these structures lack cofactors essential for their structural integrity and molecular function (e.g. hemoglobin lacks a bound heme), key ions essential for structural integrity (e.g. zinc-finger motifs) or catalysis (e.g. Ca2+ or Zn2+ in metalloproteases), and ligands that are important for biological function (e.g. kinase structures lack ADP or ATP). Here, we present AlphaFill, an algorithm based on sequence and structure similarity, to "transplant" such "missing" small molecules and ions from experimentally determined structures to predicted protein models. These publicly available structural annotations are mapped to predicted protein models, to help scientists interpret biological function and design experiments.

Molecular Modeling and Docking Studies on Phyto-compounds against Caspase-3, BRCA1, and Rb

Breast cancer is one of the well-known diseases analyzed in women compared to men worldwide. There are few studies about plant compounds that have been identified to have anticancer properties. Consequently, phyto-compounds have the capability of evolving new drugs. In this research, the three-dimensional (3D) structure of breast cancer cell line proteins, caspase-3, breast cancer susceptibility type 1 (BRCA1), and retinoblastoma (Rb) were generated, and docking with plant compounds (ferulic acid and quercetin, respectively) was studied. Swiss model was used to build the 3D structure of protein models. Then, the protein models were assessed using the validation tools (PROCHECK, ProQ, ERRAT, and Verify 3D programs). Lastly, the protein was docked successfully with ferulic acid (PubChem ID: 445858) and quercetin (PubChem ID: 5280343), respectively, using the SwissDock server and visualized with Discovery Studio (DS) 4.0 software. The results show that the protein models were stable after the validation process. The binding energy of the protein-phyto-compound complexes (Rb-Ferulic acid and Rb-Quercetin) were -6.6 and -7.8 kcal/mol, respectively. These proteins had a stable bond with their phyto-compounds. The toxicity prediction analysis revealed that ferulic acid (PubChem ID: 445858) is safe to use as a drug. This current study of the protein-phytocompound-complex interaction will help in designing new clinical medications.

Real-Time Structure Search and Structure Classification for AlphaFold Protein Models

AlphaFold2 showed a substantial improvement in the accuracy of protein structure prediction. Following the release of the software, whole-proteome protein structure predictions by AlphaFold2 for 21 organisms were made publicly available. Here, we developed the infrastructure, 3D-AF-Surfer, to enable real-time structure-based search for the AlphaFold2 models by combining molecular surface representation with 3D Zernike descriptors and deep neural networks.