Isolation and characterization of α ‐amylase encoding gene in Bacillus amyloliquefaciens PAS

Amylolytic bacteria are a source of amylase, which is an essential enzyme to support microalgae growth in the bioreactor for microalgae culture. In a previous study, the highest bacterial isolate to hydrolyze amylum (namely PAS) was successfully isolated from Ranu Pani, Indonesia, and it was identified as Bacillus amyloliquefaciens. That bacterial isolate (B. amyloliquefaciens PAS) also has been proven to accelerate Chlorella vulgaris growth in the mini bioreactor. This study aims to detect, isolate, and characterize the PAS’s α‐amylase encoding gene. This study was conducted with DNA extraction, amplification of α‐amylase gene with polymerase chain reaction (PCR) method with the specific primers, DNA sequencing, phylogenetic tree construction, and protein modeling. The result showed that α‐amylase was successfully detected in PAS bacterial isolate. The α‐amylase DNA fragment was obtained 1,468 bp and that translated sequence has an identity of about 98.3% compared to the B. amylolyquefaciens α‐amylase 3BH4 in the Protein Data Bank (PDB). The predicted 3D protein model of the PAS’s α‐amylase encoding gene has amino acid variations that predicted affect the protein’s structure in the small region. This research will be useful for further research to produce recombinant α‐amylase.

Artificial Neural Network Analysis-Based Immune-Related Signatures of Primary Non-Response to Infliximab in Patients With Ulcerative Colitis

Infliximab (IFX) is an effective medication for ulcerative colitis (UC) patients. However, one-third of UC patients show primary non-response (PNR) to IFX. Our study analyzed three Gene Expression Omnibus (GEO) datasets and used the RobustRankAggreg (RRA) algorithm to assist in identifying differentially expressed genes (DEGs) between IFX responders and non-responders. Then, an artificial intelligence (AI) technology, artificial neural network (ANN) analysis, was applied to validate the predictive value of the selected genes. The results showed that the combination of CDX2, CHP2, HSD11B2, RANK, NOX4, and VDR is a good predictor of patients’ response to IFX therapy. The range of repeated overall area under the receiver-operating characteristic curve (AUC) was 0.850 ± 0.103. Moreover, we used an independent GEO dataset to further verify the value of the six DEGs in predicting PNR to IFX, which has a range of overall AUC of 0.759 ± 0.065. Since protein detection did not require fresh tissue and can avoid multiple biopsies, our study tried to discover whether the key information, analyzed by RNA levels, is suitable for protein detection. Therefore, immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with IFX and a receiver-operating characteristic (ROC) analysis were used to further explore the clinical application value of the six DEGs at the protein level. The IHC staining of colon tissues from UC patients confirmed that VDR and RANK are significantly associated with IFX efficacy. Total IHC scores lower than 5 for VDR and lower than 7 for RANK had an AUC of 0.828 (95% CI: 0.665–0.991, p = 0.013) in predicting PNR to IFX. Collectively, we identified a predictive RNA model for PNR to IFX and explored an immune-related protein model based on the RNA model, including VDR and RANK, as a predictor of IFX non-response, and determined the cutoff value. The result showed a connection between the RNA and protein model, and both two models were available. However, the composite signature of VDR and RANK is more conducive to clinical application, which could be used to guide the preselection of patients who might benefit from pharmacological treatment in the future.

The SPICA Coarse-Grained Force Field for Proteins and Peptides

ABSTRACTA coarse-grained (CG) model for peptides and proteins was developed as an extension of the SPICA (Surface Property fItting Coarse grAined) force field (FF). The model was designed to examine membrane proteins that are fully compatible with the lipid membranes of the SPICA FF. A preliminary version of this protein model was created using thermodynamic properties, including the surface tension and density in the SPICA (formerly called SDK) FF. In this study, we improved the CG protein model to facilitate molecular dynamics (MD) simulation with a reproduction of multiple properties from both experiments and all-atom (AA) simulations. The side chain analogs reproduced the transfer free energy profiles across the lipid membrane and demonstrated reasonable dimerization free energies in water compared to those from AA-MD. A series of peptides/proteins adsorbed or penetrated into the membrane simulated by the CG-MD correctly predicted the penetration depths and tilt angles of peripheral and transmembrane peptides/proteins comparable to those in the orientation of protein in membrane (OPM) database. In addition, the dimerization free energies of several transmembrane helices within a lipid bilayer were comparable to those from experimental estimation. Application studies on a series of membrane protein assemblies, scramblases, and poliovirus capsids demonstrated a good performance of the SPICA FF.

Rapid screening of high expressing Escherichia coli colonies using a novel dicistronic-autoinducible system

Background Identification of high-expressing colonies is one of the main concerns in the upstream process of recombinant protein development. The common method to screen high-producing colonies is SDS-PAGE, a laborious and time-consuming process, which is based on a random and qualitative way. The current study describes the design and development of a rapid screening system composed of a dicistronic expression system containing a reporter (enhanced green fluorescent protein, eGFP), protein model (staphylokinase, SAK), and a self-inducible system containing heat shock protein 27 (Hsp27). Results Dicistronic-autoinducible system expressed eGFP and SAK successfully in 5-ml and 1-L culture volumes. High expressing colonies were identified during 6 h via fluorescent signals. In addition, the biological activity of the protein model was confirmed semi-quantitatively and quantitatively through radial caseinolytic and chromogenic methods, respectively. There was a direct correlation between eGFP fluorescent intensity and SAK activity. The correlation and linearity of expression between the two genes were respectively confirmed with Pearson correlation and linear regression. Additionally, the precision, limit of detection (LOD), and limit of quantification (LOQ) were determined. The expression of eGFP and SAK was stable during four freeze–thaw cycles. In addition, the developed protocol showed that the transformants can be inoculated directly to the culture, saving time and reducing the error-prone step of colony picking. Conclusion The developed system is applicable for rapid screening of high-expressing colonies in most research laboratories. This system can be investigated for other recombinant proteins expressed in E. coli with a potential capability for automation and use at larger scales.

Accurate Description of Protein–Protein Recognition and Protein Aggregation with the Implicit-Solvent-Based PACSAB Protein Model

We used the PACSAB protein model, based on the implicit solvation approach, to simulate protein–protein recognition and study the effect of helical structure on the association of aggregating peptides. After optimization, the PACSAB force field was able to reproduce correctly both the correct binding interface in ubiquitin dimerization and the conformational ensemble of the disordered protein activator for hormone and retinoid receptor (ACTR). The PACSAB model allowed us to predict the native binding of ACTR with its binding partner, reproducing the refolding upon binding mechanism of the disordered protein.