Computational analysis of carboxylesterase genes and proteins in non-pathogenic food bacterium Alicyclobacillus acidocaldarius: insights from proteogenomics.

Over recent years, Alicyclobacillus acidocaldarius, a Gram-positive nonpathogenic rod-shaped thermo-acid-tolerant bacterium, has posed numerous challenges for the fruit juice industry. However, the bacterium's unique characteristics, particularly its nonpathogenic and thermophilic capabilities, offer significant opportunities for genetic exploration by biotechnologists. This study presents the computational proteogenomics report on the carboxylesterase (CE) enzyme in A. acidocaldarius, shedding light on structural and evolutional of CEs from this bacterium. Our analysis revealed that the average molecular weight of CEs in A. acidocaldarius was 41 kDa, with an isoelectric point around 5. The amino acid composition favored negative amino acids over positive ones. The aliphatic index and hydropathicity were approximately 88 and - 0.15, respectively. While the protein sequence showed no disulfide bonds in the CEs' structure, the presence of Cys amino acids was observed in the structure of CEs. Phylogenetic analysis presented more than 99% similarity between CEs, indicating their close evolutionary relationship. By applying homology modeling, the 3-dimensional structural models of the carboxylesterase were constructed, which with the help of structural conservation and solvent accessibility analysis highlighted key residues and regions responsible for enzyme stability and conformation. The specific patterns presented the total solvent accessibility of less than 25 (Å2) was in considerable position as well as Gly residues were noticeably have high accessibility to solvent in all structures. Ala was the more frequent amino acids in the conserved-SASA of carboxylesterases. Furthermore, unsupervised agglomerative hierarchical clustering based on solvent accessibility feature successfully clustered and even distinguished this enzyme from proteases from the same genome. These findings contribute to a deeper understanding of the nonpathogenic A. acidocaldarius carboxylesterase and its potential applications in biotechnology. Additionally, structural analysis of CEs would help to address potential solutions in fruit juice industry with utilization of computational structural biology.

Application of Hierarchical Clustering to Analyze Solvent-Accessible Surface Area Patterns in Amycolatopsis lipases.

The wealth of biological databases provides a valuable asset to understand evolution at a molecular level. This research presents the machine learning approach, an unsupervised agglomerative hierarchical clustering analysis of invariant solvent accessible surface areas and conserved structural features of Amycolatopsis eburnea lipases to exploit the enzyme stability and evolution. Amycolatopsis eburnea lipase sequences were retrieved from biological database. Six structural conserved regions and their residues were identified. Total Solvent Accessible Surface Area (SASA) and structural conserved-SASA with unsupervised agglomerative hierarchical algorithm were clustered lipases in three distinct groups (99/96%). The minimum SASA of nucleus residues was related to Lipase-4. It is clearly shown that the overall side chain of SASA was higher than the backbone in all enzymes. The SASA pattern of conserved regions clearly showed the evolutionary conservation areas that stabilized Amycolatopsis eburnea lipase structures. This research can bring new insight in protein design based on structurally conserved SASA in lipases with the help of a machine learning approach.

Computational characterizations of GDP-mannose 4,6-dehydratase (NoeL) Rhizobial proteins.

A growing body of evidence suggests that Nod Factors molecules are the critical structural components in nitrogen fixation. These molecules have been implicated in plant-microbe signaling. Many enzymes involved in Nod factors biosynthesis; however, the enzymes that decorate (modify) nod factor main structure play a vital role. Here, the computational analysis of GDP-mannose 4,6-dehydratase (NoeL) proteins with great impact in modification of nod factor structure in four genomes of agriculturally important rhizobia (Bradyrhizobium, Mesorhizobium, Rhizobium, Sinorhizobium) presented. The NoeL number of amino acids was in the range of 147 (M5AMF5) to 372 (A0A023XWX0, Q89TZ1). The molecular weights were around 41 KDa. The results showed that the strain-specific purification strategy should apply as the pI of the sequences varied significantly (in the range of 5.59 to 9.12). The enzyme sequences and eight 3-dimensional structures predicted with homology modeling and machine learning representing the phylogenetic tree revealed the stability of enzymes in different conditions (Instability and Aliphatic index); however, this stability is also strain-specific. Disulphide bonds were observed in some species; however, the pattern was not detected in all members of the same species. Alpha helix was the dominant secondary structure predicted in all cytoplasmic NoeL. All models were homo-tetramer with acceptable sequence identity, GMEAN and coverage (60, - 1.80, 88, respectively). Additionally, Ramachandran maps showed that more than 94% of residues are in favored regions. We also highlight several key characterizations of NoeL from four rhizobia genomes annotation. These findings provide novel insights into the complexity and diversity of NoeL enzymes among important rhizobia and suggest considering a broader framework of biofilm for future research.