ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa

Disclosing the locale of transmembrane proteins within cellular alcove by machine learning approach: systematic review and meta analysis

Protein subcellular localization is a promising research question in Proteomics and associated fields, including Biological Sciences, Biomedical Engineering, Computational Biology, Bioinformatics, Proteomics, Artificial Intelligence, and Biophysics. However, computational techniques are preferred to explore this attribute for a massive number of proteins. The byproduct of this conjunction yields diversified location identifiers of proteins. These protein subcellular localization identifiers are unique regarding the database used, organisms, Machine Learning Technique, and accuracy. Despite the availability of these identifiers, the majority of the work has been done on the subcellular localization of proteins and, less work has been done specifically on locations of transmembrane proteins. This systematic review accounts for computational techniques implemented on transmembrane protein localization. Moreover, a literature search on PubMed, Science Direct, and IEEE Databases disclosed no systematic review or meta-analysis on the cell's transmembrane protein locale. A Systematic review was formed under the guidelines of PRISMA by using Science Direct, PubMed, and IEEE Databases. Journal publications from 2000 to 2023 were taken into consideration and screened. This review has focused only on computational studies rather than experimental techniques. 1004 studies were reviewed and were categorized as relevant and non-relevant according to inclusion and exclusion criteria. All the screening was done through Endnote after importing citations. This systematic review characterizes the gap in targeting the locale of the transmembrane protein and will aid researchers in exploring its new horizons.Communicated by Ramaswamy H. Sarma.

Comparative Analysis of Apicoplast-Targeted Protein Extension Lengths in Apicomplexan Parasites

In general, the mechanism of protein translocation through the apicoplast membrane requires a specific extension of a functionally important region of the apicoplast-targeted proteins. The corresponding signal peptides were detected in many apicomplexans but not in the majority of apicoplast-targeted proteins in Toxoplasma gondii. In T. gondii signal peptides are either much diverged or their extension region is processed, which in either case makes the situation different from other studied apicomplexans. We propose a statistic method to compare extensions of the functionally important regions of apicoplast-targeted proteins. More specifically, we provide a comparison of extension lengths of orthologous apicoplast-targeted proteins in apicomplexan parasites. We focus on results obtained for the model species T. gondii, Neospora caninum, and Plasmodium falciparum. With our method, cross species comparisons demonstrate that, in average, apicoplast-targeted protein extensions in T. gondii are 1.5-fold longer than in N. caninum and 2-fold longer than in P. falciparum. Extensions in P. falciparum less than 87 residues in size are longer than the corresponding extensions in N. caninum and, reversely, are shorter if they exceed 88 residues.

Base motif recognition and design of DNA templates for fluorescent silver clusters by machine learning

Discriminative base motifs within DNA templates for fluorescent silver clusters are identified using methods that combine large experimental data sets with machine learning tools for pattern recognition. Combining the discovery of certain multibase motifs important for determining fluorescence brightness with a generative algorithm, the probability of selecting DNA templates that stabilize fluorescent silver clusters is increased by a factor of >3.