Adam Voshall

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on major histocompatibility complex class I (MHC-I) molecules, collectively termed peptide–MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting cancer neoantigens, accurately predicting which peptides bind to MHC alleles is critical. Current computational methods for pMHC-I binding prediction fall into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and pMHC binding energetics. Although sequence-based methods are widely used, their performance depends on the size and quality of the training data. Structure-based approaches, by contrast, can generalize better across diverse MHC alleles, but they traditionally depend on identifying a single global minimum-energy conformation, an assumption that may be inadequate for the promiscuous binding of MHC-I molecules. To address these limitations, we developed STRUMP-I (STRUcture-based pMHC Prediction for class I), a novel pMHC-I binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine learning features. In the standard benchmark set, STRUMP-I achieved performance comparable to state-of-the-art sequence-based models overall and showed a clear advantage for alleles with limited or imbalanced representation. Furthermore, STRUMP-I complemented sequence-based methods by removing method-specific false positives and improving precision, with a more favorable precision–recall tradeoff than AF-FT. These evaluations reinforced the value of STRUMP-I as a structure-informed prioritization method, particularly for underrepresented alleles and as a high-precision post-prediction filter.

Farnesol salvage, a two-step pathway converting farnesol to farnesyl pyrophosphate (FPP), occurs in bacteria, plants, and animals. This paper investigates the presence of this pathway in fungi. Through bioinformatics, biochemistry, and physiological analyses, we demonstrate its absence in the yeasts Saccharomyces cerevisiae and Candida albicans , suggesting a likely absence across fungi. We screened 1,053 fungal genomes, including 34 from C. albicans , for potential homologs to four genes ( Arabidopsis thaliana AtFOLK , AtVTE5 , AtVTE6 , and Plasmodium falciparum PfPOLK ) known to accomplish farnesol/prenol salvage in other organisms. Additionally, we showed that ³ H-farnesol was not converted to FPP or any other phosphorylated prenol, and exogenous farnesol was not metabolized within 90 minutes at any phase of growth and did not rescue cells from the toxic effects of atorvastatin, but it did elevate the levels of intracellular farnesol (F _i ). All these experiments were conducted with C. albicans . In sum, we found no evidence for farnesol salvage in fungi. IMPORTANCE: The absence of farnesol salvage constitutes a major difference in the metabolic capabilities of fungi. In terms of fungal physiology, the lack of farnesol salvage pathways relates to how farnesol acts as a quorum-sensing molecule in Candida albicans and why farnesol should be investigated for use in combination with other known antifungal antibiotics. Its absence is essential for a model (K. W. Nickerson et al., Microbiol Mol Biol Rev 88:e00081-22, 2024), wherein protein farnesylation, protein chaperones, and the unfolded protein response are combined under the unifying umbrella of a cell’s intracellular farnesol (F _i ). In terms of human health, farnesol should have at least two different modes of action depending on whether those cells have farnesol salvage. Because animals have farnesol salvage, we can now see the importance of dietary prenols as well as the potential importance of farnesol in treating neurodegenerative diseases such as Parkinson’s disease, Alzheimer’s disease, and multiple sclerosis.

Small RNAs (sRNAs) associate with ARGONAUTE (AGO) proteins forming effector complexes with key roles in gene regulation and defense responses against molecular parasites. In multicellular eukaryotes, extensive duplication and diversification of RNA interference (RNAi) components have resulted in intricate pathways for epigenetic control of gene expression. The unicellular alga Chlamydomonas reinhardtii also has a complex RNAi machinery, including 3 AGOs and 3 DICER-like proteins. However, little is known about the biogenesis and function of most endogenous sRNAs. We demonstrate here that Chlamydomonas contains uncommonly long (>26 nt) sRNAs that associate preferentially with AGO1. Somewhat reminiscent of animal PIWI-interacting RNAs, these >26 nt sRNAs are derived from moderately repetitive genomic clusters and their biogenesis is DICER-independent. Interestingly, the sequences generating these >26-nt sRNAs have been conserved and amplified in several Chlamydomonas species. Moreover, expression of these longer sRNAs increases substantially under nitrogen or sulfur deprivation, concurrently with the downregulation of predicted target transcripts. We hypothesize that the transposon-like sequences from which >26-nt sRNAs are produced might have been ancestrally targeted for silencing by the RNAi machinery but, during evolution, certain sRNAs might have fortuitously acquired endogenous target genes and become integrated into gene regulatory networks.

The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the dataset depend on each other. This article surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing.

Small RNAs (sRNAs) associate with Argonaute (AGO) proteins forming effector complexes with key roles in gene regulation and defense responses against molecular parasites. In multicellular eukaryotes, extensive duplication and diversification of RNA interference (RNAi) components have resulted in intricate pathways for epigenetic control of gene expression. The unicellular alga Chlamydomonas reinhardtii also has a complex RNAi machinery, including three AGOs and three Dicer-like (DCL) proteins. However, little is known about the biogenesis and function of most endogenous sRNAs. We demonstrate here that Chlamydomonas contains uncommonly long sRNAs (>26 nt), which associate preferentially with AGO1. Somewhat reminiscent of animal PIWI-interacting RNAs, these long sRNAs are derived from moderately repetitive genomic clusters and their biogenesis appears to be Dicer-independent. Interestingly, long sRNA encoding sequences have been conserved and amplified in phylogenetically related Chlamydomonas species. Additionally, expression of several long sRNAs increases substantially under nutrient deprivation, correlating with the downregulation of predicted target transcripts. We hypothesize that the transposon-like sequences encoding long sRNAs might have been ancestrally targeted for silencing by the RNAi machinery but, during evolution, some long sRNAs might have fortuitously acquired endogenous target genes and become integrated into gene regulatory networks.

Animals and fungi produce cholesterol and ergosterol, respectively, while plants produce the phytosterols stigmasterol, campesterol, and β‐sitosterol in various combinations. The recent sequencing of many algal genomes allows the detailed reconstruction of the sterol metabolic pathways. Here, we characterized sterol synthesis in two sequenced Chlorella spp., the free‐living C. sorokiniana, and symbiotic C. variabilis NC64A. Chlamydomonas reinhardtii was included as an internal control and Coccomyxa subellipsoidea as a plant‐like outlier. We found that ergosterol was the major sterol produced by Chlorella spp. and C. reinhardtii, while C. subellipsoidea produced the three phytosterols found in plants. In silico analysis of the C. variabilis NC64A, C. sorokiniana, and C. subellipsoidea genomes identified 22 homologs of sterol biosynthetic genes from Arabidopsis thaliana, Saccharomyces cerevisiae, and C. reinhardtii. The presence of CAS1, CPI1, and HYD1 in the four algal genomes suggests the higher plant cycloartenol branch for sterol biosynthesis, confirming that algae and fungi use different pathways for ergosterol synthesis. Phylogenetic analysis for 40 oxidosqualene cyclases (OSCs) showed that the nine algal OSCs clustered with the cycloartenol cyclases, rather than the lanosterol cyclases, with the OSC for C. subellipsoidea positioned in between the higher plants and the eight other algae. With regard to why C. subellipsoidea produced phytosterols instead of ergosterol, we identified 22 differentially conserved positions where C. subellipsoidea CAS and A. thaliana CAS1 have one amino acid while the three ergosterol producing algae have another. Together, these results emphasize the position of the unicellular algae as an evolutionary transition point for sterols.