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ABSTRACT The contractile vacuole complex (CVC) is a dynamic and morphologically complex membrane organelle, comprising a large vesicle (bladder) linked with a tubular reticulum (spongiome). CVCs provide key osmoregulatory roles across diverse eukaryotic lineages, but probing the mechanisms underlying their structure and function is hampered by the limited tools available for in vivo analysis. In the experimentally tractable ciliate Tetrahymena thermophila, we describe four proteins that, as endogenously tagged constructs, localize specifically to distinct CVC zones. The DOPEY homolog Dop1p and the CORVET subunit Vps8Dp localize both to the bladder and spongiome but with different local distributions that are sensitive to osmotic perturbation, whereas the lipid scramblase Scr7p colocalizes with Vps8Dp. The H+-ATPase subunit Vma4 is spongiome specific. The live imaging permitted by these probes revealed dynamics at multiple scales including rapid exchange of CVC-localized and soluble protein pools versus lateral diffusion in the spongiome, spongiome extension and branching, and CVC formation during mitosis. Although the association with DOP1 and VPS8D implicate the CVC in endosomal trafficking, both the bladder and spongiome might be isolated from bulk endocytic input. 

Proteomics plays a vital role in biomedical research in the post-genomic era. With the technological revolution and emerging computational and statistic models, proteomic methodology has evolved rapidly in the past decade and shed light on solving complicated biomedical problems. Here, we summarize scientific research and clinical practice of existing and emerging high-throughput proteomics approaches, including mass spectrometry, protein pathway array, next-generation tissue microarrays, single-cell proteomics, single-molecule proteomics, Luminex, Simoa and Olink Proteomics. We also discuss important computational methods and statistical algorithms that can maximize the mining of proteomic data with clinical and/or other 'omics data. Various principles and precautions are provided for better utilization of these tools. In summary, the advances in high-throughput proteomics will not only help better understand the molecular mechanisms of pathogenesis, but also to identify the signature signaling networks of specific diseases. Thus, modern proteomics have a range of potential applications in basic research, prognostic oncology, precision medicine, and drug discovery. 

Abstract MotivationMHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed. ResultsWe presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental data. These computed motifs demonstrated high similarities with those derived from experimental data. In addition, we found that the motifs could be used for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods. Availability and implementationThe software can be found in https://github.com/minrq/pMHC. Supplementary informationSupplementary data are available at Bioinformatics online. 

This book constitutes the refereed proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2023, held in Istanbul, Turkey, from April 16–19, 2023. The 11 regular and 33 short papers presented in this book were carefully reviewed and selected from 188 submissions. The papers report on original research in all areas of computational molecular biology and bioinformatics.