ArticleBMC bioinformatics2026
A structural bioinformatics framework for prioritizing pH-sensitive proteins from 3D structural features.
Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcidic extracellular pH is a defining feature of many solid tumors and can influence the structural stability and activity of proteins involved in cancer progression. However, no unified computational framework exists for systematically assessing the pH-sensitivity of proteins using structural information.
methodsWe developed a structural bioinformatics framework that quantifies pH-sensitivity by integrating five protonation-relevant descriptors derived from protein sequences and 3D structures: total histidine count, histidine proportion, ionizable residues with predicted pKa values in acidic ranges, solvent-exposed histidines, and helix-associated histidines. pKa values were obtained using PROPKA 3.0, and all feature extraction steps were implemented through custom Python scripts. The framework was applied to cancer-associated secretory and membrane proteins to prioritize those whose structures may be particularly susceptible to acidic tumor conditions.
resultsThe framework effectively ranked proteins according to their structural susceptibility to acidic pH and revealed distinct distributions of pH-sensitive features across secretory and membrane classes. Several high-scoring proteins, including ITGB8, LGR5, FZD7, HLA-E, ANGPTL2, and BST2 emerged as candidates potentially affected by protonation-linked perturbations in acidic tumor microenvironments. Enrichment analyses of ranked subsets demonstrated how score-derived protein groups can support downstream biological interpretation.
conclusionThis study presents a reproducible and extensible structural scoring framework and applies it to the systematic prioritization of cancer-associated proteins potentially vulnerable to acidic tumor pH. By combining 3D structural descriptors with protonation predictions, the approach offers a computational tool for investigating pH-dependent protein behavior in cancer biology and other contexts where local acidity shapes molecular function.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.