Biology subjects

Dayhoff, G. W.

Publications and source records attributed to Dayhoff, G. W..

7 recordsLinked to original sources

bioRxiv2026

Predicted condensation-related signatures localize to two C-terminal regions of the prion protein across cervid PRNP variants associated with chronic wasting disease

Genetic variation in cervid PRNP influences chronic wasting disease (CWD) susceptibility and progression, but it remains unclear whether disease-associated substitutions produce localized differences in sequence-derived properties relevant to prion protein (PrPC) condensation. We compared a white-tailed deer (WTD) reference sequence with 10 modeled variants on the same mature PrP scaffold (residues 24-233). For each sequence, we calculated intrinsic disorder, FuzDrop per-residue droplet-promoting propensity (pDP) and binding-mode entropy (Sbind), predicted residue-level pKa values, and a discrete-charge summary at pH 7.0. The overall profiles preserve a long N-terminal droplet-promoting region, whereas variant-associated differences are concentrated in selected intervals. In the FuzDrop outputs, residues 126-140 show allele-dependent changes in pDP near the droplet-promoting threshold, and residues 220-230 show variant-specific changes in Sbind, including lower Sbind for S225F/Y and higher Sbind for elk-like Q226E. These differences occur without large changes in overall intrinsic disorder. KaML pKa predictions identify near-neutral titratable sites that provide electrostatic context for these local predictor differences. Independent atomistic simulations of monomeric PrP further detected variant-sensitive backbone behavior and intramolecular contacts involving these intervals. Together, the analysis identifies residues 126-140 and 220-230 as computationally defined candidate intervals that overlap regions implicated independently in prion conversion or CWD fibril structure, motivating direct tests of cervid PrP condensation and maturation.

bioinformatics↗

bioRxiv2026

Mathematical genomics of SRY proteins and their associated HMG-Box domains across mammals

Male sex determination in mammals depends on sex-determining region Y (SRY), a transcription factor whose high-mobility group box (HMG box) binds and bends DNA to activate SOX9. The divergent N- and C-terminal domains lack sequence homology and have been dismissed as passive linkers, yet alignment-based homology cannot reveal whether they retain a hidden compositional grammar. We applied quantitative genomics to 65 full-length mammalian SRY proteins and 38 HMG-box subsequences across 75 mammals, integrating amino acid and dipeptide compositions, Shannon entropy, dominant-residue profiles, hydropathy-based n-gram spectra, longest hydropathy stretches, and cumulative proximity. The HMG box is tightly constrained: basic residues dominated, cysteine was nearly absent, entropy clustered near 4.0, polar content averaged 58.44\%, and 25 of 38 HMG boxes shared 57.97\% polar residues, whereas full-length SRY varied more, with rodents showing high polar and glutamine-rich profiles and cetaceans low polar content. Proximity-based clustering was more taxonomically consistent at the HMG level, while terminal regions blurred domain-level signals. Across 65 SRY proteins and 38 HMG domains, intrinsic disorder was the dominant predicted state, and a polyQ-rich rodent SRY illustrated how low-complexity repeats can produce misleading ordered predictions, underscoring the need for consensus disorder analysis in rapidly evolving intrinsically disordered regions. Cumulative proximity analysis identified \textit{Bos taurus} and \textit{Bos grunniens} as the only pair proximally clustered at both the full-length SRY and SRY-HMG levels, establishing multivariate proximity as a stringent criterion of compositional similarity satisfied by a single mammalian pair. These results suggest that SRY and its HMG box each carry a distinct compositional grammar, quantifiable only through alignment-independent signatures, with compositional individuality being the rule rather than the exception. These quantitative signatures provide a framework for interpreting SRY variation, assessing clinical mutations, and understanding evolutionary pressures shaping sex determination. Denser taxon sampling, structural modeling, and experimental tests are needed to clarify whether HMG-box drift and terminal-region variation affect DNA binding, bending, or reproductive function.

bioinformatics↗

bioRxiv2025

Illuminating the Druggable Proteome with an AI Protein Profiling Platform

Most human proteins lack chemical probes or pharmaceutical modulators, leaving much of the proteome unexplored. 1,2 Activity-based protein profiling has enabled proteome-scale discovery of protein-ligand interactions and covalent inhibitors, 3-5 but remains limited by probe chemistry, protein abundance, and discordant ligandability assignments across studies. 6-9 Machine-learning (ML) models can in principle generate proteomewide ligandability maps, but the predictive utility of current models is limited by the requirement for structures and the use of incomplete and weak training labels. 9-12 Here we developed an artificial intelligence protein profiling (AiPP), a sequence-based multitask platform built on the ESMC protein language model, 13,14 to accelerate proteomewide therapeutic discovery and target identification. While its primary task is identification of covalently ligandable cysteines, seven additional task heads and two external modules provide broader context by annotating reversible ligand-binding residues, disordered molecular recognition features, cysteine functional context, and reactivities. Central to the development is the LatentLift clustering approach, which leverages latent space similarities to reconcile conflicting experimental labels and facilitate model training. Applied to the human proteome, AiPP generated a cysteine-directed ligandability atlas that overcomes the limitations of current chemo-proteomic maps. As a proof of concept, we demonstrate that AiPP can guide the discovery of covalent allosteric inhibitors targeting the previously undruggable protein tyrosine phosphatase PTPN6. By linking sequence, ligandability and biological context, AiPP provides an open resource for therapeutic discovery, while LatentLift offers a general strategy for harmonizing proteomics data for ML applications.

biophysics↗

bioRxiv2025

CDKN1A (p21Cip/Waf1) stabilizes Cyclin D3 by inhibiting its phosphorylation-dependent nuclear export following butyrate treatment

Butyrate-mediated inhibition of cell proliferation is part of the preventive role of dietary fiber against colorectal cancer (CRC). This effect notably involves the cyclin-dependent kinase inhibitor CDKN1A (p21Cip/Waf1) in human intestinal cells, yet the underlying molecular mechanisms remain incompletely understood. Previously, we observed a paradoxical increase in cyclin D3 (CCND3)--but not cyclin D1--levels upon butyrate exposure. Here, we demonstrate that the butyrate-induced accumulation of CCND3 protein results both from mRNA increase and a CDKN1A-dependent protein stabilization, specifically extending its nuclear half-life. Proteomic analyses of CCND3 co-immunoprecipitates identified complexes involving CDKN1A, CDK4, CDK6, and the CRC-associated kinase CDK5, particularly enriched in butyrate-treated cells. Phosphorylation at a conserved Thr residue, crucial for CCND nuclear export and subsequent proteasomal degradation, was notably reduced following butyrate treatment and inversely correlated with CDKN1A expression levels. Structural modeling based on AlphaFold2, complemented by molecular dynamics simulations, revealed possible differential interactions between CDKN1A and cyclins D1 and D3, predicting that CCND3-Thr283 becomes structurally buried upon CDKN1A binding, limiting its phosphorylation. Our findings provide novel mechanistic insights into how CDKN1A might regulate CCND3 stability, highlighting previously unexplored roles of cyclin D3-containing complexes in cell cycle arrest induced by butyrate.

cell biology↗

bioRxiv2025

Protein Electrostatic Properties are Fine-Tuned Through Evolution

Ionization states play crucial roles in protein function, yet predicting protein pKa values remains a formidable challenge despite decades of research. Here we present KaML-ESM2 and KaML-ESMC, neural network task heads built on ESM protein language models (pLMs) and trained on the PKAD-3r experimental dataset augmented via GAINES, a latent-space sampling strategy for addressing data scarcity. KaML-ESM2/ESMC significantly outperform current structure- and sequence-based approaches across four benchmarks, achieving root-mean-square errors of about 0.5 units across six titratable residue types in native proteins. Performance degradation on 89 buried engineered OBTRUDEs (iOnizable suBsTitutions foR bUrieD rEsidues) that lack evolutionary support (KaML-ESMC RMSE = 1.89) reveals a key limitation of the current framework, which may be overcome through supervised training. Based on these and other data presented in the work, we hypothesize that protein sequence, through its evolutionary context, encodes not only structure and function but indirectly also electrostatic characteristics. We applied KaML-ESM2 to the human proteome, demonstrating that predicted pKa values can potentially identify functional sites and infer catalytic mechanisms. We offer KaML, a sequence-based, end-to-end platform to support applications spanning biological exploration, drug design, protein engineering, and biomolecular simulation. Although additional research is needed, GAINES could offer a general framework for addressing data scarcity in machine learning approaches for protein-related problems.

biophysics↗

bioRxiv2023

Chaotic aging: Intrinsically disordered proteins in aging-related processes

1.The development of aging is associated with the disruption of key cellular processes manifested as well-established hallmarks of aging. Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) have no stable tertiary structure that provide them a power to be configurable hubs in signaling cascades and regulate many processes, potentially including those related to aging. There is a need to clarify the roles of IDPs/IDRs in aging. The dataset of 1624 aging-related proteins was collected from established aging databases and experimental studies. There is a noticeable presence of IDPs/IDRs, accounting for about 36% of the aging-related dataset, which is comparable to the disorder content of the whole human proteome (about 40%). A Gene Ontology analysis of the our Aging proteome reveals an abundance of IDPs/IDRs in one-third of aging-associated processes, especially in genome regulation. Signaling pathways associated with aging also contain IDPs/IDRs on different hierarchical levels. Protein-protein interaction network analysis showed that IDPs present in different clusters associated with different aging hallmarks. Protein cluster with IDPs enrichment and high liquid-liquid phase separation (LLPS) probability has "nuclear" localization and DNA-associated functions, related to aging hallmarks: genomic instability, telomere attrition, epigenetic alterations, stem cells exhaustion. Some IDPs related to aging with high LLPS propensity were identified as "dangerous" based on the prediction of their propensity to aggregation. Overall, our analyses indicate that IDPs/IDRs play significant roles in aging-associated processes, particularly in the regulation of DNA functioning. IDP aggregation, which can lead to loss-of-function and toxicity, could be critically harmful to the cell. A structure-based analysis of aging and the identification of proteins that are particularly susceptible to disturbances can enhance our understanding of the molecular mechanisms of aging and open up new avenues for slowing it down.

bioinformatics↗

bioRxiv2020

Variability of Accessory Proteins Rules the SARS-CoV-2 Pathogenicity

The coronavirus disease 2019 (COVID-19) is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which is pandemic with an estimated fatality rate less than 1% is ongoing. SARS-CoV-2 accessory proteins ORF3a, ORF6, ORF7a, ORF7b, ORF8, and ORF10 with putative functions to manipulate host immune mechanisms such as interferons, immune signaling receptor NLRP3 (NOD-, LRR-, and pyrin domain-containing 3) inflammasome, inflammatory cytokines such as interleukin 1{beta} (IL-1{beta}) are critical in COVID-19 pathology. Outspread variations of each of the six accessory proteins of all complete proteomes (available as of October 26, 2020, in the National Center for Biotechnology Information depository) of SARS-CoV-2, were observed across six continents. Across all continents, the decreasing order of percentage of unique variations in the accessory proteins was found to be ORF3a>ORF8>ORF7a>ORF6>ORF10>ORF7b. The highest and lowest unique variations of ORF3a were observed in South America and Oceania, respectively. This finding suggests that the wide variations of accessory proteins seem to govern the pathogenicity of SARS-CoV-2, and consequently, certain propositions and recommendations can be made in the public interest.

bioinformatics↗