{"doi":"10.1101/2023.01.27.525976","title":"Role of Strong Localized vs. Weak Distributed Interactions in Disordered Protein Phase Separation","abstract":"Abstract Interaction strength and localization are critical parameters controlling the single-chain and condensed-state properties of intrinsically disordered proteins (IDPs). Here, we decipher these relationships using coarse-grained heteropolymers comprised of hydrophobic (H) and polar (P) monomers as model IDPs. We systematically vary the fraction of P monomers X P and employ two distinct particle-based models that include either strong localized attractions between only H–H pairs (HP model) or weak distributed attractions between both H–H and H–P pairs (HP+ model). To compare different sequences and models, we first carefully tune the attraction strength for all sequences to match the single-chain radius of gyration. Interestingly, we find that this procedure produces similar conformational ensembles, nonbonded potential energies, and chain-level dynamics for single chains of almost all sequences in both models, with some deviations for the HP model at large X P . However, we observe a surprisingly rich phase behavior for the sequences in both models that deviates from the expectation that similarity at the single-chain level will translate to similar phase-separation propensity. Coexistence between dilute and dense phases is only observed up to a model-dependent X P despite the presence of favorable interchain interactions, which we quantify using the second virial coefficient. Instead, the limited number of attractive sites (H monomers) leads to the self-assembly of finite-sized clusters of different sizes depending on X P . Our findings strongly suggest that models with distributed interactions favor the formation of liquid-like condensates over a much larger range of sequence compositions compared to models with localized interactions.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":391646,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9545,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":846442,"name":"Dinesh Sundaravadivelu Devarajan","orcid":"0000-0001-7825-0999","position":1,"is_corresponding":false},{"id":336290,"name":"Michael P. Howard","orcid":"0000-0002-9561-4165","position":2,"is_corresponding":false},{"id":257361,"name":"Young C. Kim","orcid":null,"position":3,"is_corresponding":false},{"id":271234,"name":"Arash Nikoubashman","orcid":"0000-0003-0563-825X","position":4,"is_corresponding":false},{"id":231182,"name":"Jeetain Mittal","orcid":"0000-0002-9725-6402","position":5,"is_corresponding":false},{"id":868548,"name":"Shiv Rekhi","orcid":"0009-0007-3625-903X","position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":null,"created_at":"2026-07-19T01:18:47.408273Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}