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Papers

Benchmarking residue-resolution protein coarse-grained models for simulations of biomolecular condensates

https://doi.org/10.1371/journal.pcbi.1012737

 

Abstract

Intracellular liquid–liquid phase separation (LLPS) of proteins and nucleic acids is a fundamental mechanism by which cells compartmentalize their components and perform essential biological functions. Molecular simulations play a crucial role in providing microscopic insights into the physicochemical processes driving this phenomenon. In this study, we systematically compare six state-of-the-art sequence-dependent residue-resolution models to evaluate their performance in reproducing the phase behaviour and material properties of condensates formed by seven variants of the low-complexity domain (LCD) of the hnRNPA1 protein (A1-LCD)—a protein implicated in the pathological liquid-to-solid transition of stress granules. Specifically, we assess the HPS, HPS-cation–π, HPS-Urry, CALVADOS2, Mpipi, and Mpipi-Recharged models in their predictions of the condensate saturation concentration, critical solution temperature, and condensate viscosity of the A1-LCD variants. Our analyses demonstrate that, among the tested models, Mpipi, Mpipi-Recharged, and CALVADOS2 provide accurate descriptions of the critical solution temperatures and saturation concentrations for the multiple A1-LCD variants tested. Regarding the prediction of material properties for condensates of A1-LCD and its variants, Mpipi-Recharged stands out as the most reliable model. Overall, this study benchmarks a range of residue-resolution coarse-grained models for the study of the thermodynamic stability and material properties of condensates and establishes a direct link between their performance and the ranking of intermolecular interactions these models consider.

요약

 

  1. 연구 목적: 이 논문은 다양한 잔기-해상도 단백질 거대모형(coarse-grained models)을 비교하여 생체분자 응축물의 상거동과 물질적 특성을 예측하는 성능을 평가하고자 하였습니다​.
  2. 연구 대상: 연구진은 hnRNPA1 단백질의 저복잡성 도메인(A1-LCD) 변이체 7종을 대상으로 실험 및 시뮬레이션을 통해 응축물의 포화 농도, 임계 용액 온도, 점도 등을 분석하였습니다​.
  3. 주요 결과: 다양한 거대모형 중 Mpipi-Recharged 모델이 가장 정확하게 임계 온도와 포화 농도를 예측하였으며, 응축물의 점도 예측에서도 뛰어난 성능을 보였습니다​.
  4. 연구 의의: 본 연구는 단백질 상분리 연구에서 거대모형의 신뢰성을 검증하고, 생체분자 응축물의 물리적 특성을 설명하는 데 중요한 통찰을 제공하였습니다​.
  5. 향후 방향: 향후 연구에서는 다양한 단백질 시스템에 거대모형을 적용하여 생체분자 상거동 예측의 정확성을 더욱 높이는 방향으로 발전시킬 필요가 있습니다​