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Structural characterization of intrinsically disordered proteins (IDPs) is paramount and challenging in structural biology. In this regard, a de novo computational protocol is introduced to build heterogeneous structural libraries for amyloid-β (Aβ) as a critical IDP. This method combines the strength of the simulated annealing - in jumping over energy barriers and escaping from traps - with short conventional molecular dynamics simulations to quickly explore local regions of the conformational space. The protocol efficiency and reliability in building Aβ conformational library is compared with two widely used simulation methods, replica exchange molecular dynamics and multiple trajectory sampling. The probability distribution functions of various structural and energetic features are constructed for each library, and also the diversity and convergence rates in these protocols were compared. Our results show that the suggested protocol is a successful computational method in the generation of a diverse conformational library of the Aβ monomer in agreement with experimental data. This method focuses on visiting more conformations in less computational time without paying attention to the statistical weight of each state in the library. We believe that the suggested computational technique can be used for generating a reasonable starting pool for subsequent reweighting with experimental data to obtain a statistical ensemble., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The human protein disulfide isomerase (hPDI), is an essential four-domain multifunctional enzyme. As a result of disulfide shuffling in its terminal domains, hPDI exists in two oxidation states with different conformational preferences which are important for substrate binding and functional activities. Here, we address the redox-dependent conformational dynamics of hPDI through molecular dynamics (MD) simulations. Collective domain motions are identified by the principal component analysis of MD trajectories and redox-dependent opening-closing structure variations are highlighted on projected free energy landscapes. Then, important structural features that exhibit considerable differences in dynamics of redox states are extracted by statistical machine learning methods. Mapping the structural variations to time series of residue interaction networks also provides a holistic representation of the dynamical redox differences. With emphasizing on persistent long-lasting interactions, an approach is proposed that compiled these time series networks to a single dynamic residue interaction network (DRIN). Differential comparison of DRIN in oxidized and reduced states reveals chains of residue interactions that represent potential allosteric paths between catalytic and ligand binding sites of hPDI.