Rapid Conformational Fluctuations of Disordered HIV-1 Fusion Peptide in Solution

The HIV-1 Nucleocapsid Regulates Its Own Condensation by Phase-Separated Activity-Enhancing Sequestration of the Viral Protease during Maturation

A growing number of studies indicate that mRNAs and long ncRNAs can affect protein populations by assembling dynamic ribonucleoprotein (RNP) granules. These phase-separated molecular ‘sponges’, stabilized by quinary (transient and weak) interactions, control proteins involved in numerous biological functions. Retroviruses such as HIV-1 form by self-assembly when their genomic RNA (gRNA) traps Gag and GagPol polyprotein precursors. Infectivity requires extracellular budding of the particle followed by maturation, an ordered processing of ∼2400 Gag and ∼120 GagPol by the viral protease (PR). This leads to a condensed gRNA-NCp7 nucleocapsid and a CAp24-self-assembled capsid surrounding the RNP. The choreography by which all of these components dynamically interact during virus maturation is one of the missing milestones to fully depict the HIV life cycle. Here, we describe how HIV-1 has evolved a dynamic RNP granule with successive weak–strong–moderate quinary NC-gRNA networks during the sequential processing of the GagNC domain. We also reveal two palindromic RNA-binding triads on NC, KxxFxxQ and QxxFxxK, that provide quinary NC-gRNA interactions. Consequently, the nucleocapsid complex appears properly aggregated for capsid reassembly and reverse transcription, mandatory processes for viral infectivity. We show that PR is sequestered within this RNP and drives its maturation/condensation within minutes, this process being most effective at the end of budding. We anticipate such findings will stimulate further investigations of quinary interactions and emergent mechanisms in crowded environments throughout the wide and growing array of RNP granules.

Recent Advances in Computational Protocols Addressing Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDP) are abundant in the human genome and have recently emerged as major therapeutic targets for various diseases. Unlike traditional proteins that adopt a definitive structure, IDPs in free solution are disordered and exist as an ensemble of conformations. This enables the IDPs to signal through multiple signaling pathways and serve as scaffolds for multi-protein complexes. The challenge in studying IDPs experimentally stems from their disordered nature. Nuclear magnetic resonance (NMR), circular dichroism, small angle X-ray scattering, and single molecule Förster resonance energy transfer (FRET) can give the local structural information and overall dimension of IDPs, but seldom provide a unified picture of the whole protein. To understand the conformational dynamics of IDPs and how their structural ensembles recognize multiple binding partners and small molecule inhibitors, knowledge-based and physics-based sampling techniques are utilized in-silico, guided by experimental structural data. However, efficient sampling of the IDP conformational ensemble requires traversing the numerous degrees of freedom in the IDP energy landscape, as well as force-fields that accurately model the protein and solvent interactions. In this review, we have provided an overview of the current state of computational methods for studying IDP structure and dynamics and discussed the major challenges faced in this field.

Assessment of the Sampling Performance of Multiple-Copy Dynamics versus a Unique Trajectory

The goal of the present study was to ascertain the differential performance of a long molecular dynamics trajectory versus several shorter ones starting from different points in the phase space and covering the same sampling time. For this purpose, we selected the 16-mer peptide Bak₁₆BH3 as a model for study and carried out several samplings in explicit solvent. These samplings included an 8 μs trajectory (sampling S1); two 4 μs trajectories (sampling S2); four 2 μs trajectories (sampling S3); eight 1 μs trajectories (sampling S4); 16 0.5 μs trajectories (sampling S5), and 80 0.1 μs trajectories (sampling S6). Moreover, the 8 μs trajectory was further extended to 16 μs to have reference values of the diverse properties measured. The diverse samplings were compared qualitatively and quantitatively. Among the former, we carried out a comparison of the conformational profiles of the peptide using cluster analysis. Moreover, we also gained insight into the interchange among these structures along the sampling process. Among the latter, we computed the number of new conformational patterns sampled with time using strings defined from the conformations attained by each of the residues in the peptide. We also compared the locations and depths of the obtained minima on the free energy surface using principal component analysis. Finally, we also compared the helical profiles per residue at the end of the sampling process. The results suggest that a few short molecular dynamics trajectories may provide better sampling than one unique trajectory. Moreover, this procedure can also be advantageous to avoid getting trapped in a local minimum. However, caution should be exercised since short trajectories need to be long enough to overcome local barriers surrounding the starting point and the required sampling time depends on the number of degrees of freedom of the system under study. An effective way to gain insight into the minimum MD trajectory length is to monitor the convergence of different structural features, as shown in the present work.

Interaction of peptides with cell membranes: insights from molecular modeling

The investigation of the interaction of peptides with cell membranes is the focus of active research. It can enhance the understanding of basic membrane functions such as membrane transport, fusion, and signaling processes, and it may shed light on potential applications of peptides in biomedicine. In this review, we will present current advances in computational studies on the interaction of different types of peptides with the cell membrane. Depending on the properties of the peptide, membrane, and external environment, the peptide-membrane interaction shows a variety of different forms. Here, on the basis of recent computational progress, we will discuss how different peptides could initiate membrane pores, translocate across the membrane, induce membrane endocytosis, produce membrane curvature, form fibrils on the membrane surface, as well as interact with functional membrane proteins. Finally, we will present a conclusion summarizing recent progress and providing some specific insights into future developments in this field.