Ensemble-Based Thermodynamics of the Fuzzy Binding between Intrinsically Disordered Proteins and Small-Molecule Ligands

High-throughput structure determination of an intrinsically disordered protein using cell-free protein crystallization

Intrinsically disordered proteins (IDPs) play a crucial role in various biological phenomena, dynamically changing their conformations in response to external environmental cues. To gain a deeper understanding of these proteins, it is essential to identify the determinants that fix their structures at the atomic level. Here, we developed a pipeline for rapid crystal structure analysis of IDP using a cell-free protein crystallization (CFPC) method. Through this approach, we successfully demonstrated the determination of the structure of an IDP to uncover the key determinants that stabilize its conformation. Specifically, we focused on the 11-residue fragment of c-Myc, which forms an α-helix through dimerization with a binding partner protein. This fragment was strategically recombined with an in-cell crystallizing protein and was expressed in a cell-free system. The resulting crystal structures of the c-Myc fragment were successfully determined at a resolution of 1.92 Å and we confirmed that they are identical to the structures of the complex with the native binding partner protein. This indicates that the environment of the scaffold crystal can fix the structure of c-Myc. Significantly, these crystals were obtained directly from a small reaction mixture (30 µL) incubated for only 72 h. Analysis of eight crystal structures derived from 22 mutants revealed two hydrophobic residues as the key determinants responsible for stabilizing the α-helical structure. These findings underscore the power of our CFPC screening method as a valuable tool for determining the structures of challenging target proteins and elucidating the essential molecular interactions that govern their stability.

Repurposing of drug molecules from FDA database against Hepatitis C virus E2 protein using ensemble docking approach

Hepatitis C virus, a member of the Flaviviridae family and genus Hepacivirus, is an enveloped, positively single stranded RNA virus. Its surface consists of a heterodimer of E1 and E2 proteins which play a crucial role in receptor binding and membrane fusion. In this study we have used in silico virtual screening by utilizing ensemble docking on the approved drugs. These drugs can bind with high efficiency to the 36 prominent conformations of the CD81 binding site clustered from a total of 3 µs MD simulation data on the E2 protein. We started with 9213 compounds from the FDA list of drugs and progressively came down to 5 compounds which have been seen to bind with very high efficiency to not only all the conformations but also the two predicted druggable pockets that encompass the CD81 binding site. MM/PBSA binding energy calculations also point to the highly stable interaction of the compounds to the E2 protein. This study may in future broaden the arsenal of therapeutics for use against HCV infection and lead to more effective care against the virus.

More is simpler: Decomposition of ligand-binding affinity for proteins being disordered

In statistical mechanics, it is well known that the huge number of degrees of freedom does not complicate the problem as it seems, but actually greatly simplifies the analysis (e.g., to give a Boltzmann distribution). Here, we reveal that the ensemble averaging from the vast conformations of intrinsically disordered proteins (IDPs) greatly simplifies the nature of binding affinity, which can be reliably decomposed into a sum of the ligandability of IDP and the capacity of ligand. Such an unexpected regularity is applied to facilitate the virtual screening upon IDPs. It also provides essential insight in understanding the specificity difference between IDPs and conventional ordered proteins since the specificity is caused by deviation from the baseline behavior of protein-ligand binding.

Reinforcement learning to boost molecular docking upon protein conformational ensemble

Intrinsically disordered proteins (IDPs) are widely involved in human diseases and thus are attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and tens of thousands of conformations. Here, we propose a reversible upper confidence bound (UCB) algorithm for the virtual screening of IDPs to address the influence of the conformation ensemble. The docking process is dynamically arranged so that attempts are focused near the boundary to separate top ligands from the bulk accurately. It is demonstrated in the example of transcription factor c-Myc that the average docking number per ligand can be greatly reduced while the performance is merely slightly affected. This study suggests that reinforcement learning is highly efficient in solving the bottleneck of virtual screening due to the conformation ensemble in the rational drug design of IDPs.

Structure-based peptide design targeting intrinsically disordered proteins: Novel histone H4 and H2A peptidic inhibitors

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Intrinsically disordered proteins/protein regions (IDPs/IDPRs) are emerging drug targets.

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Lack of fast methods hinders the discovery of inhibitors for IDPs/ IDPRs.

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Fast and inexpensive structure-based approaches have been developed.

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The developed methods were applied to succesfully design inhibitors targeting the disordered tail of histone H4 and H2A.

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The presented methods can be widely used to identify inhibitors for other IDPs/IDPRs.

A growing body of research has demonstrated that targeting intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) is feasible and represents a new trending strategy in drug discovery. However, the number of inhibitors targeting IDPs/IDPRs is increasing slowly due to limitations of the methods that can be used to accelerate the discovery process. We have applied structure-based methods to successfully develop the first peptidic inhibitor (HIPe - Histone Inhibitory Peptide) that targets histone H4 that are released from NETs (Neutrophil Extracellular Traps). HIPe binds stably to the disordered N-terminal tail of histone H4, thereby preventing histone H4-induced cell death. Recently, by utilisation of the same state-of-the-art approaches, we have developed a novel peptidic inhibitor (CHIP - Cyclical Histone H2A Interference Peptide) that binds to NET-resident histone H2A, which results in a blockade of monocyte adhesion and consequently reduction in atheroprogression. Here, we present comprehensive details on the computational methods utilised to design and develop HIPe and CHIP. We have exploited protein–protein complexes as starting structures for rational peptide design and then applied binding free energy methods to predict and prioritise binding strength of the designed peptides with histone H4 and H2A. By doing this way, we have modelled only around 20 peptides and from these were able to select 4–5 peptides, from a total of more than a trillion candidate peptides, for functional characterisation in different experiments. The developed computational protocols are generic and can be widely used to design and develop novel inhibitors for other disordered proteins.