Inhibitor design by wrapping packing defects in HIV-1 proteins

Targeting Biomolecular Condensation and Protein Aggregation against Cancer

Biomolecular condensates, membrane-less entities arising from liquid-liquid phase separation, hold dichotomous roles in health and disease. Alongside their physiological functions, these condensates can transition to a solid phase, producing amyloid-like structures implicated in degenerative diseases and cancer. This review thoroughly examines the dual nature of biomolecular condensates, spotlighting their role in cancer, particularly concerning the p53 tumor suppressor. Given that over half of the malignant tumors possess mutations in the TP53 gene, this topic carries profound implications for future cancer treatment strategies. Notably, p53 not only misfolds but also forms biomolecular condensates and aggregates analogous to other protein-based amyloids, thus significantly influencing cancer progression through loss-of-function, negative dominance, and gain-of-function pathways. The exact molecular mechanisms underpinning the gain-of-function in mutant p53 remain elusive. However, cofactors like nucleic acids and glycosaminoglycans are known to be critical players in this intersection between diseases. Importantly, we reveal that molecules capable of inhibiting mutant p53 aggregation can curtail tumor proliferation and migration. Hence, targeting phase transitions to solid-like amorphous and amyloid-like states of mutant p53 offers a promising direction for innovative cancer diagnostics and therapeutics.

Critical amino acids in the TM2 of EAAT2 are essential for membrane-bound localization, substrate binding, transporter function and anion currents

Excitatory amino acid transporter 2 (EAAT2), the gene of which is known as solute carrier family 1 member 2 (SLC1A2), is an important membrane-bound transporter that mediates approximately 90% of the transport and clearance of l-glutamate at synapses in the central nervous system (CNS). Transmembrane domain 2 (TM2) of EAAT2 is close to hairpin loop 2 (HP2) and far away from HP1 in the inward-facing conformation. In the present study, 14 crucial amino acid residues of TM2 were identified via alanine-scanning mutations. Further analysis in EAAT2-transfected HeLa cells in vitro showed that alanine substitutions of these residues resulted in a decrease in the efficiency of trafficking/targeting to the plasma membrane and/or reduced functionality of membrane-bound, which resulted in impaired transporter activity. After additional mutations, the transporter activities of some alanine-substitution mutants recovered. Specifically, the P95A mutant decreased EAAT2-associated anion currents. The Michaelis constant (Km ) values of the mutant proteins L85A, L92A and L101A were increased significantly, whereas R87 and P95A were decreased significantly, indicating that the mutations L85A, L92A and L101A reduced the affinity of the transporter and the substrate, whereas R87A and P95A enhanced this affinity. The maximum velocity (Vmax) values of all 14 alanine mutant proteins were decreased significantly, indicating that all these mutations reduced the substrate transport rate. These results suggest that critical residues in TM2 affect not only the protein expression and membrane-bound localization of EAAT2, but also its interactions with substrates. Additionally, our findings elucidate that the P95A mutant decreased EAAT2-related anion currents. Our results indicate that the TM2 of EAAT2 plays a vital role in the transport process. The key residues in TM2 affect protein expression in the membrane, substrate transport and the anion currents of EAAT2.

Artificial Intelligence Steering Molecular Therapy in the Absence of Information on Target Structure and Regulation

Protein associations are at the core of biological activity, and the drug-based disruption of dysfunctional associations poses a major challenge to targeted therapy. The problem becomes daunting when the structure and regulated modulation of the complex are unknown. To address the challenge, we leverage an artificial intelligence platform that learns from structural and epistructural data and infers regulation-susceptible regions that also generate interfacial tension between protein and water, thereby promoting protein associations. The input consists of sequence-derived 1D-features. The network is configured with evolutionarily coupled residues and taught to search for phosphorylation-modulated binding epitopes. The discovery platform is benchmarked against a PDB-derived testing set and validated against experimental data on a therapeutic disruptor designed according to the inferred epitope for a large deregulated complex known to be recruited in heart failure. Thus, dysfunctional "molecular brakes" of cardiac contractility get released through a therapeutic intervention guided by artificial intelligence.

Protein structural defects enable pharmaceutical targeting while functionalizing the M2 proton channel

The influenza M2 (22-46) proton channel is therapeutically targetable and a prototype for proton transport across membranes. Conduction initiation, requiring a hydronium formed with exceptionally high pKa, remains nebulous. We tackle the problem by focusing on the dynamic interplay between protein structure and solvent interface. We identify two packing defects in the protein subunits that predict exactly the low and high-affinity drug-binding sites. The latter defect frustrates water coordination, enhancing water basicity and stabilizing the nearby hydronium that forms upon proton penetration in the channel. Thus, the trigger of proton conduction is directly related to the high-affinity binding site. The findings, in quantitative agreement with affinity measurements, are consistent with the targetable functional nature of protein packing defects. These findings enable the design of proton-conducting biomimetic materials, where the epistructure may be engineered to tune the basicity of interfacial water.

Deep Learning to Therapeutically Target Unreported Complexes

The disruption of large protein-protein (PP) interfaces remains a challenge in targeted therapy. Designing drugs that compete with binding partners is daunting, especially when the structure of the protein complex is unknown. To address the problem we propose a deep protein databank (PDB) learning platform to discover targetable epitopes for complex-disruptive leads.

Aggregation tendencies in the p53 family are modulated by backbone hydrogen bonds

The p53 family of proteins is comprised of p53, p63 and p73. Because the p53 DNA binding domain (DBD) is naturally unstable and possesses an amyloidogenic sequence, it is prone to form amyloid fibrils, causing loss of functions. To develop p53 therapies, it is necessary to understand the molecular basis of p53 instability and aggregation. Light scattering, thioflavin T (ThT) and high hydrostatic pressure (HHP) assays showed that p53 DBD aggregates faster and to a greater extent than p63 and p73 DBDs, and was more susceptible to denaturation. The aggregation tendencies of p53, p63, and p73 DBDs were strongly correlated with their thermal stabilities. Molecular Dynamics (MD) simulations indicated specific regions of structural heterogeneity unique to p53, which may be promoted by elevated incidence of exposed backbone hydrogen bonds (BHBs). The results indicate regions of structural vulnerability in the p53 DBD, suggesting new targetable sites for modulating p53 stability and aggregation, a potential approach to cancer therapy.

Molecular dynamics simulations to investigate the aggregation behaviors of the Abeta(17-42) oligomers

The amyloid beta-peptides (Abetas) are the main protein components of amyloid deposits in Alzheimer's disease (AD). Detailed knowledge of the structure and assembly dynamics of Abeta is important for the development of properly targeted AD therapeutics. So far, the process of the monomeric Abeta assembling into oligomeric fibrils and the mechanism underlying the aggregation process remain unclear. In this study, several molecular dynamics simulations were conducted to investigate the aggregation behaviors of the Abeta(17-42) oligomers associated with various numbers of monomers (dimer, trimer, tetramer, and pentamer). Our results showed that the structural stability of the Abeta(17-42) oligomers increases with increasing the number of monomer. We further demonstrated that the native hydrophobic contacts are positive correlated with the beta-sheet contents, indicating that hydrophobic interaction plays an important role in maintaining the structural stability of the Abeta(17-42) oligomers, particularly for those associated with more monomers. Our results also showed that the stability of the C-terminal hydrophobic segment 2 (residues 30-42) is higher than that of the N-terminal hydrophobic segment 1 (residues 17-21), suggesting that hydrophobic segment 2 may act as the nucleation site for aggregation. We further identified that Met35 residue initiates the hydrophobic interactions and that the intermolecular contact pairs, Gly33-Gly33 and Gly37-Gly37, form a stable "molecular notch", which may mediate the packing of the beta-sheet involving many other hydrophobic residues during the early stage of amyloid-like fibril formation.

Water mediated ligand functional group cooperativity: the contribution of a methyl group to binding affinity is enhanced by a COO(-) group through changes in the structure and thermodynamics of the hydration waters of ligand-thermolysin complexes

Ligand functional groups can modulate the contributions of one another to the ligand-protein binding thermodynamics, producing either positive or negative cooperativity. Data presented for four thermolysin phosphonamidate inhibitors demonstrate that the differential binding free energy and enthalpy caused by replacement of a H with a Me group, which binds in the well-hydrated S2' pocket, are more favorable in presence of a ligand carboxylate. The differential entropy is however less favorable. Dissection of these differential thermodynamic parameters, X-ray crystallography, and density-functional theory calculations suggest that these cooperativities are caused by variations in the thermodynamics of the complex hydration shell changes accompanying the H→Me replacement. Specifically, the COO(-) reduces both the enthalpic penalty and the entropic advantage of displacing water molecules from the S2' pocket and causes a subsequent acquisition of a more enthalpically, less entropically, favorable water network. This study contributes to understanding the important role water plays in ligand-protein binding.

Water's potential role: Insights from studies of the p53 core domain

Soluble proteins with amyloidogenic propensity such as the tumor suppressor protein p53 have high proportion of incompletely desolvated backbone H bonds (HB). Such bonds are vulnerable to water attack, thus potentially leading to the misfolding of these proteins. However, it is still not clear how the surrounding solvent influences the protein native states. To address this, systematic surveys by molecular dynamics simulations and entropy analysis were performed on the p53 core domain in this work. We examined seven wild/mutant X-ray structures and observed two types of water-network hydration in three "hot hydration centers" (DNA- or small molecule- binding surfaces of the p53 core domain). The "tight" water, resulting from the local collective hydrogen-bond interactions, is probably fundamental to the protein structural stability. The second type of water is highly "dynamical" and exchanges very fast within the bulk solution, which is unambiguously assisted by the local protein motions. An entropy mapping of the solvent around the protein and a temperature perturbation analysis further present the main features of the p53 hydration network. The particular environment created by different water molecules around the p53 core domain also partly explains the structural vulnerabilities of this protein.

tCONCOORD-GUI: visually supported conformational sampling of bioactive molecules

Conformational flexibility of bioactive molecules poses a major challenge to computational biology. tCONCOORD generates structure ensembles based on geometrical considerations and has been successfully applied to predict protein conformational flexibility and essential degrees of freedom. We have now developed a graphical user interface (GUI) for tCONCOORD, which substantially facilitates the simulation setup and provides valuable insights into the structure analysis and constraint definition process in tCONCOORD. Moreover, users can influence the constraint definition process by interactively turning interactions on and off, defining completely rigid or flexible regions, or by applying artifical constraints that cause a biased sampling of the conformational space. This interface offers a versatile environment not only for the setup and analysis of tCONCOORD simulations, but also for molecular modeling and structure analysis in general. Both tCONCOORD* and the tCONCOORD-GUI(dagger) are distributed freely.

Geometry-based sampling of conformational transitions in proteins

The fast and accurate prediction of protein flexibility is one of the major challenges in protein science. Enzyme activity, signal transduction, and ligand binding are dynamic processes involving essential conformational changes ranging from small side chain fluctuations to reorientations of entire domains. In the present work, we describe a reimplementation of the CONCOORD approach, termed tCONCOORD, which allows a computationally efficient sampling of conformational transitions of a protein based on geometrical considerations. Moreover, it allows for the extraction of the essential degrees of freedom, which, in general, are the biologically relevant ones. The method rests on a reliable estimate of the stability of interactions observed in a starting structure, in particular those interactions that change during a conformational transition. Applications to adenylate kinase, calmodulin, aldose reductase, T4-lysozyme, staphylococcal nuclease, and ubiquitin show that experimentally known conformational transitions are faithfully predicted.

Kinase packing defects as drug targets

Protein kinases constitute major targets in molecular cancer therapy. The structural conservation of kinases causes specificity problems in most drug inhibitors, often resulting in dangerous side effects. Here we survey recent approaches in drug design that exploit a molecular marker for specificity: the pattern of packing defects. These packing defects are solvent-exposed intramolecular hydrogen bonds that may be protected by drugs upon association. In this light, we review design strategies to achieve paralogue discrimination, to control cross reactivity and to overcome drug resistance induced by target mutations.

Chemogenomic approaches to rational drug design

Paradigms in drug design and discovery are changing at a significant pace. Concomitant to the sequencing of over 180 several genomes, the high-throughput miniaturization of chemical synthesis and biological evaluation of a multiple compounds on gene/protein expression and function opens the way to global drug-discovery approaches, no more focused on a single target but on an entire family of related proteins or on a full metabolic pathway. Chemogenomics is this emerging research field aimed at systematically studying the biological effect of a wide array of small molecular-weight ligands on a wide array of macromolecular targets. Since the quantity of existing data (compounds, targets and assays) and of produced information (gene/protein expression levels and binding constants) are too large for manual manipulation, information technologies play a crucial role in planning, analysing and predicting chemogenomic data. The present review will focus on predictive in silico chemogenomic approaches to foster rational drug design and derive information from the simultaneous biological evaluation of multiple compounds on multiple targets. State-of-the-art methods for navigating in either ligand or target space will be presented and concrete drug design applications will be mentioned.

Modulating drug impact by wrapping target proteins

Molecular therapy requires a careful control of specificity. The authors review the recent advances in this regard focusing on a novel marker for ligand-target interaction, the solvent-exposed hydrogen bond or dehydron. Dehydrons promote their own dehydration and are not conserved across homolog proteins. Thus, the so-called wrapping technology is geared at enhancing drug specificity and hinges on an analysis of interfacial dehydrons in target-ligand complexes to assess microenvironmental changes occurring on association. Dehydron differences across purported targets have been exploited to redesign drugs in order to enhance selectivity. Tested wrapping modifications to cancer drugs are reviewed. Distance matrices defined by comparing dehydron patterns across targets correlate strongly with pharmacologic distances. This fact suggests a broad applicability of the wrapping technology, ultimately leading to molecular therapies with tighter control of side effects.

Feature-similarity protein classifier as a ligand engineering tool

Kinases have been often targeted in drug therapy aimed at blocking signaling pathways. However, the conservation of protein structure across homologs often leads to uncontrolled cross-reactivity. On the other hand, sticky packing defects in proteins are typically not conserved across homologs, making them ligand-anchoring sites potentially important to enhance selectivity. Thus, we introduce a hierarchical clustering of PDB-reported kinases according to packing differences. This kinome partitioning is highly correlated with proximity relations arising from the pharmacological profiling of kinases. A variable packing sensitivity is observed for individual drugs, with highly promiscuous ligands being the most insensitive to packing differences. Our classifier enables a strategy to design selective inhibitors.

Packing defects as selectivity switches for drug-based protein inhibitors

The conservation of structure across homolog proteins often diffuses the impact of drug-based inhibition by promoting alternative protein-ligand associations that may lead to toxic side effects. However, sticky packing defects are typically not conserved across homologs, making them valuable a priori targets to enhance specificity. By introducing a homology to quantify packing differences among proteins, we enable a previously undescribed strategy for the design of highly selective drug inhibitors involving ligands that wrap nonconserved packing defects. The selectivity of these ligands is validated by performing affinity assays on a cancer-related pharmacokinome. Minor reengineering of a powerful inhibitor guided by wrapping differences across its target kinome can selectively direct its impact toward a specific kinase. Thus, nonconserved packing defects may be used as selectivity switches across homolog targets, using spatial displacements of packing defects across aligned protein structures.

Protein promiscuity: drug resistance and native functions--HIV-1 case

The association of a drug with its target protein has the effect of blocking the protein activity and is termed a promiscuous function to distinguish from the protein's native function (Tawfik and associates, Nat. Genet. 37, 73-6, 2005). Obviously, a protein has not evolved naturally for drug association or drug resistance. Promiscuous protein functions exhibit unique traits of evolutionary adaptability, or evolvability, which is dependent on the induction of novel phenotypic traits by a small number of mutations. These mutations might have small effects on native functions, but large effects on promiscuous function; for example, an evolving protein could become increasingly drug resistant while maintaining its original function. Ariel Fernandez, in his opinion piece, notes that drug-binding "promiscuity" can hardly be dissociated from native functions; a dominant approach to drug discovery is the protein-native-substrate transition-state mimetic strategy. Thus, man-made ligands (e.g. drugs) have been successfully crafted to restrain enzymatic activity by focusing on the very same structural features that determine the native function. Using the successful inhibition of HIV-1 protease as an example, Fernandez illustrates how drug designers have employed naturally evolved features of the protein to suppress its activity. Based on these arguments, he dismisses the notion that drug binding is quintessentially promiscuous, even though in principle, proteins did not evolve to associate with man made ligands. In short, Fernandez argues that there may not be separate protein domains that one could term promiscuous domains. While acknowledging that drugs may bind promiscuously or in a native-like manner a la Fernandez, Tawfik maintains the role of evolutionary adaptation, even when a drug binds native-like. In the case of HIV-1 protease, drugs bind natively, and the initial onset of mutations results in drug resistance in addition to a dramatic decline in enzymatic activity and fitness of the virus. A chain of compensatory mutations follows this, and then the virus becomes fully fit and drug resistant. Ben Berkhout and Rogier Sanders subscribe to the evolution of new protein functions through gene duplication. With two identical protein domains, one domain can be released from a constraint imposed by the original function and it is thus free to move in sequence space toward a new function without loss of the original function. They emphasize that the forced evolution of drug-resistance differs significantly from the spontaneous evolution of an additional protein function. For instance, the latter process could proceed gradually on an evolutionary time scale, whereas the acquisition of drug-resistance is an all or nothing process for a virus, leading to the failure or success of therapy. They find no evidence to the thesis that resistance-mutations appear more rapidly in promiscuous domains than native domains. Berkhout and Sanders illustrate the genetic plasticity of HIV-1 by citing examples in which well-conserved amino acid residues of catalytic domains are forced to mutate under drug-pressure. HIV drug resistance biology is very complex. Instead of a viral protein, a drug can be targeted at a cellular protein. For example, Berkhout and Sanders claim, a drug targeted at the cellular protein CCR5 inhibits the binding of the viral envelope glycoprotein (Env) to CCR5. However, Env mutates so that it binds to the CCR5-drug complex and develops drug resistance. Interestingly, CCR5 has not evolved to bind to Env, but to a series of chemokines. Andrzej Kloczkowski, Taner Sen, and Bob Jernigan point out the importance of protein motions for binding. They believe it is likely that different ligands can bind to the diverse protein conformations sampled in the course of normal protein conformational fluctuations. They have been applying simple elastic network models to extract the motions as normal modes, which yield relatively small numbers of conformations that are useful for developing protein mechanisms; while these are typically small motions, for some proteins they can be quite large in scale. One of the major advantages of the approach is that only relatively small numbers of modes are important contributors to the overall motion -- so the approach provides a way to systematically map out a protein's motions. These models successfully represent the conformational fluctuations manifested in the crystallographic B-factors, and often suggest motions related to protein functional behaviors, such as those observed for reverse transcriptase, where two dominant hinges clearly relate to the processing steps -- one showing anti-correlation between the polymerase and ribonuclease H sites related to the translation and positioning of the nucleic acid chain, and another for opening and closing the polymerase site. Disordered proteins represent a more extreme case where the set of accessible conformations is much larger; thus they could offer up a broader range of possible binding forms. Whether evolution controls the functional motions for proteins remains little studied. Intriguingly, buried in the existing databases of protein-protein interactions may be information that can shed light on the extent of promiscuous binding among proteins themselves. Within these data there are cases where large numbers of diverse proteins have been shown to interact with a single protein; some of these could represent promiscuous protein-protein binding. Uncovering these promiscuous behaviors could be important for comprehending the details of how proteins can bind promiscuously to one another, and can exhibit even greater promiscuity in their binding to small molecules. The evolutionary routes, the dynamics of the target protein, and the many other aspects that need to be addressed while designing a drug that may dodge drug resistance, indicate the complexity and multi-disciplinary nature of the issue of drug resistance.

Prion and water: tight and dynamical hydration sites have a key role in structural stability

The propensity to form fibril in disease-related proteins is a widely studied phenomenon, but its correlation, if any, with structural characteristics of the associated proteins is not clearly understood. However, the observation has been made that some proteins that readily form amyloid have a significant number of backbone H bonds that are exposed to solvent molecules, suggesting that these regions have a propensity toward protein interaction and aggregation [Fernandez, A. & Scheraga, H. A. (2003) Proc. Natl. Acad. Sci. USA 100, 113-118]. High-resolution x-ray structures of the sheep and human C-terminal prion protein have provided a useful description of surface and partially buried waters. By molecular dynamics simulations, we investigated the structural role of these water molecules. The solvent dynamical behavior on the protein surface reveals significant features about the stability and the potential interactions of the prion protein. The protein presents regions of tightly bound conserved waters that are necessary to hold in place local elements of the fold, as well as regions where the local water is in fast exchange with bulk water. These results are evidenced by a map of the spatial distribution entropy of the solvent around the protein. The particular behavior of the solvent around these regions may be crucial in the folding stability and in terms of aggregation loci.