Energetics and Dynamics Across the Bcl-2-Regulated Apoptotic Pathway Reveal Distinct Evolutionary Determinants of Specificity and Affinity

Calculated hydration free energies become less accurate with increases in molecular weight

In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect-the hydration free energy (HFE) of small molecules-is often insufficiently analyzed. While most benchmarking studies report excellent accuracies of calculated hydration free energies-usually within 2 kcal/mol of experimental values-we find that deeper analysis reveals significant shortcomings. Herein, we report a dependence of HFE prediction errors on ligand molecular weight-the higher the weight, the bigger the prediction error and the higher the probability the calculated result is erroneous by a large amount. We show that in the drug-like molecular weight region, HFE predictions can easily be off by 5 kcal/mol or more. This is likely to be highly problematic in a drug discovery and development setting. We make our HFE results and molecular descriptors freely and fully available in order to encourage deeper analysis of future molecular dynamics results and facilitate development of the next generation of force fields.

Innate Conformational Dynamics Drive Binding Specificity in Anti-Apoptotic Proteins Mcl-1 and Bcl-2

The structurally conserved B-cell lymphoma 2 (Bcl-2) family of protein function to promote or inhibit apoptosis through an exceedingly complex web of specific, intrafamilial protein-protein interactions. The critical role of these proteins in lymphomas and other cancers has motivated a widespread interest in understanding the molecular mechanisms that drive specificity in Bcl-2 family interactions. However, the high degree of structural similarity among Bcl-2 homologues has made it difficult to rationalize the highly specific (and often divergent) binding behavior exhibited by these proteins using conventional structural arguments. In this work, we use time-resolved hydrogen deuterium exchange mass spectrometry to explore shifts in conformational dynamics associated with binding partner engagement in the Bcl-2 family proteins Bcl-2 and Mcl-1. Using this approach combined with homology modeling, we reveal that Mcl-1 binding is driven by a large-scale shift in conformational dynamics, while Bcl-2 complexation occurs primarily through a classical charge compensation mechanism. This work has implications for understanding the evolution of internally regulated biological systems composed of structurally similar proteins and for the development of drugs targeting Bcl-2 family proteins for promotion of apoptosis in cancer.

Virtual Screening and Hit Selection of Natural Compounds as Acetylcholinesterase Inhibitors

Acetylcholinesterase (AChE) is one of the classical targets in the treatment of Alzheimer's disease (AD). Inhibition of AChE slows down the hydrolysis of acetycholine and increases choline levels, improving the cognitive function. The achieved success of plant-based natural drugs acting as AChE inhibitors, such as galantamine (GAL) from Galanthus genus and huperzine A from Huperzia serrate (approved drug in China), in the treatment of AD, and the fact that natural compounds (NCs) are considered as safer and less toxic compared to synthetic drugs, led us to screen the available NCs (almost 150,000) in the ZINC12 database for AChE inhibitory activity. The compounds were screened virtually by molecular docking, filtered for suitable ADME properties, and 32 ligands from 23 structural groups were selected. The stability of the complexes was estimated via 1 μs molecular dynamics simulation. Ten compounds formed stable complexes with the enzyme and had a vendor and a reasonable price per mg. They were tested for AChE inhibitory and antioxidant activity. Five compounds showed weak AChE inhibition and three of them exhibited high antioxidant activity.

Designing BH3-Mimetic Peptide Inhibitors for the Viral Bcl-2 Homologues A179L and BHRF1: Importance of Long-Range Electrostatic Interactions

Viruses have evolved strategies to prevent apoptosis of infected cells at early stages of infection. The viral proteins (vBcl-2s) from specific viral genes adopt a helical fold that is structurally similar to that of mammalian antiapoptotic Bcl-2 proteins and exhibit little sequence similarity. Hence, vBcl-2 homologues are attractive targets to prevent viral infection. However, very few studies have focused on developing inhibitors for vBcl-2 homologues. In this study, we have considered two vBcl-2 homologues, A179L from African swine fever virus and BHRF1 from Epstein-Barr virus. We generated two sets of 8000 randomized BH3-like sequences from eight wild-type proapoptotic BH3 peptides. During this process, the four conserved hydrophobic residues and an Asp residue were retained at their respective positions, and all other positions were substituted randomly without any bias. We constructed 8000 structures each for A179L and BHRF1 in complex with BH3-like sequences. Histograms of interaction energies calculated between the peptide and the protein resulted in negatively skewed distributions. The BH3-like peptides with high helical propensities selected from the negative tail of the respective interaction energy distributions exhibited more favorable interactions with A179L and BHRF1, and they are rich in basic residues. Molecular dynamics studies and electrostatic potential maps further revealed that both acidic and basic residues favorably interact with A179L, while only basic residues have the most favorable interactions with BHRF1. As in mammalian homologues, the role of long-range interactions and nonhotspot residues has to be taken into account while designing specific BH3-mimetic inhibitors for vBcl-2 homologues.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Computational Design of BH3-Mimetic Peptide Inhibitors That Can Bind Specifically to Mcl-1 or Bcl-XL: Role of Non-Hot Spot Residues

Interactions between pro- and anti-apoptotic Bcl-2 proteins decide the fate of the cell. The BH3 domain of pro-apoptotic Bcl-2 proteins interacts with the exposed hydrophobic groove of their anti-apoptotic counterparts. Through their design and development, BH3 mimetics that target the hydrophobic groove of specific anti-apoptotic Bcl-2 proteins have the potential to become anticancer drugs. We have developed a novel computational method for designing sequences with BH3 domain features that can bind specifically to anti-apoptotic Mcl-1 or Bcl-X_L. In this method, we retained the four highly conserved hydrophobic and aspartic residues of wild-type BH3 sequences and randomly substituted all other positions to generate a large number of BH3-like sequences. We modeled 20000 complex structures with Mcl-1 or Bcl-X_L using the BH3-like sequences derived from five wild-type pro-apoptotic BH3 peptides. Peptide-protein interaction energies calculated from these models for each set of BH3-like sequences resulted in negatively skewed extreme value distributions. The selected BH3-like sequences from the extreme negative tail regions have highly favorable interaction energies with Mcl-1 or Bcl-X_L. They are enriched in acidic and basic residues when they bind to Mcl-1 and Bcl-X_L, respectively. With the charged residues often away from the binding interface, the overall electric field generated by the charged residues results in strong long-range electrostatic interaction energies between the peptide and the protein giving rise to high specificity. Cell viability studies of representative BH3-like peptides further validated the predicted specificity. This study has revealed the importance of non-hot spot residues in BH3-mimetic peptides in providing specificity to a particular anti-apoptotic protein.

Cellular polyamines condense hyperphosphorylated Tau, triggering Alzheimer's disease

Many gaps in our understanding of Alzheimer's disease remain despite intense research efforts. One such prominent gap is the mechanism of Tau condensation and fibrillization. One viewpoint is that positively charged Tau is condensed by cytosolic polyanions. However, this hypothesis is likely based on an overestimation of the abundance and stability of cytosolic polyanions and an underestimation of crucial intracellular constituents - the cationic polyamines. Here, we propose an alternative mechanism grounded in cellular biology. We describe extensive molecular dynamics simulations and analysis on physiologically relevant model systems, which suggest that it is not positively charged, unmodified Tau that is condensed by cytosolic polyanions but negatively charged, hyperphosphorylated Tau that is condensed by cytosolic polycations. Our work has broad implications for anti-Alzheimer's research and drug development and the broader field of tauopathies in general, potentially paving the way to future etiologic therapies.

Bridging solvent molecules mediate RNase A - Ligand binding

Due to its high catalytic activity and readily available supply, ribonuclease A (RNase A) has become a pivotal enzyme in the history of protein science. Moreover, this great interest has carried over to computational chemistry and molecular dynamics, where RNase A has become a model system for various types of studies, all the while being an important drug design target in its own right. Here, we present a detailed molecular dynamics study of RNase–ligand binding involving 22 compounds, spanning nearly five orders of magnitude in affinity, and totaling 8.8 μs of sampling with the standard Amber parameters and an additional 8.8 μs of sampling with a modified potential. We show that short-lived, solvent-mediated bridging interactions are crucial to RNase–ligand binding. We characterize the behavior of bridging solvent molecules, uncovering a power-law dependence between the lifetime of a solvent bridge and the probability of its occurrence. We also demonstrate that from an energetic perspective, bridging solvent in RNase A–ligand binding behaves like part of the enzyme, rather than the ligands. Moreover, we describe an automated pipeline for the detection and processing of bridging interactions, and offer an independent assessment of the performance of the state-of-the-art fixed-charge force fields. Thus, our work has broad implications for drug design and computational chemistry in general.

Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

An improved knowledge of protein-protein interactions is essential for better understanding of metabolic and signaling networks, and cellular function. Progress tends to be based on structure determination and predictions using known structures, along with computational methods based on evolutionary information or detailed atomistic descriptions. We hypothesized that for the case of interactions across a common interface, between proteins from a pair of paralogue families or within a family of paralogues, a relatively simple interface description could distinguish between binding and non-binding pairs. Using binding data for several systems, and large-scale comparative modeling based on known template complex structures, it is found that charge-charge interactions (for groups bearing net charge) are generally a better discriminant than buried non-polar surface. This is particularly the case for paralogue families that are less divergent, with more reliable comparative modeling. We suggest that electrostatic interactions are major determinants of specificity in such systems, an observation that could be used to predict binding partners.