Stereo-selectivity of enantiomeric inhibitors to ubiquitin-specific protease 7 (USP7) dissected by molecular docking, molecular dynamics simulations, and binding free energy calculations.

The ubiquitin-specific protease 7 (USP7), as a member of deubiquitination enzymes, represents an attractive therapeutic target for various cancers, including prostate cancer and liver cancer. The change of the inhibitor stereocenter from the S to R stereochemistry (S-ALM → R-ALM34) markedly improved USP7 inhibitory activity. However, the molecular mechanism for the stereo-selectivity of enantiomeric inhibitors to USP7 is still unclear. In this work, molecular docking, molecular dynamics (MD) simulations, molecular mechanics/Generalized-Born surface area (MM/GBSA) calculations, and free energy landscapes were performed to address this mystery. MD simulations revealed that S-ALM34 showed a high degree of conformational flexibility compared to the R-ALM34 counterpart, and S-ALM34 binding led to the enhanced intradomain motions of USP7, especially the BL1 and BL2 loops and the two helices α4 and α5. MM/GBSA calculations showed that the binding strength of R-ALM34 to USP7 was stronger than that of S-ALM34 by - 4.99 kcal/mol, a similar trend observed by experimental data. MM/GBSA free energy decomposition was further performed to differentiate the ligand-residue spectrum. These analyses not only identified the hotspot residues interacting with R-ALM34, but also revealed that the hydrophobic interactions from F409, K420, H456, and Y514 play the major determinants in the binding of R-ALM34 to USP7. This result is anticipated to shed light on energetic basis and conformational dynamics information to aid in the design of more potent and selective inhibitors targeting USP7.

Computational Design and Optimization of Peptide Inhibitors for SIRT2.

Sirtuin 2 (SIRT2), an NAD+-dependent deacetylase, is crucial for regulating vital physiological processes, including aging, DNA repair, and cell cycle progression. Its abnormal activity is linked to diseases such as Parkinson's disease, cancer, and metabolic disorders, making it a potential target for therapeutic intervention. While small molecule inhibitors have been studied, peptide-based inhibitors offer a promising alternative due to their selectivity and bioavailability. This study explores the effects of converting the naturally occurring cyclic inhibitor peptide of SIRT2 (S2iL5) into a non-cyclic form by replacing a residue with FAK (LYS + CF3CO-). The new peptide sequence, Tyr-His-Thr-Tyr-His-Val-FAK (LYS)-Arg-Arg-Thr-Asn-Tyr-Tyr-Cys, was modeled to confirm its stable conformation. Docking studies and MM/GBSA calculations showed that the non-cyclic peptide had a better binding free energy (-50.66 kcal/mol) compared to the cyclic S2iL5 (-49.44 kcal/mol). Further mutations generated 160,000 unique peptides, screened using a machine learning-based QSAR model. Three promising peptides (Peptide 1: YGGNNVKRRTNYYC, Peptide 2: YMGEWVKRRTNYYC, and Peptide 3: YGGNGVKRRTNYYC) were selected and further modeled. Molecular dynamics (MD) analyses demonstrated that Peptide 1 and Peptide 2 had significant potential as SIRT2 inhibitors, showing moderate stability and some structural flexibility. Their best binding free energies were -59.07 kcal/mol and -46.01 kcal/mol, respectively. The study aimed to enhance peptide flexibility and binding affinity, suggesting that optimized peptide-based inhibitors can interact effectively with SIRT2. However, further experimental validation is necessary to confirm these computational predictions and evaluate the therapeutic potential of the identified peptides.

Microsecond Molecular Dynamics Simulation to Gain Insight Into the Binding of MRTX1133 and Trametinib With KRASG12D Mutant Protein for Drug Repurposing.

The Kirsten Rat Sarcoma (KRAS) G12D mutant protein is a primary driver of pancreatic ductal adenocarcinoma, necessitating the identification of targeted drug molecules. Repurposing of drugs quickly finds new uses, speeding treatment development. This study employs microsecond molecular dynamics simulations to unveil the binding mechanisms of the FDA-approved MEK inhibitor trametinib with KRASG12D, providing insights for potential drug repurposing. The binding of trametinib was compared with clinical trial drug MRTX1133, which demonstrates exceptional activity against KRASG12D, for better understanding of interaction mechanism of trametinib with KRASG12D. The resulting stable MRTX1133-KRASG12D complex reduces root mean square deviation (RMSD) values, in Switch I and II domains, highlighting its potential for inhibiting KRASG12D. MRTX1133's robust interaction with Tyr64 and disruption of Tyr96-Tyr71-Arg68 network showcase its ability to mitigate the effects of the G12D mutation. In contrast, trametinib employs a distinctive binding mechanism involving P-loop, Switch I and II residues. Extended simulations to 1 µs reveal sustained network interactions with Tyr32, Thr58, and GDP, suggesting a role of trametinib in maintaining KRASG12D in an inactive state and impede the further cell signaling. The decomposition binding free energy values illustrate amino acids' contributions to binding energy, elucidating ligand-protein interactions and molecular stability. The machine learning approach reveals that van der Waals interactions among the residues play vital role in complex stability and the potential amino acids involved in drug-receptor interactions of each complex. These details provide a molecular-level understanding of drug binding mechanisms, offering essential knowledge for further drug repurposing and potential drug discovery.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants.

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

The density-threshold affinity: Calculating lipid binding affinities from unbiased coarse-grained molecular dynamics simulations.

Many membrane proteins are sensitive to their local lipid environment. As structural methods for membrane proteins have improved, there is growing evidence of direct, specific binding of lipids to protein surfaces. Unfortunately the workhorse of understanding protein-small molecule interactions, the binding affinity for a given site, is experimentally inaccessible for these systems. Coarse-grained molecular dynamics simulations can be used to bridge this gap, and are relatively straightforward to learn. Such simulations allow users to observe spontaneous binding of lipids to membrane proteins and quantify localized densities of individual lipids or lipid fragments. In this chapter we outline a protocol for extracting binding affinities from these localized distributions, known as the "density threshold affinity." The density threshold affinity uses an adaptive and flexible definition of site occupancy that alleviates the need to distinguish between "bound'' lipids and bulk lipids that are simply diffusing through the site. Furthermore, the method allows "bead-level" resolution that is suitable for the case where lipids share binding sites, and circumvents ambiguities about a relevant reference state. This approach provides a convenient and straightforward method for comparing affinities of a single lipid species for multiple sites, multiple lipids for a single site, and/or a single lipid species modeled using multiple forcefields.

Martini 3 Coarse-Grained Model for the Cofactors Involved in Photosynthesis.

As a critical step in advancing the simulation of photosynthetic complexes, we present the Martini 3 coarse-grained (CG) models of key cofactors associated with light harvesting (LHCII) proteins and the photosystem II (PSII) core complex. Our work focuses on the parametrization of beta-carotene, plastoquinone/quinol, violaxanthin, lutein, neoxanthin, chlorophyll A, chlorophyll B, and heme. We derived the CG parameters to match the all-atom reference simulations, while structural and thermodynamic properties of the cofactors were compared to experimental values when available. To further assess the reliability of the parameterization, we tested the behavior of these cofactors within their physiological environments, specifically in a lipid bilayer and bound to photosynthetic complexes. The results demonstrate that our CG models maintain the essential features required for realistic simulations. This work lays the groundwork for detailed simulations of the PSII-LHCII super-complex, providing a robust parameter set for future studies.

Identification of potential modulators for human GPD1 by docking-based virtual screening, molecular dynamics simulations, binding free energy calculations, and DeLA-drug analysis.

Cytosolic Glycerol-3-phosphate dehydrogenase 1 (GPD1, EC 1.1.1.8) plays a pivotal role in regulating the Embden-Meyerhof glucose glycolysis pathway (E-M pathway), as well as in conditions such as Huntington's disease, cancer, and its potential role as a specific marker for Dormant Glioma Stem Cells. In this study, we conducted virtual screening using the ZINC database ( http://zinc.docking.org/ ) and the GPD1 structure to identify potential GPD1 modulators. The investigation involved screening active candidate ligands using ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) parameters, combined with molecular docking, pose analysis, and interaction analysis based on Lipinski and Veber criteria. Subsequently, the top 10 ligands were subjected to 200 ns all-atom molecular dynamics (M.D.) simulations, and binding free energies were calculated. The findings revealed that specific residues, namely TRP14, PRO94, LYS120, ASN151, THR264, ASP260, and GLN298, played a crucial role in ensuring system stability. Furthermore, through a comprehensive analysis involving molecular docking, molecular M.D., and DeLA-Drug, we identified 10 promising small molecules. These molecules represent potential lead compounds for developing effective therapeutics targeting GPD1-associated diseases, thereby contributing to a deeper understanding of GPD1-associated mechanisms. This study's significance lies in identifying key residues associated with GPD1 and discovering valuable small molecules, providing a foundation for further research and development.

Long-timescale atomistic simulations uncover loss-of-function mechanisms of uncharacterized Angiogenin mutants associated with ALS.

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease characterized by progressive degeneration of motor neurons, resulting in respiratory failure and mortality within 3-5 years. Mutations in the Angiogenin (ANG) cause loss of ribonucleolytic and nuclear translocation activities, contributing to ALS pathogenesis. This study focused on investigating two uncharacterized ANG mutations, T11S and R122H, newly identified in the Project Mine consortium. Using extensive computational analysis, including structural modeling and microsecond-timescale molecular dynamics (MD) simulations, we observed conformational changes in the catalytic residue His114 of ANG induced by T11S and R122H mutations. These alterations impaired ribonucleolytic activity, as inferred through molecular docking and binding free energy calculations. Gibbs free energy landscape and residue-residue interaction network analysis further supported our findings, revealing the energetic states and allosteric pathway from the mutated site to His114. Additionally, we assessed the binding of NCI-65828, an inhibitor of ribonucleolytic activity of ANG, and found reduced effectiveness in binding to T11S and R122H mutants when His114 assumed a non-native conformation. This highlights the crucial role of His114 and its association with ALS. Elucidating the relationship between physical structure and functional dynamics of frequently mutated ANG mutants is essential for understanding ALS pathogenesis and developing more effective therapeutic interventions.

A λ-dynamics investigation of insulin Wakayama and other A3 variant binding affinities to the insulin receptor.

Insulin Wakayama is a clinical insulin variant where a conserved valine at the third residue on insulin's A chain (ValA3) is replaced with a leucine (LeuA3), impairing insulin receptor (IR) binding by 140-500 fold. This severe impact on binding from such a subtle modification has posed an intriguing problem for decades. Although experimental investigations of natural and unnatural A3 mutations have highlighted the sensitivity of insulin-IR binding to minor changes at this site, an atomistic explanation of these binding trends has remained elusive. We investigate this problem computationally using λ-dynamics free energy calculations to model structural changes in response to perturbations of the ValA3 side chain and to calculate associated relative changes in binding free energy (ΔΔGbind). The Wakayama LeuA3 mutation and seven other A3 substitutions were studied in this work. The calculated ΔΔGbind results showed high agreement compared to experimental binding potencies with a Pearson correlation of 0.88 and a mean unsigned error of 0.68 kcal/mol. Extensive structural analyses of λ-dynamics trajectories revealed that critical interactions were disrupted between insulin and the insulin receptor as a result of the A3 mutations. This investigation also quantifies the effect that adding an A3 Cδ atom or losing an A3 Cγ atom has on insulin's binding affinity to the IR. Thus, λ-dynamics was able to successfully model the effects of subtle modifications to insulin's A3 side chain on its protein-protein interactions with the IR and shed new light on a decades-old mystery: the exquisite sensitivity of hormone-receptor binding to a subtle modification of an invariant insulin residue.

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants.

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r = - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

Investigating the role of functional mutations in leucine binding to Sestrin2 in aging and age-associated degenerative pathologies using structural and molecular simulation approaches.

Leucine is the native known ligand of Sestrin2 (Sesn2) and its interaction with Sesn2 is particularly important, as it influences the activity of mTOR in aging and its associated pathologies. It is important to find out how leucine interacts with Sesn2 and how mutations in the binding pocket of leucine affect the binding of leucine. Therefore, this study was committed to investigating the impact of non-synonymous mutations by incorporating a broad spectrum of simulation techniques, from molecular dynamics to free energy calculations. Our study was designed to model the atomic-scale interactions between leucine and mutant forms of Sesn2. Our results demonstrated that the interaction paradigm for the mutants has been altered thus showing a significant decline in the hydrogen bonding network. Moreover, these mutations compromised the dynamic stability by altering the conformational flexibility, sampling time, and leucine-induced structural constraints that consequently caused variation in the binding and structural stability. Molecular dynamics-based flexibility analysis revealed that the regions 217-339 and 371-380 demonstrated a higher fluctuation. Noteworthy, these regions correspond to a linker (217-339) and a loop (371-380) that cover the leucine binding cavity that is critical for the 'latch' mechanism in the N-terminal, which is essential for leucine binding. Further validation of reduced binding and modified internal motions caused by the mutants was obtained through binding free energy calculations, principal components analysis (PCA), and free energy landscape (FEL) analysis. By unraveling the molecular intricacies of Sesn2-leucine interactions and their mutations, we hope to pave the way for innovative strategies to combat the inevitable tide of aging and its associated diseases.Communicated by Ramaswamy H. Sarma.

Resolving coupled pH titrations using alchemical free energy calculations.

In a protein, nearby titratable sites can be coupled: the (de)protonation of one may affect the other. The degree of this interaction depends on several factors and can influence the measured p K a . Here, we derive a formalism based on double free energy differences ( Δ Δ G ) for quantifying the individual site p K a values of coupled residues. As Δ Δ G values can be obtained by means of alchemical free energy calculations, the presented approach allows for a convenient estimation of coupled residue p K a s in practice. We demonstrate that our approach and a previously proposed microscopic p K a formalism, can be combined with alchemical free energy calculations to resolve pH-dependent protein p K a values. Toy models and both, regular and constant-pH molecular dynamics simulations, alongside experimental data, are used to validate this approach. Our results highlight the insights gleaned when coupling and microstate probabilities are analyzed and suggest extensions to more complex enzymatic contexts. Furthermore, we find that naïvely computed p K a values that ignore coupling, can be significantly improved when coupling is accounted for, in some cases reducing the error by half. In short, alchemical free energy methods can resolve the p K a values of both uncoupled and coupled residues.

Understanding the riddle of amine oxidase flavoenzyme reactivity on the stereoisomers of N-methyl-dopa and N-methyl-tyrosine.

Enzymes are usually stereospecific against chiral substrates, which is commonly accepted for the amine oxidase family of enzymes as well. However, the FsqB (fumisoquin biosynthesis gene B) enzyme that belongs to the family of sarcosine oxidase and oxidizes L-N-methyl-amino acids, shows surprising activity for both enantiomers of N-methyl-dopa. The aim of this study is to understand the mechanism behind this behavior. Primary docking experiments showed that tyrosine and aspartate residues (121 and 315 respectively) are located on the ceiling of the active site of FsqB and may play a role in fixing the N-methyl-dopa via its catechol moiety and allowing both stereoisomers of this substrate to be in close proximity of the N5 atom of the isoalloxazine ring of the cofactor. Three experimental approaches were used to prove this hypothesis which are: (1) studying the oxidative ability of the variants Y121F and D315A on N-methyl-dopa substrates in comparison with N-methyl-tyrosine substrates; (2) studying the FsqB WT and variants catalyzed biotransformation via high-performance liquid chromatography (HPLC); (3) molecular dynamics simulations to characterize the underlying mechanisms of the molecular recognition. First, we found that the chemical characteristics of the catechol moiety of N-methyl-dopa are important to explain the differences between N-methyl-dopa and N-methyl-tyrosine. Furthermore, we found that Y121 and D315 are specific in FsqB and not found in the model enzyme sarcosine oxidase. The on-bench and theoretical mutagenesis studies show that Y121 residue has a major role in fixing the N-methyl-dopa substrates close to the N5 atom of the isoalloxazine ring of the cofactor. Simultaneously, D315 has a supportive role in this mechanism. Jointly, the experimental and theoretical approaches help to solve the riddle of FsqB amine oxidase substrate specificity.

PACAP key interactions with PAC1, VPAC1, and VPAC2 identified by molecular dynamics simulations.

The neuropeptide pituitary adenylate cyclase-activating polypeptide (PACAP) belongs to the glucagon/secretin family. PACAP interacts with the pituitary adenylate cyclase-activating polypeptide receptor type 1 (PAC1) and vasoactive intestinal peptide receptors 1 and 2 (VPAC1 and VPAC2), exhibiting functions in the immune, endocrine, and nervous systems. This peptide is upregulated in numerous instances of brain injury, acting as a neuroprotective agent. It can also suppress HIV-1 and SARS-CoV-2 viral replication in vitro. This work aimed to identify, in each peptide-receptor system, the most relevant residues for complex stability and interaction energy communication via Molecular Dynamics (MD), Free Energy calculations, and Protein-energy networks, thus revealing in detail the underlying mechanisms of activation of these receptors. Hydrogen bond formation, interaction energies, and computational alanine scanning between PACAP and its receptors showed that His1, Asp3, Arg12, Arg14, and Lys15 are crucial to the peptide's stability. Furthermore, several PACAP interactions with structurally conserved positions deemed necessary in GPCR B1 activation, including Arg2.60, Lys2.67, and Glu7.42, were significant for the peptide's stability within the receptors. According to the protein-energy network, the connection between Asp3 of PACAP and the receptors' conserved Arg2.60 represents a critical energy communication hub in all complexes. Additionally, the ECDs of the receptors were also found to function as energy communication hubs for PACAP. Although the overall binding mode of PACAP in the three receptors was found to be highly conserved, Arg12 and Tyr13 of PACAP were more prominent in complex with PAC1, while Ser2 of PACAP was with VPAC2. The detailed analyses performed in this work pave the way for using PACAP and its receptors as therapeutic targets.Communicated by Ramaswamy H. Sarma.

Virtual screening and identification of promising therapeutic compounds against drug-resistant Mycobacterium tuberculosis ß-ketoacyl-acyl carrier protein synthase I (KasA).

The emergence of new Mycobacterium tuberculosis (Mtb) strains resistant to the key drugs currently used in the clinic for tuberculosis treatment can substantially reduce the probability of therapy success, causing the relevance and importance of studies on the development of novel potent antibacterial agents targeting different vulnerable spots of Mtb. In this study, 28,860 compounds from the library of bioactive molecules were screened to identify novel potential inhibitors of ß-ketoacyl-acyl carrier protein synthase I (KasA), one of the key enzymes involved in the biosynthesis of mycolic acids of the Mtb cell wall. In doing so, we used a structure-based virtual screening approach to drug repurposing that included high-throughput docking of the C171Q KasA enzyme with compounds from the library of bioactive molecules including the FDA-approved drugs and investigational drug candidates, assessment of the binding affinity for the docked ligand/C171Q KasA complexes, and molecular dynamics simulations followed by binding free energy calculations. As a result, post-modeling analysis revealed 6 top-ranking compounds exhibiting a strong attachment to the malonyl binding site of the enzyme, as evidenced by the values of binding free energy which are significantly lower than those predicted for the KasA inhibitor TLM5 used in the calculations as a positive control. In light of the data obtained, the identified compounds are suggested to form a good basis for the development of new antitubercular molecules of clinical significance with activity against the KasA enzyme of Mtb.Communicated by Ramaswamy H. Sarma.

Structural and thermodynamic framework for PIEZO1 modulation by small molecules.

Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1's mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics-based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1's binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure-activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.

In silico identification of colchicine derivatives as novel and potential inhibitors based on molecular docking and dynamic simulations targeting multifactorial drug targets involved in Alzheimer's disease.

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, characterized by a gradual and steady deterioration in cognitive function over time. At least 50 million people worldwide are considered to have AD or another form of dementia. AD is marked by a gradual decline in cognitive abilities, memory deterioration and neurodegenerative transformations within the brain. The intricate and multifaceted nature of polygenic AD presents significant challenges within the landscape of drug development. The pathophysiology of AD unfolds in a non-linear and dynamic pattern, encompassing various systems and giving rise to a multitude of factors and hypotheses that contribute to the disease's onset. These encompass theories such as the beta-amyloid hypothesis, cholinergic hypothesis, tau hypothesis, oxidative stress and more. In the realm of drug development, polypharmacological drug profiles have emerged as a strategy that can yield combined or synergistic effects, effectively mitigating undesirable side effects and significantly enhancing the therapeutic efficacy of essential medications. With this concept in mind, our in-silico study sought to delve into the binding interactions of a diverse array of colchicine derivative compounds. These derivatives are chosen for their potential anti-inflammatory, antioxidant, anti-neurodegenerative and neuroprotective properties against Alzheimer's and other neurodegenerative diseases. We investigated compound interactions with AD-related targets, utilizing comprehensive molecular docking and dynamic simulations. COM111X showed impressive docking with acetylcholinesterase, indicating potential as an anti-Alzheimer's drug. COM112Y displayed strong docking scores with PDE4D and butyrylcholinesterase, suggesting dual inhibition for Alzheimer's treatment. Further in vitro and in vivo studies are warranted to explore these findings.Communicated by Ramaswamy H. Sarma.

Computational studies to explore inhibitors against the cyclin-dependent kinase 12/13 enzyme: an insilco pharmacophore modeling, molecular docking and dynamics approach.

Cancer is enlisted among the deadliest disease all over the world. The cyclin-dependent kinases 12 and 13 have been identified as cell cycle regulators. They conduct transcription and co-transcriptional processes by phosphorylating the C-terminal of RNA polymerase-II. Inhibition of CDK12 and 13 selectively presents a novel strategy to treat triple-negative breast cancer, but dual inhibitors are still lacking. Here, we report the screening of the natural product compound class against the dual CDK12/13 enzyme by employing various in silico methods. Complexes of CDK12 enzymes are used to form common feature pharmacophore models, whereas we perform receptor-based pharmacophore modelling on CDK13 enzyme owing to the availability of a single PDB. On conducting screening over the representative pharmacophores, the common drug-like screened natural products were shortlisted for conducting molecular docking studies. After molecular docking calculations, the candidates that showed crucial interaction with CDK12 and CDK13 enzymes were shortlisted for simulation studies. Five common docked candidates were selected for molecular dynamics simulations and free energy calculations. Based on the cut-off criteria of free energy calculations, one common hit was selected as the dual CDK12/13 inhibitor. The outcome concluded that the hit with ID CNP0386383 possesses drug-like properties, displays crucial interaction in the binding pocket, and shows stable dynamic behaviour and higher binding energy than the experimentally reported inhibitor of both CDK12 and CDK13 enzymes.Communicated by Ramaswamy H. Sarma.

Decoding molecular mechanism of species-selective targeting of fungal versus human HSP90 using multiple replica molecular dynamics simulations and binding free energy calculations.

As a highly evolutionarily conserved molecular chaperone, heat shock protein (HSP90), plays an important role in virulence traits, representing a therapeutic target for the treatment of fungal infections. The close evolutionary relationship between fungi and their human hosts poses a key challenge for the development of selective antifungal agents. In this work, molecular docking, multiple replica microsecond-based molecular dynamics (MD) simulations, and binding free energy calculations were performed to decode molecular mechanism of species-selective targeting of fungal versus human HSP90 triggered by the compound A11. MD simulations reveal that binding of compound A11 to human HSP90 nucleotide-binding domain (NBD) leads to obvious conformational changes relative to fungal HSP90 NBD. Binding free energy calculations show that the binding of compound A11 to fungal HSP90 NBD is stronger than that to human HSP90 NBD. Per residue-based free energy decomposition analysis was used to evaluate the inhibitor - residue interaction profile. The results efficiently identify the hot spot residues that play vital roles in favorable binding of compound A11 to fungal HSP90 NBD. This study is expected to provide a useful guidance for the development of selective inhibitors toward fungal HSP90.Communicated by Ramaswamy H. Sarma.

On the Path to Optimal Alchemistry.

Alchemical free energy calculations have become a standard and widely used tool, in particular for calculating and comparing binding affinities of drugs. Although methods to compute such free energies have improved significantly over the last decades, the choice of path between the end states of interest is usually still the same as two decades ago. We will show that there is a fundamentally arbitrary, implicit choice of parametrization of this path. To address this, the notion of the length of a path or a metric is required. A metric recently introduced in the context of the accelerated weight histogram method also proves to be very useful here. We demonstrate that this metric can not only improve the efficiency of sampling along a given path, but that it can also be used to improve the actual choice of path. For a set of relevant use cases, the combination of these improvements can increase the efficiency of alchemical free energy calculations by up to a factor 16.