Residue-specific free energy analysis in ligand bindings to JAK2

Discovery of novel inhibitors of SARS-CoV-2 main protease

Corona Virus Disease 2019 (COVID-19), referred to as 'New Coronary Pneumonia', is a type of acute infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. M^pro is one of the main targets for treating COVID-19. The current research on M^pro mainly focuses on the repurposing of old drugs, and there are only a few novel ligands that inhibit M^pro. In this research, we used computational free energy calculation to screen a compound library against M^pro, and discovered four novel compounds with the two best compounds (AG-690/13507628 and AG-690/13507724) having experimental measured IC₅₀ of just under 3 μM and low cell toxicity. Detailed decomposition of the interactions between the inhibitors and M^pro reveals key interacting residues and interactions that determine the activity. The results from this study should provide a basis for further development of anti-SARS-CoV-2 drugs.Communicated by Ramaswamy H. Sarma.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: I. Standard procedure

Despite the massive application of end-point free energy methods in protein-ligand and protein-protein interactions, computational understandings about their performance in relatively simple and prototypical host-guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host-guest dataset containing 13 host-guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host-guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein-ligand binding, the standard end-point calculations cannot produce useful outcomes in host-guest binding and thus are not recommended unless alterations are performed.

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Discovery of Novel Inhibitors of CDK2 Using Docking and Physics-based Binding Free Energy Calculation

Cyclin-dependent kinase (CDK) is a serine/threonine protein kinase family that cooperates with cyclin and plays an important role in the regulation of cell cycle. Cyclin-dependent kinase 2 is an important member of the CDK family and holds great promise as an anti-cancer drug target. In this study, we used molecular docking and physics-based binding free energy calculation method AS-IE that explicitly calculated protein-ligand binding entropy to discover novel inhibitors of CDK2. A total of 17 inhibitors were discovered with the best IC₅₀ reaching ~2 μM. Decomposition of the binding free energy using AS-IE reveals key protein-ligand interactions that determines the activity. These results provided a good example of drug design using physics-based free energy calculation method such as AS-IE and the novel compounds offered a good start point for further development of CDK2 inhibitors.

Analysis of the binding modes of the first- and second-generation antiandrogens with respect to F876L mutation

Androgen receptor (AR) is an important target for the treatment of prostate cancer, and mutations in the AR have an important impact on the resistance of existing drugs. In this work, we performed molecular dynamics simulations of the existing marketed antiandrogens flutamide, nilutamide, bicalutamide, enzalutamide, apalutamide, darolutamide, and its main metabolite ORM15341 in complex with the wild-type and F876L mutant AR. We calculated the residue-specific binding free energy contribution of the wild-type and mutant ARs with the AS-IE method and analyzed the hotspot residues and the binding free energy contributions of specific residues before and after the mutation. In addition, we analyzed the total binding obtained by adding residue binding energy contributions and compared the results with experimental values. The obtained residue-specific binding information should be very helpful in understanding the mechanism of drug resistance with respect to specific mutations and in the design of new generation drugs against possible new mutations.

Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands

Androgen receptor (AR) is an important therapeutic target for the treatment of diseases such as prostate cancer, hypogonadism, muscle wasting, etc. In this study, the complex structures of the AR ligand-binding domain (LBD) with fifteen ligands were analyzed by molecular dynamics simulations combined with the alanine-scanning-interaction-entropy method (ASIE). The quantitative free energy contributions of the pocket residues were obtained and hotspot residues are quantitatively identified. Our calculation shows that that these hotspot residues are predominantly hydrophobic and their interactions with binding ligands are mainly van der Waals interactions. The total binding free energies obtained by summing over binding contributions by individual residues are in good correlation with the experimental binding data. The current quantitative analysis of binding mechanism of AR to ligands provides important insight on the design of future inhibitors.

Binding selectivity of inhibitors toward the first over the second bromodomain of BRD4: theoretical insights from free energy calculations and multiple short molecular dynamics simulations

Bromodomain-containing protein 4 (BRD4) plays an important role in mediating gene transcription involved in cancers and non-cancer diseases such as acute heart failure and inflammatory diseases. In this work, multiple short molecular dynamics (MSMD) simulations are integrated with a molecular mechanics generalized Born surface area (MM-GBSA) approach to decipher binding selectivity of three inhibitors 8NS, 82Y, and 837 toward two domains BD1 and BD2 of BRD4. The results demonstrate that the enthalpy effects play critical roles in selectivity identification of inhibitors toward BD1 and BD2, determining that 8NS has better selectivity toward BD2 than BD1, while 82Y and 837 more favorably bind to BD1 than BD2. A residue-based free-energy decomposition method was used to calculate an inhibitor-residue interaction spectrum and unveil contributions of separate residues to binding selectivity. The results identify six common residues, containing (P82, P375), (V87, V380), (L92, L385), (L94, L387), (N140, N433), and (I146, V439) individually belonging to (BD1, BD2) of BRD4, and yield a considerable binding difference of inhibitors to BD1 and BD2, suggesting that these residues play key roles in binding selectivity of inhibitors toward BD1 and BD2 of BRD4. Therefore, these results provide useful dynamics information and a structure affinity relationship for the development of highly selective inhibitors targeting BD1 and BD2 of BRD4.

Binding Modes of Small-Molecule Inhibitors to the EED Pocket of PRC2

Polycomb Polycomb repressive complex 2 (PRC2) plays a key role in silencing epigenetic gene through trimethylation of lysine 27 on histone 3 (H3K27). Dysregulations of PRC2 caused by overexpression and mutations of the core subunits of PRC2 have been implicated in many cancers. The core subunits EZH1/2 are histone-lysine N-methyltransferases that function as the enzymatic component of PRC2. While the core subunit EED is a scaffolding protein to support EZH1/2 and binds JARID2K116me3/H3K27me3 to enhance the enzymatic activity of PRC2 through allosteric activation. Recently, several small molecules that compete with JARI2K116me3 and H3K27me3 have been reported. These molecules selectively bind to the JARID2K116me3/H3K27me3-binding pocket of EED, thereby preventing the allosteric regulation of PRC2. These first-in-class PRC2 inhibitors show robust suppression in DLBCL cell lines, demonstrating anticancer drugs that target the EED subunit of PRC2 are viable. In this study, we used the recently developed MM/GBSA_IE and the alanine scanning method to analyze the hot spots in EED/inhibitor interactions. The analysis of these hot and warm spots helps us to understand the fundamental differences between inhibitors. Our results give a quantitative explanation on why the binding affinities of EED/A-395 interactions are stronger than that of EED/EED226 while their binding modes are similar and provide valuable insights for rational design of novel EED inhibitors.

Study of SHMT2 Inhibitors and Their Binding Mechanism by Computational Alanine Scanning

Mitochondrial serine hydroxymethyl transferase isoform 2 (SHMT2) has attracted increasing attention as a pivotal catalyzing regulator of the serine/glycine pathway in the one-carbon metabolism of cancer cells. However, few inhibitors that target this potential anticancer target have been discovered. Quantitative characterization of the interactions between SHMT2 and its known inhibitors should benefit future discovery of novel inhibitors. In this study, we employed a recently developed alanine-scanning-interaction-entropy method to quantitatively calculate the residue-specific binding free energy of 28 different SHMT2 inhibitors that originate from the same skeleton. Major contributing residues from SHMT2 and chemical groups from the inhibitors were identified, and the binding energy of each residue was quantitatively determined, revealing essential features of the protein-inhibitor interaction. The most important contributing residue is Y105 of the B chain followed by L166 of the A chain. The calculated protein-ligand binding free energies are in good agreement with the experimental results and showed better correlation and smaller errors compared with those obtained using the conventional MM/GBSA with the normal mode method. These results may aid the rational design of more effective SHMT2 inhibitors.

Protein-Ligand Interaction Energy-Based Entropy Calculations: Fundamental Challenges For Flexible Systems

Entropy calculations represent one of the most challenging steps in obtaining the binding free energy in biomolecular systems. A novel computationally effective approach (IE) was recently proposed to calculate the entropy based on the computation of protein-ligand interaction energy directly from molecular dynamics (MD) simulations. We present a study focused on the application of this method to flexible molecular systems and compare its performance with well-established normal mode (NM) and quasiharmonic (QH) entropy calculation approaches. Our results demonstrated that the IE method is intended for calculating entropy change for binding partners in fixed conformations, as by the original definition of IE, and is not applicable to the molecular complexes in which the interacting partners undergo significant conformational changes during the binding process.