On the Use of Interaction Entropy and Related Methods to Estimate Binding Entropies

StreaMD: the toolkit for high-throughput molecular dynamics simulations

Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.

Evaluation of an Affinity-Enhanced Anti-SARS-CoV2 Nanobody Design Workflow Using Machine Learning and Molecular Dynamics

In silico optimization of protein binding has received a great deal of attention in the recent years. Since in silico prefiltering of strong binders is fast and cheap compared to in vitro library screening methods, the advent of powerful hardware and advanced machine learning algorithms has made this strategy more accessible and preferred. These advances have already impacted the global response to pandemic threats. In this study, we proposed and tested a workflow for designing nanobodies targeting the SARS-CoV-2 spike protein receptor binding domain (S-RBD) using machine learning techniques complemented by molecular dynamics simulations. We evaluated the feasibility of this workflow using a test set of 3 different nanobodies and 2 different S-RBD variants, from in silico design and bacterial expression to binding assays of the designed nanobody mutants. We successfully designed nanobodies that were subsequently tested against both the wild-type (Wuhan type) and the delta variant S-RBD and found 2 of them to be stronger binders compared to the wild-type nanobody. We use this case study to describe both the strengths and weaknesses of this in silico assisted nanobody design strategy.

Sanggenol B, a plant bioactive, as a safer alternative to tackle cancer by antagonising human FGFR

Fibroblast Growth Receptor Factor (FGFR) are a family of proteins which are, in addition to their biological role, are involved in various pathological functions, such as cancer cellular proliferation, and metastasis. Deregulation of FGFRs at various points could result in malignancy. A conformational transition of the DFG (Asp-Phe-Gly) motif can switch the enzyme from a catalytically active (DFG-in) to an inactive (DFG-out) state. There are a few FDFR inhibitors which have received approval from the FDA, but these have adverse side effects. Hence, there is a demand for safer alternatives. With this aim, Ligand and Structure based virtual screening was carried to identify suitable lead molecule. In this process, Four Featured atom-based 3D Pharmacophore with quantitative structure-activity relationship analysis (3D-QSAR) was developed. The External validation of the hypothesis was carried invoking criteria such as Area under the ROC curve. Natural plant compound databases such as the Traditional Chinese medicine, NPACT and the ZINC Natural databases were chosen for pharmacophore filtering, which was followed by virtual screening against FGFR isoforms. The compound Sanggenol B was identified as the most suitable lead molecule. Structural stability of the protein-ligand complex and interactions of the ligand (Sanggenol B & the reference compound Ponatinib) with FGFR were analysed for 1000 ns (triplicate) by means of molecular simulation and the binding free energy was calculated using MMGBSA. Sanggenol B (PubChem CID: 15233694) binds effectively at the active site with favourable energies and is proposed as a safe alternative from a natural source.Communicated by Ramaswamy H. Sarma.

Exploring transmembrane allostery in the MexB: DB08385 variant as a promising inhibitor-like candidate against Pseudomonas aeruginosa antibiotic resistance: a computational study

Pseudomonas aeruginosa, a formidable pathogen renowned for its antimicrobial resistance, poses a significant threat to immunocompromised individuals. In this regard, the MexAB-OprM efflux pump acts as a pivotal line of defense by extruding antimicrobials from bacterial cells. The inner membrane homotrimeric protein MexB captures antibiotics and translocates them into the outer membrane OprM channel protein connected through the MexA adaptor protein. Despite extensive efforts, competitive inhibitors targeting the tight (T) protomer of the MexB protein have not received FDA approval for medical use. Over the past few years, allosteric inhibitors have become popular as alternatives to the classical competitive inhibitor-based approach because of their higher specificity, lower dosage, and reduced toxicological effects. Hence, in this study, we unveiled the existence of a transmembrane allosteric binding pocket of MexB inspired by the recent discovery of an important allosteric inhibitor, BDM88855, for the homolog AcrB protein. While repurposing BDM88855 proved ineffective in controlling the MexB loose (L) protomer, our investigation identified a promising alternative: a chlorine-containing variant of DB08385 (2-Cl DB08385 or Variant 1). Molecular dynamics simulations, including binding free energy estimation coupled with heterogeneous dielectric implicit membrane model (implicit-membrane MM/PBSA), interaction entropy (IE) analysis and potential of mean force (PMF) calculation, demonstrated Variant 1's superior binding affinity to the transmembrane pocket, displaying the highest energy barrier in the ligand unbinding process. To elucidate the allosteric crosstalk between the transmembrane and porter domain of MexB, we employed the 'eigenvector centrality' measure in the linear mutual information obtained from the protein correlation network. Notably, this study confirmed the presence of an allosteric transmembrane site in the MexB L protomer. In addition to this, Variant 1 emerged as a potent regulator of allosteric crosstalk, inducing an 'O-L intermediate state' in the MexB L protomer. This induced state might hold the potential to diminish substrate intake into the access pocket, leading to the ineffective efflux of antibiotics.

Convergence criteria for single-step free-energy calculations: the relation between the Π bias measure and the sample variance

Free energy calculations play a crucial role in simulating chemical processes, enzymatic reactions, and drug design. However, assessing the reliability and convergence of these calculations remains a challenge. This study focuses on single-step free-energy calculations using thermodynamic perturbation. It explores how the sample distributions influence the estimated results and evaluates the reliability of various convergence criteria, including Kofke's bias measure Π and the standard deviation of the energy difference ΔU, σ ΔU . The findings reveal that for Gaussian distributions, there is a straightforward relationship between Π and σ ΔU , free energies can be accurately approximated using a second-order cumulant expansion, and reliable results are attainable for σ ΔU up to 25 kcal mol-1. However, interpreting non-Gaussian distributions is more complex. If the distribution is skewed towards more positive values than a Gaussian, converging the free energy becomes easier, rendering standard convergence criteria overly stringent. Conversely, distributions that are skewed towards more negative values than a Gaussian present greater challenges in achieving convergence, making standard criteria unreliable. We propose a practical approach to assess the convergence of estimated free energies.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Binding Evolution of the Dengue Virus Envelope Against DC-SIGN: A Combined Approach of Phylogenetics and Molecular Dynamics Analyses Over 30 Years of Dengue Virus in Brazil

The Red Queen Hypothesis (RQH), derived from Lewis Carroll's "Through the Looking-Glass", postulates that organisms must continually adapt in response to each other to maintain relative fitness. Within the context of host-pathogen interactions, the RQH implies an evolutionary arms race, wherein viruses evolve to exploit hosts and hosts evolve to resist viral invasion. This study delves into the dynamics of the RQH in the context of virus-cell interactions, specifically focusing on virus receptors and cell receptors. We observed multiple virus-host systems and noted patterns of co-evolution. As viruses evolved receptor-binding proteins to effectively engage with cell receptors, cells countered by altering their receptor genes. This ongoing mutual adaptation cycle has influenced the molecular intricacies of receptor-ligand interactions. Our data supports the RQH as a driving force behind the diversification and specialization of both viral and host cell receptors. Understanding this co-evolutionary dance offers insights into the unpredictability of emerging viral diseases and potential therapeutic interventions. Future research is crucial to dissect the nuanced molecular changes and the broader ecological consequences of this ever-evolving battle. Here, we combine phylogenetic inferences, structural modeling, and molecular dynamics analyses to describe the epidemiological characteristics of major Brazilian DENV strains that circulated from 1990 to 2022 from a combined perspective, thus providing us with a more detailed picture on the dynamics of such interactions over time.

Molecular Insights into the Variability in Infection and Immune Evasion Capabilities of SARS-CoV-2 Variants: A Sequence and Structural Investigation of the RBD Domain

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continuously emerge, an increasing number of mutations are accumulating in the Spike protein receptor-binding domain (RBD) region. Through sequence analysis of various Variants of Concern (VOC), we identified that they predominantly fall within the ο lineage although recent variants introduce any novel mutations in the RBD. Molecular dynamics simulations were employed to compute the binding free energy of these variants with human Angiotensin-converting enzyme 2 (ACE2). Structurally, the binding interface of the ο RBD displays a strong positive charge, complementing the negatively charged binding interface of ACE2, resulting in a significant enhancement in the electrostatic potential energy for the ο variants. Although the increased potential energy is partially offset by the rise in polar solvation free energy, enhanced electrostatic potential contributes to the long-range recognition between the ο variant's RBD and ACE2. We also conducted simulations of glycosylated ACE2-RBD proteins. The newly emerged ο (JN.1) variant has added a glycosylation site at N-354@RBD, which significantly weakened its binding affinity with ACE2. Further, our interaction studies with three monoclonal antibodies across multiple SARS-CoV-2 strains revealed a diminished neutralization efficacy against the ο variants, primarily attributed to the electrostatic repulsion between the antibodies and RBD interface. Considering the characteristics of the ο variant and the trajectory of emerging strains, we propose that newly developed antibodies against SARS-CoV-2 RBD should have surfaces rich in negative potential and, postbinding, exhibit strong van der Waals interactions. These findings provide invaluable guidance for the formulation of future therapeutic strategies.

In silico investigation of falcipain-2 inhibition by hybrid benzimidazole-thiosemicarbazone antiplasmodial agents: A molecular docking, molecular dynamics simulation, and kinetics study

The emergence of artemisinin-resistant variants of Plasmodium falciparum necessitates the urgent search for novel antimalarial drugs. In this regard, an in silico study to screen antimalarial drug candidates from a series of benzimidazole-thiosemicarbazone hybrid molecules with interesting antiplasmodial properties and explore their falcipain-2 (FP2) inhibitory potentials has been undertaken herein. FP2 is a key cysteine protease that degrades hemoglobin in Plasmodium falciparum and is an important biomolecular target in the development of antimalarial drugs. Pharmacokinetic properties, ADMET profiles, MM/GBSA-based binding free energies, reaction mechanisms, and associated barrier heights have been investigated. DFT, molecular dynamics simulation, molecular docking, and ONIOM methods were used. From the results obtained, four 4N-substituted derivatives of the hybrid molecule (E)-2-(1-(5-chloro-1H-benzo[d]imidazol-2-yl)ethylidene)hydrazine-1-carbothioamide (1A) denoted 1B, 1C, 1D, and 1E are drug-like and promising inhibitors of FP2, exhibiting remarkably small inhibitory constants (5.94 × 10-14 - 2.59 × 10-04 n M) and favorable binding free energies (-30.32 to -17.17 kcal/mol). Moreover, the ONIOM results have revealed that 1B and possibly 1C and 1D may act as covalent inhibitors of FP2. The rate-determining step of the thermodynamically favorable covalent binding mechanism occurs across a surmountable barrier height of 24.18 kcal/mol in water and 28.42 kcal/mol in diethyl ether. Our findings are useful for further experimental investigations on the antimalarial activities of the hybrid molecules studied.

Tetrasulfide bond boosts the anti-tumor efficacy of dimeric prodrug nanoassemblies

Dimeric prodrug nanoassemblies (DPNAs) stand out as promising strategies for improving the efficiency and safety of chemotherapeutic drugs. The success of trisulfide bonds (-SSS-) in DPNAs makes polysulfide bonds a worthwhile focus. Here, we explore the comprehensive role of tetrasulfide bonds (-SSSS-) in constructing superior DPNAs. Compared to trisulfide and disulfide bonds, tetrasulfide bonds endow DPNAs with superlative self-assembly stability, prolonged blood circulation, and high tumor accumulation. Notably, the ultra-high reduction responsivity of tetrasulfide bonds make DPNAs a highly selective "tumor bomb" that can be ignited by endogenous reducing agents in tumor cells. Furthermore, we present an "add fuel to the flames" strategy to intensify the reductive stress at tumor sites by replenishing exogenous reducing agents, making considerable progress in selective tumor inhibition. This work elucidates the crucial role of tetrasulfide bonds in establishing intelligent DPNAs, alongside the combination methodology, propelling DPNAs to new heights in potent cancer therapy.

Investigation of Zika virus methyl transferase inhibitors using steered molecular dynamics

Zika virus (ZIKV) spread is considered a major public health threat by the World Health Organization (WHO). There are no vaccines or drugs available to control the infection of the Zika virus, therefore a highly effective medicinal molecule is urgently required. In this study, a computationally intensive investigation was performed to identify a potent natural compound that could inhibit the ZIKV NS5 methyltransferase. This research approach is based on target-based drug identification principles where the native inhibitor SAH (S-adenosylhomocysteine) of ZIKV NS5 methyltransferase was selected as a reference. High-throughput virtual screening and tanimoto similarity coefficient were applied to the natural compound library for ranking the potential candidates. The top five compounds were selected for interaction analysis, MD simulation, total binding free energy through MM/GBSA, and steered MD simulation. Among these compounds, Adenosine 5'-monophosphate monohydrate, Tubercidin, and 5-Iodotubercidin showed stable binding to the protein compared to the native compound, SAH. These three compounds also showed less fluctuations in RMSF in contrast to native compound. Additionally, the same interacting residues observed in SAH also made strong interactions with these three compounds. Adenosine 5'-monophosphate monohydrate and 5-Iodotubercidin had greater total binding free energies than the reference ligand. Moreover, the dissociation resistance of all three compounds was equivalent to that of the reference ligand. This study suggested binding properties of three-hit compounds that could be used to develop drugs against Zika virus infections.Communicated by Ramaswamy H. Sarma.

In silico anti-viral assessment of phytoconstituents in a traditional (Siddha Medicine) polyherbal formulation - Targeting Mpro and pan-coronavirus post-fusion Spike protein

Background and aim

Novel nature of the viral pathogen SARS-CoV-2 and the absence of standard drugs for treatment, have been a major challenge to combat this deadly infection. Natural products offer safe and effective remedy, for which traditional ethnic medicine can provide leads. An indigenous poly-herbal formulation, Kabasura Kudineer from Siddha system of medicine was evaluated here using a combination of computational approaches, to identify potential inhibitors against two anti-SARS-CoV-2 targets - post-fusion Spike protein (structural protein) and main protease (Mpro, non-structural protein).

Experimental procedure

We docked 32 phytochemicals from the poly-herbal formulation against viral post-fusion Spike glycoprotein and Mpro followed by molecular dynamics using Schrodinger software. Drug-likeness analysis was performed using machine learning (ML) approach and pkCSM.

Results

The binding affinity of the phytochemicals in Kabasura Kudineer revealed the following top-five bioactives: Quercetin > Luteolin > Chrysoeriol > 5-Hydroxy-7,8-Dimethoxyflavone > Scutellarein against Mpro target, and Gallic acid > Piperlonguminine > Chrysoeriol > Elemol > Piperine against post-fusion Spike protein target. Quercetin and Gallic acid exhibited binding stability in complexation with their respective viral-targets and favourable free energy change as revealed by the molecular dynamics simulations and MM-PBSA analysis. In silico predicted pharmacokinetic profiling of these ligands revealed appropriate drug-likeness properties.

Conclusion

These outcomes provide: (a) potential mechanism for the anti-viral efficacy of the indigenous Siddha formulation, targeting Mpro and post-fusion Spike protein (b) top bioactive lead-molecules that may be developed as natural product-based anti-viral pharmacotherapy and their pleiotropic protective effects may be leveraged to manage co-morbidities associated with COVID-19.

The molecular mechanism of the effects of the anti-neuropathic ligands on the modulation of the Sigma-2 receptor: An in-silico study

Neuropathic pain (NP) is a prevalent medical condition that lacks an effective treatment. Recently, the Sigma-2 receptor (S2R) has been proposed as a potential therapeutic target for NP. Some highly-selective S2R ligands (UKH1114, CM398, and YTD) have shown promising results in vivo, but the molecular interaction between the S2R and these ligands has been scarcely investigated. This work explores changes in the S2R upon interaction with the three mentioned ligands using in silico approaches. The results indicated that the ICL1, H1, ICL2, and ECL are the most dynamic regions of S2R in all systems. Binding interaction analysis identified amino acids with significant contribution to the binding free energy. Notably, the UKH1114-S2R simulation trajectory revealed that small alterations in the ICL1, H1, ICL2, and ECL form a new stable opening in the S2R, linking the occluded S2R binding pocket to the endoplasmic reticulum lumen, providing more evidence for the assumptions about the EBP and S2R mechanism of function. Further, the agreement between the membrane parameters in our study and experimental values confirms the validity of the MD simulations. Overall, this study provides new insights into the interaction between anti-NP ligands and the S2R.

Insights into the selectivity of a brain-penetrant CDK4/6 vs CDK1/2 inhibitor for glioblastoma used in multiple replica molecular dynamics simulations

Cyclin dependent kinases (CDKs) play an important role in cell cycle regulation and their dysfunction is associated with many cancers. That is why CDKs have been attractive targets for the treatment of cancer. Glioblastoma is a cancer caused by the aberrant expression of CDK4/6, so exploring the mechanism of the selection of CDK4/6 toward inhibitors relative to the other family members CDK1/2 is essential. In this work, multiple replica molecular dynamics (MRMD) simulations, principal component analysis (PCA), free energy landscapes (FELs), molecular mechanics Poisson-Boltzmann/Generalized Born surface area (MM-PB/GBSA) and other methods were integrated to decipher the selectively binding mechanism of the inhibitor N1J to CDK4/6 and CDK1/2. Molecular electrostatic potential (MESP) analysis provides an explanation for the N1J selectivity. Residue-based free energy decomposition reveals that most of the hot residues are located at the same location of CDKs proteins, but the different types of residues in different proteins cause changes in binding energy, which is considered as a potential developmental direction to improve the selectivity of inhibitors to CDK4/6. These results provide insights into the source of inhibitor and CDK4/6 selectivity for the future development of more selective inhibitors.Communicated by Ramaswamy H. Sarma.

Assessment of Different Parameters on the Accuracy of Computational Alanine Scanning of Protein-Protein Complexes with the Molecular Mechanics/Generalized Born Surface Area Method

Computational alanine scanning with the molecular mechanics generalized Born surface area (MM/GBSA) method constitutes a widely used approach for identifying critical residues at protein-protein interfaces. Despite its popularity, the MM/GBSA method still has certain drawbacks due to its dependence on many factors. Here, we performed a systematical study on the impact of four different parameters, namely, the internal dielectric constant, the generalized Born model, the entropic term, and the inclusion of structural waters on the accuracy of computational alanine scanning calculations with the MM/GBSA method. Our results show that the internal dielectric constant is the most critical parameter for getting accurate predictions. The introduction of entropy and interfacial water molecules decreased the quality of the predictions, while the generalized Born model had little to no effect. Considering the significance of the internal dielectric value, we proposed a methodology based on the energetic predominance of a particular set of amino acids at the protein-protein interface for selecting an appropriate value for this variable. We hope that these results serve as a guideline for future studies of protein-protein complexes using the MM/GBSA method.

In silico study of selective inhibition mechanism of S-adenosyl-L-methionine analogs for human DNA methyltransferase 3A

Epigenetic mechanisms leading to transcriptional regulation, including DNA methylation, are frequently dysregulated in diverse cancers. Interfering with aberrant DNA methylation performed by DNA cytosine methyltransferases (DNMTs) is a clinically validated approach. In particular, the selective inhibition of the de novo DNMT3A and DNMT3B enzymes, whose expression is limited to early embryogenesis, adult stem cells, and in cancers, is particularly attractive; such selectivity is likely to attenuate the dose limiting toxicity shown by current, non-selective DNMT inhibitors. We use molecular dynamics (MD) based computational analysis to study known small molecule binders of DNMT3A, then propose reversible, tight binding, and selective inhibitors that exploit the Asn¹¹⁹²/Arg⁶⁸⁸ difference between the maintenance DNMT1 and DNMT3A near the active site. A similar strategy exploiting the presence of a unique active site cysteine Cys⁶⁶⁶ is used to propose DNMT3A-selective irreversible inhibitors. We report our results of relative binding energies of the known and proposed compounds estimated using MM/GBSA and umbrella sampling (US) techniques, and our evaluation of other end-point binding free energy calculation methods for these receptors. These calculations offer insight into the potential for small molecules to selectively target the active site of DNMT3A.

Binding site hotspot map of PI3Kα and mTOR in the presence of selective and dual ATP-competitive inhibitors

The PI3K/Akt/mTOR signaling pathway plays a pivotal role in cellular metabolism, growth and survival. PI3Kα hyperactivation impairs downstream signaling, including mTOR regulation, and are linked to poor prognosis and refractory cancer treatment. To support multi-target drug discovery, we took advantage from existing PI3Kα and mTOR crystallographic structures to map similarities and differences in their ATP-binding pockets in the presence of selective or dual inhibitors. Molecular dynamics and MM/PBSA calculations were employed to study the binding profile and identify the relative contribution of binding site residues. Our analysis showed that while varying parameters of solute and solvent dielectric constant interfered in the absolute binding free energy, it had no effect in the relative per residue contribution. In all complexes, the most important interactions were observed within 3-3.5 Å from inhibitors, responding for ∼75-100% of the total calculated interaction energy. While closest residues are essential for the strength of the binding of all ligands, more distant residues seem to have a larger impact on the binding of the dual inhibitor, as observed for PI3Kα residues Phe934, Lys802 and Asp805 and, mTOR residues Leu2192, Phe2358, Leu2354, Lys2187 and Tyr2225. A detailed description of individual residue contribution in the presence of selective or dual inhibitors is provided as an effort to improve the understanding of molecular mechanisms controlling multi-target inhibition. This work provides key information to support further studies seeking the rational design of potent PI3K/mTOR dual inhibitors for cancer treatment.Communicated by Ramaswamy H. Sarma.

Is the Triggering of PD-L1 Dimerization a Potential Mechanism for Food-Derived Small Molecules in Cancer Immunotherapy? A Study by Molecular Dynamics

Using small molecules to inhibit the PD-1/PD-L1 pathway is an important approach in cancer immunotherapy. Natural compounds such as capsaicin, zucapsaicin, 6-gingerol and curcumin have been proposed to have anticancer immunologic functions by downregulating the PD-L1 expression. PD-L1 dimerization promoted by small molecules was recently reported to be a potential mechanism to inhibit the PD-1/PD-L1 pathway. To clarify the molecular mechanism of such compounds on PD-L1 dimerization, molecular docking and molecular dynamics simulations were performed. The results evidenced that these compounds could inhibit PD-1/PD-L1 interactions by directly targeting PD-L1 dimerization. Binding free energy calculations showed that capsaicin, zucapsaicin, 6-gingerol and curcumin have strong binding ability with the PD-L1 dimer, where the affinities of them follow the trend of zucapsaicin > capsaicin > 6-gingerol ≈ curcumin. Analysis by residue energy decomposition, contact numbers and nonbonded interactions revealed that these compounds have a tight interaction with the C-sheet, F-sheet and G-sheet fragments of the PD-L1 dimer, which were also involved in the interactions with PD-1. Moreover, non-polar interactions between these compounds and the key residues Ile54, Tyr56, Met115 and Ala121 play a key role in stabilizing the protein−ligand complexes in solution, in which the 4′-hydroxy-3′-methoxyphenyl group and the carbonyl group of zucapsaicin, capsaicin, 6-ginger and curcumin were significant for the complexation of small molecules with the PD-L1 dimer. The conformational variations of these complexes were further analyzed by free energy landscape (FEL) and principal component analysis (PCA) and showed that these small molecules could make the structure of dimers more stable. This work provides a mechanism insight for food-derived small molecules blocking the PD-1/PD-L1 pathway via directly targeting the PD-L1 dimerization and offers theoretical guidance to discover more effective small molecular drugs in cancer immunotherapy.

Glycosylated Flavonoid Compounds as Potent CYP121 Inhibitors of Mycobacterium tuberculosis

Due to the concerning rise in the number of multiple- and prolonged-drug-resistant (MDR and XDR) Mycobacterium tuberculosis (Mtb) strains, unprecedented demand has been created to design and develop novel therapeutic drugs with higher efficacy and safety. In this study, with a focused view on implementing an in silico drug design pipeline, a diverse set of glycosylated flavonoids were screened against the Mtb cytochrome-P450 enzyme 121 (CYP121), which is established as an approved drug target for the treatment of Mtb infection. A total of 148 glycosylated flavonoids were screened using structure-based virtual screening against the crystallized ligand, i.e., the L44 inhibitor, binding pocket in the Mtb CYP121 protein. Following this, only the top six compounds with the highest binding scores (kcal/mol) were considered for further intermolecular interaction and dynamic stability using 100 ns classical molecular dynamics simulation. These results suggested a considerable number of hydrogen and hydrophobic interactions and thermodynamic stability in comparison to the reference complex, i.e., the CYP121-L44 inhibitor. Furthermore, binding free energy via the MMGBSA method conducted on the last 10 ns interval of MD simulation trajectories revealed the substantial affinity of glycosylated compounds with Mtb CYP121 protein against reference complex. Notably, both the docked poses and residual energy decomposition via the MMGBSA method demonstrated the essential role of active residues in the interactions with glycosylated compounds by comparison with the reference complex. Collectively, this study demonstrates the viability of these screened glycosylated flavonoids as potential inhibitors of Mtb CYP121 for further experimental validation to develop a therapy for the treatment of drug-resistant Mtb strains.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

BRD4: quantum mechanical protein–ligand binding free energies using the full-protein DFT-based QM-PBSA method

Fully quantum mechanical approaches to calculating protein–ligand free energies of binding have the potential to reduce empiricism and explicitly account for all physical interactions responsible for protein–ligand binding.

gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS

Molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area is one of the most popular methods to estimate binding free energies. This method has been proven to balance accuracy and computational efficiency, especially when dealing with large systems. As a result of its popularity, several programs have been developed for performing MM/PB(GB)SA calculations within the GROMACS community. These programs, however, present several limitations. Here we present gmx_MMPBSA, a new tool to perform end-state free energy calculations from GROMACS molecular dynamics trajectories. gmx_MMPBSA provides the user with several options, including binding free energy calculations with different solvation models (PB, GB, or 3D-RISM), stability calculations, computational alanine scanning, entropy corrections, and binding free energy decomposition. Noteworthy, several promising methodologies to calculate relative binding free energies such as alanine scanning with variable dielectric constant and interaction entropy have also been implemented in gmx_MMPBSA. Two additional tools-gmx_MMPBSA_test and gmx_MMPBSA_ana-have been integrated within gmx_MMPBSA to improve its usability. Multiple illustrating examples can be accessed through gmx_MMPBSA_test, while gmx_MMPBSA_ana provides fast, easy, and efficient access to different graphics plotted from gmx_MMPBSA output files. The latest version (v1.4.3, 26/05/2021) is available free of charge (documentation, test files, and tutorials included) at https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA.