Exploring the Ligand Binding/Unbinding Pathway by Selectively Enhanced Sampling of Ligand in a Protein-Ligand Complex

MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity

The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a critical role for it to bind in the target. It is a big issue to figure out the bioactive conformation in deep learning without the co-crystal structure or highly precise molecular simulations. In this study, we developed a deep learning framework of Multi-Conformation Point Network (MCPNET) to construct classification and regression models, respectively, based on electrostatic potential distributions on vdW surfaces around multiple conformations of the compound using a dataset of compounds with developmental toxicity in zebrafish embryo. MCPNET applied 3D multi-conformational surface point cloud to extract the molecular features for model training, which may be critical for capturing the structural diversity of compounds. The models achieved an accuracy of 85 % on the classification task and R2 of 0.66 on the regression task, outperforming traditional machine learning models and other deep learning models. The key feature of our model is its interpretability with the component visualization to identify the factors contributing to the prediction and to understand the compound action mechanism. MCPNET may predict the conformation quietly close to the bioactive conformation of a compound by attention-based multi-conformation pooling mechanism. Our results demonstrated the potential of deep learning based on 3D molecular representations in accurately predicting developmental toxicity. The source code is publicly available at https://github.com/Superlit-CC/MCPNET.

Complete reconstruction of dasatinib unbinding pathway from c-Src kinase by supervised molecular dynamics simulation method; assessing efficiency and trustworthiness of the method

Over the past years, rational drug design has gained lots of attention since employing it gave the world targeted therapy and more effective treatment solutions. Structure-based drug design (SBDD) is an excellent tool in rational drug design that takes advantage of accurate methods such as unbiased molecular dynamics (UMD) simulation for designing and optimizing molecular entities by understanding the binding and unbinding pathways of the binders. Supervised molecular dynamics (SuMD) simulation is a branch of UMD in which long-duration simulations are turned into short simulations, called replica, and a specific parameter is monitored throughout the simulation. In this work, we utilized this strategy to reconstruct the unbinding pathway of the anticancer drug dasatinib from its target protein, the c-Src kinase. Several unbinding events with valuable details were achieved. Then, to assess the efficiency and trustworthiness of the SuMD method, the unbinding pathway was also reconstructed by conventional UMD simulation, which uncovered some of the limitations of this method, such as limited sampling of the active site and finding the metastable states in the unbinding pathway. Furthermore, in times like these, when the world is desperate to find treatments for the Covid-19 disease, we think these methods are of exceptional value.Communicated by Ramaswamy H. Sarma.

Advances in computational methods for ligand binding kinetics

Binding kinetic parameters can be correlated with drug efficacy, which in recent years led to the development of various computational methods for predicting binding kinetic rates and gaining insight into protein-drug binding paths and mechanisms. In this review, we introduce and compare computational methods recently developed and applied to two systems, trypsin-benzamidine and kinase-inhibitor complexes. Methods involving enhanced sampling in molecular dynamics simulations or machine learning can be used not only to predict kinetic rates, but also to reveal factors modulating the duration of residence times, selectivity, and drug resistance to mutations. Methods which require less computational time to make predictions are highlighted, and suggestions to reduce the error of computed kinetic rates are presented.

Seafood Paramyosins as Sources of Anti-Angiotensin-Converting-Enzyme and Anti-Dipeptidyl-Peptidase Peptides after Gastrointestinal Digestion: A Cheminformatic Investigation

Paramyosins, muscle proteins occurring exclusively in invertebrates, are abundant in seafoods. The potential of seafood paramyosins (SP) as sources of anti-angiotensin-converting-enzyme (ACE) and anti-dipeptidyl-peptidase (DPP-IV) peptides is underexplored. This in silico study investigated the release of anti-ACE and anti-DPP-IV peptides from SP after gastrointestinal (GI) digestion. We focused on SP of the common octopus, Humboldt squid, Japanese abalone, Japanese scallop, Mediterranean mussel, Pacific oyster, sea cucumber, and Whiteleg shrimp. SP protein sequences were digested on BIOPEP-UWM, followed by identification of known anti-ACE and anti-DPP-IV peptides liberated. Upon screening for high-GI-absorption, non-allergenicity, and non-toxicity, shortlisted peptides were analyzed via molecular docking and dynamic to elucidate mechanisms of interactions with ACE and DPP-IV. Potential novel anti-ACE and anti-DPP-IV peptides were predicted by SwissTargetPrediction. Physicochemical and pharmacokinetics of peptides were predicted with SwissADME. GI digestion liberated 2853 fragments from SP. This comprised 26 known anti-ACE and 53 anti-DPP-IV peptides exhibiting high-GI-absorption, non-allergenicity, and non-toxicity. SwissTargetPrediction predicted three putative anti-ACE (GIL, DL, AK) and one putative anti-DPP-IV (IAL) peptides. Molecular docking found most of the anti-ACE peptides may be non-competitive inhibitors, whereas all anti-DPP-IV peptides likely competitive inhibitors. Twenty-five nanoseconds molecular dynamics simulation suggests the stability of these screened peptides, including the three predicted anti-ACE and one predicted anti-DPP-IV peptides. Seven dipeptides resembling approved oral-bioavailable peptide drugs in physicochemical and pharmacokinetic properties were revealed: AY, CF, EF, TF, TY, VF, and VY. In conclusion, our study presented in silico evidence for SP being a promising source of bioavailable and safe anti-ACE and anti-DPP-IV peptides following GI digestions.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Decoding molecular mechanism underlying binding of drugs to HIV-1 protease with molecular dynamics simulations and MM-GBSA calculations

HIV-1 protease (PR) is thought to be efficient targets of anti-AIDS drug design. Molecular dynamics (MD) simulations and multiple post-processing analysis technologies were applied to decipher molecular mechanism underlying binding of three drugs Lopinavir (LPV), Nelfinavir (NFV) and Atazanavir (ATV) to the PR. Binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) suggest that compensation between binding enthalpy and entropy plays a vital role in binding of drugs to PR. Dynamics analyses show that binding of LPV, NFV and ATV highly affects structural flexibility, motion modes and dynamics behaviour of the PR, especially for two flaps. Computational alanine scanning and interaction network analysis verify that although three drugs have structural difference, they share similar binding modes to the PR and common interaction clusters with the PR. The current findings also confirm that residues located interaction clusters, such as Asp25/Asp25', Gly27/Gly27', Ala28/Ala28', Asp29, Ile47/Ile47', Gly49/Gly49', Ile50/Ile50', Val82/Val82' and Ile84/Ile84, can be used as efficient targets of clinically available inhibitors towards the PR.

Comparative analysis of the unbinding pathways of antiviral drug Indinavir from HIV and HTLV1 proteases by supervised molecular dynamics simulation

Determining the unbinding pathways of potential small molecule compounds from their target proteins is of great significance for designing efficacious treatment solutions. One of these potential compounds is the approved HIV-1 protease inhibitor, Indinavir, which has a weak effect on the HTLV-1 protease. In this work, by employing the SuMD method, we reconstructed the unbinding pathways of Indinavir from HIV and HTLV-1 proteases to compare and understand the mechanism of the unbinding and to discover the reasons for the lack of inhibitory activity of Indinavir against the HTLV-1 protease. We achieved multiple unbinding events from both HIV and HTLV-1 proteases in which the RMSD values of Indinavir reached over 40 Å. Also, we found that the mobility and fluctuations of the flap region are higher in the HTLV-1 protease, making the drug less stable. We realized that critically positioned aromatic residues such as Trp98/Trp98' and Phe67/Phe67' in the HTLV-1 protease could make strong π-Stacking interactions with Indinavir in the unbinding pathway, which are unfavorable for the stability of Indinavir in the active site. The details found in this study can make a reasonable explanation for the lack of inhibitory activity of this drug against HTLV-1 protease. We believe the details discovered in this work can help design more effective and selective inhibitors for the HTLV-1 protease.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Secondary Metabolic Profile as a Tool for Distinction and Characterization of Cultivars of Black Pepper (Piper nigrum L.) Cultivated in Pará State, Brazil

This study evaluated the chemical compositions of the leaves and fruits of eight black pepper cultivars cultivated in Pará State (Amazon, Brazil). Hydrodistillation and gas chromatography-mass spectrometry were employed to extract and analyze the volatile compounds, respectively. Sesquiterpene hydrocarbons were predominant (58.5-90.9%) in the cultivars "Cingapura", "Equador", "Guajarina", "Iaçará", and "Kottanadan", and "Bragantina", "Clonada", and "Uthirankota" displayed oxygenated sesquiterpenoids (50.6-75.0%). The multivariate statistical analysis applied using volatile composition grouped the samples into four groups: γ-Elemene, curzerene, and δ-elemene ("Equador"/"Guajarina", I); δ-elemene ("Iaçará"/"Kottanadan"/"Cingapura", II); elemol ("Clonada"/"Uthirankota", III) and α-muurolol, bicyclogermacrene, and cubebol ("Bragantina", IV). The major compounds in all fruit samples were monoterpene hydrocarbons such as α-pinene, β-pinene, and limonene. Among the cultivar leaves, phenolics content (44.75-140.53 mg GAE·g^-1 FW), the enzymatic activity of phenylalanine-ammonia lyase (20.19-57.22 µU·mL^-1), and carotenoids (0.21-2.31 µg·mL^-1) displayed significant variations. Due to black pepper's susceptibility to Fusarium infection, a molecular docking analysis was carried out on Fusarium protein targets using each cultivar's volatile components. F. oxysporum endoglucanase was identified as the preferential protein target of the compounds. These results can be used to identify chemical markers related to the susceptibility degree of black pepper cultivars to plant diseases prevalent in Pará State.

Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics

Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with <1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD_Dual simulations were also <1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

Effect of Protein Flexibility from Coarse-Grained Elastic Network Parameterizations on the Calculation of Free Energy Profiles of Ligand Binding

The characterization of the affinity and binding mechanism of specific molecules to a protein active site is scientifically and industrially relevant for many applications. In principle, this information can be obtained using molecular dynamics (MD) simulations by calculating the free energy profile of the process. However, this is a computationally demanding calculation. Currently, coarse-grained (CG) force fields are very well implemented for MD simulations of biomolecular systems. These computationally efficient force fields are a major advantage to the study of large model systems and/or those requiring long simulation times. The Martini model is currently one of the most popular CG force fields for these systems. For the specific case of protein simulations, to correctly maintain the macromolecular three-dimensional structure, the Martini model needs to include an elastic network (EN). In this work, the effect of protein flexibility, as induced by three EN models compatible with the Martini force field, was tested on the calculation of free energy profiles for protein-ligand binding. The EN models used were ElNeDyn, GoMartini, and GEN. The binding of triolein (TOG) and triacetin (TAG) to a lipase protein (thermomyces lanuginosa lipase-TLL) was used as a case study. The results show that inclusion of greater flexibility in the CG parameterization of proteins is of high importance in the calculation of the free energy profiles of protein-ligand systems. However, care must be taken in order to avoid unjustified large protein deformations. In addition, due to molecular flexibility there may be no absolute need for the center of the ligand to reach the center of the protein-binding site. The calculation of the energy profile to a distance of about 0.5 nm from the active site center can be sufficient to differentiate the affinity of different ligands to a protein.