Flexibility and binding affinity in protein-ligand, protein-protein and multi-component protein interactions: limitations of current computational approaches

Synthesis, antimicrobial activity and molecular docking study of benzyl functionalized benzimidazole silver(I) complexes

In this study, a series of N-functionalized benzimidazole silver(I) complexes were prepared and characterized by FT-IR, 1H, 13C{1H} NMR spectroscopy, and elemental analysis. Synthesized N-benzylbenzimidazole silver(I) complexes were evaluated for their antimicrobial activities against bacteria Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and the fungal strains Candida albicans and Candida glabrata. The results indicated that N-alkylbenzimidazole silver(I) complexes exhibited good antimicrobial activity compared to N-alkylbenzimidazole derivatives. Especially, complex 2e presented perfect antimicrobial activity than the other complexes. The characterized molecules were optimized by DFT-based calculation methods and the optimized molecules were analyzed in detail by molecular docking methods against bacterial DNA-gyrase and CYP51. The amino acid residues detected for both target molecules are consistent with expectations, and the calculated binding affinities and inhibition constants are promising for further studies. A series of N-alkylbenzimidazole silver(I) complexes were synthesized and fully characterized by means of 1H NMR, 13C NMR, and FT-IR spectroscopies. Synthesized N-alkylbenzimidazole silver(I) complexes were investigated for their antimicrobial activities against bacteria Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and the fungal strains Candida albicans and Candida glabrata. All complexes showed better activity according to Ampicilin against Pseudomonas aeruginosa. The molecules which were firstly optimized by DFT-based calculation methods were also analyzed by molecular docking methods against DNA gyrase of E. Coli and CYP51. 338 × 190 mm (96 × 96 DPI).

Synergistic Inhibitory Effect of Quercetin and Cyanidin-3O-Sophoroside on ABCB1

The human ABCB1 (P-glycoprotein, Pgp) protein is an active exporter expressed in the plasma membrane of cells forming biological barriers. In accordance with its broad substrate spectrum and tissue expression pattern, it affects the pharmacokinetics of numerous chemotherapeutic drugs and it is involved in unwanted drug-drug interactions leading to side effects or toxicities. When expressed in tumor tissues, it contributes to the development of chemotherapy resistance in malignancies. Therefore, the understanding of the molecular details of the ligand-ABCB1 interactions is of crucial importance. In a previous study, we found that quercetin (QUR) hampers both the transport and ATPase activity of ABCB1, while cyandin-3O-sophroside (C3S) stimulates the ATPase activity and causes only a weak inhibition of substrate transport. In the current study, when QUR and C3S were applied together, both a stronger ATPase inhibition and a robust decrease in substrate transport were observed, supporting their synergistic ABCB1 inhibitory effect. Similar to cyclosporine A, a potent ABCB1 inhibitor, co-treatment with QUR and C3S shifted the conformational equilibrium to the "inward-facing" conformer of ABCB1, as it was detected by the conformation-selective UIC2 mAb. To gain deeper insight into the molecular details of ligand-ABCB1 interactions, molecular docking experiments and MD simulations were also carried out. Our in silico studies support that QUR and C3S can bind simultaneously to ABCB1. The most favourable ligand-ABCB1 interaction is obtained when C3S binds to the central substrate binding site and QUR occupies the "access tunnel". Our results also highlight that the strong ABCB1 inhibitory effect of the combined treatment with QUR and C3S may be exploited in chemotherapy protocols for the treatment of multidrug-resistant tumors or for improving drug delivery through pharmacological barriers.

An immunoinformatic approach towards development of a potent and effective multi-epitope vaccine against monkeypox virus (MPXV)

Monkeypox is a viral zoonotic disease, often transmitted to humans from animals. While the whole world is haggling with the COVID-19 pandemic, the emergence of the monkeypox virus (MPXV) arose as a new challenge to mankind. Till date, numerous cases related to the MPXV have been reported in several countries across the globe, but, its momentary distribution in the current time has left everyone in fright with increasing mortality and limited clinically approved treatments. Therefore, it is of immense importance to develop a potent and highly effective vaccine capable of inducing desired immunogenic responses against the highly contagious MPXV. Herein, using various immunoinformatic and computational biology tools, we made an attempt to develop a multi-epitope vaccine construct against the MPXV which is antigenic, non-allergen and non-toxic in nature and capable of exhibiting immunogenic behavior. The sequence of vaccine construct was designed using the proposed 4 MHC-I, 3 MHC-II and 4 B-cell epitopes linked with suitable adjuvant and linkers. The modeled structure of the vaccine construct was used to assess its interaction with the Toll-like Receptor 4 (TLR4) using ClusPro and HADDOCK. All-atoms molecular dynamics simulation of the MPXV vaccine construct-TLR4 complex followed by a high level of gene expression of the construct within the bacterial system affirmed its stability along with induction of immunogenic response within the host cell. Altogether, our immunoinformatic approach aid in the development of a stable chimeric vaccine construct against MPXV and needs further experimental validation for its immunological relevance and usefulness as a vaccine candidate.Communicated by Ramaswamy H. Sarma.

Recognition of a Flexible Protein Loop in Taspase 1 by Multivalent Supramolecular Tweezers

Many natural proteins contain flexible loops utilizing well-defined complementary surface regions of their interacting partners and usually undergo major structural rearrangements to allow perfect binding. The molecular recognition of such flexible structures is still highly challenging due to the inherent conformational dynamics. Notably, protein-protein interactions are on the other hand characterized by a multivalent display of complementary binding partners to enhance molecular affinity and specificity. Imitating this natural concept, we here report the rational design of advanced multivalent supramolecular tweezers that allow addressing two lysine and arginine clusters on a flexible protein surface loop. The protease Taspase 1, which is involved in cancer development, carries a basic bipartite nuclear localization signal (NLS) and thus interacts with Importin α, a prerequisite for proteolytic activation. Newly established synthesis routes enabled us to covalently fuse several tweezer molecules into multivalent NLS ligands. The resulting bi- up to pentavalent constructs were then systematically compared in comprehensive biochemical assays. In this series, the stepwise increase in valency was robustly reflected by the ligands' gradually enhanced potency to disrupt the interaction of Taspase 1 with Importin α, correlated with both higher binding affinity and inhibition of proteolytic activity.

Evaluation of immune evasion in SARS-CoV-2 Delta and Omicron variants

Emerging SARS-CoV-2 variants with higher transmissibility and immune escape remain a persistent threat across the globe. This is evident from the recent outbreaks of the Delta (B.1.617.2) and Omicron variants. These variants have originated from different continents and spread across the globe. In this study, we explored the genomic and structural basis of these variants for their lineage defining mutations of the spike protein through computational analysis, protein modeling, and molecular dynamic (MD) simulations. We further experimentally validated the importance of these deletion mutants for their immune escape using a pseudovirus-based neutralization assay, and an antibody (4A8) that binds directly to the spike protein's NTD. Delta variant with the deletion and mutations in the NTD revealed a better rigidity and reduced flexibility as compared to the wild-type spike protein (Wuhan isolate). Furthermore, computational studies of 4A8 monoclonal antibody (mAb) revealed a reduced binding of Delta variant compared to the wild-type strain. Similarly, the MD simulation data and virus neutralization assays revealed that the Omicron also exhibits immune escape, as antigenic beta-sheets appear to be disrupted. The results of the present study demonstrate the higher possibility of immune escape and thereby achieved better fitness advantages by the Delta and Omicron variants, which warrants further demonstrations through experimental evidences. Our study, based on in-silico computational modelling, simulations, and pseudovirus-based neutralization assay, highlighted and identified the probable mechanism through which the Delta and Omicron variants are more pathogenically evolved with higher transmissibility as compared to the wild-type strain.

Computer Simulations Aimed at Exploring Protein Aggregation and Dissociation

Protein aggregation can lead to well-defined structures that are functional, but is also the cause of the death of neuron cells in many neurodegenerative diseases. The complexity of the molecular events involved in the aggregation kinetics of amyloid proteins and the transient and heterogeneous characters of all oligomers prevent high-resolution structural experiments. As a result, computer simulations have been used to determine the atomic structures of amyloid proteins at different association stages as well as to understand fibril dissociation. In this chapter, we first review the current computer simulation methods used for aggregation with some atomistic and coarse-grained results aimed at better characterizing the early formed oligomers and amyloid fibril formation. Then we present the applications of non-equilibrium molecular dynamics simulations to comprehend the dissociation of protein assemblies.

Can the local electric field be a descriptor of catalytic activity? A case study on chorismate mutase

The current theoretical perception of enzymatic activity is highly reliant on the determination of the activation energy of the reactions, which is often calculated using computationally demanding quantum mechanical calculations. With the ever-increasing use of bioengineering techniques that produce too many variants of the same enzyme, a fast and accurate way to study the relative efficiency of enzymes is currently in high demand. Here, we propose the local electric field (LEF) of the enzyme along the reaction axis as a descriptor for the enzymatic activity using the example of chorismate mutase in its native form and several variants (R90A, R90G, and R90K/C88S). The study shows a direct correlation between the calculated enzymatic EF and the enzymatic activity for all the complexes. MD simulations of the Michaelis complex and the transition state analog (TSA) show a stabilizing force on the TSA due to the enzymatic EF. QM/MM and QM-only DFT calculations in the presence of an external electric field (EEF) oriented along the reaction axis show that the electric field can interact with the dipole moment of the TS, thereby stabilizing it and thus lowering the activation energy.

Optimization of covalent docking for organophosphates interaction with Anopheles acetylcholinesterase

Organophosphates (OPs) used as potent insecticides for malaria vector control, covalently phosphorylate the catalytic serine residue of Anopheles gambiae AChE (AgAChE) in a reaction that liberates their leaving groups. In the recent 10-year insecticide use assessment, OPs were the most frequently used World Health Organization prequalified insecticides. Molecular modelling programs are best suited to display molecular interactions between ligands and the target proteins. The docking modes that generate ligand poses closer to the binding site show high accuracy in predicting the ligand binding mode. The implicit solvation approach such as molecular mechanics-generalized born surface area (MM-GBSA) is a more reliable method to predict ligand onformations and binding affinities. Apart from covalent docking studies being scarce, current molecular docking programs do not adequately possess the covalent docking reaction algorithm to display the molecular mechanism of OPs at the AgAChE catalytic site. This results into OP docking studies commonly being conducted through noncovalent pannels. The aim of this study was to establish the optimim covalent docking system for OPs through manual customization of Schrödinger's Glide covalent docking reaction algorithm. To achieve this, a newly customized covalent reaction algorithm was assessed on a set of ligands covering aromatic, non-aromatic and hydrophobic OPs and compared to the noncovalent docking results in terms of reliability based on the reported X-ray diffraction molecular interactions and crystal poses. The study established that by virtue of omitting the well-known OP hydrolysis, noncovalent mode suggested molecular interactions that were further from the catalytic triad and could not otherwise occur when the molecule is hydrolyzed as in the customized covalent docking mode. Moreover, the MM-GBSA concurred with the optimized covalent docking in eliminating such inaccurate molecular interactions. Additionally, the covalent docking mode confined the interactions and ligand poses to the catalytic site indicating relatively high accuracy and reliability. This study reports the optimized covalent docking panel that effectively confirmed the molecular mechanisms of OPs, as well as indentifying the corresponding amino acid residues required to stabilize the aromatic, non-aromatic and hydrophobic OPs at the AgAChE catalytic site in line with the reported X-ray diffraction studies. As such, the proposed manual customization of the Schrödinger's Glide covalent docking platform can be used to reliably predict molecular interactions between OPs and AgAChE target.

Mutations of SARS-CoV-2 RBD May Alter Its Molecular Structure to Improve Its Infection Efficiency

The receptor-binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mediates the viral-host interaction and is a target for most neutralizing antibodies. Nevertheless, SARS-CoV-2 RBD mutations pose a threat due to their role in host cell entry via the human angiotensin-converting enzyme 2 receptor that might strengthen SARS-CoV-2 infectivity, viral load, or resistance against neutralizing antibodies. To understand the molecular structural link between RBD mutations and infectivity, the top five mutant RBDs (i.e., N501Y, E484K L452R, S477N, and N439K) were selected based on their recorded case numbers. These mutants along with wild-type (WT) RBD were studied through all-atom molecular dynamics (MD) simulations of 100 ns. The principal component analysis and the free energy landscape were used too. Interestingly, N501Y, N439K, and E484K mutations were observed to increase the rigidity in some RBD regions while increasing the flexibility of the receptor-binding motif (RBM) region, suggesting a compensation of the entropy penalty. However, S477N and L452R RBDs were observed to increase the flexibility of the RBM region while maintaining similar flexibility in other RBD regions in comparison to WT RBD. Therefore, both mutations (especially S477N) might destabilize the RBD structure, as loose conformation compactness was observed. The destabilizing effect of S477N RBD was consistent with previous work on S477N mutation. Finally, the free energy landscape results showed that mutations changed WT RBD conformation while local minima were maintained for all mutant RBDs. In conclusion, RBD mutations definitely impact the WT RBD structure and conformation as well as increase the binding affinity to angiotensin-converting enzyme receptor.

Analyzing In Silico the Relationship Between the Activation of the Edema Factor and Its Interaction With Calmodulin

Molecular dynamics (MD) simulations have been recorded on the complex between the edema factor (EF) of Bacilllus anthracis and calmodulin (CaM), starting from a structure with the orthosteric inhibitor adefovir bound in the EF catalytic site. The starting structure has been destabilized by alternately suppressing different co-factors, such as adefovir ligand or ions, revealing several long-distance correlations between the conformation of CaM, the geometry of the CaM/EF interface, the enzymatic site and the overall organization of the complex. An allosteric communication between CaM/EF interface and the EF catalytic site, highlighted by these correlations, was confirmed by several bioinformatics approaches from the literature. A network of hydrogen bonds and stacking interactions extending from the helix V of of CaM, and the residues of the switches A, B and C, and connecting to catalytic site residues, is a plausible candidate for the mediation of allosteric communication. The greatest variability in volume between the different MD conditions was also found for cavities present at the EF/CaM interface and in the EF catalytic site. The similarity between the predictions from literature and the volume variability might introduce the volume variability as new descriptor of allostery.

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Computational studies play an increasingly important role in chemistry and biophysics, mainly thanks to improvements in hardware and algorithms. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational simulations are mainly used to optimize promising new compounds by estimating their binding affinity to proteins. This is challenging due to the complexity of the simulated system. To assess the present and future value of simulation for drug discovery, we review key applications of advanced methods for sampling complex free-energy landscapes at near nonergodicity conditions and for estimating the rate coefficients of very slow processes of pharmacological interest. We outline the statistical mechanics and computational background behind this research, including methods such as steered molecular dynamics and metadynamics. We review recent applications to pharmacology and drug discovery and discuss possible guidelines for the practitioner. Recent trends in machine learning are also briefly discussed. Thanks to the rapid development of methods for characterizing and quantifying rare events, simulation's role in drug discovery is likely to expand, making it a valuable complement to experimental and clinical approaches.

Binding Site Switch by Dispersion Interactions: Rotational Signatures of Fenchone-Phenol and Fenchone-Benzene Complexes

Non-covalent interactions between molecules determine molecular recognition and the outcome of chemical and biological processes. Characterising how non-covalent interactions influence binding preferences is of crucial importance in advancing our understanding of these events. Here, we analyse the interactions involved in smell and specifically the effect of changing the balance between hydrogen-bonding and dispersion interactions by examining the complexes of the common odorant fenchone with phenol and benzene, mimics of tyrosine and phenylalanine residues, respectively. Using rotational spectroscopy and quantum chemistry, two isomers of each complex have been identified. Our results show that the increased weight of dispersion interactions in these complexes changes the preferred binding site in fenchone and sets the basis for a better understanding of the effect of different residues in molecular recognition and binding events.

Effect of mutation on structure, function and dynamics of receptor binding domain of human SARS-CoV-2 with host cell receptor ACE2: a molecular dynamics simulations study

Recent studies have pointed the role of angiotensin-converting enzyme-II (ACE2) in mediating the entry of SARS-CoV-2 to the host cell by binding to the receptor-binding domain (RBD) of viral spike protein, and successive priming by cellular proteases initiates the infection. SARS-CoV replication rate and disease severity is controlled by the binding affinity of RBD with ACE2. To understand, how mutations in the conserved residues of RBD affect the molecular interaction with ACE2, we generated five alanine mutants i.e. Y449A, N487A, Y489A, N501A and Y505A in the receptor binding motif (RBM) of the ACE2-RBD SARS-CoV-2 complex (PDB: 6M0J). Computational site directed mutagenesis induced dynamics in wild-type and mutant complexes were extensively studied through all-atoms molecular dynamics (MD) simulations of 150 ns. In silico mutational analysis revealed loss of important intermolecular hydrogen bonds and other non-bonded contacts, critical for molecular recognition of SARS-CoV-2 RBD to ACE2, which is well supported by saturation mutagenesis study of binding interface residues. MD simulations results showed that RBM motif is flexible, where mutant residues are relatively more mobile than corresponding wild-type residues. Global motion analysis through principal component studies revealed that RBD exhibits protuberant in-ward motion towards the human ACE2 binding interface which may be crucial for molecular interaction. Conclusively, the present finding are in congruence with previous experimental reports and provides detailed information on the structural basis of receptor binding by human SARS-CoV-2, which will crucial for the development of novel inhibitors or drugs to combat against SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Structures of the intrinsically disordered Aβ, tau and α-synuclein proteins in aqueous solution from computer simulations

Intrinsically disordered proteins (IDPs) play many biological roles in the human proteome ranging from vesicular transport, signal transduction to neurodegenerative diseases. The Aβ and tau proteins, and the α-synuclein protein, key players in Alzheimer's and Parkinson's diseases, respectively are fully disordered at the monomer level. The structural heterogeneity of the monomeric and oligomeric states and the high self-assembly propensity of these three IDPs have precluded experimental structural determination. Simulations have been used to determine the atomic structures of these IDPs. In this article, we review recent computer models to capture the equilibrium ensemble of Aβ, tau and α-synuclein proteins at different association steps in aqueous solution and present new results of the PEP-FOLD framework on α-synuclein monomer.

Tau R3-R4 Domain Dimer of the Wild Type and Phosphorylated Ser356 Sequences. I. In Solution by Atomistic Simulations

In Alzheimer's disease, neurofibrillary lesions correlate with cognitive deficits and consist of inclusions of tau protein with cross-β structure. A stable dimeric form of soluble tau has been evidenced in the cells, but its high-resolution structure is missing in solution. We know, however, that cryo-electron microscopy (c-EM) of full-length tau in the brain of an individual with AD displays a core of eight β-sheets with a C-shaped architecture spanning the R3-R4 repeat domain, while the rest of the protein is very flexible. To address the conformational ensemble of the dimer, we performed atomistic replica exchange molecular dynamics simulations on the tau R3-R4 domain starting from the c-EM configuration. We find that the wild type tau R3-R4 dimer explores elongated, U-shaped, V-shaped, and globular forms rather than the C-shape. Phosphorylation of Ser356, pSer356, is known to block the interaction between the tau protein and the amyloid-β42 peptide. Standard molecular dynamics simulations of this phosphorylated sequence for a total of 5 μs compared to its wild type counterpart show a modulation of the population of β-helices and accessible topologies and a decrease of intermediates near the fibril-like conformers.

Impact of the Astaxanthin, Betanin, and EGCG Compounds on Small Oligomers of Amyloid Aβ40 Peptide

There is experimental evidence that the astaxanthin, betanin, and epigallocatechin-3-gallate (EGCG) compounds slow down the aggregation kinetics and the toxicity of the amyloid-β (Aβ) peptide. How these inhibitors affect the self-assembly at the atomic level remains elusive. To address this issue, we have performed for each ligand atomistic replica exchange molecular dynamic (REMD) simulations in an explicit solvent of the Aβ_11-40 trimer from the U-shape conformation and MD simulations starting from Aβ_1-40 dimer and tetramer structures characterized by different intra- and interpeptide conformations. We find that the three ligands have similar binding free energies on small Aβ₄₀ oligomers but very distinct transient binding sites that will affect the aggregation of larger assemblies and fibril elongation of the Aβ₄₀ peptide.

Stability of Aβ11-40 Trimers with Parallel and Antiparallel β-Sheet Organizations in a Membrane-Mimicking Environment by Replica Exchange Molecular Dynamics Simulation

The aggregation of the amyloid (Aβ) peptide of 39-43 amino acids into plaques is observed in the brain of Alzheimer's disease (AD) patients, but the mechanisms underlying the neurotoxicity of Aβ oligomers are still elusive. One suggested initial mechanism is related to the implications of amyloid membrane interactions, but characterization of these assemblies is challenging by experimental means. In this study, we have explored the stability of a trimer of Aβ11-40 in parallel and antiparallel β-sheet structures for the wild-type sequence and its F20W mutant in a dipalmitoylphosphatidylcholine membrane using atomistic replica exchange molecular dynamic simulations. We show that both the U-shape organization and the assembly of β-hairpins are maintained in the membrane and are resistant to the mutation F20W. In contrast the models are destabilized by the F19P mutation. Overall, our results indicate that these two assemblies represent minimal seeds or nuclei for the formation of either amyloid fibrils, a variety of β-barrel pores, or various aggregates for many Aβ sequences in a membrane-mimicking environment.

Aggregation of disease-related peptides

Protein misfolding and aggregation of amyloid proteins is the fundamental cause of more than 20 diseases. Molecular mechanisms of the self-assembly and the formation of the toxic aggregates are still elusive. Computer simulations have been intensively used to study the aggregation of amyloid peptides of various amino acid lengths related to neurodegenerative diseases. We review atomistic and coarse-grained simulations of short amyloid peptides aimed at determining their transient oligomeric structures and the early and late aggregation steps.

Binding site matching in rational drug design: algorithms and applications

Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

Adequate prediction for inhibitor affinity of Aβ40 protofibril using the linear interaction energy method

The search for efficient inhibitors targeting Aβ oligomers and fibrils is an important issue in Alzheimer's disease treatment. As a consequence, an accurate and computationally cheap approach to estimate the binding affinity for many ligands interacting with Aβ peptides is very important. Here, the calculated binding free energies of 30 ligands interacting with 12Aβ11-40 peptides using the linear interaction energy (LIE) approach are found to be in good correlation with experimental data (R = 0.79). The binding affinities of these complexes are also calculated by using free energy perturbation (FEP) and molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) methods. The time-consuming FEP method provides results with similar correlation (R = 0.72), whereas MM/PBSA calculations show very low correlation with experimental data (R = 0.27). In all complexes, van der Waals interactions contribute much more than electrostatic interactions. The LIE model, which is much less time-consuming than both the FEP and MM/PBSA methods, opens the door to accurate and rapid affinity prediction of ligands with Aβ peptides and the design of new ligands.

Optimization of a Deep-Learning Method Based on the Classification of Images Generated by Parameterized Deep Snap a Novel Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR) Analysis

Numerous chemical compounds are distributed around the world and may affect the homeostasis of the endocrine system by disrupting the normal functions of hormone receptors. Although the risks associated with these compounds have been evaluated by acute toxicity testing in mammalian models, the chronic toxicity of many chemicals remains due to high cost of the compounds and the testing, etc. However, computational approaches may be promising alternatives and reduce these evaluations. Recently, deep learning (DL) has been shown to be promising prediction models with high accuracy for recognition of images, speech, signals, and videos since it greatly benefits from large datasets. Recently, a novel DL-based technique called DeepSnap was developed to conduct QSAR analysis using three-dimensional images of chemical structures. It can be used to predict the potential toxicity of many different chemicals to various receptors without extraction of descriptors. DeepSnap has been shown to have a very high capacity in tests using Tox21 quantitative qHTP datasets. Numerous parameters must be adjusted to use the DeepSnap method but they have not been optimized. In this study, the effects of these parameters on the performance of the DL prediction model were evaluated in terms of the loss in validation as an indicator for evaluating the performance of the DL using the toxicity information in the Tox21 qHTP database. The relations of the parameters of DeepSnap such as (1) number of molecules per SDF split into (2) zoom factor percentage, (3) atom size for van der waals percentage, (4) bond radius, (5) minimum bond distance, and (6) bond tolerance, with the validation loss following quadratic function curves, which suggests that optimal thresholds exist to attain the best performance with these prediction models. Using the parameter values set with the best performance, the prediction model of chemical compounds for CAR agonist was built using 64 images, at 105° angle, with AUC of 0.791. Thus, based on these parameters, the proposed DeepSnap-DL approach will be highly reliable and beneficial to establish models to assess the risk associated with various chemicals.

Self-adaptive multiscaling algorithm for efficient simulations of many-protein systems in crowded conditions

A method is described for the efficient simulation of multiprotein systems in crowded environments. It is based on an adaptive, reversible structural coarsening algorithm that preserves relevant physical features of the proteins across scales. Water is treated implicitly whereas all the other components of the aqueous solution, such as ions, cosolutes, or osmolytes, are treated in atomic detail. The focus is on the analytical adaptation of the solvent model to different levels of molecular resolutions, which allows continuous, on-the-fly transitions between scales. This permits the analytical calculation of forces during dynamics and preserves detailed balance in Monte Carlo simulations. A major computational speedup can be achieved in systems containing hundreds of proteins without cutting off the long-range interactions. The method can be combined with a self-adaptive configurational-bias sampling technique described previously, designed to detect strong, weak, or ultra-weak protein associations and shown to improve sampling efficiency and convergence. The implementation aims to simulate early stages of multimeric complexation, aggregation, or self-assembly. The method can be adopted as the basis for a more general algorithm to identify vertices, edges, and hubs in protein interaction networks or to predict critical steps in signal transduction pathways.

Generation of Pairwise Potentials Using Multidimensional Data Mining

The rapid development of molecular structural databases provides the chemistry community access to an enormous array of experimental data that can be used to build and validate computational models. Using radial distribution functions collected from experimentally available X-ray and NMR structures, a number of so-called statistical potentials have been developed over the years using the structural data mining strategy. These potentials have been developed within the context of the two-particle Kirkwood equation by extending its original use for isotropic monatomic systems to anisotropic biomolecular systems. However, the accuracy and the unclear physical meaning of statistical potentials have long formed the central arguments against such methods. In this work, we present a new approach to generate molecular energy functions using structural data mining. Instead of employing the Kirkwood equation and introducing the "reference state" approximation, we model the multidimensional probability distributions of the molecular system using graphical models and generate the target pairwise Boltzmann probabilities using the Bayesian field theory. Different from the current statistical potentials that mimic the "knowledge-based" PMF based on the 2-particle Kirkwood equation, the graphical-model-based structure-derived potential developed in this study focuses on the generation of lower-dimensional Boltzmann distributions of atoms through reduction of dimensionality. We have named this new scoring function GARF, and in this work we focus on the mathematical derivation of our novel approach followed by validation studies on its ability to predict protein-ligand interactions.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Phosphorylation promotes binding affinity of Rap-Raf complex by allosteric modulation of switch loop dynamics

The effects of phosphorylation of a serine residue on the structural and dynamic properties of Ras-like protein, Rap, and its interactions with effector protein Ras binding domain (RBD) of Raf kinase, in the presence of GTP, are investigated via molecular dynamics simulations. The simulations show that phosphorylation significantly effects the dynamics of functional loops of Rap which participate in the stability of the complex with effector proteins. The effects of phosphorylation on Rap are significant and detailed conformational analysis suggest that the Rap protein, when phosphorylated and with GTP ligand, samples different conformational space as compared to non-phosphorylated protein. In addition, phosphorylation of SER11 opens up a new cavity in the Rap protein which can be further explored for possible drug interactions. Residue network analysis shows that the phosphorylation of Rap results in a community spanning both Rap and RBD and strongly suggests transmission of allosteric effects of local alterations in Rap to distal regions of RBD, potentially affecting the downstream signalling. Binding free energy calculations suggest that phosphorylation of SER11 residue increases the binding between Rap and Raf corroborating the network analysis results. The increased binding of the Rap-Raf complex can have cascading effects along the signalling pathways where availability of Raf can influence the oncogenic effects of Ras proteins. These simulations underscore the importance of post translational modifications like phosphorylation on the functional dynamics in proteins and can be an alternative to drug-targeting, especially in notoriously undruggable oncoproteins belonging to Ras-like GTPase family.

Anti-inflammatory activity of dimethyl octenol and oleanene tetrol isolated from Trianthema decandra L

Dimethyl octenol from chloroform extract and oleanene tetrol from water extract of Trianthema decandra (TD) were isolated and characterized by using HPLC, UV, FT-IR, NMR, LC-MS and CHNS, their structure were elucidated from their respective spectral data. The anti-inflammatory activity of chloroform extract, water extract, dimethyl octenol and oleanene tetrol of T. decandra were studied and underlying cellular and molecular mechanisms of action were investigated in vitro and in vivo using macrophage-like cell line (RAW264.7 cells) and type II collagen induced arthritis mice models. Nitric oxide production was inhibited and TNF-α secretion was supressed in stimulated RAW cells treated with the chloroform extract and dimethyl octenol of T. decandra. Further, the chloroform and water extract, dimethyl octenol and oleanene tetrol inhibited protein denaturation and stabilized HRBC membranes in vitro. Reduction in inflammation as a measure of paw diameter was recorded in all the treated animals when compared to control animals. Catalase, peroxidase and glutathione peroxidase levels significantly increased in the joint tissue of treated groups. The possible mechanism of action of these compounds was studied using in silico molecular docking methods with phospholipase A₂ (PLA₂), cycloxygenase-1 (COX-1) and cycloxygenase-2 (COX-2) as targets. Among the three target proteins, the inhibition of the inflammatory protein PLA₂ and COX-2 towards dimethyl octenol and oleanene tetrol respectively. Our results contribute towards confirmation of the traditional use of TD and its compounds for the therapy of rheumatoid arthritis and other inflammatory joint disorders.

Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor

Akt1 and Akt3 are important serine/threonine-specific protein kinases involved in G2 phase required by cancer cells to maintain cell cycle and to prevent cell death. Accordingly, inhibitors of these kinases should have potent anti-cancer properties. This prompted us to use pharmacophore/QSAR modeling to identify optimal binding models and physicochemical descriptors that explain bioactivity variation within a set of 74 diverse Akt3 inhibitors. Two successful orthogonal pharmacophores were identified and further validated using receiver operating characteristic (ROC) curve analyses. The pharmacophoric models and associated QSAR equation were applied to screen the national cancer institute (NCI) list of compounds for new Akt3 inhibitors. Six hits showed significant experimental anti-Akt3 IC₅₀ values, out of which one compound exhibited dual low micromolar anti-Akt1 and anti-Akt3 inhibitory profiles.

Vitamin D receptor (VDR) non-synonymous single nucleotide polymorphisms (nsSNPs) affect the calcitriol drug response - A theoretical insight

Pharmacogenetics and pharmacogenomics have become presumptive with advancements in next-generation sequencing technology. In complex diseases, distinguishing the feasibility of pathogenic and neutral disease-causing variants is a time consuming and expensive process. Recent drug research and development processes mainly rely on the relationship between the genotype and phenotype through Single nucleotide polymorphisms (SNPs). The SNPs play an indispensable role in elucidating the individual's vulnerability to disease and drug response. The understanding of the interplay between these leads to the establishment of personalized medicine. In order to address this issue, we developed a computational pipeline of vitamin D receptor (VDR) for SNP centered study by application of elegant molecular docking and molecular dynamics simulation approaches. In a few SNPs the volume of the binding cavities has increased in mutant structures when compared to the wild type, indicating a weakening in interaction (699.1 ų in wild type Vs. 738.8 in Leu230Val, 820.7 ų in Arg247Leu). This also differently reflected in the H-bond interactions and binding free energies -169.93 kcal/mol (wild type) Vs -156.43 kcal/mol (R154W), -105.49 kcal/mol (R274L) in Leu230Val and Arg247Leu respectively. Although we could not find noteworthy changes in the binding free energies and binding pocket in the remaining mutations, the H-bond interactions made these SNPs deleterious. Thus, we further analyzed the H-bond interactions and distances using molecular dynamics (MD) simulation studies.

Molecular Docking at a Glance

The current chapter introduces different aspects of molecular docking technique in order to give an overview to the readers about the topics which will be dealt with throughout this volume. Like many other fields of science, molecular docking studies has experienced a lagging period of slow and steady increase in terms of acquiring attention of scientific community as well as its frequency of application, followed by a pronounced era of exponential expansion in theory, methodology, areas of application and performance due to developments in related technologies such as computational resources and theoretical as well as experimental biophysical methods. In the following sections the evolution of molecular docking will be reviewed and its different components including methods, search algorithms, scoring functions, validation of the methods, and area of applications plus few case studies will be touched briefly.

A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces

T-cell receptors (TCRs) have emerged as a new class of therapeutics, most prominently for cancer where they are the key components of new cellular therapies as well as soluble biologics. Many studies have generated high affinity TCRs in order to enhance sensitivity. Recent outcomes, however, have suggested that fine manipulation of TCR binding, with an emphasis on specificity may be more valuable than large affinity increments. Structure-guided design is ideally suited for this role, and here we studied the generality of structure-guided design as applied to TCRs. We found that a previous approach, which successfully optimized the binding of a therapeutic TCR, had poor accuracy when applied to a broader set of TCR interfaces. We thus sought to develop a more general purpose TCR design framework. After assembling a large dataset of experimental data spanning multiple interfaces, we trained a new scoring function that accounted for unique features of each interface. Together with other improvements, such as explicit inclusion of molecular flexibility, this permitted the design new affinity-enhancing mutations in multiple TCRs, including those not used in training. Our approach also captured the impacts of mutations and substitutions in the peptide/MHC ligand, and recapitulated recent findings regarding TCR specificity, indicating utility in more general mutational scanning of TCR-pMHC interfaces.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Detection and characterization of nonspecific, sparsely populated binding modes in the early stages of complexation

A method is proposed to study protein-ligand binding in a system governed by specific and nonspecific interactions. Strong associations lead to narrow distributions in the proteins configuration space; weak and ultraweak associations lead instead to broader distributions, a manifestation of nonspecific, sparsely populated binding modes with multiple interfaces. The method is based on the notion that a discrete set of preferential first-encounter modes are metastable states from which stable (prerelaxation) complexes at equilibrium evolve. The method can be used to explore alternative pathways of complexation with statistical significance and can be integrated into a general algorithm to study protein interaction networks. The method is applied to a peptide-protein complex. The peptide adopts several low-population conformers and binds in a variety of modes with a broad range of affinities. The system is thus well suited to analyze general features of binding, including conformational selection, multiplicity of binding modes, and nonspecific interactions, and to illustrate how the method can be applied to study these problems systematically. The equilibrium distributions can be used to generate biasing functions for simulations of multiprotein systems from which bulk thermodynamic quantities can be calculated.

Multiple binding modes for palmitate to barley lipid transfer protein facilitated by the presence of proline 12

Molecular dynamics simulations have been used to characterise the binding of the fatty acid ligand palmitate in the barley lipid transfer protein 1 (LTP) internal cavity. Two different palmitate binding modes (1 and 2), with similar protein-ligand interaction energies, have been identified using a variety of simulation strategies. These strategies include applying experimental protein-ligand atom-atom distance restraints during the simulation, or protonating the palmitate ligand, or using the vacuum GROMOS 54B7 force-field parameter set for the ligand during the initial stages of the simulations. In both the binding modes identified the palmitate carboxylate head group hydrogen bonds with main chain amide groups in helix A, residues 4 to 19, of the protein. In binding mode 1 the hydrogen bonds are to Lys 11, Cys 13, and Leu 14 and in binding mode 2 to Thr 15, Tyr 16, Val 17, Ser 24 and also to the OH of Thr 15. In both cases palmitate binding exploits irregularity of the intrahelical hydrogen-bonding pattern in helix A of barley LTP due to the presence of Pro 12. Simulations of two variants of barley LTP, namely the single mutant Pro12Val and the double mutant Pro12Val Pro70Val, show that Pro 12 is required for persistent palmitate binding in the LTP cavity. Overall, the work identifies key MD simulation approaches for characterizing the details of protein-ligand interactions in complexes where NMR data provide insufficient restraints.

Chemical specificity and conformational flexibility in proteinase-inhibitor interaction: scaffolds for promiscuous binding

One of the most important roles of proteins in cellular milieu is recognition of other biomolecules including other proteins. Protein-protein complexes are involved in many essential cellular processes. Interfaces of protein-protein complexes are traditionally known to be conserved in evolution and less flexible than other solvent interacting tertiary structural surface. But many examples are emerging where these features do not hold good. An understanding of inter-play between flexibility and sequence conservation is emerging, providing a fresh dimension to the paradigm of sequence-structure-function relationship. The functional manifestation of the inter-relation between sequence conservation and flexibility of interface is exemplified in this review using proteinase-inhibitor protein complexes.

Toward in silico biomolecular manipulation through static modes: atomic scale characterization of HIV-1 protease flexibility

Probing biomolecular flexibility with atomic-scale resolution is a challenging task in current computational biology for fundamental understanding and prediction of biomolecular interactions and associated functions. This paper makes use of the static mode method to study HIV-1 protease considered as a model system to investigate the full biomolecular flexibility at the atomic scale, the screening of active site biomechanical properties, the blind prediction of allosteric sites, and the design of multisite strategies to target deformations of interest. Relying on this single calculation run of static modes, we demonstrate that in silico predictive design of an infinite set of complex excitation fields is reachable, thanks to the storage of the static modes in a data bank that can be used to mimic single or multiatom contact and efficiently anticipate conformational changes arising from external stimuli. All along this article, we compare our results to data previously published and propose a guideline for efficient, predictive, and custom in silico experiments.

Structure-based design of small-molecule protein–protein interaction modulators: the story so far

As the pivotal role of protein–protein interactions in cell growth, transcriptional activity, intracellular trafficking, signal transduction and pathological conditions has been assessed, experimental and in silico strategies have been developed to design protein–protein interaction modulators. State-of-the-art structure-based design methods, mainly pharmacophore modeling and docking, which have succeeded in the identification of enzyme inhibitors, receptor agonists and antagonists, and new tools specifically conceived to target protein–protein interfaces (e.g., hot-spot and druggable pocket prediction methods) have been applied in the search for small-molecule protein–protein interaction modulators. Many successful applications of structure-based design approaches that, despite the challenge of targeting protein–protein interfaces with small molecules, have led to the identification of micromolar and submicromolar hits are reviewed here.