Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

PPLK+C: A Bioinformatics Tool for Predicting Peptide Ligands of Potassium Channels Based on Primary Structure Information

Potassium channels play a key role in regulating the flow of ions through the plasma membrane, orchestrating many cellular processes including cell volume regulation, hormone secretion and electrical impulse formation. Ligand peptides of potassium channels are molecules used in basic and applied research and are now considered promising alternatives in the treatment of many diseases, such as cardiovascular diseases and cancer. Currently, there are various bioinformatics tools focused on the prediction of peptides with different activities. However, none of the current tools can predict ligand peptides of potassium channels. In this work, we developed a tool called PPLK⁺C; this is the first tool that can predict peptide ligands of potassium channels. We also evaluated several amino acid molecular features and four machine-learning algorithms for the prediction of potassium channel ligand peptides: random forest, nearest neighbors, support vector machine and artificial neural network. All the biological data used in this study for training and validating models were obtained from peptides with experimentally verified activity. PPLK⁺C is a bioinformatics software written in the Python programming language, which showed a high predictive capacity with a model generated with the random forest algorithm: 0.77 sensitivity, 0.94 specificity, 0.91 accuracy and 0.70 Matthews correlation coefficient. PPLK⁺C is a novel tool with a friendly interface that can be used for the discovery of novel ligand peptides of potassium channels with high reliability, using only primary structure information.

Combinatorial library generation, molecular docking and molecular dynamics simulations for enhancing the isoflavone scaffold in phosphodiesterase inhibition

Isoflavones are listed among the most widely studied natural compounds in light of their several biological properties, one of which consists in their ability to inhibit phosphodiesterases (PDEs).

Tweaking α -Galactoceramides: Probing the Dynamical Mechanisms of Improved Recognition for Invariant Natural Killer T-cell Receptor in Cancer Immunotherapeutics

BACKGROUND

The last few decades have witnessed groundbreaking research geared towards immune surveillance mechanisms and have yielded significant improvements in the field of cancer immunotherapy. This approach narrows down on the development of therapeutic agents that either activate or enhance the recognitive function of the immune system to facilitate the destruction of malignant cells. The α -galactosylceramide derivative, KRN7000, is an immunotherapeutic agent that has gained attention due to its pharmacological ability to activate CD1d-restricted invariant natural killer T(iNKT) cells with notable potency against cancer cells in mouse models; a therapeutic success was not well replicated in human models. Dual structural modification of KRN7000 entailing the incorporation of hydrocinnamoyl ester on C6" and C4-OH truncation of the sphingoid base led to the development of AH10-7 which, interestingly, exhibited high potency in human cells.

OBJECTIVE/METHODS

Therefore, to gain molecular insights into the structural dynamics and selective mechanisms of AH10-7 for human variants, we employed integrative molecular dynamics simulations and thermodynamic calculations to investigate the inhibitory activities of KRN7000 andAH10-7 on hTCR-CD1d towards activating iNKT.

RESULTS

Interestingly, our findings revealed that AH10-7 exhibited higher affinity binding and structural effects on hTCR-CD1d, as mediated by the incorporated hydrocinnamoyl ester moiety which accounted for stronger intermolecular interactions with 'non-common' binding site residues.

CONCLUSION

Findings extracted from this study further reveal important molecular and structural perspectives that could aid in the design of novel α-GalCer derivatives for cancer immunotherapeutics.

Spontaneous Ligand Access Events to Membrane-Bound Cytochrome P450 2D6 Sampled at Atomic Resolution

The membrane-anchored enzyme Cytochrome P450 2D6 (CYP2D6) is involved in the metabolism of around 25% of marketed drugs and its metabolic performance shows a high interindividual variation. While it was suggested that ligands access the buried active site of the enzyme from the membrane, no proof from unbiased simulations has been provided to support this hypothesis. Laboratory experiments fail to capture the access process which is suspected to influence binding kinetics. Here, we applied unbiased molecular dynamics (MD) simulations to investigate the access of ligands to wild-type CYP2D6, as well as the allelic variant CYP2D6*53. In multiple simulations, substrates accessed the active site of the enzyme from the protein-membrane interface to ultimately adopt a conformation that would allow a metabolic reaction. We propose the necessary steps for ligand access and the results suggest that the increased metabolic activity of CYP2D6*53 might be caused by a facilitated ligand uptake.

Effect of Kinase Inhibiting RNase Attenuator (KIRA) Compounds on the Formation of Face-to-Face Dimers of Inositol-Requiring Enzyme 1: Insights from Computational Modeling

Inositol-requiring enzyme 1α (IRE1α) is a transmembrane dual kinase/ribonuclease protein involved in propagation of the unfolded protein response (UPR). Inositol-requiring enzyme 1α is currently being explored as a potential drug target due to the growing evidence of its role in variety of disease conditions. Upon activation, IRE1 cleaves X-box binding protein 1 (XBP1) mRNA through its RNase domain. Small molecules targeting the kinase site are known to either increase or decrease RNase activity, but the allosteric relationship between the kinase and RNase domains of IRE1α is poorly understood. Subsets of IRE1 kinase inhibitors (known as “KIRA” compounds) bind to the ATP-binding site and allosterically impede the RNase activity. The KIRA compounds are able to regulate the RNase activity by stabilizing the monomeric form of IRE1α. In the present work, computational analysis, protein–protein and protein–ligand docking studies, and molecular dynamics simulations were applied to different IRE1 dimer systems to provide structural insights into the perturbation of IRE1 dimers by small molecules kinase inhibitors that regulate the RNase activity. By analyzing structural deviations, energetic components, and the number of hydrogen bonds in the interface region, we propose that the KIRA inhibitors act at an early stage of IRE1 activation by interfering with IRE1 face-to-face dimer formation thus disabling the activation of the RNase domain. This work sheds light on the mechanism of action of KIRA compounds and may assist in development of further compounds in, for example, cancer therapeutics. The work also provides information on the sequence of events and protein–protein interactions initiating the unfolded protein response.

A Model for the Homotypic Interaction between Na+,K+-ATPase β1 Subunits Reveals the Role of Extracellular Residues 221-229 in Its Ig-Like Domain

The Na⁺, K⁺-ATPase transports Na⁺ and K⁺ across the membrane of all animal cells. In addition to its ion transporting function, the Na⁺, K⁺-ATPase acts as a homotypic epithelial cell adhesion molecule via its β₁ subunit. The extracellular region of the Na⁺, K⁺-ATPase β₁ subunit includes a single globular immunoglobulin-like domain. We performed Molecular Dynamics simulations of the ectodomain of the β₁ subunit and a refined protein-protein docking prediction. Our results show that the β₁ subunit Ig-like domain maintains an independent structure and dimerizes in an antiparallel fashion. Analysis of the putative interface identified segment Lys221-Tyr229. We generated triple mutations on YFP-β₁ subunit fusion proteins to assess the contribution of these residues. CHO fibroblasts transfected with mutant β₁ subunits showed a significantly decreased cell-cell adhesion. Association of β₁ subunits in vitro was also reduced, as determined by pull-down assays. Altogether, we conclude that two Na⁺, K⁺-ATPase molecules recognize each other by a large interface spanning residues 221–229 and 198–207 on their β₁ subunits.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

How Docking Programs Work

Protein-ligand docking simulations are of central interest for computer-aided drug design. Docking is also of pivotal importance to understand the structural basis for protein-ligand binding affinity. In the last decades, we have seen an explosion in the number of three-dimensional structures of protein-ligand complexes available at the Protein Data Bank. These structures gave further support for the development and validation of in silico approaches to address the binding of small molecules to proteins. As a result, we have now dozens of open source programs and web servers to carry out molecular docking simulations. The development of the docking programs and the success of such simulations called the attention of a broad spectrum of researchers not necessarily familiar with computer simulations. In this scenario, it is essential for those involved in experimental studies of protein-ligand interactions and biophysical techniques to have a glimpse of the basics of the protein-ligand docking simulations. Applications of protein-ligand docking simulations to drug development and discovery were able to identify hits, inhibitors, and even drugs. In the present chapter, we cover the fundamental ideas behind protein-ligand docking programs for non-specialists, which may benefit from such knowledge when studying molecular recognition mechanism.

Ligand Pathways in Nuclear Receptors

Nuclear receptors (NRs) are ligand-inducible transcription factors that play an essential role in a multitude of physiological processes as well as diseases, rendering them attractive drug targets. Crystal structures revealed the binding site of NRs to be buried in the core of the protein, with no obvious route for ligands to access this cavity. The process of ligand binding is known to be an often-neglected contributor to the efficacy of drug candidates and is thought to influence the selectivity and specificity of NRs. While experimental methods generally fail to highlight the dynamic processes of ligand access or egress on the atomistic scale, computational methods have provided fundamental insight into the pathways connecting the buried binding pocket to the surrounding environment. Methods based on molecular dynamics (MD) and Monte Carlo simulations have been applied to identify pathways and quantify their capability to transport ligands. Here, we systematically review findings of more than 20 years of research in the field, including the applied methodology and controversies. Further, we establish a unified nomenclature to describe the pathways with respect to their location relative to protein secondary structure elements and summarize findings relevant to drug design. Lastly, we discuss the effect of NR interaction partners such as coactivators and corepressors, as well as mutations on the pathways.

Trends in therapeutic antibody affinity maturation: From in-vitro towards next-generation sequencing approaches

Current advances in antibody engineering driving the strongest growth area in biotherapeutic agents development. Affinity improvement that is mainly important for biological activity and clinical efficacy of therapeutic antibodies, has still remained a challenging task. In the human body, during a course of immune response affinity maturation increase antibody activity by several rounds of somatic hypermutation and clonal selection in the germinal center. The final outputs are antibodies representing higher affinity and specificity against a particular antigen. In the realm of biotechnology, exploring of mutations which improve antibody affinity while preserving its specificity and stability is an extremely time-consuming and laborious process. Recent advances in computational algorithms and DNA sequencing technologies help researchers to redesign antibody structure to achieve desired properties such as improved binding affinity. In this review, we briefly described the principle of affinity maturation and different corresponding in vitro techniques. Also, we recapitulated the most recent advancements in the field of antibody affinity maturation including computational approaches and next-generation sequencing (NGS).

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

Engineering enzyme access tunnels

Enzymes are efficient and specific catalysts for many essential reactions in biotechnological and pharmaceutical industries. Many times, the natural enzymes do not display the catalytic efficiency, stability or specificity required for these industrial processes. The current enzyme engineering methods offer solutions to this problem, but they mainly target the buried active site where the chemical reaction takes place. Despite being many times ignored, the tunnels and channels connecting the environment with the active site are equally important for the catalytic properties of enzymes. Changes in the enzymatic tunnels and channels affect enzyme activity, specificity, promiscuity, enantioselectivity and stability. This review provides an overview of the emerging field of enzyme access tunnel engineering with case studies describing design of all the aforementioned properties. The software tools for the analysis of geometry and function of the enzymatic tunnels and channels and for the rational design of tunnel modifications will also be discussed. The combination of new software tools and enzyme engineering strategies will provide enzymes with access tunnels and channels specifically tailored for individual industrial processes.

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.

Molecular Modeling Approaches for the Prediction of Selected Pharmacokinetic Properties

Poor profiles of potential drug candidates, including pharmacokinetic properties, have been acknowledged as a significant hindrance to the development of modern therapeutics. Contemporary drug discovery and development would be incomplete without the aid of molecular modeling (in-silico) techniques, allowing the prediction of pharmacokinetic properties such as clearance, unbound fraction, volume of distribution and bioavailability. As with all models, in-silico approaches are subject to their interpretability, a trait that must be balanced with accuracy when considering the development of new methods. The best models will always require reliable data to inform them, presenting significant challenges, particularly when appropriate in-vitro or in-vivo data may be difficult or time-consuming to obtain. This article seeks to review some of the key in-silico techniques used to predict key pharmacokinetic properties and give commentary on the current and future directions of the field.