Exploring Conformational Change of Adenylate Kinase by Replica Exchange Molecular Dynamic Simulation

Toward the Prediction of Binding Events in Very Flexible, Allosteric, Multidomain Proteins

Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico, particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico, particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.

Structure-based drug design and molecular dynamics studies of an allosteric modulator targeting the protein-protein interaction site of PDK1

CONTEXT

Protein-protein interaction interfaces play a major role in cell signaling pathways. There is always a great interest in developing protein-protein interaction (PPI) inhibitors of kinases, as they are challenging due to their hydrophobicity, flat surface, specificity, potency, etc. 3 Phosphoinositide-dependent kinase-1 (PDK1), which is involved in the PI3K/PDK1/AKT pathway, is a cancer target that has gained insight for the past two decades. PDK1 possesses a protein interaction fragment (PIF) pocket, which is a well-known PPI that targets allosteric modulators. This work focusses on energy-based pharmacophore model development which on virtual screening could yield novel scaffolds towards the drug designing objective for the kind of PDK1 modulators. A novel pyrazolo pyridine molecule was identified as an allosteric modulator that binds to the PPI site. The metadynamics simulations with free energy profiles further revealed the conformational allosteric changes stimulated on the protein structure upon ligand binding. The cytotoxic activity (IC50-20 μM) of the identified compound against five different cancer cell lines and cell cycle analysis supported the anticancer activity of the identified compound.

METHODS

All the computational works were carried out by the most commonly used Schrodinger Suite software. The pharmacophore was validated by Receiver Operation Characteristics (ROC) and screening against allosteric Enamine database library. The Optimized Potential Liquid Simulations (OPLS-2005) was used to minimize the structures for molecular docking calculations, and inbuilt scoring method of ranking the compounds based on docking score and Glide energy was used. Molecular dynamics simulations were conducted by Desmond implemented in Maestro. The hit compound was purchased from Enamine and tested against different cancer cell lines by MTT assay, apoptosis by western blotting technique, and by flow cytometry analysis.

Tackling Hysteresis in Conformational Sampling: How to Be Forgetful with MEMENTO

The structure of proteins has long been recognized to hold the key to understanding and engineering their function, and rapid advances in structural biology and protein structure prediction are now supplying researchers with an ever-increasing wealth of structural information. Most of the time, however, structures can only be determined in free energy minima, one at a time. While conformational flexibility may thus be inferred from static end-state structures, their interconversion mechanisms─a central ambition of structural biology─are often beyond the scope of direct experimentation. Given the dynamical nature of the processes in question, many studies have attempted to explore conformational transitions using molecular dynamics (MD). However, ensuring proper convergence and reversibility in the predicted transitions is extremely challenging. In particular, a commonly used technique to map out a path from a starting to a target conformation called steered MD (SMD) can suffer from starting-state dependence (hysteresis) when combined with techniques such as umbrella sampling (US) to compute the free energy profile of a transition. Here, we study this problem in detail on conformational changes of increasing complexity. We also present a new, history-independent approach that we term "MEMENTO" (Morphing End states by Modelling Ensembles with iNdependent TOpologies) to generate paths that alleviate hysteresis in the construction of conformational free energy profiles. MEMENTO utilizes template-based structure modelling to restore physically reasonable protein conformations based on coordinate interpolation (morphing) as an ensemble of plausible intermediates, from which a smooth path is picked. We compare SMD and MEMENTO on well-characterized test cases (the toy peptide deca-alanine and the enzyme adenylate kinase) before discussing its use in more complicated systems (the kinase P38α and the bacterial leucine transporter LeuT). Our work shows that for all but the simplest systems SMD paths should not in general be used to seed umbrella sampling or related techniques, unless the paths are validated by consistent results from biased runs in opposite directions. MEMENTO, on the other hand, performs well as a flexible tool to generate intermediate structures for umbrella sampling. We also demonstrate that extended end-state sampling combined with MEMENTO can aid the discovery of collective variables on a case-by-case basis.

Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins

In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.

Molecular dynamics study on the behavior and binding mechanism of target protein Transgelin-2 with its agonist TSG12 for anti-asthma drug discovery

Transgelin-2 (TG2) is a novel promising therapeutic target for the treatment of asthma as it plays an important role in relaxing airway smooth muscles and reducing pulmonary resistance in asthma. The compound TSG12 is the only reported TG2 agonist with in vivo anti-asthma activity. However, the dynamic behavior and ligand binding sites of TG2 and its binding mechanism with TSG12 remain unclear. In this study, we performed 12.6 μs molecular dynamics (MD) simulations for apo-TG2 and TG2-TSG12 complex, respectively. The results suggested that the apo-TG2 has 4 most populated conformations, and that its binding of the agonist could expand the conformation distribution space of the protein. The simulations revealed 3 potential binding sites in 3 most populated conformations, one of which is induced by the agonist binding. Free energy decomposition uncovered 8 important residues with contributions stronger than -1 kcal/mol. Computational alanine scanning for the important residues by 100 ns conventional MD simulation for each mutated TG2-TSG12 complexes demonstrated that E27, R49 and F52 are essential residues for the agonist binding. These results should be helpful to understand the dynamic behavior of TG2 and its binding mechanism with the agonist TSG12, which could provide some structural insights into the novel mechanism for anti-asthma drug development.

Myofilament-associated proteins with intrinsic disorder (MAPIDs) and their resolution by computational modeling

The cardiac sarcomere is a cellular structure in the heart that enables muscle cells to contract. Dozens of proteins belong to the cardiac sarcomere, which work in tandem to generate force and adapt to demands on cardiac output. Intriguingly, the majority of these proteins have significant intrinsic disorder that contributes to their functions, yet the biophysics of these intrinsically disordered regions (IDRs) have been characterized in limited detail. In this review, we first enumerate these myofilament-associated proteins with intrinsic disorder (MAPIDs) and recent biophysical studies to characterize their IDRs. We secondly summarize the biophysics governing IDR properties and the state-of-the-art in computational tools toward MAPID identification and characterization of their conformation ensembles. We conclude with an overview of future computational approaches toward broadening the understanding of intrinsic disorder in the cardiac sarcomere.

Role of Repeated Conformational Transitions in Substrate Binding of Adenylate Kinase

The catalytic cycle of the enzyme adenylate kinase involves large conformational motions between open and closed states. A previous single-molecule experiment showed that substrate binding tends to accelerate both the opening and the closing rates and that a single turnover event often involves multiple rounds of conformational switching. In this work, we showed that the repeated conformational transitions of adenylate kinase are essential for the relaxation of incorrectly bound substrates into the catalytically competent conformation by combining all-atom and coarse-grained molecular simulations. In addition, free energy calculations based on all-atom and coarse-grained models demonstrated that the enzyme with incorrectly bound substrates has much a lower free energy barrier for domain opening compared to that with the correct substrate conformation, which may explain the the acceleration of the domain opening rate by substrate binding. The results of this work provide mechanistic understanding to previous experimental observations and shed light onto the interplay between conformational dynamics and enzyme catalysis.

Red cell adenylate kinase deficiency in China: molecular study of 2 new mutations (413G > A, 223dupA)

Background

Adenylate kinase (AK) is a monomolecular enzyme widely found in a variety of organisms. It mainly catalyses the reversible transfer of adenosine nucleotide phosphate groups and plays an important role in maintaining energy metabolism. AK deficiency is a rare genetic disorder that is related to haemolytic anaemia. Chronic haemolytic anaemia associated with AK deficiency is a rare condition, and only 14 unrelated families have been reported thus far. Moreover, only 11 mutations have been identified in the AK1 gene, with only 3 cases of psychomotor impairment.

Case presentation

The patient was a 3-year-old boy with severe haemolytic anaemia and psychomotor retardation. A molecular study of the patient’s AK gene revealed 2 different mutations: a heterozygous missense mutation in exon 6 (c.413G > A) and a heterozygous frameshift mutation in exon 5 (c.223dupA). Molecular modelling analyses indicated that AK gene inactivation resulted in a lack of AK activity. The patient recovered after regular blood transfusion therapy.

Conclusions

AK1 deficiency was diagnosed on the basis of low enzymatic activity and the identification of a mutation in the AK1 gene located on chromosome 9q. Here, we report the first case of moderate red cell AK1 deficiency associated with chronic nonspherocytic haemolytic anaemia (CNSHA) in China. The genetic mutations were confirmed by Sanger sequencing. The variants were classified as pathogenic by bioinformatics tools, such as ACMG/AMP guidelines, Mutation Taster, SIFT, MACP, REVEL and PolyPhen2.2. Based on our evidence and previous literature reports, we speculate that the site of the AK1 gene c.413G > A (p.Arg138His) mutation may be a high-frequency mutation site and the other mutation (c.223dupA) might be related to the neuropathogenicity caused by AK1 deficiency. NGS should be a part of newborn to early childhood screening to diagnose rare and poorly diagnosed genetic diseases as early as possible.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01248-2.

Probing the competitive inhibitor efficacy of frog-skin alpha helical AMPs identified against ACE2 binding to SARS-CoV-2 S1 spike protein as therapeutic scaffold to prevent COVID-19

In COVID-19 infection, the SARS-CoV-2 spike protein S1 interacts to the ACE2 receptor of human host, instigating the viral infection. To examine the competitive inhibitor efficacy of broad spectrum alpha helical AMPs extracted from frog skin, a comparative study of intermolecular interactions between viral S1 and AMPs was performed relative to S1-ACE2p interactions. The ACE2 binding region with S1 was extracted as ACE2p from the complex for ease of computation. Surprisingly, the Spike-Dermaseptin-S9 complex had more intermolecular interactions than the other peptide complexes and importantly, the S1-ACE2p complex. We observed how atomic displacements in docked complexes impacted structural integrity of a receptor-binding domain in S1 through conformational sampling analysis. Notably, this geometry-based sampling approach confers the robust interactions that endure in S1-Dermaseptin-S9 complex, demonstrating its conformational transition. Additionally, QM calculations revealed that the global hardness to resist chemical perturbations was found more in Dermaseptin-S9 compared to ACE2p. Moreover, the conventional MD through PCA and the torsional angle analyses indicated that Dermaseptin-S9 altered the conformations of S1 considerably. Our analysis further revealed the high structural stability of S1-Dermaseptin-S9 complex and particularly, the trajectory analysis of the secondary structural elements established the alpha helical conformations to be retained in S1-Dermaseptin-S9 complex, as substantiated by SMD results. In conclusion, the functional dynamics proved to be significant for viral Spike S1 and Dermaseptin-S9 peptide when compared to ACE2p complex. Hence, Dermaseptin-S9 peptide inhibitor could be a strong candidate for therapeutic scaffold to prevent infection of SARS-CoV-2.

Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic

Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

Increasing the Sampling Efficiency of Protein Conformational Change by Combining a Modified Replica Exchange Molecular Dynamics and Normal Mode Analysis

Understanding conformational change at an atomic level is significant when determining a protein functional mechanism. Replica exchange molecular dynamics (REMD) is a widely used enhanced sampling method to explore protein conformational space. However, REMD with an explicit solvent model requires huge computational resources, immensely limiting its application. In this study, a variation of parallel tempering metadynamics (PTMetaD) with the omission of solvent-solvent interactions in exchange attempts and the use of low-frequency modes calculated by normal-mode analysis (NMA) as collective variables (CVs), namely ossPTMetaD, is proposed with the aim to accelerate MD simulations simultaneously in temperature and geometrical spaces. For testing the performance of ossPTMetaD, five protein systems with diverse biological functions and motion patterns were selected, including large-scale domain motion (AdK), flap movement (HIV-1 protease and BACE1), and DFG-motif flip in kinases (p38α and c-Abl). The simulation results showed that ossPTMetaD requires much fewer numbers of replicas than temperature REMD (T-REMD) with a reduction of ∼70% to achieve a similar exchange ratio. Although it does not obey the detailed balance condition, ossPTMetaD provides consistent results with T-REMD and experimental data. The high accessibility of the large conformational change of protein systems by ossPTMetaD, especially in simulating the very challenging DFG-motif flip of protein kinases, demonstrated its high efficiency and robustness in the characterization of the large-scale protein conformational change pathway and associated free energy profile.

Computational Insights into the Conformational Accessibility and Binding Strength of SARS-CoV-2 Spike Protein to Human Angiotensin-Converting Enzyme 2

The spike protein of SARS-CoV-2 (CoV-2-S) mediates the virus entry into human cells. Experimental studies have shown the stronger binding affinity of the RBD (receptor binding domain) of CoV-2-S to angiotensin-converting enzyme 2 (ACE2) as compared to that of SARS-CoV spike (CoV-S). However, a similar or weaker binding affinity of CoV-2-S compared to that of CoV-S is observed if entire spikes are used in the bioassay. To explore the underlying mechanism, we calculated the binding affinities of the RBDs to ACE2 and simulated the transitions between ACE2-inaccessible and -accessible conformations. We found that the ACE2-accessible angle of CoV-2-S is 52.2° and that the ACE2 binding strength of CoV-2-S RBD is much stronger than that of CoV-S RBD. However, CoV-2-S has much less of an ACE2-accessible conformation and is much more difficult to shift from ACE2-inaccessible to -accessible than CoV-S, making the binding affinity of the entire protein decrease. Further analysis revealed key interactional residues for strong binding and five potential ligand-binding pockets for drug research.

An Information-Theory-Based Approach for Optimal Model Reduction of Biomolecules

In theoretical modeling of a physical system, a crucial step consists of the identification of those degrees of freedom that enable a synthetic yet informative representation of it. While in some cases this selection can be carried out on the basis of intuition and experience, straightforward discrimination of the important features from the negligible ones is difficult for many complex systems, most notably heteropolymers and large biomolecules. We here present a thermodynamics-based theoretical framework to gauge the effectiveness of a given simplified representation by measuring its information content. We employ this method to identify those reduced descriptions of proteins, in terms of a subset of their atoms, that retain the largest amount of information from the original model; we show that these highly informative representations share common features that are intrinsically related to the biological properties of the proteins under examination, thereby establishing a bridge between protein structure, energetics, and function.

D3Targets-2019-nCoV: a webserver for predicting drug targets and for multi-target and multi-site based virtual screening against COVID-19

A highly effective medicine is urgently required to cure coronavirus disease 2019 (COVID-19). For the purpose, we developed a molecular docking based webserver, namely D3Targets-2019-nCoV, with two functions, one is for predicting drug targets for drugs or active compounds observed from clinic or in vitro/in vivo studies, the other is for identifying lead compounds against potential drug targets via docking. This server has its unique features, (1) the potential target proteins and their different conformations involving in the whole process from virus infection to replication and release were included as many as possible; (2) all the potential ligand-binding sites with volume larger than 200 Å3 on a protein structure were identified for docking; (3) correlation information among some conformations or binding sites was annotated; (4) it is easy to be updated, and is accessible freely to public (https://www.d3pharma.com/D3Targets-2019-nCoV/index.php). Currently, the webserver contains 42 proteins [20 severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) encoded proteins and 22 human proteins involved in virus infection, replication and release] with 69 different conformations/structures and 557 potential ligand-binding pockets in total. With 6 examples, we demonstrated that the webserver should be useful to medicinal chemists, pharmacologists and clinicians for efficiently discovering or developing effective drugs against the SARS-CoV-2 to cure COVID-19.

Perturb-Scan-Pull: A Novel Method Facilitating Conformational Transitions in Proteins

Conformational transitions in proteins facilitate precise physiological functions. Therefore, it is crucial to understand the mechanisms underlying these processes to modulate protein function. Yet, studying structural and dynamical properties of proteins is notoriously challenging due to the complexity of the underlying potential energy surfaces (PES). We have previously developed the perturbation-response scanning (PRS) method to identify key residues that participate in the communication network responsible for specific conformational transitions. PRS is based on a residue-by-residue scan of the protein to determine the subset of residues/forces which provide the closest conformational change leading to a target conformational state, inasmuch as linear response theory applies to these motions. Here, we develop a novel method to further evaluate if conformational transitions may be triggered on the PES. We aim to study functionally relevant conformational transitions in proteins by using results obtained from PRS and feeding them as inputs to steered molecular dynamics simulations. The success and the transferability of the method are evaluated on three protein systems having different complexities of motion on the PES: calmodulin, adenylate kinase, and bacterial ferric binding protein. We find that the method captures the target conformation, while providing key residues and the optimum paths with relatively low free energy profiles.