Computational analysis and prediction of the binding motif and protein interacting partners of the Abl SH3 domain

Advances in the labelling and selective manipulation of synapses

Synapses are highly specialized neuronal structures that are essential for neurotransmission, and they are dynamically regulated throughout the lifetime. Although accumulating evidence indicates that these structures are crucial for information processing and storage in the brain, their precise roles beyond neurotransmission are yet to be fully appreciated. Genetically encoded fluorescent tools have deepened our understanding of synaptic structure and function, but developing an ideal methodology to selectively visualize, label and manipulate synapses remains challenging. Here, we provide an overview of currently available synapse labelling techniques and describe their extension to enable synapse manipulation. We categorize these approaches on the basis of their conceptual bases and target molecules, compare their advantages and limitations and propose potential modifications to improve their effectiveness. These methods have broad utility, particularly for investigating mechanisms of synaptic function and synaptopathy.

Robust and predictive 3D-QSAR models for predicting the activities of novel oxadiazole derivatives as multifunctional anti-Alzheimer agents

In recent years, Alzheimer disease (AD) as a neurodegenerative disorder has been increasing annually with the aging of the global population, therefore, development of novel anti-AD drugs is imperative. Studies have proven that glycogen synthase kinase-3β (GSK-3β) is a pivotal factor in the development of AD. Therefore, GSK-3β inhibitors would provide powerful means to treat the disorders, such as AD. To in-depth study the structure-activity relationship of a series of oxadiazole derivatives as multifunctional anti-Alzheimer agents, computational three dimensional quantitative structure-activity relationship (3D-QSAR) studies, molecular docking and molecular dynamics were conducted. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods were conducted to build up the 3D-QSAR models, and exhibited significant results (R cv 2 = 0.692, R pred 2 = 0.6885/CoMFA, R cv 2 = 0.696, R pred 2 = 0.6887/CoMSIA). The accuracy of the 3D-QSAR models was validated by external validation and applicability domain analysis. The derived contour maps provided structural information for designing novel compounds to improve the inhibitory activities. Additionally, molecular docking and molecular dynamics were also employed to investigate the bonding interactions and stability of this series of inhibitors in the active site of GSK-3β, and the results revealed that the importance of residues Ile62, Asn64 Val70, Tyr128, Val129 and Leu182 for ligand binding to the receptor GSK-3β. All the results would be of great help for the discovery of new GSK-3β agents that can solve the problem of AD.

Mechanistic Insights into How the Single Point Mutation Change the Autoantibody Repertoire

A recent study showed that just one point mutation F33 to Y in the complementarity-determining region 1 of heavy chain (H-CDR1) could lead to the auto-antibody losing its DNA binding ability. However, the potential molecular mechanisms have not been well elucidated. In this study, we investigated how the antibody lost the DNA binding ability caused by mutation F33 to Y in the H-CDR1. We found that the electrostatic force was not the primary driving force for the interaction between anti-DNA antibodies and the antigen single strand DNA (ssDNA), and that the H-CDR2 largely contributed to the binding of antigen ssDNA, even larger than H-CDR1. The H-F33Y mutation could increase the hydrogen-bond interaction but impair the pi-pi stacking interaction between the antibody and ssDNA. We further found that F33H, W98H and Y95L in the wiletype antibody could form the stable pi-pi stacking interaction with the nucleotide bases of ssDNA. However, the Y33 in mutant could not form the parallel sandwich pi-pi stacking interaction with the ssDNA. To further confirm the importance of pi-pi stacking, the wildtype antibody and the mutants (F33YH, F33AH, W98AH and Y95AL) were experimentally expressed in CHO cells and purified, and the results from ELISA clearly showed that all the mutants lost the ssDNA binding ability. Taken together, our findings may not only deepen the understanding of the underlying interaction mechanism between autoantibody and antigen, but also broad implications in the field of antibody engineer.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Systematic profiling of Taxol resistance and sensitivity to tubulin missence mutations at molecular and cellular levels

Taxol (paclitaxel) is the first approved microtubule-stabilizing agent (MSA) by binding stoichiometrically to tubulin, which is considered to be one of the most significant advances in first-line chemotherapy against diverse tumors. However, a large number of residue missence mutations harboring in the tubulin have been observed to cause acquired drug resistance, largely limiting the clinical application of Taxol and its analogs in chemotherapy. A systematic investigation of the intermolecular interactions between the Taxol and various tubulin mutants would help to establish a comprehensive picture of drug response to tubulin mutations in clinical treatment of cancer, and to design new MSA agents with high potency and selectivity to overcome drug resistance. In this study, we described an integration of in silico analysis and in vitro assay (iSiV) to profile Taxol against a panel of 149 clinically observed, cancer-associated missence mutations in β-tubulin at molecular and cellular levels, aiming to a systematic understanding of molecular mechanism and biological implication underlying drug resistance and sensitivity conferring from tubulin mutations. It is revealed that the Taxol-resistant mutations can be classified into three types: (I) nonbonded interaction broken due to mutation, (II) steric hindrance caused by mutation, and (III) conformational change upon mutation. In addition, we identified three new Taxol-resistant mutations (C239Y, T274I, and R320P) that can largely reduce the binding affinity of Taxol to tubulin at molecular level, in which the T274I and R320P were observed to considerably impair the antitumor activity of Taxol at cellular level. Moreover, a novel drug-susceptible mutation (M363T) was also identified, which improves Taxol affinity by 2.6-fold and decreases Taxol antitumor EC50 values from 29.4 to 18.7 μM.

In silico studies of a novel scaffold of benzoxazole derivatives as anticancer agents by 3D-QSAR, molecular docking and molecular dynamics simulations

The vascular endothelial growth factor receptor-2 kinases (VEGFR-2) expressed on tumor cells and vessels are attractive targets for cancer treatment. Potent inhibitors for the VEGFR-2 receptor are novel strategies to develop anti-cancer drugs. In this work, template ligand-based 3D-QSAR studies were performed on a series of benzoxazole derivatives toward different cell lines (HepG2, HCT-116 and MCF-7). Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques were used to generate 3D-QSAR models. Good predictability was derived for the optimal CoMFA models (HepG2: R_cv² = 0.509, R_pred² = 0.5128; HCT-116: R_cv² = 0.574, R_pred² = 0.5597; MCF-7: R_cv² = 0.568, R_pred² = 0.5057) and CoMSIA models (HepG2: R_cv² = 0.711, R_pred² = 0.6198; HCT-116: R_cv² = 0.531, R_pred² = 0.5804; MCF-7: R_cv² = 0.669, R_pred² = 0.6577). In addition, the contour maps derived from CoMFA and CoMSIA models were also generated to illustrate the relationship between different fields and the inhibitory activities. Moreover, molecular docking and molecular dynamics (MD) simulations were also conducted to understand the binding modes and the potential interactions between the receptor and the inhibitors. Some key residues (Leu35, Val43, Lys63, Leu84, Gly117, Leu180 and Asp191) were pointed out for stabilizing the inhibitors in the binding pocket. The binding free energies for the inhibitors agreed well with the experimental inhibitory activity and indicated that steric, electrostatic and hydrogen bond interactions are the main driving force for inhibitor-receptor binding. Overall, a good consistency between theoretical 3D-SQAR and molecular docking and MD simulation studies would provide directions for the design of new candidates, avoiding time-consuming and costly synthesis and biological evaluations. On the whole, the results derived from this study could expand the understanding of benzoxazole derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity targeting VEGFR-2.

Heterogeneous and Allosteric Role of Surface Hydration for Protein-Ligand Binding

Atomistic-level understanding of surface hydration mediating protein-protein interactions and ligand binding has been a challenge due to the dynamic nature of water molecules near the surface. We develop a computational method to evaluate the solvation free energy based on the density map of the first hydration shell constructed from all-atom molecular dynamics simulation and use it to examine the binding of two intrinsically disordered ligands to their target protein domain. One ligand is from the human protein, and the other is from the 1918 Spanish flu virus. We find that the viral ligand incurs a 6.9 kcal/mol lower desolvation penalty upon binding to the target, which is consistent with its stronger binding affinity. The difference arises from the spatially fragmented and nonuniform water density profiles of the first hydration shell. In particular, residues that are distal from the ligand-binding site contribute to a varying extent to the desolvation penalty, among which the "entropy hotspot" residues contribute significantly. Thus, ligand binding alters hydration on remote sites in addition to affecting the binding interface. The nonlocal effect disappears when the conformational motion of the protein is suppressed. The present results elucidate the interplay between protein conformational dynamics and surface hydration. Our approach of measuring the solvation free energy based on the water density of the first hydration shell is tolerant of the conformational fluctuation of protein, and we expect it to be applicable to investigating a broad range of biomolecular interfaces.

Lactose and Galactose Promote the Crystallization of Human Galectin-10

Galectin-10 (Gal-10) forms Charcot-Leyden crystals (CLCs), which play a key role in the symptoms of asthma and allergies and some other diseases. Gal-10 has a carbohydrate-binding site; however, neither the Gal-10 dimer nor the CLCs can bind sugars. To investigate the monomer-dimer equilibrium of Gal-10, high-performance size-exclusion chromatography (SEC) was employed to separate serial dilutions of Gal-10 with and without carbohydrates. We found that both the dimerization and crystallization of Gal-10 were promoted by lactose/galactose binding. A peak position shift for the monomer was observed after treatment with either lactose or galactose, implying that the polarity of the monomer was reduced by lactose/galactose binding. Further experiments indicated that alkaline conditions of pH 8.8 mimicked the lactose/galactose-binding environment, and the time interval between monomers and dimers in the chromatogram decreased from 0.8 min to 0.4 min. Subsequently, the electrostatic potential of the Gal-10 monomers was computed. After lactose/galactose binding, the top side of the monomer shifted from negatively charged to electrically neutral, allowing it to interact with the carbohydrate-binding site of the opposing subunit during dimerization. Since lactose/galactose promotes the crystallization of Gal-10, our findings implied that dairy-free diets (free of lactose/galactose) might be beneficial to patients with CLC-related diseases.

Identification of the structural features of quinazoline derivatives as EGFR inhibitors using 3D-QSAR modeling, molecular docking, molecular dynamics simulations and free energy calculations

Epidermal growth factor receptor (EGFR) is a promising target for the treatment of different types of malignant tumors. Therefore, a combined molecular modeling study was performed on a series of quinazoline derivatives as EGFR inhibitors. The optimum ligand-based CoMFA and CoMSIA models showed reliable and satisfactory predictability (with R²_cv=0.681, R²_ncv=0.844, R²_pred=0.8702 and R²_cv=0.643, R²_ncv=0.874, R²_pred=0.6423). The derived contour maps provide structural features to improve inhibitory activity. Furthermore, the contour maps, molecular docking, and molecular dynamics (MD) simulations have good consistency, illustrating that the derived models are reliable. In addition, MD simulations and binding free energy calculations were also carried out to understand the conformational fluctuations at the binding pocket of the receptor. The results indicate that hydrogen bond, hydrophobic and electrostatic interactions play significant roles on activity and selectivity. Furthermore, amino acids Val31, Lys50, Thr95, Leu149 and Asp160 are considered as essential residues to participate in the ligand-receptor interactions. Overall, this work would offer reliable theoretical basis for future structural modification, design and synthesis of novel EGFR inhibitors with good potency.Communicated by Ramaswamy H. Sarma.

Rational design of stapled helical peptides as antidiabetic PPARγ antagonists to target coactivator site by decreasing unfavorable entropy penalty instead of increasing favorable enthalpy contribution

Peroxisome proliferator-activated receptor γ (PPARγ) is a ligand-activated transcription factor belonging to the nuclear hormone receptor and has been exploited as a well-established druggable target for the treatment of diabetes mellitus (DM). Traditionally, small-molecule compounds have been developed to attack at the ligand site and Ser273 phosphorylation site of PPARγ. In this study, we derived helical peptide segments from the LXXLL motif region of coactivator proteins as antidiabetic PPARγ antagonists, which were expected to competitively disrupt the native interaction between PPARγ and its cognate coactivators by rebinding at PPARγ coactivator site. Structural analysis, dynamics simulation and energetics dissection revealed that these peptides cannot be well folded into active helical structure when splitting from the protein context of their parent coactivators and exhibit a large flexibility and intrinsic disorder in the free state, which would, therefore, incur a considerable entropy penalty upon rebinding to PPARγ. Hydrocarbon stapling strategy was employed to constrain these free coactivator peptides into ordered helical conformation, thus largely minimizing unfavorable entropy penalty but having only a moderate effect on favorable enthalpy contribution. The computational findings were further substantiated by fluorescence-based assays; the binding affinity of three potent SRC1, NCoA6 and p300 coactivator peptides to PPARγ was observed to be improved by 7.2-fold, 4.2-fold and 5.7-fold upon the stapling, which were also measured to have an efficient competitive potency with their unstapled counterparts for PPARγ coactivator site, with CC₅₀ = 0.096, 0.12 and 0.18 μM, respectively.

Structure-based analysis and rational design of human peroxiredoxin-1's C-terminus-derived peptides to target sulfiredoxin-1 in pancreatic cancer

Human peroxiredoxin (PRX) family of antioxidant enzymes reduces hydrogen peroxide and alkyl hydroperoxide involved in the redox signaling, among which the widely documented PRX1 is a versatile molecule regulating cell growth, differentiation and apoptosis, and has been implicated in the tumorigensis of pancreatic cancer. In this study, we systematically examined the complex crystal structure of PRX1 with its cognate interacting partner sulfiredoxin-1 (SRX1) at molecular level, and found that the PRX1-SRX1 association is a typical peptide-mediated protein-protein interaction, where a 18-mer C-terminal tail (CTT) segment of PRX1 was identified to be primarily responsible for the interaction, which contributes ~80% and ~ 55% to the total binding potency of SRX1 to PRX1 monomer and homodimer, respectively. We also demonstrated that the SRX1 exhibits a strong global selectivity for PRX1 CTT tail over other PRX family proteins. Next, the intermolecular interaction between PRX1 CTT tail and SRX1 was investigated at structural, energetic and dynamic levels, from which a 9-mer core region of PRX1 CTT tail was defined as the SRX1-binding hotspot. Biophysical assays substantiated that the CTT and CTTc peptides (out of PRX1 protein context) can bind in an independent manner and possess a close affinity to SRX1. Based on the CTTc sketch a computational combinatorial library consisting of 216 designed peptide derivatives was rationally generated, from which the top-5 hits were found to have comparable affinity with CTT peptide and improved affinity relative to CTTc peptide. They can be used as structurally reduced lead molecular entities to further develop new peptidic agents for therapeutic purpose to disrupt the native PRX1-SRX1 interaction by competing with PRX1 CTT tail for the peptide-binding pocket of SRX1.

Combination of multiple methods and views for recognition, transportation, and structure-guided modification of lysine-specific demethylase phenylcyclopropylamine inhibitor

Lysine-Specific Demethylase 1 (LSD1) is a typical histone-specific demethylase, which plays an important role in protein methylation modification. It is a member of the amine oxidase family (MAO) that specifically removes methyl groups from monomethylated H3K4, dimethylated H3K4 and H3K9 sites associated with tumorigenesis. Phenylcyclopropylamine derivatives are a class of specific LSD1 inhibitors, drawing attention due to their high efficiency. Here, extensive molecular dynamics (MD) simulations are combined with a three-dimensional quantitative structure-activity relationship (3D-QSAR) in order to design a new phenylcyclopropylamine inhibitor from multiple perspectives. In a ligand-oriented point of view, a 3D-QSAR model with comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) can be built based on the 55 phenylcyclopropylamine compounds targeting LSD1 obtained experimentally. The aromatic and piperazine rings are identified as the potential key groups regulating the activity of the compounds. In an interaction-oriented view, the representative compound is defined with the highest inhibitory efficiency. The binding and delivery mechanism and conformational dependence of activity, including channel and dynamic properties, are studied using RAMD and umbrella sampling technologies. The direct hydrogen bond and conjugated interactions are identified as a major driving force in this procedure. The dominant region of the phenylcyclopropylamine influences the free energy and detects the key residues in recognition and delivery. On the basis of both the ligand and interaction, a series of new inhibitor structures were designed, and two of them showed better efficiency. In order to select the inhibitor with a longer residence time, a comparison is conducted between the designed inhibitors and the experimentally obtained inhibitor from the perspective of static binding and dynamic delivery properties. This work creates new guidance for the phenylcyclopropylamine inhibitor design of LDS1 by combining the ligand and receptor, considering both static and dynamic properties. This scheme could be applied in other inhibitor design systems.

Interleukin-1α, Interleukin-1β and Interleukin-1 Receptor Antagonist Share a Common U-shaped Recognition Epitope on Interleukin-1 Receptor Surface

Interleukin-1 (IL-1) plays a central role in the regulation of immune and inflammatory responses. There are two forms of IL-1 agonists (IL-1α and IL-1β) and one form of IL-1 antagonist (IL-1Ra); they share a similar binding mode to IL-1 receptor (IL-1R) but exhibit opposite biological functions on the receptor. In this study, the intermolecular interactions of IL-1R receptor with IL-1α, IL-1β and IL-1Ra ligands were systematically investigated at structural, energetic and dynamic levels. It was found that the receptor primarily adopts a U-shaped, double-stranded and linear/conformational-hybrid epitope to commonly interact with the three ligands. The epitope covers a common protein segment (residues 107-127), which is fully located within in the C2T2 subsdomain of IL-1R extracellular domain (ECD) and contributes ~40% to the total binding energy of IL-1R/ligand association. The epitope is natively folded into an ordered conformation in IL-1R protein context but would become largely disordered out of the context. Here, we adopted a disulfide bridge to staple U-shaped epitope-derived peptides, which can be effectively constrained into a native-like conformation and thus exhibit an improved affinity to ligands as compared to their unstapled counterpart, with affinity increase by up to ~15-fold. These disulfide bridges were designed to point out of ligand/peptide complex interface and thus would not disrupt the direct complex interaction. Energetic decomposition imparted that the stapling has only a modest influence on the interaction enthalpy and desolvation effect of ligand/peptide binding, but can substantially reduce entropy penalty upon the binding. For a peptide, the stapling-addressed entropic reduction can be roughly regarded as a constant, which only improves peptide affinity to these ligands, but does not change peptide selectivity over different ligands. This article is protected by copyright. All rights reserved.

Predicting residues involved in anti-DNA autoantibodies with limited neural networks

Abstract

Computer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for functional prediction. A potentially strong target for computational approach is autoimmune antibodies, which are the result of broken tolerance in the immune system where it cannot distinguish “self” from “non-self” resulting in attack of its own structures (proteins and DNA, mainly). The information on structure, function, and pathogenicity of autoantibodies may assist in engineering RVD against autoimmune diseases. Current computational approaches exploit large datasets curated with extensive domain knowledge, most of which include the need for many resources and have been applied indirectly to problems of interest for DNA, RNA, and monomer protein binding. We present a novel method for discovering potential binding sites. We employed long short-term memory (LSTM) models trained on FASTA primary sequences to predict protein binding in DNA-binding hydrolytic antibodies (abzymes). We also employed CNN models applied to the same dataset for comparison with LSTM. While the CNN model outperformed the LSTM on the primary task of binding prediction, analysis of internal model representations of both models showed that the LSTM models recovered sub-sequences that were strongly correlated with sites known to be involved in binding. These results demonstrate that analysis of internal processes of LSTM models may serve as a powerful tool for primary sequence analysis.

Graphical abstract

Genomic stratification and differential natural selection signatures among human norovirus genogroup II isolates

Noroviruses (NoVs), which are members of the family Caliciviridae, are the most common cause of gastroenteritis in humans. Ten NoV genogroups have been reported so far. Of these, genogroup II (GII) is the most prevalent, and it causes serious infections worldwide. The complete genome sequences of NoV GII isolates from different geographical regions were retrieved from the public database. The model-based clustering approach, implemented in the STRUCTURE resource, was employed for assessment of genetic composition. The MEGA X and IQ Tree tools were used for phylogenetic analysis. Genome-wide natural selection analysis was performed using maximum-likelihood-based methods. The demographic features of NoV GII genome sequences were assessed using the BEAST package. All of the NoV GII sequences initially clustered into two main subpopulations at significant K = 2, where the genotype GII.4 samples clearly split from the rest of the genotypes. This indicates a marked genetic distinction between norovirus GII.4 and non-GII.4 samples. Phylogenetic analysis showed the presence of five distinct subclades for genotype GII.2 and seven subclades for GII.4 samples. Several isolates with admixed ancestry were identified that constituted distinct subclusters in the phylogenetic tree. No continental-specific genetic distinctions were observed among the NoV GII samples. Significant genomic signatures of both positive and negative natural selection were identified across the NoV GII genes. A differential pattern of positive selection signals was inferred between the GII.4 and non-GII.4 genotypes. The demographic analysis revealed an increase in the effective population size of NoV GII during 2009-2010, followed by a rapid fall in 2015.

Interleukin-25 recognition by its unique receptor IL-17Rb via two discrete linear and cyclic epitopes

Interleukin-17 (IL-17) is a family of pro-inflammatory cytokines and has been involved in the pathogenesis of chronic inflammatory and autoimmune diseases. The IL-17E, also known as IL-25, is a distinct member of this family that binds to its unique receptor IL-17Rb to induce the activation of nuclear factor kappa-light-chain enhancer of activated B cells. Here, we systematically examined the intermolecular recognition and association of IL-25 with IL-17Rb and demonstrated that the IL-25 primarily adopts two discrete linear and cyclic epitopes to interact with IL-17Rb. The two epitopes are separately located in the monomers 1 and 2 of IL-25 homodimer and cover sequences ¹²⁵ DPRGNSELLYHN¹³⁶ and ⁷⁷ ELDRDLNRLPQDLY⁹⁰ . They totally contribute 71.6% binding energy to the full-length IL-25. The linear epitope targets a site spanning over the extracellular fnIIID1 and fnIIID2 domains of IL-17Rb, while the cyclic epitope primarily binds at the fnIIID1 domain. In addition, we also found that the linear and cyclic epitopes are natively folded into ordered single-stranded and double-stranded conformations in IL-25 protein context, respectively, but would become largely disordered when splitting from the context to be free peptides, which, however, cannot bind effectively to IL-17Rb as them in the native state. In this respect, we extended the cyclic epitope to cover the whole IL-25 double-stranded region and added a disulfide bridge across its two strands at three selected anchor residue pairs. It is revealed that the disulfide-stapled peptides can be constrained into a native-like conformation and thus exhibit an improved binding potency to IL-17Rb as compared to their unstapled counterpart.

E484K mutation in SARS-CoV-2 RBD enhances binding affinity with hACE2 but reduces interactions with neutralizing antibodies and nanobodies: Binding free energy calculation studies

The pandemic of the COVID-19 disease caused by SARS-CoV-2 has led to more than 200 million infections and over 4 million deaths worldwide. The progress in the developments of effective vaccines and neutralizing antibody therapeutics brings hopes to eliminate the threat of COVID-19. However, SARS-CoV-2 continues to mutate, and several new variants have been emerged. Among the various naturally-occurring mutations, the E484K mutation shared by many variants attracted serious concerns, which may potentially enhance the receptor binding affinity and reduce the immune response. In the present study, the molecular mechanism behind the impacts of E484K mutation on the binding affinity of the receptor-binding domain (RBD) with the receptor human angiotensin-converting enzyme 2 (hACE2) was investigated by using the molecular dynamics (MD) simulations combined with the molecular mechanics-generalized Born surface area (MMGBSA) method. Our results indicate that the E484K mutation results in more favorable electrostatic interactions compensating the burial of the charged and polar groups upon the binding of RBD with hACE2, which significantly improves the RBD-hACE2 binding affinity. Besides that, the E484K mutation also causes the conformational rearrangements of the loop region containing the mutant residue, which leads to tighter binding interface of RBD with hACE2 and formation of some new hydrogen bonds. The tighter binding interface and the new hydrogen bonds formation also contribute to the improved binding affinity of RBD to the receptor hACE2. In addition, six neutralizing antibodies and nanobodies complexed with RBD were selected to explore the effects of E484K mutation on the recognition of these antibodies to RBD. The simulation results show that the E484K mutation significantly reduces the binding affinities to RBD for most of the studied neutralizing antibodies/nanobodies, and the decrease in the binding affinities is mainly owing to the unfavorable electrostatic interactions caused by the mutation. Our studies revealed that the E484K mutation may improve the binding affinity between RBD and the receptor hACE2, implying more transmissibility of the E484K-containing variants, and weaken the binding affinities between RBD and the studied neutralizing antibodies/nanobodies, indicating reduced effectiveness of these antibodies/nanobodies. Our results provide valuable information for the effective vaccine development and antibody/nanobody drug design.

Computational design and experimental substantiation of conformationally constrained peptides from the complex interfaces of transcriptional enhanced associate domains with their cofactors in gastric cancer

Transcriptional enhanced associate domains (Teads) are the downstream effectors of the hippo signaling pathway and have been recognized as attractive druggable targets of gastric cancer. The biological function of Teads is regulated by diverse cofactors. In this study, the intermolecular interactions of Teads with their cognate cofactors were systematically characterized at structural, thermodynamic and dynamic levels. The Teads possess a double-stranded helical hairpin that is surrounded by three independent structural elements β-sheet, α-helix and Ω-loop of cofactor proteins and plays a central role in recognition and association with cofactors. A number of functional peptides were split from the hairpin region at Tead-cofactor complex interfaces, which, however, cannot maintain in native conformation without the support of protein context and would therefore incur a considerable entropy penalty upon competitively rebinding to the interfaces. Here, we further used disulfide and hydrocarbon bridges to cyclize and staple the hairpin and helical peptides, respectively. The chemical modification strategies were demonstrated to effectively constrain peptide conformation into active state and to largely reduce peptide flexibility in free state, thus considerably improving their affinity. Since the cyclization and stapling only minimize the indirect entropy cost but do not influence the direct enthalpy effect upon peptide binding, the designed conformationally constrained peptides can retain in their native selectivity over different cofactors. This is particularly interesting because it means that the cyclized/stapled, affinity-improved peptides can specifically compete with their parent Teads for the cofactor arrays as they share consistent target specificity.

Insight into the LFA-1/SARS-CoV-2 Orf7a Complex by Protein-Protein Docking, Molecular Dynamics, and MM-GBSA Calculations

In the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome, open reading frames (ORFs) encode for viral accessory proteins. Among these, Orf7a structurally resembles the members of the immunoglobulin (Ig) superfamily and intracellular adhesion molecules (ICAMs), in particular. ICAMs are involved in integrin binding through lymphocyte function-associated antigen 1 (LFA-1). Based on such considerations and on previous findings on SARS-CoV, it has been postulated that the formation of the LFA-1/Orf7a complex could contribute to SARS-CoV-2 infectivity and pathogenicity. With the current work, we aim at providing insight into this macromolecular assembly, taking advantage of the recently reported SARS-CoV-2 Orf7a structure. Protein-protein docking, molecular dynamics (MD) simulations, and a Molecular Mechanical-Generalized Born Surface Area (MM-GBSA)-based stage were enrolled to provide refined models.

Structure-based derivation and optimization of YAP-like coactivator-derived peptides to selectively target TEAD family transcription factors by hydrocarbon stapling and cyclization

Human transcriptional enhanced associate domain (TEAD) family consists of four paralogous transcription factors that function to modulate gene expression by interacting with YAP-like coactivators and have been recognized as potential therapeutic targets of diverse diseases including lung cancer and gastric tumor. Here, we attempt to explore the systematic interaction profile between the 4 TEAD proteins and the peptides derived from the binding sites of 8 known YAP-like coactivators, in order to analyze the binding affinity and recognition specificity of these peptides toward the TEAD family, and to design hydrocarbon-stapled/cyclized peptides that can target the specific interaction profile for each coactivator. Structural, energetic, and dynamic investigations of TEAD-coactivator interactions reveal that the coactivators adopt three independent secondary structure regions (β-strand, α-helix, and Ω-loop) to surround on the surface of TEAD proteins, in which the α-helical and Ω-loop regions are primarily responsible for the interactions. Five α-helical peptides and four Ω-loop peptides are derived from the 8 YAP-like coactivators, and their systematic binding profile toward the 4 TEAD proteins is created, and hydrocarbon stapling and cyclization strategies are employed to constrain the free α-helical and Ω-loop peptides into their native conformations, respectively, thus effectively promoting peptide binding to TEADs. The all-hydrocarbon and disulfide bridges are designed to point out the TEAD-peptide complex interface, which would not disrupt the direct intermolecular interaction between the TEAD and peptide. Therefore, the stapling and cyclization only improve peptide binding affinity to these TEADs, but do not alter peptide recognition specificity over different TEADs.

Molecular-level understanding of the hTAS2R1 receptor-bitter tasting tetra-peptide binding: a structural biology study based on computational approaches

Effective computational approaches for bitter-tasting peptides have been developed and analyzed in the present work.

Computational Design of PDZ-Peptide Binding

This chapter describes two computational methods for PDZ-peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for conformational sampling with a Poisson-Boltzmann (PB) Linear Interaction Energy for scoring. A new CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative binding free energies. A detailed protocol is described based on the Proteus CPD software. The medium-throughput approach can be performed with standard MD and PB software, such as NAMD and Charmm. For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around 0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of parameter fitting before it can be applied, and its transferability to other protein families is still untested.

Molecular Dynamics Revealing a Detour-Forward Release Mechanism of Tacrine: Implication for the Specific Binding Characteristics in Butyrylcholinesterase

Butyrylcholinesterase (BChE) is a non-specific enzyme with clinical pharmacological and toxicological significance, which was a renewed interest as therapeutic target in Alzheimer's disease (AD) nowadays. Here, all-atom molecular dynamics simulations of butyrylcholinesterase with tacrine complex were designed to characterize inhibitor binding modes, strengths, and the hydrogen-bond dependent non-covalent release mechanism. Four possible release channels were identified, and the most favorable channel was determined by random acceleration molecular dynamics molecular dynamics (RAMD MD) simulations. The thermodynamic and dynamic properties as well as the corresponding Detour-forward delivery mechanism were determined according to the classical molecular dynamics (MD) simulations accompanied with umbrella sampling. The free energy barrier of the tacrine release process for the most beneficial pathway is about 10.95 kcal/mol, which is related to the non-covalent interactions from the surrounding residues, revealing the specific binding characteristics in the active site. The residues including Asp70, Ser79, Trp82, Gly116, Thr120, Tyr332, and His438 were identified to play major roles in the stabilization of tacrine in the pocket of BChE, where hydrogen bonding and π-π interactions are significant factors. Tyr332 and Asp70, which act as gate keepers, play crucial roles in the substrate delivery. The present results provide a basic understanding for the ligand transport mechanism depending on the BChE enzymatic environment, which is useful for the design of BChE inhibitors in the future.

Predicted Hotspot Residues Involved in Allosteric Signal Transmission in Pro-Apoptotic Peptide-Mcl1 Complexes

Mcl1 is a primary member of the Bcl–2 family—anti–apoptotic proteins (AAP)—that is overexpressed in several cancer pathologies. The apoptotic regulation is mediated through the binding of pro-apoptotic peptides (PAPs) (e.g., Bak and Bid) at the canonical hydrophobic binding groove (CBG) of Mcl1. Although all PAPs form amphipathic α-helices, their amino acid sequences vary to different degree. This sequence variation exhibits a central role in the binding partner selectivity towards different AAPs. Thus, constructing a novel peptide or small organic molecule with the ability to mimic the natural regulatory process of PAP is essential to inhibit various AAPs. Previously reported experimental binding free energies (BFEs) were utilized in the current investigation aimed to understand the mechanistic basis of different PAPs targeted to mMcl1. Molecular dynamics (MD) simulations used to estimate BFEs between mMcl1—PAP complexes using Molecular Mechanics-Generalized Born Solvent Accessible (MMGBSA) approach with multiple parameters. Predicted BFE values showed an excellent agreement with the experiment (R² = 0.92). The van–der Waals (ΔG_vdw) and electrostatic (ΔG_ele) energy terms found to be the main energy components that drive heterodimerization of mMcl1—PAP complexes. Finally, the dynamic network analysis predicted the allosteric signal transmission pathway involves more favorable energy contributing residues. In total, the results obtained from the current investigation may provide valuable insights for the synthesis of a novel peptide or small organic inhibitor targeting Mcl1.

nAPOLI: A Graph-Based Strategy to Detect and Visualize Conserved Protein-Ligand Interactions in Large-Scale

Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions. Nonetheless, there is not an all-in-one tool that couples large-scale statistical, visual, and interactive analysis of conserved protein-ligand interactions. Therefore, we propose nAPOLI (Analysis of PrOtein-Ligand Interactions), a web server that combines large-scale analysis of conserved interactions in protein-ligand complexes at the atomic-level, interactive visual representations, and comprehensive reports of the interacting residues/atoms to detect and explore conserved non-covalent interactions. We demonstrate the potential of nAPOLI in detecting important conserved interacting residues through four case studies: two involving a human cyclin-dependent kinase 2 (CDK2), one related to ricin, and other to the human nuclear receptor subfamily 3 (hNR3). nAPOLI proved to be suitable to identify conserved interactions according to literature, as well as highlight additional interactions. Finally, we illustrate, with a virtual screening ligand selection, how nAPOLI can be widely applied in structural biology and drug design. nAPOLI is freely available at bioinfo.dcc.ufmg.br/napoli/.

HawkDock: a web server to predict and analyze the protein-protein complex based on computational docking and MM/GBSA

Protein–protein interactions (PPIs) play an important role in the different functions of cells, but accurate prediction of the three-dimensional structures for PPIs is still a notoriously difficult task. In this study, HawkDock, a free and open accessed web server, was developed to predict and analyze the structures of PPIs. In the HawkDock server, the ATTRACT docking algorithm, the HawkRank scoring function developed in our group and the MM/GBSA free energy decomposition analysis were seamlessly integrated into a multi-functional platform. The structures of PPIs were predicted by combining the ATTRACT docking and the HawkRank re-scoring, and the key residues for PPIs were highlighted by the MM/GBSA free energy decomposition. The molecular visualization was supported by 3Dmol.js. For the structural modeling of PPIs, HawkDock could achieve a better performance than ZDOCK 3.0.2 in the benchmark testing. For the prediction of key residues, the important residues that play an essential role in PPIs could be identified in the top 10 residues for ∼81.4% predicted models and ∼95.4% crystal structures in the benchmark dataset. To sum up, the HawkDock server is a powerful tool to predict the binding structures and identify the key residues of PPIs. The HawkDock server is accessible free of charge at http://cadd.zju.edu.cn/hawkdock/.

Entropy Hotspots for the Binding of Intrinsically Disordered Ligands to a Receptor Domain

Proline-rich motifs (PRMs) are widely used for mediating protein-protein interactions with weak binding affinities. Because they are intrinsically disordered when unbound, conformational entropy plays a significant role for the binding. However, residue-level differences of the entropic contribution in the binding of different ligands remain not well understood. We use all-atom molecular dynamics simulation and the maximal information spanning tree formalism to analyze conformational entropy associated with the binding of two PRMs, one from the Abl kinase and the other from the nonstructural protein 1 of the 1918 Spanish influenza A virus, to the N-terminal SH3 (nSH3) domain of the CrkII protein. Side chains of the stably folded nSH3 experience more entropy change upon ligand binding than the backbone, whereas PRMs involve comparable but heterogeneous entropy changes among the backbone and side chains. In nSH3, two conserved nonpolar residues forming contacts with the PRM experience the largest side-chain entropy loss. In contrast, the C-terminal charged residues of PRMs that form polar contacts with nSH3 experience the greatest side-chain entropy loss, although their "fuzzy" nature is attributable to the backbone that remains relatively flexible. Thus, residues that form high-occupancy contacts between nSH3 and PRM do not reciprocally contribute to entropy loss. Furthermore, certain surface residues of nSH3 distal to the interface with PRMs gain entropy, indicating a nonlocal effect of ligand binding. Comparing between the PRMs from cAbl and nonstructural protein 1, the latter involves a larger side-chain entropy loss and forms more contacts with nSH3. Consistent with experiments, this indicates stronger binding of the viral ligand at the expense of losing the flexibility of side chains, whereas the backbone experiences less entropy loss. The entropy "hotspots" as identified in this study will be important for tuning the binding affinity of various ligands to a receptor.

Biophysical prediction of protein-peptide interactions and signaling networks using machine learning

In mammalian cells, much of signal transduction is mediated by weak protein-protein interactions between globular peptide-binding domains (PBDs) and unstructured peptidic motifs in partner proteins. The number and diversity of these PBDs (over 1,800 are known), low binding affinities, and sensitivity of binding properties to minor sequence variation represent a substantial challenge to experimental and computational analysis of PBD specificity and the networks PBDs create. Here we introduce a bespoke machine learning approach, hierarchical statistical mechanical modelling (HSM), capable of accurately predicting the affinities of PBD-peptide interactions across multiple protein families. By synthesizing biophysical priors within a modern machine learning framework, HSM outperforms existing computational methods and high-throughput experimental assays. HSM models are interpretable in familiar biophysical terms at three spatial scales: the energetics of protein-peptide binding, the multi-dentate organization of protein-protein interactions, and the global architecture of signaling networks.

Molecular basis for the interaction between human choline kinase alpha and the SH3 domain of the c-Src tyrosine kinase

Choline kinase alpha is a 457-residue protein that catalyzes the reaction between ATP and choline to yield ADP and phosphocholine. This metabolic action has been well studied because of choline kinase's link to cancer malignancy and poor patient prognosis. As the myriad of x-ray crystal structures available for this enzyme show, chemotherapeutic drug design has centered on stopping the catalytic activity of choline kinase and reducing the downstream metabolites it produces. Furthermore, these crystal structures only reveal the catalytic domain of the protein, residues 80-457. However, recent studies provide evidence for a non-catalytic protein-binding role for choline kinase alpha. Here, we show that choline kinase alpha interacts with the SH3 domain of c-Src. Co-precipitation assays, surface plasmon resonance, and crystallographic analysis of a 1.5 Å structure demonstrate that this interaction is specific and is mediated by the poly-proline region found N-terminal to the catalytic domain of choline kinase. Taken together, these data offer strong evidence that choline kinase alpha has a heretofore underappreciated role in protein-protein interactions, which offers an exciting new way to approach drug development against this cancer-enhancing protein.

Molecular modeling and rational design of hydrocarbon-stapled/halogenated helical peptides targeting CETP self-binding site: Therapeutic implication for atherosclerosis

The human plasma cholesteryl ester transfer protein (CETP) collects triglycerides from very-/low-density lipoproteins (V/LDL) and exchanges them for cholesteryl esters from high-density lipoproteins (HDL), which has recognized as an important therapeutic target for atherosclerosis. The protein has a C-terminal amphipathic α-helix that serves as self-binding peptide to fulfill biological function by dynamically binding to/unbinding from its cognate site (termed self-binding site) in the same protein. Previously, we successfully derived and halogenated the helical peptide to competitively disrupt the self-binding behavior of CETP C-terminal tail. However, the halogenated peptides have only a limited affinity increase as compared to native helical peptide (∼3-fold), thus exhibiting only a moderate competitive potency. Here, instead of optimizing the direct intermolecular interaction of peptide with CETP self-binding site we attempt to further improve the peptide competitive potency by reducing its conformational flexibility with hydrocarbon-stapling technique. Computational analysis reveals that the helical peptide has large intrinsic disorder in unbound free state, which would incur a considerable entropy penalty upon rebinding to the self-binding site. All-hydrocarbon bridge is designed and optimized on native and halogenated peptides in terms of the helical pattern and binding mode of self-binding peptide. Dynamics simulation and circular dichroism indicate that the stapling can considerably reduce peptide disorder in free state. Energetics calculation and fluorescence assay conform that the binding affinity of stapled/halogenated peptides is improved substantially (by > 5-fold), thus exhibiting an effective competition potency with native peptide for the self-binding site. Structural examination suggests that the binding modes and nonbonded interactions of native and halogenated peptides are not influenced essentially due to the stapling.

Systematic profiling of SH3-mediated Tau-Partner interaction network in Alzheimer's disease by integrating in silico analysis and in vitro assay

The aberrant assembly of microtubule-associated protein Tau (τ) into insoluble aggregates is closely related to Alzheimer's disease (AD), which is elicited from Tau phosphorylation events and regulated by the specific intermolecular recognition between the proline-rich PxxP motifs of Tau and the SH3 domains of its diverse partner proteins/kinases. Here, we attempt to create a systematic interaction profile for the 10 SH3 domains of previously reported Tau partners across all the 18 Tau PxxP peptides. A number of biologically functional SH3-PxxP interaction events are identified from the profile and then tested using fluorescence spectroscopy. It is revealed that (i) the region (residues 520-560) precedent to the tubulin-binding partial repeats of Tau protein is an important target of SH3 domains, where contains the three PxxP peptides τp^527-536, τp^530-539 and τp^547-556 that exhibit different binding profiles towards the investigated SH3 domains, (ii) as compared to τp^527-536 and τp^547-556, the τp^530-539 peptide located between them has only a modest binding potency to most SH3 domains, suggesting that the three peptides contribute unevenly to Tau-SH3 interactions, and (iii) some other Tau PxxP peptides, particularly those within the residue range 490-510 that is neighboring to the region 520-560, can also interact effectively with several SH3 domains. The SH3 domain of the well known Tau partner kinase Fyn is determined to have high or moderate affinity for an array of Tau PxxP peptides, including τp^137-146, τp^493-502, τp^527-536 and τp^547-556 (K_d ranges 15.7-85.6 μM).

Is protein context responsible for peptide-mediated interactions?

Many cell signaling pathways are orchestrated by the weak, transient, and reversible peptide-mediated interactions (PMIs). Here, the role of protein context in contributing to the stability and specificity of PMIs is investigated systematically.

Discovery of High Affinity Receptors for Dityrosine through Inverse Virtual Screening and Docking and Molecular Dynamics

Dityrosine is the product of oxidation that has been linked to a number of serious pathological conditions. Evidence indicates that high amounts of dityrosine exist in oxidized milk powders and some milk related foodstuffs, further reducing the nutritional value of oxidized proteins. Therefore, we hypothesize that some receptors related to special diseases would be targets for dityrosine. However, the mechanisms of the interaction of dityrosine with probable targets are still unknown. In the present work, an inverse virtual screening approach was performed to screen possible novel targets for dityrosine. Molecular docking studies were performed on a panel of targets extracted from the potential drug target database (PDTD) to optimize and validate the screening results. Firstly, two different conformations cis- and trans- were found for dityrosine during minimization. Moreover, Tubulin (αT) (−11.0 kcal/mol) was identified as a target for cis-dityrosine (CDT), targets including αT (−11.2 kcal/mol) and thyroid hormone receptor beta-1 (−10.7 kcal/mol) presented high binding affinities for trans-dityrosine (TDT). Furthermore, in order to provide binding complexes with higher precision, the three docked systems were further refined by performing thermo dynamic simulations. A series of techniques for searching for the most stable binding pose and the calculation of binding free energy are elaborately provided in this work. The major interactions between these targets and dityrosine were hydrophobic, electrostatic and hydrogen bonding. The application of inverse virtual screening method may facilitate the prediction of unknown targets for known ligands, and direct future experimental assays.

Molecular dynamics and free energy studies on the Drosophila melanogaster and Leptinotarsa decemlineata ecdysone receptor complexed with agonists: Mechanism for binding and selectivity

The ecdysone receptor is a nuclear hormone receptor that plays a pivotal role in the insect metamorphosis and development. To address the molecular mechanisms of binding and selectivity, the interactions of two typical agonists Ponasterone A and 20-Hydroxyecdysone with Drosophila melanogaster (DME) and Leptinotarsa decemlineata ecdysone (LDE) receptors were investigated by homology modeling, molecular docking, molecular dynamic simulation, and thermodynamic analysis. We discover that 1) the L5-loop, L11-loop, and H12 helix for DME, L7-loop, and L11-loop for LDE are more flexible, which affect the global dynamics of the ligand-binding pocket, thus facilitating the ligand recognition of ecdysone receptor; 2) several key residues (Thr55/Thr37, Phe109/Phe91, Arg95/Arg77, Arg99/Arg81, Phe108/Leu90, and Ala110/Val92) are responsible for the binding of the proteins; 3) the binding-free energy is mainly contributed by the van der Waals forces as well as the electrostatic interactions of ligand and receptor; 4) the computed binding-free energy difference between DME-C1 and LDE-C1 is -4.65 kcal/mol, explains that C1 can form many more interactions with the DME; 5) residues Phe108/Leu90 and Ala110/Val92 have relatively position and orientation difference in the two receptors, accounting most likely for the ligand selectivity of ecdysone receptor from different orders of insects. This study underscores the expectation that different insect pests should be able to discriminate among compounds from different as yet undiscovered compounds, and the results firstly show a structural and functional relay between the agonists and receptors (DME and LDE), which can provide an avenue for the development of target-specific insecticides. Communicated by Ramaswamy H. Sarma.

Rational cyclization-based minimization of entropy penalty upon the binding of Nrf2-derived linear peptides to Keap1: A new strategy to improve therapeutic peptide activity against sepsis

Nrf2 is a critical regulator of innate immune response and survival during sepsis, which is constitutively degraded through binding to the Keap1 adapter protein of E3 ubiquitin ligase. Two linear peptides DLG and ETG derived from, respectively, the low-affinity and high-affinity motifs of Nrf2 binding site exhibit self-binding affinity to Keap1 central hole (active pocket); they can be exploited as therapeutic self-inhibitory peptides to disrupt the Nrf2-Keap1 interaction. Molecular dynamics simulation and binding energetics decomposition reveal that the two peptides possess large flexibility and intrinsic disorder in unbound free state, and thus would incur a considerable entropy penalty upon binding to Keap1. In order to improve Keap1-peptide binding affinity (or free energy ΔG), instead of traditionally increasing favorable enthalpy contribution (ΔH) we herein describe a rational peptide cyclization strategy to minimize unfavorable entropy penalty (ΔS) upon the binding of Nrf2-derived linear peptides to Keap1. Crystal structure analysis impart that the native active conformations of DLG and ETG peptides bound with Keap1 are folded into U-shape and hairpin configurations, respectively, and adopt their turning head to insert into the central hole of Keap1. Here, cyclization is designed by adding a disulfide bond across the two arms of DLG U-shape or ETG hairpin, which would not influence the direct intermolecular interaction between Keap1 and peptide as well as desolvation effect involved in the interaction, but can effectively constrain the conformational flexibility and disorder of the two peptides in free state, thus largely minimizing entropy penalty upon the binding. Both free energy calculation and binding affinity assay substantiate that the cyclization, as might be expected, can moderately or considerably enhance peptide binding potency to Keap1, with affinity (dissociation constant K_d) increase by 1.4-7.5-fold for designed cyclic peptides relative to their linear counterparts.

Design, cyclization, and optimization of MMP13-TIMP1 interaction-derived self-inhibitory peptides against chondrocyte senescence in osteoarthritis

The matrix metallopeptidase 13 (MMP13) is a central regulator of chondrocyte senescence that contributes to the development and progression of osteoarthritis (OA). In the present study, the native inhibitory structure of MMP13 in complex with its natural cognate inhibitor, the tissue inhibitor of metalloproteinases 1 (TIMP1), was modeled at atomic level using a grafting-based structural bioinformatics method with existing crystal structures. The modeled complex structure was then examined in detail, from which a TIMP1 inhibitory site that directly inserts into the active site of MMP13 enzyme was identified. The inhibitory site contains a coiled inhibitory loop (ILP) and a stretched N-terminal tail (NTT); they are highly structured in the intact MMP13-TIMP1 complex interface, but exhibit a large flexibility and intrinsic disorder when split from the interface context. In vitro binding assays demonstrated that the isolated ILP and NTT peptides cannot effectively rebind at the MMP13 active site (K_d > ~100 μM or = n.d.), although they have all key interacting residues in the enzyme inhibition. In silico simulations revealed that splitting of the peptide segments from TIMP1 inhibitory site does not influence the direct intermolecular interaction between MMP13 and the peptides substantially; instead, the large conformational flexibility of these isolated peptides in absence of interface context is primarily responsible for the affinity impairment, which would incur a considerable entropy penalty upon the peptide binding to MMP13. An extended version of ILP peptide, namely eILP (⁶³TPAMESVCGY⁷²), was redesigned with a rational strategy to derive a number of its cyclized counterparts by introducing a disulfide bridge across the peptide two-termini; the redesign reduces the peptide flexibility in free state and constrains the peptide pre-folding to a native-like conformation, which would help the peptide binding with minimized entropy penalty. Binding assays substantiated that the affinity K_d values of four designed cyclic peptides (, , and ) were improved to 23, 67, 42 and 18 μM, respectively, from the 96 μM of linear eILP peptide.

Computational Design of Antiangiogenic Peptibody by Fusing Human IgG1 Fc Fragment and HRH Peptide: Structural Modeling, Energetic Analysis, and Dynamics Simulation of Its Binding Potency to VEGF Receptor

Peptibodies represent a new class of biological therapeutics with combination of peptide activity and antibody-like properties. Previously, we discovered a novel peptide HRH that exhibited a dose-dependent angiogenesis-suppressing effect by targeting vascular endothelial growth factor receptors (VEGFRs). Here, we computationally designed an antiangiogenic peptibody, termed as PbHRH, by fusing the HRH peptide to human IgG1 Fc fragment using the first approved peptibody drug Romiplostim as template. The biologically active peptide of Romiplostim is similar with HRH peptide; both of them have close sequence lengths and can fold into a α-helical conformation in free state. Molecular dynamics simulations revealed that the HRH functional domain is highly flexible, which is functionally independent of Fc fragment in the designed PbHRH peptibody. Subsequently, the intermolecular interactions between VEGFR-1 domain 2 (D2) and PbHRH were predicted, clustered and refined into three representatives. Conformational analysis and energetic evaluation unraveled that the PbHRH can adopt multiple binding modes to block the native VEGF-A binding site of VEGFR-1 D2 with its HRH functional domain, although the binding effectiveness of HRH segments in peptibody context seems to be moderately decreased relative to that of free HRH peptide. Overall, it is suggested that integrating HRH peptide into PbHRH peptibody does not promote the direct intermolecular interaction between VEGFR-1 D2 and HRH. Instead, the peptibody may indirectly help to improve the pharmacokinetic profile and bioavailability of HRH.

Assessing the performance of the MM/PBSA and MM/GBSA methods. 6. Capability to predict protein-protein binding free energies and re-rank binding poses generated by protein-protein docking

Understanding protein-protein interactions (PPIs) is quite important to elucidate crucial biological processes and even design compounds that interfere with PPIs with pharmaceutical significance. Protein-protein docking can afford the atomic structural details of protein-protein complexes, but the accurate prediction of the three-dimensional structures for protein-protein systems is still notoriously difficult due in part to the lack of an ideal scoring function for protein-protein docking. Compared with most scoring functions used in protein-protein docking, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) methodologies are more theoretically rigorous, but their overall performance for the predictions of binding affinities and binding poses for protein-protein systems has not been systematically evaluated. In this study, we first evaluated the performance of MM/PBSA and MM/GBSA to predict the binding affinities for 46 protein-protein complexes. On the whole, different force fields, solvation models, and interior dielectric constants have obvious impacts on the prediction accuracy of MM/GBSA and MM/PBSA. The MM/GBSA calculations based on the ff02 force field, the GB model developed by Onufriev et al. and a low interior dielectric constant (εin = 1) yield the best correlation between the predicted binding affinities and the experimental data (rp = -0.647), which is better than MM/PBSA (rp = -0.523) and a number of empirical scoring functions used in protein-protein docking (rp = -0.141 to -0.529). Then, we examined the capability of MM/GBSA to identify the possible near-native binding structures from the decoys generated by ZDOCK for 43 protein-protein systems. The results illustrate that the MM/GBSA rescoring has better capability to distinguish the correct binding structures from the decoys than the ZDOCK scoring. Besides, the optimal interior dielectric constant of MM/GBSA for re-ranking docking poses may be determined by analyzing the characteristics of protein-protein binding interfaces. Considering the relatively high prediction accuracy and low computational cost, MM/GBSA may be a good choice for predicting the binding affinities and identifying correct binding structures for protein-protein systems.

Structural modeling of osteoarthritis ADAMTS4 complex with its cognate inhibitory protein TIMP3 and rational derivation of cyclic peptide inhibitors from the complex interface to target ADAMTS4

The ADAMTS4 (a disintegrin and metalloproteinase with thrombospondin motifs 4) enzyme is a matrix-associated zinc metalloendopeptidase that plays an essential role in the degradation of cartilage aggrecan in arthritic diseases and has been recognized as one of the most primary targets for therapeutic intervention in osteoarthritis (OA). Here, we reported computational modeling of the atomic-level complex structure of ADAMTS4 with its cognate inhibitory protein TIMP3 based on high-resolution crystal template. By systematically examining the modeled complex structure we successfully identified a short inhibitory loop (⁶²EASESLC⁶⁸) in TIMP3 N-terminal inhibitory domain (NID) that directly participates in blocking the enzyme's active site, which, and its extended versions, were then broken from the full-length protein to serve as the peptide inhibitor candidates of ADAMTS4. Atomistic molecular dynamics simulation, binding energetic analysis, and fluorescence-based assay revealed that the TIMP3-derived linear peptides can only bind weakly to the enzyme (K_d = 74 ± 8 μM), which would incur a considerable entropy penalty due to the high conformational flexibility and intrinsic disorder of these linear peptides. In this respect, we proposed a cyclization strategy to improve enzyme-peptide binding affinity by, instead of traditionally maximizing enthalpy contribution, minimizing entropy cost of the binding, where a disulfide bond was added across the two terminal residues of linear peptides, resulting in a number of TIMP3-derived cyclic peptides. Our studies confirmed that the cyclization, as might be expected, can promote peptide binding capability against ADAMTS4 substantially, with affinity increase by 3-fold, 9-fold and 7-fold for cyclic peptides , and , respectively.

Amplification of F-Actin Disassembly and Cellular Repulsion by Growth Factor Signaling

Extracellular cues that regulate cellular shape, motility, and navigation are generally classified as growth promoting (i.e., growth factors/chemoattractants and attractive guidance cues) or growth preventing (i.e., repellents and inhibitors). Yet, these designations are often based on complex assays and undefined signaling pathways and thus may misrepresent direct roles of specific cues. Here, we find that a recognized growth-promoting signaling pathway amplifies the F-actin disassembly and repulsive effects of a growth-preventing pathway. Focusing on Semaphorin/Plexin repulsion, we identified an interaction between the F-actin-disassembly enzyme Mical and the Abl tyrosine kinase. Biochemical assays revealed Abl phosphorylates Mical to directly amplify Mical Redox-mediated F-actin disassembly. Genetic assays revealed that Abl allows growth factors and Semaphorin/Plexin repellents to combinatorially increase Mical-mediated F-actin disassembly, cellular remodeling, and repulsive axon guidance. Similar roles for Mical in growth factor/Abl-related cancer cell behaviors further revealed contexts in which characterized positive effectors of growth/guidance stimulate such negative cellular effects as F-actin disassembly/repulsion.

Structure-based design of competitive ligands to target Spon2 in gastric cancer: An integration of molecular modeling and in vitro assay

Spon2 is a proto-oncogene matrix protein that plays an essential role in the tumorigenesis and metastasis of gastric cancer. The protein has recently been found to function as a guanine nucleotide exchange factor through the activation of RhoGTPase. Here, computational modeling and bioinformatics analysis were employed to investigate the molecular mechanism and biological implication underlying Spon2 autoinhibition. It is revealed that the binding of PxxP motif to SH domain can stabilize the intramolecular interaction between the N-terminal helix and DH domain of Spon2, thus shifting the protein into an autoinhibitory state. Here, we proposed releasing Spon2 autoinhibition by targeting SH domain with competitive peptide ligands. To verify this notion, the PxxP sequence was adopted as the start to derive an array of efficient SH binders by using a structure-based rational design strategy, which were then substantiated with fluorescence spectroscopy analysis and guanine nucleotide exchange test. Consequently, the obtained peptide ligands were determined to have a moderate or high affinity for SH domain; they can also enhance Spon2 exchange activity by 1.2-6.1 folds, exhibiting a significant correlation with their SH-binding affinity (Pearson's coefficient=0.92). In addition, neutral substitution of conserved residues in a high-affinity peptide ligand can largely reduce its Spon2-activating potency, confirming that the designed peptide activates Spon2 by competitively disrupting SH-PxxP interaction.