Prediction of binding affinities between the human amphiphysin-1 SH3 domain and its peptide ligands using homology modeling, molecular dynamics and molecular field analysis

Molecular insight into the systematic affinity and selectivity of partner recognition sites between the WW1 and WW2 domains of human KIBRA neuroprotein

Human KIBRA, a member of the WWC family proteins, is an upstream regulator of the Salvador/Warts/Hippo (SWH) signaling pathway and predominately expressed in nervous system. The protein has two functionally regulatory domains WW1 and WW2 at N-terminal region, which recognize and bind to the PY-motif segments of their partner proteins to serve as a signaling scaffold role in the SWH pathway. The two domains are highly conserved, but their downstream ligands and biological functions may not be fully consistent. In this study, we attempted to systematically profile the PY-motif affinity to and selectivity between KIBRA WW1 and WW2 domains involved in partner recognition sites. Ontology mining was used to enrich the KIBRA-interacting proteins in literature libraries, from which a variety of PY-motif peptide segments were identified, and their binding behavior to each domain was then analyzed by integrating computational modeling and experimental assay. Most PY-motif peptides were found to interact potently with WW1 and WW2, but they generally only exhibit a moderate or modest selectivity between the two domains. Subsequently, several representative peptides were further examined in detail to elucidate the molecular mechanism underlying their affinity and selectivity. It is revealed that the middle motif region of PY-motif peptides is primarily responsible for the affinity and stability of peptide binding, but only contributes marginally to peptide selectivity. Instead, the N-terminal region and, particularly, C-terminal region of PY-motif peptides play a crucial role in the selectivity. Hydrophobic contacts and hydrogen bonds confer stability and specificity to the domain-peptide interaction, respectively.

Systematic Modeling, Prediction, and Comparison of Domain-Peptide Affinities: Does it Work Effectively With the Peptide QSAR Methodology?

The protein-protein association in cellular signaling networks (CSNs) often acts as weak, transient, and reversible domain-peptide interaction (DPI), in which a flexible peptide segment on the surface of one protein is recognized and bound by a rigid peptide-recognition domain from another. Reliable modeling and accurate prediction of DPI binding affinities would help to ascertain the diverse biological events involved in CSNs and benefit our understanding of various biological implications underlying DPIs. Traditionally, peptide quantitative structure-activity relationship (pQSAR) has been widely used to model and predict the biological activity of oligopeptides, which employs amino acid descriptors (AADs) to characterize peptide structures at sequence level and then statistically correlate the resulting descriptor vector with observed activity data via regression. However, the QSAR has not yet been widely applied to treat the direct binding behavior of large-scale peptide ligands to their protein receptors. In this work, we attempted to clarify whether the pQSAR methodology can work effectively for modeling and predicting DPI affinities in a high-throughput manner? Over twenty thousand short linear motif (SLiM)-containing peptide segments involved in SH3, PDZ and 14-3-3 domain-medicated CSNs were compiled to define a comprehensive sequence-based data set of DPI affinities, which were represented by the Boehringer light units (BLUs) derived from previous arbitrary light intensity assays following SPOT peptide synthesis. Four sophisticated MLMs (MLMs) were then utilized to perform pQSAR modeling on the set described with different AADs to systematically create a variety of linear and nonlinear predictors, and then verified by rigorous statistical test. It is revealed that the genome-wide DPI events can only be modeled qualitatively or semiquantitatively with traditional pQSAR strategy due to the intrinsic disorder of peptide conformation and the potential interplay between different peptide residues. In addition, the arbitrary BLUs used to characterize DPI affinity values were measured via an indirect approach, which may not very reliable and may involve strong noise, thus leading to a considerable bias in the modeling. The R prd 2 = 0.7 can be considered as the upper limit of external generalization ability of the pQSAR methodology working on large-scale DPI affinity data.

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.

Structure-Based Design and Development of Chemical Probes Targeting Putative MOR-CCR5 Heterodimers to Inhibit Opioid Exacerbated HIV-1 Infectivity

Crystal structures of ligand-bound G-protein-coupled receptors provide tangible templates for rationally designing molecular probes. Herein, we report the structure-based design, chemical synthesis, and biological investigations of bivalent ligands targeting putative mu opioid receptor C-C motif chemokine ligand 5 (MOR-CCR5) heterodimers. The bivalent ligand VZMC013 possessed nanomolar level binding affinities for both the MOR and CCR5, inhibited CCL5-stimulated calcium mobilization, and remarkably improved anti-HIV-1BaL activity over previously reported bivalent ligands. VZMC013 inhibited viral infection in TZM-bl cells coexpressing CCR5 and MOR to a greater degree than cells expressing CCR5 alone. Furthermore, VZMC013 blocked human immunodeficiency virus (HIV)-1 entry in peripheral blood mononuclear cells (PBMC) cells in a concentration-dependent manner and inhibited opioid-accelerated HIV-1 entry more effectively in phytohemagglutinin-stimulated PBMC cells than in the absence of opioids. A three-dimensional molecular model of VZMC013 binding to the MOR-CCR5 heterodimer complex is constructed to elucidate its mechanism of action. VZMC013 is a potent chemical probe targeting MOR-CCR5 heterodimers and may serve as a pharmacological agent to inhibit opioid-exacerbated HIV-1 entry.

Exploring the putative mechanism of allosteric modulations by mixed-action kappa/mu opioid receptor bitopic modulators

The modulation and selectivity mechanisms of seven mixed-action kappa opioid receptor (KOR)/mu opioid receptor (MOR) bitopic modulators were explored. Molecular modeling results indicated that the 'message' moiety of seven bitopic modulators shared the same binding mode with the orthosteric site of the KOR and MOR, whereas the 'address' moiety bound with different subdomains of the allosteric site of the KOR and MOR. The 'address' moiety of seven bitopic modulators bound to different subdomains of the allosteric site of the KOR and MOR may exhibit distinguishable allosteric modulations to the binding affinity and/or efficacy of the 'message' moiety. Moreover, the 3-hydroxy group on the phenolic moiety of the seven bitopic modulators induced selectivity to the KOR over the MOR.

Computational insights into the molecular mechanisms of differentiated allosteric modulation at the mu opioid receptor by structurally similar bitopic modulators

Targeting the mu opioid receptor (MOR) by applying orthosteric ligands is the most frequently employed method to treat opioid use disorder (OUD). Unfortunately, most of MOR orthosteric ligands produce severe side effects, mainly due to their low selectivity over other opioid receptors. In contrast, some G protein-coupled receptor allosteric modulators have been reported to exhibit high subtype selectivity and can effectively modulate the potency and/or efficacy of orthosteric ligands. Recently, NAQ and its analog NCQ were identified as novel MOR bitopic modulators. Interestingly, NAQ and NCQ were similar in structure but exhibited different efficacy profiles to the MOR. NAQ exhibited an antagonism activity to the MOR while NCQ showed a partial agonism activity to the MOR. In the present study, molecular modeling methods were applied to explore the putative molecular mechanisms of their different functional profiles to the MOR. When NAQ binding with the inactive MOR, the 'address' portion of NAQ interacted with the MOR allosteric site but showed no significant allosteric modulation of the efficacy of the 'message' portion of NAQ. However, when NCQ binding with the inactive and active MOR, the 'address' portion of NCQ seemed to be able to positively modulate the efficacy of the 'message' portion of NCQ at varying levels. Evidentially, the substituents at the 1'- and 4'-positions of the isoquinoline ring of NCQ seemed to play a critical role in the modulatory function of the 'address' portion of NCQ. These findings will be invaluable to develop our next generation of MOR bitopic modulators with high affinity and subtype selectivity to potentially treat OUD.

Molecular Docking and Molecular Dynamics Studies on Selective Synthesis of α-Amyrin and β-Amyrin by Oxidosqualene Cyclases from Ilex Asprella

Amyrins are the immediate precursors of many pharmaceutically important pentacyclic triterpenoids. Although various amyrin synthases have been identified, little is known about the relationship between protein structures and the constituent and content of the products. IaAS1 and IaAS2 identified from Ilex asprella in our previous work belong to multifunctional oxidosqualene cyclases and can produce α-amyrin and β-amyrin at different ratios. More than 80% of total production of IaAS1 is α-amyrin; while IaAS2 mainly produces β-amyrin with a yield of 95%. Here, we present a molecular modeling approach to explore the underlying mechanism for selective synthesis. The structures of IaAS1 and IaAS2 were constructed by homology modeling, and were evaluated by Ramachandran Plot and Verify 3D program. The enzyme-product conformations generated by molecular docking indicated that ASP484 residue plays an important role in the catalytic process; and TRP611 residue of IaAS2 had interaction with β-amyrin through π–σ interaction. MM/GBSA binding free energy calculations and free energy decomposition after 50 ns molecular dynamics simulations were performed. The binding affinity between the main product and corresponding enzyme was higher than that of the by-product. Conserved amino acid residues such as TRP257; TYR259; PHE47; TRP534; TRP612; and TYR728 for IaAS1 (TRP257; TYR259; PHE473; TRP533; TRP611; and TYR727 for IaAS2) had strong interactions with both products. GLN450 and LYS372 had negative contribution to binding affinity between α-amyrin or β-amyrin and IaAS1. LYS372 and ARG261 had strong repulsive effects for the binding of α-amyrin with IaAS2. The importance of Lys372 and TRP612 of IaAS1, and Lys372 and TRP611 of IaAS2, for synthesizing amyrins were confirmed by site-directed mutagenesis. The different patterns of residue–product interactions is the cause for the difference in the yields of two products.

Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design

Background:

Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.

Methods:

We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.

Results:

Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.

Conclusion:

There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

Characterization of 17-Cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6α-(indole-7-carboxamido)morphinan (NAN) as a Novel Opioid Receptor Modulator for Opioid Use Disorder Treatment

The opioid crisis is a significant public health issue with more than 115 people dying from opioid overdose per day in the United States. The aim of the present study was to characterize the in vitro and in vivo pharmacological effects of 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6α-(indole-7-carboxamido)morphinan (NAN), a μ opioid receptor (MOR) ligand that may be a potential candidate for opioid use disorder treatment that produces less withdrawal signs than naltrexone. The efficacy of NAN was compared to varying efficacy ligands at the MOR, and determined at the δ opioid receptor (DOR) and κ opioid receptor (KOR). NAN was identified as a low efficacy partial agonist for G-protein activation at the MOR and DOR, but had relatively high efficacy at the KOR. In contrast to high efficacy MOR agonists, NAN did not induce MOR internalization, downregulation, or desensitization, but it antagonized agonist-induced MOR internalization and stimulation of intracellular Ca2+ release. Opioid withdrawal studies conducted using morphine-pelleted mice demonstrated that NAN precipitated significantly less withdrawal signs than naltrexone at similar doses. Furthermore, NAN failed to produce fentanyl-like discriminative stimulus effects in rats up to doses that produced dose- and time-dependent antagonism of fentanyl. Overall, these results provide converging lines of evidence that NAN functions mainly as a MOR antagonist and support further consideration of NAN as a candidate medication for opioid use disorder treatment.

Structure-Activity Relationship Studies of 6α- and 6β-Indolylacetamidonaltrexamine Derivatives as Bitopic Mu Opioid Receptor Modulators and Elaboration of the "Message-Address Concept" To Comprehend Their Functional Conversion

Structure-activity relationship (SAR) studies of numerous opioid ligands have shown that introduction of a methyl or ethyl group on the tertiary amino group at position 17 of the epoxymorphinan skeleton generally results in a mu opioid receptor (MOR) agonist while introduction of a cyclopropylmethyl group typically leads to an antagonist. Furthermore, it has been shown that introduction of heterocyclic ring systems at position 6 can favor antagonism. However, it was reported that 17-cyclopropylmethyl-3,14β-dihydroxy-4,5α-epoxy-6β-[(2'-indolyl)acetamido]morphinan (INTA), which bears a cyclopropylmethyl group at position 17 and an indole ring at position 6, acted as a MOR agonist. We herein report a SAR study on INTA with a series of its complementary derivatives to understand how introduction of an indole moiety with α or β linkage at position 6 of the epoxymorphinan skeleton may influence ligand function. Interestingly, one of INTA derivatives, compound 15 (NAN) was identified as a MOR antagonist both in vitro and in vivo. Molecular modeling studies revealed that INTA and NAN may interact with different domains of the MOR allosteric binding site. In addition, INTA may interact with W293 and N150 residues found in the orthosteric site to stabilize MOR activation conformation while NAN does not. These results suggest that INTA and NAN may be bitopic ligands and the type of allosteric interactions with the MOR influence their functional activity. These insights along with our enriched comprehension of the "message-address" concept will to benefit future ligand design.

Understanding the role of glucose regulated protein 170 (GRP170) as a nucleotide exchange factor through molecular simulations

Glucose Regulated Protein 170 (GRP170), also called Oxygen Regulated Protein 150 (ORP150), is a major molecular chaperone resident in the endoplasmic reticulum (ER). It belongs to the heat shock protein (HSP70) super family and can be induced by conditions such as hypoxia, ischemia and interferences in calcium homeostasis. It was recently reported that GRP170 may act as a nucleotide exchange factor (NEF) for GRP78 or binding immunoglobulin protein (BiP), and the ER canonical HSP70. However, little is known about the mechanism underlying its NEF activity. In this study, two homology models of GRP170 were constructed based on the X-ray crystal structures of ADP and ATP bound HSP110, a cytosolic homolog of GRP170, in order to characterize the differences in the binding modes of both ligands. It was observed that the differences in the binding modes of ADP and ATP led to a conformation change in the substrate binding domain which could potentially influence the binding of its substrates such as BiP. Our findings help understand the effect of nucleotide binding on the function of this chaperone protein as a NEF as well as the structural differences between GRP170 and its family members.

Exploring the binding mechanisms of diaminopimelic acid analogs to meso-diaminopimelate dehydrogenase by molecular modeling

Meso-Diaminopimelic acid (meso-2,6-diamino-heptanedioic acid, DAP) is an important component of the cell wall of many bacteria. Meso-diaminopimelate dehydrogenase (m-Ddh) is a critical enzyme in the process of converting tetrahydrodipicolinate to DAP. Here, we are proposing that DAP analogs targeting m-Ddh may be considered as potential antibiotics. Four DAP analogs without significant structural change from DAP have been obtained and their inhibitory potencies against m-Ddh from the P. gingivalis strain W83 show significant differences from that of DAP. However, their inhibitory mechanisms as for how simple structural change influences the inhibitory potency remain unknown. Therefore, we employed molecular modeling methods to obtain insight into the inhibitory mechanisms of DAP and analogs with m-Ddh. The predicted binding mode of DAP was highly consistent with the experimental structural data and disclosed the important roles played by the binding pocket residues. According to our predictions, the isoxazoline ring of compounds 1 and 2 and the double bonds in compounds 3 and 4 had distinct influences on these compounds' binding to m-Ddh. This enriched understanding of the inhibitory mechanisms of DAP and these four analogs to m-Ddh has provided new and relevant information for future rational development of potent inhibitors targeting m-Ddh.

Computer-aided design of amino acid-based therapeutics: a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

Binding mode analyses of NAP derivatives as mu opioid receptor selective ligands through docking studies and molecular dynamics simulation

Mu opioid receptor selective antagonists are highly desirable because of their utility as pharmacological probes for receptor characterization and functional studies. Furthermore, the mu opioid receptors act as an important target in drug abuse and addiction treatment. Previously, we reported NAP as a novel lead compound with high selectivity and affinity towards the mu opioid receptor. Based on NAP, we have synthesized its derivatives and further characterized their binding affinities and selectivity towards the receptor. NMP and NGP were identified as the two most selective MOR ligands among NAP derivatives. In the present study, molecular modeling methods were applied to assess the dual binding modes of NAP derivatives, particularly on NMP and NGP, in three opioid receptors, in order to analyze the effects of structural modifications on the pyridyl ring of NAP on the binding affinity and selectivity. The results indicated that the steric hindrance, electrostatic, and hydrophobic effects caused by the substituents on the pyridyl ring of NAP contributed complimentarily on the binding affinity and selectivity of NAP derivatives to three opioid receptors. Analyses of these contributions provided insights on future design of more potent and selective mu opioid receptor ligands.

Rational Molecular Design of Potent PLK1 PBD Domain-binding Phosphopeptides Using Preferential Amino Acid Building Blocks

Polo-like kinase 1 (PLK1) is an important regulator in diverse aspects of the cell cycle and proliferation. The protein has a highly conserved polo-box domain (PBD) present in C-terminal noncatalytic region, which exhibits a relatively broad sequence specificity in recognizing and binding phosphorylated substrates to control substrate phosphorylation by the kinase. In order to elucidate the structural basis, thermodynamic property, and biological implication underlying PBD-substrate recognition and association, a systematic amino acid preference profile of phosphopeptide interaction with PLK1 PBD domain was established via virtual mutagenesis analysis and mutation energy calculation, from which the contribution of different amino acids at each residue position of two reference phosphopeptides to domain-peptide binding was characterized comprehensively and quantitatively. With the profile, we are able to determine the favorable, neutral, and unfavorable amino acid types for each position of PBD-binding phosphopeptides, and we also explored the molecular origin of the broad sequence specificity in PBD-substrate recognition. To practice computational findings, the profile was further employed to guide rational design of potent PBD binders; three 6-mer phosphopeptides (i.e., IQSpSPC, LQSpTPF, and LNSpTPT) were successfully developed, which can efficiently target PBD domain with high affinity (Kd = 5.7 ± 1.1, 0.75 ± 0.18, and 7.2 ± 2.6 μm, resp.) as measured by a fluorescence anisotropy assay. The complex structure of PLK1 PBD domain with a newly designed, potent phosphopeptide LQSpTPF as well as diverse noncovalent chemical forces, such as H-bonds and hydrophobic interactions at the complex interface, were examined in detail to reveal the molecular mechanism of high affinity and stability of the complex system.

Computational study on the drug resistance mechanism of HCV NS3 protease to BMS-605339

NS3 protease plays a vital role in the replication of the hepatitis C virus (HCV). BMS-605339 is a novel linear tetra-peptide α-ketoamide inhibitor of NS3 protease and shows specificity for HCV NS3 protease genotype 1a and genotype 1b. Mutation at the key site 168 of the HCV NS3 protease can induce resistance to BMS-605339, which greatly affects the antiviral therapy efficacy to hepatitis C. In the present study, we employed molecular dynamics simulations, free energy calculations, and free energy decomposition to explore the drug resistance mechanism of BMS-605339 due to the three representative mutations D168C/Y/V. The free energy decomposition analysis indicates that the decrease in the binding affinity is mainly attributed to the decrease in both van der Waals and electrostatic interactions. After detailed analysis of our calculated results, we observed that the break of the salt bridge between residues 155 and 168 caused by the mutations D168C/Y/V is the original reason for the decrease in the binding ability between BMS-605339 and the mutant NS3 proteases. The obtained results will reveal the drug resistance mechanism between BMS-605339 and the mutant NS3 proteases, and provide valuable clue for designing novel and more potent drugs to HCV NS3 protease.

Molecular modeling study on the drug resistance mechanism of NS5B polymerase to TMC647055

NS5B polymerase plays an important role in viral replication machinery. TMC647055 (TMC) is a novel and potent non-nucleoside inhibitor of the HCV NS5B polymerase. However, mutations that result in drug resistance to TMC have been reported. In this study, we used molecular dynamics (MD) simulations, binding free energy calculations, and free energy decomposition to investigate the drug resistance mechanism of HCV to TMC resulting from L392I, P495T, P495S, and P495L mutations in NS5B polymerase. From the calculated results we determined that the decrease in the binding affinity between TMC and NS5B(L392I) polymerase is mainly caused by the extra methyl group at the CB atom of Ile. The polarity of the side-chain of residue 495 has no distinct influence on residue 495 binding with TMC, whereas the smaller size of the side-chain of residue 495 causes a substantial decrease in the van der Walls interaction between TMC and residue 495. Moreover, the longer length of the side-chain of residue 495 has a significant effect on the electrostatic interaction between TMC and Arg-503. Finally, we performed the same calculations and detailed analysis on other 3 mutations (L392V, P495V, and P495I). The results further confirmed our conclusions. The computational results not only reveal the drug resistance mechanism between TMC647055 and NS5B polymerase, but also provide valuable information for the rational design of more potent non-nucleoside inhibitors targeting HCV NS5B polymerase.

Computational study on the drug resistance mechanism of HCV NS5B RNA-dependent RNA polymerase mutants V494I, V494A, M426A, and M423T to Filibuvir

Filibuvir, a potent non-nucleoside inhibitor of the hepatitis C virus (HCV) NS5B RNA-dependent RNA polymerase (RdRp), has shown great promise in phase IIb clinical trial. However, drug resistant mutations towards Filibuvir have been identified. In the present study, the drug resistance mechanism of wild-type (WT) and mutant NS5B polymerases (including V494I, V494A, M426A, and M423T) toward Filibuvir was investigated by molecular modeling methods. The predicted binding free energy of these five complexes is highly consistent with the experimental EC50 values of Filibuvir to the wild-type and mutant NS5B RdRps, V494I<WT<V494A<M426A<M423T. Analysis of the individual energy terms indicates that the loss of binding affinity is mainly attributed to the decrease in the van der Waals interaction contribution. Through detailed analysis of the interaction between FBV and RdRp(V494I), RdRp(WT), RdRp(V494A), RdRp(M426A), and RdRp(M423T), several conclusions are made. Firstly, the smaller size of residue 494 side chain results in the smaller binding affinity between FBV and RdRp. Secondly, the poor inhibition capacity of Filibuvir toward RdRp(M426A) is mainly due to the decrease in the van der Walls interaction between Filibuvir and residue Leu-497(M426A) caused by the spatial structure change of Ala-426(M426A). Thirdly, the decrease in the binding affinity in mutation M423T is attributed to the smaller binding cave and the cyclopentyl group of Filibuvir exposing outside the cave. Our computational results will provide valuable information for developing more potent and selective inhibitors toward HCV NS5B polymerase.

Computational study on the molecular mechanisms of drug resistance of Narlaprevir due to V36M, R155K, V36M+R155K, T54A, and A156T mutations of HCV NS3/4A protease

Narlaprevir is a novel NS3/4A protease inhibitor of hepatitis C virus (HCV), and it has been tested in a phase II clinical trial recently. However, distinct drug-resistance of Narlaprevir has been discovered. In our study, the molecular mechanisms of drug-resistance of Narlaprevir due to the mutations V36M, R155K, V36M+R155K, T54A, and A156T of NS3/4A protease have been investigated by molecular dynamics (MD) simulations, free energy calculations, and free energy decomposition analysis. The predicted binding free energies of Narlaprevir towards the wild-type and five mutants show that the mutations V36M, R155K, and T54A lead to low-level drug resistance and the mutations V36M+R155K and A156T lead to high-level drug resistance, which is consistent with the experimental data. The analysis of the individual energy terms indicates that the van der Waals contribution is important for distinguishing the binding affinities of these six complexes. These findings again show that the combination of different molecular modeling techniques is an efficient way to interpret the molecular mechanism of drug-resistance. Our work mainly elaborates the molecular mechanism of drug-resistance of Narlaprevir and further provides valuable information for developing novel, safer, and more potent HCV antiviral drugs in the near future.

Molecular dynamics study of segment peptides of Bax, Bim, and Mcl-1 BH3 domain of the apoptosis-regulating proteins bound to the anti-apoptotic Mcl-1 protein

Mcl-1 has emerged as a potential therapeutic target in the treatment of several malignancies. Peptides representing BH3 region of pro-apoptotic proteins have been shown to bind the hydrophobic cleft of anti-apoptotic Mcl-1 and this segment is responsible for modulating the apoptotic pathways in living cells. Understanding the molecular basis of protein-peptide interaction is required to develop potent inhibitors specific for Mcl-1. Molecular dynamics simulations were performed for Mcl-1 in complex with three different BH3 peptides derived from Mcl-1, Bax, and Bim. Accordingly, the calculated binding free energies using MM-PBSA method are obtained and comparison with the experimentally determined binding free energies is made. The interactions involving two conserved charged residues (Aspi, and Arg/Lysi-4) and three upstream conserved hydrophobic residues (Leui-5, Ile/Vali-2, and Glyi-1, respectively) of BH3 peptides play the important roles in the structural stability of the complexes. The calculated results exhibit that the interactions of Bim BH3 peptides to Mcl-1 is stronger than the complex with Bax 19BH3 peptides. The hydrophobic residues (position i - 9, i - 8 and i + 2) of BH3 peptides can be involved in their inhibitory specificity. The calculated results can be used for designing more effective MCL-1 inhibitors.

Targeting the TGF-β receptor with kinase inhibitors for scleroderma therapy

Scleroderma (systemic sclerosis) is a connective tissue disease that affects various organ systems; the treatment of scleroderma is still difficult and remains a challenge to the clinician. Recently, kinase inhibitors have shown great potential against fibrotic diseases and, specifically, the transforming growth factor-β receptor (TGF-βR) was found as a new and promising target for scleroderma therapy. In the current study, we propose that the large pool of existing kinase inhibitors could be exploited for inhibiting the TGF-βR to suppress scleroderma. In this respect, we developed a modeling protocol to systematically profile the inhibitory activities of 169 commercially available kinase inhibitors against the TGF-βR, from which five promising candidates were selected and tested using a standard kinase assay protocol. Consequently, two molecular entities, namely the PKB inhibitor MK-2206 and the mTOR C1/C2 inhibitor AZD8055, showed high potency when bound to the TGF-βR, with IC50 values of 97 and 86 nM, respectively, which are close to those of the recently developed TGF-βR selective inhibitors SB525334 and LY2157299 (IC50 = 14.3 and 56 nM, respectively). We also performed atomistic molecular dynamics simulations and post-molecular mechanics/Poisson-Boltzmann surface area analyses to dissect the structural basis and energetic properties of intermolecular interactions between the TGF-βR kinase domain and these potent compounds, highlighting intensive nonbonded networks across the tightly packed interface of non-cognate TGF-βR-inhibitor complexes.

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis. Five hundred and thirty-one physicochemical property parameters of amino acids were used as descriptors to characterize the structure of peptides. These high-dimensional descriptors then go through a feature selection process given by the binary matrix shuffling filter (BMSF) to obtain a set of important low-dimensional features. Each descriptor that passes the BMSF filtering also receives a weight defined through its contribution to reduce the estimation error. These selected features served as the predictors for subsequent sample selection and modeling. Based on the weighted Euclidean distances between samples, a common range was determined with high-dimensional semivariogram and then used as a threshold to select the near-neighbor samples from the training set. For each sample to be predicted, the QSAR model was established using SVR with the weighted, selected features based on the exclusive set of near-neighbor training samples. Prediction was conducted for each test sample accordingly. The performances of this pipeline are tested with the QSAR analysis of angiotensin-converting enzyme inhibitors and HLA-A*0201 data sets. Improved prediction accuracy was obtained in both applications. This pipeline can optimize the QSAR modeling from both the feature selection and sample selection perspectives. This leads to improved accuracy over single selection methods. We expect this pipeline to have extensive application prospect in the field of regression prediction.

An index for characterization of natural and non-natural amino acids for peptidomimetics

Bioactive peptides and peptidomimetics play a pivotal role in the regulation of many biological processes such as cellular apoptosis, host defense, and biomineralization. In this work, we develop a novel structural matrix, Index of Natural and Non-natural Amino Acids (NNAAIndex), to systematically characterize a total of 155 physiochemical properties of 22 natural and 593 non-natural amino acids, followed by clustering the structural matrix into 6 representative property patterns including geometric characteristics, H-bond, connectivity, accessible surface area, integy moments index, and volume and shape. As a proof-of-principle, the NNAAIndex, combined with partial least squares regression or linear discriminant analysis, is used to develop different QSAR models for the design of new peptidomimetics using three different peptide datasets, i.e., 48 bitter-tasting dipeptides, 58 angiotensin-converting enzyme inhibitors, and 20 inorganic-binding peptides. A comparative analysis with other QSAR techniques demonstrates that the NNAAIndex method offers a stable and predictive modeling technique for in silico large-scale design of natural and non-natural peptides with desirable bioactivities for a wide range of applications.

Computational insights into the selectivity mechanism of APP-IP over matrix metalloproteinases

In this work, selectivity mechanism of APP-IP inhibitor (β-amyloid precursor protein-derived inhibitory peptide) over matrix metalloproteinases (MMPs including MMP-2, MMP-7, MMP-9 and MMP-14) was investigated by molecular modeling methods. Among MMPs, MMP-2 is the most favorable one for APP-IP interacting based on our calculations. The predicted binding affinities can give a good explanation of the activity difference of inhibitor APP-IP. In Comparison with MMP-2/APP-IP complex, the side chain of Tyr214(MMP-7) makes the binding pocket so shallow that the whole side chain of Tyr3(APP-IP) can not be fully embraced, thus unfavorable for the N-terminal of APP-IP binding to MMP-7. The poor selectivity of APP-IP toward MMP-9 is mainly related with the decrease of interaction between the APP-IP C-terminal and MMP-9 due to the bulky side chains of Pro193 and Gln199, which is in agreement with experiment. The mutations at residues P193A and Q199G of MMP-9 alternate the binding pattern of the C-terminal of APP-IP by forming two new hydrogen bonds and hydrophobic interactions with MMP-9. The mutants favor the binding affinity of MMP-9 largely. For MMP-14/APP-IP, the large steric effect of Phe204(MMP-14) and the weak contributions of the polar residues Asn231(MMP-14) and Thr190(MMP-14) could explain why MMP-14 is non-selective for APP-IP interacting. Here, the molecular modeling methods were successfully employed to explore the selective inhibitor of MMPs, and our work gives valuable information for future rational design of selective peptide inhibitors toward individual MMP.

Molecular dynamics and free energy studies of chirality specificity effects on aminobenzo[a]quinolizine inhibitors binding to DPP-IV

The aminobenzo[a]quinolizines were investigated as a novel class of DPP-IV inhibitors. The stereochemistry of this class plays an important role in the bioactivity. In this study, the mechanisms of how different configuration of three chiral centers of this class influences the binding affinity were investigated by molecular dynamics simulations, free energy decomposition analysis. The S configuration for chiral center 3* is decisive for isomers to maintain high bioactivity; the chirality effect of chiral center 2* on the binding affinity is largely dependent, while the S configuration for chiral center 2* is preferable to R configuration for the bioactivity gain; the effect of chiral center 11b* on the binding affinity is insignificant. The chirality specificity for three chiral centers is responsible for distinction of two van der Waals contacts with Tyr547 and Phe357, and of H-bonding interactions with Arg125 and Glu206. Particularly, the Arg125 to act as a bridge in the H-bonding network contributes to stable H-bonding interactions of isomer in DPP-IV active site.

Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models

Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.

Transient protein-protein interaction of the SH3-peptide complex via closely located multiple binding sites

Protein-protein interactions play an essential role in cellular processes. Certain proteins form stable complexes with their partner proteins, whereas others function by forming transient complexes. The conventional protein-protein interaction model describes an interaction between two proteins under the assumption that a protein binds to its partner protein through a single binding site. In this study, we improved the conventional interaction model by developing a Multiple-Site (MS) model in which a protein binds to its partner protein through closely located multiple binding sites on a surface of the partner protein by transiently docking at each binding site with individual binding free energies. To test this model, we used the protein-protein interaction mediated by Src homology 3 (SH3) domains. SH3 domains recognize their partners via a weak, transient interaction and are therefore promiscuous in nature. Because the MS model requires large amounts of data compared with the conventional interaction model, we used experimental data from the positionally addressable syntheses of peptides on cellulose membranes (SPOT-synthesis) technique. From the analysis of the experimental data, individual binding free energies for each binding site of peptides were extracted. A comparison of the individual binding free energies from the analysis with those from atomistic force fields gave a correlation coefficient of 0.66. Furthermore, application of the MS model to 10 SH3 domains lowers the prediction error by up to 9% compared with the conventional interaction model. This improvement in prediction originates from a more realistic description of complex formation than the conventional interaction model. The results suggested that, in many cases, SH3 domains increased the protein complex population through multiple binding sites of their partner proteins. Our study indicates that the consideration of general complex formation is important for the accurate description of protein complex formation, and especially for those of weak or transient protein complexes.

Molecular dynamics simulation and free energy calculation studies of the binding mechanism of allosteric inhibitors with p38α MAP kinase

p38 MAP kinase is a promising target for anti-inflammatory treatment. The classical kinase inhibitors imatinib and sorafenib as well as BI-1 and BIRB-796 were reported to bind in the DFG-out form of human p38α, known as type II or allosteric kinase inhibitors. Although DFG-out conformation has attracted great interest in the design of type II kinase inhibitors, the structural requirements for binding and mechanism of stabilization of DFG-out conformation remain unclear. As allosteric inhibition is important to the selectivity of kinase inhibitor, herein the binding modes of imatinib, sorafenib, BI-1 and BIRB-796 to p38α were investigated by molecular dynamics simulation. Binding free energies were calculated by molecular mechanics/Poisson-Boltzmann surface area method. The predicted binding affinities can give a good explanation of the activity difference of the studied inhibitors. Furthermore, binding free energies decomposition analysis and further structural analysis indicate that the dominating effect of van der Waals interaction drives the binding process, and key residues, such as Lys53, Gly71, Leu75, Ile84, Thr106, Met109, Leu167, Asp168, and Phe169, play important roles by forming hydrogen bond, salt bridge, and hydrophobic interactions with the DFG-out conformation of p38α. Finally, we also conducted a detailed analysis of BI-1, imatinib, and sorafenib binding to p38α in comparison with BIRB-796 exploited for gaining potency as well as selectivity of p38 inhibitors. These results are expected to be useful for future rational design of novel type II p38 inhibitors.

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

Structural basis of specific binding between Aurora A and TPX2 by molecular dynamics simulations

In the present study, the impacts of G198N and W128F mutations on the recognition between Aurora A and targeting protein of Xenopus kinesin-like protein 2 (TPX2) were investigated using molecular dynamics (MD) simulations, free energy calculations, and free energy decomposition analysis. The predicted binding free energy of the wild-type complex is more favorable than those of three mutants, indicating that both single and double mutations are unfavorable for the Aurora A and TPX2 binding. It is also observed that the mutations alternate the binding pattern between Aurora A and TPX2, especially the downstream of TPX2. An intramolecular hydrogen bond between the atom OD of Asp11(TPX2) and the atom HE1 of Trp34(TPX2) disappear in three mutants and thus lead to the instability of the secondary structure of TPX2. The combination of different molecular modeling techniques is an efficient way to understand how mutation has impacts on the protein-protein binding and our work gives valuable information for the future design of specific peptide inhibitors for Aurora A.

Prediction of peptides binding to the PKA RIIalpha subunit using a hierarchical strategy

MOTIVATION

Favorable interaction between the regulatory subunit of the cAMP-dependent protein kinase (PKA) and a peptide in A-kinase anchoring proteins (AKAPs) is critical for translocating PKA to the subcellular sites where the enzyme phosphorylates its substrates. It is very hard to identify AKAPs peptides binding to PKA due to the high sequence diversity of AKAPs.

RESULTS

We propose a hierarchical and efficient approach, which combines molecular dynamics (MD) simulations, free energy calculations, virtual mutagenesis (VM) and bioinformatics analyses, to predict peptides binding to the PKA RIIα regulatory subunit in the human proteome systematically. Our approach successfully retrieved 15 out of 18 documented RIIα-binding peptides. Literature curation supported that many newly predicted peptides might be true AKAPs. Here, we present the first systematic search for AKAP peptides in the human proteome, which is useful to further experimental identification of AKAPs and functional analysis of their biological roles.

Identifying and reducing error in cluster-expansion approximations of protein energies

Protein design involves searching a vast space for sequences that are compatible with a defined structure. This can pose significant computational challenges. Cluster expansion is a technique that can accelerate the evaluation of protein energies by generating a simple functional relationship between sequence and energy. The method consists of several steps. First, for a given protein structure, a training set of sequences with known energies is generated. Next, this training set is used to expand energy as a function of clusters consisting of single residues, residue pairs, and higher order terms, if required. The accuracy of the sequence-based expansion is monitored and improved using cross-validation testing and iterative inclusion of additional clusters. As a trade-off for evaluation speed, the cluster-expansion approximation causes prediction errors, which can be reduced by including more training sequences, including higher order terms in the expansion, and/or reducing the sequence space described by the cluster expansion. This article analyzes the sources of error and introduces a method whereby accuracy can be improved by judiciously reducing the described sequence space. The method is applied to describe the sequence-stability relationship for several protein structures: coiled-coil dimers and trimers, a PDZ domain, and T4 lysozyme as examples with computationally derived energies, and SH3 domains in amphiphysin-1 and endophilin-1 as examples where the expanded pseudo-energies are obtained from experiments. Our open-source software package Cluster Expansion Version 1.0 allows users to expand their own energy function of interest and thereby apply cluster expansion to custom problems in protein design.

The molecular mechanism studies of chirality effect of PHA-739358 on Aurora kinase A by molecular dynamics simulation and free energy calculations

Aurora kinase family is one of the emerging targets in oncology drug discovery and several small molecules targeting aurora kinases have been discovered and evaluated under early phase I/II trials. Among them, PHA-739358 (compound 1r) is a 3-aminopyrazole derivative with strong activity against Aurora A under early phase II trial. Inhibitory potency of compound 1r (the benzylic substituent at the pro-R position) is 30 times over that of compound 1s (the benzylic substituent at the pro-S position). In present study, the mechanism of how different configurations influence the binding affinity was investigated using molecular dynamics (MD) simulations, free energy calculations and free energy decomposition analysis. The predicted binding free energies of these two complexes are consistent with the experimental data. The analysis of the individual energy terms indicates that although the van der Waals contribution is important for distinguishing the binding affinities of these two inhibitors, the electrostatic contribution plays a more crucial role in that. Moreover, it is observed that different configurations of the benzylic substituent could form different binding patterns with protein, thus leading to variant inhibitory potency of compounds 1r and 1s. The combination of different molecular modeling techniques is an efficient way to interpret the chirality effects of inhibitors and our work gives valuable information for the chiral drug design in the near future.

Predicting the binding affinity of epitope-peptides with HLA-A*0201 by encoding atom-pair non-covalent interaction information between receptor and ligands

A structure-based method was used to characterize the non-covalent interactions of HLA-A*0201 with its peptide ligands. In this procedure, protein and peptide atoms were classified into 16 types in terms of their chemical property and local environment, and a 16 x 16 matrix was then defined to describe the interaction mode of 256 atom-pairs between the receptor and ligand in a complex structure. Three biologically related chemical forces as electrostatic, van der Waals, and hydrophobic potentials were separately calculated for each element of the matrix to yield 768 structural descriptors encoding the detailed information about the non-covalent interactions involved in protein-peptide binding. We employed this method to perform quantitative structure-activity relationship (QSAR) study of a data panel consisting of 419 non-apeptides with known binding affinities to HLA-A*0201 protein. Several QSAR models were constructed using partial least square regression (PLS) coupled with or without genetic algorithm (GA)-variable selection, and these models were validated rigorously and investigated systematically by using external test set and one-way analysis of variance. Results show that diverse properties have significant contributions to the HLA-A*0201-peptide binding. Particularly, the hydrophobicity and electrostatic property at the anchor residues of peptides confer a significant specificity and stability for the bound complexes.