Forcefield_NCAA: ab initio charge parameters to aid in the discovery and design of therapeutic proteins and peptides with unnatural amino acids and their application to complement inhibitors of the compstatin family

Accurate estimates of dynamical statistics using memory

Many chemical reactions and molecular processes occur on time scales that are significantly longer than those accessible by direct simulations. One successful approach to estimating dynamical statistics for such processes is to use many short time series of observations of the system to construct a Markov state model, which approximates the dynamics of the system as memoryless transitions between a set of discrete states. The dynamical Galerkin approximation (DGA) is a closely related framework for estimating dynamical statistics, such as committors and mean first passage times, by approximating solutions to their equations with a projection onto a basis. Because the projected dynamics are generally not memoryless, the Markov approximation can result in significant systematic errors. Inspired by quasi-Markov state models, which employ the generalized master equation to encode memory resulting from the projection, we reformulate DGA to account for memory and analyze its performance on two systems: a two-dimensional triple well and the AIB9 peptide. We demonstrate that our method is robust to the choice of basis and can decrease the time series length required to obtain accurate kinetics by an order of magnitude.

Structural dynamics influences the antibacterial activity of a cell-penetrating peptide (KFF)3K

Given the widespread demand for novel antibacterial agents, we modified a cell-penetrating peptide (KFF)3K to transform it into an antibacterial peptide. Namely, we inserted a hydrocarbon staple into the (KFF)3K sequence to induce and stabilize its membrane-active secondary structure. The staples were introduced at two positions, (KFF)3K[5-9] and (KFF)3K[2-6], to retain the initial amphipathic character of the unstapled peptide. The stapled analogues are protease resistant contrary to (KFF)3K; 90% of the stapled (KFF)3K[5-9] peptide remained undigested after incubation in chymotrypsin solution. The stapled peptides showed antibacterial activity (with minimal inhibitory concentrations in the range of 2-16 µM) against various Gram-positive and Gram-negative strains, contrary to unmodified (KFF)3K, which had no antibacterial effect against any strain at concentrations up to 32 µM. Also, both stapled peptides adopted an α-helical structure in the buffer and micellar environment, contrary to a mostly undefined structure of the unstapled (KFF)3K in the buffer. We found that the antibacterial activity of (KFF)3K analogues is related to their disruptive effect on cell membranes and we showed that by stapling this cell-penetrating peptide, we can induce its antibacterial character.

On the plasticity of amyloid formation: The impact of destabilizing small to large substitutions on islet amyloid polypeptide amyloid formation

Amyloids are partially ordered, proteinaceous, β-sheet rich deposits that have been implicated in a wide range of diseases. An even larger set of proteins that do not normally form amyloid in vivo can be induced to do so in vitro. A growing number of structures of amyloid fibrils have been reported and a common feature is the presence of a tightly packed core region in which adjacent monomers pack together in extremely tight interfaces, often referred to as steric zippers. A second common feature of many amyloid fibrils is their polymorphous nature. We examine the consequences of disrupting the tight packing in amyloid fibrils on the kinetics of their formation using the 37 residue polypeptide hormone islet amyloid polypeptide (IAPP, amylin) as a model system. IAPP forms islet amyloid in vivo and is aggressively amyloidogenic in vitro. Six Cryo-EM structures of IAPP amyloid fibrils are available and in all Gly24 is in the core of the structured region and makes tight contacts with other residues. Calculations using the ff14SBonlysc forcefield in Amber20 show that substitutions with larger amino acids significantly disrupt close packing and are predicted to destabilize the various fibril structures. However, Gly to 2-amino butyric acid (2-carbon side chain) and Gly to Leu substitutions actually enhance the rate of amyloid formation. A Pro substitution slows, but does not prevent amyloid formation.

Engineering of enzymes using non-natural amino acids

In enzyme engineering, the main targets for enhancing properties are enzyme activity, stereoselective specificity, stability, substrate range, and the development of unique functions. With the advent of genetic code extension technology, non-natural amino acids (nnAAs) are able to be incorporated into proteins in a site-specific or residue-specific manner, which breaks the limit of 20 natural amino acids for protein engineering. Benefitting from this approach, numerous enzymes have been engineered with nnAAs for improved properties or extended functionality. In this review, we focus on applications and strategies for using nnAAs in enzyme engineering. Notably, approaches to computational modelling of enzymes with nnAAs are also addressed. Finally, we discuss the bottlenecks that currently need to be addressed in order to realise the broader prospects of this genetic code extension technique.

A CHO-Based Cell-Free Dual Fluorescence Reporter System for the Straightforward Assessment of Amber Suppression and scFv Functionality

Incorporation of noncanonical amino acids (ncAAs) with bioorthogonal reactive groups by amber suppression allows the generation of synthetic proteins with desired novel properties. Such modified molecules are in high demand for basic research and therapeutic applications such as cancer treatment and in vivo imaging. The positioning of the ncAA-responsive codon within the protein’s coding sequence is critical in order to maintain protein function, achieve high yields of ncAA-containing protein, and allow effective conjugation. Cell-free ncAA incorporation is of particular interest due to the open nature of cell-free systems and their concurrent ease of manipulation. In this study, we report a straightforward workflow to inquire ncAA positions in regard to incorporation efficiency and protein functionality in a Chinese hamster ovary (CHO) cell-free system. As a model, the well-established orthogonal translation components Escherichia coli tyrosyl-tRNA synthetase (TyrRS) and tRNATyr_CUA were used to site-specifically incorporate the ncAA p-azido-l-phenylalanine (AzF) in response to UAG codons. A total of seven ncAA sites within an anti-epidermal growth factor receptor (EGFR) single-chain variable fragment (scFv) N-terminally fused to the red fluorescent protein mRFP1 and C-terminally fused to the green fluorescent protein sfGFP were investigated for ncAA incorporation efficiency and impact on antigen binding. The characterized cell-free dual fluorescence reporter system allows screening for ncAA incorporation sites with high incorporation efficiency that maintain protein activity. It is parallelizable, scalable, and easy to operate. We propose that the established CHO-based cell-free dual fluorescence reporter system can be of particular interest for the development of antibody-drug conjugates (ADCs).

Computational Site Saturation Mutagenesis of Canonical and Non-Canonical Amino Acids to Probe Protein-Peptide Interactions

Technologies for discovering peptides as potential therapeutics have rapidly advanced in recent years with significant interest from both academic and pharmaceutical labs. These advancements in turn drive the need for new computational tools to design peptides for purposes of advancing lead molecules into the clinic. Here we report the development and application of a new automated tool, AutoRotLib, for parameterizing a diverse set of non-canonical amino acids (NCAAs), N-methyl, or peptoid residues for use with the computational design program Rosetta. In addition, we developed a protocol for designing thioether-cyclized macrocycles within Rosetta, due to their common application in mRNA display using the RaPID platform. To evaluate the utility of these new computational tools, we screened a library of canonical and NCAAs on both a linear peptide and a thioether macrocycle, allowing us to quickly identify mutations that affect peptide binding and subsequently measure our results against previously published data. We anticipate in silico screening of peptides against a diverse chemical space will be a fundamental component for peptide design and optimization, as more amino acids can be explored in a single in silico screen than an in vitro screen. As such, these tools will enable maturation of peptide affinity for protein targets of interest and optimization of peptide pharmacokinetics for therapeutic applications.

Deciphering the Effect of Lysine Acetylation on the Misfolding and Aggregation of Human Tau Fragment 171IPAKTPPAPK180 Using Molecular Dynamic Simulation and the Markov State Model

The formation of neurofibrillary tangles (NFT) with β-sheet-rich structure caused by abnormal aggregation of misfolded microtubule-associated protein Tau is a hallmark of tauopathies, including Alzheimer’s Disease. It has been reported that acetylation, especially K174 located in the proline-rich region, can largely promote Tau aggregation. So far, the mechanism of the abnormal acetylation of Tau that affects its misfolding and aggregation is still unclear. Therefore, revealing the effect of acetylation on Tau aggregation could help elucidate the pathogenic mechanism of tauopathies. In this study, molecular dynamics simulation combined with multiple computational analytical methods were performed to reveal the effect of K174 acetylation on the spontaneous aggregation of Tau peptide ¹⁷¹IPAKTPPAPK¹⁸⁰, and the dimerization mechanism as an early stage of the spontaneous aggregation was further specifically analyzed by Markov state model (MSM) analysis. The results showed that both the actual acetylation and the mutation mimicking the acetylated state at K174 induced the aggregation of the studied Tau fragment; however, the effect of actual acetylation on the aggregation was more pronounced. In addition, acetylated K174 plays a major contributing role in forming and stabilizing the antiparallel β-sheet dimer by forming several hydrogen bonds and side chain van der Waals interactions with residues I171, P172, A173 and T175 of the corresponding chain. In brief, this study uncovered the underlying mechanism of Tau peptide aggregation in response to the lysine K174 acetylation, which can deepen our understanding on the pathogenesis of tauopathies.

An allosteric regulation mechanism of Arabidopsis Serine/Threonine kinase 1 (SIK1) through phosphorylation

The Arabidopsis Serine/Threonine Kinase 1 (SIK1) is a Sterile 20 (STE20)/Hippo orthologue that is also categorized as a Mitogen-Activated Protein Kinase Kinase Kinase Kinase (MAP4K). Like its animal and fungi orthologues, SIK1 is required for cell cycle exit, cell expansion, polarity establishment, as well as pathogenic response. The catalytic activity of SIK1, like other MAPKs, is presumably regulated by its phosphorylation states. Since no crystal structure for SIK1 has been reported yet, we built structural models for SIK1 kinase domain in different phosphorylation states with different pocket conformation to see how this kinase may be regulated. Using computational structural biology methods, we outlined a conduction path in which a phosphorylation site on the A-loop regulates the catalytic activity of SIK1 by controlling the closing or opening of the catalytic pocket at the G-loop. Furthermore, with analyses on the dynamic motions and in vitro kinase assay, we confirmed that three key residues in this conduction path, Lys278, Glu295, and Arg370, are indeed important for the kinase activity of SIK1. Since these residues are conserved in all STE20 kinases examined, the regulatory mechanism that we discovered may be common in STE20 kinases.

Cyclosporin A: Conformational Complexity and Chameleonicity

The chameleonic behavior of cyclosporin A (CsA) was investigated through conformational ensembles employing multicanonical molecular dynamics simulations that could sample the cis and trans isomers of N-methylated amino acids; these assessments were conducted in explicit water, dimethyl sulfoxide, acetonitrile, methanol, chloroform, cyclohexane (CHX), and n-hexane (HEX) using AMBER ff03, AMBER10:EHT, AMBER12:EHT, and AMBER14:EHT force fields. The conformational details were discussed employing the free-energy landscapes (FELs) at T = 300 K; it was observed that the experimentally determined structures of CsA were only a part of the conformational space. Comparing the ROESY measurements in CHX-d12 and HEX-d14, the major conformations in those apolar solvents were essentially the same as that in CDCl₃ except for the observation of some sidechain rotamers. The effects of the metal ions on the conformations, including the cis/trans isomerization, were also investigated. Based on the analysis of FELs, it was concluded that the AMBER ff03 force field best described the experimentally derived conformations, indicating that CsA intrinsically formed membrane-permeable conformations and that the metal ions might be the key to the cis/trans isomerization of N-methylated amino acids before binding a partner protein.

Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches.

An atomic perspective on improving daptomycin's activity

BACKGROUND

Recently, through comprehensive medicinal chemistry efforts, we have found a new daptomycin analogue, termed kynomycin, showing enhanced activity against both methicillin-resistant S. aureus and vancomycin-resistant Enterococcus in vitro and in vivo, with improved pharmacokinetics and lower cytotoxicity than daptomycin.

METHODS

In this study we compared the physicochemical properties of kynomycin with those of daptomycin from an atomic perspective by using Nuclear Magnetic Resonance spectroscopy and Molecular Dynamics simulations.

RESULTS AND CONCLUSION

We observed that kynurenine methylation changes daptomycin's key physicochemical properties; its calcium dependent oligomerization efficiency is improved and the modified kynurenine strengths contacts with the lipid tail and tryptophan residues. In addition, it is observed that, compared to daptomycin, kynomycin tetramer is more stable and binds stronger to calcium. The combined experiments provide key clues for the improved antibacterial activity of kynomycin.

GENERAL SIGNIFICANCE

We expect that this approach will help study the calcium binding and oligomerization features of new calcium dependent peptide antibiotics.

Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations

Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.

A predictive coarse-grained model for position-specific effects of post-translational modifications

Biomolecules undergo liquid-liquid phase separation (LLPS), resulting in the formation of multicomponent protein-RNA membraneless organelles in cells. However, the physiological and pathological role of post-translational modifications (PTMs) on the biophysics of phase behavior is only beginning to be probed. To study the effect of PTMs on LLPS in silico, we extend our transferable coarse-grained model of intrinsically disordered proteins to include phosphorylated and acetylated amino acids. Using the parameters for modified amino acids available for fixed-charge atomistic force fields, we parameterize the size and atomistic hydropathy of the coarse-grained-modified amino acid beads and, hence, the interactions between the modified and natural amino acids. We then elucidate how the number and position of phosphorylated and acetylated residues alter the protein's single-chain compactness and its propensity to phase separate. We show that both the number and the position of phosphorylated threonines/serines or acetylated lysines can serve as a molecular on/off switch for phase separation in the well-studied disordered regions of Fused in Sarcoma (FUS) and DDX3X, respectively. We also compare modified residues to their commonly used PTM mimics for their impact on chain properties. Importantly, we show that the model can predict and capture experimentally measured differences in the phase behavior for position-specific modifications, showing that the position of modifications can dictate phase separation. In sum, this model will be useful for studying LLPS of post-translationally modified intrinsically disordered proteins and predicting how modifications control phase behavior with position-specific resolution.

Exploring the Distinct Binding and Activation Mechanisms for Different CagA Oncoproteins and SHP2 by Molecular Dynamics Simulations

CagA is a major virulence factor of Helicobacter pylori. H. pylori CagA is geographically subclassified into East Asian CagA and Western CagA, which are characterized by the presence of a EPIYA-D or EPIYA-C segment. The East Asian CagA is more closely associated with gastric cancer than the Western CagA. In this study, molecular dynamic (MD) simulations were performed to investigate the binding details of SHP2 and EPIYA segments, and to explore the allosteric regulation mechanism of SHP2. Our results show that the EPIYA-D has a stronger binding affinity to the N-SH2 domain of SHP2 than EPIYA-C. In addition, a single EPIYA-D binding to N-SH2 domain of SHP2 can cause a deflection of the key helix B, and the deflected helix B could squeeze the N-SH2 and PTP domains to break the autoinhibition pocket of SHP2. However, a single EPIYA-C binding to the N-SH2 domain of SHP2 cannot break the autoinhibition of SHP2 because the secondary structure of the key helix B is destroyed. However, the tandem EPIYA-C not only increases its binding affinity to SHP2, but also does not significantly break the secondary structure of the key helix B. Our study can help us better understand the mechanism of gastric cancer caused by Helicobacter pylori infection.

Development and Testing of Force Field Parameters for Phenylalanine and Tyrosine Derivatives

Theoretical analyses are valuable for the exploration of the effects of unnatural amino acids on enzyme functions; however, many necessary parameters for unnatural amino acids remain lacking. In this study, we developed and tested force field parameters compatible with Amber ff14SB for 18 phenylalanine and tyrosine derivatives. The charge parameters were derived from ab initio calculations using the RESP fitting approach and then adjusted to reproduce the benchmark relative energies (at the MP2/TZ level) of the α- and β-backbones for each unnatural amino acid dipeptide. The structures optimized under the proposed force field parameters for the 18 unnatural amino acid dipeptides in both the α- and β-backbone forms were in good agreement with their QM structures, as the average RMSD was as small as 0.1 Å. The force field parameters were then tested in their application to seven proteins containing unnatural amino acids. The RMSDs of the simulated configurations of these unnatural amino acids were approximately 1.0 Å compared with those of the crystal structures. The vital interactions between proteins and unnatural amino acids in five protein–ligand complexes were also predicted using MM/PBSA analysis, and they were largely consistent with experimental observations. This work will provide theoretical aid for drug design involving unnatural amino acids.

Computing Proton-Coupled Redox Potentials of Fluorotyrosines in a Protein Environment

The oxidation of tyrosine to form the neutral tyrosine radical via proton-coupled electron transfer is essential for a wide range of biological processes. The precise measurement of the proton-coupled redox potentials of tyrosine (Y) in complex protein environments is challenging mainly because of the highly oxidizing and reactive nature of the radical state. Herein, a computational strategy is presented for predicting proton-coupled redox potentials in a protein environment. In this strategy, both the reduced Y-OH and oxidized Y-O^• forms of tyrosine are sampled with molecular dynamics using a molecular mechanical force field. For a large number of conformations, a quantum mechanical/molecular mechanical (QM/MM) electrostatic embedding scheme is used to compute the free-energy differences between the reduced and oxidized forms, including the zero-point energy and entropic contributions as well as the impact of the protein electrostatic environment. This strategy is applied to a series of fluorinated tyrosine derivatives embedded in a de novo α-helical protein denoted as α₃Y. The force fields for both the reduced and oxidized forms of these noncanonical fluorinated tyrosine residues are parameterized for general use. The calculated relative proton-coupled redox potentials agree with experimentally measured values with a mean unsigned error of 24 mV. Analysis of the simulations illustrates that hydrogen-bonding interactions between tyrosine and water increase the redox potentials by ∼100-250 mV, with significant variations because of the fluctuating protein environment. This QM/MM approach enables the calculation of proton-coupled redox potentials of tyrosine and other residues such as tryptophan in a variety of protein systems.

Differential effects of serine side chain interactions in amyloid formation by islet amyloid polypeptide

Islet amyloid polypeptide (IAPP), a 37 residue polypeptide, is the main protein component of islet amyloid deposits produced in the pancreas in Type 2 diabetes. Human IAPP contains five serine residues at positions 19, 20, 28, 29, and 34. Models of the IAPP amyloid fibril indicate a structure composed of two closely aligned columns of IAPP monomers with each monomer contributing to two intermolecular β-strands. Ser 19 and Ser 20 are in the partially ordered β-turn region, which links the two strands, whereas Ser 28, Ser 29, and Ser 34 are in the core region of the amyloid fibril. Ser 29 is involved in contacts between the two columns of monomers and is the part of the steric zipper interface. We undertook a study of individual serine substitutions with the hydrophobic isostere 2-aminobutyric acid (2-Abu) to examine the site-specific role of serine side chains in IAPP amyloid formation. All five variants formed amyloid. The Ser 19 to 2-Abu mutant accelerates amyloid formation by a factor of 3 to 4, while the Ser 29 to 2-Abu mutation modestly slows the rate of amyloid formation. 2-Abu replacements at the other sites had even smaller effects. The data demonstrate that the cross-column interactions made by residue 29 are not essential for amyloid formation and also show that cross-strand networks of hydrogen-bonded Ser side chains, so called Ser-ladders, are not required for IAPP amyloid formation. The effect of the Ser 19 to 2-Abu mutant suggests that residues in this region are important for amyloid formation by IAPP.

Exploring the Allosteric Mechanism of Src Homology-2 Domain-Containing Protein Tyrosine Phosphatase 2 (SHP2) by Molecular Dynamics Simulations

The Src homology-2 (SH2) domain-containing protein tyrosine phosphatase 2 (SHP2, encoded by PTPN11) is a critical allosteric phosphatase for many signaling pathways. Programmed cell death 1 (PD-1) could be phosphorylated at its immunoreceptor tyrosine-based inhibitory motif (ITIM) and immunoreceptor tyrosine-based switch motif (ITSM) and can bind to SHP2 to initiate T cell inactivation. Although the interaction of SHP2-PD-1 plays an important role in the immune process, the complex structure and the allosteric regulation mechanism remain unknown. In this study, molecular dynamics (MD) simulations were performed to study the binding details of SHP2 and PD-1, and explore the allosteric regulation mechanism of SHP2. The results show that ITIM has a preference to bind to the N-SH2 domain and ITSM has almost the same binding affinity to the N-SH2 and C-SH2 domain. Only when ITIM binds to the N-SH2 domain and ITSM binds to the C-SH2 domain can the full activation of SHP2 be obtained. The binding of ITIM and ITSM could change the motion mode of SHP2 and switch it to the activated state.

Identification of Conomarphin Variants in the Conus eburneus Venom and the Effect of Sequence and PTM Variations on Conomarphin Conformations

Marine cone snails belonging to the Conidae family make use of neuroactive peptides in their venom to capture prey. Here we report the proteome profile of the venom duct of Conus eburneus, a cone snail belonging to the Tesseliconus clade. Through tandem mass spectrometry and database searching against the C. eburneus transcriptome and the ConoServer database, we identified 24 unique conopeptide sequences in the venom duct. The majority of these peptides belong to the T and M gene superfamilies and are disulfide-bonded, with cysteine frameworks V, XIV, VI/VII, and III being the most abundant. All seven of the Cys-free peptides are conomarphin variants belonging to the M superfamily that eluted out as dominant peaks in the chromatogram. These conomarphins vary not only in amino acid residues in select positions along the backbone but also have one or more post-translational modifications (PTMs) such as proline hydroxylation, C-term amidation, and γ-carboxylation of glutamic acid. Using molecular dynamics simulations, the conomarphin variants were predicted to predominantly have hairpin-like or elongated structures in acidic pH. These two structures were found to have significant differences in electrostatic properties and the inclusion of PTMs seems to complement this disparity. The presence of polar PTMs (hydroxyproline and γ-carboxyglutamic acid) also appear to stabilize hydrogen bond networks in these conformations. Furthermore, these predicted structures are pH sensitive, becoming more spherical and compact at higher pH. The subtle conformational variations observed here might play an important role in the selection and binding of the peptides to their molecular targets.

Linkage between Proximal and Distal Movements of P450cam Induced by Putidaredoxin

Putidaredoxin (Pdx) is the exclusive reductase and a structural effector for P450cam (CYP101A1). However, the mechanism of how Pdx modulates the conformational states of P450cam remains elusive. Here we report a putative communication pathway for the Pdx-induced conformational change in P450cam using results of double electron-electron resonance (DEER) spectroscopy and molecular dynamics simulations. Use of solution state DEER measurements allows us to observe subtle conformational changes in the internal helices in P450cam among closed, open, and P450cam-Pdx complex states. Molecular dynamics simulations and dynamic network analysis suggest that Pdx binding is coupled to small coordinated movements of several regions of P450cam, including helices C, B', I, G, and F. These changes provide a linkage between the Pdx binding site on the proximal side of the enzyme and helices F/G on the distal side and the site of the largest movement resulting from the Pdx-induced closed-to-open transition. This study provides a detailed rationale for how Pdx exerts its long-recognized effector function at the active site from its binding site on the opposite face of the enzyme.

Phosphorylation of Histone Deacetylase 8: Structural and Mechanistic Analysis of the Phosphomimetic S39E Mutant

Histone deacetylase (HDAC) enzymes that catalyze removal of acetyl-lysine post-translational modifications are frequently post-translationally modified. HDAC8 is phosphorylated within the deacetylase domain at conserved residue serine 39, which leads to decreased catalytic activity. HDAC8 phosphorylation at S39 is unique in its location and function and may represent a novel mode of deacetylation regulation. To better understand the impact of phosphorylation of HDAC8 on enzyme structure and function, we performed crystallographic, kinetic, and molecular dynamics studies of the S39E HDAC8 phosphomimetic mutant. This mutation decreases the level of deacetylation of peptides derived from acetylated nuclear and cytoplasmic proteins. However, the magnitude of the effect depends on the peptide sequence and the identity of the active site metal ion [Zn(II) vs Fe(II)], with the value of kcat/KM for the mutant decreasing 9- to >200-fold compared to that of wild-type HDAC8. Furthermore, the dissociation rate constant of the active site metal ion increases by ∼10-fold. S39E HDAC8 was crystallized in complex with the inhibitor Droxinostat, revealing that phosphorylation of S39, as mimicked by the glutamate side chain, perturbs local structure through distortion of the L1 loop. Molecular dynamics simulations of both S39E and phosphorylated S39 HDAC8 demonstrate that the perturbation of the L1 loop likely occurs because of the lost hydrogen bond between D29 and S39. Furthermore, the S39 perturbation causes structural changes that propagate through the protein scaffolding to influence function in the active site. These data demonstrate that phosphorylation plays an important regulatory role for HDAC8 by affecting ligand binding, catalytic efficiency, and substrate selectivity.

Computational structural enzymology methodologies for the study and engineering of fatty acid synthases, polyketide synthases and nonribosomal peptide synthetases

Various computational methodologies can be applied to enzymological studies on enzymes in the fatty acid, polyketide, and non-ribosomal peptide biosynthetic pathways. These multi-domain complexes are called fatty acid synthases, polyketide synthases, and non-ribosomal peptide synthetases. These mega-synthases biosynthesize chemically diverse and complex bioactive molecules, with the intermediates being chauffeured between catalytic partners via a carrier protein. Recent efforts have been made to engineer these systems to expand their product diversity. A major stumbling block is our poor understanding of the transient protein-protein and protein-substrate interactions between the carrier protein and its many catalytic partner domains and product intermediates. The innate reactivity of pathway intermediates in two major classes of polyketide synthases has frustrated our mechanistic understanding of these interactions during the biosynthesis of these natural products, ultimately impeding the engineering of these systems for the generation of engineered natural products. Computational techniques described in this chapter can aid data interpretation or used to generate testable models of these experimentally intractable transient interactions, thereby providing insight into key interactions that are difficult to capture otherwise, with the potential to expand the diversity in these systems.

Drug-induced activation of integrin alpha IIb beta 3 leads to minor localized structural changes

Integrins are transmembrane proteins involved in hemostasis, wound healing, immunity and cancer. In response to intracellular signals and ligand binding, integrins adopt different conformations: the bent (resting) form; the intermediate extended form; and the ligand-occupied active form. An integrin undergoing such conformational dynamics is the heterodimeric platelet receptor αIIbβ3. Although the dramatic rearrangement of the overall structure of αIIbβ3 during the activation process is potentially related to changes in the protein secondary structure, this has not been investigated so far in a membrane environment. Here we examine the Mn2+- and drug-induced activation of αIIbβ3 and the impact on the structure of this protein reconstituted into liposomes. By quartz crystal microbalance with dissipation monitoring and activation assays we show that Mn2+ induces binding of the conformation-specific antibody PAC-1, which only recognizes the extended, active integrin. Circular dichroism spectroscopy reveals, however, that Mn2+-treatment does not induce major secondary structural changes of αIIbβ3. Similarly, we found that treatment with clinically relevant drugs (e.g. quinine) led to the activation of αIIbβ3 without significant changes in protein secondary structure. Molecular dynamics simulation studies revealed minor local changes in the beta-sheet probability of several extracellular domains of the integrin. Our experimental setup represents a new approach to study transmembrane proteins, especially integrins, in a membrane environment and opens a new way for testing drug binding to integrins under clinically relevant conditions.

CoCo-MD: A Simple and Effective Method for the Enhanced Sampling of Conformational Space

CoCo ("complementary coordinates") is a method for ensemble enrichment based on principal component analysis (PCA) that was developed originally for the investigation of NMR data. Here we investigate the potential of the CoCo method, in combination with molecular dynamics simulations (CoCo-MD), to be used more generally for the enhanced sampling of conformational space. Using the alanine penta-peptide as a model system, we find that an iterative workflow, interleaving short multiple-walker MD simulations with long-range jumps through conformational space informed by CoCo analysis, can increase the rate of sampling of conformational space up to 10 times for the same computational effort (total number of MD timesteps). Combined with the reservoir-REMD method, free energies can be readily calculated. An alternative, approximate but fast and practically useful, alternative approach to unbiasing CoCo-MD generated data is also described. Applied to cyclosporine A, we can achieve far greater conformational sampling than has been reported previously, using a fraction of the computational resource. Simulations of the maltose binding protein, begun from the "open" state, effectively sample the "closed" conformation associated with ligand binding. The PCA-based approach means that optimal collective variables to enhance sampling need not be defined in advance by the user but are identified automatically and are adaptive, responding to the characteristics of the developing ensemble. In addition, the approach does not require any adaptations to the associated MD code and is compatible with any conventional MD package.

Rapid and accurate structure-based therapeutic peptide design using GPU accelerated thermodynamic integration

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability.

Elucidating the multi-targeted anti-amyloid activity and enhanced islet amyloid polypeptide binding of β-wrapins

β-wrapins are engineered binding proteins stabilizing the β-hairpin conformations of amyloidogenic proteins islet amyloid polypeptide (IAPP), amyloid-β, and α-synuclein, thus inhibiting their amyloid propensity. Here, we use computational and experimental methods to investigate the molecular recognition of IAPP by β-wrapins. We show that the multi-targeted, IAPP, amyloid-β, and α-synuclein, binding properties of β-wrapins originate mainly from optimized interactions between β-wrapin residues and sets of residues in the three amyloidogenic proteins with similar physicochemical properties. Our results suggest that IAPP is a comparatively promiscuous β-wrapin target, probably due to the low number of charged residues in the IAPP β-hairpin motif. The sub-micromolar affinity of β-wrapin HI18, specifically selected against IAPP, is achieved in part by salt-bridge formation between HI18 residue Glu10 and the IAPP N-terminal residue Lys1, both located in the flexible N-termini of the interacting proteins. Our findings provide insights towards developing novel protein-based single- or multi-targeted therapeutics.

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble. By applying an energetic cutoff, followed by geometric clustering, we demonstrate the striking robustness and efficiency of the approach in identifying highly populated conformational states of cyclic peptides. The resulting structural and thermodynamic information is benchmarked against interproton distances from NMR experiments and conformational states identified by X-ray crystallography. Using three different model systems of varying size and flexibility, we show that the method reliably reproduces experimentally determined structural ensembles and is capable of identifying key conformational states that include the bioactive conformation. Thus, the described approach is a robust method to generate conformations of peptidic macrocycles and holds promise for structure-based drug design.

MELD-Path Efficiently Computes Conformational Transitions, Including Multiple and Diverse Paths

The molecular actions of proteins occur along reaction coordinates. Current computer methods have limited ability to explore them. We describe a fast protocol called MELD-path that (1) efficiently samples relevant conformational states via MELD, an accelerator of Molecular Dynamics (MD), (2) seeds multiple short MD trajectories from MELD states, and then (3) constructs Markov State Models (MSM) that give the routes and kinetics. We tested the method against extensive (multi μs) MD simulations of the right-handed- to left-handed-helix transition of a 9-mer peptide of AIB, the symmetry of which allows us to establish convergence. MELD-path finds all the metastable states, their correct relative populations, and the full ensemble of routes, not just a single assumed route. For this transition, we find a very broad route structure. MELD-path is highly parallelizable and efficient, yielding the full route map in a few days of computation. We believe MELD-path could be a general and rapid way to explore mechanistic processes in biomolecules on the computer.

Molecular Modeling of Chemoreceptor:Ligand Interactions

Docking algorithms have been widely used to elucidate ligand:receptor interactions that are important in biological function. Here, we introduce an in-house developed docking-refinement protocol that combines the following innovative features. (1) The use of multiple short molecular dynamics (MD) docking simulations, with residues within the binding pocket of the receptor unconstrained, so that the binding modes of the ligand in the binding pocket may be exhaustively examined. (2) The initial positioning of the ligand within the binding pocket based on complementary shape, and the use of both harmonic and quartic spherical potentials to constrain the ligand in the binding pocket during multiple short docking simulations. (3) The selection of the most probable binding modes generated by the short docking simulations using interaction energy calculations, as well as the subsequent application of all-atom MD simulations and physical-chemistry based free energy calculations to elucidate the most favorable binding mode of the ligand in complex with the receptor. In this chapter, we provide step-by-step instructions on how to computationally investigate the binding of small-molecule ligands to protein receptors by examining as control and test cases, respectively, the binding of L-serine and R-3,4-dihydroxymandelic acid (R-DHMA) to the Escherichia coli chemoreceptor Tsr. Similar computational strategies can be used for the molecular modeling of a series of ligand:protein receptor interactions.

Conformational study of the 3,6-dihydro-2H-1,4-oxazin-2-one fragment in 8-tert-butyl-7-methoxy-8-methyl-9-oxa-6-azaspiro[4.5]decane-2,10-dione stereoisomers

Unnatural cyclic α-amino acids play an important role in the search for biologically active compounds and macromolecules. Enantiomers of natural amino acids with a D configuration are not naturally encoded, but can be chemically synthesized. The crystal structures of two enantiomers obtained by a method of stereoselective synthesis, namely (5R,8S)-8-tert-butyl-7-methoxy-8-methyl-9-oxa-6-azaspiro[4.5]decane-2,10-dione, (1), and (5S,8R)-8-tert-butyl-7-methoxy-8-methyl-9-oxa-6-azaspiro[4.5]decane-2,10-dione, (2), both C₁₄H₂₁NO₄, were determined by X-ray diffraction. Both enantiomers crystallize isostructurally in the space group P2₁, with one molecule in the asymmetric unit and with the same packing motif. The crystal structures are stabilized by C-H...O hydrogen bonds, resulting in the formation of chains along the [100] and [010] directions. The conformation of the 3,6-dihydro-2H-1,4-oxazin-2-one fragment was compared with other crystal structures possessing this heterocyclic moiety. The comparison showed that the title compounds are not exceptional among structures containing the 3,6-dihydro-2H-1,4-oxazin-2-one fragment. The planar moiety was more frequently observed in derivatives in which this fragment was not condensed with other rings.

Base-Promoted Synthesis of β-Substituted-Tryptophans via a Simple and Convenient Three-Component Condensation of Nickel(II) Glycinate

A three-component reaction of nickel(II) glycinate was conducted for the convenient synthesis of β-substituted-tryptophans. The reaction worked smoothly under mild conditions and the procedure was simple and easy to handle.

Peptidomimetic therapeutics: scientific approaches and opportunities

Natural endogenously occurring peptides exhibit desirable medicinal properties, but are often limited in application by rapid proteolysis and inadequate membrane permeability. However, editing naturally occurring peptide sequences to develop peptidomimetic analogs created a promising class of therapeutics that can augment or inhibit molecular interactions. Here, we discuss a variety of chemical modifications, including l to d isomerization, cyclization, and unnatural amino acid substitution, as well as design strategies, such as attachment to cell-penetrating peptides, which are used to develop peptidomimetics. We also provide examples of approved peptidomimetics and discuss several compounds in clinical trials.

PEPlife: A Repository of the Half-life of Peptides

Short half-life is one of the key challenges in the field of therapeutic peptides. Various studies have reported enhancement in the stability of peptides using methods like chemical modifications, D-amino acid substitution, cyclization, replacement of labile aminos acids, etc. In order to study this scattered data, there is a pressing need for a repository dedicated to the half-life of peptides. To fill this lacuna, we have developed PEPlife (http://crdd.osdd.net/raghava/peplife), a manually curated resource of experimentally determined half-life of peptides. PEPlife contains 2229 entries covering 1193 unique peptides. Each entry provides detailed information of the peptide, like its name, sequence, half-life, modifications, the experimental assay for determining half-life, biological nature and activity of the peptide. We also maintain SMILES and structures of peptides. We have incorporated web-based modules to offer user-friendly data searching and browsing in the database. PEPlife integrates numerous tools to perform various types of analysis such as BLAST, Smith-Waterman algorithm, GGSEARCH, Jalview and MUSTANG. PEPlife would augment the understanding of different factors that affect the half-life of peptides like modifications, sequence, length, route of delivery of the peptide, etc. We anticipate that PEPlife will be useful for the researchers working in the area of peptide-based therapeutics.

Strategies to Develop Inhibitors of Motif-Mediated Protein-Protein Interactions as Drug Leads

Protein-protein interactions are fundamental for virtually all functions of the cell. A large fraction of these interactions involve short peptide motifs, and there has been increased interest in targeting them using peptide-based therapeutics. Peptides benefit from being specific, relatively safe, and easy to produce. They are also easy to modify using chemical synthesis and molecular biology techniques. However, significant challenges remain regarding the use of peptides as therapeutic agents. Identification of peptide motifs is difficult, and peptides typically display low cell permeability and sensitivity to enzymatic degradation. In this review, we outline the principal high-throughput methodologies for motif discovery and describe current methods for overcoming pharmacokinetic and bioavailability limitations.

SATPdb: a database of structurally annotated therapeutic peptides

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

Background

In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues.

Results

PEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81–4.05 Å.

Conclusions

In summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/.

Reviewers

This article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users.

CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides

CPPsite 2.0 (http://crdd.osdd.net/raghava/cppsite/) is an updated version of manually curated database (CPPsite) of cell-penetrating peptides (CPPs). The current version holds around 1850 peptide entries, which is nearly two times than the entries in the previous version. The updated data were curated from research papers and patents published in last three years. It was observed that most of the CPPs discovered/ tested, in last three years, have diverse chemical modifications (e.g. non-natural residues, linkers, lipid moieties, etc.). We have compiled this information on chemical modifications systematically in the updated version of the database. In order to understand the structure-function relationship of these peptides, we predicted tertiary structure of CPPs, possessing both modified and natural residues, using state-of-the-art techniques. CPPsite 2.0 also maintains information about model systems (in vitro/in vivo) used for CPP evaluation and different type of cargoes (e.g. nucleic acid, protein, nanoparticles, etc.) delivered by these peptides. In order to assist a wide range of users, we developed a user-friendly responsive website, with various tools, suitable for smartphone, tablet and desktop users. In conclusion, CPPsite 2.0 provides significant improvements over the previous version in terms of data content.

New compstatin peptides containing N-terminal extensions and non-natural amino acids exhibit potent complement inhibition and improved solubility characteristics

Compstatin peptides are complement inhibitors that bind and inhibit cleavage of complement C3. Peptide binding is enhanced by hydrophobic interactions; however, poor solubility promotes aggregation in aqueous environments. We have designed new compstatin peptides derived from the W4A9 sequence (Ac-ICVWQDWGAHRCT-NH2, cyclized between C2 and C12), based on structural, computational, and experimental studies. Furthermore, we developed and utilized a computational framework for the design of peptides containing non-natural amino acids. These new compstatin peptides contain polar N-terminal extensions and non-natural amino acid substitutions at positions 4 and 9. Peptides with α-modified non-natural alanine analogs at position 9, as well as peptides containing only N-terminal polar extensions, exhibited similar activity compared to W4A9, as quantified via ELISA, hemolytic, and cell-based assays, and showed improved solubility, as measured by UV absorbance and reverse-phase HPLC experiments. Because of their potency and solubility, these peptides are promising candidates for therapeutic development in numerous complement-mediated diseases.

CancerPPD: a database of anticancer peptides and proteins

CancerPPD (http://crdd.osdd.net/raghava/cancerppd/) is a repository of experimentally verified anticancer peptides (ACPs) and anticancer proteins. Data were manually collected from published research articles, patents and from other databases. The current release of CancerPPD consists of 3491 ACP and 121 anticancer protein entries. Each entry provides comprehensive information related to a peptide like its source of origin, nature of the peptide, anticancer activity, N- and C-terminal modifications, conformation, etc. Additionally, CancerPPD provides the information of around 249 types of cancer cell lines and 16 different assays used for testing the ACPs. In addition to natural peptides, CancerPPD contains peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, CancerPPD stores predicted tertiary structures as well as peptide sequences in SMILES format. Tertiary structures of peptides were predicted using the state-of-art method, PEPstr and secondary structural states were assigned using DSSP. In order to assist users, a number of web-based tools have been integrated, these include keyword search, data browsing, sequence and structural similarity search. We believe that CancerPPD will be very useful in designing peptide-based anticancer therapeutics.

Structure-based inhibition of protein-protein interactions

Protein-protein interactions (PPIs) are emerging as attractive targets for drug design because of their central role in directing normal and aberrant cellular functions. These interactions were once considered "undruggable" because their large and dynamic interfaces make small molecule inhibitor design challenging. However, landmark advances in computational analysis, fragment screening and molecular design have enabled development of a host of promising strategies to address the fundamental molecular recognition challenge. An attractive approach for targeting PPIs involves mimicry of protein domains that are critical for complex formation. This approach recognizes that protein subdomains or protein secondary structures are often present at interfaces and serve as organized scaffolds for the presentation of side chain groups that engage the partner protein(s). Design of protein domain mimetics is in principle rather straightforward but is enabled by a host of computational strategies that provide predictions of important residues that should be mimicked. Herein we describe a workflow proceeding from interaction network analysis, to modeling a complex structure, to identifying a high-affinity sub-structure, to developing interaction inhibitors. We apply the design procedure to peptidomimetic inhibitors of Ras-mediated signaling.