Recapitulation and design of protein binding peptide structures and sequences

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations

We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Subsequently, molecular dynamics simulations are employed to narrow down the selection of peptides for experimental assays. We apply this computational strategy to design peptide inhibitors that specifically target β-catenin and NF-κB essential modulator. Among the twelve β-catenin inhibitors, six exhibit improved binding affinity compared to the parent peptide. Notably, the best C-terminal peptide binds β-catenin with an IC50 of 0.010 ± 0.06 μM, which is 15-fold better than the parent peptide. For NF-κB essential modulator, two of the four tested peptides display substantially enhanced binding compared to the parent peptide. Collectively, this study underscores the successful integration of deep learning and structure-based modeling and simulation for target specific peptide design.

Isolation of Tobacco Mosaic Virus-Binding Peptides for Biotechnology Applications

Tobacco mosaic virus (TMV) was the first virus to be discovered and it is now widely used as a tool for biological research and biotechnology applications. TMV particles can be decorated with functional molecules by genetic engineering or bioconjugation. However, this can destabilize the nanoparticles, and/or multiple rounds of modification may be necessary, reducing product yields and preventing the display of certain cargo molecules. To overcome these challenges, we used phage display technology and biopanning to isolate a TMV-binding peptide (TBPT25 ) with strong binding properties (IC50 =0.73 μM, KD =0.16 μM), allowing the display of model cargos via a single mixing step. The TMV-binding peptide is specific for TMV but does not recognize free coat proteins and can therefore be used to decorate intact TMV or detect intact TMV particles in crude plant sap.

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.

Evaluation of Computationally Designed Peptides against TWEAK, a Cytokine of the Tumour Necrosis Factor Ligand Family

The tumour necrosis factor-like weak inducer of apoptosis (TWEAK) is a member of the tumour necrosis factor ligand family and has been shown to be overexpressed in tumoral cells together with the fibroblast growth factor–inducible 14 (Fn14) receptor. TWEAK-Fn14 interaction triggers a set of intracellular pathways responsible for tumour cell invasion and migration, as well as proliferation and angiogenesis. Hence, modulation of the TWEAK-Fn14 interaction is an important therapeutic goal. The targeting of protein-protein interactions by external agents, e.g., drugs, remains a substantial challenge. Given their intrinsic features, as well as recent advances that improve their pharmacological profiles, peptides have arisen as promising agents in this regard. Here, we report, by in silico structural design validated by cell-based and in vitro assays, the discovery of four peptides able to target TWEAK. Our results show that, when added to TWEAK-dependent cellular cultures, peptides cause a down-regulation of genes that are part of TWEAK-Fn14 signalling pathway. The direct, physical interaction between the peptides and TWEAK was further elucidated in an in vitro assay which confirmed that the bioactivity shown in cell-based assays was due to the targeting of TWEAK. The results presented here are framed within early pre-clinical drug development and therefore these peptide hits represent a starting point for the development of novel therapeutic agents. Our approach exemplifies the powerful combination of in silico and experimental efforts to quickly identify peptides with desirable traits.

Peptidomimetics in Silico

Endogenous peptides as part of physiological processes are targets of interest when it comes to finding desirable therapeutics which are able to modulate molecular interactions. The major limits presented by peptides when they are used as drugs have motivated the research of the synthesis of peptidomimetics obtained through chemical modification and the use of in silico approaches. Here recent works on the discovery of peptidomimetics by computational methods are reported. Together with molecular dynamic simulations, the use of pharmacophore research simulations helps to gain insight into and understand the molecular determinants underlying the physiological processes.

Targeting trimeric transmembrane domain 5 of oncogenic latent membrane protein 1 using a computationally designed peptide

A peptide inhibitor was designed in silico and validated experimentally to disrupt homotrimeric transmembrane helix assembly.

Protein–protein interactions are involved in diverse biological processes. These interactions are therefore vital targets for drug development. However, the design of peptide modulators targeting membrane-based protein–protein interactions is a challenging goal owing to the lack of experimentally-determined structures and efficient protocols to probe their functions. Here we employed rational peptide design and molecular dynamics simulations to design a membrane-insertable peptide that disrupts the strong trimeric self-association of the fifth transmembrane domain (TMD5) of the oncogenic Epstein–Barr virus (EBV) latent membrane protein-1 (LMP-1). The designed anti-TMD5 peptide formed 1 : 2 heterotrimers with TMD5 in micelles and inhibited TMD5 oligomerization in bacterial membranes. Moreover, the designed peptide inhibited LMP-1 homotrimerization based on NF-κB activity in EVB positive lymphoma cells. The results indicated that the designed anti-TMD5 peptide may represent a promising starting point for elaboration of anti-EBV therapeutics via inhibition of LMP-1 oligomerization. To the best of our knowledge, this represents the first example of disrupting homotrimeric transmembrane helices using a designed peptide inhibitor.

Assessing the capability of in silico mutation protocols for predicting the finite temperature conformation of amino acids

Mutation protocols are a key tool in computational biophysics for modelling unknown side chain conformations. In particular, these protocols are used to generate the starting structures for molecular dynamics simulations. The accuracy of the initial side chain and backbone placement is crucial to obtain a stable and quickly converging simulation. In this work, we assessed the performance of several mutation protocols in predicting the most probable conformer observed in finite temperature molecular dynamics simulations for a set of protein-peptide crystals differing only by single-point mutations in the peptide sequence. Our results show that several programs which predict well the crystal conformations fail to predict the most probable finite temperature configuration. Methods relying on backbone-dependent rotamer libraries have, in general, a better performance, but even the best protocol fails in predicting approximately 30% of the mutations.

PixelDB: Protein-peptide complexes annotated with structural conservation of the peptide binding mode

PixelDB, the Peptide Exosite Location Database, compiles 1966 non-redundant, high-resolution structures of protein-peptide complexes filtered to minimize the impact of crystal packing on peptide conformation. The database is organized to facilitate study of structurally conserved versus non-conserved elements of protein-peptide engagement. PixelDB clusters complexes based on the structural similarity of the peptide-binding protein, and by comparing complexes within a cluster highlights examples of domains that engage peptides using more than one binding mode. PixelDB also identifies conserved peptide core structural motifs characteristic of each binding mode. Peptide regions that flank core motifs often make non-structurally conserved interactions with the protein surface in regions we call exosites. Many examples establish that exosite contacts can be important for enhancing protein binding and interaction specificity. PixelDB provides a resource for computational and structural biologists to study, model, and predict core-motif and exosite-contacting peptide interactions. PixelDB is available to the community without restriction in a convenient flat-file format with accompanying visualization tools.

Inhibitory mechanism of peptides with a repeating hydrophobic and hydrophilic residue pattern on interleukin-10

Interleukin 10 (IL-10) is a cytokine that is able to downregulate inflammation. Its overexpression is directly associated with the difficulty in the clearance of chronic viral infections, such as chronic hepatitis B, hepatitis C and HIV infection, and infection-related cancer. IL-10 signaling blockade has been proposed as a promising way of clearing chronic viral infection and preventing tumor growth in animal models. Recently, we have reported that peptides with a helical repeating pattern of hydrophobic and hydrophilic residues are able to inhibit IL-10 significantly both in vitro and in vivo. ¹ In this work, we seek to further study the inhibiting mechanism of these peptides using sequence-modified peptides. As evidenced by both experimental and molecular dynamics simulation in concert the N-terminal hydrophobic peptide constructed with repeating hydrophobic and hydrophilic pattern of residues is more likely to inhibit IL10. In addition, the sequence length and the ability of protonation are also important for inhibition activity.

De novo design of helical peptides to inhibit tumor necrosis factor-α by disrupting its trimer formation

Helical peptide TNFα inhibitors were designed by targeting their dimer structure.

Effects of HCM cTnI mutation R145G on troponin structure and modulation by PKA phosphorylation elucidated by molecular dynamics simulations

Cardiac troponin (cTn) is a key molecule in the regulation of human cardiac muscle contraction. The N-terminal cardiac-specific peptide of the inhibitory subunit of troponin, cTnI (cTnI(1-39)), is a target for phosphorylation by protein kinase A (PKA) during β-adrenergic stimulation. We recently presented evidence indicating that this peptide interacts with the inhibitory peptide (cTnl(137-147)) when S23 and S24 are phosphorylated. The inhibitory peptide is also the target of the point mutation cTnI-R145G, which is associated with hypertrophic cardiomyopathy (HCM), a disease associated with sudden death in apparently healthy young adults. It has been shown that both phosphorylation and this mutation alter the cTnC-cTnI (C-I) interaction, which plays a crucial role in modulating contractile activation. However, little is known about the molecular-level events underlying this modulation. Here, we computationally investigated the effects of the cTnI-R145G mutation on the dynamics of cTn, cTnC Ca(2+) handling, and the C-I interaction. Comparisons were made with the cTnI-R145G/S23D/S24D phosphomimic mutation, which has been used both experimentally and computationally to study the cTnI N-terminal specific effects of PKA phosphorylation. Additional comparisons between the phosphomimic mutations and the real phosphorylations were made. For this purpose, we ran triplicate 150 ns molecular dynamics simulations of cTnI-R145G Ca(2+)-bound cTnC(1-161)-cTnI(1-172)-cTnT(236-285), cTnI-R145G/S23D/S24D Ca(2+)-bound cTnC(1-161)-cTnI(1-172)-cTnT(236-285), and cTnI-R145G/PS23/PS24 Ca(2+)-bound cTnC(1-161)-cTnI(1-172)-cTnT(236-285), respectively. We found that the cTnI-R145G mutation did not impact the overall dynamics of cTn, but stabilized crucial Ca(2+)-coordinating interactions. However, the phosphomimic mutations increased overall cTn fluctuations and destabilized Ca(2+) coordination. Interestingly, cTnI-R145G blunted the intrasubunit interactions between the cTnI N-terminal extension and the cTnI inhibitory peptide, which have been suggested to play a crucial role in modulating troponin function during β-adrenergic stimulation. These findings offer a molecular-level explanation for how the HCM mutation cTnI-R145G reduces the modulation of cTn by phosphorylation of S23/S24 during β-adrenergic stimulation.

Docking small peptides remains a great challenge: an assessment using AutoDock Vina

There is a growing interest in the mechanisms and the prediction of how flexible peptides bind proteins, often in a highly selective and conserved manner. While both existing small-molecule docking methods and custom protocols can be used, even short peptides make difficult targets owing to their high torsional flexibility. Any benchmarking should therefore start with those. We compiled a meta-data set of 47 complexes with peptides up to five residues, based on 11 related studies from the past decade. Although their highly varying strategies and constraints preclude direct, quantitative comparisons, we still provide a comprehensive overview of the reported results, using a simple yet stringent measure: the quality of the top-scoring peptide pose. Using the entire data set, this is augmented by our own benchmark of AutoDock Vina, a freely available, fast and widely used docking tool. It particularly addresses non-expert users and was therefore implemented in a highly integrated manner. Guidelines addressing important issues such as the amount of sampling required for result reproducibility are so far lacking. Using peptide docking as an example, this is the first study to address these issues in detail. Finally, to encourage further, standardized benchmarking efforts, the compiled data set is made available in an accessible, transparent and extendable manner.

Knowledge-based modeling of peptides at protein interfaces: PiPreD

MOTIVATION

Protein-protein interactions (PPIs) underpin virtually all cellular processes both in health and disease. Modulating the interaction between proteins by means of small (chemical) agents is therefore a promising route for future novel therapeutic interventions. In this context, peptides are gaining momentum as emerging agents for the modulation of PPIs.

RESULTS

We reported a novel computational, structure and knowledge-based approach to model orthosteric peptides to target PPIs: PiPreD. PiPreD relies on a precompiled and bespoken library of structural motifs, iMotifs, extracted from protein complexes and a fast structural modeling algorithm driven by the location of native chemical groups on the interface of the protein target named anchor residues. PiPreD comprehensive and systematically samples the entire interface deriving peptide conformations best suited for the given region on the protein interface. PiPreD complements the existing technologies and provides new solutions for the disruption of selected interactions.

AVAILABILITY AND IMPLEMENTATION

Database and accessory scripts and programs are available upon request to the authors or at http://www.bioinsilico.org/PIPRED.

CONTACT

narcis.fernandez@gmail.com.

Computational studies of the effect of the S23D/S24D troponin I mutation on cardiac troponin structural dynamics

During β-adrenergic stimulation, cardiac troponin I (cTnI) is phosphorylated by protein kinase A (PKA) at sites S23/S24, located at the N-terminus of cTnI. This phosphorylation has been shown to decrease KCa and pCa50, and weaken the cTnC-cTnI (C-I) interaction. We recently reported that phosphorylation results in an increase in the rate of early, slow phase of relaxation (kREL,slow) and a decrease in its duration (tREL,slow), which speeds up the overall relaxation. However, as the N-terminus of cTnI (residues 1-40) has not been resolved in the whole cardiac troponin (cTn) structure, little is known about the molecular-level behavior within the whole cTn complex upon phosphorylation of the S23/S24 residues of cTnI that results in these changes in function. In this study, we built up the cTn complex structure (including residues cTnC 1-161, cTnI 1-172, and cTnT 236-285) with the N-terminus of cTnI. We performed molecular-dynamics (MD) simulations to elucidate the structural basis of PKA phosphorylation-induced changes in cTn structure and Ca(2+) binding. We found that introducing two phosphomimic mutations into sites S23/S24 had no significant effect on the coordinating residues of Ca(2+) binding site II. However, the overall fluctuation of cTn was increased and the C-I interaction was altered relative to the wild-type model. The most significant changes involved interactions with the N-terminus of cTnI. Interestingly, the phosphomimic mutations led to the formation of intrasubunit interactions between the N-terminus and the inhibitory peptide of cTnI. This may result in altered interactions with cTnC and could explain the increased rate and decreased duration of slow-phase relaxation seen in myofibrils.

DINC: a new AutoDock-based protocol for docking large ligands

Background

Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow.

Results

In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC.

Conclusions

Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://dinc.kavrakilab.org. Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.

Computational design of protein-small molecule interfaces

The computational design of proteins that bind small molecule ligands is one of the unsolved challenges in protein engineering. It is complicated by the relatively small size of the ligand which limits the number of intermolecular interactions. Furthermore, near-perfect geometries between interacting partners are required to achieve high binding affinities. For apolar, rigid small molecules the interactions are dominated by short-range van der Waals forces. As the number of polar groups in the ligand increases, hydrogen bonds, salt bridges, cation-π, and π-π interactions gain importance. These partial covalent interactions are longer ranged, and additionally, their strength depends on the environment (e.g. solvent exposure). To assess the current state of protein-small molecule interface design, we benchmark the popular computer algorithm Rosetta on a diverse set of 43 protein-ligand complexes. On average, we achieve sequence recoveries in the binding site of 59% when the ligand is allowed limited reorientation, and 48% when the ligand is allowed full reorientation. When simulating the redesign of a protein binding site, sequence recovery among residues that contribute most to binding was 52% when slight ligand reorientation was allowed, and 27% when full ligand reorientation was allowed. As expected, sequence recovery correlates with ligand displacement.

Peptides: minimal drug surrogates to interrogate and interfere with protein function

The interactome in normal and disease cells is a key area for study and therapeutic targeting, yet few molecules have been developed that can interfere with protein–protein interactions within cells. Peptides and homologues are potential reagents to target PPI.

Simulating molecular mechanisms of the MDM2-mediated regulatory interactions: a conformational selection model of the MDM2 lid dynamics

Diversity and complexity of MDM2 mechanisms govern its principal function as the cellular antagonist of the p53 tumor suppressor. Structural and biophysical studies have demonstrated that MDM2 binding could be regulated by the dynamics of a pseudo-substrate lid motif. However, these experiments and subsequent computational studies have produced conflicting mechanistic models of MDM2 function and dynamics. We propose a unifying conformational selection model that can reconcile experimental findings and reveal a fundamental role of the lid as a dynamic regulator of MDM2-mediated binding. In this work, structure, dynamics and energetics of apo-MDM2 are studied as a function of posttranslational modifications and length of the lid. We found that the dynamic equilibrium between "closed" and "semi-closed" lid forms may be a fundamental characteristic of MDM2 regulatory interactions, which can be modulated by phosphorylation, phosphomimetic mutation as well as by the lid size. Our results revealed that these factors may regulate p53-MDM2 binding by fine-tuning the thermodynamic equilibrium between preexisting conformational states of apo-MDM2. In agreement with NMR studies, the effect of phosphorylation on MDM2 interactions was more pronounced with the truncated lid variant that favored the thermodynamically dominant closed form. The phosphomimetic mutation S17D may alter the lid dynamics by shifting the thermodynamic equilibrium towards the ensemble of "semi-closed" conformations. The dominant "semi-closed" lid form and weakened dependence on the phosphorylation seen in simulations with the complete lid can provide a rationale for binding of small p53-based mimetics and inhibitors without a direct competition with the lid dynamics. The results suggested that a conformational selection model of preexisting MDM2 states may provide a robust theoretical framework for understanding MDM2 dynamics. Probing biological functions and mechanisms of MDM2 regulation would require further integration of computational and experimental studies and may help to guide drug design of novel anti-cancer therapeutics.

Ionic channels as targets for drug design: a review on computational methods

Ion channels are involved in a broad range of physiological and pathological processes. The implications of ion channels in a variety of diseases, including diabetes, epilepsy, hypertension, cancer and even chronic pain, have signaled them as pivotal drug targets. Thus far, drugs targeting ion channels were developed without detailed knowledge of the molecular interactions between the lead compounds and the target channels. In recent years, however, the emergence of high-resolution structures for a plethora of ion channels paves the way for computer-assisted drug design. Currently, available functional and structural data provide an attractive platform to generate models that combine substrate-based and protein-based approaches. In silico approaches include homology modeling, quantitative structure-activity relationships, virtual ligand screening, similarity and pharmacophore searching, data mining, and data analysis tools. These strategies have been frequently used in the discovery and optimization of novel molecules with enhanced affinity and specificity for the selected therapeutic targets. In this review we summarize recent applications of in silico methods that are being used for the development of ion channel drugs.

Computational design of the sequence and structure of a protein-binding peptide

The de novo design of protein-binding peptides is challenging because it requires the identification of both a sequence and a backbone conformation favorable for binding. We used a computational strategy that iterates between structure and sequence optimization to redesign the C-terminal portion of the RGS14 GoLoco motif peptide so that it adopts a new conformation when bound to Gα(i1). An X-ray crystal structure of the redesigned complex closely matches the computational model, with a backbone root-mean-square deviation of 1.1 Å.

Computational design of an endo-1,4-beta-xylanase ligand binding site

The field of computational protein design has experienced important recent success. However, the de novo computational design of high-affinity protein-ligand interfaces is still largely an open challenge. Using the Rosetta program, we attempted the in silico design of a high-affinity protein interface to a small peptide ligand. We chose the thermophilic endo-1,4-β-xylanase from Nonomuraea flexuosa as the protein scaffold on which to perform our designs. Over the course of the study, 12 proteins derived from this scaffold were produced and assayed for binding to the target ligand. Unfortunately, none of the designed proteins displayed evidence of high-affinity binding. Structural characterization of four designed proteins revealed that although the predicted structure of the protein model was highly accurate, this structural accuracy did not translate into accurate prediction of binding affinity. Crystallographic analyses indicate that the lack of binding affinity is possibly due to unaccounted for protein dynamics in the 'thumb' region of our design scaffold intrinsic to the family 11 β-xylanase fold. Further computational analysis revealed two specific, single amino acid substitutions responsible for an observed change in backbone conformation, and decreased dynamic stability of the catalytic cleft. These findings offer new insight into the dynamic and structural determinants of the β-xylanase proteins.

Salt bridges: geometrically specific, designable interactions

Salt bridges occur frequently in proteins, providing conformational specificity and contributing to molecular recognition and catalysis. We present a comprehensive analysis of these interactions in protein structures by surveying a large database of protein structures. Salt bridges between Asp or Glu and His, Arg, or Lys display extremely well-defined geometric preferences. Several previously observed preferences are confirmed, and others that were previously unrecognized are discovered. Salt bridges are explored for their preferences for different separations in sequence and in space, geometric preferences within proteins and at protein-protein interfaces, co-operativity in networked salt bridges, inclusion within metal-binding sites, preference for acidic electrons, apparent conformational side chain entropy reduction on formation, and degree of burial. Salt bridges occur far more frequently between residues at close than distant sequence separations, but, at close distances, there remain strong preferences for salt bridges at specific separations. Specific types of complex salt bridges, involving three or more members, are also discovered. As we observe a strong relationship between the propensity to form a salt bridge and the placement of salt-bridging residues in protein sequences, we discuss the role that salt bridges might play in kinetically influencing protein folding and thermodynamically stabilizing the native conformation. We also develop a quantitative method to select appropriate crystal structure resolution and B-factor cutoffs. Detailed knowledge of these geometric and sequence dependences should aid de novo design and prediction algorithms.

De novo design of peptide-calcite biomineralization systems

Many organisms produce complex, hierarchically structured, inorganic materials via protein-influenced crystal growth--a process known as biomineralization. Understanding this process would shed light on hard-tissue formation and guide efforts to develop biomaterials. We created and tested a computational method to design protein-biomineralization systems. The algorithm folds a protein from a fully extended structure and simultaneously optimizes the fold, orientation, and sequence of the protein adsorbed to a crystal surface. We used the algorithm to design peptides (16 residues) to modify calcite (CaCO(3)) crystallization. We chemically synthesized six peptides that were predicted to bind different states of a calcite growth plane. All six peptides dramatically affected calcite crystal growth (as observed by scanning electron microscopy), and the effects were dependent on the targeted state of the {001} growth plane. Additionally, we synthesized and assayed scrambled variants of all six designed peptides to distinguish cases where sequence composition determines the interactions versus cases where sequence order (and presumably structure) plays a role. Scrambled variants of negatively charged peptides also had dramatic effects on calcite crystallization; in contrast, scrambled variants of positively charged peptides had a variable effect on crystallization, ranging from dramatic to mild. Special emphasis is often placed on acidic protein residues in calcified tissue mineralization; the work presented here suggests an important role for basic residues as well. In particular, this work implicates a potential role for basic residues in sequence-order specificity for peptide-mineral interactions.

Structure-based prediction of protein-peptide specificity in Rosetta

Protein-peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein-peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure-based prediction of protein-peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide-binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide-binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure-based approaches can provide atomic-level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress.

A physical model for PDZ-domain/peptide interactions

The PDZ domain is an interaction motif that recognizes and binds the C-terminal peptides of target proteins. PDZ domains are ubiquitous in nature and help assemble multiprotein complexes that control cellular organization and signaling cascades. We present an optimized energy function to predict the binding free energy (ΔΔG) of PDZ domain/peptide interactions computationally. Geometry-optimized models of PDZ domain/peptide interfaces were built using Rosetta, and protein and peptide side chain and backbone degrees of freedom are minimized simultaneously. Using leave-one-out cross-validation, Rosetta’s energy function is adjusted to reproduce experimentally determined ΔΔG values with a correlation coefficient of 0.66 and a standard deviation of 0.79 kcal mol⁻¹. The energy function places an increased weight on hydrogen bonding interactions when compared to a previously developed method to analyze protein/protein interactions. Binding free enthalpies (ΔΔH) and entropies (ΔS) are predicted with reduced accuracies of R = 0.60 and R = 0.17, respectively. The computational method improves prediction of PDZ domain specificity from sequence and allows design of novel PDZ domain/peptide interactions.

The structural basis of peptide-protein binding strategies

Peptide-protein interactions are very prevalent, mediating key processes such as signal transduction and protein trafficking. How can peptides overcome the entropic cost involved in switching from an unstructured, flexible peptide to a rigid, well-defined bound structure? A structure-based analysis of peptide-protein interactions unravels that most peptides do not induce conformational changes on their partner upon binding, thus minimizing the entropic cost of binding. Furthermore, peptides display interfaces that are better packed than protein-protein interfaces and contain significantly more hydrogen bonds, mainly those involving the peptide backbone. Additionally, "hot spot" residues contribute most of the binding energy. Finally, peptides tend to bind in the largest pockets available on the protein surface. Our study is based on peptiDB, a new and comprehensive data set of 103 high-resolution peptide-protein complex structures. In addition to improved understanding of peptide-protein interactions, our findings have direct implications for the structural modeling, design, and manipulation of these interactions.

DynaDock: A new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well-established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein-peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics-based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft-core potentials for the protein-peptide interactions and applies a new optimization scheme to the soft-core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft-core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein-peptide complexes with 2-16mer peptides. Docking poses with peptide RMSD values <2.10 A from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 A. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD < or = 2.10 A.

Identification of structural mechanisms of HIV-1 protease specificity using computational peptide docking: implications for drug resistance

Drug-resistant mutations (DRMs) in HIV-1 protease are a major challenge to antiretroviral therapy. Protease-substrate interactions that are determined to be critical for native selectivity could serve as robust targets for drug design that are immune to DRMs. In order to identify the structural mechanisms of selectivity, we developed a peptide-docking algorithm to predict the atomic structure of protease-substrate complexes and applied it to a large and diverse set of cleavable and noncleavable peptides. Cleavable peptides showed significantly lower energies of interaction than noncleavable peptides with six protease active-site residues playing the most significant role in discrimination. Surprisingly, all six residues correspond to sequence positions associated with drug resistance mutations, demonstrating that the very residues that are responsible for native substrate specificity in HIV-1 protease are altered during its evolution to drug resistance, suggesting that drug resistance and substrate selectivity may share common mechanisms.

Characterization of a key neutralizing epitope on pertussis toxin recognized by monoclonal antibody 1B7

Despite more than five decades of research and vaccination, infection by Bordetella pertussis remains a serious disease with no specific treatments or validated correlates of protective immunity. Of the numerous monoclonal antibodies binding pertussis toxin (PTx) that have been produced and characterized, murine IgG2a monoclonal antibody 1B7 is uniquely neutralizing in all in vitro assays and in vivo murine models of infection. 1B7 binds an epitope on the enzymatically active S1 subunit of PTx (PTx-S1) with some linear elements, but previous work with S1 scanning peptides, phage-displayed peptide libraries, and S1 truncation/deletion variants was unable to more precisely define the epitope. Using computational docking algorithms, alanine scanning mutagenesis, and surface plasmon resonance, we characterize the epitope bound by 1B7 on PTx-S1 in molecular detail and define energetically important interactions between residues at the interface. Six residues on PTx-S1 and six residues on 1B7 were identified that, when altered to alanine, resulted in variants with significantly reduced affinity for the native partner. Using this information, a model of the 1B7-S1 interaction was developed, indicating a predominantly conformational epitope located on the base of S1 near S4. The location of this epitope is consistent with previous data and is shown to be conserved across several naturally occurring strain variants, including PTx-S1A, -B (Tohama-I), -D, and -E (18-323) in addition to the catalytically inactive 9K/129G variant. This highly neutralizing but poorly immunogenic epitope may represent an important target for next-generation vaccine development, identification of immune correlates, and passive immunization strategies for pertussis.

Antigenic characteristics of rhinovirus chimeras designed in silico for enhanced presentation of HIV-1 gp41 epitopes [corrected]

The development of an effective AIDS vaccine remains the most promising long-term strategy to combat human immunodeficiency virus (HIV)/AIDS. Here, we report favorable antigenic characteristics of vaccine candidates isolated from a combinatorial library of human rhinoviruses displaying the ELDKWA epitope of the gp41 glycoprotein of HIV-1. The design principles of this library emerged from the application of molecular modeling calculations in conjunction with our knowledge of previously obtained ELDKWA-displaying chimeras, including knowledge of a chimera with one of the best 2F5-binding characteristics obtained to date. The molecular modeling calculations identified the energetic and structural factors affecting the ability of the epitope to assume conformations capable of fitting into the complementarity determining region of the ELDKWA-binding, broadly neutralizing human mAb 2F5. Individual viruses were isolated from the library following competitive immunoselection and were tested using ELISA and fluorescence quenching experiments. Dissociation constants obtained using both techniques revealed that some of the newly isolated chimeras bind 2F5 with greater affinity than previously identified chimeric rhinoviruses. Molecular dynamics simulations of two of these same chimeras confirmed that their HIV inserts were partially preorganized for binding, which is largely responsible for their corresponding gains in binding affinity. The study illustrates the utility of combining structure-based experiments with computational modeling approaches for improving the odds of selecting vaccine component designs with preferred antigenic characteristics. The results obtained also confirm the flexibility of HRV as a presentation vehicle for HIV epitopes and the potential of this platform for the development of vaccine components against AIDS.

EM-fold: De novo folding of alpha-helical proteins guided by intermediate-resolution electron microscopy density maps

In medium-resolution (7-10 A) cryo-electron microscopy (cryo-EM) density maps, alpha helices can be identified as density rods whereas beta-strand or loop regions are not as easily discerned. We are proposing a computational protein structure prediction algorithm "EM-Fold" that resolves the density rod connectivity ambiguity by placing predicted alpha helices into the density rods and adding missing backbone coordinates in loop regions. In a benchmark of 11 mainly alpha-helical proteins of known structure a native-like model is identified in eight cases (rmsd 3.9-7.9 A). The three failures can be attributed to inaccuracies in the secondary structure prediction step that precedes EM-Fold. EM-Fold has been applied to the approximately 6 A resolution cryo-EM density map of protein IIIa from human adenovirus. We report the first topological model for the alpha-helical 400 residue N-terminal region of protein IIIa. EM-Fold also has the potential to interpret medium-resolution density maps in X-ray crystallography.

The second wave of synthetic biology: from modules to systems

Synthetic biology is a research field that combines the investigative nature of biology with the constructive nature of engineering. Efforts in synthetic biology have largely focused on the creation and perfection of genetic devices and small modules that are constructed from these devices. But to view cells as true 'programmable' entities, it is now essential to develop effective strategies for assembling devices and modules into intricate, customizable larger scale systems. The ability to create such systems will result in innovative approaches to a wide range of applications, such as bioremediation, sustainable energy production and biomedical therapies.

Peptidic modulators of protein-protein interactions: progress and challenges in computational design

With the decline in productivity of drug-development efforts, novel approaches to rational drug design are being introduced and developed. Naturally occurring and synthetic peptides are emerging as novel promising compounds that can specifically and efficiently modulate signaling pathways in vitro and in vivo. We describe sequence-based approaches that use peptides to mimic proteins in order to inhibit the interaction of the mimicked protein with its partners. We then discuss a structure-based approach, in which protein-peptide complex structures are used to rationally design and optimize peptidic inhibitors. We survey flexible peptide docking techniques and discuss current challenges and future directions in the rational design of peptidic inhibitors.

Challenges in the computational design of proteins

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

PROTDES: CHARMM toolbox for computational protein design

We present an open-source software able to automatically mutate any residue positions and find the best aminoacids in an arbitrary protein structure without requiring pairwise approximations. Our software, PROTDES, is based on CHARMM and it searches automatically for mutations optimizing a protein folding free energy. PROTDES allows the integration of molecular dynamics within the protein design. We have implemented an heuristic optimization algorithm that iteratively searches the best aminoacids and their conformations for an arbitrary set of positions within a structure. Our software allows CHARMM users to perform protein design calculations and to create their own procedures for protein design using their own energy functions. We show this by implementing three different energy functions based on different solvent treatments: surface area accessibility, generalized Born using molecular volume and an effective energy function. PROTDES, a tutorial, parameter sets, configuration tools and examples are freely available at http://soft.synth-bio.org/protdes.html.

High-resolution design of a protein loop

Despite having irregular structure, protein loops often adopt specific conformations that are critical to protein function. Most studies in de novo protein design have focused on creating proteins with regular elements of secondary structure connected by very short loops or turns. To design longer protein loops that adopt specific conformations, we have developed a protocol within the Rosetta molecular modeling program that iterates between optimizing the sequence and conformation of a loop in search of low-energy sequence-structure pairs. We have tested the procedure by designing 10-residue loops for the connection between the second and third strand in the beta-sandwich protein tenascin. Three low-energy designs from 7,200 flexible backbone trajectories were selected for experimental characterization. All three designs, called LoopA, LoopB, and LoopC, adopt stable folded structures. High-resolution crystal structures of LoopA and LoopB have been solved. LoopB adopts a structure very similar to the design model (0.46 A rmsd), and all but one of the side chains are modeled in the correct rotamers. LoopA crystallized at low pH in a structure that differs dramatically from our design model. It forms a strand-swapped dimer mediated by hydrogen bonds to protonated glutamic acids. Gel filtration indicates that the protein is not a dimer at neutral pH. These results suggest that the high-resolution design of protein loops is possible; however, they also highlight how small changes in protein energetics can dramatically perturb the low free energy structure of a protein.

Computational design of antibody-affinity improvement beyond in vivo maturation

Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti-epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.

Substrate-induced conformational changes and dynamics of UDP-N-acetylgalactosamine:polypeptide N-acetylgalactosaminyltransferase-2

O-Glycan biosynthesis is initiated by the transfer of N-acetylgalactosamine (GalNAc) from a nucleotide sugar donor (UDP-GalNAc) to Ser/Thr residues of an acceptor substrate. The detailed transfer mechanism, catalyzed by the UDP-GalNAc polypeptide:N-acetyl-alpha-galactosaminyltransferases (ppGalNAcTs), remains unclear despite structural information available for several isoforms in complex with substrates at various stages along the catalytic pathway. We used all-atom molecular dynamics simulations with explicit solvent and counterions to study the conformational dynamics of ppGalNAcT-2 in several enzymatic states along the catalytic pathway. ppGalNAcT-2 is simulated both in the presence and in the absence of substrates and reaction products to examine the role of conformational changes in ligand binding. In multiple 40-ns-long simulations of more than 600 ns total run time, we studied systems ranging from 45,000 to 95,000 atoms. Our simulations accurately identified dynamically active regions of the protein, as previously revealed by the X-ray structures, and permitted a detailed, atomistic description of the conformational changes of loops near the active site and the characterization of the ensemble of structures adopted by the transferase complex on the transition pathway between the ligand-bound and ligand-free states. In particular, the conformational transition of a functional loop adjacent to the active site from closed (active) to open (inactive) is correlated with the rotameric state of the conserved residue W331. Analysis of water dynamics in the active site revealed that internal water molecules have an important role in enhancing the enzyme flexibility. We also found evidence that charged side chains in the active site rearrange during site opening to facilitate ligand binding. Our results are consistent with the single-displacement transfer mechanism previously proposed for ppGalNAcTs based on X-ray structures and mutagenesis data and provide new evidence for possible functional roles of certain amino acids conserved across several isoforms.

Progress in computational protein design

Current progress in computational structure-based protein design is reviewed in the areas of methodology and applications. Foundational advances include new potential functions, more efficient ways of computing energetics, flexible treatments of solvent, and useful energy function approximations, as well as ensemble-based approaches to scoring designs for inclusion of entropic effects, improvements to guaranteed and to stochastic search techniques, and methods to design combinatorial libraries for screening and selection. Applications include new approaches and successes in the design of specificity for protein folding, binding, and catalysis, in the redesign of proteins for enhanced binding affinity, and in the application of design technology to study and alter enzyme catalysis. Computational protein design continues to mature and advance.

Maintaining solvent accessible surface area under rotamer substitution for protein design

Although quantities derived from solvent accessible surface areas (SASA) are useful in many applications in protein design and structural biology, the computational cost of accurate SASA calculation makes SASA-based scores difficult to integrate into commonly used protein design methodologies. We demonstrate a method for maintaining accurate SASA during a Monte Carlo search of sequence and rotamer space for a fixed protein backbone. We extend the fast Le Grand and Merz algorithm (Le Grand and Merz, J Comput Chem, 14, 349), which discretizes the solvent accessible surface for each atom by placing dots on a sphere and combines Boolean masks to determine which dots are exposed. By replacing semigroup operations with group operations (from Boolean logic to counting dot coverage) we support SASA updates. Our algorithm takes time proportional to the number of atoms affected by rotamer substitution, rather than the number of atoms in the protein. For design simulations with a one hundred residue protein our approach is approximately 145 times faster than performing a Le Grand and Merz SASA calculation from scratch following each rotamer substitution. To demonstrate practical effectiveness, we optimize a SASA-based measure of protein packing in the complete redesign of a large set of proteins and protein-protein interfaces.

The Use of Quantum Molecular Calculations to Guide a Genetic Algorithm: A Way to Search for New Chemistry

The process of gene-based molecular evolution has been simulated in silico by using massively parallel density functional theory quantum calculations, coupled with a genetic algorithm, to test for fitness with respect to a target chemical reaction in populations of genetically encoded molecules. The goal of this study was the identification of transition-metal complexes capable of mediating a known reaction, namely the cleavage of N(2) to give the metal nitride. Each complex within the search space was uniquely specified by a nanogene consisting of an eight-digit number. Propagation of an individual nanogene into successive generations was determined by the fitness of its phenotypic molecule to perform the target reaction and new generations were created by recombination and mutation of surviving nanogenes. In its simplest implementation, the quantum-directed genetic algorithm (QDGA) quickly located a local minimum on the evolutionary fitness hypersurface, but proved incapable of progressing towards the global minimum. A strategy for progressing beyond local minima consistent with the Darwinian paradigm by the use of environmental variations coupled with mass extinctions was therefore developed. This allowed for the identification of nitriding complexes that are very closely related to known examples from the chemical literature. Examples of mutations that appear to be beneficial at the genetic level but prove to be harmful at the phenotypic level are described. As well as revealing fundamental aspects of molecular evolution, QDGA appears to be a powerful tool for the identification of lead compounds capable of carrying out a target chemical reaction.