PeptideMine - A webserver for the design of peptides for protein-peptide binding studies derived from protein-protein interactomes

Background  

Signal transduction events often involve transient, yet specific, interactions between structurally conserved protein domains and polypeptide sequences in target proteins. The identification and validation of these associating domains is crucial to understand signal transduction pathways that modulate different cellular or developmental processes. Bioinformatics strategies to extract and integrate information from diverse sources have been shown to facilitate the experimental design to understand complex biological events. These methods, primarily based on information from high-throughput experiments, have also led to the identification of new connections thus providing hypothetical models for cellular events. Such models, in turn, provide a framework for directing experimental efforts for validating the predicted molecular rationale for complex cellular processes. In this context, it is envisaged that the rational design of peptides for protein-peptide binding studies could substantially facilitate the experimental strategies to evaluate a predicted interaction. This rational design procedure involves the integration of protein-protein interaction data, gene ontology, physico-chemical calculations, domain-domain interaction data and information on functional sites or critical residues.     

Targeted inhibition of amyloidogenesis using a non-toxic,serum stable strategically designed cyclic peptide with therapeutic implications

Amyloidogenic disorders are currently rising as a global health issue, prompting more and more studies dedicated to the development of effective targeted therapeutics. The innate affinity of these amyloidogenic proteins towards the biomembranes adds further complexities to the systems. Our previous studies have shown that biologically active peptides can effectively target amyloidogenesis serving as an efficient therapeutic alternative in several amyloidogenic disorders. The structural uniqueness of the PWWP motif in the de novo designed heptapeptide, KR7 (KPWWPRR-NH₂) was demonstrated to target insulin fiber elongation specifically. By working on insulin, an important model system in amyloidogenic studies, we gained several mechanistic insights into the inhibitory actions at the protein-peptide interface. Here, we report a second-generation non-toxic and serum stable cyclic peptide, based primarily on the PWWP motif that resulted in complete inhibition of insulin fibrillation both in the presence and absence of the model membranes. Using both low- and high-resolution spectroscopic techniques, we could delineate the specific mechanism of inhibition, at atomistic resolution. Our studies put forward an effective therapeutic intervention that redirects the default aggregation kinetics towards off-pathway fibrillation. Based on the promising results, this novel cyclic peptide can be considered an excellent lead to design pharmaceutical molecules against amyloidogenesis.     

Rationally designed interfacial peptides are efficient in vitro inhibitors of HIV-1 capsid assembly with antiviral activity

Virus capsid assembly constitutes an attractive target for the development of antiviral therapies; a few experimental inhibitors of this process for HIV-1 and other viruses have been identified by screening compounds or by selection from chemical libraries. As a different, novel approach we have undertaken the rational design of peptides that could act as competitive assembly inhibitors by mimicking capsid structural elements involved in intersubunit interfaces. Several discrete interfaces involved in formation of the mature HIV-1 capsid through polymerization of the capsid protein CA were targeted. We had previously designed a peptide, CAC1, that represents CA helix 9 (a major part of the dimerization interface) and binds the CA C-terminal domain in solution. Here we have mapped the binding site of CAC1, and shown that it substantially overlaps with the CA dimerization interface. We have also rationally modified CAC1 to increase its solubility and CA-binding affinity, and designed four additional peptides that represent CA helical segments involved in other CA interfaces. We found that peptides CAC1, its derivative CAC1M, and H8 (representing CA helix 8) were able to efficiently inhibit the in vitro assembly of the mature HIV-1 capsid. Cocktails of several peptides, including CAC1 or CAC1M plus H8 or CAI (a previously discovered inhibitor of CA polymerization), or CAC1M+H8+CAI, also abolished capsid assembly, even when every peptide was used at lower, sub-inhibitory doses. To provide a preliminary proof that these designed capsid assembly inhibitors could eventually serve as lead compounds for development of anti-HIV-1 agents, they were transported into cultured cells using a cell-penetrating peptide, and tested for antiviral activity. Peptide cocktails that drastically inhibited capsid assembly in vitro were also able to efficiently inhibit HIV-1 infection ex vivo. This study validates a novel, entirely rational approach for the design of capsid assembly interfacial inhibitors that show antiviral activity.     

Cell-permeable chameleonic peptides: Exploiting conformational dynamics in de novo cyclic peptide design

Membrane-traversing peptides offer opportunities for targeting intracellular proteins and oral delivery. Despite progress in understanding the mechanisms underlying membrane traversal in natural cell-permeable peptides, there are still several challenges to designing membrane-traversing peptides with diverse shapes and sizes. Conformational flexibility appears to be a key determinant of membrane permeability of large macrocycles. We review recent developments in the design and validation of chameleonic cyclic peptides, which can switch between alternative conformations to enable improved permeability through cell membranes, while still maintaining reasonable solubility and exposed polar functional groups for target protein binding. Finally, we discuss the principles, strategies, and practical considerations for rational design, discovery, and validation of permeable chameleonic peptides.     

A hidden Markov model for predicting protein interfaces

Protein-protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.     

VitAL: Viterbi Algorithm for de novo Peptide Design

Background

Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.

Methodology/Principal Findings

A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.

Conclusions/Significance

The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.     

In vitro selection of nucleic acids and proteins: What are we learning?

For almost a decade, in vitro selection experiments have been used to isolate novel nucleic acids, peptides and proteins according to their function. Selection experiments have altered our perception of molecular mimicry and catalysis, and they appear to be more facile than rational design at generating biopolymers with desired properties. New methods that have been developed improve the power of functional strategies in ways that nature has already discovered - by expanding library size and facilitating the recombination of positive mutations. Recent structural information on a number of selected and evolved molecules highlights future challenges for design via rational approaches.     

Peptide-mediated interactions in biological systems: new discoveries and applications

Peptide-mediated interactions play very important roles in cellular processes. Recent years have seen much activity in the discovery of new bioactive peptides, and interactions mediated by protein-peptide binding events. At the same time, computational approaches continue to be developed that allow protein-peptide interactions to be discovered with great accuracy. There are also a growing number of chemicals that can target these interactions with various applications in disease. Both new discoveries and predictions suggest that these protein-peptide interactions play greater roles in cellular processes than previously thought. We propose that projects to uncover the protein-peptide repertoire used in Nature in a systematic way will have numerous applications in molecular biology and medicine.     

Specific peptides for the therapeutic targeting of oncogenes

Tumors are dependent on oncogenic proteins for their maintenance and survival. The ideal cancer therapy would include drugs that specifically target these proteins. Many such proteins function through interfaces that can be difficult to target effectively with small molecules. However, recent advances in cell-permeable peptide technology, improving cellular penetration and stability, raise the possibility that specific peptide interference of oncogenic proteins could be successfully translated to the clinic. Several active anti-tumor peptides were recently described. For example, a stable peptide inhibitor of the Hsp90 ATP-binding pocket killed a wide range of tumors in vitro and in vivo, and a peptide inhibitor of the BCL6 oncoprotein was active in B-cell lymphomas; both peptides functioned without toxicity to normal tissues.     

Structure-lipophilicity relationships of peptides and peptidomimetics

Summary Understanding the physicochemical and structural properties of peptides are important prerequisites for the rational design of bioactive peptides and peptidomimetics. The present contribution reviews methods used for the assessment and prediction of lipophilicity (or hydrophobicity) and their correlation with structural elements of peptides and closely related peptidomimetics.     

Leveraging structure determination with fragment screening for infectious disease drug targets: MECP synthase from Burkholderia pseudomallei

As part of the Seattle Structural Genomics Center for Infectious Disease, we seek to enhance structural genomics with ligand-bound structure data which can serve as a blueprint for structure-based drug design. We have adapted fragment-based screening methods to our structural genomics pipeline to generate multiple ligand-bound structures of high priority drug targets from pathogenic organisms. In this study, we report fragment screening methods and structure determination results for 2C-methyl-D-erythritol-2,4-cyclo-diphosphate (MECP) synthase from Burkholderia pseudomallei, the gram-negative bacterium which causes melioidosis. Screening by nuclear magnetic resonance spectroscopy as well as crystal soaking followed by X-ray diffraction led to the identification of several small molecules which bind this enzyme in a critical metabolic pathway. A series of complex structures obtained with screening hits reveal distinct binding pockets and a range of small molecules which form complexes with the target. Additional soaks with these compounds further demonstrate a subset of fragments to only bind the protein when present in specific combinations. This ensemble of fragment-bound complexes illuminates several characteristics of MECP synthase, including a previously unknown binding surface external to the catalytic active site. These ligand-bound structures now serve to guide medicinal chemists and structural biologists in rational design of novel inhibitors for this enzyme.     

Protein engineering in bioelectrocatalysis

Electrochemistry of redox proteins is a broadly applicable technology with important applications in biosensors, biofuel cells and chemical syntheses. Escalating attention in this area is driven by remarkable progress in designing efficient interfaces for transferring electrons between electrode surfaces and redox proteins. Research in interface design is slowly shifting from modifying electrode surfaces towards the engineering of redox proteins. Protein engineering, which encompasses rational design, directed evolution and combined methods, offers many powerful methods and strategies for improving the electron transfer properties of redox proteins.     

Discovery of nonpeptide,peptidomimetic peptidase inhibitors that target alternate enzyme active site conformations

Structure-generating programs provide rational methods to rapidly design novel scaffolds targeting the biologic receptor of choice. Recent research has demonstrated proteins equilibrate between families of conformations (ensembles) for which drug design may target. New methods are currently being developed utilizing structure-generating programs to target alternate enzyme conformations in an attempt to overcome the challenge of developing therapeutically useful molecules. These new methods provide the potential to overcome bioavailability problems encountered with peptide and peptide-like molecules by identifying novel small molecule scaffolds.     

RNABindRPlus: A Predictor that Combines Machine Learning and Sequence Homology-Based Methods to Improve the Reliability of Predicted RNA-Binding Residues in Proteins

Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.     

Structural resiliency of an EGF-like subdomain bound to its target protein, thrombin.

The thrombin-bound structures of native peptide fragments from the fifth EGF-like domain of thrombomodulin were determined by use of NMR and transferred NOE spectroscopy. The bound peptides assume an EGF-like structure of an antiparallel beta-sheet, a novel structural motif observed for a bound peptide in protein-peptide complexes. There is a remarkable structural resiliency of this structure motif manifested in its ability to accommodate a different number of residues within the disulfide loop. Docking experiments revealed that the key contacts with thrombin are hydrophobic interactions between the side chains of residues Ile 414 and Ile 424 of thrombomodulin and a hydrophobic pocket on the thrombin surface. Residues Leu 415, Phe 419, and Ile 420, which would have been buried in intact EGF-like domains, are unfavorably exposed in the complex of thrombin with the EGF-like thrombomodulin fragment, thus providing a rationale for the enhancement of binding affinity upon the deletion of Ile 420. The unique beta-sheet structures of the bound peptides are specified by the presence of disulfide bridges in the peptides because a corresponding linear thrombomodulin fragment folds into a sheet structure with a different backbone topology. The different bound conformations for the linear and the cyclized peptides indicate that side-chain interactions within a specific environment may dictate the folding of bound peptides in protein-peptide complexes.     

Precise structural determination of weakly binding peptides by utilizing dihedral angle constraints

Structural determination of target-bound conformations of peptides is of primary importance for the optimization of peptide ligands and peptide–mimetic design. In the structural determination of weakly binding ligands, transferred nuclear Overhauser effect (TrNOE) methods have been widely used. However, not many distance constraints can be obtained from small peptide ligands by TrNOE, especially for peptides bound to a target molecule in an extended conformation. Therefore, for precise structural determination of weakly binding peptides, additional structural constraints are required. Here, we present a strategy to systematically introduce dihedral angle constraints obtained from multiple transferred cross-correlated relaxation experiments and demonstrate precise structures of weakly binding peptides. As a result, we could determine the bioactive conformations of phage-derived peptide ligands and define their core binding motifs.     

Bacteria use structural imperfect mimicry to hijack the host interactome

Bacteria use protein-protein interactions to infect their hosts and hijack fundamental pathways, which ensures their survival and proliferation. Hence, the infectious capacity of the pathogen is closely related to its ability to interact with host proteins. Here, we show that hubs in the host-pathogen interactome are isolated in the pathogen network by adapting the geometry of the interacting interfaces. An imperfect mimicry of the eukaryotic interfaces allows pathogen proteins to actively bind to the host’s target while preventing deleterious effects on the pathogen interactome. Understanding how bacteria recognize eukaryotic proteins may pave the way for the rational design of new antibiotic molecules.     

Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.