PaFlexPepDock: parallel ab-initio docking of peptides onto their receptors with full flexibility based on Rosetta

Prediction and Modeling of Protein–Protein Interactions Using “Spotted” Peptides with a Template-Based Approach

Protein–peptide interactions (PpIs) are a subset of the overall protein–protein interaction (PPI) network in the living cell and are pivotal for the majority of cell processes and functions. High-throughput methods to detect PpIs and PPIs usually require time and costs that are not always affordable. Therefore, reliable in silico predictions represent a valid and effective alternative. In this work, a new algorithm is described, implemented in a freely available tool, i.e., “PepThreader”, to carry out PPIs and PpIs prediction and analysis. PepThreader threads multiple fragments derived from a full-length protein sequence (or from a peptide library) onto a second template peptide, in complex with a protein target, “spotting” the potential binding peptides and ranking them according to a sequence-based and structure-based threading score. The threading algorithm first makes use of a scoring function that is based on peptides sequence similarity. Then, a rerank of the initial hits is performed, according to structure-based scoring functions. PepThreader has been benchmarked on a dataset of 292 protein–peptide complexes that were collected from existing databases of experimentally determined protein–peptide interactions. An accuracy of 80%, when considering the top predicted 25 hits, was achieved, which performs in a comparable way with the other state-of-art tools in PPIs and PpIs modeling. Nonetheless, PepThreader is unique in that it is able at the same time to spot a binding peptide within a full-length sequence involved in PPI and model its structure within the receptor. Therefore, PepThreader adds to the already-available tools supporting the experimental PPIs and PpIs identification and characterization.

Cytotoxic T-lymphocyte elicited therapeutic vaccine candidate targeting cancer against MAGE-A11 carcinogenic protein

Immunotherapy is a breakthrough approach for cancer treatment and prevention. By exploiting the fact that cancer cells have overexpression of tumor antigens responsible for its growth and progression, which can be identified and removed by boosting the immune system. In silico techniques have provided efficient ways for developing preventive measures to ward off cancer. Herein, we have designed a potent cytotoxic T-lymphocyte epitope to elicit a desirable immune response against carcinogenic melanoma-associated antigen-A11. Potent epitope was predicted using reliable algorithms and characterized by advanced computational avenue CABS molecular dynamics simulation, for full flexible binding with HLA-A*0201 and androgen receptor to large-scale rearrangements of the complex system. Results showed the potent immunogenic construct (KIIDLVHLL), from top epitopes using five algorithms. Molecular docking analyses showed the strong binding of epitope with HLA-A*0201 and androgen receptor with docking score of -780.6 and -641.06 kcal/mol, respectively. Molecular dynamics simulation analysis revealed strong binding of lead epitope with androgen receptor by involvement of 127 elements through atomic-model study. Full flexibility study showed stable binding of epitope with an average root mean square deviation (RMSD) 2.21 Å and maximum RMSD value of 6.48 Å in optimal cluster density area. The epitope also showed remarkable results with radius of gyration 23.0777 Å, world population coverage of 39.08% by immune epitope database, and transporter associated with antigen processing (TAP) affinity IC50 value of 2039.65 nm. Moreover, in silico cloning approach confirmed the expression and translation capacity of the construct within a suitable expression vector. The present study paves way for a potential immunogenic construct for prevention of cancer.

Recent Advances and Computational Approaches in Peptide Drug Discovery

BACKGROUND

Drug design and development is a vast field that requires huge investment along with a long duration for providing approval to suitable drug candidates. With the advancement in the field of genomics, the information about druggable targets is being updated at a fast rate which is helpful in finding a cure for various diseases.

METHODS

There are certain biochemicals as well as physiological advantages of using peptide-based therapeutics. Additionally, the limitations of peptide-based drugs can be overcome by modulating the properties of peptide molecules through various biomolecular engineering techniques. Recent advances in computational approaches have been helpful in studying the effect of peptide drugs on the biomolecular targets. Receptor - ligand-based molecular docking studies have made it easy to screen compatible inhibitors against a target.Furthermore, there are simulation tools available to evaluate stability of complexes at the molecular level. Machine learning methods have added a new edge by enabling accurate prediction of therapeutic peptides.

RESULTS

Peptide-based drugs are expected to take over many popular drugs in the near future due to their biosafety, lower off-target binding chances and multifunctional properties.

CONCLUSION

This article summarises the latest developments in the field of peptide-based therapeutics related to their usage, tools, and databases.

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.

Antimicrobial Peptide Designing and Optimization Employing Large-Scale Flexibility Analysis of Protein-Peptide Fragments

The mankind relies on the use of antibiotics for a healthy life. The epidemic-like emergence of drug-resistant bacterial strains is increasingly becoming one of the leading causes of morbidity and mortality, which gives rise to design a potential antimicrobial peptide (AMP). Here, we have designed the potential AMP using the extensive dynamics simulation since protein-peptide interactions are linked to large conformational changes. Therefore, we have employed the advanced computational avenue CABS molecular docking method that enabled the flexible peptide-protein molecular docking with a large-scale rearrangement of the protein. Lead AMP was investigated against the wild-type (WT) and mutant-PBP5 (MT-PBP5) proteins (antiresistance property). AMP20 showed strong interactions with wtPBP5 and mtPBP5 and involvement of a large number of elements in interactions determined through an atomic model study. Full flexibility analysis showed the stable interaction of AMP20 with both the wild-type and mutant form of PBP5 with root-mean-square deviation (RMSD) values of ∼4.51 and 4.85 Å, respectively. Moreover, peptide dynamics showed involvement of all residues of AMP20 through contact map analysis, and extensive simulation confirmed the stable interaction of AMP20, with lower values of RMSD, radius of gyration, and root-mean-square fluctuation. This study paves the way for a potential approach to design the AMP with amino acid walking and large-scale conformational rearrangements of amino acids.

Towards Unraveling the Histone Code by Fragment Blind Docking

Histones serve as protein spools for winding the DNA in the nucleosome. High variability of their post-translational modifications result in a unique code system often responsible for the pathomechanisms of epigenetics-based diseases. Decoding is performed by reader proteins via complex formation with the N-terminal peptide tails of histones. Determination of structures of histone-reader complexes would be a key to unravel the histone code and the design of new drugs. However, the large number of possible histone complex variations imposes a true challenge for experimental structure determination techniques. Calculation of such complexes is difficult due to considerable size and flexibility of peptides and the shallow binding surfaces of the readers. Moreover, location of the binding sites is often unknown, which requires a blind docking search over the entire surface of the target protein. To accelerate the work in this field, a new approach is presented for prediction of the structure of histone H3 peptide tails docked to their targets. Using a fragmenting protocol and a systematic blind docking method, a collection of well-positioned fragments of the H3 peptide is produced. After linking the fragments, reconstitution of anchoring regions of the target-bound H3 peptide conformations was possible. As a first attempt of combination of blind and fragment docking approaches, our new method is named fragment blind docking (FBD).

Protein-peptide molecular docking with large-scale conformational changes: the p53-MDM2 interaction

Protein-peptide interactions are often associated with large-scale conformational changes that are difficult to study either by classical molecular modeling or by experiment. Recently, we have developed the CABS-dock method for flexible protein-peptide docking that enables large-scale rearrangements of the protein chain. In this study, we use CABS-dock to investigate the binding of the p53-MDM2 complex, an element of the cell cycle regulation system crucial for anti-cancer drug design. Experimental data suggest that p53-MDM2 binding is affected by significant rearrangements of a lid region - the N-terminal highly flexible MDM2 fragment; however, the details are not clear. The large size of the highly flexible MDM2 fragments makes p53-MDM2 intractable for exhaustive binding dynamics studies using atomistic models. We performed extensive dynamics simulations using the CABS-dock method, including large-scale structural rearrangements of MDM2 flexible regions. Without a priori knowledge of the p53 peptide structure or its binding site, we obtained near-native models of the p53-MDM2 complex. The simulation results match well the experimental data and provide new insights into the possible role of the lid fragment in p53 binding. The presented case study demonstrates that CABS-dock methodology opens up new opportunities for protein-peptide docking with large-scale changes of the protein receptor structure.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

AnchorDock: Blind and Flexible Anchor-Driven Peptide Docking

The huge conformational space stemming from the inherent flexibility of peptides is among the main obstacles to successful and efficient computational modeling of protein-peptide interactions. Current peptide docking methods typically overcome this challenge using prior knowledge from the structure of the complex. Here we introduce AnchorDock, a peptide docking approach, which automatically targets the docking search to the most relevant parts of the conformational space. This is done by precomputing the free peptide's structure and by computationally identifying anchoring spots on the protein surface. Next, a free peptide conformation undergoes anchor-driven simulated annealing molecular dynamics simulations around the predicted anchoring spots. In the challenging task of a completely blind docking test, AnchorDock produced exceptionally good results (backbone root-mean-square deviation ≤ 2.2Å, rank ≤15) for 10 of 13 unbound cases tested. The impressive performance of AnchorDock supports a molecular recognition pathway that is driven via pre-existing local structural elements.