HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm

Improving Protein-Peptide Docking Results via Pose-Clustering and Rescoring with a Combined Knowledge-Based and MM-GBSA Scoring Function

Protein-peptide docking, which predicts the complex structure between a protein and a peptide, is a valuable computational tool in peptide therapeutics development and the mechanistic investigation of peptides involved in cellular processes. Although current peptide docking approaches are often able to sample near-native peptide binding modes, correctly identifying those near-native modes from decoys is still challenging because of the extremely high complexity of the peptide binding energy landscape. In this study, we have developed an efficient postdocking rescoring protocol using a combined scoring function of knowledge-based ITScorePP potentials and physics-based MM-GBSA energies. Tested on five benchmark/docking test sets, our postdocking strategy showed an overall significantly better performance in binding mode prediction and score-rmsd correlation than original docking approaches. Specifically, our postdocking protocol outperformed original docking approaches with success rates of 15.8 versus 10.5% for pepATTRACT on the Global_57 benchmark, 5.3 versus 5.3% for CABS-dock on the Global_57 benchmark, 17.0 versus 11.3% for FlexPepDock on the LEADS-PEP data set, 40.3 versus 33.9% for HPEPDOCK on the Local_62 benchmark, and 64.2 versus 52.8% for HPEPDOCK on the LEADS-PEP data set when the top prediction was considered. These results demonstrated the efficacy and robustness of our postdocking protocol.

Targeting Tumors Using Peptides

To penetrate solid tumors, low molecular weight (Mw < 10 KDa) compounds have an edge over antibodies: their higher penetration because of their small size. Because of the dense stroma and high interstitial fluid pressure of solid tumors, the penetration of higher Mw compounds is unfavored and being small thus becomes an advantage. This review covers a wide range of peptidic ligands—linear, cyclic, macrocyclic and cyclotidic peptides—to target tumors: We describe the main tools to identify peptides experimentally, such as phage display, and the possible chemical modifications to enhance the properties of the identified peptides. We also review in silico identification of peptides and the most salient non-peptidic ligands in clinical stages. We later focus the attention on the current validated ligands available to target different tumor compartments: blood vessels, extracelullar matrix, and tumor associated macrophages. The clinical advances and failures of these ligands and their therapeutic conjugates will be discussed. We aim to present the reader with the state-of-the-art in targeting tumors, by using low Mw molecules, and the tools to identify new ligands.

Challenges and opportunities of automated protein-protein docking: HDOCK server vs human predictions in CAPRI Rounds 38-46

Protein-protein docking plays an important role in the computational prediction of the complex structure between two proteins. For years, a variety of docking algorithms have been developed, as witnessed by the critical assessment of prediction interactions (CAPRI) experiments. However, despite their successes, many docking algorithms often require a series of manual operations like modeling structures from sequences, incorporating biological information, and selecting final models. The difficulties in these manual steps have significantly limited the applications of protein-protein docking, as most of the users in the community are nonexperts in docking. Therefore, automated docking like a web server, which can give a comparable performance to human docking protocol, is pressingly needed. As such, we have participated in the blind CAPRI experiments for Rounds 38-45 and CASP13-CAPRI challenge for Round 46 with both our HDOCK automated docking web server and human docking protocol. It was shown that our HDOCK server achieved an "acceptable" or higher CAPRI-rated model in the top 10 submitted predictions for 65.5% and 59.1% of the targets in the docking experiments of CAPRI and CASP13-CAPRI, respectively, which are comparable to 66.7% and 54.5% for human docking protocol. Similar trends can also be observed in the scoring experiments. These results validated our HDOCK server as an efficient automated docking protocol for nonexpert users. Challenges and opportunities of automated docking are also discussed.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

In silico Analysis, Molecular Docking, Molecular Dynamic, Cloning, Expression and Purification of Chimeric Protein in Colorectal Cancer Treatment

Introduction

Colorectal cancer (CRC) is a type of cancer in humans that leads to high mortality and morbidity. CD166 and CD326 are immunoglobulins that are associated with cell migration. These molecules are included in tumorigenesis of CRC and serve a great marker of CRC stem cells. In the present study, we devised a novel chimeric protein including the V₁-domain of the CD166 and two epitopes of CD326 to use in diagnostic or therapeutic applications.

Methods

In silico techniques were launched to characterize the properties and structure of the protein. We have predicted physicochemical properties, structures, stability, MHC class I binding properties and ligand-receptor interaction of this chimeric protein by means of computational bioinformatics tools and servers. The sequence of chimeric gene was optimized for expression in prokaryotic host using online tools and cloned into pET-28a plasmid. The recombinant pET28a was transformed into the E. coli BL21DE3. Expression of recombinant protein was examined by SDS-PAGE and Western blotting.

Results

The designed chimeric protein retained high stability and the same immunogenicity as of the original proteins. Bioinformatics data indicated that the epitopes of the synthetic chimeric protein might induce B-cell- and T-cell-mediated immune responses. Furthermore, a gene was synthesized using the codon bias of a prokaryotic expression system. This synthetic gene expressed a bacterial expression system. The recombinant protein with molecular weights of 27kDa was expressed and confirmed by anti-his Western blot analysis.

Conclusion

The designed recombinant protein may be useful as a CRC diagnostic tool and for developing a protective vaccine against CRC.

Modulation of adiponectin receptors AdipoR1 and AdipoR2 by phage display-derived peptides in in vitro and in vivo models

Type 2 diabetes (T2D) is often linked to metabolic syndrome, which assembles various risk factors related to obesity. Plasma levels of adiponectin are decreased in T2D and obese subjects. Aiming to develop a peptide able to bind adiponectin receptors and modulate their signalling pathways, a 12-amino acid sequence homologous in AdipoR1/R2 has been targeted by phage display with a linear 12-mer peptide library. The selected peptide P17 recognises AdipoR1/R2 expressed by skeletal muscle, liver and pancreatic islets. In HepaRG and C2C12 cells, P17 induced the activation of AMPK (AMPKα-pT172) and the expression of succinate dehydrogenase and glucokinase; no cytotoxic effects were observed on HepaRG cells. In db/db mice, P17 promoted body weight and glycaemia stabilisation, decreased plasma triglycerides to the range of healthy mice and increased adiponectin (in high fat-fed mice) and insulin (in chow-fed mice) levels. It restored to the range of healthy mice the tissue levels and subcellular distribution of AdipoR1/R2, AMPKα-pT172 and PPARα-pS12. In liver, P17 reduced steatosis and apoptosis. The docking of P17 to AdipoR is reminiscent of the binding mechanism of adiponectin. To conclude, we have developed an AdipoR1/AdipoR2-targeted peptide that modulates adiponectin signalling pathways and has therapeutic relevance for T2D and obesity associated pathologies.

In silico evaluation of marine fish proteins as nutritional supplements for COVID-19 patients

To date, no specific drug has been discovered for the treatment of COVID-19 and hence, patients are in a state of anxiety.

PepPro: A Nonredundant Structure Data Set for Benchmarking Peptide-Protein Computational Docking

We present a nonredundant benchmark, coined PepPro, for testing peptide-protein docking algorithms. Currently, PepPro contains 89 nonredundant experimentally determined peptide-protein complex structures, with peptide sequence lengths ranging from 5 to 30 amino acids. The benchmark covers peptides with distinct secondary structures, including helix, partial helix, a mixture of helix and β-sheet, β-sheet formed through binding, β-sheet formed through self-folding, and coil. In addition, unbound proteins' structures are provided for 58 complexes and can be used for testing the ability of a docking algorithm handling the conformational changes of proteins during the binding process. PepPro should benefit the docking community for the development and improvement of peptide docking algorithms. The benchmark is available at http://zoulab.dalton.missouri.edu/PepPro_benchmark. © 2019 Wiley Periodicals, Inc.

PepBDB: a comprehensive structural database of biological peptide-protein interactions

Summary

A structural database of peptide-protein interactions is important for drug discovery targeting peptide-mediated interactions. Although some peptide databases, especially for special types of peptides, have been developed, a comprehensive database of cleaned peptide-protein complex structures is still not available. Such cleaned structures are valuable for docking and scoring studies in structure-based drug design. Here, we have developed PepBDB-a curated Peptide Binding DataBase of biological complex structures from the Protein Data Bank (PDB). PepBDB presents not only cleaned structures but also extensive information about biological peptide-protein interactions, and allows users to search the database with a variety of options and interactively visualize the search results.

Availability and implementation

PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/.

Docking Flexible Cyclic Peptides with AutoDock CrankPep

While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep (ADCP), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.

Rational design of linear tripeptides against the aggregation of human mutant SOD1 protein causing amyotrophic lateral sclerosis

Formation of protein aggregation is considered a hallmark feature of various neurological diseases. Amyotrophic lateral sclerosis is one such devastating neurodegenerative disorder characterized by mutation in Cu/Zn superoxide dismutase protein (SOD1). In our study, we contemplated the most aggregated and pathogenic mutant A4V in a viewpoint of finding a therapeutic regime by inhibiting the formation of the aggregates with the aid of tripeptides since new perspectives in the field of drug design in the current era are being focused on peptide-based drugs. Reports from the experimental study have stipulated that the SOD1 derived peptide, "LSGDHCIIGRTLVVHEKADD" was found to have the inhibitory activity against aggregated SOD1 protein. Moreover, it was determined that the hexapeptide, "LSGDHC" was the key factor in inhibiting the aggregates of SOD1. Accordingly, we utilized the computerized algorithms and programs on determining the binding efficiency and inhibitory activity of hexapeptide on mutant SOD1. Following that, we incorporated a cutting-edge methodology with the use of molecular docking, affinity predictions, alanine scanning, steered molecular dynamics (SMD) and discrete molecular dynamics (DMD) in designing the de novo tripeptides, which could act against the aggregated mutant SOD1 protein. Upon examining the results from the various conformational studies, we identified that CGH had an enhanced binding affinity and inhibitory activity against the aggregated mutant SOD1 protein than other tripeptides and hexapeptide. Thus, our study could be a lead for state-of-the-art design in peptide-based drugs for doctoring the cureless ALS disorder.

Dynamics and Mechanisms in the Recruitment and Transference of Histone Chaperone CIA/ASF1

The recruitment and transference of proteins through protein-protein interactions is a general process involved in various biological functions in cells. Despite the importance of this general process, the dynamic mechanism of how proteins are recruited and transferred from one interacting partner to another remains unclear. In this study, we investigated the dynamic mechanisms of recruitment and translocation of histone chaperone CIA/ASF1 for nucleosome disassembly by exploring the conformational space and the free energy profile of unbound DBD(CCG1) and CIA/ASF1-bound DBD(CCG1) systems through extensive molecular dynamics simulations. It was found that there exists three metastable conformational states for DBD(CCG1), an unbound closed state, a CIA/ASF1-bound half-open state, and an open state. The free energy landscape shows that the closed state and the half-open state are separated by a high free energy barrier, while the half-open state and the open state are connected with a moderate free energy increase. The high free energy barrier between the closed and half-open states explains why DBD(CCG1) can recruit CIA/ASF1 and remain in the binding state during the transportation. In addition, the asymmetric binding of CIA/ASF1 on DBD(CCG1) allows DBD(CCG1) to adopt the open state by moving one of its two domains, such that the exposed domain of DBD(CCG1) is able to recognize the acetylated histone H4 tails. As such, CIA/ASF1 has a chance to translocate from DBD(CCG1) to histone, which is also facilitated by the moderate energy increase from the bound half-open state to the open state of DBD(CCG1). These findings suggest that the recruitment and transference of histone chaperone CIA/ASF1 is highly favored by its interaction with DBD(CCG1) via conformational selection and asymmetric binding, which may represent a general mechanism of similar biological processes.

Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes

A significant number of protein-protein interactions (PPIs) are mediated through the interactions between proteins and peptide segments, and therefore determination of protein-peptide interactions (PpIs) is critical to gain an in-depth understanding of the PPI network and even design peptides or small molecules capable of modulating PPIs. Computational approaches, especially molecular docking, provide an efficient way to model PpIs, and a reliable scoring function that can recognize the correct binding conformations for protein-peptide complexes is one of the most important components in protein-peptide docking. The end-point binding free energy calculation methods, such as MM/GBSA and MM/PBSA, are theoretically more rigorous than most empirical and semi-empirical scoring functions designed for protein-peptide docking, but their performance in predicting binding affinities and binding poses for protein-peptide systems has not been systematically assessed. In this study, we first evaluated the capability of MM/GBSA and MM/PBSA with different solvation models, interior dielectric constants (εin) and force fields to predict the binding affinities for 53 protein-peptide complexes. For the 19 short peptides with 5-12 residues, MM/PBSA based on the minimized structures in explicit solvent with the ff99 force field and εin = 2 yields the best correlation between the predicted binding affinities and the experimental data (rp = 0.748), while for the 34 medium-size peptides with 20-25 residues, MM/GBSA based on 1 ns of molecular dynamics (MD) simulations in implicit solvent with the ff03 force field, the GBOBC1 model and a low interior dielectric constant (εin = 1) yields the best accuracy (rp = 0.735). Then, we assessed the rescoring capability of MM/PBSA and MM/GBSA to distinguish the correct binding conformations from the decoys for 112 protein-peptide systems. The results illustrate that MM/PBSA based on the minimized structures with the ff99 or ff14SB force field and MM/GBSA based on the minimized structures with the ff03 force field show excellent capability to recognize the near-native binding poses for the short and medium-size peptides, respectively, and they outperform the predictions given by two popular protein-peptide docking algorithms (pepATTRACT and HPEPDOCK). Therefore, MM/PBSA and MM/GBSA are powerful tools to predict the binding affinities and identify the correct binding poses for protein-peptide systems.

A Comprehensive Review on Current Advances in Peptide Drug Development and Design

Protein-protein interactions (PPIs) execute many fundamental cellular functions and have served as prime drug targets over the last two decades. Interfering intracellular PPIs with small molecules has been extremely difficult for larger or flat binding sites, as antibodies cannot cross the cell membrane to reach such target sites. In recent years, peptides smaller size and balance of conformational rigidity and flexibility have made them promising candidates for targeting challenging binding interfaces with satisfactory binding affinity and specificity. Deciphering and characterizing peptide-protein recognition mechanisms is thus central for the invention of peptide-based strategies to interfere with endogenous protein interactions, or improvement of the binding affinity and specificity of existing approaches. Importantly, a variety of computation-aided rational designs for peptide therapeutics have been developed, which aim to deliver comprehensive docking for peptide-protein interaction interfaces. Over 60 peptides have been approved and administrated globally in clinics. Despite this, advances in various docking models are only on the merge of making their contribution to peptide drug development. In this review, we provide (i) a holistic overview of peptide drug development and the fundamental technologies utilized to date, and (ii) an updated review on key developments of computational modeling of peptide-protein interactions (PepPIs) with an aim to assist experimental biologists exploit suitable docking methods to advance peptide interfering strategies against PPIs.

Predicting protein-peptide interaction sites using distant protein complexes as structural templates

Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details of the interactions. However, because of peptide flexibility and the transient nature of protein-peptide interactions, peptides are difficult to study experimentally. Thus, computational methods for predicting structural information about protein-peptide interactions are needed. Here we present InterPep, a pipeline for predicting protein-peptide interaction sites. It is a novel pipeline that, given a protein structure and a peptide sequence, utilizes structural template matches, sequence information, random forest machine learning, and hierarchical clustering to predict what region of the protein structure the peptide is most likely to bind. When tested on its ability to predict binding sites, InterPep successfully pinpointed 255 of 502 (50.7%) binding sites in experimentally determined structures at rank 1 and 348 of 502 (69.3%) among the top five predictions using only structures with no significant sequence similarity as templates. InterPep is a powerful tool for identifying peptide-binding sites; with a precision of 80% at a recall of 20% it should be an excellent starting point for docking protocols or experiments investigating peptide interactions. The source code for InterPred is available at http://wallnerlab.org/InterPep/ .

Benchmarking of different molecular docking methods for protein-peptide docking

Background

Molecular docking studies on protein-peptide interactions are a challenging and time-consuming task because peptides are generally more flexible than proteins and tend to adopt numerous conformations. There are several benchmarking studies on protein-protein, protein-ligand and nucleic acid-ligand docking interactions. However, a series of docking methods is not rigorously validated for protein-peptide complexes in the literature. Considering the importance and wide application of peptide docking, we describe benchmarking of 6 docking methods on 133 protein-peptide complexes having peptide length between 9 to 15 residues. The performance of docking methods was evaluated using CAPRI parameters like FNAT, I-RMSD, L-RMSD.

Result

Firstly, we performed blind docking and evaluate the performance of the top docking pose of each method. It was observed that FRODOCK performed better than other methods with average L-RMSD of 12.46 Å. The performance of all methods improved significantly for their best docking pose and achieved highest average L-RMSD of 3.72 Å in case of FRODOCK. Similarly, we performed re-docking and evaluated the performance of the top and best docking pose of each method. We achieved the best performance in case of ZDOCK with average L-RMSD 8.60 Å and 2.88 Å for the top and best docking pose respectively. Methods were also evaluated on 40 protein-peptide complexes used in the previous benchmarking study, where peptide have length up to 5 residues. In case of best docking pose, we achieved the highest average L-RMSD of 4.45 Å and 2.09 Å for the blind docking using FRODOCK and re-docking using AutoDock Vina respectively.

Conclusion

The study shows that FRODOCK performed best in case of blind docking and ZDOCK in case of re-docking. There is a need to improve the ranking of docking pose generated by different methods, as the present ranking scheme is not satisfactory. To facilitate the scientific community for calculating CAPRI parameters between native and docked complexes, we developed a web-based service named PPDbench (http://webs.iiitd.edu.in/raghava/ppdbench/).

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2449-y) contains supplementary material, which is available to authorized users.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.