Cyclization and Docking Protocol for Cyclic Peptide-Protein Modeling Using HADDOCK2.4

Post-Docking Refinement of Peptide or Protein-RNA Complexes Using Thermal Titration Molecular Dynamics (TTMD): A Stability Insight

RNA-protein interactions drive and regulate fundamental cellular processes like transcription and translation. Despite being still limited, the growing body of structural data significantly contributes to the characterization of these interactions. However, RNA complexes involving proteins or peptides are not always available due to the structural determination challenges that this biopolymer entails. Consequently, modeling approaches like molecular docking are exploited to generate complexes relevant to structural and pharmaceutical purposes, including analysis of putative drug targets. Docking methods, despite their widespread adoption, are often hindered by limitations in scoring accuracy, which affects the ranking of the generated poses. Postdocking refining methods, including molecular dynamics (MD) approaches, have been developed to tackle this issue. Thermal Titration Molecular Dynamics (TTMD) is an enhanced sampling molecular dynamics technique that has been previously effectively applied to refine protein or RNA-small-molecule docking poses. This study presents the first application of TTMD to RNA-peptide complexes, validating this method on more complex systems and extending its applicability domain. Our findings showcase the capability of this technique to refine peptide-RNA docking poses, correctly identifying native binding modes among decoys for different pharmaceutically relevant targets.

Exploring key features of selectivity in somatostatin receptors through molecular dynamics simulations

Somatostatin receptors (SSTRs) are widely distributed throughout the human body and play crucial roles in various physiological processes. They are recognized as key targets for both radiotherapy and radiodiagnosis due to their overexpression in several cancer types. However, the discovery and design of selective drugs for each of the five isoforms have been significantly hindered by the lack of complete structural information. In this study, we conducted a systematic computational analysis of all five SSTRs in complex with the endogenous ligand somatostatin to elucidate their structural and dynamic features. We thoroughly characterized each isoform using available experimental structures for SSTR2 and SSTR4, as well as AlphaFold2 models for SSTR1, SSTR3, and SSTR5. By performing multi-copy μs-long molecular dynamics simulations, we examined the differences and similarities in dynamical behavior and somatostatin binding among all SSTRs. Our analysis focused on understanding the opening and closing movements of the extracellular loop 2, which are crucial for ligand binding and recognition. Interestingly, we observed a unique conformation of somatostatin within the binding pocket of SSTR5 in which the loop can partially close, as compared to the other isoforms. Fingerprint analyses provided distinct interaction patterns of somatostatin with all receptors, thus enabling precise guidelines for the discovery and development of more selective somatostatin-based pharmaceuticals tailored for precision medicine therapies.

Discovery of Cyclic Peptide Inhibitors Targeted on TNFα-TNFR1 from Computational Design and Bioactivity Verification

Activating tumor necrosis factor receptor 1 (TNFR1) with tumor necrosis factor alpha (TNFα) is one of the key pathological mechanisms resulting in the exacerbation of rheumatoid arthritis (RA) immune response. Despite various types of drugs being available for the treatment of RA, a series of shortcomings still limits their application. Therefore, developing novel peptide drugs that target TNFα-TNFR1 interaction is expected to expand therapeutic drug options. In this study, the detailed interaction mechanism between TNFα and TNFR1 was elucidated, based on which, a series of linear peptides were initially designed. To overcome its large conformational flexibility, two different head-to-tail cyclization strategies were adopted by adding a proline-glycine (GP) or cysteine-cysteine (CC) to form an amide or disulfide bond between the N-C terminal. The results indicate that two cyclic peptides, R1_CC4 and α_CC8, exhibit the strongest binding free energies. α_CC8 was selected for further optimization using virtual mutations through in vitro activity and toxicity experiments due to its optimal biological activity. The L16R mutant was screened, and its binding affinity to TNFR1 was validated using ELISA assays. This study designed a novel cyclic peptide structure with potential anti-inflammatory properties, possibly bringing an additional choice for the treatment of RA in the future.

Modeling Protein-Glycan Interactions with HADDOCK

The term glycan refers to a broad category of molecules composed of monosaccharide units linked to each other in a variety of ways, whose structural diversity is related to different functions in living organisms. Among others, glycans are recognized by proteins with the aim of carrying information and for signaling purposes. Determining the three-dimensional structures of protein-glycan complexes is essential both for the understanding of the mechanisms glycans are involved in and for applications such as drug design. In this context, molecular docking approaches are of undoubted importance as complementary approaches to experiments. In this study, we show how high ambiguity-driven DOCKing (HADDOCK) can be efficiently used for the prediction of protein-glycan complexes. Using a benchmark of 89 complexes, starting from their bound or unbound forms, and assuming some knowledge of the binding site on the protein, our protocol reaches a 70% and 40% top 5 success rate on bound and unbound data sets, respectively. We show that the main limiting factor is related to the complexity of the glycan to be modeled and the associated conformational flexibility.

CircPDIA3/miR-449a/XBP1 feedback loop curbs pyroptosis by inhibiting palmitoylation of the GSDME-C domain to induce chemoresistance of colorectal cancer

Although oxaliplatin (OXA) is widely used in the frontline treatment of colorectal cancer (CRC), CRC recurrence is commonly observed due to OXA resistance. OXA resistance is associated with a number of factors, including abnormal regulation of pyroptosis. It is therefore important to elucidate the abnormal regulatory mechanism underlying pyroptosis. Here, we identified that the circular RNA circPDIA3 played an important role in chemoresistance in CRC. CircPDIA3 could induce chemoresistance in CRC by inhibiting pyroptosis both in vitro and in vivo. Mechanistically, RIP, RNA pull-down and co-IP assays revealed that circPDIA3 directly bonded to the GSDME-C domain, subsequently enhanced the autoinhibitory effect of the GSDME-C domain through blocking the GSDME-C domain palmitoylation by ZDHHC3 and ZDHHC17, thereby restraining pyroptosis. Additionally, it was found that the circPDIA3/miR-449a/XBP1 positive feedback loop increased the expression of circPDIA3 to induce chemoresistance. Furthermore, our clinical data and patient-derived tumor xenograft (PDX) models supported the positive association of circPDIA3 with development of chemoresistance in CRC patients. Taken together, our findings demonstrated that circPDIA3 could promote chemoresistance by amplifying the autoinhibitory effect of the GSDME-C domain through inhibition of the GSDME-C domain palmitoylation in CRC. This study provides novel insights into the mechanism of circRNA in regulating pyroptosis and providing a potential therapeutic target for reversing chemoresistance of CRC.

Leveraging machine learning models for peptide-protein interaction prediction

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein-protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide-protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide-protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide-protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide-protein interactions.

Abatement of the binding of human hexokinase II enzyme monomers by in-silico method with the design of inhibitory peptides

The hexokinase II enzyme is bound to the (VDAC1) channel in the form of a dimer and prevents the release of cell death factors from mitochondria to the cytoplasm. Studies have shown that blocking the binding of hexokinase II enzyme to (VDAC1) led to the initiation of apoptosis in cancer cells. No peptide has been designed so far to inhibit hexokinase II. The aim of this study was to inhibit the dimerization of enzyme subunits in order to inhibition the formation of (VDAC1) and the hexokinase II complex. In this study, the molecular dynamics simulation of the enzyme in monomer and dimer states was investigated in terms of RMSF, RMSD and radius of gyration. The following process involves extracting and designing variable-length peptides from the interacting segments of enzyme monomers. Using molecular dynamics simulation, the stability of the peptide was determined in terms of RMSD. Molecular docking was used to investigate the interaction between the designed peptides. Finally, the inhibitory effect of peptides on subunit association was measured using dynamic light scattering (DLS) technique. Our results showed that the designed peptides, which mimic common amino acids in dimerization, interrupt the bona fide form of the enzyme subunits. The result of this study provides a new way to disrupt the assembly process and thereby decreased the function of the hexokinase II.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00201-8.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Comprehensive Evaluation of 10 Docking Programs on a Diverse Set of Protein-Cyclic Peptide Complexes

Cyclic peptides have emerged as a highly promising class of therapeutic molecules owing to their favorable pharmacokinetic properties, including stability and permeability. Currently, many clinically approved cyclic peptides are derived from natural products or their derivatives, and the development of molecular docking techniques for cyclic peptide discovery holds great promise for expanding the applications and potential of this class of molecules. Given the availability of numerous docking programs, there is a pressing need for a systematic evaluation of their performance, specifically on protein-cyclic peptide systems. In this study, we constructed an extensive benchmark data set called CPSet, consisting of 493 protein-cyclic peptide complexes. Based on this data set, we conducted a comprehensive evaluation of 10 docking programs, including Rosetta, AutoDock CrankPep, and eight protein-small molecule docking programs (i.e., AutoDock, AudoDock Vina, Glide, GOLD, LeDock, rDock, MOE, and Surflex). The evaluation encompassed the assessment of the sampling power, docking power, and scoring power of these programs. The results revealed that all of the tested protein-small molecule docking programs successfully sampled the binding conformations when using the crystal conformations as the initial structures. Among them, rDock exhibited outstanding performance, achieving a remarkable 94.3% top-100 sampling success rate. However, few programs achieved successful predictions of the binding conformations using tLEaP-generated conformations as the initial structures. Within this scheme, AutoDock CrankPep yielded the highest top-100 sampling success rate of 29.6%. Rosetta's scoring function outperformed the others in selecting optimal conformations, resulting in an impressive top-1 docking success rate of 87.6%. Nevertheless, all the tested scoring functions displayed limited performance in predicting binding affinity, with MOE@Affinity dG exhibiting the highest Pearson's correlation coefficient of 0.378. It is therefore suggested to use an appropriate combination of different docking programs for given tasks in real applications. We expect that this work will offer valuable insights into selecting the appropriate docking programs for protein-cyclic peptide complexes.

Unlocking novel therapies: cyclic peptide design for amyloidogenic targets through synergies of experiments, simulations, and machine learning

Existing therapies for neurodegenerative diseases like Parkinson's and Alzheimer's address only their symptoms and do not prevent disease onset. Common therapeutic agents, such as small molecules and antibodies struggle with insufficient selectivity, stability and bioavailability, leading to poor performance in clinical trials. Peptide-based therapeutics are emerging as promising candidates, with successful applications for cardiovascular diseases and cancers due to their high bioavailability, good efficacy and specificity. In particular, cyclic peptides have a long in vivo stability, while maintaining a robust antibody-like binding affinity. However, the de novo design of cyclic peptides is challenging due to the lack of long-lived druggable pockets of the target polypeptide, absence of exhaustive conformational distributions of the target and/or the binder, unknown binding site, methodological limitations, associated constraints (failed trials, time, money) and the vast combinatorial sequence space. Hence, efficient alignment and cooperation between disciplines, and synergies between experiments and simulations complemented by popular techniques like machine-learning can significantly speed up the therapeutic cyclic-peptide development for neurodegenerative diseases. We review the latest advancements in cyclic peptide design against amyloidogenic targets from a computational perspective in light of recent advancements and potential of machine learning to optimize the design process. We discuss the difficulties encountered when designing novel peptide-based inhibitors and we propose new strategies incorporating experiments, simulations and machine learning to design cyclic peptides to inhibit the toxic propagation of amyloidogenic polypeptides. Importantly, these strategies extend beyond the mere design of cyclic peptides and serve as template for the de novo generation of (bio)materials with programmable properties.

Sequence composition and location of CRE motifs affect the binding ability of CcpA protein

Bacillus catabolite control protein (CcpA) mediates carbon catabolite repression (CCR) by binding with catabolite response elements (CREs) of genes or operons. Although numerous CREs had been predicted and identified, the influence of the changes in sequence and structure of CREs on recognition and binding for CcpA has yet to be unclear. This study aimed at revealing how CcpA could bind such diverse sites and focused on the analysis of multiple mutants of the CRE motif derived from the α-amylase promoter. Molecular docking and free energy calculation insights into the binding ability between the CRE sequences composition and CcpA protein. Disruption of conserved nucleotides in the CRE motifs, as well as altering the symmetric structure of the CRE sequences and the relative position of the displaced CRE motifs near the transcription start site contribute to some extent to weakening the strength of CcpA - dependent regulation. These main factors contribute to the understanding of the subtle changes in CRE motifs leading to differential regulatory effects of CcpA. Finally, an engineered promoter with a high level of transcription was obtained, and elevated extracellular enzyme activity was achieved in the expression system of Bacillus amyloliquefaciens, including alkaline protease, keratinase, aminopeptidase and acid-stable alpha amylase. The study also provides a reference for the application of other promoters with CRE motifts.

Design of Cyclic Peptides Targeting Protein-Protein Interactions Using AlphaFold

More than 930,000 protein-protein interactions (PPIs) have been identified in recent years, but their physicochemical properties differ from conventional drug targets, complicating the use of conventional small molecules as modalities. Cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target protein-cyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational methods. Recently, AlphaFold with a cyclic offset has enabled predicting the structure of cyclic peptides, thereby enabling de novo cyclic peptide designs. We developed a cyclic peptide complex offset to enable the structural prediction of target proteins and cyclic peptide complexes and found AlphaFold2 with a cyclic peptide complex offset can predict structures with high accuracy. We also applied the cyclic peptide complex offset to the binder hallucination protocol of AfDesign, a de novo protein design method using AlphaFold, and we could design a high predicted local-distance difference test and lower separated binding energy per unit interface area than the native MDM2/p53 structure. Furthermore, the method was applied to 12 other protein-peptide complexes and one protein-protein complex. Our approach shows that it is possible to design putative cyclic peptide sequences targeting PPI.

Comparative Binding Ability of Human Monoclonal Antibodies against Omicron Variants of SARS-CoV-2: An In Silico Investigation

Mutation(s) in the spike protein is the major characteristic trait of newly emerged SARS-CoV-2 variants such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Delta-plus. Omicron (B.1.1.529) is the latest addition and it has been characterized by high transmissibility and the ability to escape host immunity. Recently developed vaccines and repurposed drugs exert limited action on Omicron strains and hence new therapeutics are immediately needed. Herein, we have explored the efficiency of twelve therapeutic monoclonal antibodies (mAbs) targeting the RBD region of the spike glycoprotein against all the Omicron variants bearing a mutation in spike protein through molecular docking and molecular dynamics simulation. Our in silico evidence reveals that adintivimab, beludivimab, and regadanivimab are the most potent mAbs to form strong biophysical interactions and neutralize most of the Omicron variants. Considering the efficacy of mAbs, we incorporated CDRH3 of beludavimab within the framework of adintrevimab, which displayed a more intense binding affinity towards all of the Omicron variants viz. BA.1, BA.2, BA.2.12.1, BA.4, and BA.5. Furthermore, the cDNA of chimeric mAb was cloned in silico within pET30ax for recombinant production. In conclusion, the present study represents the candidature of human mAbs (beludavimab and adintrevimab) and the therapeutic potential of designed chimeric mAb for treating Omicron-infected patients.

Accurate Prediction for Protein-Peptide Binding Based on High-Temperature Molecular Dynamics Simulations

The structural characterization of protein-peptide interactions is fundamental to elucidating biological processes and designing peptide drugs. Molecular dynamics (MD) simulations are extensively used to study biomolecular systems. However, simulating the protein-peptide binding process is usually quite expensive. Based on our previous studies, herein, we propose a simple and effective method to predict the binding site and pose of the peptide simultaneously using high-temperature (high-T) MD simulations with the RSFF2C force field. Thousands of binding events (nonspecific or specific) can be sampled during microseconds of high-T MD. From density-based clustering analysis, the structures of all of the 12 complexes (nine with linear peptides and three with cyclic peptides) can be successfully predicted with root-mean-square deviation (RMSD) < 2.5 Å. By directly simulating the process of the ligand binding onto the receptor, our method approaches experimental precision for the first time, significantly surpassing previous protein-peptide docking methods in terms of accuracy.