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

Investigating the Interaction Mechanism of CAT-BT-Br and Key Residue Mutations for Castration-Resistant Prostate Cancer through Molecular Dynamics Simulation

Prostate cancer is the second most common cancer in men, second only to lung cancer. Castration-resistant prostate cancer (CRPC) was formerly known as hormone-resistant prostate cancer. The aim of this study is to reveal the effect of key residue mutations on the binding mechanism between catalase (CAT) and benzaldehyde thiourea derivatives (BT-Br), providing theoretical support for the development of novel CAT inhibitors. This article analyzes the structural stability, binding energy and decomposition, hydrogen bonding, etc. of wild-type (WT) and multiple mutations systems. The results showed that, in addition to the R203A mutant, all mutation systems significantly enhanced the binding ability of CAT to BT-Br, and their binding free energy contribution mainly came from van der Waals interactions. Hydrogen bond analysis shows that the hydrogen bond occupancy rate of the WT system is relatively low, while mutations such as V302A have a hydrogen bond occupancy rate as high as 93.05%, indicating a significant enhancement in their binding ability. In addition, mutations have limited impact on the overall stability of proteins, but some mutations such as Y215A and V302A significantly alter the binding site and direction of proteins. The results of principal component analysis (PCA) in other systems are consistent with those of root mean square fluctuation (RMSF) analysis, and the binding site shows little movement. This study not only elucidates the microscopic effects of key residue mutations on the binding mechanism between CAT and BT-Br but also provides new targets and drug design ideas for prostate cancer treatment based on iron death induction strategies.

Main methods and tools for peptide development based on protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) regulate essential physiological and pathological processes. Due to their large and shallow binding surfaces, PPIs are often considered challenging drug targets for small molecules. Peptides offer a viable alternative, as they can bind these targets, acting as regulators or mimicking interaction partners. This review focuses on competitive peptides, a class of orthosteric modulators that disrupt PPI formation. We provide a concise yet comprehensive overview of recent advancements in in-silico peptide design, highlighting computational strategies that have improved the efficiency and accuracy of PPI-targeting peptides. Additionally, we examine cutting-edge experimental methods for evaluating PPI-based peptides. By exploring the interplay between computational design and experimental validation, this review presents a structured framework for developing effective peptide therapeutics targeting PPIs in various diseases.

From Apo to Ligand-Bound: Unraveling PPARγ-LBD Conformational Shifts via Advanced Molecular Dynamics

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor whose ligand-induced conformational changes, primarily driven by helix 12 (H12) repositioning, regulate transcriptional activity. However, the precise mechanism remains elusive. In this study, we performed classical molecular dynamics (cMD) simulations of the PPARγ ligand binding domain (LBD) in complex with two agonists (BRL, 3EA), a partial agonist (GW0072), and an antagonist (EKP), generating 3 μs trajectories for each system. To gain deeper insights, we integrated machine learning-assisted clustering with MD simulations, revealing a favorable trend in binding free energy (ΔG b), suggesting enhanced complex stability. A case study on EKP demonstrated that, despite fitting within the binding site, it failed to induce rapid LBD or H12 rearrangements in the apo agonist-induced conformation. Additionally, we investigated the apo-state conformations of PPARγ-LBD influenced by agonist and antagonist ligands, utilizing cMD and Gaussian accelerated molecular dynamics (GaMD) over a cumulative 6 μs (3 μs cMD + 3 μs GaMD). Key residues known to modulate PPARγ function upon mutation were analyzed, and simulations confirmed the high stability of both apo and ligand-bound conformations. Notably, in the apo state, specific H12 residues interacted with other PPARγ-LBD regions, preventing disorder and abrupt transitions. These findings guided the selection of collective variables (CVs) for well-tempered metadynamics (WT-MetaD) simulations, which-in the apo-agonist state-captured the H12 shift from agonist- to antagonist-like conformations, consistent with resolved X-ray structures. Overall, this computational framework provides novel insights into PPARγ-LBD conformational dynamics and establishes a valuable approach for rationally assessing the effects of modulators on PPARγ activity.

Identification and inhibition of PIN1-NRF2 protein-protein interactions through computational and biophysical approaches

NRF2 is a transcription factor responsible for coordinating the expression of over a thousand cytoprotective genes. Although NRF2 is constitutively expressed, its stability is modulated by the redox-sensitive protein KEAP1 and other conditional binding partner regulators. The new era of NRF2 research has highlighted the cooperation between NRF2 and PIN1 in modifying its cytoprotective effect. Despite numerous studies, the understanding of the PIN1-NRF2 interaction remains limited. Herein, we described the binding interaction of PIN1 and three different 14-mer long phospho-peptides mimicking NRF2 protein using computer-based, biophysical, and biochemical approaches. According to our computational analyses, the residues positioned in the WW domain of PIN1 (Ser16, Arg17, Ser18, Tyr23, Ser32, Gln33, and Trp34) were found to be crucial for PIN1-NRF2 interactions. Biophysical FP assays were used to verify the computational prediction. The data demonstrated that Pintide, a peptide predominantly interacting with the PIN1 WW-domain, led to a significant reduction in the binding affinity of the NRF2 mimicking peptides. Moreover, we evaluated the impact of known PIN1 inhibitors (juglone, KPT-6566, and EGCG) on the PIN1-NRF2 interaction. Among the inhibitors, KPT-6566 showed the most potent inhibitory effect on PIN1-NRF2 interaction within an IC50 range of 0.3-1.4 µM. Furthermore, our mass spectrometry analyses showed that KPT-6566 appeared to covalently modify PIN1 via conjugate addition, rather than disulfide exchange of the sulfonyl-acetate moiety. Altogether, such inhibitors would also be highly valuable molecular probes for further investigation of PIN1 regulation of NRF2 in the cellular context and potentially pave the way for drug molecules that specifically inhibit the cytoprotective effects of NRF2 in cancer.

A Study on the Binding Mechanism and the Impact of Key Residue Mutations between SND1 and MTDH Peptide through Molecular Dynamics Simulations

Metastasis of breast cancer is the main cause of death for patients with breast cancer. The interaction between metadherin (MTDH) and staphylococcal nuclease domain 1 (SND1) plays a pivotal role in promoting breast cancer development. However, the binding details between MTDH and SND1 remain unclear. In this study, we employed all-atom molecular dynamics simulations (MDs) and conducted binding energy calculations to investigate the binding details and the impact of key residue mutations on binding. The mutations in key residues have not significantly affected the overall stability of the structure and the fluctuation of residues near the binding site; they have exerted a substantial impact on the binding of SND1 and MTDH peptide. The electrostatic interactions and van der Waals interactions play an important role in the binding of SND1 and the MTDH peptide. The mutations in the key residues have a significant impact on electrostatic and van der Waals interactions, resulting in weakened binding. The energy contributions of key residues mainly come from the electrostatic energy and van der Waals interactions of the side chain. In addition, the key residues form an intricate and stable network of hydrogen bonds and salt-bridge interactions with the MTDH peptide. The mutations in key residues have directly disrupt the interactions formed between SND1 and MTDH peptide, consequently leading to changes in the binding mode of the MTDH peptide. These analyses unveil the detailed atomic-level interaction mechanism between SND1 and the MTDH peptide, providing a molecular foundation for the development of antibreast cancer drugs.

QPH-FR: A Novel Quinoa Peptide Enhances Chemosensitivity by Targeting Leucine-Rich Repeat-Containing G Protein-Coupled Receptor 5 in Colorectal Cancer

Chemoresistance is one of the difficulties in the treatment of colorectal cancer (CRC), and the enhanced stemness of tumor cells is the underlying contributing factor. Leucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5) is a classical marker of CRC stem cells and can be an important potential target for CRC chemotherapy. Quinoa, a protein-rich plant, offers potential as a source of high-quality active peptides. Novelly, the study obtained quinoa protein hydrolysate (QPH) from whole quinoa grains by simulated digestion. In vivo experiments revealed that the tumor volume in the 5-FU+QPH group decreased from 145.90 ± 13.35 to 94.49 ± 13.05 mm3 in the 5-FU group, suggesting that QPH enhances the chemosensitivity of CRC. Further, the most effective peptide QPH-FR from 631 peptides in QPH was screened by activity prediction, molecular docking, and experimental validation. Mechanistically, QPH-FR competitively suppressed the formation of the LGR5/RSPO1 complex by binding to LGR5, causing RNF43/ZNRF3 to ubiquitinate the FZD receptor, thereby suppressing the Wnt/β-catenin signaling pathway and exerting stemness inhibition. In summary, the study proposes that a novel peptide QPH-FR from quinoa elucidates the mechanism by which QPH-FR targets LGR5 to enhance chemosensitivity, providing theoretical support for the development of chemotherapeutic adjuvant drugs based on plant peptides.

Exploring binding modes of the selected inhibitors to SND1 by all-atom molecular dynamics simulations

Breast cancer is the leading cause of cancer-related deaths in women. Previous studies have indicated that disrupting the interaction between Metadherin (MTDH) and Staphylococcal nuclease domain containing 1 (SND1) can inhibit breast cancer development. Understanding the binding mode of small molecule inhibitors with SND1 is of great significance for designing drugs targeting the MTDH-SND1 complex. In this study, we conducted all-atom molecular dynamics (MD) simulations in solution and performed binding energy calculations to gain insights into the binding mechanism of small molecules to SND1. The binding site of SND1 for small molecules is relatively rigid, and the binding of the small molecule and the mutation of key residues have little effect on the conformation of the binding site. SND1 binds more tightly to C26-A6 than to C26-A2, as C26-A2 undergoes a 180° directional change during the simulation process. The key residue mutations have a direct effect on the position and orientation of small molecule in the binding site. The key residues make primary contributions to the binding energy through van der Waals interaction and nonpolar solvation energy, although the contribution from nonpolar solvation is relatively minor. The key residue mutations also affect the formation of hydrogen bonds and ultimately the stability of the small molecule-SND1 complex.Communicated by Ramaswamy H. Sarma.

Evaluation of phytochemicals from Thymus serpyllum as potential drug candidates to manage oxidative stress in transition dairy cows

Dairy cows undergo immense stress and experience autoimmune reactions during the transition period, majorly due to the generation of ROS in the body. So, pharmacological approaches are needed to manage oxidative stress in the transition cows. Recently, the use of phytochemicals as feed additives in cows' nutrition has gained interest in managing various disease conditions. In the current study, we have evaluated the potential effects of phytochemicals derived from methanolic extract of Thymus serpyllum against oxidative stress and autoimmunity via inhibition of bovine nuclear factor kappa B (NF-κB). The free radical scavenging activity of Thymus serpyllum seed and leaf extracts was 71.8 and 75.6%, respectively at 100 µg/mL concentration. Similarly, both extracts displayed radicals reducing power and inhibition of lipid-peroxidation maximally at 100 µg/mL. A total of 52 bioactive compounds were identified when the plant extract was characterized by the GC-MS analysis, and five (Thymol, Luteolin 7-o-glucuronide, Rosmarinic acid, Apigenin 6,8-di-c-glucoside, Kaempferol) had binding free energy values of -11.6433, -10.002, -8.2615, -7.1714, -6.4870, respectively, in complexes with bovine NF-κB. Through computational analysis, the screened compounds showed good pharmacokinetic parameters, including non-toxicity, non-carcinogenic, high gastrointestinal absorption and thus can serve as potential drug candidates. MD simulation studies predicted the stability of complexes and the complex of Kaempferol was most stable based on RSMD value and MM/GBSA binding energy. The biochemical assays and computational studies indicated that Thymus serpyllum could be used as a promising feed additive in dairy cows to manage oxidative stress during the transition period.Communicated by Ramaswamy H. Sarma.

Revealing the binding mechanism of BACE1 inhibitors through molecular dynamics simulations

Alzheimer's disease is a debilitating neurodegenerative disorder, and the Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key therapeutic target in its treatment. This study employs molecular dynamics simulations and binding energy analysis to investigate the binding interactions between BACE1 and four selected small molecules: CNP520, D9W, NB641, and NB360. The binding model analysis indicates that the binding of BACE1 with four molecules are stable, except the loop regions show significant fluctuation. The binding free energy analyses reveal that NB360 exhibits the highest binding affinity with BACE1, surpassing other molecules (CNP520, D9W, and NB641). Detailed energy component assessments highlight the critical roles of electrostatic interactions and van der Waals forces in the binding process. Furthermore, residue contribution analysis identifies key amino acids influencing the binding process across all systems. Hydrogen bond analysis reveals a limited number of bonds between BACE1 and each small molecule, highlighting the importance of structural modifications to enable more stable hydrogen bonds. This research provides valuable insights into the molecular mechanisms of potential Alzheimer's disease therapeutics, guiding the way for improved drug design and the development of effective treatments targeting BACE1.

Antimicrobial Peptide Recognition Motif of the Substrate Binding Protein SapA from Nontypeable Haemophilus influenzae

Nontypeable Haemophilus influenzae (NTHi) is an opportunistic pathogen associated with respiratory diseases, including otitis media and exacerbations of chronic obstructive pulmonary disease. NTHi exhibits resistance to killing by host antimicrobial peptides (AMPs) mediated by SapA, the substrate binding protein of the sensitivity to antimicrobial peptides (Sap) transporter. However, the specific mechanisms by which SapA selectively binds various AMPs such as defensins and cathelicidin are unknown. In this study, we report mutational analyses of both defensin AMPs and the SapA binding pocket to define the specificity of AMP recognition. Bactericidal assays revealed that NTHi lacking SapA are more susceptible to human beta defensins and LL-37, while remaining highly resistant to a human alpha defensin. In contrast to homologues, our research underscores the distinct specificity of NTHi SapA, which selectively recognizes and binds to peptides containing the charged-hydrophobic motif PKE and RRY. These findings provide valuable insight into the divergence of SapA among bacterial species and NTHi SapA's ability to selectively interact with specific AMPs to mediate resistance.

Development of a novel EphA2-targeting radioligand for SPECT imaging in different tumor models

The erythropoietin-producing hepatoma A2 receptor (EphA2) is a tyrosine kinase, which is overexpressed in tumors while having lower expression in normal tissues, making it an excellent target for tumor diagnosis and treatment. Peptide radiotracers offer unique advantages in tumor diagnosis and therapy and have been approved for clinical use. In this study, a high-affinity EPHA2-targeted radiotracer, 99mTc-HYNIC-PEG4-EPH-3, was developed and designed based on linear peptides. 99mTc-HYNIC-PEG4-EPH-3 exhibited superior water solubility and stability. And 99mTc-HYNIC-PEG4-EPH-3 could specifically target EphA2-expressing tumors, particularly with a tumor-to-non-target (T/NT) ratio >4.7 excluding kidneys. As a result of excellent biodistribution and tumor targeting capability of 99mTc-HYNIC-PEG4-EPH-3, it might be a promising candidate drug for clinical diagnosis of EphA2-overexpressing tumors.

A dual-population multi-objective evolutionary algorithm driven by generative adversarial networks for benchmarking and protein-peptide docking

Multi-objective optimization problems (MOPs) are characterized as optimization problems in which multiple conflicting objective functions are optimized simultaneously. To solve MOPs, some algorithms used machine learning models to drive the evolutionary algorithms, leading to the design of a variety of model-based evolutionary algorithms. However, model collapse occurs during the generation of candidate solutions, which results in local optima and poor diversity in model-based evolutionary algorithms. To address this problem, we propose a dual-population multi-objective evolutionary algorithm driven by Wasserstein generative adversarial network with gradient penalty (DGMOEA), where the dual-populations coordinate and cooperate to generate high-quality solutions, thus improving the performance of the evolutionary algorithm. We compare the proposed algorithm with the 7 state-of-the-art algorithms on 20 multi-objective benchmark functions. Experimental results indicate that DGMOEA achieves significant results in solving MOPs, where the metrics IGD and HV outperform the other comparative algorithms on 15 and 18 out of 20 benchmarks, respectively. Our algorithm is evaluated on the LEADS-PEP dataset containing 53 protein-peptide complexes, and the experimental results on solving the protein-peptide docking problem indicated that DGMOEA can effectively reduce the RMSD between the generated and the original peptide's 3D poses and achieve more competitive results.

Allergenicity evaluation of fermented milk prepared by co-fermentation of Lactobacillus plantarum 7-2 and commercial starters after in vitro digestive

Milk allergy is one of the most common food allergies, in which αS-casein is the major milk allergen. Under optimized conditions, mixed starter (containing Lactobacillus plantarum 7-2 and commercial starter) effectively degraded αS-casein of skimmed milk and reduced the pressure of stomach digestion. The fermented milk prepared by mixed starter was determined by ELISA, the antigenicity of αS-casein was reduced by 77.53%. Compared with the fermented milk prepared by commercial starter, label-free quantitative proteomics demonstrated that the mixed starter more efficiently degraded the epitopes of major milk allergens and influenced the digestion pattern of the fermented milk. Therefore, L. plantarum 7-2 shows positive potential in reducing the antigenicity of αS-casein and others. In addition, this study predicted that the new epitopes produced in the fermentation process could induce immunity using molecular simulation.

Rational design, synthesis and structural characterization of peptides and peptidomimetics to target Hsp90/Cdc37 interaction for treating hepatocellular carcinoma

Heat shock protein 90 (Hsp90) and cell division cycle 37 (Cdc37) work together as a molecular chaperone complex to regulate the activity of a multitude of client protein kinases. These kinases belong to a wide array of intracellular signaling networks that mediate multiple cellular processes including proliferation. As a result, Hsp90 and Cdc37 represent innovative therapeutic targets in various cancers (such as leukemia, multiple myeloma, and hepatocellular carcinoma (HCC)) in which their expression levels are elevated. Conventional small molecule Hsp90 inhibitors act by blocking the conserved adenosine triphosphate (ATP) binding site. However, by targeting less conserved sites in a more specific manner, peptides and peptidomimetics (modified peptides) hold potential as more efficacious and less toxic alternatives to the conventional small molecule inhibitors. Using a rational approach, we herein developed bioactive peptides targeting Hsp90/Cdc37 interaction. A six amino acid linear peptide derived from Cdc37, KTGDEK, was designed to target Hsp90. We used in silico computational docking to first define its mode of interaction, and binding orientation, and then conjugated the peptide with a cell penetrating peptide, TAT, and a fluorescent dye to confirm its ability to colocalize with Hsp90 in HCC cells. Based on the parent linear sequence, we developed a peptidomimetics library of pre-cyclic and cyclic derivatives. These peptidomimetics were evaluated for their binding affinity to Hsp90, and bioactivity in HCC cell lines. Among them, a pre-cyclic peptidomimetic demonstrates high binding affinity and bioactivity in HCC cells, causing reduced cell proliferation that is associated with induction of cell apoptosis, and down-regulation of phosphorylated MEK1/2. Overall, this generalized approach of rational design, structural optimization, and cellular validation of 'drug-like' peptidomimetics against Hsp90/Cdc37 offers a feasible and promising way to design novel therapeutic agents for malignancies and other diseases that are dependent on this molecular chaperone complex.

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Fludarabine, a Potential DNA-Dependent RNA Polymerase Inhibitor, as a Prospective Drug against Monkeypox Virus: A Computational Approach

Monkeypox is a zoonotic contagious disease that has recently re-emerged in different countries worldwide. Due to the lack of an effective treatment that eliminates the virus, there is an urgent need to find effective drugs to stop the spread of the multi-country outbreak. The current study aimed to use computational methods to quickly identify potentially effective drugs against the Monkeypox virus (MPXV). Three MPXV proteins were targeted in this study due to their essential role in viral replication (a DNA-Dependent RNA Polymerase subunit (A6R)), a protein involved in cell entry (D8L), and a protein catalyzing the envelopment of intracellular mature virus particles (F13L). We virtually screened a library of 1615 FDA-approved compounds, utilizing different in-silico approaches including computational modeling, molecular docking, molecular dynamic (MD) simulation, and MM-GBSA. The compound Fludarabine was found to have the best docking score (−7.53 kcal/mol) in relation to the MPXV A6R protein. Additionally, Fludarabine showed in-silico activity on the D8L and F13L proteins. During the whole period of the 100 ns MD simulation, the complex of A6R and Fludarabine exhibited the best stability. This stability was reflected in a good score of MM-GBSA, with an average value of −44.62 kcal/mole in a range between −53.26 and −35.49 and a low value of standard deviation (3.76). Furthermore, Fludarabine blocked efficiently the Asn175 residue which has an important role in the attachment of the virus to a host cell. The results of this study recommend more in vitro studies on this compound, as a starting point to develop a novel treatment against MPXV.

Docking cyclic peptides formed by a disulfide bond through a hierarchical strategy

MOTIVATION

Cyclization is a common strategy to enhance the therapeutic potential of peptides. Many cyclic peptide drugs have been approved for clinical use, in which the disulfide-driven cyclic peptide is one of the most prevalent categories. Molecular docking is a powerful computational method to predict the binding modes of molecules. For protein-cyclic peptide docking, a big challenge is considering the flexibility of peptides with conformers constrained by cyclization.

RESULTS

Integrating our efficient peptide 3D conformation sampling algorithm MODPEP2.0 and knowledge-based scoring function ITScorePP, we have proposed an extended version of our hierarchical peptide docking algorithm, named HPEPDOCK2.0, to predict the binding modes of the peptide cyclized through a disulfide against a protein. Our HPEPDOCK2.0 approach was extensively evaluated on diverse test sets and compared with the state-of-the-art cyclic peptide docking program AutoDock CrankPep (ADCP). On a benchmark dataset of 18 cyclic peptide-protein complexes, HPEPDOCK2.0 obtained a native contact fraction of above 0.5 for 61% of the cases when the top prediction was considered, compared with 39% for ADCP. On a larger test set of 25 cyclic peptide-protein complexes, HPEPDOCK2.0 yielded a success rate of 44% for the top prediction, compared with 20% for ADCP. In addition, HPEPDOCK2.0 was also validated on two other test sets of 10 and 11 complexes with apo and predicted receptor structures, respectively. HPEPDOCK2.0 is computationally efficient and the average running time for docking a cyclic peptide is about 34 min on a single CPU core, compared with 496 min for ADCP. HPEPDOCK2.0 will facilitate the study of the interaction between cyclic peptides and proteins and the development of therapeutic cyclic peptide drugs.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/hpepdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond

Cyclic peptides formed by disulfide bonds have been one large group of common drug candidates in drug development. Structural information of a peptide is essential to understand its interaction with its target. However, due to the high flexibility of peptides, it is difficult to sample the near-native conformations of a peptide. Here, we have developed an extended version of our MODPEP approach, named MODPEP2.0, to fast generate the conformations of cyclic peptides formed by a disulfide bond. MODPEP2.0 builds the three-dimensional (3D) structures of a cyclic peptide from scratch by assembling amino acids one by one onto the cyclic fragment based on the constructed rotamer and cyclic backbone libraries. Being tested on a data set of 193 diverse cyclic peptides, MODPEP2.0 obtained a considerable advantage in both accuracy and computational efficiency, compared with other sampling algorithms including PEP-FOLD, ETKDG, and modified ETKDG (mETKDG). MODPEP2.0 achieved a high sampling accuracy with an average C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α RMSD of 2.20 Å and 1.66 Å when 10 and 100 conformations were considered, respectively, compared with 3.41 Å and 2.62 Å for PEP-FOLD, 3.44 Å and 3.16 Å for ETKDG, 3.09 Å and 2.72 Å for mETKDG. MODPEP2.0 also reproduced experimental peptide structures for 81.35% of the test cases when an ensemble of 100 conformations were considered, compared with 54.95%, 37.50% and 50.00% for PEP-FOLD, ETKDG, and mETKDG. MODPEP2.0 is computationally efficient and can generate 100 peptide conformations in one second. MODPEP2.0 will be useful in sampling cyclic peptide structures and modeling related protein-peptide interactions, facilitating the development of cyclic peptide drugs.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.

NLDock: a Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes

Nucleic acid-ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid-ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein-ligand interactions, only a few docking programs were presented for nucleic acid-ligand interactions. Here, we have developed a fast nucleic acid-ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid-ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency.

Improving Docking Power for Short Peptides Using Random Forest

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions

Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.

Investigation of beta-lactoglobulin derived bioactive peptides against SARS-CoV-2 (COVID-19): In silico analysis

The coronavirus disease of 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which started in late 2019 in Wuhan, China spread to the whole world in a short period of time, and thousands of people have died due to this epidemic. Although scientists have been searching for methods to manage SARS-CoV-2, there is no specific medication against COVID-19 as of yet. Two main approaches should be followed in the treatment of SARS-CoV-2; one of which is to neutralize the virus, and the other is to inhibit the host cell membrane receptors, where SARS-CoV-2 will bind. In this study, peptides derived from beta-lactoglobulin, which inactivates both the virus and its receptors in the host cell, were identified using computer-based in silico analysis. The beta-lactoglobulin derived peptides used in this study were obtained by the treatment of goat milk whey fraction with trypsin. The structure of the peptides was characterized by the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF/MS), and six beta-lactoglobulin derived peptides were selected as candidate peptides. Subsequently, the effects of peptides on SARS-CoV-2 and host cells were identified using virtual screening. According to the results of this in silico analysis, Ala-Leu-Pro-Met-His-Ile-Arg (ALMPHIR) and Ile-Pro-Ala-Val-Phe-Lys (IPAVFK) peptides were evaluated as potential candidates to be used in the treatment of SARS-CoV-2 after the future in vitro and in vivo studies.

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This in silico study used a hypothesis-driven peptidomics strategy.

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The beta-lactoglobulin derived peptides have potential effects against SARS-CoV-2.

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ALMPHIR and IPAVFK are potential candidates among these peptides.

Thermodynamic Dissection of the Intercalation Binding Process of Doxorubicin to dsDNA with Implications of Ionic and Solvent Effects

Doxorubicin (DOX) is a cancer drug that binds to dsDNA through intercalation. A comprehensive microsecond timescale molecular dynamics study is performed for DOX with 16 tetradecamer dsDNA sequences in explicit aqueous solvent, in order to investigate the intercalation process at both binding stages (conformational change and insertion binding stages). The molecular mechanics generalized Born surface area (MM-GBSA) method is adapted to quantify and break down the binding free energy (BFE) into its thermodynamic components, for a variety of different solution conditions as well as different DNA sequences. Our results show that the van der Waals interaction provides the largest contribution to the BFE at each stage of binding. The sequence selectivity depends mainly on the base pairs located downstream from the DOX intercalation site, with a preference for (AT)₂ or (TA)₂ driven by the favorable electrostatic and/or van der Waals interactions. Invoking the quartet sequence model proved to be most successful to predict the sequence selectivity. Our findings also indicate that the aqueous bathing solution (i.e., water and ions) opposes the formation of the DOX-DNA complex at every binding stage, thus implying that the complexation process preferably occurs at low ionic strength and is crucially dependent on solvent effects.

Based on the Virtual Screening of Multiple Pharmacophores, Docking and Molecular Dynamics Simulation Approaches toward the Discovery of Novel HPPD Inhibitors

4-Hydroxyphenylpyruvate dioxygenase (HPPD) is an iron-dependent non-heme oxygenase involved in the catabolic pathway of tyrosine, which is an important enzyme in the transformation of 4-hydroxyphenylpyruvic acid to homogentisic acid, and thus being considered as herbicide target. Within this study, a set of multiple structure-based pharmacophore models for HPPD inhibitors were developed. The ZINC and natural product database were virtually screened, and 29 compounds were obtained. The binding mode of HPPD and its inhibitors obtained through molecular docking study showed that the residues of Phe424, Phe381, His308, His226, Gln307 and Glu394 were crucial for activity. Molecular-mechanics-generalized born surface area (MM/GBSA) results showed that the coulomb force, lipophilic and van der Waals (vdW) interactions made major contributions to the binding affinity. These efforts will greatly contribute to design novel and effective HPPD inhibitory herbicides.