PepVis: An integrated peptide virtual screening pipeline for ensemble and flexible docking protocols

Novel Peptides from Sturgeon Ovarian Protein Hydrolysates Prevent Oxidative Stress-Induced Dysfunction in Osteoblast Cells: Purification, Identification, and Characterization

This study aimed to explore antioxidant peptides derived from sturgeon (Acipenser schrenckii) ovaries that exhibit antiosteoporotic effects in oxidative-induced MC3T3-E1 cells. The F3-15 component obtained from sturgeon ovarian protein hydrolysates (SOPHs) via gel filtration and RP-HPLC significantly increased the cell survival rate (from 49.38 ± 2.88 to 76.26 ± 2.09%). Two putative antioxidant-acting peptides, FDWDRL (FL6) and FEGPPFKF (FF8), were screened from the F3-15 faction via liquid chromatography-tandem mass spectrometry (LC-MS/MS) and through prediction by computer simulations. Molecular docking results indicated that the possible antioxidant mechanisms of FL6 and FF8 involved blocking the active site of human myeloperoxidase (hMPO). The in vitro tests showed that FL6 and FF8 were equally adept at reducing intracellular ROS levels, increasing the activity of antioxidant enzymes, and protecting cells from oxidative injuries by inhibiting the mitogen-activated protein kinase (MAPK) pathway and activating the phosphoinositide-3 kinase (PI3K)/protein kinase B (AKT)/glycogen synthase kinase-3β (GSK-3β) signaling pathway. Moreover, both peptides could increase differentiation and mineralization abilities in oxidatively damaged MC3T3-E1 cells. Furthermore, FF8 exhibited high resistance to pepsin and trypsin, showcasing potential for practical applications.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Assessing the fast prediction of peptide conformers and the impact of non-natural modifications

We present an assessment of different approaches to predict peptide structures using modeling tools. Several small molecule, protein, and peptide-focused methodologies were used for the fast prediction of conformers for peptides shorter than 30 amino acids. We assessed the effect of including restraints based on annotated or predicted secondary structure motifs. A number of peptides in bound conformations and in solution were collected to compare the tools. In addition, we studied the impact of changing single amino acids to non-natural residues using molecular dynamics simulations. Deep learning methods such as AlphaFold2, or the combination of physics-based approaches with secondary structure information, produce the most accurate results for natural sequences. In the case of peptides with non-natural modifications, modeling the peptide containing natural amino acids first and then modifying and simulating the peptide using benchmarked force fields is a recommended pipeline. The results can guide the modeling of oligopeptides for drug discovery projects.

Computer-Aided Analysis of West Sub-Saharan Africa Snakes Venom towards the Design of Epitope-Based Poly-Specific Antivenoms

Snakebite envenomation is a neglected tropical disease that causes over 100,000 deaths each year. The only effective treatment consists of antivenoms derived from animal sera, but these have been deemed with highly variable potency and are usually inaccessible and too costly for victims. The production of antivenoms by venom-independent techniques, such as the immunization with multi-epitope constructs, could circumvent those drawbacks. Herein, we present a knowledge-based pipeline to prioritize potential epitopes of therapeutic relevance from toxins of medically important snakes in West Sub-Saharan Africa. It is mainly based on sequence conservation and protein structural features. The ultimately selected 41 epitopes originate from 11 out of 16 snake species considered of highest medical importance in the region and 3 out of 10 of those considered as secondary medical importance. Echis ocellatus, responsible for the highest casualties in the area, would be covered by 12 different epitopes. Remarkably, this pipeline is versatile and customizable for the analysis of snake venom sequences from any other region of the world.

A Computer-Aided Approach for the Discovery of D-Peptides as Inhibitors of SARS-CoV-2 Main Protease

The SARS-CoV-2 main protease, also known as 3-chymotrypsin-like protease (3CLpro), is a cysteine protease responsible for the cleavage of viral polyproteins pp1a and pp1ab, at least, at eleven conserved sites, which leads to the formation of mature nonstructural proteins essential for the replication of the virus. Due to its essential role, numerous studies have been conducted so far, which have confirmed 3CLpro as an attractive drug target to combat Covid-19 and have reported a vast number of inhibitors and their co-crystal structures. Despite all the ongoing efforts, D-peptides, which possess key advantages over L-peptides as therapeutic agents, have not been explored as potential drug candidates against 3CLpro. The current work fills this gap by reporting an in silico approach for the discovery of D-peptides capable of inhibiting 3CLpro that involves structure-based virtual screening (SBVS) of an in-house library of D-tripeptides and D-tetrapeptides into the protease active site and subsequent rescoring steps, including Molecular Mechanics Generalized-Born Surface Area (MM-GBSA) free energy calculations and molecular dynamics (MD) simulations. In vitro enzymatic assays conducted for the four top-scoring D-tetrapeptides at 20 μM showed that all of them caused 55–85% inhibition of 3CLpro activity, thus highlighting the suitability of the devised approach. Overall, our results present a promising computational strategy to identify D-peptides capable of inhibiting 3CLpro, with broader application in problems involving protein inhibition.

Divide-and-link peptide docking: a fragment-based peptide docking protocol

Protein-peptide interactions are crucial for various important cellular regulations, and are also a basis for understanding protein-protein interactions, protein folding and peptide drug design. Due to the limited structural data obtained using experimental methods, it is necessary to predict protein-peptide interaction modes using computational methods. In the present work, we designed a fragment-based docking protocol, Divide-and-Link Peptide Docking (DLPepDock), to predict protein-peptide binding modes. This protocol contains the following steps: dividing the peptide into fragments and separately docking the fragments using a third-party small molecular docking tool, linking the docked fragmental poses to form the whole peptide conformations via fragmental coordinate transformation using our in-house program, removing unreasonable poses according to several geometrical filters, extracting representative conformations after clustering for further minimization using the steepest descent and conjugation gradient methods based on a full-atom molecular force field and finally scoring using the MM/PBSA binding energy calculation implemented in Amber. When tested on the LEADS-PEP benchmark data set of 26 diverse complexes with peptides of 6-12 residues, FlexPepDock ab initio and AutoDock CrankPep achieved superior results. DLPepDock performed better than the other 15 docking protocols implemented in nine docking programs (HPepDock, DockThor, rDock, Glide, LeDock, AutoDock, AutoDock Vina, Surflex, and GOLD). The Linux scripts to call the third-party tools and run all the calculations.

Virtual screening of peptides with high affinity for SARS-CoV-2 main protease

The current pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused more than 2,000,000 deaths worldwide. Currently, vaccine development and drug repurposing have been the main strategies to find a COVID-19 treatment. However, the development of new drugs could be the solution if the main strategies fail. Here, a virtual screening of pentapeptides was applied in order to identify peptides with high affinity to SARS-CoV-2 main protease (M^pro). Over 70,000 peptides were screened employing a genetic algorithm that uses a docking score as the fitness function. The algorithm was coupled with a RESTful API to persist data and avoid redundancy. The docking exhaustiveness was adapted to the number of peptides in each virtual screening step, where the higher the number of peptides, the lower the docking exhaustiveness. Two potential peptides were selected (HHYWH and HYWWT), which have higher affinity to M^pro than to human proteases. Albeit preliminary, the data presented here provide some basis for the rational design of peptide-based drugs to treat COVID-19.

Structure-based design of small molecule and peptide inhibitors for selective targeting of ROCK1: an integrative computational approach

Rho-associated, coiled-coil-containing protein kinase (ROCK1) regulates cell contraction, morphology, and motility by phosphorylating its downstream targets. ROCK1 is a proven target for many pathological conditions like cancer, atherosclerosis, glaucoma, neuro-degeneration, etc. Though many kinase inhibitors are available, there is a dearth of studies on repurposing approved drugs and novel peptide inhibitors that could potentially target ROCK1. Hence, in this study, an extensive integration of open-source pipelines was employed to probe the potential inhibitors (ligand/peptide) for targeting ROCK1. To start with, a systematic enrichment analysis was performed to delineate the most optimal ROCK1 crystal structure that can be harnessed for drug design. A comparative analysis of conformational flexibility between monomeric and dimeric forms was also performed to prioritize the optimal assembly for structural studies. Subsequently, Virtual screening of FDA-approved drugs in Drugbank was performed using POAP pipeline. Further, the top hits were probed for binding affinity, crucial interaction fingerprints, and complex stability during MD simulation. In parallel, a combinatorial tetrapeptide library was also virtually screened against ROCK1 using the PepVis pipeline. Following which, all these shortlisted inhibitors (compounds/peptides) were subjected to Kinomerun analysis to infer other potential kinase targets. Finally, Polydatin and conivaptan were prioritized as the most potential repurposable inhibitors, and WWWF, WWVW as potential inhibitory peptides for targeting ROCK1. The prioritized inhibitors are highly promising for use in therapeutics, as these are resultants of the multilevel stringent filtration process. The computational strategies implemented in this study could potentially serve as a scaffold towards selective inhibitor design for other kinases.Communicated by Ramaswamy H. Sarma.

Production, characterization and molecular docking of antioxidant peptides from peptidome of kinema fermented with proteolytic Bacillus spp

Kinema is an alkaline traditionally fermented soybean product popularly consumed in Sikkim Himalayan region. Kinema was prepared by soybean fermented with different species of Bacillus and analyzed for peptide content, antioxidant activity and consequence of gastrointestinal enzymes (pepsin and pancreatin) on the antioxidant effect. Antioxidant effect was enhanced during soybean fermentation using different starters, which further increased during gastrointestinal digestion. The peptides formed during soybean fermentation were analyzed using LC-MS/MS. Soybean fermented using different starters resulted in the production of some common peptides and a large number of unique peptides, which may affect the functional property of kinema. Peptides having antioxidative amino acids (histidine, phenylalanine, methionine, tryptophan and tyrosine) and significant GRAVY value were selected for their molecular interaction with myeloperoxidase (MPO), a key enzyme responsible for elevated oxidative stress. A peptide SEDDVFVIPAAYPF produced in kinema fermented using Bacillus licheniformis 1G had interaction with four out of five catalytic residues identified in MPO. Kinema prepared using specific starter can produce unique peptides responsible for specific health benefits.

Structural based study to identify new potential inhibitors for dual specificity tyrosine-phosphorylation- regulated kinase

Background and Objectives The Dual-specificity tyrosine-phosphorylation regulated kinase-1A (DYRK1A) a serine/threonine kinase that has freshly gained recognition as an essential drug target due to the discovery of its involvement in pathological diseases. The development of new potent inhibitors of DYRK1A would contribute to clarify the molecular mechanisms of associated diseases. It would administer a new lead compound for molecular-targeted protein, which was the primary focus of our study. Methods The library of in-house synthesized pyrrolone-fused benzosuberene (PBS) compounds was docked with DYRK1A receptor. Further, molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) estimations were conducted to confirm our docking outcomes and compared the stability of chosen complexes with the 2C3 (standard molecule) complex. Results This study reports Ligand15, Ligand14, and Ligand11 as potent inhibitors which showed higher ligand efficiency, binding affinity, lipophilic ligand efficiency, and favorable torsion values as compared to 2C3. Conclusion The stated methodologies revealed a unique mechanism of active site binding. The binding interactions within the active site showed that the chosen molecules had notable interactions than the standard molecule, which led to the generation of potential compounds to inhibit DYRK1A.

KinomeRun: An interactive utility for kinome target screening and interaction fingerprint analysis towards holistic visualization on kinome tree

Kinases are key targets for many of the pathological conditions. Inverse screening of ligands serves as an essential mode to identify potential kinase targets in modern drug discovery research. Hence, we intend to develop KinomeRun, a robust pipeline for inverse screening and kinome tree visualization through the seamless integration of kinome structures, docking and kinome-drug interaction fingerprint analysis. In this pipeline, the hurdle of residue numbering in kinome is also resolved by creating a common index file with the conserved kinase pocket residues for comparative interaction analysis. KinomeRun can be used to screen the ligands of interest docked against multiple kinase structures in parallel around the kinase binding site and also to filter out the targets with unique interaction patterns. This automation is essential for prioritization of kinase targets that show specificity for a given drug and will also serve as a crucial tool kit for holistic approaches in kinase drug discovery. KinomeRun is developed using python and bash programming language and is distributed freely under the GNU GPL licence-3.0 and can be downloaded at https://github.com/inpacdb/KinomeRun. The tutorial videos for installation, target screening and customized filtration are available at https://www.youtube.com/playlist?list=PLuIaEFtMVgQ7v__WigQH9ilGVxrfI1LKs and also be downloaded for offline viewing from the github link.

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.

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/ .

Identification and molecular docking study of fish roe-derived peptides as potent BACE 1, AChE, and BChE inhibitors

Acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and beta-secretase 1 (BACE 1) play vital roles in the development and progression of Alzheimer's disease (AD).