Docking small peptides remains a great challenge: an assessment using AutoDock Vina

Hybrid Diffusion Model for Stable, Affinity-Driven, Receptor-Aware Peptide Generation

The convergence of biotechnology and artificial intelligence has the potential to transform drug development, especially in the field of therapeutic peptide design. Peptides are short chains of amino acids with diverse therapeutic applications that offer several advantages over small molecular drugs, such as targeted therapy and minimal side effects. However, limited oral bioavailability and enzymatic degradation have limited their effectiveness. With advances in deep learning techniques, innovative approaches to peptide design have become possible. In this work, we demonstrate HYDRA, a hybrid deep learning approach that leverages the distribution modeling capabilities of a diffusion model and combines it with a binding affinity maximization algorithm that can be used for de novo design of peptide binders for various target receptors. As an application, we have used our approach to design therapeutic peptides targeting proteins expressed by Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) genes. The ability of HYDRA to generate peptides conditioned on the target receptor's binding sites makes it a promising approach for developing effective therapies for malaria and other diseases.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Specifically targeting antimicrobial peptides for inhibition of Candidatus Liberibacter asiaticus

AIMS

Huanglongbing (citrus greening) is a plant disease putatively caused by the unculturable Gram-negative bacterium Candidatus Liberibacter asiaticus (CLas), and it has caused severe damage to citrus plantations worldwide. There are no definitive treatments for this disease, and conventional disease control techniques have shown limited efficacy. This work presents an in silico evaluation of using specifically targeting anti-microbial peptides (STAMPs) consisting of a targeting segment and an antimicrobial segment to inhibit citrus greening by inhibiting the BamA protein of CLas, which is an outer membrane protein crucial for bacterial viability.

METHODS AND RESULTS

Initially, a set of peptides with a high affinity toward BamA protein were screened and evaluated via molecular docking and molecular dynamics simulations and were verified in vitro via bio-layer interferometry (BLI). In silico studies and BLI experiments indicated that two peptides, HASP2 and HASP3, showed stable binding to BamA. Protein structures for STAMPs were created by fusing known anti-microbial peptides (AMPs) with the selected short peptides. The binding of STAMPs to BamA was assessed using molecular docking and binding energy calculations. The attachment of high-affinity short peptides significantly reduced the free energy of binding for AMPs, suggesting that it would make it easier for the STAMPs to bind to BamA. Efficacy testing in vitro using a closely related CLas surrogate bacterium showed that STAMPs had greater inhibitory activity than AMP alone.

CONCLUSIONS

In silico and in vitro results indicate that the STAMPs can inhibit CLas surrogate Rhizobium grahamii more effectively compared to AMPs, suggesting that STAMPs can achieve better inhibition of CLas, potentially via enhancing the site specificity of AMPs.

Novel kokumi peptides from yeast extract and their taste mechanism via an in silico study

Yeast extract, a widely utilized natural substance in the food industry and biopharmaceutical field, holds significant potential for flavor enhancement. Kokumi peptides within yeast extracts were isolated through ultrafiltration and gel chromatography, followed by identification using liquid chromatography tandem mass spectrometry (LC-MS/MS). Two peptides, IQGFK and EDFFVR, were identified and synthesized using solid-phase methods based on molecular docking outcomes. Sensory evaluations and electronic tongue analyses conducted with chicken broth solutions revealed taste thresholds of 0.12 mmol L-1 for IQGFK and 0.16 mmol L-1 for EDFFVR, respectively, and both peptides exhibited kokumi properties. Additionally, through molecular dynamics simulations, the binding mechanisms between these peptides and the calcium-sensing receptor (CaSR) were explored. The findings indicated stable binding of both peptides to the receptor. IQGFK primarily interacted through electrostatic interactions, with key binding sites including Asp275, Asn102, Pro274, Trp70, Tyr218, and Ser147. EDFFVR mainly engaged via van der Waals energy and polar solvation free energy, with key binding sites being Asp275, Ile416, Pro274, Arg66, Ala298, and Tyr218. This suggests that both peptides can activate the CaSR, thereby inducing kokumi activity. This study provides a theoretical foundation and reference for the screening and identification of kokumi peptides, successfully uncovering two novel kokumi peptides derived from yeast extract.

Antihypertensive peptide resources map of ribulose-1,5-bisphosphate carboxylase/oxygenases (RuBisCO) in angiosperms: Revealed by an integrated in silico and in vitro approach

As the most abundant protein on earth, ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) has been considered a promising resource of functional foods. This study aimed to explore the full potential of plant RuBisCO proteins as precursors of antihypertensive peptides on a large scale. In total, 12,766 RuBisCO large subunit and 1,020 RuBisCO small subunit sequences of angiosperms were collected for simulated proteolysis and evaluation of antihypertensive potential, revealing a vast reservoir of antihypertensive peptides. Moreover, RuBisCO-derived novel antihypertensive peptides TTVW, TMW, and VPCL were identified with in vitro IC50 of 12.89 ± 0.82, 23.97 ± 1.02, and 339.12 ± 21.64 μM, respectively. Notably, TTVW and TMW are noncompetitive inhibitors predicted to bound adjacent to the catalytic region of ACE, while VPCL is a competitive inhibitor predicted to bound to the central active site inside ACE. Overall, this work provides a powerful theoretical guidance in developing antihypertensive functional foods utilizing plant RuBisCO.

Invited review: Camel milk-derived bioactive peptides and diabetes-Molecular view and perspectives

In dairy science, camel milk (CM) constitutes a center of interest for scientists due to its known beneficial effect on diabetes as demonstrated in many in vitro, in vivo, and clinical studies and trials. Overall, CM had positive effects on various parameters related to glucose transport and metabolism as well as the structural and functional properties of the pancreatic β-cells and insulin secretion. Thus, CM consumption may help manage diabetes; however, such a recommendation will become rationale and clinically conceivable only if the exact molecular mechanisms and pathways involved at the cellular levels are well understood. Moreover, the application of CM as an alternative antidiabetic tool may first require the identification of the exact bioactive molecules behind such antidiabetic properties. In this review, we describe the advances in our knowledge of the molecular mechanisms reported to be involved in the beneficial effects of CM in managing diabetes using different in vitro and in vivo models. This mainly includes the effects of CM on the different molecular pathways controlling (1) insulin receptor signaling and glucose uptake, (2) the pancreatic β-cell structure and function, and (3) the activity of key metabolic enzymes in glucose metabolism. Moreover, we described the current status of the identification of CM-derived bioactive peptides and their structure-activity relationship study and characterization in the context of molecular markers related to diabetes. Such an overview will not only enrich our scientific knowledge of the plausible mode of action of CM in diabetes but should ultimately rationalize the claim of the potential application of CM against diabetes. This will pave the way toward new directions and ideas for developing a new generation of antidiabetic products taking benefits from the chemical composition of CM.

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).

A New Advanced Approach: Design and Screening of Affinity Peptide Ligands Using Computer Simulation Techniques

Peptides acquire target affinity based on the combination of residues in their sequences and the conformation formed by their flexible folding, an ability that makes them very attractive biomaterials in therapeutic, diagnostic, and assay fields. With the development of computer technology, computer-aided design and screening of affinity peptides has become a more efficient and faster method. This review summarizes successful cases of computer-aided design and screening of affinity peptide ligands in recent years and lists the computer programs and online servers used in the process. In particular, the characteristics of different design and screening methods are summarized and categorized to help researchers choose between different methods. In addition, experimentally validated sequences are listed, and their applications are described, providing directions for the future development and application of computational peptide screening and design.

Discovery of selective and potent USP22 inhibitors via structure-based virtual screening and bioassays exerting anti-tumor activity

Ubiquitin-specific protease 22 (USP22) plays a prominent role in tumor development, invasion, metastasis and immune reprogramming, which has been proposed as a potential therapeutic target for cancer. Herein, we employed a structure-based discovery and biological evaluation and discovered that Rottlerin (IC50 = 2.53 μM) and Morusin (IC50 = 8.29 μM) and as selective and potent USP22 inhibitors. Treatment of HCT116 cells and A375 cells with each of the two compounds resulted in increased monoubiquitination of histones H2A and H2B, as well as reduced protein expression levels of Sirt1 and PD-L1, all of which are known as USP22 substrates. Additionally, our study demonstrated that the administration of Rottlerin or Morusin resulted in an increase H2Bub levels, while simultaneously reducing the expression of Sirt1 and PD-L1 in a manner dependent on USP22. Furthermore, Rottlerin and Morusin were found to enhance the degradation of PD-L1 and Sirt1, as well as increase the polyubiquitination of endogenous PD-L1 and Sirt1 in HCT116 cells. Moreover, in an in vivo syngeneic tumor model, Rottlerin and Morusin exhibited potent antitumor activity, which was accompanied by an enhanced infiltration of T cells into the tumor tissues. Using in-depth molecular dynamics (MD) and binding free energy calculation, conserved residue Leu475 and non-conserved residue Arg419 were proven to be crucial for the binding affinity and inhibitory function of USP22 inhibitors. In summary, our study established a highly efficient approach for USP22-specific inhibitor discovery, which lead to identification of two selective and potent USP22 inhibitors as potential drugs in anticancer therapy.

Construction of Histone-Protein Complex Structures by Peptide Growing

The structures of histone complexes are master keys to epigenetics. Linear histone peptide tails often bind to shallow pockets of reader proteins via weak interactions, rendering their structure determination challenging. In the present study, a new protocol, PepGrow, is introduced. PepGrow uses docked histone fragments as seeds and grows the full peptide tails in the reader-binding pocket, producing atomic-resolution structures of histone-reader complexes. PepGrow is able to handle the flexibility of histone peptides, and it is demonstrated to be more efficient than linking pre-docked peptide fragments. The new protocol combines the advantages of popular program packages and allows fast generation of solution structures. AutoDock, a force-field-based program, is used to supply the docked peptide fragments used as structural seeds, and the building algorithm of Modeller is adopted and tested as a peptide growing engine. The performance of PepGrow is compared to ten other docking methods, and it is concluded that in situ growing of a ligand from a seed is a viable strategy for the production of complex structures of histone peptides at atomic resolution.

Machine Learning Advances in Predicting Peptide/Protein-Protein Interactions Based on Sequence Information for Lead Peptides Discovery

Peptides have shown increasing advantages and significant clinical value in drug discovery and development. With the development of high-throughput technologies and artificial intelligence (AI), machine learning (ML) methods for discovering new lead peptides have been expanded and incorporated into rational drug design. Predictions of peptide-protein interactions (PepPIs) and protein-protein interactions (PPIs) are both opportunities and challenges in computational biology, which will help to better understand the mechanisms of disease and provide the impetus for the discovery of lead peptides. This paper comprehensively reviews computational models for PepPI and PPI predictions. It begins with an introduction of various databases of peptide ligands and target proteins. Then it discusses data formats and feature representations for proteins and peptides. Furthermore, classical ML methods and emerging deep learning (DL) methods that can be used to train prediction models of PepPI and PPI are classified into four categories, and their advantages and disadvantages are analyzed. To assess the relative performance of different models, different validation protocols and evaluation indexes are discussed. The goal of this review is to help researchers quickly get started to develop computational frameworks using these integrated resources and eventually promote the discovery of lead peptides.

In Silico Exploration of Metabolically Active Peptides as Potential Therapeutic Agents against Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is regarded as a fatal neurodegenerative disease that is featured by progressive damage of the upper and lower motor neurons. To date, over 45 genes have been found to be connected with ALS pathology. The aim of this work was to computationally identify unique sets of protein hydrolysate peptides that could serve as therapeutic agents against ALS. Computational methods which include target prediction, protein-protein interaction, and peptide-protein molecular docking were used. The results showed that the network of critical ALS-associated genes consists of ATG16L2, SCFD1, VAC15, VEGFA, KEAP1, KIF5A, FIG4, TUBA4A, SIGMAR1, SETX, ANXA11, HNRNPL, NEK1, C9orf72, VCP, RPSA, ATP5B, and SOD1 together with predicted kinases such as AKT1, CDK4, DNAPK, MAPK14, and ERK2 in addition to transcription factors such as MYC, RELA, ZMIZ1, EGR1, TRIM28, and FOXA2. The identified molecular targets of the peptides that support multi-metabolic components in ALS pathogenesis include cyclooxygenase-2, angiotensin I-converting enzyme, dipeptidyl peptidase IV, X-linked inhibitor of apoptosis protein 3, and endothelin receptor ET-A. Overall, the results showed that AGL, APL, AVK, IIW, PVI, and VAY peptides are promising candidates for further study. Future work would be needed to validate the therapeutic properties of these hydrolysate peptides by in vitro and in vivo approaches.

Interaction of Positively Charged Oligopeptides with Blood Plasma Proteins

In this project, we combine two areas of research, experimental characterization and molecular docking studies of the interaction of positively charged oligopeptides with crucial blood plasma proteins. The investigated peptides are rich in NH₂ groups of amino acid side chains from Dap, Orn, Lys, and Arg residues, which are relevant in protein interaction. The peptides are 9- and 11-mer with the following sequences: (Lys-Dab-Dab-Gly-Orn-Pro-His-Lys-Arg-Lys-Dbt), (Lys-Dab-Ala-Gly-Orn-Pro-His-Lys-Arg), and (Lys-Dab-Dab-Gly-Orn-Pro-Phe(2-F)-Lys-Arg). The net charge of the compound strongly depends on the pH environment and it is an important aspect of protein binding. The studied oligopeptides exhibit therapeutic properties: anti-inflammatory activity and the capacity to diminish reactive oxygen species (ROS). Therefore, the mechanism of potential binding with blood plasma components is the next challenge. The binding interaction has been investigated under pseudo-physiological conditions with the main blood plasma proteins: albumin (BSA), α1-acid glycoprotein (AAG), and γ-globulin fraction (GGF). The biomolecular quenching constant (k_q) and binding constant (K_b) were obtained by fluorescence spectroscopy at various temperatures. Simultaneously, the changes in the secondary structure of proteins were monitored by circular dichroism (CD) and infrared spectroscopy (IR) by quantity analysis. Moreover, molecular docking studies were conducted to estimate the binding affinity, the binding domain, and the chemical nature of these interactions. The results show that the investigated oligopeptides could be mainly transported by albumin, and the binding domain I is the most favored cavity. The BSA and GGF are able to form stable complexes with the studied compounds as opposed to AAG. The binding reactions are spontaneous processes. The highest binding constants were determined for Lys-Dab-Dab-Gly-Orn-Pro-His-Lys-Arg-Lys-Dbt peptide, in which the values of the binding constants K_b to BSA and GGF were 10.1 × 10⁴ dm³mol^-1 and 3.39 × 10³ dm³mol^-1, respectively. The positively charged surface of peptides participated in salt bridge interaction with proteins; however, hydrogen bonds were also formed. The secondary structure of BSA and GGF after contact with peptides was changed. A reduction in the α-helix structure was observed with an increase in the β-sheet and β-turn and random coil structures.

tLyp-1: A peptide suitable to target NRP-1 receptor

Targeting vascular endothelial growth factor receptor (VEFGR) and its co-receptor neuropilin-1 (NRP-1) is an interesting vascular strategy. tLyp-1 is a tumor-homing and penetrating peptide of 7 amino acids (CGNKRTR). It is a truncated form of Lyp-1 (CGNKRTRGC), which is known to target NRP-1 receptor, with high affinity and specificity. It is mediated by endocytosis via C-end rule (CendR) internalization pathway. The aim of this study is to evaluate the importance of each amino acid in the tLyp-1 sequence through alanine-scanning (Ala-scan) technique, during which each of the amino acid in the sequence was systematically replaced by alanine to produce 7 different analogues. In silico approach through molecular docking and molecular dynamics are employed to understand the interaction between the peptide and its analogues with the NRP-1 receptor, followed by in vitro ligand binding assay study. The C-terminal Arg is crucial in the interaction of tLyp-1 with NRP-1 receptor. Substituting this residue dramatically reduces the affinity of this peptide which is clearly seen in this study. Lys-4 is also important in the interaction, which is confirmed via the in vitro study and the MM-PBSA analysis. The finding in this study supports the CendR, in which the presence of R/K-XX-R/K motif is essential in the binding of a ligand with NRP-1 receptor. This presented work will serve as a guide in the future work pertaining the development of active targeting agent towards NRP-1 receptor.

Molecular Characterization, Purification, and Mode of Action of Enterocin KAE01 from Lactic Acid Bacteria and Its In Silico Analysis against MDR/ESBL Pseudomonas aeruginosa

Bacteriocins are gaining immense importance in therapeutics since they show significant antibacterial potential. This study reports the bacteriocin KAE01 from Enterococcus faecium, along with its characterization, molecular modeling, and antibacterial potency, by targeting the matrix protein of Pseudomonas aeruginosa. The bacteriocin was purified by using ammonium sulfate precipitation and fast protein liquid chromatography (FPLC), and its molecular weight was estimated as 55 kDa by means of SDS-PAGE. The bacteriocin was found to show stability in a wide range of pH values (2.0-10.0) and temperatures (100 °C for 1 h and 121 °C for 15 min). Antimicrobial screening of the purified peptide against different strains of P. aeruginosa showed its significant antibacterial potential. Scanning electron microscopy of bacteriocin-induced bacterial cultures revealed significant changes in the cellular morphology of the pathogens. In silico molecular modeling of KAE01, followed by molecular docking of the matrix protein (qSA) of P. aeruginosa and KAE01, supported the antibacterial potency and SEM findings of this study.

Immunoinformatics Approach for Epitope-Based Vaccine Design: Key Steps for Breast Cancer Vaccine

Vaccines are an upcoming medical intervention for breast cancer. By targeting the tumor antigen, cancer vaccines can be designed to train the immune system to recognize tumor cells. Therefore, along with technological advances, the vaccine design process is now starting to be carried out with more rational methods such as designing epitope-based peptide vaccines using immunoinformatics methods. Immunoinformatics methods can assist vaccine design in terms of antigenicity and safety. Common protocols used to design epitope-based peptide vaccines include tumor antigen identification, protein structure analysis, T cell epitope prediction, epitope characterization, and evaluation of protein-epitope interactions. Tumor antigen can be divided into two types: tumor associated antigen and tumor specific antigen. We will discuss the identification of tumor antigens using high-throughput technologies. Protein structure analysis comprises the physiochemical, hydrochemical, and antigenicity of the protein. T cell epitope prediction models are widely available with various prediction parameters as well as filtering tools for the prediction results. Epitope characterization such as allergenicity and toxicity can be done in silico as well using allergenicity and toxicity predictors. Evaluation of protein-epitope interactions can also be carried out in silico with molecular simulation. We will also discuss current and future developments of breast cancer vaccines using an immunoinformatics approach. Finally, although prediction models have high accuracy, the opposite can happen after being tested in vitro and in vivo. Therefore, further studies are needed to ensure the effectiveness of the vaccine to be developed. Although epitope-based peptide vaccines have the disadvantage of low immunogenicity, the addition of adjuvants can be a solution.

Towards rational computational peptide design

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

Spectral and theoretical study of SARS-CoV-2 ORF10 protein interaction with endogenous and exogenous macroheterocyclic compounds

The coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 coronavirus has spread rapidly around the world in a matter of weeks. Most of the current recommendations developed for the use of antivirals in COVID-19 were developed during the initial waves of the pandemic, when resources were limited and administrative or pragmatic criteria took precedence. The choice of drugs for the treatment of COVID-19 was carried out from drugs approved for medical use. COVID-19 is a serious public health problem and the search for drugs that can relieve the disease in infected patients at various stages is still necessary. Therefore, the search for effective drugs with inhibitory and/or virucidal activity is a paramount task. Accessory proteins of the virus play a significant role in the pathogenesis of the disease, as they modulate the host's immune response. This paper studied the interaction of one of the SARS-CoV-2 accessory proteins ORF10 with macroheterocyclic compounds - protoporphyrin IX d.m.e., Fe(III)protoporphyrin d.m.e. and 5,10,15,20-tetrakis(3'-pyridyl)chlorin tetraiodide, which are potential inhibitors and virucidal agents. The SARS-CoV-2 ORF10 protein shows the highest affinity for Chlorin, which binds hydrophobically to the alpha structured region of the protein. Protoporphyrin is able to form several complexes with ORF10 close in energy, with alpha- and beta-molecular recognition features, while Fe(III)protoporphyrin forms complexes with the orientation of the porphyrin macrocycle parallel to the ORF10 alpha-helix. Taking into account the nature of the interaction with ORF10, it has been suggested that Chlorin may have virucidal activity upon photoexposure. The SARS-CoV-2 ORF10 protein was expressed in Escherichia coli cells, macroheterocyclic compounds were synthesized, and the structure was confirmed. The interaction between macrocycles with ORF10 was studied by spectral methods. The results of in silico studies were confirmed by experimental data.

Tools and techniques for rational designing of antimicrobial peptides for aquaculture

Fisheries and aquaculture industries remain essential sources of food and nutrition for millions of people worldwide. Indiscriminate use of antibiotics has led to the emergence of antimicrobial-resistant bacteria and posed a severe threat to public health. Researchers have opined that antimicrobial peptides (AMPs) can be the best possible alternative to curb the rising tide of antimicrobial resistance in aquaculture. AMPs may also help to achieve the objectives of one health approach. The natural AMPs are associated with several shortcomings, like less in vivo stability, toxicity to host cell, high cost of production and low potency in a biological system. In this review, we have provided a comprehensive outline about the strategies for designing synthetic mimics of natural AMPs with high potency. Moreover, the freely available AMP databases and the information about the molecular docking tools are enlisted. We also provided in silico template for rationally designing the AMPs from fish piscidins or other peptides. The rationally designed piscidin (rP1 and rp2) may be used to tackle microbial infections in aquaculture. Further, the protocol can be used to develop the truncated mimics of natural AMPs having more potency and protease stability.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Molecular modelling studies and identification of novel phytochemical inhibitor of DLL3

Prostate cancer has been recently considered the most diagnosed cancer in male. DLL3 is overexpressed in CRPC-NE but not in localised prostate cancer or BPH. There are no effective treatments for neuroendocrine differentiated prostate cancer due to a lack of understanding of DLL3 structure and function. The structure of DLL3 is not yet determined using any experimental techniques. Hence, the structure-based drug discovery approach against prostate cancer has not shown great success. In present study, molecular modelling techniques were employed to generate three-dimensional structure of DLL3 and performed its thorough structural analysis. Further, all-atom molecular dynamics simulation was performed to obtain energetically favourable conformation. Further, we used a virtual screening using a library of >13800 phytochemicals from the IMPPAT database and other literature to select the best possible phytochemical inhibitor for DLL3 and identified the top five compounds. Relative binding affinity was calculated using the MM-PBSA approach. ADMET properties of the screened compounds reveal the toxic effect of Gnemonol C. We believe these studied physicochemical properties, functional domain identification, and binding site identification would be very useful to gain more structural and functional insights of DLL3; also, it can be used to infer their pharmacodynamics properties of DLL3 which was recently reported as an important prostate cancer target. The current study also proposes that Ergosterol Peroxide, Dioslupecin A, Mulberrofuran K, and Caracurine V have strong affinities and could serve as plausible inhibitors against DLL3. We believe this study would further help develop better drug candidates against neuroendocrine prostate cancer.Communicated by Ramaswamy H. Sarma.

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.

Molecular coevolution of nuclear and nucleolar localization signals inside basic domain of HIV-1 Tat

During evolution, viruses had to adapt to an increasingly complex environment of eukaryotic cells. Viral proteins that need to enter the cell nucleus or associate with nucleoli possess nuclear localization signals (NLSs) and nucleolar localization signals (NoLSs) for nuclear and nucleolar accumulation, respectively. As viral proteins are relatively small, acquisition of novel sequences seems to be a more complicated task for viruses than for eukaryotes. Here, we carried out a comprehensive analysis of the basic domain (BD) of HIV-1 Tat to show how viral proteins might evolve with NLSs and NoLSs without an increase in protein size. The HIV-1 Tat BD is involved in several functions, the most important being the transactivation of viral transcription. The BD also functions as an NLS, although it is substantially longer than a typical NLS. It seems that different regions in the BD could function as NLSs due to its enrichment with positively charged amino acids. Additionally, the high positive net charge inevitably causes the BD to function as an NoLS through a charge-specific mechanism. The integration of NLSs and NoLSs into functional domains enriched with positively charged amino acids might be a mechanism that allows the condensation of different functional sequences in small protein regions and, as a result, to reduce protein size, influencing the origin and evolution of NLSs and NoLSs in viruses. IMPORTANCE Here, we investigated the molecular mechanism of NLS and NoLS integration into the basic domain of HIV-1 Tat (⁴⁹RKKRRQRRR⁵⁷), and found that these two supplementary functions (i.e., function of NLS and NoLS) are embedded in the basic domain amino acid sequence. The integration of NLSs and NoLSs into functional domains of viral proteins enriched with positively charged amino acids is a mechanism that allows the concentration of different functions within small protein regions. Integration of NLS and NoLS into functional protein domains might have influenced the viral evolution, as this could prevent an increase in the protein size.

Theoretical and experimental study of interaction of macroheterocyclic compounds with ORF3a of SARS-CoV-2

The pandemic infectious disease (Covid-19) caused by the coronavirus (SARS-CoV2) is spreading rapidly around the world. Covid-19 does an irreparable harm to the health and life of people. It also has a negative financial impact on the economies of most countries of the world. In this regard, the issue of creating drugs aimed at combating this disease is especially acute. In this work, molecular docking was used to study the docking of 23 compounds with QRF3a SARS-CoV2. The performed in silico modeling made it possible to identify leading compounds capable of exerting a potential inhibitory and virucidal effect. The leading compounds include chlorin (a drug used in PDT), iron(III)protoporphyrin (endogenous porphyrin), and tetraanthraquinone porphyrazine (an exogenous substance). Having taken into consideration the localization of ligands in the QRF3a SARS-CoV2, we have made an assumption about their influence on the pathogenesis of Covid-19. The interaction of chlorin, iron(III)protoporphyrin and protoporphyrin with the viral protein ORF3a were studied by fluorescence and UV–Vis spectroscopy. The obtained experimental results confirm the data of molecular docking. The results showed that a viral protein binds to endogenous porphyrins and chlorins, moreover, chlorin is a competitive ligand for endogenous porphyrins. Chlorin should be considered as a promising drug for repurposing.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

In silico Screening and Behavioral Validation of a Novel Peptide, LCGA-17, With Anxiolytic-Like Properties

The aim of the study was to develop better anxiolytics and antidepressants. We focused on GABA_A receptors and the α2δ auxiliary subunit of V-gated Ca²⁺ channels as putative targets because they are established as mediators of efficacious anxiolytics, antidepressants, and anticonvulsants. We further focused on short peptides as candidate ligands because of their high safety and tolerability profiles. We employed a structural bioinformatics approach to develop novel tetrapeptides with predicted affinity to GABA_A receptors and α2δ. In silico docking studies of one of these peptides, LCGA-17, showed a high binding score for both GABA_A receptors and α2δ, combined with anxiolytic-like properties in a Danio rerio behavioral screen. LCGA-17 showed anxiolytic-like effects in the novel tank test, the light–dark box, and the social preference test, with efficacy comparable to fluvoxamine and diazepam. In binding assays using rat brain membranes, [³H]-LCGA-17 was competed more effectively by gabapentinoid ligands of α2δ than ligands of GABA_A receptors, suggesting that α2δ represents a likely target for LCGA-17. [³H]-LCGA-17 binding to brain lysates was unaffected by competition with ligands for GABA_B, glutamate, dopamine, serotonin, and other receptors, suggesting specific interaction with α2δ. Dose-finding studies in mice using acute administration of LCGA-17 (i.p.) demonstrated anxiolytic-like effects in the open field test, elevated plus maze, and marble burying tests, as well as antidepressant-like properties in the forced swim test. The anxiolytic effects were effectively blocked by bicuculline. Therefore, LCGA-17 is a novel candidate anxiolytic and antidepressant that may act through α2δ, with possible synergism by GABA_A receptors.

Possible therapeutic targets and promising drugs based on unsymmetrical hetaryl-substituted porphyrins to combat SARS-CoV-2

Coronavirus disease 2019 is a serious disease that causes acute respiratory syndrome and negatively affects the central nervous system. SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) crosses the blood-brain barrier due to the S protein on the surface of the viral particles. Thus, it is important to develop compounds that not only have an inhibitory effect but are also capable of completely deactivating the S-protein function. This study describes the purposeful modification of porphyrins and proposes compounds, asymmetrically hetaryl-substituted porphyrins with benzothiazole, benzoxazole, and N-methylbenzimidazole residues, to deactivate the S-protein functions. Molecular docking of SARS-CoV-2 proteins with hetaryl-substituted porphyrins showed that the viral proteins S-, N-, and nsp13 exhibited the highest binding affinity. Hetaryl-substituted porphyrins form strong complexes (13-14 kcal/mol) with the receptor-binding domain of the S protein, while the distance from the porphyrins to the receptor-binding motif (RBM) does not exceed 20 Å; therefore, RBM can be oxidized by ¹O₂, which is generated by porphyrin. Hetaryl-substituted porphyrins interact with the nucleocapsid protein in the serine/arginine-rich region, and a number of vulnerable amino acid residues are located in the photooxidation zone. This damage complicates the packaging of viral RNA into new virions. High-energy binding of hetaryl-substituted porphyrins with the N- and C-terminal domains of nsp13 was observed. This binding blocks the action of nsp13 as an enzyme of exoribonuclease and methyltransferase, thereby preventing RNA replication and processing. A procedure for the synthesis of hetaryl-substituted porphyrins was developed, new compounds were obtained, their structures were identified, and their photocatalytic properties were studied.

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.

Interfacial binding sites for cholesterol on GABAA receptors and competition with neurosteroids

γ-Aminobutyric acid type A (GABA_A) receptors in the brain are located in the outer membranes of brain cells where the concentration of cholesterol is high. Of the 25 available high-resolution structures available for GABA_A receptors, none were determined in the presence of cholesterol, but four include resolved molecules of cholesterol hemisuccinate (CHS). Here, a molecular docking procedure is used to sweep the transmembrane (TM) surfaces of the receptors for cholesterol binding sites. Cholesterol docking poses determined in this way match 89% of the resolved CHS when CHS molecules deemed unlikely to represent typical bound cholesterols are excluded. The receptors are pentameric, and their TM surfaces consist of a set of five facets, each including pairs of TM helices from two adjacent subunits. Each facet contains hydrophobic hollows running from one side of the membrane to the other, within which are six potential binding sites for cholesterol, three on each side of the membrane. High-resolution structures of GABA_A receptors with bound neurosteroids show that neurosteroids bind in these cholesterol binding sites, so the binding of neurosteroids and cholesterol will be competitive.

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.

Modeling the binding of protoporphyrin IX, verteporfin, and chlorin e6 to SARS-CoV-2 proteins

In this work, we analyze the latest data on the molecular docking of a range of SARS-CoV-2 proteins to protoporphyrin IX, verteporfin, and chlorin e6, as well as consider the prospects for using chlorins and porphyrins as agents for photoinactivation of the SARS2 virus.

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions

Large contact surfaces of protein-protein interactions (PPIs) remain to be an ongoing issue in the discovery and design of small molecule modulators. Peptides are intrinsically capable of exploring larger surfaces, stable, and bioavailable, and therefore bear a high therapeutic value in the treatment of various diseases, including cancer, infectious diseases, and neurodegenerative diseases. Given these promising properties, a long way has been covered in the field of targeting PPIs via peptide design strategies. In silico tools have recently become an inevitable approach for the design and optimization of these interfering peptides. Various algorithms have been developed to scrutinize the PPI interfaces. Moreover, different databases and software tools have been created to predict the peptide structures and their interactions with target protein complexes. High-throughput screening of large peptide libraries against PPIs; "hotspot" identification; structure-based and off-structure approaches of peptide design; 3D peptide modeling; peptide optimization strategies like cyclization; and peptide binding energy evaluation are among the capabilities of in silico tools. In the present study, the most recent advances in the field of in silico approaches for the design of interfering peptides against PPIs will be reviewed. The future perspective of the field and its advantages and limitations will also be pinpointed.

Genetically Encoded Fragment-Based Discovery from Phage-Displayed Macrocyclic Libraries with Genetically Encoded Unnatural Pharmacophores

Genetically encoded macrocyclic peptide libraries with unnatural pharmacophores are valuable sources for the discovery of ligands for many targets of interest. Traditionally, generation of such libraries employs "early stage" incorporation of unnatural building blocks into the chemically or translationally produced macrocycles. Here, we describe a divergent late-stage approach to such libraries starting from readily available starting material: genetically encoded libraries of peptides. A diketone linchpin 1,5-dichloropentane-2,4-dione converts peptide libraries displayed on phage to 1,3-diketone bearing macrocyclic peptides (DKMP): shelf-stable precursors for Knorr pyrazole synthesis. Ligation of diverse hydrazine derivatives onto DKMP libraries displayed on phage that carries silent DNA-barcodes yields macrocyclic libraries in which the amino acid sequence and the pharmacophore are encoded by DNA. Selection of this library against carbonic anhydrase enriched macrocycles with benzenesulfonamide pharmacophore and nanomolar Kd. The methodology described in this manuscript can graft diverse pharmacophores into many existing genetically encoded phage libraries and significantly increase the value of such libraries in molecular discoveries.

Enfuvirtide, an HIV-1 fusion inhibitor peptide, can act as a potent SARS-CoV-2 fusion inhibitor: an in silico drug repurposing study

Regarding the urgency of therapeutic measures for coronavirus disease 2019 (COVID-19) pandemic, the use of available drugs with FDA approval is preferred because of the less time and cost required for their development. In silico drug repurposing is an accurate way to speed up the screening of the existing FDA-approved drugs to find a therapeutic option for COVID-19. The similarity in SARS-CoV-2 and HIV-1 fusion mechanism to host cells can be a key point for Inhibit SARS-CoV-2 entry into host cells by HIV fusion inhibitors. Accordingly, in this study, an HIV-1 fusion inhibitor called Enfuvirtide (Enf) was selected. The affinity and essential residues involving in the Enf binding to the S2 protein of SARS-CoV-2, HIV-1 gp41 protein and angiotensin-converting enzyme 2 (ACE-2) as a negative control, was evaluated using molecular docking. Eventually, Enf-S2 and Enf-gp41 protein complexes were simulated by molecular dynamics (MD) in terms of binding affinity and stability. Based on the most important criteria such as docking score, cluster size, energy and dissociation constant, the strongest interaction was observed between Enf with the S2 protein. In addition, MD results confirmed that Enf-S2 protein interaction was remarkably stable and caused the S2 protein residues to undergo the fewest fluctuations. In conclusion, it can be stated that Enf can act as a strong SARS-CoV-2 fusion inhibitor and demonstrates the potential to enter the clinical trial phase of COVID-19.

Communicated by Ramaswamy H. Sarma

Design and Generation of Self-Assembling Peptide Virus-like Particles with Intrinsic GPCR Inhibitory Activity

Synthetic analogs of the second transmembrane domain (TM) containing a portion of the extracellular loop 1 of G-protein-coupled receptors (GPCR) can serve as biased antagonists of the corresponding receptor. Analogs with negative charges added to the extracellular end self-assemble into round structures. Addition of polyethylene glycol chains of defined length to the C-terminus of the peptides prevents super aggregation and results in highly uniform particles that can fuse with cell membranes spontaneously. Added PEG chains slow down cell fusion, while attachment of receptor ligands to the surface of particles results in receptor-mediated membrane fusion and cell-selective delivery. Critical assembly concentration of TM peptide particles is in the nanomolar range and thus requires nontraditional methods of determination. In this chapter, we outline sequence selection and design of self-assembling GPCR antagonists, methods of the preparation of the nanoparticles, and biophysical methods of particle characterization. The protocols allow for straightforward rational design, generation, and characterization of self-assembling GPCR antagonists for a variety of applications.

A comprehensive application: Molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine

Traditional Chinese medicine (TCM) has been used for more than 2000 years in China. TCM has received wide attention recently due to its unique charm. At the same time, its main obstacles have attracted wide attention, including vagueness of drug composition and treatment mechanism. With the development of virtual screening technology, more and more Chinese medicine compounds have been studied to discover the potential active components and mechanisms of action. Molecular docking is a computer technology based on structural design. Network pharmacology establishes powerful and comprehensive databases to understand the relationship between TCM and disease network. In this review, emergent uses and applications of two techniques and further superiorities of the two techniques when embarked to boil down into a tidy system were illustrated. A combination of the two provides a theoretical basis and technical support for the construction of modern TCM based on the compatibility of components and accelerates the realization of two basic elements as well, including the clearness of the pharmacodynamic substances and explanation of the effect of TCM.

The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes

MOTIVATION

Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs.

RESULTS

Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins.

AVAILABILITY AND IMPLEMENTATION

ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recent Advances and Computational Approaches in Peptide Drug Discovery

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Docking of peptides to GPCRs using a combination of CABS-dock with FlexPepDock refinement

The structural description of peptide ligands bound to G protein-coupled receptors (GPCRs) is important for the discovery of new drugs and deeper understanding of the molecular mechanisms of life. Here we describe a three-stage protocol for the molecular docking of peptides to GPCRs using a set of different programs: (1) CABS-dock for docking fully flexible peptides; (2) PD2 method for the reconstruction of atomistic structures from C-alpha traces provided by CABS-dock and (3) Rosetta FlexPepDock for the refinement of protein–peptide complex structures and model scoring. We evaluated the proposed protocol on the set of seven different GPCR–peptide complexes (including one containing a cyclic peptide), for which crystallographic structures are available. We show that CABS-dock produces high resolution models in the sets of top-scored models. These sets of models, after reconstruction to all-atom representation, can be further improved by Rosetta high-resolution refinement and/or minimization, leading in most of the cases to sub-Angstrom accuracy in terms of interface root-mean-square-deviation measure.

Molecular Structure, Binding Affinity, and Biological Activity in the Epigenome

Development of valid structure–activity relationships (SARs) is a key to the elucidation of pathomechanisms of epigenetic diseases and the development of efficient, new drugs. The present review is based on selected methodologies and applications supplying molecular structure, binding affinity and biological activity data for the development of new SARs. An emphasis is placed on emerging trends and permanent challenges of new discoveries of SARs in the context of proteins as epigenetic drug targets. The review gives a brief overview and classification of the molecular background of epigenetic changes, and surveys both experimental and theoretical approaches in the field. Besides the results of sophisticated, cutting edge techniques such as cryo-electron microscopy, protein crystallography, and isothermal titration calorimetry, examples of frequently used assays and fast screening techniques are also selected. The review features how different experimental methods and theoretical approaches complement each other and result in valid SARs of the epigenome.