PepCrawler: a fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors

The role and future prospects of artificial intelligence algorithms in peptide drug development

Peptide medications have been more well-known in recent years due to their many benefits, including low side effects, high biological activity, specificity, effectiveness, and so on. Over 100 peptide medications have been introduced to the market to treat a variety of illnesses. Most of these peptide medications are developed on the basis of endogenous peptides or natural peptides, which frequently required expensive, time-consuming, and extensive tests to confirm. As artificial intelligence advances quickly, it is now possible to build machine learning or deep learning models that screen a large number of candidate sequences for therapeutic peptides. Therapeutic peptides, such as those with antibacterial or anticancer properties, have been developed by the application of artificial intelligence algorithms.The process of finding and developing peptide drugs is outlined in this review, along with a few related cases that were helped by AI and conventional methods. These resources will open up new avenues for peptide drug development and discovery, helping to meet the pressing needs of clinical patients for disease treatment. Although peptide drugs are a new class of biopharmaceuticals that distinguish them from chemical and small molecule drugs, their clinical purpose and value cannot be ignored. However, the traditional peptide drug research and development has a long development cycle and high investment, and the creation of peptide medications will be substantially hastened by the AI-assisted (AI+) mode, offering a new boost for combating diseases.

Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

Molecular Docking of Intrinsically Disordered Proteins: Challenges and Strategies

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions

Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

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.

Reverse engineering approach: a step towards a new era of vaccinology with special reference to Salmonella

INTRODUCTION

Salmonella is responsible for causing enteric fever, septicemia, and gastroenteritis in humans. Due to high disease burden and emergence of multi- and extensively drug-resistant Salmonella strains, it is becoming difficult to treat the infection with existing battery of antibiotics as we are not able to discover newer antibiotics at the same pace at which the pathogens are acquiring resistance. Though vaccines against Salmonella are available commercially, they have limited efficacy. Advancements in genome sequencing technologies and immunoinformatics approaches have solved the problem significantly by giving rise to a new era of vaccine designing, i.e. 'Reverse engineering.' Reverse engineering/vaccinology has expedited the vaccine identification process. Using this approach, multiple potential proteins/epitopes can be identified and constructed as a single entity to tackle enteric fever.

AREAS COVERED

This review provides details of reverse engineering approach and discusses various protein and epitope-based vaccine candidates identified using this approach against typhoidal Salmonella.

EXPERT OPINION

Reverse engineering approach holds great promise for developing strategies to tackle the pathogen(s) by overcoming the limitations posed by existing vaccines. Progressive advancements in the arena of reverse vaccinology, structural biology, and systems biology combined with an improved understanding of host-pathogen interactions are essential components to design new-generation vaccines.

Approaches for evaluation of novel CPP-based cargo delivery systems

Cell penetrating peptides (CPPs) can be broadly defined as relatively short synthetic, protein derived or chimeric peptides. Their most remarkable property is their ability to cross cell barriers and facilitate the translocation of cargo, such as drugs, nucleic acids, peptides, small molecules, dyes, and many others across the plasma membrane. Over the years there have been several approaches used, adapted, and developed for the evaluation of CPP efficacies as delivery systems, with the fluorophore attachment as the most widely used approach. It has become progressively evident, that the evaluation method, in order to lead to successful outcome, should concede with the specialties of the delivery. For characterization and assessment of CPP-cargo a combination of research tools of chemistry, physics, molecular biology, engineering, and other fields have been applied. In this review, we summarize the diverse, in silico, in vitro and in vivo approaches used for evaluation and characterization of CPP-based cargo delivery systems.

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.

Inhibition of Viral Membrane Fusion by Peptides and Approaches to Peptide Design

Fusion of lipid-enveloped viruses with the cellular plasma membrane or the endosome membrane is mediated by viral envelope proteins that undergo large conformational changes following binding to receptors. The HIV-1 fusion protein gp41 undergoes a transition into a "six-helix bundle" after binding of the surface protein gp120 to the CD4 receptor and a co-receptor. Synthetic peptides that mimic part of this structure interfere with the formation of the helix structure and inhibit membrane fusion. This approach also works with the S spike protein of SARS-CoV-2. Here we review the peptide inhibitors of membrane fusion involved in infection by influenza virus, HIV-1, MERS and SARS coronaviruses, hepatitis viruses, paramyxoviruses, flaviviruses, herpesviruses and filoviruses. We also describe recent computational methods used for the identification of peptide sequences that can interact strongly with protein interfaces, with special emphasis on SARS-CoV-2, using the PePI-Covid19 database.

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.

Peptide-tiling screens of cancer drivers reveal oncogenic protein domains and associated peptide inhibitors

Gene fragments derived from structural domains mediating physical interactions can modulate biological functions. Utilizing this, we developed lentiviral overexpression libraries of peptides comprehensively tiling high-confidence cancer driver genes. Toward inhibiting cancer growth, we assayed ~66,000 peptides, tiling 65 cancer drivers and 579 mutant alleles. Pooled fitness screens in two breast cancer cell lines revealed peptides, which selectively reduced cellular proliferation, implicating oncogenic protein domains important for cell fitness. Coupling of cell-penetrating motifs to these peptides enabled drug-like function, with peptides derived from EGFR and RAF1 inhibiting cell growth at IC50s of 27-63 μM. We anticipate that this peptide-tiling (PepTile) approach will enable rapid de novo mapping of bioactive protein domains and associated interfering peptides.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

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.

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.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Therapeutic peptides for the treatment of cystic fibrosis: Challenges and perspectives

Cystic fibrosis (CF) is the most common amongst rare genetic diseases, affecting more than 70.000 people worldwide. CF is characterized by a dysfunctional chloride channel, termed cystic fibrosis conductance regulator (CFTR), which leads to the production of a thick and viscous mucus layer that clogs the lungs of CF patients and traps pathogens, leading to chronic infections and inflammation and, ultimately, lung damage. In recent years, the use of peptides for the treatment of respiratory diseases, including CF, has gained growing interest. Therapeutic peptides for CF include antimicrobial peptides, inhibitors of proteases, and modulators of ion channels, among others. Peptides display unique features that make them appealing candidates for clinical translation, like specificity of action, high efficacy, and low toxicity. Nevertheless, the intrinsic properties of peptides, together with the need of delivering these compounds locally, e.g. by inhalation, raise a number of concerns in the development of peptide therapeutics for CF lung disease. In this review, we discuss the challenges related to the use of peptides for the treatment of CF lung disease through inhalation, which include retention within mucus, proteolysis, immunogenicity and aggregation. Strategies for overcoming major shortcomings of peptide therapeutics will be presented, together with recent developments in peptide design and optimization, including computational analysis and high-throughput screening.

Characterization of Three Osmotin-Like Proteins from Plumeria rubra and Prospection for Adiponectin Peptidomimetics

BACKGROUND

Osmotin-Like Proteins (OLPs) have been purified and characterized from different plant tissues, including latex fluids. Besides its defensive role, tobacco osmotin seems to induce adiponectin-like physiological effects, acting as an agonist. However, molecular information about this agonistic effect on adiponectin receptors has been poorly exploited and other osmotins have not been investigated yet.

OBJECTIVE AND METHODS

The present study involved the characterization of three OLPs from Plumeria rubra latex and molecular docking studies to evaluate the interaction between them and adiponectin receptors (AdipoR1 and AdipoR2).

RESULTS

P. rubra Osmotin-Like Proteins (PrOLPs) exhibited molecular masses from 21 to 25 kDa and isoelectric points ranging from 4.4 to 7.7. The proteins have 16 cysteine residues, which are involved in eight disulfide bonds, conserved in the same positions as other plant OLPs. The threedimensional (3D) models exhibited the three typical domains of OLPs, and molecular docking analysis showed that two PrOLP peptides interacted with two adiponectin receptors similarly to tobacco osmotin peptide.

CONCLUSION

As observed for tobacco osmotin, the latex osmotins of P. rubra exhibited compatible interactions with adiponectin receptors. Therefore, these plant defense proteins (without known counterparts in humans) are potential tools to study modulation of glucose metabolism in type II diabetes, where adiponectin plays a pivotal role in homeostasis.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.

Approaches for the discovery of new cell-penetrating peptides

Introduction: The capability of cell-penetrating peptides (CPP), also known as protein transduction domains (PTD), to enter into cells possibly with an attached cargo, makes their application as delivery vectors or as direct therapeutics compelling. They are generally biocompatible, nontoxic, and easy to synthesize and modify. Three decades after the discovery of the first CPPs, ~2,000 CPP sequences have been identified, and many more predicted. Nevertheless, the field has a strong commitment to authenticate new, more efficient, and specific CPPs.Areas covered: Although a scattering of CPPs have been found by chance, various systematic approaches have been developed and refined over the years to directly aid the identification and depiction of new peptide-based delivery vectors or therapeutics. Here, the authors give an overview of CPPs, and review various approaches of discovering new ones. An emphasis is placed on in silico methods, as these have advanced rapidly in recent years.Expert opinion: Although there are many known CPPs, there is a need to find more efficient and specific CPPs. Several approaches are used to identify such sequences. The success of these approaches depends on the advancement of others and the successful prediction of CPP sequences relies on experimental data.

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.

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

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

Targeting Tumors Using Peptides

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

Design of Disruptors of the Hsp90-Cdc37 Interface

The molecular chaperone Hsp90 is a ubiquitous ATPase-directed protein responsible for the activation and structural stabilization of a large clientele of proteins. As such, Hsp90 has emerged as a suitable candidate for the treatment of a diverse set of diseases, such as cancer and neurodegeneration. The inhibition of the chaperone through ATP-competitive inhibitors, however, was shown to lead to undesirable side effects. One strategy to alleviate this problem is the development of molecules that are able to disrupt specific protein–protein interactions, thus modulating the activity of Hsp90 only in the particular cellular pathway that needs to be targeted. Here, we exploit novel computational and theoretical approaches to design a set of peptides that are able to bind Hsp90 and compete for its interaction with the co-chaperone Cdc37, which is found to be responsible for the promotion of cancer cell proliferation. In spite of their capability to disrupt the Hsp90–Cdc37 interaction, no important cytotoxicity was observed in human cancer cells exposed to designed compounds. These findings imply the need for further optimization of the compounds, which may lead to new ways of interfering with the Hsp90 mechanisms that are important for tumour growth.

Mechanisms Inspired Targeting Peptides

Peptides, as a large group of molecules, are composed of amino acid residues and can be divided into linear or cyclic peptides according to the structure. Over 13,000 molecules of natural peptides have been found and many of them have been well studied. In artificial peptide libraries, the number of peptide diversity could be up to 1 × 10¹³. Peptides have more complex structures and higher affinity to target proteins comparing with small molecular compounds. Recently, the development of targeting cancer immune checkpoint (CIP) inhibitors is having a very important role in tumor therapy. Peptides targeting ligands or receptors in CIP have been designed based on three-dimensional structures of target proteins or directly selected by random peptide libraries in biological display systems. Most of these targeting peptides work as inhibitors of protein-protein interaction and improve CD8+ cytotoxic T-lymphocyte (CTL) activation in the tumor microenvironment, for example, PKHB1, Ar5Y4 and TPP1. Peptides could be designed to regulate CIP protein degradation in vivo, such as PD-LYSO and PD-PALM. Besides its use in developing therapeutic drugs for targeting CIP, targeting peptides could be used in drug's targeted delivery and diagnosis in tumor immune therapy.

Protein-peptide docking using CABS-dock and contact information

CABS-dock is a computational method for protein-peptide molecular docking that does not require predefinition of the binding site. The peptide is treated as fully flexible, while the protein backbone undergoes small fluctuations and, optionally, large-scale rearrangements. Here, we present a specific CABS-dock protocol that enhances the docking procedure using fragmentary information about protein-peptide contacts. The contact information is used to narrow down the search for the binding peptide pose to the proximity of the binding site. We used information on a single-chosen and randomly chosen native protein-peptide contact to validate the protocol on the peptiDB benchmark. The contact information significantly improved CABS-dock performance. The protocol has been made available as a new feature of the CABS-dock web server (at http://biocomp.chem.uw.edu.pl/CABSdock/).

SHORT ABSTRACT

CABS-dock is a tool for flexible docking of peptides to proteins. In this article, we present a protocol for CABS-dock docking driven by information about protein-peptide contact(s). Using information on individual protein-peptide contacts allows to improve the accuracy of CABS-dock docking.

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

Protein-peptide interactions are crucial in many cellular functions. Therefore, determining the structure of protein-peptide complexes is important for understanding the molecular mechanism of related biological processes and developing peptide drugs. HPEPDOCK is a novel web server for blind protein-peptide docking through a hierarchical algorithm. Instead of running lengthy simulations to refine peptide conformations, HPEPDOCK considers the peptide flexibility through an ensemble of peptide conformations generated by our MODPEP program. For blind global peptide docking, HPEPDOCK obtained a success rate of 33.3% in binding mode prediction on a benchmark of 57 unbound cases when the top 10 models were considered, compared to 21.1% for pepATTRACT server. HPEPDOCK also performed well in docking against homology models and obtained a success rate of 29.8% within top 10 predictions. For local peptide docking, HPEPDOCK achieved a high success rate of 72.6% on a benchmark of 62 unbound cases within top 10 predictions, compared to 45.2% for HADDOCK peptide protocol. Our HPEPDOCK server is computationally efficient and consumed an average of 29.8 mins for a global peptide docking job and 14.2 mins for a local peptide docking job. The HPEPDOCK web server is available at http://huanglab.phys.hust.edu.cn/hpepdock/.

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

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

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

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

Benchmarking of different molecular docking methods for protein-peptide docking

Background

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

Result

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

Conclusion

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

Electronic supplementary material

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

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

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

Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides

Given the importance of peptide-mediated protein interactions in cellular processes, protein-peptide docking has received increasing attention. Here, we have developed a Hierarchical flexible Peptide Docking approach through fast generation and ensemble docking of peptide conformations, which is referred to as HPepDock. Tested on the LEADS-PEP benchmark data set of 53 diverse complexes with peptides of 3-12 residues, HPepDock performed significantly better than the 11 docking protocols of five small-molecule docking programs (DOCK, AutoDock, AutoDock Vina, Surflex, and GOLD) in predicting near-native binding conformations. HPepDock was also evaluated on the 19 bound/unbound and 10 unbound/unbound protein-peptide complexes of the Glide SP-PEP benchmark and showed an overall better performance than Glide SP-PEP+MM-GBSA and FlexPepDock in both bound and unbound docking. HPepDock is computationally efficient, and the average running time for docking a peptide is ∼15 min with the range from about 1 min for short peptides to around 40 min for long peptides.

Computer-aided design of amino acid-based therapeutics: a review

During the last two decades, the pharmaceutical industry has progressed from detecting small molecules to designing biologic-based therapeutics. Amino acid-based drugs are a group of biologic-based therapeutics that can effectively combat the diseases caused by drug resistance or molecular deficiency. Computational techniques play a key role to design and develop the amino acid-based therapeutics such as proteins, peptides and peptidomimetics. In this study, it was attempted to discuss the various elements for computational design of amino acid-based therapeutics. Protein design seeks to identify the properties of amino acid sequences that fold to predetermined structures with desirable structural and functional characteristics. Peptide drugs occupy a middle space between proteins and small molecules and it is hoped that they can target "undruggable" intracellular protein-protein interactions. Peptidomimetics, the compounds that mimic the biologic characteristics of peptides, present refined pharmacokinetic properties compared to the original peptides. Here, the elaborated techniques that are developed to characterize the amino acid sequences consistent with a specific structure and allow protein design are discussed. Moreover, the key principles and recent advances in currently introduced computational techniques for rational peptide design are spotlighted. The most advanced computational techniques developed to design novel peptidomimetics are also summarized.

Protein-peptide docking: opportunities and challenges

Peptides have recently attracted much attention as promising drug candidates. Rational design of peptide-derived therapeutics usually requires structural characterization of the underlying protein-peptide interaction. Given that experimental characterization can be difficult, reliable computational tools are needed. In recent years, a variety of approaches have been developed for 'protein-peptide docking', that is, predicting the structure of the protein-peptide complex, starting from the protein structure and the peptide sequence, including variable degrees of information about the peptide binding site and/or conformation. In this review, we provide an overview of protein-peptide docking methods and outline their capabilities, limitations, and applications in structure-based drug design. Key challenges are also briefly discussed, such as modeling of large-scale conformational changes upon binding, scoring of predicted models, and optimal inclusion of varied types of experimental data and theoretical predictions into an integrative modeling process.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.

Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes

Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

Identification of peptide-mediated interactions between human PTTG and SH3 domains in pALL gene expression profile

Human pituitary tumor-transforming gene (PTTG) plays an essential role in the development and progression of pediatric acute lymphoblastic leukemia (pALL). PTTG has two SH3-binding peptide motifs that can be recognized by a variety of SH3-containing proteins in the pALL through peptide-mediated interactions. In this study, the gene expression profile of pALL was examined in detail by integrating computational modeling and experimental assay, aiming to identify those potential partner proteins of human PTTG. The binding potency of domain candidates to peptide motifs was ranked using knowledge-based scoring and fluorescence titration. A number of SH3 domains found in a variety of pALL proteins were identified as potent binders with moderate or high affinity for PTTG. It is revealed that the PTTG peptide motifs show different affinity profiles for various candidate proteins, indicating that the PTTG selectivity is optimized across pALL gene expression space. The PTTG peptides were then mutated rationally to target the SH3 domains of identified partner proteins by competing with the native peptide motifs.

Integrated In Silico-In Vitro Identification and Characterization of the SH3-Mediated Interaction between Human PTTG and its Cognate Partners in Medulloblastoma

The human pituitary tumor-transforming gene is an oncogenic protein which serves as a central hub in the cellular signaling network of medulloblastoma. The protein contains two vicinal PxxP motifs at its C terminus that are potential binding sites of peptide-recognition SH3 domains. Here, a synthetic protocol that integrated in silico analysis and in vitro assay was described to identify the SH3-binding partners of pituitary tumor-transforming gene in the gene expression profile of medulloblastoma. In the procedure, a variety of structurally diverse, non-redundant SH3 domains with high gene expression in medulloblastoma were compiled, and their three-dimensional structures were either manually retrieved from the protein data bank database or computationally modeled through bioinformatics technique. The binding capability of these domains towards the two PxxP-containing peptides m1p: ¹⁶¹LGPPSPVK¹⁶⁸ and m2p: ¹⁶⁸KMPSPPWE¹⁷⁵ of pituitary tumor-transforming gene were ranked by structure-based scoring and fluorescence-based assay. Consequently, a number of SH3 domains, including MAP3K and PI3K, were found to have moderate or high affinity for m1p and/or m2p. Interestingly, the two overlapping peptides exhibits a distinct binding profile to these identified domain partners, suggesting that the binding selectivity of m1p and m2p is optimized across the medulloblastoma expression spectrum by competing for domain candidates. In addition, two redesigned versions of m1p peptide ware obtained via a structure-based rational mutation approach, which exhibited an increased affinity for the domain as compared to native peptide.

Protein-Peptide Interaction Design: PepCrawler and PinaColada

In this chapter we present two methods related to rational design of inhibitory peptides: PepCrawler: A tool to derive binding peptides from protein-protein complexes and the prediction of protein-peptide complexes. Given an initial protein-peptide complex, the method detects improved predicted peptide binding conformations which bind the protein with higher affinity. This program is a robotics motivated algorithm, representing the peptide as a robotic arm moving among obstacles and exploring its conformational space in an efficient way. PinaColada: A peptide design program for the discovery of novel peptide candidates that inhibit protein-protein interactions. PinaColada uses PepCrawler while introducing sequence mutations, in order to find novel inhibitory peptides for PPIs. It uses the ant colony optimization approach to explore the peptide's sequence space, while using PepCrawler in the refinement stage.

Application of the ATTRACT Coarse-Grained Docking and Atomistic Refinement for Predicting Peptide-Protein Interactions

Peptide-protein interactions are abundant in the cell and form an important part of the interactome. Large-scale modeling of peptide-protein complexes requires a fully blind approach; i.e., simultaneously predicting the peptide-binding site and the peptide conformation to high accuracy. Here, we present one of the first fully blind peptide-protein docking protocols, pepATTRACT. It combines a coarse-grained ensemble docking search of the entire protein surface with two stages of atomistic flexible refinement. pepATTRACT yields high-quality predictions for 70 % of the cases when tested on a large benchmark of peptide-protein complexes. This performance in fully blind mode is similar to state-of-the-art local docking approaches that use information on the location of the binding site. Limiting the search to the peptide-binding region, the resulting pepATTRACT-local approach further improves the performance. Docking scripts for pepATTRACT and pepATTRACT-local can be generated via a web interface at www.attract.ph.tum.de/peptide.html . Here, we explain how to set up a docking run with the pepATTRACT web interface and demonstrate its usage by an application on binding of disordered regions from tumor suppressor p53 to a partner protein.

Short peptides interfering with signaling pathways as new therapeutic tools for cancer treatment

Short peptides have many advantages, such as low molecular weight, selectivity for a specific target, organelles or cells with minimal toxicity. We describe properties of short peptides, which interfere with communication networks in tumor cells and within microenvironment of malignant gliomas, the most common brain tumors. We focus on ligand/receptor axes and intracellular signaling pathways critical for gliomagenesis that could be targeted with interfering peptides. We review structures and efficacy of organelle-specific and cell-penetrating peptides and describe diverse chemical modifications increasing proteolytic stability and protecting synthetic peptides against degradation. We report results of application of short peptides in glioma therapy clinical trials, their rises and falls. The most advanced examples of therapeutics such as short interfering peptides combined with cell-penetrating peptides that show good effectiveness in disease models are presented. It is foreseen that identification of peptides with better clinical properties may improve their success rates in clinical trials.

AnchorDock for Blind Flexible Docking of Peptides to Proteins

Due to increasing interest in peptides as signaling modulators and drug candidates, several methods for peptide docking to their target proteins are under active development. The "blind" docking problem, where the peptide-binding site on the protein surface is unknown, presents one of the current challenges in the field. AnchorDock protocol was developed by Ben-Shimon and Niv to address this challenge.This protocol narrows the docking search to the most relevant parts of the conformational space. This is achieved by pre-folding the free peptide and by computationally detecting anchoring spots on the surface of the unbound protein. Multiple flexible simulated annealing molecular dynamics (SAMD) simulations are subsequently carried out, starting from pre-folded peptide conformations, constrained to the various precomputed anchoring spots.Here, AnchorDock is demonstrated using two known protein-peptide complexes. A PDZ-peptide complex provides a relatively easy case due to the relatively small size of the protein, and a typical peptide conformation and binding region; a more challenging example is a complex between USP7_N-term and a p53-derived peptide, where the protein is larger, and the peptide conformation and a binding site are generally assumed to be unknown. AnchorDock returned native-like solutions ranked first and third for the PDZ and USP7 complexes, respectively. We describe the procedure step by step and discuss possible modifications where applicable.

Detection of Peptide-Binding Sites on Protein Surfaces Using the Peptimap Server

Peptide-mediated interactions are of primordial importance to the cell, and the structure of such interaction provides an important starting point for their further characterization. In many cases, the structure of the peptide-protein complex has not been solved by experiment, and modeling tools need to be applied to generate structural models of the interaction. PeptiMap is a protocol that identifies the peptide-binding site when only the structure of the receptor is known, but no information about where the peptide binds is available. This is achieved by mapping the surface for solvents to identify ligand-binding sites, similar in approach to ANCHORMAP in which amino acids are mapped. Peptimap is a free open access web-based server. It can be accessed at http://peptimap.cluspro.org .

Template-Based Prediction of Protein-Peptide Interactions by Using GalaxyPepDock

We introduce a web server called GalaxyPepDock that predicts protein-peptide interactions based on templates. With the continuously increasing size of the protein structure database, the probability of finding related proteins for templates is increasing. GalaxyPepDock takes a protein structure and a peptide sequence as input and returns protein-peptide complex structures as output. Templates for protein-peptide complex structures are selected from the structure database considering similarity to the target protein structure and to putative protein-peptide interactions as estimated by protein structure alignment and peptide sequence alignment. Complex structures are then built from the template structures by template-based modeling. By further structure refinement that performs energy-based optimization, structural aspects that are missing in the template structures or that are not compatible with the given protein and peptide are refined. During the refinement, flexibilities of both protein and peptide induced by binding are considered. The atomistic protein-peptide interactions predicted by GalaxyPepDock can offer important clues for designing new peptides with desired binding properties.

Highly Flexible Protein-Peptide Docking Using CABS-Dock

Protein-peptide molecular docking is a difficult modeling problem. It is even more challenging when significant conformational changes that may occur during the binding process need to be predicted. In this chapter, we demonstrate the capabilities and features of the CABS-dock server for flexible protein-peptide docking. CABS-dock allows highly efficient modeling of full peptide flexibility and significant flexibility of a protein receptor. During CABS-dock docking, the peptide folding and binding process is explicitly simulated and no information about the peptide binding site or its structure is used. This chapter presents a successful CABS-dock use for docking a potentially therapeutic peptide to a protein target. Moreover, simulation contact maps, a new CABS-dock feature, are described and applied to the docking test case. Finally, a tutorial for running CABS-dock from the command line or command line scripts is provided. The CABS-dock web server is available from http://biocomp.chem.uw.edu.pl/CABSdock/ .

InteractoMIX: a suite of computational tools to exploit interactomes in biological and clinical research

Virtually all the biological processes that occur inside or outside cells are mediated by protein–protein interactions (PPIs). Hence, the charting and description of the PPI network, initially in organisms, the interactome, but more recently in specific tissues, is essential to fully understand cellular processes both in health and disease. The study of PPIs is also at the heart of renewed efforts in the medical and biotechnological arena in the quest of new therapeutic targets and drugs. Here, we present a mini review of 11 computational tools and resources tools developed by us to address different aspects of PPIs: from interactome level to their atomic 3D structural details. We provided details on each specific resource, aims and purpose and compare with equivalent tools in the literature. All the tools are presented in a centralized, one-stop, web site: InteractoMIX (http://interactomix.com).

PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex

Structure determination of linear peptides of 5–50 amino acids in aqueous solution and interacting with proteins is a key aspect in structural biology. PEP-FOLD3 is a novel computational framework, that allows both (i) de novo free or biased prediction for linear peptides between 5 and 50 amino acids, and (ii) the generation of native-like conformations of peptides interacting with a protein when the interaction site is known in advance. PEP-FOLD3 is fast, and usually returns solutions in a few minutes. Testing PEP-FOLD3 on 56 peptides in aqueous solution led to experimental-like conformations for 80% of the targets. Using a benchmark of 61 peptide–protein targets starting from the unbound form of the protein receptor, PEP-FOLD3 was able to generate peptide poses deviating on average by 3.3Å from the experimental conformation and return a native-like pose in the first 10 clusters for 52% of the targets. PEP-FOLD3 is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PEP-FOLD3.