Rational, computer-enabled peptide drug design: principles, methods, applications and future directions

Advances in Drug Discovery and Design using Computer-aided Molecular Modeling

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Genetics of amyotrophic lateral sclerosis: seeking therapeutic targets in the era of gene therapy

Amyotrophic lateral sclerosis (ALS) is an intractable disease that causes respiratory failure leading to mortality. The main locus of ALS is motor neurons. The success of antisense oligonucleotide (ASO) therapy in spinal muscular atrophy (SMA), a motor neuron disease, has triggered a paradigm shift in developing ALS therapies. The causative genes of ALS and disease-modifying genes, including those of sporadic ALS, have been identified one after another. Thus, the freedom of target choice for gene therapy has expanded by ASO strategy, leading to new avenues for therapeutic development. Tofersen for superoxide dismutase 1 (SOD1) was a pioneer in developing ASO for ALS. Improving protocols and devising early interventions for the disease are vital. In this review, we updated the knowledge of causative genes in ALS. We summarized the genetic mutations identified in familial ALS and their clinical features, focusing on SOD1, fused in sarcoma (FUS), and transacting response DNA-binding protein. The frequency of the C9ORF72 mutation is low in Japan, unlike in Europe and the United States, while SOD1 and FUS are more common, indicating that the target mutations for gene therapy vary by ethnicity. A genome-wide association study has revealed disease-modifying genes, which could be the novel target of gene therapy. The current status and prospects of gene therapy development were discussed, including ethical issues. Furthermore, we discussed the potential of axonal pathology as new therapeutic targets of ALS from the perspective of early intervention, including intra-axonal transcription factors, neuromuscular junction disconnection, dysregulated local translation, abnormal protein degradation, mitochondrial pathology, impaired axonal transport, aberrant cytoskeleton, and axon branching. We simultaneously discuss important pathological states of cell bodies: persistent stress granules, disrupted nucleocytoplasmic transport, and cryptic splicing. The development of gene therapy based on the elucidation of disease-modifying genes and early intervention in molecular pathology is expected to become an important therapeutic strategy in ALS.

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.

An Overview of Antiviral Peptides and Rational Biodesign Considerations

Viral diseases have contributed significantly to worldwide morbidity and mortality throughout history. Despite the existence of therapeutic treatments for many viral infections, antiviral resistance and the threat posed by novel viruses highlight the need for an increased number of effective therapeutics. In addition to small molecule drugs and biologics, antimicrobial peptides (AMPs) represent an emerging class of potential antiviral therapeutics. While AMPs have traditionally been regarded in the context of their antibacterial activities, many AMPs are now known to be antiviral. These antiviral peptides (AVPs) have been shown to target and perturb viral membrane envelopes and inhibit various stages of the viral life cycle, from preattachment inhibition through viral release from infected host cells. Rational design of AMPs has also proven effective in identifying highly active and specific peptides and can aid in the discovery of lead peptides with high therapeutic selectivity. In this review, we highlight AVPs with strong antiviral activity largely curated from a publicly available AMP database. We then compile the sequences present in our AVP database to generate structural predictions of generic AVP motifs. Finally, we cover the rational design approaches available for AVPs taking into account approaches currently used for the rational design of AMPs.

Design and synthesis of five-membered heterocyclic derivatives of istradefylline with comparable pharmacological activity

Parkinson's disease (PD) is a common degenerative disease of the central nervous system among the elderly. Istradefylline, an FDA-approved adenosine A_2A receptor antagonist (anti-PD drug), has good efficacy. However, it has been reported that the double bond of istradefylline is easily converted into cis-configuration when exposed to an indoor environment or direct light in a dilute solution. In order to find more stable adenosine A_2A receptor antagonists with similar pharmacological efficacy to istradefylline, the compounds series I-1 (12 compounds) was designed by maintaining the xanthine skeleton of istradefylline unchanged and replacing the trans-double bond with thiazole or benzothiazole and other biologically active heterocyclic compounds. These compounds were synthesized via multi-step experiment and successfully confirmed through different characterization techniques for their ability to inhibit cAMP formation in A_2A AR overexpressing cells. The thiazole derivative of istradefylline (Compound I-1-11, I-1-12) exhibited significant activity (IC₅₀  = 16.74 ± 4.11 μM, 10.36 ± 3.09 μM), as compared to istradefylline (IC₅₀  = 5.05 ± 1.32 μM). In addition, the molecular docking of benzothiazole derivatives I-1-11 and thiazole derivatives I-1-12 with higher inhibition rate were carried out and compared with istradefylline. The molecular docking results showed that I-1-11 and I-1-12 anchored in the same site as that of XAC (3REY) with predicted affinity binding energy -6.63 kcal/mol and - 6.75 kcal/mol, respectively. Validation through dynamics simulation also showed stable interactions, with fluctuations <3 Å and MM/GBSA energy <-20 kcal/mol. Hence, this study could provide a basis for the rational design of adenosine A_2A receptor antagonists with better potency.

Computational Evolution Protocol for Peptide Design

Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.

Insights on peptide topology in the computational design of protein ligands: the example of lysozyme binding peptides

Herein, we compared the ability of linear and cyclic peptides generated in silico to target different protein sites: internal pockets and solvent-exposed sites. We selected human lysozyme (HuL) as a model target protein combined with the computational evolution of linear and cyclic peptides. The sequence evolution of these peptides was based on the PARCE algorithm. The generated peptides were screened based on their aqueous solubility and HuL binding affinity. The latter was evaluated by means of scoring functions and atomistic molecular dynamics (MD) trajectories in water, which allowed prediction of the structural features of the protein-peptide complexes. The computational results demonstrated that cyclic peptides constitute the optimal choice for solvent exposed sites, while both linear and cyclic peptides are capable of targeting the HuL pocket effectively. The most promising binders found in silico were investigated experimentally by surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), and electrospray ionization mass spectrometry (ESI-MS) techniques. All tested peptides displayed dissociation constants in the micromolar range, as assessed by SPR; however, both NMR and ESI-MS suggested multiple binding modes, at least for the pocket binding peptides. A detailed NMR analysis confirmed that both linear and cyclic pocket peptides correctly target the binding site they were designed for.

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.

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.

Design, synthesis and biological evaluation of anticholinesterase peptides: Fragment-based vs. template-based peptide design

The prevalence of Alzheimer's disease (AD) has become a substantial global concern. Approved AChE inhibitors have been used for symptomatic treatment of AD. Binding of amyloid β (Aβ) to the peripheral anionic site of AChE facilitates the formation of Aβ plaques. Blocking this proposed protein-protein interaction by inhibition of the peripheral anionic site of AChE, in addition to increasing the level of ACh, reduces the Aβ aggregation and might qualify to slow down the progression of disease besides the palliative treatment. Targeting protein-protein interactions consider as one of the most challenging issues in the realm of drug design in which peptides have potentials to excel in. In the present study, we applied two virtual fragment-based and template-based approaches to design peptidic inhibitors of the PAS of AChE. Based on the in silico studies, high scored peptides p2 (WTWYGYWVW) and p10 (NHRMLTRRY) obtained from fragment-based and template-based design respectively. Regarding in vitro results, p2 (IC₅₀ = 16 ± 3.2 μM) and p10 (IC₅₀ = 23.6 ± 4.9 μM) showed significant AChE inhibitory effects. The molecular mechanism of inhibition studied by Lineweaver-Burk plots was mixed inhibition for both peptides. The in vitro results conformed to the in silico results and showed that both peptides occupied the CAS and PAS of AChE. The comparison of two peptide-design approaches revealed that the fragment-based design had more chemical diversity and showed priority to the template-based design. According to the obtained results, peptidic inhibitors of AChE designed by the proposed fragment-based approach might be more efficient in comparison to traditional approaches.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

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.

Synthesis, Molecular Docking, and Preliminary Evaluation of 2-(1,2,3-Triazoyl)benzaldehydes As Multifunctional Agents for the Treatment of Alzheimer's Disease

We described here our results on the use of thiourea as a ligand in the copper catalysed azide-alkyne cycloaddition (CuAAC) of 2-azidobenzaldehyde with alkynes. Reactions were performed reacting 2-azidobenzaldehyde with a range of terminal alkynes using 10 mol % of copper iodide as a catalyst, 20 mol % of thiourea as a ligand, triethylamine as base, DMSO as solvent at 100 °C under nitrogen atmosphere. The corresponding 2-(1H-1,2,3-triazoyl)-benzaldehydes (2-TBH) were obtained in moderated to excellent yields and according our experiments, the use of thiourea decreases the formation of side products. The obtained compounds were screened for their binding affinity with multiple therapeutic targets of AD by molecular docking: β-secretase (BACE), glycogen synthase kinase (GSK-3β) and acetylcholinesterase (AChE). The three compounds with highest affinity, 5 a (2-(4-phenyl-1H-1,2,3-triazol-1-yl)benzaldehyde), 5 b (2-(4-(p-tolyl)-1H-1,2,3-triazol-1-yl)benzaldehyde), and 5 d (2-(4-(4-(tert-butyl)phenyl)-1H-1,2,3-triazol-1-yl)benzaldehyde) were selected and evaluated on its antioxidant effect, in view of select the most promising one to perform the in vivo validation. Due the antioxidant potential ally to the affinity with BACE, GSK-3β and AChE, compound 5 b was evaluated in a mouse model of AD induced by intracerebroventricular injection of streptozotocin (STZ). Our results indicate that 5 b (1 mg/kg) treatment during 20 days is able to reverse the cognitive and memory impairment induced by STZ trough the modulation of AChE activity, amyloid cascade and GSK-3β expression.

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.

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

Human papillomavirus (HPV) is the most common sexually transmitted infection in the world and the main cause of cervical cancer. Nowadays, the virus-like particles (VLPs) based on L1 proteins have been considered as the best candidate for vaccine development against HPV infections. Two commercial HPV (Gardasil and Cervarix) are available. These HPV VLP vaccines induce genotype-limited protection. The major impediments such as economic barriers especially gaps in financing obstructed the optimal delivery of vaccines in developing countries. Thus, many efforts are underway to develop the next generation of vaccines against other types of high-risk HPV. In this study, we developed DNA constructs (based on L1 and L2 genes) that were potentially immunogenic and highly conserved among the high-risk HPV types. The framework of analysis include (1) B-cell epitope mapping, (2) T-cell epitope mapping (i.e., CD4⁺ and CD8⁺ T cells), (3) allergenicity assessment, (4) tap transport and proteasomal cleavage, (5) population coverage, (6) global and template-based docking, and (7) data collection, analysis, and design of the L1 and L2 DNA constructs. Our data indicated the 8-epitope candidates for helper T-cell and CTL in L1 and L2 sequences. For the L1 and L2 constructs, combination of these peptides in a single universal vaccine could involve all world population by the rate of 95.55% and 96.33%, respectively. In vitro studies showed high expression rates of multiepitope L1 (~57.86%) and L2 (~68.42%) DNA constructs in HEK-293T cells. Moreover, in vivo studies indicated that the combination of L1 and L2 DNA constructs without any adjuvant or delivery system induced effective immune responses, and protected mice against C3 tumor cells (the percentage of tumor-free mice: ~66.67%). Thus, the designed L1 and L2 DNA constructs would represent promising applications for HPV vaccine development.

Charges' interaction in polyelectrolyte (nano)complexing of His6-OPH with peptides: Unpredictable results due to imperfect or useless concept?

Quorum Quenching (QQ) enzymes can be used to prevent bacterial antibiotic resistance by degradation of Quorum Sensing (QS) signaling molecules, for example N-acyl homoserine lactones (AHLs). This paper is aimed at the in silico investigation of the possible combinations of hexahistidine-tagged organophosphorus hydrolase (His₆-OPH) with antimicrobial peptides (AMPs) to improve the enzyme activity and, promisingly, stability. This shall help creating a nanosized QQ preparation capable to hydrolyze different AHLs and possessing an antimicrobial activity. To achieve this, binding of AMPs and His₆-OPH was simulated by molecular docking, and various interaction parameters (affinity, charge, contact area, etc.) of the generated models were studied. Both anionic and cationic polypeptides were shown to bind to His₆-OPH with negligible effect of their charge, that significantly deviates from the charge-to-charge interaction concept. The (nano)complexes of His₆-OPH with Indolicidin and Temporin A appear to have the most balanced characteristics which were issued experimentally also.

Computational identification of self-inhibitory peptides from white spot syndrome virus envelope protein VP28

Since effective chemotherapeutics or preventive measures are still unavailable, finding feasible approaches against white spot syndrome virus (WSSV) has always been the vital subject in shrimp farming field. Envelope proteins are the ideal targets for antiviral strategies development due to their indispensable roles in virus entry, and inhibitory peptides targeting them have been proved to be promising in blocking virus infection. In this study, the Wimley-White interfacial hydrophobicity scale (WWIHS) in combination with known structural data was applied to identify potential inhibitory peptides that targeted the envelope protein VP28 of WSSV. Results showed that two potential inhibitory peptides were identified, one of which exhibited not only obvious antiviral activity, but also broad-spectrum antimicrobial activity. The inhibitory peptide identified here can serve as a lead compound for anti-WSSV strategies development.

Rational modulator design by exploitation of protein-protein complex structures

The horizon of drug discovery is currently expanding to target and modulate protein-protein interactions (PPIs) in globular proteins and intrinsically disordered proteins that are involved in various diseases. To either interrupt or stabilize PPIs, the 3D structure of target protein-protein (or protein-peptide) complexes can be exploited to rationally design PPI modulators (inhibitors or stabilizers) through structure-based molecular design. In this review, we present an overview of experimental and computational methods that can be used to determine 3D structures of protein-protein complexes. Several approaches including rational and in silico methods that can be applied to design peptides, peptidomimetics and small compounds by utilization of determined 3D protein-protein/peptide complexes are summarized and illustrated.

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.

In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design

Plants are extensively used in traditional medicine, and several plant antimicrobial peptides have been described as potential alternatives to conventional antibiotics. However, after more than four decades of research no plant antimicrobial peptide is currently used for treating bacterial infections, due to their length, post-translational modifications or  high dose requirement for a therapeutic effect . Here we report the design of antimicrobial peptides derived from a guava glycine-rich peptide using a genetic algorithm. This approach yields guavanin peptides, arginine-rich α-helical peptides that possess an unusual hydrophobic counterpart mainly composed of tyrosine residues. Guavanin 2 is characterized as a prototype peptide in terms of structure and activity. Nuclear magnetic resonance analysis indicates that the peptide adopts an α-helical structure in hydrophobic environments. Guavanin 2 is bactericidal at low concentrations, causing membrane disruption and triggering hyperpolarization. This computational approach for the exploration of natural products could be used to design effective peptide antibiotics.

Antimicrobial peptides are considered promising alternatives to antibiotics. Here the authors developed a computational algorithm that starts with peptides naturally occurring in plants and optimizes this starting material to yield new variants which are highly distinct from the parent peptide.

Exploring the nature of the H-bonds between the human class II MHC protein, HLA-DR1 (DRB*0101) and the influenza virus hemagglutinin peptide, HA306-318, using the quantum theory of atoms in molecules

The nature of the H-bonds between the human protein HLA-DR1 (DRB*0101) and the hemagglutinin peptide HA306-318 has been studied using the Quantum Theory of Atoms in Molecules for the first time. We have found four H-bond groups: one conventional CO··HN bond group and three nonconventional CO··HC, π··HC involving aromatic rings and HN··HC_aliphatic groups. The calculated electron density at the determined H-bond critical points suggests the follow protein pocket binding trend: P1 (2,311) >> P9 (1.109) > P4 (0.950) > P6 (0.553) > P7 (0.213) which agrees and reveal the nature of experimental findings, showing that P1 produces by a long way the strongest binding of the HLA-DR1 human protein molecule with the peptide backbone as consequence of the vast number of H-bonds in the P1 area and at the same time the largest specific binding of the peptide Tyr308 residue with aromatic residues located at the binding groove floor. The present results suggest the topological analysis of the electronic density as a valuable tool that allows a non-arbitrary partition of the pockets binding energy via the calculated electron density at the determined critical points.

PeptideNavigator: An interactive tool for exploring large and complex data sets generated during peptide-based drug design projects

There is growing interest in peptide-based drug design and discovery. Due to their relatively large size, polymeric nature, and chemical complexity, the design of peptide-based drugs presents an interesting "big data" challenge. Here, we describe an interactive computational environment, PeptideNavigator, for naturally exploring the tremendous amount of information generated during a peptide drug design project. The purpose of PeptideNavigator is the presentation of large and complex experimental and computational data sets, particularly 3D data, so as to enable multidisciplinary scientists to make optimal decisions during a peptide drug discovery project. PeptideNavigator provides users with numerous viewing options, such as scatter plots, sequence views, and sequence frequency diagrams. These views allow for the collective visualization and exploration of many peptides and their properties, ultimately enabling the user to focus on a small number of peptides of interest. To drill down into the details of individual peptides, PeptideNavigator provides users with a Ramachandran plot viewer and a fully featured 3D visualization tool. Each view is linked, allowing the user to seamlessly navigate from collective views of large peptide data sets to the details of individual peptides with promising property profiles. Two case studies, based on MHC-1A activating peptides and MDM2 scaffold design, are presented to demonstrate the utility of PeptideNavigator in the context of disparate peptide-design projects.

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/ .

An unexpected way forward: towards a more accurate and rigorous protein-protein binding affinity scoring function by eliminating terms from an already simple scoring function

A fundamental and unsolved problem in biophysical chemistry is the development of a computationally simple, physically intuitive, and generally applicable method for accurately predicting and physically explaining protein-protein binding affinities from protein-protein interaction (PPI) complex coordinates. Here, we propose that the simplification of a previously described six-term PPI scoring function to a four term function results in a simple expression of all physically and statistically meaningful terms that can be used to accurately predict and explain binding affinities for a well-defined subset of PPIs that are characterized by (1) crystallographic coordinates, (2) rigid-body association, (3) normal interface size, and hydrophobicity and hydrophilicity, and (4) high quality experimental binding affinity measurements. We further propose that the four-term scoring function could be regarded as a core expression for future development into a more general PPI scoring function. Our work has clear implications for PPI modeling and structure-based drug design.