PRO_LIGAND: an approach to de novo molecular design. 4. Application to the design of peptides

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.

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.

Techniques assisting peptide vaccine and peptidomimetic design. Sidechain exposure in the SARS-CoV-2 spike glycoprotein

The aim of the present study is to discuss the design of peptide vaccines and peptidomimetics against SARS-COV-2, to develop and apply a method of protein structure analysis that is particularly appropriate to applying and discussing such design, and also to use that method to summarize some important features of the SARS-COV-2 spike protein sequence. A tool for assessing sidechain exposure in the SARS-CoV-2 spike glycoprotein is described. It extends to assessing accessibility of sidechains by considering several different three-dimensional structure determinations of SARS-CoV-2 and SARS-CoV-1 spike protein. The method is designed to be insensitive to a distance limit for counting neighboring atoms and the results are in good agreement with the physical chemical properties and exposure trends of the 20 naturally occurring sidechains. The spike protein sequence is analyzed with comment regarding exposable character. It includes studies of complexes with antibody elements and ACE2. These indicate changes in exposure at sites remote to those at which the antibody binds. They are of interest concerning design of synthetic peptide vaccines, and for peptidomimetics as a basis of drug discovery. The method was also developed in order to provide linear (one-dimensional) information that can be used along with other bioinformatics data of this kind in data mining and machine learning, potentially as genomic data regarding protein polymorphisms to be combined with more traditional clinical data.

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Bioinformatics studies are carried out on SARS-CoV-2 spike, studying solvent exposure.

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The methods are particularly suited for synthetic vaccines and d-amino acid peptidomimetics.

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Methods of generating d-amino acid peptidomimetics are described and reviewed.

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The effect of antibody binding in stabilizing loop conformation and exposing remote sites is noted.

COVID-19 Coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance

This paper continues a recent study of the spike protein sequence of the COVID-19 virus (SARS-CoV-2). It is also in part an introductory review to relevant computational techniques for tackling viral threats, using COVID-19 as an example. Q-UEL tools for facilitating access to knowledge and bioinformatics tools were again used for efficiency, but the focus in this paper is even more on the virus. Subsequence KRSFIEDLLFNKV of the S2′ spike glycoprotein proteolytic cleavage site continues to appear important. Here it is shown to be recognizable in the common cold coronaviruses, avian coronaviruses and possibly as traces in the nidoviruses of reptiles and fish. Its function or functions thus seem important to the coronaviruses. It might represent SARS-CoV-2 Achilles’ heel, less likely to acquire resistance by mutation, as has happened in some early SARS vaccine studies discussed in the previous paper. Preliminary conformational analysis of the receptor (ACE2) binding site of the spike protein is carried out suggesting that while it is somewhat conserved, it appears to be more variable than KRSFIEDLLFNKV. However compounds like emodin that inhibit SARS entry, apparently by binding ACE2, might also have functions at several different human protein binding sites. The enzyme 11β-hydroxysteroid dehydrogenase type 1 is again argued to be a convenient model pharmacophore perhaps representing an ensemble of targets, and it is noted that it occurs both in lung and alimentary tract. Perhaps it benefits the virus to block an inflammatory response by inhibiting the dehydrogenase, but a fairly complex web involves several possible targets.

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This paper “drills down” into the studies of the author's previous COVID-19 paper.

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Designing vaccine and drugs must seek to avoid escape mutations.

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Subsequence KRSFIEDLLFNKV seems recognizable across many coronaviruses.

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The ACE2 binding domain is a target, but shows variation.

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A steroid dehydrogenase is argued to remain an interesting model pharmacophore.

New Data Structure for Computational Molecular Design with Atomic or Fragment Resolution

A new molecular data structure and molecular structure operation algorithms are proposed for general purpose molecular design. The data structure allows for a variety of molecular operations for creating new molecules. Two types of molecular operations were developed, unimolecular and bimolecular operations. In unimolecular operations, a child molecule can be created from a parent via addition of a functional group, deletion of a fragment, mutation of an atom, etc. In bimolecular operations, children molecules are generated from two parent molecules through combination or crossover (hybridization). These molecular operations are essential for the creation and modification of molecules for the purpose of molecular design. The data structure is capable of representing linear, branched, multifunctional, and multivalent compounds. Algorithms are developed for deriving the molecular data structure of a molecule from its atomic coordinates and vice versa. We show that this new molecular data structure and the developed algorithms, referred to as Molecular Assembling and Representation Suite, allow one to generate a comprehensive library of new molecules via performing every possible molecular structure modification.

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).

Knowledge-based modeling of peptides at protein interfaces: PiPreD

MOTIVATION

Protein-protein interactions (PPIs) underpin virtually all cellular processes both in health and disease. Modulating the interaction between proteins by means of small (chemical) agents is therefore a promising route for future novel therapeutic interventions. In this context, peptides are gaining momentum as emerging agents for the modulation of PPIs.

RESULTS

We reported a novel computational, structure and knowledge-based approach to model orthosteric peptides to target PPIs: PiPreD. PiPreD relies on a precompiled and bespoken library of structural motifs, iMotifs, extracted from protein complexes and a fast structural modeling algorithm driven by the location of native chemical groups on the interface of the protein target named anchor residues. PiPreD comprehensive and systematically samples the entire interface deriving peptide conformations best suited for the given region on the protein interface. PiPreD complements the existing technologies and provides new solutions for the disruption of selected interactions.

AVAILABILITY AND IMPLEMENTATION

Database and accessory scripts and programs are available upon request to the authors or at http://www.bioinsilico.org/PIPRED.

CONTACT

narcis.fernandez@gmail.com.

Peptides: minimal drug surrogates to interrogate and interfere with protein function

The interactome in normal and disease cells is a key area for study and therapeutic targeting, yet few molecules have been developed that can interfere with protein–protein interactions within cells. Peptides and homologues are potential reagents to target PPI.

Discovery of a potent peptidic cyclophilin A inhibitor Trp-Gly-Pro

Through virtual screening of a rationally built database consisting of 40 peptides, we identified three short peptides. After testing these three synthetic peptides, we found that the peptide Trp-Gly-Pro (WGP) showed comparable inhibitory ability as positive control cyclosporine A (CsA) on CypA-mediated PPIase activity with IC50 values of 33.11 nM and 10.25 nM, respectively. The peptide WGP had same order of CypA-binding affinity as CsA with dissociation equilibrium constant KD of 3.41×10(-6) and 6.42×10(-6) M, respectively. This peptide could also inhibit HIV-1IIIB infection. This study provides a novel strategy for rational design and development of peptidic drugs.

VitAL: Viterbi algorithm for de novo peptide design

Background

Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.

Methodology/Principal Findings

A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.

Conclusions/Significance

The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.

ENPDA: an evolutionary structure-based de novo peptide design algorithm

One of the goals of computational chemists is to automate the de novo design of bioactive molecules. Despite significant advances in computational approaches to ligand design and binding energy evaluation, novel procedures for ligand design are required. Evolutionary computation provides a new approach to this design endeavor. We propose an evolutionary tool for de novo peptide design, based on the evaluation of energies for peptide binding to a user-defined protein surface patch. Special emphasis has been placed on the evaluation of the proposed peptides, leading to two different evaluation heuristics. The software developed was successfully tested on the design of ligands for the proteins prolyl oligopeptidase, p53, and DNA gyrase.

Computer-based de novo design of drug-like molecules

Ever since the first automated de novo design techniques were conceived only 15 years ago, the computer-based design of hit and lead structure candidates has emerged as a complementary approach to high-throughput screening. Although many challenges remain, de novo design supports drug discovery projects by generating novel pharmaceutically active agents with desired properties in a cost- and time-efficient manner. In this review, we outline the various design concepts and highlight current developments in computer-based de novo design.

Genomic Messaging System and DNA Mark-Up Language for Information-Based Personalized Medicine with Clinical and Proteome Research Applications

The convergence of clinical medicine and the Life Sciences, commencing with opportunities in clinical trials and clinically linked medical research, presents many novel challenges. The Genomic Messaging System (GMS) described here was originally developed as a tool for assembling clinical genomic records of individual and collective patients, and was then generalized to become a flexible workflow component that will link clinical records to a variety of computational biology research tools, for research and ultimately for a more personalized, focused, and preventative healthcare system. Prominent among the applications linked are protein science applications, including the rapid automated modeling of patient proteins with their individual structural polymorphisms. In an initial study, GMS formed the basis of a fully automated system for modeling patient proteins with structural polymorphisms as a basis for drug selection and ultimately design on an individual patient basis.

Computation of the physio-chemical properties and data mining of large molecular collections

Very large data sets of molecules screened against a broad range of targets have become available due to the advent of combinatorial chemistry. This information has led to the realization that ADME (absorption, distribution, metabolism, and excretion) and toxicity issues are important to consider prior to library synthesis. Furthermore, these large data sets provide a unique and important source of information regarding what types of molecular shapes may interact with specific receptor or target classes. Thus, the requirement for rapid and accurate data mining tools became paramount. To address these issues Pharmacopeia, Inc. formed a computational research group, The Center for Informatics and Drug Discovery (CIDD).* In this review we cover the work done by this group to address both in silico ADME modeling and data mining issues faced by Pharmacopeia because of the availability of a large and diverse collection (over 6 million discrete compounds) of drug-like molecules. In particular, in the data mining arena we discuss rapid docking tools and how we employ them, and we describe a novel data mining tool based on a ID representation of a molecule followed by a molecular sequence alignment step. For the ADME area we discuss the development and application of absorption, blood-brain barrier (BBB) and solubility models. Finally, we summarize the impact the tools and approaches might have on the drug discovery process.

Soft docking an L and a D peptide to an anticholera toxin antibody using internal coordinate mechanics

BACKGROUND

The tremendous increase in sequential and structural information is a challenge for computer-assisted modelling to predict the binding modes of interacting biomolecules. One important area is the structural understanding of protein-peptide interactions, information that is increasingly important for the design of biologically active compounds.

RESULTS

We predicted the three-dimensional structure of a complex between the monoclonal antibody TE33 and its cholera-toxin-derived peptide epitope VPGSQHID. Using the internal coordinate mechanics (ICM) method of flexible docking, the bound conformation of the initially extended peptide epitope to the antibody crystal or modelled structure reproduced the known binding conformation to a root mean square deviation of between 1.9 A and 3.1 A. The predicted complexes are in good agreement with binding data obtained from substitutional analyses in which each epitope residue is replaced by all other amino acids. Furthermore, a de novo prediction of the recently discovered TE33-binding D peptide dwGsqhydp (single-letter amino acid code where D amino acids are represented by lower-case letters) explains results obtained from binding studies with 172 peptide analogues.

CONCLUSIONS

Despite the difficulties arising from the huge conformational space of a peptide, this approach allowed the prediction of the correct binding orientation and the majority of essential binding features of a peptide-antibody complex.

PRO_SELECT: combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritized for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.

RASSE: a new method for structure-based drug design

A novel method, RASSE, has been developed to suggest reasonable structures which can fit well to the binding sites of receptors. Molecules are generated by an iterative growing procedure in which atoms are added to existing fragments. Potential ligands are then picked out by special scoring rules. This atomgrowing based method is characterized by combinatorial searching of atom types and conformations. To some extent, it is the computer simulation of combinatorial chemistry. This method has been applied to the design of inhibitors for E. coli dihydrofolate reductase and human phospholipase A2. The results demonstrate that this program is capable of generating reasonable structures, thus proving its power in drug design.

Towards the automatic design of synthetically accessible protein ligands: peptides, amides and peptidomimetics

The computer program LUDI for the de novo design of protein ligands was extended so that it is now able to take into account the synthetic accessibility of the constructed molecules. As an example, the design of peptides, amides and peptidomimetics using amino acids as building blocks is described. Two new libraries containing natural and non-natural amino acids were constructed for this purpose. Conformational flexibility is taken into account by using multiple conformers for each amino acid. The program was applied to the design of ligands for the enzymes elastase, renin and thermolysin.

PRO_LIGAND: an approach to de novo molecular design. 6. Flexible fitting in the design of peptides

This paper describes the further development of the functionality of our in-house de novo design program, PRO_LIGAND. In particular, attention is focused on the implementation and validation of the 'direct tweak' method for the construction of conformationally flexible molecules, such as peptides, from molecular fragments. This flexible fitting method is compared to the original method based on libraries of prestored conformations for each fragment. It is shown that the directed tweak method produces results of comparable quality, with significant time savings. By removing the need to generate a set of representative conformers for any new library fragment, the flexible fitting method increases the speed and simplicity with which new fragments can be included in a fragment library and also reduces the disk space required for library storage. A further improvement to the molecular construction process within PRO_LIGAND is the inclusion of a constrained minimisation procedure which relaxes fragments onto the design model and can be used to reject highly strained structures during the structure generation phase. This relaxation is shown to be very useful in simple test cases, but restricts diversity for more realistic examples. The advantages and disadvantages of these additions to the PRO_LIGAND methodology are illustrated by three examples: similar design to an alpha helix region of dihydrofolate reductase, complementary design to the active site of HIV-1 protease and similar design to an epitope region of lysozyme.