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

Future Perspective: Harnessing the Power of Artificial Intelligence in the Generation of New Peptide Drugs

The expansive field of drug discovery is continually seeking innovative approaches to identify and develop novel peptide-based therapeutics. With the advent of artificial intelligence (AI), there has been a transformative shift in the generation of new peptide drugs. AI offers a range of computational tools and algorithms that enables researchers to accelerate the therapeutic peptide pipeline. This review explores the current landscape of AI applications in peptide drug discovery, highlighting its potential, challenges, and ethical considerations. Additionally, it presents case studies and future prospectives that demonstrate the impact of AI on the generation of new peptide drugs.

A unified conformational selection and induced fit approach to protein-peptide docking

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.

Applying internal coordinate mechanics to model the interactions between 8R-lipoxygenase and its substrate

Background

Lipoxygenases (LOX) play pivotal roles in the biosynthesis of leukotrienes and other biologically active potent signalling compounds. Developing inhibitors for LOX is of high interest to researchers. Modelling the interactions between LOX and its substrate arachidonic acid is critical for developing LOX specific inhibitors. Currently, there are no LOX-substrate structures. Recently, the structure of a coral LOX, 8R-LOX, which is 41% sequence identical to the human 5-LOX was solved to 1.85Å resolution. This structure provides a foundation for modelling enzyme-substrate interactions.

Methods

In this research, we applied a computational method, Internal Coordinate Mechanics (ICM), to model the interactions between 8R-LOX and its substrate arachidonic acid. Docking arachidonic acid to 8R-LOX was performed. The most favoured docked ligand conformations were retained. We compared the results of our simulation with a proposed model and concluded that the binding pocket identified in this study agrees with the proposed model partially.

Results

The results showed that the conformation of arachidonic acid docked into the ICM-identified docking site has less energy than that docked into the manually defined docking site for pseudo wild type 8R-LOX. The mutation at I805 resulted in no docking pocket found near Fe atom. The energy of the arachidonic acid conformation docked into the manually defined docking site is higher in mutant 8R-LOX than in wild type 8R-LOX. The arachidonic acid conformations are not productive conformations.

Conclusions

We concluded that, for the wild type 8R-LOX, the conformation of arachidonic acid docked into the ICM-identified docking site is more stable than that docked into the manually defined docking site. Mutation affects the structure of the putative active site pocket of 8R-LOX, and leads no docking pockets around the catalytic Fe atom. The docking simulation in a mutant 8R-LOX demonstrated that the structural change due to the mutation impacts the enzyme activity. Further research and analysis is required to obtain the 8R-LOX-substrate model.

Structure of an anti-cholera toxin antibody Fab in complex with an epitope-derivedD-peptide: a case of polyspecific recognition

The structure of a complex of the anti-cholera toxin antibody TE33 Fab (fragment antibody) with the D-peptide vpGsqhyds was solved to 1.78 A resolution. The D-peptide was derived from the linear L-peptide epitope VPGSQHIDS by a stepwise transformation. Despite the very similar amino acid sequence-the only difference is a tyrosine residue in position 7-there are marked differences in the individual positions with respect to their contribution to the peptide overall affinity as ascertained by a complete substitutional analysis. This is reflected by the X-ray structure of the TE33 Fab/D-peptide complex where there is an inverted orientation of the D-peptide as compared with the known structure of a corresponding complex containing the epitope L-peptide, with the side chains establishing different contacts within the binding site of TE33. The D- and L-peptide affinities are comparable and the surface areas buried by complex formation are almost the same. Thus the antibody TE33 provides a typical example for polyspecific binding behavior of IgG family antibodies.

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.

Molecular basis for the binding polyspecificity of an anti-cholera toxin peptide 3 monoclonal antibody

The onset of autoimmune diseases is proposed to involve binding promiscuity of antibodies (Abs) and T-cells, an often reported yet poorly understood phenomenon. Here, we attempt to approach two questions: first, is binding promiscuity a general feature of monoclonal antibodies (mAbs) and second, what is the molecular basis for polyspecificity? To this end, the anti-cholera toxin peptide 3 (CTP3) mAb TE33 was investigated for polyspecific binding properties. Screening of phage display libraries identified two epitope-unrelated peptides that specifically bound TE33 with affinities similar to or 100-fold higher than the wild-type epitope. Substitutional analyses revealed distinct key residue patterns recognized by the antibody suggesting a unique binding mode for each peptide. A database query with one of the consensus motifs and a subsequent binding study uncovered 45 peptides (derived from heterologous proteins) that bound TE33. To better understand the structural basis of the observed polyspecificity we modeled the new cyclic epitope in complex with TE33. The interactions between this peptide and TE33 suggested by our model are substantially different from the interactions observed in the X-ray structure of the wild-type epitope complex. However, the overall binding conformation of the peptides is similar. Together, our results support the theory of a general polyspecific potential of mAbs.

ICFF: a new method to incorporate implicit flexibility into an internal coordinate force field

We introduce a new method to accurately "project" a Cartesian force field onto an internal coordinate molecular model with fixed-bond geometry. The algorithm automatically generates the Internal Coordinate Force Field (ICFF), which is a close approximation of the "source" Cartesian force field. The ICFF method reduces the number of free variables in a model by at least 10-fold and facilitates the fast convergence of geometry optimizations, an advantage that is critical for many applications such as the docking of flexible ligands or conformational modeling of macromolecules. Although covalent geometry is fixed in an ICFF model, implicit flexibility is incorporated into the force field parameters in the following two ways. First, we formulate an empirical torsion energy term in ICFF as a sixfold Fourier series and develop a procedure to calculate the Fourier coefficients from the conformational energy profiles of the fully flexible Cartesian model. The ICFF torsion parameters thus represent not only torsion component of the source force field, but also bond bending, bond stretching, and "1-4" van der Waals interactions. Second, we use a soft polynomial repulsion function for "1-5" and "1-6" interactions to mimic the flexibility of bonds, connecting these atoms. Also, we suggest a way to use a local part of the Cartesian force field to automatically generate fixed covalent geometries, compatible with the ICFF energy function. Here, we present an implementation of the ICFF algorithm, which employs the MMFF94s Cartesian force field as a "source." Extensive benchmarking of ICFF with a representative set of organic molecules demonstrates that the implicit flexibility model accurately reproduces MMFF94s equilibrium conformational energy differences (RMSD approximately 0.64 kcal) and, most importantly, detailed torsion energy profiles (RMSD approximately 0.37 kcal). This accuracy is characteristic of the method, because all the ICFF parameters (except one scaling factor in the "1-5,1-6" repulsion term) are derived directly from the source Cartesian force field and do not depend on any particular molecular set. In contrast, the rigid geometry model with the MMFF94s energy function yields highly biased estimations in this test with the RMSD exceeding 1.2 kcal for the equilibrium energy comparisons and approximately 3.4 kcal for the torsion energy profiles.

Efficient docking of peptides to proteins without prior knowledge of the binding site

Reliability in docking of ligand molecules to proteins or other targets is an important challenge for molecular modeling. Applications of the docking technique include not only prediction of the binding mode of novel drugs, but also other problems like the study of protein-protein interactions. Here we present a study on the reliability of the results obtained with the popular AutoDock program. We have performed systematical studies to test the ability of AutoDock to reproduce eight different protein/ligand complexes for which the structure was known, without prior knowledge of the binding site. More specifically, we look at factors influencing the accuracy of the final structure, such as the number of torsional degrees of freedom in the ligand. We conclude that the Autodock program package is able to select the correct complexes based on the energy without prior knowledge of the binding site. We named this application blind docking, as the docking algorithm is not able to "see" the binding site but can still find it. The success of blind docking represents an important finding in the era of structural genomics.

Docking multiple conformations of a flexible ligand into a protein binding site using NMR restraints

A method is described for docking a large, flexible ligand using intra-ligand conformational restraints from exchange-transferred NOE (etNOE) data. Numerous conformations of the ligand are generated in isolation, and a subset of representative conformations is selected. A crude model of the protein-ligand complex is used as a template for overlaying the selected ligand structures, and each complex is conformationally relaxed by molecular mechanics to optimize the interaction. Finally, the complexes were assessed for structural quality. Alternative approaches are described for the three steps of the method: generation of the initial docking template; selection of a subset of ligand conformations; and conformational sampling of the complex. The template is generated either by manual docking using interactive graphics or by a computational grid-based search of the binding site. A subset of conformations from the total number of peptides calculated in isolation is selected based on either low energy and satisfaction of the etNOE restraints, or a cluster analysis of the full set. To optimize the interactions in the complex, either a restrained Monte Carlo-energy minimization (MCM) protocol or a restrained simulated annealing (SA) protocol were used. This work produced 53 initial complexes of which 8 were assessed in detail. With the etNOE conformational restraints, all of the approaches provide reasonable models. The grid-based approach to generate an initial docking template allows a large volume to be sampled, and as a result, two distinct binding modes were identified for a fifteen-residue peptide binding to an enzyme active site.

Docking of combinatorial peptide libraries into a broadly cross-reactive human IgM

A monoclonal IgM cryoglobulin with diverse binding behavior was isolated from a patient (Mez) with Waldenström's macroglobulinemia. It gave very high titers in the binding of combinatorially synthesized libraries of peptides ranging in size from two to eight residues. The crystal structure of Mez Fv revealed that the binding site was divided into two cavities of unequal volumes with dimensions and chemical properties that were compatible with the binding of peptides. Access to this unique combination of structural information and peptide binding data led us to carry out Mez-peptide docking simulations to gain insight into the Mez binding propensities. In the present article, the results for docking of five peptide libraries are combined with discussions of the methods and approximations involved in the docking process. We analyze the origins of peptide binding affinity for Mez IgM in terms of its cross-reactivity and its structural preferences.