MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.

The precise prediction of major histocompatibility complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFold's capabilities by fine-tuning it with a specialized dataset consisting of exclusively high-resolution class I MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFold's original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of class I MHC-peptide interaction prediction. A comparative analysis was conducted against the homology-modeling-based method Pandora as well as the AlphaFold multimer model. Our results demonstrate that our fine-tuned model outperforms others in terms of root-mean-square deviation (median value for Cα atoms for peptides is 0.66 Å) and also provides enhanced predicted local distance difference test scores, offering a more reliable assessment of the predicted structures. These advances have substantial implications for computational immunology, potentially accelerating the development of novel therapeutics and vaccines by providing a more precise computational lens through which to view MHC-peptide interactions.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2.

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites.

The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.

Mapping of antibody epitopes based on docking and homology modeling.

Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody-antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

Accurate ligand-protein docking in CASP15 using the ClusPro LigTBM server.

In the ligand prediction category of CASP15, the challenge was to predict the positions and conformations of small molecules binding to proteins that were provided as amino acid sequences or as models generated by the AlphaFold2 program. For most targets, we used our template-based ligand docking program ClusPro ligTBM, also implemented as a public server available at https://ligtbm.cluspro.org/. Since many targets had multiple chains and a number of ligands, several templates, and some manual interventions were required. In a few cases, no templates were found, and we had to use direct docking using the Glide program. Nevertheless, ligTBM was shown to be a very useful tool, and by any ranking criteria, our group was ranked among the top five best-performing teams. In fact, all the best groups used template-based docking methods. Thus, it appears that the AlphaFold2-generated models, despite the high accuracy of the predicted backbone, have local differences from the x-ray structure that make the use of direct docking methods more challenging. The results of CASP15 confirm that this limitation can be frequently overcome by homology-based docking.

High Accuracy Prediction of PROTAC Complex Structures.

The design of PROteolysis-TArgeting Chimeras (PROTACs) requires bringing an E3 ligase into proximity with a target protein to modulate the concentration of the latter through its ubiquitination and degradation. Here, we present a method for generating high-accuracy structural models of E3 ligase-PROTAC-target protein ternary complexes. The method is dependent on two computational innovations: adding a "silent" convolution term to an efficient protein-protein docking program to eliminate protein poses that do not have acceptable linker conformations and clustering models of multiple PROTACs that use the same E3 ligase and target the same protein. Results show that the largest consensus clusters always have high predictive accuracy and that the ensemble of models can be used to predict the dissociation rate and cooperativity of the ternary complex that relate to the degrading activity of the PROTAC. The method is demonstrated by applications to known PROTAC structures and a blind test involving PROTACs against BRAF mutant V600E. The results confirm that PROTACs function by stabilizing a favorable interaction between the E3 ligase and the target protein but do not necessarily exploit the most energetically favorable geometry for interaction between the proteins.

Use of Solvent Mapping for Characterizing the Binding Site and for Predicting the Inhibition of the Human Ether-á-Go-Go-Related K+ Channel.

Molecular dynamics was used to optimize the droperidol-hERG complex obtained from docking. To accommodate the inhibitor, residues T623, S624, V625, G648, Y652, and F656 did not move significantly during the simulation, while F627 moved significantly. Binding sites in cryo-EM structures and in structures obtained from molecular dynamics simulations were characterized using solvent mapping and Atlas ligands, which were negative images of the binding site, were generated. Atlas ligands were found to be useful for identifying human ether-á-go-go-related potassium channel (hERG) inhibitors by aligning compounds to them or by guiding the docking of compounds in the binding site. A molecular dynamics optimized structure of hERG led to improved predictions using either compound alignment to the Atlas ligand or docking. The structure was also found to be suitable to define a strategy for lowering inhibition based on the proposed binding mode of compounds in the channel.

Conservation of Allosteric Ligand Binding Sites in G-Protein Coupled Receptors.

Despite the growing number of G protein-coupled receptor (GPCR) structures, only 39 structures have been cocrystallized with allosteric inhibitors. These structures have been studied by protein mapping using the FTMap server, which determines the clustering of small organic probe molecules distributed on the protein surface. The method has found druggable sites overlapping with the cocrystallized allosteric ligands in 21 GPCR structures. Mapping of Alphafold2 generated models of these proteins confirms that the same sites can be identified without the presence of bound ligands. We then mapped the 394 GPCR X-ray structures available at the time of the analysis (September 2020). Results show that for each of the 21 structures with bound ligands there exist many other GPCRs that have a strong binding hot spot at the same location, suggesting potential allosteric sites in a large variety of GPCRs. These sites cluster at nine distinct locations, and each can be found in many different proteins. However, ligands binding at the same location generally show little or no similarity, and the amino acid residues interacting with these ligands also differ. Results confirm the possibility of specifically targeting these sites across GPCRs for allosteric modulation and help to identify the most likely binding sites among the limited number of potential locations. The FTMap server is available free of charge for academic and governmental use at https://ftmap.bu.edu/.

Novel p.G250A Mutation Associated with Chronic Pancreatitis Highlights Misfolding-Prone Region in Carboxypeptidase A1 (CPA1).

Inborn mutations in the digestive protease carboxypeptidase A1 (CPA1) gene may be associated with hereditary and idiopathic chronic pancreatitis (CP). Pathogenic mutations, such as p.N256K, cause intracellular retention and reduced secretion of CPA1, accompanied by endoplasmic reticulum (ER) stress, suggesting that mutation-induced misfolding underlies the phenotype. Here, we report the novel p.G250A CPA1 mutation found in a young patient with CP. Functional properties of the p.G250A mutation were identical to those of the p.N256K mutation, confirming its pathogenic nature. We noted that both mutations are in a catalytically important loop of CPA1 that is stabilized by the Cys248-Cys271 disulfide bond. Mutation of either or both Cys residues to Ala resulted in misfolding, as judged by the loss of CPA1 secretion and intracellular retention. We re-analyzed seven previously reported CPA1 mutations that affect this loop and found that all exhibited reduced secretion and caused ER stress of varying degrees. The magnitude of ER stress was proportional to the secretion defect. Replacing the naturally occurring mutations with Ala (e.g., p.V251A for p.V251M) restored secretion, with the notable exception of p.N256A. We conclude that the disulfide-stabilized loop of CPA1 is prone to mutation-induced misfolding, in most cases due to the disruptive nature of the newly introduced side chain. We propose that disease-causing CPA1 mutations exhibit abolished or markedly reduced secretion with pronounced ER stress, whereas CPA1 mutations with milder misfolding phenotypes may be associated with lower disease risk or may not be pathogenic at all.

Assessing the binding properties of CASP14 targets and models.

An important question is how well the models submitted to CASP retain the properties of target structures. We investigate several properties related to binding. First we explore the binding of small molecules as probes, and count the number of interactions between each residue and such probes, resulting in a binding fingerprint. The similarity between two fingerprints, one for the X-ray structure and the other for a model, is determined by calculating their correlation coefficient. The fingerprint similarity weakly correlates with global measures of accuracy, and GDT_TS higher than 80 is a necessary but not sufficient condition for the conservation of surface binding properties. The advantage of this approach is that it can be carried out without information on potential ligands and their binding sites. The latter information was available for a few targets, and we explored whether the CASP14 models can be used to predict binding sites and to dock small ligands. Finally, we tested the ability of models to reproduce protein-protein interactions by docking both the X-ray structures and the models to their interaction partners in complexes. The analysis showed that in CASP14 the quality of individual domain models is approaching that offered by X-ray crystallography, and hence such models can be successfully used for the identification of binding and regulatory sites, as well as for assembling obligatory protein-protein complexes. Success of ligand docking, however, often depends on fine details of the binding interface, and thus may require accounting for conformational changes by simulation methods.

Exploring the structural origins of cryptic sites on proteins.

Molecular dynamics (MD) simulations of proteins reveal the existence of many transient surface pockets; however, the factors determining what small subset of these represent druggable or functionally relevant ligand binding sites, called "cryptic sites," are not understood. Here, we examine multiple X-ray structures for a set of proteins with validated cryptic sites, using the computational hot spot identification tool FTMap. The results show that cryptic sites in ligand-free structures generally have a strong binding energy hot spot very close by. As expected, regions around cryptic sites exhibit above-average flexibility, and close to 50% of the proteins studied here have unbound structures that could accommodate the ligand without clashes. Nevertheless, the strong hot spot neighboring each cryptic site is almost always exploited by the bound ligand, suggesting that binding may frequently involve an induced fit component. We additionally evaluated the structural basis for cryptic site formation, by comparing unbound to bound structures. Cryptic sites are most frequently occluded in the unbound structure by intrusion of loops (22.5%), side chains (19.4%), or in some cases entire helices (5.4%), but motions that create sites that are too open can also eliminate pockets (19.4%). The flexibility of cryptic sites frequently leads to missing side chains or loops (12%) that are particularly evident in low resolution crystal structures. An interesting observation is that cryptic sites formed solely by the movement of side chains, or of backbone segments with fewer than five residues, result only in low affinity binding sites with limited use for drug discovery.

Interaction Energetics and Druggability of the Protein-Protein Interaction between Kelch-like ECH-Associated Protein 1 (KEAP1) and Nuclear Factor Erythroid 2 Like 2 (Nrf2).

Development of small molecule inhibitors of protein-protein interactions (PPIs) is hampered by our poor understanding of the druggability of PPI target sites. Here, we describe the combined application of alanine-scanning mutagenesis, fragment screening, and FTMap computational hot spot mapping to evaluate the energetics and druggability of the highly charged PPI interface between Kelch-like ECH-associated protein 1 (KEAP1) and nuclear factor erythroid 2 like 2 (Nrf2), an important drug target. FTMap identifies four binding energy hot spots at the active site. Only two of these are exploited by Nrf2, which alanine scanning of both proteins shows to bind primarily through E79 and E82 interacting with KEAP1 residues S363, R380, R415, R483, and S508. We identify fragment hits and obtain X-ray complex structures for three fragments via crystal soaking using a new crystal form of KEAP1. Combining these results provides a comprehensive and quantitative picture of the origins of binding energy at the interface. Our findings additionally reveal non-native interactions that might be exploited in the design of uncharged synthetic ligands to occupy the same site on KEAP1 that has evolved to bind the highly charged DEETGE binding loop of Nrf2. These include π-stacking with KEAP1 Y525 and interactions at an FTMap-identified hot spot deep in the binding site. Finally, we discuss how the complementary information provided by alanine-scanning mutagenesis, fragment screening, and computational hot spot mapping can be integrated to more comprehensively evaluate PPI druggability.

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45.

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.

ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking.

Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org, is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.

Benchmark Sets for Binding Hot Spot Identification in Fragment-Based Ligand Discovery.

Binding hot spots are regions of proteins that, due to their potentially high contribution to the binding free energy, have high propensity to bind small molecules. We present benchmark sets for testing computational methods for the identification of binding hot spots with emphasis on fragment-based ligand discovery. Each protein structure in the set binds a fragment, which is extended into larger ligands in other structures without substantial change in its binding mode. Structures of the same proteins without any bound ligand are also collected to form an unbound benchmark. We also discuss a set developed by Astex Pharmaceuticals for the validation of hot and warm spots for fragment binding. The set is based on the assumption that a fragment that occurs in diverse ligands in the same subpocket identifies a binding hot spot. Since this set includes only ligand-bound proteins, we added a set with unbound structures. All four sets were tested using FTMap, a computational analogue of fragment screening experiments to form a baseline for testing other prediction methods, and differences among the sets are discussed.

Template-based modeling by ClusPro in CASP13 and the potential for using co-evolutionary information in docking.

As a participant in the joint CASP13-CAPRI46 assessment, the ClusPro server debuted its new template-based modeling functionality. The addition of this feature, called ClusPro TBM, was motivated by the previous CASP-CAPRI assessments and by the proven ability of template-based methods to produce higher-quality models, provided templates are available. In prior assessments, ClusPro submissions consisted of models that were produced via free docking of pre-generated homology models. This method was successful in terms of the number of acceptable predictions across targets; however, analysis of results showed that purely template-based methods produced a substantially higher number of medium-quality models for targets for which there were good templates available. The addition of template-based modeling has expanded ClusPro's ability to produce higher accuracy predictions, primarily for homomeric but also for some heteromeric targets. Here we review the newest additions to the ClusPro web server and discuss examples of CASP-CAPRI targets that continue to drive further development. We also describe ongoing work not yet implemented in the server. This includes the development of methods to improve template-based models and the use of co-evolutionary information for data-assisted free docking.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Protein-protein docking by fast generalized Fourier transforms on 5D rotational manifolds.

Energy evaluation using fast Fourier transforms (FFTs) enables sampling billions of putative complex structures and hence revolutionized rigid protein-protein docking. However, in current methods, efficient acceleration is achieved only in either the translational or the rotational subspace. Developing an efficient and accurate docking method that expands FFT-based sampling to five rotational coordinates is an extensively studied but still unsolved problem. The algorithm presented here retains the accuracy of earlier methods but yields at least 10-fold speedup. The improvement is due to two innovations. First, the search space is treated as the product manifold [Formula: see text], where [Formula: see text] is the rotation group representing the space of the rotating ligand, and [Formula: see text] is the space spanned by the two Euler angles that define the orientation of the vector from the center of the fixed receptor toward the center of the ligand. This representation enables the use of efficient FFT methods developed for [Formula: see text] Second, we select the centers of highly populated clusters of docked structures, rather than the lowest energy conformations, as predictions of the complex, and hence there is no need for very high accuracy in energy evaluation. Therefore, it is sufficient to use a limited number of spherical basis functions in the Fourier space, which increases the efficiency of sampling while retaining the accuracy of docking results. A major advantage of the method is that, in contrast to classical approaches, increasing the number of correlation function terms is computationally inexpensive, which enables using complex energy functions for scoring.

Ligand deconstruction: Why some fragment binding positions are conserved and others are not.

Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots--regions of the protein where interactions with a ligand contribute substantial binding free energy--the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand.

New additions to the ClusPro server motivated by CAPRI.

The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for small angle X-ray scattering data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally, we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. Proteins 2017; 85:435-444. © 2016 Wiley Periodicals, Inc.