Accounting for observed small angle X-ray scattering profile in the protein-protein docking server ClusPro

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Biophysical and Integrative Characterization of Protein Intrinsic Disorder as a Prime Target for Drug Discovery

Protein intrinsic disorder is increasingly recognized for its biological and disease-driven functions. However, it represents significant challenges for biophysical studies due to its high conformational flexibility. In addressing these challenges, we highlight the complementary and distinct capabilities of a range of experimental and computational methods and further describe integrative strategies available for combining these techniques. Integrative biophysics methods provide valuable insights into the sequence–structure–function relationship of disordered proteins, setting the stage for protein intrinsic disorder to become a promising target for drug discovery. Finally, we briefly summarize recent advances in the development of new small molecule inhibitors targeting the disordered N-terminal domains of three vital transcription factors.

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models

Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray crystallography or cryo-electron microscopy provide atomic resolution characterization of the epitope, but the data collection process is typically long and the success rate is low. On the other hand, computational methods for modeling antibody-antigen structures from the individual components frequently suffer from a high false positive rate, rarely resulting in a unique solution. Recent deep learning models for structure prediction are also successful in predicting protein-protein complexes. However, they do not perform well for antibody-antigen complexes. Small Angle X-ray Scattering (SAXS) is a reliable technique for rapid structural characterization of protein samples in solution albeit at low resolution. Here, we present an integrative approach for modeling antigen-antibody complexes using the antibody sequence, antigen structure, and experimentally determined SAXS profiles of the antibody, antigen, and the complex. The method models antibody structures using a novel deep-learning approach, NanoNet. The structures of the antibodies and antigens are represented using multiple 3D conformations to account for compositional and conformational heterogeneity of the protein samples that are used to collect the SAXS data. The complexes are predicted by integrating the SAXS profiles with scoring functions for protein-protein interfaces that are based on statistical potentials and antibody-specific deep-learning models. We validated the method via application to four Fab:EGFR and one Fab:PCSK9 antibody:antigen complexes with experimentally available SAXS datasets. The integrative approach returns accurate predictions (interface RMSD<4Å) in the top five predictions for four out of five complexes (respective interface RMSD values of 1.95, 2.18, 2.66 and 3.87Å), providing support for the utility of such a computational pipeline for epitope characterization during therapeutic antibody discovery.

Purification and characterization of inorganic pyrophosphatase for in vitro RNA transcription

Inorganic pyrophosphatase (iPPase) is an enzyme that cleaves pyrophosphate into two phosphate molecules. This enzyme is an essential component of in vitro transcription (IVT) reactions for RNA preparation as it prevents pyrophosphate from precipitating with magnesium, ultimately increasing the rate of the IVT reaction. Large-scale RNA production is often required for biochemical and biophysical characterization studies of RNA, therefore requiring large amounts of IVT reagents. Commercially purchased iPPase is often the most expensive component of any IVT reaction. In this paper, we demonstrate that iPPase can be produced in large quantities and high quality using a reasonably generic laboratory facility and that laboratory-purified iPPase is as effective as commercially available iPPase. Furthermore, using size exclusion chromatography coupled with multi-angle light scattering and dynamic light scattering, analytical ultracentrifugation, and small-angle X-ray scattering, we demonstrate that yeast iPPase can form tetramers and hexamers in solution as well as the enzymatically active dimer. Our work provides a robust protocol for laboratories involved with RNA in vitro transcription to efficiently produce active iPPase, significantly reducing the financial strain of large-scale RNA production.

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Hybrid methods for combined experimental and computational determination of protein structure

Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.

Cyclic Peptide Inhibitors of the Tsg101 UEV Protein Interactions Refined through Global Docking and Gaussian Accelerated Molecular Dynamics Simulations

Tsg101 UEV domain proteins are potential targets for virus infection therapy, especially for HIV and Ebola viruses. Peptides are key in curbing virus transmission, and cyclic peptides have a greater survival time than their linear peptides. To date, the accurate prediction of cyclic peptide-protein receptors binding conformations still is challenging because of high peptide flexibility. Here, a useful approach combined the global peptide docking, Gaussian accelerated molecular dynamics (GaMD), two-dimensional (2D) potential of mean force (PMF), normal molecular dynamics (cMD), and solvated interaction energy (SIE) techniques. Then we used this approach to investigate the binding conformations of UEV domain proteins with three cyclic peptides inhibitors. We reported the possible cyclic peptide-UEV domain protein binding conformations via 2D PMF free energy profiles and SIE free energy calculations. The residues Trp145, Tyr147, and Trp148 of the native cyclic peptide (CP1) indeed play essential roles in the cyclic peptides-UEV domain proteins interactions. Our findings might increase the accuracy of cyclic peptide-protein conformational prediction, which may facilitate cyclic peptide inhibitor design. Our approach is expected to further aid in addressing the challenges in cyclic peptide inhibitor design.

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.

Protein-Protein and Protein-Peptide Docking with ClusPro Server

The process of creating a model of the structure formed by a pair of interacting molecules is commonly referred to as docking. Protein docking is one of the most studied topics in computational and structural biology with applications to drug design and beyond. In this chapter, we describe ClusPro, a web server for protein-protein and protein-peptide docking. As an input, the server requires two Protein Data Bank (PDB) files (protein-protein mode) or a PDB file for the protein and a sequence for the ligand (protein-peptide mode). Its output consists of ten models of the resulting structure formed by the two objects upon interaction. The server typically produces results in less than 4 h. The server also provides tools (via "Advanced Options" list) for a user to fine-tune the results using any additional knowledge about the interaction process, e.g., small-angle X-ray scattering (SAXS) profile or distance restraints.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

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.

What method to use for protein-protein docking?

A number of well-established servers perform 'free' docking of proteins of known structures. In contrast, template-based docking can start from sequences if structures are available for complexes that are homologous to the target. On the basis of the results of the CAPRI-CASP structure prediction experiments, template-based methods yield more accurate predictions if good templates can be found, but generally fail without such templates. However, free global docking, or focused docking around even poor quality template-based models, can still generate acceptable docked structures in these cases. In accordance with the analysis of a benchmark set, free docking of heterodimers yields acceptable or better predictions in the top 10 models for around 40% of structures. However, it is likely that a combination of template-based and free docking methods can perform better for targets that have template structures available. Another way of improving the reliability of predictions is adding experimental information as restraints, an option built into several docking servers.

The role of small-angle scattering in structure-based screening applications

In many biomolecular interactions, changes in the assembly states and structural conformations of participants can act as a complementary reporter of binding to functional and thermodynamic assays. This structural information is captured by a number of structural biology and biophysical techniques that are viable either as primary screens in small-scale applications or as secondary screens to complement higher throughput methods. In particular, small-angle X-ray scattering (SAXS) reports the average distance distribution between all atoms after orientational averaging. Such information is important when for example investigating conformational changes involved in inhibitory and regulatory mechanisms where binding events do not necessarily cause functional changes. Thus, we summarise here the current and prospective capabilities of SAXS-based screening in the context of other methods that yield structural information. Broad guidelines are also provided to assist readers in preparing screening protocols that are tailored to available X-ray sources.

Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures

The elucidation of protein-protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

The ClusPro web server for protein-protein docking

The ClusPro server (https://cluspro.org) is a widely used tool for protein-protein docking. The server provides a simple home page for basic use, requiring only two files in Protein Data Bank (PDB) format. However, ClusPro also offers a number of advanced options to modify the search; these include the removal of unstructured protein regions, application of attraction or repulsion, accounting for pairwise distance restraints, construction of homo-multimers, consideration of small-angle X-ray scattering (SAXS) data, and location of heparin-binding sites. Six different energy functions can be used, depending on the type of protein. Docking with each energy parameter set results in ten models defined by centers of highly populated clusters of low-energy docked structures. This protocol describes the use of the various options, the construction of auxiliary restraints files, the selection of the energy parameters, and the analysis of the results. Although the server is heavily used, runs are generally completed in <4 h.

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.

FoXS, FoXSDock and MultiFoXS: Single-state and multi-state structural modeling of proteins and their complexes based on SAXS profiles

Small Angle X-ray Scattering (SAXS) is an increasingly common and useful technique for structural characterization of molecules in solution. A SAXS experiment determines the scattering intensity of a molecule as a function of spatial frequency, termed SAXS profile. Here, we describe three web servers for modeling atomic structures based on SAXS profiles. FoXS (Fast X-Ray Scattering) rapidly computes a SAXS profile of a given atomistic model and fits it to an experimental profile. FoXSDock docks two rigid protein structures based on a SAXS profile of their complex. MultiFoXS computes a population-weighted ensemble starting from a single input structure by fitting to a SAXS profile of the protein in solution. We describe the interfaces and capabilities of the servers (salilab.org/foxs), followed by demonstrating their application on Interleukin-33 (IL-33) and its primary receptor ST2.

Modification of Spherical Polyelectrolyte Brushes by Layer-by-Layer Self-Assembly as Observed by Small Angle X-ray Scattering

Multilayer modified spherical polyelectrolyte brushes were prepared through alternate deposition of positively charged poly(allylamine hydrochloride) (PAH) and negatively charged poly-l-aspartic acid (PAsp) onto negatively charged spherical poly(acrylic acid) (PAA) brushes (SPBs) on a poly(styrene) core. The charge reversal determined by the zeta potential indicated the success of layer-by-layer (LBL) deposition. The change of the structure during the construction of multilayer modified SPBs was observed by small-angle X-ray scattering (SAXS). SAXS results indicated that some PAH chains were able to penetrate into the PAA brush for the PAA-PAH double-layer modified SPBs whereas part of the PAH moved towards the outer layer when the PAsp layer was loaded to form a PAA-PAH-PAsp triple-layer system. The multilayer modified SPBs were stable upon changing the pH (5 to 9) and ionic strength (1 to 100 mM). The triple-layer modified SPBs were more tolerated to high pH (even at 11) compared to the double-layer ones. SAXS is proved to be a powerful tool for studying the inner structure of multilayer modified SPBs, which can establish guidelines for the a range of potential applications of multilayer modified SPBs.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.