FREAD revisited: Accurate loop structure prediction using a database search algorithm

An overview on olfaction in the biological, analytical, computational, and machine learning fields

Recently, the comprehension of odor perception has advanced, unveiling the mysteries of the molecular receptors within the nasal passages and the intricate mechanisms governing signal transmission between these receptors, the olfactory bulb, and the brain. This review provides a comprehensive panorama of odors, encompassing various topics ranging from the structural and molecular underpinnings of odorous substances to the physiological intricacies of olfactory perception. It extends to elucidate the analytical methods used for their identification and explores the frontiers of computational methodologies.

Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop

Protein loop modeling is a challenging yet highly nontrivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid, and deep learning (DL) methods fall substantially short of either atomic accuracy or computational efficiency. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSDs of 1.77 and 1.95 Å for the CASP13+14 and CASP15 benchmark datasets, respectively, and manifests at least 2 orders of magnitude speedup in general compared with other methods. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.

X-ray Crystallography Module in MD Simulation Program Amber 2023. Refining the Models of Protein Crystals

The MD simulation package Amber offers an attractive platform to refine crystallographic structures of proteins: (i) state-of-the-art force fields help to regularize protein coordinates and reconstruct the poorly diffracting elements of the structure, such as flexible loops; (ii) MD simulations restrained by the experimental diffraction data provide an effective strategy to optimize structural models of protein crystals, including explicitly modeled interstitial solvent as well as crystal contacts. Here, we present the new crystallography module xray, released as a part of the Amber 2023 package. This module contains functions to calculate and scale structure factors (including the contributions from bulk solvent), evaluate the maximum-likelihood-type crystallographic potential, and compute its derivative forces. The X-ray functionality of Amber no longer relies on external dependencies so that the full advantage of GPU acceleration can be taken. This makes it possible to refine in a short time hundreds of crystal models, including supercell models comprised of multiple unit cells. The new automated Amber-based refinement procedure leads to an appreciable improvement in Rfree (in some cases, by as much as 0.067) as well as MolProbity scores.

Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling

Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

Protein Loop Modeling Using AlphaFold2

The functions of proteins are largely determined by their three-dimensional (3D) structures. Loop modeling tries to predict the conformation of a relatively short stretch of protein backbone and sidechain. It is a difficult problem due to conformational variability. Recently, AlphaFold2 has achieved outstanding results in 3-D protein structure prediction and is expected to perform well on loop modeling. In this paper, we investigate the performances of AlphaFold2 variants on popular loop modeling benchmark datasets and propose an efficient protocol of using AlphaFold2 for loop modeling, called IAFLoop. To predict the structure of a loop region, IAFLoop gives a moderately extended segment of the target loop region as input to AlphaFold2, runs a fast version of AlphaFold2 using a reduced database without ensembling, and uses RMSD based consensus scores to select the final output models. Our experimental results on benchmark datasets show that IAFLoop generated highly accurate loop models. It achieves comparable performance to the original application of AlphaFold2 in terms of RMSD error, and achieving much better results on some targets, while only using half of the time. Compared to the best previous methods, IAFLoop reduces the RMSD error by almost half on the 8-residual loop dataset, and more than 70% on the 12-residual loop dataset.

Production of novel recombinant anti-EpCAM antibody as targeted therapy for breast cancer

BACKGROUND

The utilization of monoclonal antibodies (moAbs), an issue correlated with the biopharmaceutical professions, is developing and maturing. Coordinated with this conception, we produced the appealingly modeled anti-EpCAM scFv for breast cancer tumors.

METHODS

Afterward cloning and expression of recombinant antibody in Escherichia coli bacteria, the correctness of the desired antibody was checked by western blotting. Flow cytometry was utilized to determine the capacity of the recombinant antibody to append to the desired receptors in the malignant breast cancer (BC)cell line. The recombinant antibody (anti-EpCAM scFv) was examined for preclinical efficacy in reducing tumor growth, angiogenesis, and invasiveness (in vitro- in vivo).

FINDINGS

A target antibody-mediated attenuation of migration and invasion in the examined cancer cell lines was substantiated (P-value < 0.05). Grafted tumors from breast cancer in mice indicated significant and compelling suppression of tumor growth and decrement in blood supply in reaction to the recombinant anti-EpCAM intervention. Evaluations of immunohistochemical and histopathological findings revealed an enhanced response rate to the treatment.

CONCLUSION

The desired anti-EpCAM scFv can be a therapeutic tool to reduce invasion and proliferation in malignant breast cancer.

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.

Characterisation of the immune repertoire of a humanised transgenic mouse through immunophenotyping and high-throughput sequencing

Immunoglobulin loci-transgenic animals are widely used in antibody discovery and increasingly in vaccine response modelling. In this study, we phenotypically characterised B-cell populations from the Intelliselect Transgenic mouse (Kymouse) demonstrating full B-cell development competence. Comparison of the naïve B-cell receptor (BCR) repertoires of Kymice BCRs, naïve human, and murine BCR repertoires revealed key differences in germline gene usage and junctional diversification. These differences result in Kymice having CDRH3 length and diversity intermediate between mice and humans. To compare the structural space explored by CDRH3s in each species' repertoire, we used computational structure prediction to show that Kymouse naïve BCR repertoires are more human-like than mouse-like in their predicted distribution of CDRH3 shape. Our combined sequence and structural analysis indicates that the naïve Kymouse BCR repertoire is diverse with key similarities to human repertoires, while immunophenotyping confirms that selected naïve B cells are able to go through complete development.

Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops)

Antibodies and T-cell receptors have been a subject of much interest due to their central role in the immune system and their potential applications in several biotechnological and medical applications from cancer therapy to vaccine development. A unique feature of these two lymphocyte receptors is their ability to bind a huge variety of different (pathogen) targets. This ability stems from six short loops in the binding domain that have hypervariable sequence due to genetic recombination mechanism. Particularly one of these loops, the third complementarity determining region (CDR3), has the highest sequence variability and a dominant role in binding the target. However, it has also been proven the most difficult to be modeled structurally, which is vitally important for downstream tasks such as binding prediction. This difficulty stems from its variability in sequence that both reduces the possibility of finding homologues and introduces unique structural features in the loop. We present here a general protocol for modeling such loops in antibodies and T-cell receptors. We also discuss the difficulties in loop modeling and the advantages and limitations of different modeling methods.

Homology Modeling of Antibody Variable Regions: Methods and Applications

Adaptive immunity specifically protects us from antigenic challenges. Antibodies are key effector proteins of adaptive immunity, and they are remarkable in their ability to recognize a virtually limitless number of antigens. Fragment variable (F_V), the antigen-binding region of antibodies, can be split into two main components, namely, framework and complementarity determining regions. The framework (FR) consists of light-chain framework (FR_L) and heavy-chain framework (FR_H). Similarly, the complementarity determining regions (CDRs) comprises of light-chain CDRs 1-3 (CDRs L1-3) and heavy-chain CDRs 1-3 (CDRs H1-3). While FRs are relatively constant in sequence and structure across diverse antibodies, sequence variation in CDRs leading to differential conformations of CDR loops accounts for the distinct antigenic specificities of diverse antibodies. The conserved structural features in FRs and conformity of CDRs to a limited set of standard conformations allow for the accurate prediction of F_V models using homology modeling techniques. Antibody structure prediction from its amino acid sequence has numerous important applications including prediction of antibody-antigen interaction interfaces and redesign of therapeutically and biotechnologically useful antibodies with improved affinity. This chapter summarizes the current practices employed in the successful homology modeling of antibody variable regions and the potential applications of the generated homology models.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction

The inhibition of protein-protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein-protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2's prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.

Accurate positioning of functional residues with robotics-inspired computational protein design

SignificanceComputational protein design promises to advance applications in medicine and biotechnology by creating proteins with many new and useful functions. However, new functions require the design of specific and often irregular atom-level geometries, which remains a major challenge. Here, we develop computational methods that design and predict local protein geometries with greater accuracy than existing methods. Then, as a proof of concept, we leverage these methods to design new protein conformations in the enzyme ketosteroid isomerase that change the protein's preference for a key functional residue. Our computational methods are openly accessible and can be applied to the design of other intricate geometries customized for new user-defined protein functions.

Dudás E, Fantini M, Puglisi R, Louka A, Marcatili P, Cattaneo A, Pastore A. Selection and Modelling of a New Single-Domain Intrabody Against TDP-43

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder associated to deteriorating motor and cognitive functions, and short survival. The disease is caused by neuronal death which results in progressive muscle wasting and weakness, ultimately leading to lethal respiratory failure. The misbehaviour of a specific protein, TDP-43, which aggregates and becomes toxic in ALS patient’s neurons, is supposed to be one of the causes. TDP-43 is a DNA/RNA-binding protein involved in several functions related to nucleic acid metabolism. Sequestration of TDP-43 aggregates is a possible therapeutic strategy that could alleviate or block pathology. Here, we describe the selection and characterization of a new intracellular antibody (intrabody) against TDP-43 from a llama nanobody library. The structure of the selected intrabody was predicted in silico and the model was used to suggest mutations that enabled to improve its expression yield, facilitating its experimental validation. We showed how coupling experimental methodologies with in silico design may allow us to obtain an antibody able to recognize the RNA binding regions of TDP-43. Our findings illustrate a strategy for the mitigation of TDP-43 proteinopathy in ALS and provide a potential new tool for diagnostics.

Modeling a unit cell: crystallographic refinement procedure using the biomolecular MD simulation platform Amber

A procedure has been developed for the refinement of crystallographic protein structures based on the biomolecular simulation program Amber. The procedure constructs a model representing a crystal unit cell, which generally contains multiple protein molecules and is fully hydrated with TIP3P water. Periodic boundary conditions are applied to the cell in order to emulate the crystal lattice. The refinement is conducted in the form of a specially designed short molecular-dynamics run controlled by the Amber ff14SB force field and the maximum-likelihood potential that encodes the structure-factor-based restraints. The new Amber-based refinement procedure has been tested on a set of 84 protein structures. In most cases, the new procedure led to appreciably lower R free values compared with those reported in the original PDB depositions or obtained by means of the industry-standard phenix.refine program. In particular, the new method has the edge in refining low-accuracy scrambled models. It has also been successful in refining a number of molecular-replacement models, including one with an r.m.s.d. of 2.15 Å. In addition, Amber-refined structures consistently show superior MolProbity scores. The new approach offers a highly realistic representation of protein-protein interactions in the crystal, as well as of protein-water interactions. It also offers a realistic representation of protein crystal dynamics (akin to ensemble-refinement schemes). Importantly, the method fully utilizes the information from the available diffraction data, while relying on state-of-the-art molecular-dynamics modeling to assist with those elements of the structure that do not diffract well (for example mobile loops or side chains). Finally, it should be noted that the protocol employs no tunable parameters, and the calculations can be conducted in a matter of several hours on desktop computers equipped with graphical processing units or using a designated web service.

Computational Identification and Design of Complementary β-Strand Sequences

The ß-sheet is a regular secondary structure element which consists of linear segments called ß-strands. They are involved in many important biological processes, and some are known to be related to serious diseases such as neurologic disorders and amyloidosis. The self-assembly of ß-sheet peptides also has practical applications in material sciences since they can be building blocks of repeated nanostructures. Therefore, computational algorithms for identification of ß-sheet formation can offer useful insight into the mechanism of disease-prone protein segments and the construction of biocompatible nanomaterials. Despite the recent advances in structure-based methods for the assessment of atomic interactions, identifying amyloidogenic peptides has proven to be extremely difficult since they are structurally very flexible. Thus, an alternative strategy is required to describe ß-sheet formation. It has been hypothesized and observed that there are certain amino acid propensities between ß-strand pairs. Based on this hypothesis, a database search algorithm, B-SIDER, is developed for the identification and design of ß-sheet forming sequences. Given a target sequence, the algorithm identifies exact or partial matches from the structure database and constructs a position-specific score matrix. The score matrix can be utilized to design novel sequences that can form a ß-sheet specifically with the target.

The Therapeutic Antibody Profiler for Computational Developability Assessment

The need to consider an antibody's "developability" (immunogenicity, solubility, specificity, stability, manufacturability, and storability) is now well understood in therapeutic antibody design. Predicting these properties rapidly and inexpensively is critical to industrial workflows, to avoid devoting resources to non-productive candidates. Here, we describe a high-throughput computational developability assessment tool, the Therapeutic Antibody Profiler (TAP), which assesses the physicochemical "druglikeness" of an antibody candidate. Input variable domain sequences are converted to three-dimensional structural models, and then five developability-linked molecular surface descriptors are calculated and compared to advanced-stage clinical therapeutics. Values at the extremes of/outside of the distributions seen in therapeutics imply an increased risk of developability issues. Therefore, TAP, starting only from sequence information, provides a route to rapidly identifying drug candidate antibodies that are likely to have poor developability. Our web application ( opig.stats.ox.ac.uk/webapps/tap ) profiles input antibody sequences against a continually updated reference set of clinical therapeutics.

Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies

Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. Using the method, we demonstrate that sequence dissimilar but functionally similar antibodies can be found across the Coronavirus Antibody Database, with high accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than is suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis.

Antibodies are a key component of the immune system that combat pathogens by binding to a defined region of their molecular surface (known as an ‘epitope’). The ability to map which antibodies target the same epitopes is crucial when designing non-competing antibody therapeutics or predicting the influence of pathogen mutation on population immunity. While one can use laboratory experiments to deduce when pairs of antibodies engage the same epitope, such experiments are very expensive and time consuming if used to compare on the order of thousands of antibodies. In this work, we report a new computational algorithm (SPACE) that clusters antibodies that target the same epitope based on their predicted 3D structure, as binding site structure is a property often conserved between binders complementary to the same epitope. Unlike existing antibody epitope profiling tools which assume two antibodies must share a high sequence identity/similar genetic basis to engage the same region, our orthogonal method can detect broader patterns of convergent evolution across binders to different pathogen strains, and between antibodies with different genetic and even species origins.

Conformational variability of loops in the SARS-CoV-2 spike protein

The SARS‐CoV‐2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. Its flexible loop regions are known to be involved in protein binding and may adopt multiple conformations. This article identifies the S protein loops and studies their conformational variability based on the available Protein Data Bank structures. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations based on a cluster analysis. Loop modeling methods were then applied to the S protein loop targets, and the prediction accuracies discussed in relation to the characteristics of the conformational clusters identified. Loops with multiple conformations were found to be challenging to model based on a single structural template.

Molecular dynamics modeling of the Vibrio cholera Na+-translocating NADH:quinone oxidoreductase NqrB-NqrD subunit interface

The Na⁺-translocating NADH:quinone oxidoreductase (Na⁺-NQR) is the major Na⁺ pump in aerobic pathogens such as Vibrio cholerae. The interface between two of the NQR subunits, NqrB and NqrD, has been proposed to harbor a binding site for inhibitors of Na⁺-NQR. While the mechanisms underlying Na⁺-NQR function and inhibition remain underinvestigated, their clarification would facilitate the design of compounds suitable for clinical use against pathogens containing Na⁺-NQR. An in silico model of the NqrB-D interface suitable for use in molecular dynamics simulations was successfully constructed. A combination of algorithmic and manual methods was used to reconstruct portions of the two subunits unresolved in the published crystal structure and validate the resulting structure. Hardware and software optimizations that improved the efficiency of the simulation were considered and tested. The geometry of the reconstructed complex compared favorably to the published V. cholerae Na⁺-NQR crystal structure. Results from one 1 µs, three 150 ns and two 50 ns molecular dynamics simulations illustrated the stability of the system and defined the limitations of this model. When placed in a lipid bilayer under periodic boundary conditions, the reconstructed complex was completely stable for at least 1 µs. However, the NqrB-D interface underwent a non-physiological transition after 350 ns.

Current approaches to flexible loop modeling

Loops are key components of protein structures, involved in many biological functions. Due to their conformational variability, the structural investigation of loops is a difficult topic, requiring a combination of experimental and computational methods. This paper provides a brief overview of current computational approaches to flexible loop modeling, and presents the main ingredients of the most standard protocols. Despite great progress in recent years, accurately modeling the conformational variability of long flexible loops remains a challenging problem. Future advances in this field will likely come from a tight coupling of experimental and computational techniques, which would enable a better understanding of the relationships between loop sequence, structural flexibility, and functional roles. In fine, accurate loop modeling will open the road to loop design problems of interest for applications in biomedicine and biotechnology.

Crystal structure and characterization of nucleoside diphosphate kinase from Vibrio cholerae

Nucleoside diphosphate kinases (NDK) are ubiquitous enzymes that catalyse the transfer of the γ phosphate from nucleoside triphosphates (NTPs) to nucleoside diphosphate (NDPs), to maintain appropriate NTP levels in cells. NDKs are associated with signal transduction, cell development, proliferation, differentiation, tumor metastasis, apoptosis and motility. The critical role of NDK in bacterial virulence renders it a potential drug target. The present manuscript reports crystal structure and functional characterization of Vibrio cholerae NDK (VNDK). The 16 kDa VNDK was crystallized in a solution containing 30% PEG 4000, 100 mM Tris-HCl pH 8.5 and 200 mM sodium acetate in orthorhombic space group P2₁2₁2₁ with unit cell parameters a = 48.37, b = 71.21, c = 89.14 Å, α = β = γ = 90° with 2 molecules in asymmetric unit. The crystal structure was solved by molecular replacement and refined to crystallographic R_factor and R_free values of 22.8% and 25.8% respectively. VNDK exists as both dimer and tetramer in solution as confirmed by size exclusion chromatography, glutaraldehyde crosslinking and small angle X-ray scattering while the crystal structure appears to be a dimer. The biophysical characterization states that VNDK has kinase and DNase activity with maximum stability at pH 8-9 and temperature up to 40 °C. VNDK shows elevated thermolability as compared to other NDK and shows preferential binding with GTP rationalized using computational studies.

Epitope-Paratope Interaction of a Neutralizing Human Anti-Hepatitis B Virus PreS1 Antibody That Recognizes the Receptor-Binding Motif

Hepatitis B virus (HBV) is a global health burden that causes acute and chronic hepatitis. To develop an HBV-neutralizing antibody that effectively prevents HBV infection, we previously generated a human anti-preS1 monoclonal antibody (1A8) that binds to genotypes A–D and validated its HBV-neutralizing activity in vitro. In the present study, we aimed to determine the fine epitope and paratope of 1A8 to understand the mechanism of HBV neutralization. We performed alanine-scanning mutagenesis on the preS1 (aa 19–34, genotype C) and the heavy (HCDR) and light (LCDR) chain complementarity-determining regions. The 1A8 recognized the three residues (Leu22, Gly23, and Phe25) within the highly conserved receptor-binding motif (NPLGFFP) of the preS1, while four CDR residues of 1A8 were critical in antigen binding. Structural analysis of the epitope–paratope interaction by molecular modeling revealed that Leu100 in the HCDR3, Ala50 in the HCDR2, and Tyr96 in the LCDR3 closely interacted with Leu22, Gly23, and Phe25 of the preS1. Additionally, we found that 1A8 also binds to the receptor-binding motif (NPLGFLP) of infrequently occurring HBV. The results suggest that 1A8 may broadly and effectively block HBV entry and thus have potential as a promising candidate for the prevention and treatment of HBV infection.

Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C

Protein loops, connecting the α-helices and β-strands, are involved in many important biological processes. However, due to their conformational flexibility, it is still challenging to accurately determine three-dimensional (3D) structures of long loops experimentally and computationally. Herein, we present a systematic study of the protein loop structure prediction via a total of ∼850 μs molecular dynamics (MD) simulations. For a set of 15 long (10-16 residues) and solvent-exposed loops, we first evaluated the performance of four state-of-the-art loop modeling algorithms, DaReUS-Loop, Sphinx, Rosetta-NGK, and MODELLER, on each loop, and none of them could accurately predict the structures for most loops. Then, temperature replica exchange molecular dynamics (REMD) simulations were conducted with three recent force fields, RSFF2C with TIP3P water model, CHARMM36m with CHARMM-modified TIP3P, and AMBER ff19SB with OPC. We found that our recently developed residue-specific force field RSFF2C performed the best and successfully predicted 12 out of 15 loops with a root-mean-square deviation (RMSD) < 1.5 Å. As an alternative with lower computational cost, normal MD simulations at high temperatures (380, 500, and 620 K) were investigated. Temperature-dependent performance was observed for each force field, and, for RSFF2C+TIP3P, we found that three independent 100-ns MD simulations at 500 K gave comparable results with REMD simulations. These results suggest that MD simulations, especially with enhanced sampling techniques such as replica exchange, with the RSFF2C force field could be useful for accurate loop structure prediction.

Systemic cancer therapy with engineered adenovirus that evades innate immunity

Oncolytic virus therapy is a cancer treatment modality that has the potential to improve outcomes for patients with currently incurable malignancies. Although intravascular delivery of therapeutic viruses provides access to disseminated tumors, this delivery route exposes the virus to opsonizing and inactivating factors in the blood, which limit the effective therapeutic virus dose and contribute to activation of systemic toxicities. When human species C adenovirus HAdv-C5 is delivered intravenously, natural immunoglobulin M (IgM) antibodies and coagulation factor X rapidly opsonize HAdv-C5, leading to virus sequestration in tissue macrophages and promoting infection of liver cells, triggering hepatotoxicity. Here, we showed that natural IgM antibody binds to the hypervariable region 1 (HVR1) of the main HAdv-C5 capsid protein hexon. Using compound targeted mutagenesis of hexon HVR1 loop and other functional sites that mediate virus-host interactions, we engineered and obtained a high-resolution cryo-electron microscopy structure of an adenovirus vector, Ad5-3M, which resisted inactivation by blood factors, avoided sequestration in liver macrophages, and failed to trigger hepatotoxicity after intravenous delivery. Systemic delivery of Ad5-3M to mice with localized or disseminated lung cancer led to viral replication in tumor cells, suppression of tumor growth, and prolonged survival. Thus, compound targeted mutagenesis of functional sites in the virus capsid represents a generalizable approach to tailor virus interactions with the humoral and cellular arms of the immune system, enabling generation of "designer" viruses with improved therapeutic properties.

Public Baseline and shared response structures support the theory of antibody repertoire functional commonality

The naïve antibody/B-cell receptor (BCR) repertoires of different individuals ought to exhibit significant functional commonality, given that most pathogens trigger an effective antibody response to immunodominant epitopes. Sequence-based repertoire analysis has so far offered little evidence for this phenomenon. For example, a recent study estimated the number of shared ('public') antibody clonotypes in circulating baseline repertoires to be around 0.02% across ten unrelated individuals. However, to engage the same epitope, antibodies only require a similar binding site structure and the presence of key paratope interactions, which can occur even when their sequences are dissimilar. Here, we search for evidence of geometric similarity/convergence across human antibody repertoires. We first structurally profile naïve ('baseline') antibody diversity using snapshots from 41 unrelated individuals, predicting all modellable distinct structures within each repertoire. This analysis uncovers a high (much greater than random) degree of structural commonality. For instance, around 3% of distinct structures are common to the ten most diverse individual samples ('Public Baseline' structures). Our approach is the first computational method to find levels of BCR commonality commensurate with epitope immunodominance and could therefore be harnessed to find more genetically distant antibodies with same-epitope complementarity. We then apply the same structural profiling approach to repertoire snapshots from three individuals before and after flu vaccination, detecting a convergent structural drift indicative of recognising similar epitopes ('Public Response' structures). We show that Antibody Model Libraries derived from Public Baseline and Public Response structures represent a powerful geometric basis set of low-immunogenicity candidates exploitable for general or target-focused therapeutic antibody screening.

Monoclonal Antibodies: A Prospective and Retrospective View

BACKGROUND

Monoclonal Antibodies (mAbs) represent one of the most important classes of biotherapeutic agents. They are used to cure many diseases, including cancer, autoimmune diseases, cardiovascular diseases, angiogenesis-related diseases and, more recently also haemophilia. They can be highly varied in terms of format, source, and specificity to improve efficacy and to obtain more targeted applications. This can be achieved by leaving substantially unchanged the basic structural components for paratope clustering.

OBJECTIVES

The objective was to trace the most relevant findings that have deserved prestigious awards over the years, to report the most important clinical applications and to emphasize their latest emerging therapeutic trends.

RESULTS

We report the most relevant milestones and new technologies adopted for antibody development. Recent efforts in generating new engineered antibody-based formats are briefly reviewed. The most important antibody-based molecules that are (or are going to be) used for pharmacological practice have been collected in useful tables.

CONCLUSION

The topics here discussed prove the undisputed role of mAbs as innovative biopharmaceuticals molecules and as vital components of targeted pharmacological therapies.

Efficient Sampling of Protein Loop Regions Using Conformational Hashing Complemented with Random Coordinate Descent

De novo construction of loop regions is an important problem in computational structural biology. Compared to regions with well-defined secondary structure, loops tend to exhibit significant conformational heterogeneity. As a result, their structures are often ambiguous when determined using experimental data obtained by crystallography, cryo-EM, or NMR. Although structurally diverse models could provide a more relevant representation of proteins in their native states, obtaining large numbers of biophysically realistic and physiologically relevant loop conformations is a resource-consuming task. To address this need, we developed a novel loop construction algorithm, Hash/RCD, that combines knowledge-based conformational hashing with random coordinate descent (RCD). This hybrid approach achieved a closure rate of 100% on a benchmark set of 195 loops in 29 proteins that range from 3 to 31 residues. More importantly, the use of templates allows Hash/RCD to maintain the accuracy of state-of-the-art coordinate descent methods while reducing sampling time from over 400 to 141 ms. These results highlight how the integration of coordinate descent with knowledge-based sampling overcomes barriers inherent to either approach in isolation. This method may facilitate the identification of native-like loop conformations using experimental data or full-atom scoring functions by allowing rapid sampling of large numbers of loops. In this manuscript, we investigate and discuss the advantages, bottlenecks, and limitations of combining conformational hashing with RCD. By providing a detailed technical description of the Hash/RCD algorithm, we hope to facilitate its implementation by other researchers.

ProMod3-A versatile homology modelling toolbox

Computational methods for protein structure modelling are routinely used to complement experimental structure determination, thus they help to address a broad spectrum of scientific questions in biomedical research. The most accurate methods today are based on homology modelling, i.e. detecting a homologue to the desired target sequence that can be used as a template for modelling. Here we present a versatile open source homology modelling toolbox as foundation for flexible and computationally efficient modelling workflows. ProMod3 is a fully scriptable software platform that can perform all steps required to generate a protein model by homology. Its modular design aims at fast prototyping of novel algorithms and implementing flexible modelling pipelines. Common modelling tasks, such as loop modelling, sidechain modelling or generating a full protein model by homology, are provided as production ready pipelines, forming the starting point for own developments and enhancements. ProMod3 is the central software component of the widely used SWISS-MODEL web-server.

Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View

Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.

Current strategies for detecting functional convergence across B-cell receptor repertoires

Convergence across B-cell receptor (BCR) and antibody repertoires has become instrumental in prioritizing candidates in recent rapid therapeutic antibody discovery campaigns. It has also increased our understanding of the immune system, providing evidence for the preferential selection of BCRs to particular (immunodominant) epitopes post vaccination/infection. These important implications for both drug discovery and immunology mean that it is essential to consider the optimal way to combine experimental and computational technology when probing BCR repertoires for convergence signatures. Here, we discuss the theoretical basis for observing BCR repertoire functional convergence and explore factors of study design that can impact functional signal. We also review the computational arsenal available to detect antibodies with similar functional properties, highlighting opportunities enabled by recent clustering algorithms that exploit structural similarities between BCRs. Finally, we suggest future areas of development that should increase the power of BCR repertoire functional clustering.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Maturation of the Human Immunoglobulin Heavy Chain Repertoire With Age

B cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B cell system with age is poorly studied. We extensively investigated age-related alterations of naïve and antigen-experienced immunoglobulin heavy chain (IgH) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream IgH constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing IgH repertoires and stress the importance of using well-selected, age-appropriate controls in IgH studies.

DaReUS-Loop: a web server to model multiple loops in homology models

Loop regions in protein structures often have crucial roles, and they are much more variable in sequence and structure than other regions. In homology modeling, this leads to larger deviations from the homologous templates, and loop modeling of homology models remains an open problem. To address this issue, we have previously developed the DaReUS-Loop protocol, leading to significant improvement over existing methods. Here, a DaReUS-Loop web server is presented, providing an automated platform for modeling or remodeling loops in the context of homology models. This is the first web server accepting a protein with up to 20 loop regions, and modeling them all in parallel. It also provides a prediction confidence level that corresponds to the expected accuracy of the loops. DaReUS-Loop facilitates the analysis of the results through its interactive graphical interface and is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/DaReUS-Loop/.

Potent Neutralizing Antibodies against SARS-CoV-2 Identified by High-Throughput Single-Cell Sequencing of Convalescent Patients' B Cells

The COVID-19 pandemic urgently needs therapeutic and prophylactic interventions. Here, we report the rapid identification of SARS-CoV-2-neutralizing antibodies by high-throughput single-cell RNA and VDJ sequencing of antigen-enriched B cells from 60 convalescent patients. From 8,558 antigen-binding IgG1⁺ clonotypes, 14 potent neutralizing antibodies were identified, with the most potent one, BD-368-2, exhibiting an IC₅₀ of 1.2 and 15 ng/mL against pseudotyped and authentic SARS-CoV-2, respectively. BD-368-2 also displayed strong therapeutic and prophylactic efficacy in SARS-CoV-2-infected hACE2-transgenic mice. Additionally, the 3.8 Å cryo-EM structure of a neutralizing antibody in complex with the spike-ectodomain trimer revealed the antibody’s epitope overlaps with the ACE2 binding site. Moreover, we demonstrated that SARS-CoV-2-neutralizing antibodies could be directly selected based on similarities of their predicted CDR3_H structures to those of SARS-CoV-neutralizing antibodies. Altogether, we showed that human neutralizing antibodies could be efficiently discovered by high-throughput single B cell sequencing in response to pandemic infectious diseases.

Ten quick tips for homology modeling of high-resolution protein 3D structures

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

Simple Selection Procedure to Distinguish between Static and Flexible Loops

Loops are the most variable and unorganized elements of the secondary structure of proteins. Their ability to shift their shape can play a role in the binding of small ligands, enzymatic catalysis, or protein–protein interactions. Due to the loop flexibility, the positions of their residues in solved structures show the largest B-factors, or in a worst-case scenario can be unknown. Based on the loops’ movements’ timeline, they can be divided into slow (static) and fast (flexible). Although most of the loops that are missing in experimental structures belong to the flexible loops group, the computational tools for loop reconstruction use a set of static loop conformations to predict the missing part of the structure and evaluate the model. We believe that these two loop types can adopt different conformations and that using scoring functions appropriate for static loops is not sufficient for flexible loops. We showed that common model evaluation methods, are insufficient in the case of flexible solvent-exposed loops. Instead, we recommend using the potential energy to evaluate such loop models. We provide a novel model selection method based on a set of geometrical parameters to distinguish between flexible and static loops without the use of molecular dynamics simulations. We have also pointed out the importance of water network and interactions with the solvent for the flexible loop modeling.

Structural diversity of B-cell receptor repertoires along the B-cell differentiation axis in humans and mice

Most current analysis tools for antibody next-generation sequencing data work with primary sequence descriptors, leaving accompanying structural information unharnessed. We have used novel rapid methods to structurally characterize the complementary-determining regions (CDRs) of more than 180 million human and mouse B-cell receptor (BCR) repertoire sequences. These structurally annotated CDRs provide unprecedented insights into both the structural predetermination and dynamics of the adaptive immune response. We show that B-cell types can be distinguished based solely on these structural properties. Antigen-unexperienced BCR repertoires use the highest number and diversity of CDR structures and these patterns of naïve repertoire paratope usage are highly conserved across subjects. In contrast, more differentiated B-cells are more personalized in terms of CDR structure usage. Our results establish the CDR structure differences in BCR repertoires and have applications for many fields including immunodiagnostics, phage display library generation, and “humanness” assessment of BCR repertoires from transgenic animals. The software tool for structural annotation of BCR repertoires, SAAB+, is available at https://github.com/oxpig/saab_plus.

B-cell receptors (BCR) are the major components of the adaptive immune system. These are immunoglobulin molecules that bind to foreign substances known as antigens. Each individual has a huge BCR repertoire, where each individual BCR has a specific binding site composed of the complementary-determining regions (CDRs) capable of recognising a specific antigen. Drug discovery and immunodiagnostics inspired by the adaptive immune system rely on our ability to accurately interrogate the structural diversity of the binding sites of the BCR repertoire. Here we report our novel rapid pipeline, SAAB+, which has enabled us to interrogate how the structure of the CDR changes in BCR repertoires along the B-cell differentiation axis. By analysing human and mouse BCR repertoires at an unprecedented scale, we observed species-specific structural predetermination and detected CDR dynamics across multiple stages of B-cell differentiation. We showed that naïve repertoires share the highest number and diversity of CDR structures, a pattern which was highly conserved in all B-cell donors. Our results suggest that increased B-cell differentiation is associated with a personalization of CDR structure usages. Finally, we established the differences in CDR usages between humans and mice, analysis with immediate relevance for BCR repertoire “humanness” assessment and rational immunotherapeutic engineering.

Small molecules that inhibit TNF signalling by stabilising an asymmetric form of the trimer

Tumour necrosis factor (TNF) is a cytokine belonging to a family of trimeric proteins; it has been shown to be a key mediator in autoimmune diseases such as rheumatoid arthritis and Crohn’s disease. While TNF is the target of several successful biologic drugs, attempts to design small molecule therapies directed to this cytokine have not led to approved products. Here we report the discovery of potent small molecule inhibitors of TNF that stabilise an asymmetrical form of the soluble TNF trimer, compromising signalling and inhibiting the functions of TNF in vitro and in vivo. This discovery paves the way for a class of small molecule drugs capable of modulating TNF function by stabilising a naturally sampled, receptor-incompetent conformation of TNF. Furthermore, this approach may prove to be a more general mechanism for inhibiting protein–protein interactions.

While biologics have been successfully applied in TNF antagonist treatments, there are no clinically approved small molecules that target TNF. Here, the authors discover potent small molecule inhibitors of TNF, elucidate their molecular mechanism, and demonstrate TNF inhibition in vitro and in vivo.

Salivary Scavenger and Agglutinin (SALSA) Is Expressed in Mucosal Epithelial Cells and Decreased in Bronchial Epithelium of Asthmatic Horses

The Salivary Scavenger and Agglutinin (SALSA) protein is an innate immune protein with various alleged functions, including the regulation of inflammation and tissue remodeling. Transcriptomic studies of severe equine asthma (SEA) showed downregulation of the gene encoding SALSA in bronchial epithelium of asthmatic compared to non-asthmatic horses. This study aimed to characterize expression of SALSA in equine tissues by immunohistochemistry (IHC), corroborate potential differences in epithelial gene expression between asthmatic and non-asthmatic horses, and assess the structure of equine SALSA. An antibody against SALSA was validated through immunoprecipitation followed by mass spectrometry and Western blotting to recognize the equine protein. This antibody was applied to tissue microarrays (TMAs) containing 22 tissues each from four horses. A quantitative PCR assay was designed to compare gene expression for SALSA between six asthmatic and six non-asthmatic horses, before and after an asthmatic challenge, using cDNA from endoscopic bronchial biopsies as source material. The SALSA gene from bronchial cDNA samples of 10 horses, was amplified and sequenced, and translated to characterize the protein structure. Immunostaining for SALSA was detected in the mucosal surfaces of the trachea, bronchi, bronchioles, stomach, small intestine and bladder, in pancreatic and salivary gland ducts, and in uterine gland epithelium. Staining was strongest in the duodenum, and the intercalated ducts and Demilune cells of the salivary gland. SALSA was concentrated in the apical regions of the epithelial cell cytoplasm, suggestive of a secreted protein. Gene expression was significantly lower (p = 0.031) in asthmatic compared to non-asthmatic horses. Equine SALSA consisted of three to five scavenger receptor cysteine-rich (SRCR) domains, two CUB (C1r/C1s, uegf, bmp-1) domains and one Zona Pellucida domain. These domains mediate the binding of ligands involved in innate immunity. Varying numbers of SRCR domains were identified in different horses, indicating different isoforms. In summary, equine SALSA has a predilection for mucosal sites, has multiple isoforms, and has decreased expression in asthmatic horses, suggesting alterations in innate immunity in equine asthma.

Modeling Antibody-Antigen Complexes by Information-Driven Docking

Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.

Mutagenesis facilitated crystallization of GLP-1R

The class B family of G-protein-coupled receptors (GPCRs) has long been a paradigm for peptide hormone recognition and signal transduction. One class B GPCR, the glucagon-like peptide-1 receptor (GLP-1R), has been considered as an anti-diabetes drug target and there are several peptidic drugs available for the treatment of this overwhelming disease. The previously determined structures of inactive GLP-1R in complex with two negative allosteric modulators include ten thermal-stabilizing mutations that were selected from a total of 98 designed mutations. Here we systematically summarize all 98 mutations we have tested and the results suggest that the mutagenesis strategy that strengthens inter-helical hydro-phobic interactions shows the highest success rate. We further investigate four back mutations by thermal-shift assay, crystallization and molecular dynamic simulations, and conclude that mutation I1962.66bF increases thermal stability intrinsically and that mutation S2714.47bA decreases crystal packing entropy extrinsically, while mutations S1932.63bC and M2333.36bC may be dispensable since these two cysteines are not di-sulfide-linked. Our results indicate intrinsic connections between different regions of GPCR transmembrane helices and the current data suggest a general mutagenesis principle for structural determination of GPCRs and other membrane proteins.

B-SIDER: Computational Algorithm for the Design of Complementary β-Sheet Sequences

The β-sheet is an element of protein secondary structure, and intra-/intermolecular β-sheet interactions play pivotal roles in biological regulatory processes including scaffolding, transporting, and oligomerization. In nature, a β-sheet formation is tightly regulated because dysregulated β-stacking often leads to severe diseases such as Alzheimer's, Parkinson's, systemic amyloidosis, or diabetes. Thus, the identification of intrinsic β-sheet-forming propensities can provide valuable insight into protein designs for the development of novel therapeutics. However, structure-based design methods may not be generally applicable to such amyloidogenic peptides mainly owing to high structural plasticity and complexity. Therefore, an alternative design strategy based on complementary sequence information is of significant importance. Herein, we developed a database search method called β-Stacking Interaction DEsign for Reciprocity (B-SIDER) for the design of complementary β-strands. This method makes use of the structural database information and generates target-specific score matrices. The discriminatory power of the B-SIDER score function was tested on representative amyloidogenic peptide substructures against a sequence-based score matrix (PASTA 2.0) and two popular ab initio protein design score functions (Rosetta and FoldX). B-SIDER is able to distinguish wild-type amyloidogenic β-strands as favored interactions in a more consistent manner than other methods. B-SIDER was prospectively applied to the design of complementary β-strands for a splitGFP scaffold. Three variants were identified to have stronger interactions than the original sequence selected through a directed evolution, emitting higher fluorescence intensities. Our results indicate that B-SIDER can be applicable to the design of other β-strands, assisting in the development of therapeutics against disease-related amyloidogenic peptides.

High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder

Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure.

Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design

Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed.

Coagulation Factor XIIIa Inhibitor Tridegin: On the Role of Disulfide Bonds for Folding, Stability, and Function

Tridegin is a potent and specific 66mer peptide inhibitor of coagulation factor XIIIa with six cysteines involved in three disulfide bonds. Three of the 15 possible 3-disulfide-bonded isomers have been identified, which share a bridge between cysteines 19 and 25. We synthesized the three possible 2-disulfide-bonded analogues using a targeted protecting group strategy to investigate the impact of the C19-C25 bond on tridegin's folding, stability, and function. The FXIIIa inhibitory activity of the analogues was retained, which was shown by in vitro fluorogenic activity and whole blood clotting assays. Molecular dynamics simulations of wild-type tridegin and the analogues as well as molecular docking studies with FXIIIa were performed to elucidate the impact of the C19-C25 bond on conformational stability and binding mode. The strategy of selectively reducing disulfide bonds to facilitate large-scale synthesis, while retaining the functionality of disulfide-bonded peptides, has been demonstrated with our present study.

Binding site comparisons for target-centered drug discovery

INTRODUCTION

The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.