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Tandiana R, Barletta GP, Soler MA, Fortuna S, Rocchia W. Computational Mutagenesis of Antibody Fragments: Disentangling Side Chains from ΔΔ G Predictions. J Chem Theory Comput 2024; 20:2630-2642. [PMID: 38445482 DOI: 10.1021/acs.jctc.3c01225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The development of highly potent antibodies and antibody fragments as binding agents holds significant implications in fields such as biosensing and biotherapeutics. Their binding strength is intricately linked to the arrangement and composition of residues at the binding interface. Computational techniques offer a robust means to predict the three-dimensional structure of these complexes and to assess the affinity changes resulting from mutations. Given the interdependence of structure and affinity prediction, our objective here is to disentangle their roles. We aim to evaluate independently six side-chain reconstruction methods and ten binding affinity estimation techniques. This evaluation was pivotal in predicting affinity alterations due to single mutations, a key step in computational affinity maturation protocols. Our analysis focuses on a data set comprising 27 distinct antibody/hen egg white lysozyme complexes, each with crystal structures and experimentally determined binding affinities. Using six different side-chain reconstruction methods, we transformed each structure into its corresponding mutant via in silico single-point mutations. Subsequently, these structures undergo minimization and molecular dynamics simulation. We therefore estimate ΔΔG values based on the original crystal structure, its energy-minimized form, and the ensuing molecular dynamics trajectories. Our research underscores the critical importance of selecting reliable side-chain reconstruction methods and conducting thorough molecular dynamics simulations to accurately predict the impact of mutations. In summary, our study demonstrates that the integration of conformational sampling and scoring is a potent approach to precisely characterizing mutation processes in single-point mutagenesis protocols and crucial for computational antibody design.
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Affiliation(s)
- Rika Tandiana
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| | - German P Barletta
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
- The Abdus Salam International Centre for Theoretical Physics─ICTP, Strada Costiera 11, 34151 Trieste, Italy
| | - Miguel Angel Soler
- Dipartimento di Scienze Matematiche, Informatiche e Fisiche, Universita' di Udine, Via delle Scienze 206, 33100 Udine, Italy
| | - Sara Fortuna
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
| | - Walter Rocchia
- Computational MOdelling of NanosCalE and BioPhysical SysTems─CONCEPT Lab Istituto Italiano di Tecnologia (IIT), Via Melen-83, B Block, 16152 Genoa, Italy
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Soler MA, Minovski N, Rocchia W, Fortuna S. Replica-exchange optimization of antibody fragments. Comput Biol Chem 2023; 103:107819. [PMID: 36657284 DOI: 10.1016/j.compbiolchem.2023.107819] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/16/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.
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Affiliation(s)
- Miguel A Soler
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, Udine, Italy
| | - Nikola Minovski
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy
| | - Walter Rocchia
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy
| | - Sara Fortuna
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy.
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Connection between MHC class II binding and aggregation propensity: The antigenic peptide 10 of Paracoccidioides brasiliensis as a benchmark study. Comput Struct Biotechnol J 2023; 21:1746-1758. [PMID: 36890879 PMCID: PMC9986244 DOI: 10.1016/j.csbj.2023.02.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/17/2023] [Accepted: 02/17/2023] [Indexed: 02/22/2023] Open
Abstract
The aggregation of epitopes that are also able to bind major histocompatibility complex (MHC) alleles raises questions around the potential connection between the formation of epitope aggregates and their affinities to MHC receptors. We first performed a general bioinformatic assessment over a public dataset of MHC class II epitopes, finding that higher experimental binding correlates with higher aggregation-propensity predictors. We then focused on the case of P10, an epitope used as a vaccine candidate against Paracoccidioides brasiliensis that aggregates into amyloid fibrils. We used a computational protocol to design variants of the P10 epitope to study the connection between the binding stabilities towards human MHC class II alleles and their aggregation propensities. The binding of the designed variants was tested experimentally, as well as their aggregation capacity. High-affinity MHC class II binders in vitro were more disposed to aggregate forming amyloid fibrils capable of binding Thioflavin T and congo red, while low affinity MHC class II binders remained soluble or formed rare amorphous aggregates. This study shows a possible connection between the aggregation propensity of an epitope and its affinity for the MHC class II cleft.
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Medagli B, Soler MA, De Zorzi R, Fortuna S. Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders. Methods Mol Biol 2023; 2552:333-359. [PMID: 36346602 DOI: 10.1007/978-1-0716-2609-2_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.
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Affiliation(s)
- Barbara Medagli
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy.
| | - Miguel A Soler
- CONCEPT Lab, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Rita De Zorzi
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy
| | - Sara Fortuna
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy.
- CONCEPT Lab, Istituto Italiano di Tecnologia, Genova, Italy.
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Bartuzi D, Kaczor AA, Matosiuk D. Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling. Methods Mol Biol 2023; 2627:25-40. [PMID: 36959440 DOI: 10.1007/978-1-0716-2974-1_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.
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Affiliation(s)
- Damian Bartuzi
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Laboratory, Medical University of Lublin, Lublin, Poland.
| | - Agnieszka A Kaczor
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Laboratory, Medical University of Lublin, Lublin, Poland
- University of Eastern Finland, School of Pharmacy, Kuopio, Finland
| | - Dariusz Matosiuk
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modelling Laboratory, Medical University of Lublin, Lublin, Poland
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Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules. PLoS One 2022; 17:e0254969. [PMID: 35085247 PMCID: PMC8794220 DOI: 10.1371/journal.pone.0254969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 10/28/2021] [Indexed: 11/19/2022] Open
Abstract
Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference state associated to the null interaction case. Despite their significant effectiveness, the physical interpretation of knowledge-based potentials has been repeatedly questioned, with no consensus on the choice of the reference state. Here we use the fact that the Flory theorem, originally derived for chains in a dense polymer melt, holds also for chain fragments within the core of globular proteins, if the average over buried fragments collected from different non-redundant native structures is considered. After verifying that the ensuing Gaussian statistics, a hallmark of effectively non-interacting polymer chains, holds for a wide range of fragment lengths, although with significant deviations at short spatial scales, we use it to define a ‘bona fide’ reference state. Notably, despite the latter does depend on fragment length, deviations from it do not. This allows to estimate an effective interaction potential which is not biased by the presence of correlations due to the connectivity of the protein chain. We show how different sequence-independent effective statistical potentials can be derived using this approach by coarse-graining the protein representation at varying levels. The possibility of defining sequence-dependent potentials is explored.
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Soler MA, Medagli B, Semrau MS, Storici P, Bajc G, de Marco A, Laio A, Fortuna S. A consensus protocol for the in silico optimisation of antibody fragments. Chem Commun (Camb) 2019; 55:14043-14046. [PMID: 31690899 DOI: 10.1039/c9cc06182g] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We present an in silico mutagenetic protocol for improving the binding affinity of single domain antibodies (or nanobodies, VHHs). The method iteratively attempts random mutations in the interacting region of the protein and evaluates the resulting binding affinity towards the target by scoring, with a collection of scoring functions, short explicit solvent molecular dynamics trajectories of the binder-target complexes. The acceptance/rejection of each attempted mutation is carried out by a consensus decision-making algorithm, which considers all individual assessments derived from each scoring function. The method was benchmarked by evolving a single complementary determining region (CDR) of an anti-HER2 VHH hit obtained by direct panning of a phage display library. The optimised VHH mutant showed significantly enhanced experimental affinity with respect to the original VHH it matured from. The protocol can be employed as it is for the optimization of peptides, antibody fragments, and (given enough computational power) larger antibodies.
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Affiliation(s)
- Miguel A Soler
- International School for Advanced Studies (SISSA), Via Bonomea 265, 34136, Trieste, Italy.
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Ochoa R, Laio A, Cossio P. Predicting the Affinity of Peptides to Major Histocompatibility Complex Class II by Scoring Molecular Dynamics Simulations. J Chem Inf Model 2019; 59:3464-3473. [PMID: 31290667 DOI: 10.1021/acs.jcim.9b00403] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Predicting the binding affinity of peptides able to interact with major histocompatibility complex (MHC) molecules is a priority for researchers working in the identification of novel vaccines candidates. Most available approaches are based on the analysis of the sequence of peptides of known experimental affinity. However, for MHC class II receptors, these approaches are not very accurate, due to the intrinsic flexibility of the complex. To overcome these limitations, we propose to estimate the binding affinity of peptides bound to an MHC class II by averaging the score of the configurations from finite-temperature molecular dynamics simulations. The score is estimated for 18 different scoring functions, and we explored the optimal manner for combining them. To test the predictions, we considered eight peptides of known binding affinity. We found that six scoring functions correlate with the experimental ranking of the peptides significantly better than the others. We then assessed a set of techniques for combining the scoring functions by linear regression and logistic regression. We obtained a maximum accuracy of 82% for the predicted sign of the binding affinity using a logistic regression with optimized weights. These results are potentially useful to improve the reliability of in silico protocols to design high-affinity binding peptides for MHC class II receptors.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group , University of Antioquia , 050010 Medellin , Colombia
| | - Alessandro Laio
- International School for Advanced Studies (SISSA) , Via Bonomea 265 , 34136 Trieste , Italy.,The Abdus Salam International Centre for Theoretical Physics (ICTP) , Strada Costiera 11 , 34151 Trieste , Italy
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group , University of Antioquia , 050010 Medellin , Colombia.,Department of Theoretical Biophysics , Max Planck Institute of Biophysics , 60438 Frankfurt am Main , Germany
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Battisti A, Zamuner S, Sarti E, Laio A. Toward a unified scoring function for native state discrimination and drug-binding pocket recognition. Phys Chem Chem Phys 2019; 20:17148-17155. [PMID: 29900428 DOI: 10.1039/c7cp08170g] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.
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Affiliation(s)
- Anna Battisti
- International School for Advanced Studies (SISSA), Via Bonomea 265, I-34136 Trieste, Italy.
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Soler MA, Fortuna S, de Marco A, Laio A. Binding affinity prediction of nanobody-protein complexes by scoring of molecular dynamics trajectories. Phys Chem Chem Phys 2018; 20:3438-3444. [PMID: 29328338 DOI: 10.1039/c7cp08116b] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Nanobodies offer a viable alternative to antibodies for engineering high affinity binders. Their small size has an additional advantage: it allows exploiting computational protocols for optimizing their biophysical features, such as the binding affinity. The efficient prediction of this quantity is still considered a daunting task especially for modelled complexes. We show how molecular dynamics can successfully assist in the binding affinity prediction of modelled nanobody-protein complexes. The approximate initial configurations obtained by in silico design must undergo large rearrangements before achieving a stable conformation, in which the binding affinity can be meaningfully estimated. The scoring functions developed for the affinity evaluation of crystal structures will provide accurate estimates for modelled binding complexes if the scores are averaged over long finite temperature molecular dynamics simulations.
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Škrbić T, Zamuner S, Hong R, Seno F, Laio A, Trovato A. Vibrational entropy estimation can improve binding affinity prediction for non-obligatory protein complexes. Proteins 2018; 86:393-404. [DOI: 10.1002/prot.25454] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/22/2017] [Accepted: 01/05/2018] [Indexed: 01/10/2023]
Affiliation(s)
- Tatjana Škrbić
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
| | - Stefano Zamuner
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
| | - Rolando Hong
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
| | - Flavio Seno
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
- Padova Section, National Institute of Nuclear Physics (INFN); Padova Italy
| | - Alessandro Laio
- Faculty of Physics; International School for Advanced Studies (SISSA/ISAS); Trieste Italy
| | - Antonio Trovato
- Department of Physics and Astronomy “Galileo Galilei”; University of Padova; Padova Italy
- Padova Section, National Institute of Nuclear Physics (INFN); Padova Italy
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Sarti E, Gladich I, Zamuner S, Correia BE, Laio A. Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses. Proteins 2016; 84:1312-20. [DOI: 10.1002/prot.25079] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 04/27/2016] [Accepted: 05/19/2016] [Indexed: 12/28/2022]
Affiliation(s)
| | | | | | - Bruno E. Correia
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale De Lausanne; Lausanne Switzerland
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Rigid-Docking Approaches to Explore Protein-Protein Interaction Space. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016; 160:33-55. [PMID: 27830312 DOI: 10.1007/10_2016_41] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.
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