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Siva Sankari G, James R, Payva F, Sivaramakrishnan V, Vineeth Kumar TV, Kanchi S, Santhy KS. Computational analysis of sodium-dependent phosphate transporter SLC20A1/PiT1 gene identifies missense variations C573F, and T58A as high-risk deleterious SNPs. J Biomol Struct Dyn 2024; 42:4072-4086. [PMID: 37286379 DOI: 10.1080/07391102.2023.2218939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/21/2023] [Indexed: 06/09/2023]
Abstract
SLC20A1/PiT1 is a sodium-dependent inorganic phosphate transporter, initially recognized as the retroviral receptor for Gibbon Ape Leukemia Virus in humans. SNPs in SLC20A1 is associated with Combined Pituitary Hormone Deficiency and Sodium Lithium Counter transport. Using in silico techniques, we have screened the nsSNPs for their deleterious effect on the structure and function of SLC20A1. Screening with sequence and structure-based tools on 430 nsSNPs, filtered 17 nsSNPs which are deleterious. To evaluate the role of these SNPs, protein modeling and MD simulations were performed. A comparative analysis of model generated with SWISS-MODEL and AlphaFold shows that many residues are in the disallowed region of Ramachandran plot. Since SWISS-MODEL structure has a 25-residue deletion, the AlphaFold structure was used to perform MD simulation for equilibration and structure refinement. Further, to understand perturbation of energetics, we performed in silico mutagenesis and ΔΔG calculation using FoldX on MD refined structures, which yielded SNPs that are neutral (3), destabilizing (12) and stabilizing (2) on protein structure. Furthermore, to elucidate the impact of SNPs on structure, we performed MD simulations to discern the changes in RMSD, Rg, RMSF and LigPlot of interacting residues. RMSF profiles of representative SNPs revealed that A114V (neutral) and T58A (positive) were more flexible & C573F (negative) was more rigid compared to wild type, which is also reflected in the changes in number of local interacting residues in LigPlot and ΔΔG. Taken together, our results show that SNPs can lead to structural perturbations and impact the function of SLC20A1 with potential implications for disease.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- G Siva Sankari
- Centre for Wildlife Studies, Kerala Veterinary and Animal Sciences University, Wayanad, Kerala, India
| | - Remya James
- St. Joseph's College for Women, Alappuzha, Kerala, India
- Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Febby Payva
- St. Joseph's College for Women, Alappuzha, Kerala, India
- Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
| | - Venketesh Sivaramakrishnan
- Disease Biology Lab, Department of Biosciences, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Puttaparthi, Andhra Pradesh, India
| | | | - Subbarao Kanchi
- Department of Physics, Sri Sathya Sai Institute of Higher Learning, Prasanthi Nilayam, Puttaparthi, Andhra Pradesh, India
| | - K S Santhy
- Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India
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2
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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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3
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Jamshidi Parvar S, Hall BA, Shorthouse D. Interpreting the effect of mutations to protein binding sites from large-scale genomic screens. Methods 2024; 222:122-132. [PMID: 38185227 DOI: 10.1016/j.ymeth.2023.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/27/2023] [Accepted: 12/22/2023] [Indexed: 01/09/2024] Open
Abstract
Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.
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Affiliation(s)
| | - Benjamin A Hall
- UCL Department of Medical Physics and Biomedical Engineering, Mallet Place Engineering Building, University College London, Gower Street, London WC1E 6BT, UK
| | - David Shorthouse
- UCL School of Pharmacy, 29-39 Brunswick Square, London WC1N 1AX, UK.
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4
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Parisi G, Piacentini R, Incocciati A, Bonamore A, Macone A, Rupert J, Zacco E, Miotto M, Milanetti E, Tartaglia GG, Ruocco G, Boffi A, Di Rienzo L. Design of protein-binding peptides with controlled binding affinity: the case of SARS-CoV-2 receptor binding domain and angiotensin-converting enzyme 2 derived peptides. Front Mol Biosci 2024; 10:1332359. [PMID: 38250735 PMCID: PMC10797010 DOI: 10.3389/fmolb.2023.1332359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
The development of methods able to modulate the binding affinity between proteins and peptides is of paramount biotechnological interest in view of a vast range of applications that imply designed polypeptides capable to impair or favour Protein-Protein Interactions. Here, we applied a peptide design algorithm based on shape complementarity optimization and electrostatic compatibility and provided the first experimental in vitro proof of the efficacy of the design algorithm. Focusing on the interaction between the SARS-CoV-2 Spike Receptor-Binding Domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) receptor, we extracted a 23-residues long peptide that structurally mimics the major interacting portion of the ACE2 receptor and designed in silico five mutants of such a peptide with a modulated affinity. Remarkably, experimental KD measurements, conducted using biolayer interferometry, matched the in silico predictions. Moreover, we investigated the molecular determinants that govern the variation in binding affinity through molecular dynamics simulation, by identifying the mechanisms driving the different values of binding affinity at a single residue level. Finally, the peptide sequence with the highest affinity, in comparison with the wild type peptide, was expressed as a fusion protein with human H ferritin (HFt) 24-mer. Solution measurements performed on the latter constructs confirmed that peptides still exhibited the expected trend, thereby enhancing their efficacy in RBD binding. Altogether, these results indicate the high potentiality of this general method in developing potent high-affinity vectors for hindering/enhancing protein-protein associations.
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Affiliation(s)
- Giacomo Parisi
- Department of Basic and Applied Sciences for Engineering (SBAI), Università“Sapienza”, Roma, Italy
| | - Roberta Piacentini
- Department of Biochemical Sciences “Alessandro Rossi Fanelli”, Università“Sapienza”, Roma, Italy
| | - Alessio Incocciati
- Department of Biochemical Sciences “Alessandro Rossi Fanelli”, Università“Sapienza”, Roma, Italy
| | - Alessandra Bonamore
- Department of Biochemical Sciences “Alessandro Rossi Fanelli”, Università“Sapienza”, Roma, Italy
| | - Alberto Macone
- Department of Biochemical Sciences “Alessandro Rossi Fanelli”, Università“Sapienza”, Roma, Italy
| | - Jakob Rupert
- Department of Biology and Biotechnologies “Charles Darwin”, Università“Sapienza”, Roma, Italy
- Centre for Human Technologies (CHT), Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Elsa Zacco
- Centre for Human Technologies (CHT), Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Mattia Miotto
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Roma, Italy
| | - Edoardo Milanetti
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Roma, Italy
- Department of Physics, Università“Sapienza”, Roma, Italy
| | - Gian Gaetano Tartaglia
- Department of Biology and Biotechnologies “Charles Darwin”, Università“Sapienza”, Roma, Italy
- Centre for Human Technologies (CHT), Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - Giancarlo Ruocco
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Roma, Italy
- Department of Physics, Università“Sapienza”, Roma, Italy
| | - Alberto Boffi
- Department of Biochemical Sciences “Alessandro Rossi Fanelli”, Università“Sapienza”, Roma, Italy
| | - Lorenzo Di Rienzo
- Center for Life Nano and Neuro Science, Istituto Italiano di Tecnologia (IIT), Roma, Italy
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5
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Xu X, Bonvin AMJJ. DeepRank-GNN-esm: a graph neural network for scoring protein-protein models using protein language model. BIOINFORMATICS ADVANCES 2024; 4:vbad191. [PMID: 38213822 PMCID: PMC10782804 DOI: 10.1093/bioadv/vbad191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/19/2023] [Indexed: 01/13/2024]
Abstract
Motivation Protein-Protein interactions (PPIs) play critical roles in numerous cellular processes. By modelling the 3D structures of the correspond protein complexes valuable insights can be obtained, providing, e.g. starting points for drug and protein design. One challenge in the modelling process is however the identification of near-native models from the large pool of generated models. To this end we have previously developed DeepRank-GNN, a graph neural network that integrates structural and sequence information to enable effective pattern learning at PPI interfaces. Its main features are related to the Position Specific Scoring Matrices (PSSMs), which are computationally expensive to generate, significantly limits the algorithm's usability. Results We introduce here DeepRank-GNN-esm that includes as additional features protein language model embeddings from the ESM-2 model. We show that the ESM-2 embeddings can actually replace the PSSM features at no cost in-, or even better performance on two PPI-related tasks: scoring docking poses and detecting crystal artifacts. This new DeepRank version bypasses thus the need of generating PSSM, greatly improving the usability of the software and opening new application opportunities for systems for which PSSM profiles cannot be obtained or are irrelevant (e.g. antibody-antigen complexes). Availability and implementation DeepRank-GNN-esm is freely available from https://github.com/DeepRank/DeepRank-GNN-esm.
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Affiliation(s)
- Xiaotong Xu
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
| | - Alexandre M J J Bonvin
- Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht 3584 CS, The Netherlands
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6
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Kiani YS, Jabeen I. Challenges of Protein-Protein Docking of the Membrane Proteins. Methods Mol Biol 2024; 2780:203-255. [PMID: 38987471 DOI: 10.1007/978-1-0716-3985-6_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.
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Affiliation(s)
- Yusra Sajid Kiani
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Ishrat Jabeen
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
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7
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Zhu J, Long J, Li X, Lu C, Zhou X, Chen L, Qiu C, Jin Z. Improving the thermal stability and branching efficiency of Pyrococcus horikoshii OT3 glycogen branching enzyme. Int J Biol Macromol 2024; 255:128010. [PMID: 37979752 DOI: 10.1016/j.ijbiomac.2023.128010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/14/2023] [Accepted: 11/08/2023] [Indexed: 11/20/2023]
Abstract
In practical applications, the gelatinisation temperature of starch is high. Most current glycogen branching enzymes (GBEs, EC 2.4.1.18) exhibit optimum activity at moderate or low temperatures and quickly lose their activity at higher temperatures, limiting the application of GBEs in starch modification. Therefore, we used the PROSS strategy combined with PDBePISA analysis of the dimer interface to further improve the heat resistance of hyperthermophilic bacteria Pyrococcus horikoshii OT3 GBE. The results showed that the melting temperature of mutant T508K increased by 3.1 °C compared to wild-type (WT), and the optimum reaction temperature increased by 10 °C for all mutants except V140I. WT almost completely lost its activity after incubation at 95 °C for 60 h, while all of the combined mutants maintained >40 % of their residual activity. Further, the content of the α-1,6 glycosidic bond of corn starch modified by H415W and V140I/H415W was approximately 2.68-fold and 1.92-fold higher than that of unmodified corn starch and corn starch modified by WT, respectively. Additionally, the glucan chains of DP < 13 were significantly increased in mutant modified corn starch. This method has potential for improving the thermal stability of GBE, which can be applied in starch branching in the food industry.
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Affiliation(s)
- Jing Zhu
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi 214122, China
| | - Jie Long
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi 214122, China
| | - Xingfei Li
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi 214122, China
| | - Cheng Lu
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Bioengineering, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China
| | - Xing Zhou
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China
| | - Long Chen
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China
| | - Chao Qiu
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China
| | - Zhengyu Jin
- The State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; School of Food Science and Technology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China; Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, Wuxi 214122, China.
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8
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Rana MM, Nguyen DD. Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation. J Phys Chem Lett 2023; 14:10870-10879. [PMID: 38032742 DOI: 10.1021/acs.jpclett.3c02679] [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: 12/02/2023]
Abstract
Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes. GGL-PPI leverages atom-level graph coloring and multiscale weighted colored geometric subgraphs to capture structural features of biomolecules, demonstrating superior performance on three standard data sets, namely, AB-Bind, SKEMPI 1.0, and SKEMPI 2.0 data sets. The model's efficacy extends to predicting protein thermodynamic stability in a blind test set, providing unbiased predictions for both direct and reverse mutations and showcasing notable generalization. GGL-PPI's precision in predicting changes in binding free energy and stability due to mutations enhances our comprehension of protein complexes, offering valuable insights for drug design endeavors.
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Affiliation(s)
- Md Masud Rana
- Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States
| | - Duc Duy Nguyen
- Department of Mathematics, University of Kentucky, Lexington, Kentucky 40506, United States
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9
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Hernández González JE, de Araujo AS. Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States. J Chem Inf Model 2023; 63:6807-6822. [PMID: 37851531 DOI: 10.1021/acs.jcim.3c00972] [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: 10/20/2023]
Abstract
The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.
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10
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Kouba P, Kohout P, Haddadi F, Bushuiev A, Samusevich R, Sedlar J, Damborsky J, Pluskal T, Sivic J, Mazurenko S. Machine Learning-Guided Protein Engineering. ACS Catal 2023; 13:13863-13895. [PMID: 37942269 PMCID: PMC10629210 DOI: 10.1021/acscatal.3c02743] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/20/2023] [Indexed: 11/10/2023]
Abstract
Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.
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Affiliation(s)
- Petr Kouba
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Faculty of
Electrical Engineering, Czech Technical
University in Prague, Technicka 2, 166 27 Prague 6, Czech Republic
| | - Pavel Kohout
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Faraneh Haddadi
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Anton Bushuiev
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Raman Samusevich
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Jiri Sedlar
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Jiri Damborsky
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Tomas Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Prague 6, Czech Republic
| | - Josef Sivic
- Czech Institute
of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Jugoslavskych partyzanu 1580/3, 160 00 Prague 6, Czech Republic
| | - Stanislav Mazurenko
- Loschmidt
Laboratories, Department of Experimental Biology and RECETOX, Faculty
of Science, Masaryk University, Kamenice 5, 625 00 Brno, Czech
Republic
- International
Clinical Research Center, St. Anne’s
University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
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11
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Asif M, Chiou CC, Hussain MF, Hussain M, Sajid Z, Gulsher M, Raheem A, Khan A, Nasreen N, Kloczkowski A, Hassan M, Iqbal F, Chen CC. Homozygous Mutations in GDAP1 and MFN2 Genes Resulted in Autosomal Recessive Forms of Charcot-Marie-Tooth Disease in Consanguineous Pakistani Families. DNA Cell Biol 2023; 42:697-708. [PMID: 37797217 PMCID: PMC11262584 DOI: 10.1089/dna.2023.0169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/09/2023] [Accepted: 08/23/2023] [Indexed: 10/07/2023] Open
Abstract
Charcot-Marie-Tooth disease (CMT) is a heritable neurodegenerative disease of peripheral nervous system diseases in which more than 100 genes and their mutations are associated. Two consanguineous families Dera Ghazi Khan (PAK-CMT1-DG KHAN) and Layyah (PAK-CMT2-LAYYAH) with multiple CMT-affected subjects were enrolled from Punjab province in Pakistan. Basic epidemiological data were collected for the subjects. Nerve conduction study (NCS) and electromyography (EMG) were performed for the patients. Whole-exome sequencing (WES) followed by Sanger sequencing was applied to report the genetic basic of CMT. The NCS findings revealed that sensory and motor nerve conduction velocities for both families were <38 m/s. EMG presented denervation, neuropathic motor unit potential, and reduced interference pattern of peripheral nerves. WES identified that a novel nonsense mutation (c. 226 G>T) in GADP1 gene and a previously known missense mutation in MFN2 gene (c. 334 G>A) cause CMT4A (Charcot-Marie-Tooth disease type 4A) in the PAK-CMT1-DG KHAN family and CMT2A (Charcot-Marie-Tooth disease type 2A) in the PAK-CMT2-LAYYAH family, respectively. Mutations followed Mendelian pattern with autosomal recessive mode of inheritance. Multiple sequence alignment by Clustal Omega indicated that mutation-containing domain in both genes is highly conserved, and in situ analysis revealed that both mutations are likely to be pathogenic. We reported that a novel nonsense mutation and a previously known missense mutation in GAPD1 gene and MFN2 gene, respectively, cause CMT in consanguineous Pakistani families.
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Affiliation(s)
- Muhammad Asif
- Institute of Molecular Biology and Biotechnology. Bahauddin Zakariya University, Multan, Pakistan
- Institute of Zoology, Bahauddin Zakariya University, Multan, Pakistan
| | - Chien-Chun Chiou
- Department of Dermatology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan
| | | | - Manzoor Hussain
- Orthopedic Unit 1, Nishter Medical University Multan, Pakistan
| | - Zureesha Sajid
- Institute of Molecular Biology and Biotechnology. Bahauddin Zakariya University, Multan, Pakistan
- Department of Biotechnology, Institute of Biochemistry, Biotechnology and Bioinformatics, Baghdad-ul-Jadeed Campus, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Muhammad Gulsher
- Children Hospital and Institute of Child Health, Multan, Pakistan
| | - Afifa Raheem
- Institute of Zoology, Bahauddin Zakariya University, Multan, Pakistan
| | - Adil Khan
- Department of Botany and Zoology, Bacha Khan University, Charsadda, Pakistan
| | - Nasreen Nasreen
- Department of Zoology, Abdul Wali Khan University, Mardan, Pakistan
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, USA
- Department of Pediatrics, The Ohio State University, Columbus, Ohio, USA
| | - Mubashir Hassan
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Furhan Iqbal
- Institute of Zoology, Bahauddin Zakariya University, Multan, Pakistan
| | - Chien-Chin Chen
- Department of Pathology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi, Taiwan
- Department of Cosmetic Science, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
- Ph.D. Program in Translational Medicine, Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, Taichung, Taiwan
- Department of Biotechnology and Bioindustry Sciences, College of Bioscience and Biotechnology, National Cheng Kung University, Tainan, Taiwan
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12
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Salgado RC, Gomes LN, França TT, da Silva Napoleão SM, Barreiros LA, de Oliveira TS, Ishizuka EK, Ferreira JFS, Condino-Neto A. Disseminated Histoplasmosis in a Brazilian Patient with G6PD Deficiency Caused by Class I Variant. J Clin Immunol 2023; 43:1796-1798. [PMID: 37814085 DOI: 10.1007/s10875-023-01599-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/01/2023] [Indexed: 10/11/2023]
Affiliation(s)
- Ranieri Coelho Salgado
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil.
| | - Lillian Nunes Gomes
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil
| | - Tábata Takahashi França
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil
| | | | - Lucila Akune Barreiros
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil
| | - Tiago Santos de Oliveira
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil
| | - Edson Kiyotaka Ishizuka
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil
| | | | - Antonio Condino-Neto
- Department of Immunology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, Brazil.
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13
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Berber I, Erten C, Kazan H. Predator: Predicting the Impact of Cancer Somatic Mutations on Protein-Protein Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3163-3172. [PMID: 37030791 DOI: 10.1109/tcbb.2023.3262119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.
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14
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Wang Y, Zhang D, Huang L, Zhang Z, Shi Q, Hu J, He G, Guo X, Shi H, Liang L. Uncovering the interactions between PME and PMEI at the gene and protein levels: Implications for the design of specific PMEI. J Mol Model 2023; 29:286. [PMID: 37610510 DOI: 10.1007/s00894-023-05644-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/30/2023] [Indexed: 08/24/2023]
Abstract
CONTEXT Pectin methylesterase inhibitor (PMEI) can specifically bind and inhibit the activity of pectin methylesterase (PME), which has been widely used in fruit and vegetable juice processing. However, the limited three-dimensional structure, unclear action mechanism, low thermal stability and biological activity of PMEI severely limited its application. In this work, molecular recognition and conformational changes of PME and PMEI were analyzed by various molecular simulation methods. Then suggestions were proposed for improving thermal stability and affinity maturation of PMEI through semi-rational design. METHODS Phylogenetic trees of PME and PMEI were established using the Maximum likelihood (ML) method. The results show that PME and PMEI have good sequence and structure conservation in various plants, and the simulated data can be widely adopted. In this work, MD simulations were performed using AMBER20 package and ff14SB force field. Protein interaction analysis indicates that H-bonds, van der Waals forces, and the salt bridge formed of K224 with ID116 are the main driving forces for mutual molecular recognition of PME and PMEI. According to the analyses of free energy landscape (FEL), conformational cluster, and motion, the association with PMEI greatly disrupts PME's dispersed functional motion mode and biological function. By monitoring the changes of residue contact number and binding free energy, IG35M/ IG35R: IT93F and IT113W/ IT113W: ID116W mutations contribute to thermal stability and affinity maturation of the PME-PMEI complex system, respectively. This work reveals the interaction between PME and PMEI at the gene and protein levels and provides options for modifying specific PMEI.
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Affiliation(s)
- Yueteng Wang
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Derong Zhang
- School of Marxism, Chengdu Vocational & Technical College of Industry, Chengdu, 610081, China
| | - Lifen Huang
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Zelan Zhang
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Quanshan Shi
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Jianping Hu
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Gang He
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Xiaoqiang Guo
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China
| | - Hang Shi
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou, 213001, China.
| | - Li Liang
- Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, School of Pharmacy, Chengdu University, Chengdu, 610106, China.
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15
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Biswas G, Mukherjee D, Dutta N, Ghosh P, Basu S. EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations. J Mol Model 2023; 29:239. [PMID: 37423912 DOI: 10.1007/s00894-023-05626-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023]
Abstract
CONTEXT Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆Gbinding, Kd) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆Gbinding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆Gbinding without involving intermediate structural descriptors. METHODS Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆Gbinding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Prfmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.
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Affiliation(s)
- Gargi Biswas
- Department of Chemistry and Structural Biology, Weizmann Institute of Science, 7610001, Rehovot, Israel
| | - Debasish Mukherjee
- Institute of Molecular Biology gGmbH (IMB), Ackermannweg 4, 55128, Mainz, Germany
| | - Nalok Dutta
- Dept of Biochemical Engineering, Faculty of Engineering Science, University College London, London, WC1E 6BT, UK
| | - Prithwi Ghosh
- Department of Botany, Narajole Raj College, Vidyasagar University, Midnapore, 721211, India
| | - Sankar Basu
- Department of Microbiology, Asutosh College (affiliated with University of Calcutta), 92, Shyama Prasad Mukherjee Rd, Bhowanipore, 700026, Kolkata, India.
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16
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Sergeeva AP, Katsamba PS, Liao J, Sampson JM, Bahna F, Mannepalli S, Morano NC, Shapiro L, Friesner RA, Honig B. Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity. J Mol Biol 2023:168187. [PMID: 37355034 DOI: 10.1016/j.jmb.2023.168187] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 06/26/2023]
Abstract
The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.
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Affiliation(s)
- Alina P Sergeeva
- Department of Systems Biology, Columbia University Medical Center, New York, NY 10032
| | - Phinikoula S Katsamba
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Junzhuo Liao
- Department of Chemistry, Columbia University, New York, NY 10027, USA
| | - Jared M Sampson
- Department of Chemistry, Columbia University, New York, NY 10027, USA; Schrödinger, Inc., New York, NY 10036, USA
| | - Fabiana Bahna
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Seetha Mannepalli
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Nicholas C Morano
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Lawrence Shapiro
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.
| | | | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY 10032; Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA; Department of Medicine, Columbia University, New York, NY 10032.
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17
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Purisima EO, Corbeil CR, Gaudreault F, Wei W, Deprez C, Sulea T. Solvated interaction energy: from small-molecule to antibody drug design. Front Mol Biosci 2023; 10:1210576. [PMID: 37351549 PMCID: PMC10282643 DOI: 10.3389/fmolb.2023.1210576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 05/26/2023] [Indexed: 06/24/2023] Open
Abstract
Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.
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18
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Desautels TA, Arrildt KT, Zemla AT, Lau EY, Zhu F, Ricci D, Cronin S, Zost SJ, Binshtein E, Scheaffer SM, Dadonaite B, Petersen BK, Engdahl TB, Chen E, Handal LS, Hall L, Goforth JW, Vashchenko D, Nguyen S, Weilhammer DR, Lo JKY, Rubinfeld B, Saada EA, Weisenberger T, Lee TH, Whitener B, Case JB, Ladd A, Silva MS, Haluska RM, Grzesiak EA, Earnhart CG, Hopkins S, Bates TW, Thackray LB, Segelke BW, Lillo AM, Sundaram S, Bloom J, Diamond MS, Crowe JE, Carnahan RH, Faissol DM. Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2022.10.21.513237. [PMID: 36324800 PMCID: PMC9628197 DOI: 10.1101/2022.10.21.513237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3, but also revealed how quickly viral escape can curtail effective options4,5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including Evusheld™ and its constituent, cilgavimab4,6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protection in vivo against the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.
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Affiliation(s)
- Thomas A Desautels
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Kathryn T Arrildt
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Adam T Zemla
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Edmond Y Lau
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Fangqiang Zhu
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Dante Ricci
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | | | - Seth J Zost
- Vanderbilt Vaccine Center, Nashville, TN 37232, USA
| | | | - Suzanne M Scheaffer
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Bernadeta Dadonaite
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Brenden K Petersen
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | | | - Elaine Chen
- Vanderbilt Vaccine Center, Nashville, TN 37232, USA
| | | | - Lynn Hall
- Vanderbilt Vaccine Center, Nashville, TN 37232, USA
| | - John W Goforth
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Denis Vashchenko
- Applications Simulations and Quality Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sam Nguyen
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Dina R Weilhammer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Jacky Kai-Yin Lo
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Bonnee Rubinfeld
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Edwin A Saada
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Tracy Weisenberger
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Tek-Hyung Lee
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Bradley Whitener
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - James B Case
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Alexander Ladd
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Mary S Silva
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Rebecca M Haluska
- Applications Simulations and Quality Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Emilia A Grzesiak
- Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Christopher G Earnhart
- Joint Program Executive Office for Chemical, Biological, Radiological, and Nuclear Defense, US, Department of Defense, Frederick, MD 21703, USA
| | | | - Thomas W Bates
- Global Security Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Larissa B Thackray
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Brent W Segelke
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | | | - Shivshankar Sundaram
- Center for Bioengineering, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Jesse Bloom
- Basic Sciences Division and Computational Biology Program, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Howard Hughes Medical Institute, Seattle, WA 98195, USA
| | - Michael S Diamond
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Molecular Microbiology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - James E Crowe
- Vanderbilt Vaccine Center, Nashville, TN 37232, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Robert H Carnahan
- Vanderbilt Vaccine Center, Nashville, TN 37232, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Daniel M Faissol
- Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
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19
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Kim Y, Yoon T, Park WB, Na S. Predicting mechanical properties of silk from its amino acid sequences via machine learning. J Mech Behav Biomed Mater 2023; 140:105739. [PMID: 36871478 DOI: 10.1016/j.jmbbm.2023.105739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/12/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023]
Abstract
The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, is that their mechanical properties are significantly dependent on the amino acid sequence. Numerous studies have been conducted to determine the specific relationship between the amino acid sequence of silk and its mechanical properties. Still, the relationship between the amino acid sequence of silk and its mechanical properties is yet to be clarified. Other fields have adopted machine learning (ML) to establish a relationship between the inputs, such as the ratio of different input material compositions and the resulting mechanical properties. We have proposed a method to convert the amino acid sequence into numerical values for input and succeeded in predicting the mechanical properties of silk from its amino acid sequences. Our study sheds light on predicting mechanical properties of silk fiber from respective amino acid sequences.
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20
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Jung Y, Geng C, Bonvin AMJJ, Xue LC, Honavar VG. MetaScore: A Novel Machine-Learning-Based Approach to Improve Traditional Scoring Functions for Scoring Protein-Protein Docking Conformations. Biomolecules 2023; 13:121. [PMID: 36671507 PMCID: PMC9855734 DOI: 10.3390/biom13010121] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 01/11/2023] Open
Abstract
Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking-the so-called scoring problem-still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein-protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein-protein interfacial features and by using ensemble methods to combine multiple scoring functions.
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Affiliation(s)
- Yong Jung
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Cunliang Geng
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Li C. Xue
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
| | - Vasant G. Honavar
- Bioinformatics & Genomics Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
- Artificial Intelligence Research Laboratory, Pennsylvania State University, University Park, PA 16802, USA
- Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Clinical and Translational Sciences Institute, Pennsylvania State University, University Park, PA 16802, USA
- College of Information Sciences & Technology, Pennsylvania State University, University Park, PA 16802, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
- Center for Big Data Analytics and Discovery Informatics, Pennsylvania State University, University Park, PA 16823, USA
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21
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Sora V, Laspiur AO, Degn K, Arnaudi M, Utichi M, Beltrame L, De Menezes D, Orlandi M, Stoltze UK, Rigina O, Sackett PW, Wadt K, Schmiegelow K, Tiberti M, Papaleo E. RosettaDDGPrediction for high-throughput mutational scans: From stability to binding. Protein Sci 2023; 32:e4527. [PMID: 36461907 PMCID: PMC9795540 DOI: 10.1002/pro.4527] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/25/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022]
Abstract
Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.
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Affiliation(s)
- Valentina Sora
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Adrian Otamendi Laspiur
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Matteo Arnaudi
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Mattia Utichi
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Ludovica Beltrame
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Dayana De Menezes
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Matteo Orlandi
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Ulrik Kristoffer Stoltze
- Department of Clinical GeneticsCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Department of Pediatrics and Adolescent MedicineUniversity Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Olga Rigina
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Peter Wad Sackett
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
| | - Karin Wadt
- Department of Clinical GeneticsCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Kjeld Schmiegelow
- Department of Pediatrics and Adolescent MedicineUniversity Hospital RigshospitaletCopenhagenDenmark
- Institute of Clinical Medicine, Faculty of MedicineUniversity of CopenhagenCopenhagenDenmark
| | - Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Society Research CenterCopenhagenDenmark
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and TechnologyTechnical University of DenmarkLyngbyDenmark
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22
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Conti S, Karplus M. A Computational Framework for Determining the Breadth of Antibodies Against Highly Mutable Pathogens. Methods Mol Biol 2023; 2552:399-408. [PMID: 36346605 DOI: 10.1007/978-1-0716-2609-2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Highly mutable pathogens pose daunting challenges for antibody design. The usual criteria of high potency and specificity are often insufficient to design antibodies that provide long-lasting protection. This is due, in part, to the ability of the pathogen to rapidly acquire mutations that permit them to evade the designed antibodies. To overcome these limitations, design of antibodies with a larger neutralizing breadth can be pursued. Such broadly neutralizing antibodies (bnAbs) should remain targeted to a specific epitope, yet show robustness against pathogen mutability, thereby neutralizing a higher number of antigens. This is particularly important for highly mutable pathogens, like the influenza virus and the human immunodeficiency virus (HIV). The protocol describes a method for computing the "breadth" of a given antibody, an essential aspect of antibody design.
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Affiliation(s)
- Simone Conti
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
| | - Martin Karplus
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA.
- Laboratoire de Chimie Biophysique, ISIS, Université de Strasbourg, Strasbourg, France.
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23
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Bhattacharya S, Chatterji S, Chandy M, Mahajan AY, Goel G, Mishra D, Vivek P, Das P, Mandal S, Chugani A, Mittal A, Perumal RC, Ramprasad VL, Gupta R. Molecular epidemiology of SARS-CoV-2 in healthcare workers and identification of viral genomic correlates of transmissibility and vaccine break through infection: A retrospective observational study from a cancer hospital in eastern India. Indian J Med Microbiol 2023; 41:104-110. [PMID: 36244851 PMCID: PMC9558092 DOI: 10.1016/j.ijmmb.2022.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 09/01/2022] [Accepted: 09/25/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE Despite COVID vaccination with ChAdOx1 ncov-19 (COVISHIELD®) (ChAdOx1 ncov-19) a large number of healthcare workers (HCWs) were getting infected in wave-2 of the pandemic in a cancer hospital of India. It was important therefore to determine the genotypes responsible for vaccine breakthrough infections. METHODS & OBJECTIVES Retrospective observational study of HCWs. Whole genome sequencing of SARS CoV-2 using Illumina NovaSeq was done. Mutations from both waves were compared to identify genomic correlates of transmissibility and vaccine breakthrough infections. RESULTS Vaccine breakthrough infections were seen in 127 HCWs out of 1806 fully vaccinated staff (7.03%). Median number of HCWs infected per day in wave-1 was 0.92 versus 3.25 in wave-2. Majority of wave-1 samples belonged to B.1 and B.1.1 lineage. Variant of concern- Delta variant (90%), and variant of interest- Kappa variant (10%), was seen in only wave-2 samples. Total mutation observed in wave-2 samples (median = 44) was 1.8 times than wave-1 sample (median = 24). Spike protein in wave-2 samples had 13 non-synonymous mutation as compared to 8 seen in wave-1 samples. E484Q-vaccine escape mutant was detected in five samples of wave-2; T478K - highly infectious mutation was seen in 31 samples of wave-2. We identified a novelcoding disruptive in-frame deletion (c.467_472delAGTTCA, p. Glu156_Arg158delinsGly) in the Spike protein. This mutation was seen only in wave-2 (78%, n = 39) samples. CONCLUSION The circulating virus strains in wave-2 infections demonstrated a greater degree of infectivity. There was a significant change in the genotypes observed in wave-1 and wave-2 infections along with almost twice the number of mutations. We noted that vaccine breakthrough infections (although mostly mild).
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Affiliation(s)
- Sanjay Bhattacharya
- Department of Microbiology, Tata Medical Center, 14 MAR, Kolkata, 700160, India
| | - Soumyadip Chatterji
- Department of Infectious Diseases, Tata Medical Center, 14 MAR, Kolkata, 700160, India.
| | - Mammen Chandy
- Department of Clinical Hematology, Tata Medical Center, 14 MAR, Kolkata, 700160, India
| | | | - Gaurav Goel
- Department of Microbiology, Tata Medical Center, 14 MAR, Kolkata, 700160, India
| | - Deepak Mishra
- Department of Laboratory Sciences, Tata Medical Center, Kolkata, India
| | - Priyanka Vivek
- Department of Staff Health, Tata Medical Center, 14 MAR, Kolkata, 700160, India
| | - Parijat Das
- Department of Microbiology, Tata Medical Center, 14 MAR, Kolkata, 700160, India
| | - Sudipto Mandal
- Department of Microbiology, Tata Medical Center, 14 MAR, Kolkata, 700160, India
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24
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Réau M, Renaud N, Xue LC, Bonvin AMJJ. DeepRank-GNN: a graph neural network framework to learn patterns in protein-protein interfaces. Bioinformatics 2022; 39:6845451. [PMID: 36420989 PMCID: PMC9805592 DOI: 10.1093/bioinformatics/btac759] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 10/19/2022] [Accepted: 11/23/2022] [Indexed: 11/25/2022] Open
Abstract
MOTIVATION Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using convolutional neural network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. RESULTS We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNN's performance on two applications using a dedicated graph interaction neural network: (i) the scoring of docking poses and (ii) the discriminating of biological and crystal interfaces. In addition to the highly competitive performance obtained in those tasks as compared to state-of-the-art methods, we show a significant improvement in speed and storage requirement using DeepRank-GNN as compared to DeepRank. AVAILABILITY AND IMPLEMENTATION DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Li C Xue
- Center for Molecular and Biomolecular Informatics, Radboudumc, Nijmegen 6525 GA, The Netherlands
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25
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The Importance of Charge Transfer and Solvent Screening in the Interactions of Backbones and Functional Groups in Amino Acid Residues and Nucleotides. Int J Mol Sci 2022; 23:ijms232113514. [PMID: 36362296 PMCID: PMC9654426 DOI: 10.3390/ijms232113514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 10/26/2022] [Accepted: 10/28/2022] [Indexed: 11/06/2022] Open
Abstract
Quantum mechanical (QM) calculations at the level of density-functional tight-binding are applied to a protein–DNA complex (PDB: 2o8b) consisting of 3763 atoms, averaging 100 snapshots from molecular dynamics simulations. A detailed comparison of QM and force field (Amber) results is presented. It is shown that, when solvent screening is taken into account, the contributions of the backbones are small, and the binding of nucleotides in the double helix is governed by the base–base interactions. On the other hand, the backbones can make a substantial contribution to the binding of amino acid residues to nucleotides and other residues. The effect of charge transfer on the interactions is also analyzed, revealing that the actual charge of nucleotides and amino acid residues can differ by as much as 6 and 8% from the formal integer charge, respectively. The effect of interactions on topological models (protein -residue networks) is elucidated.
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26
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Conformational Stability and Denaturation Processes of Proteins Investigated by Electrophoresis under Extreme Conditions. Molecules 2022; 27:molecules27206861. [PMID: 36296453 PMCID: PMC9610776 DOI: 10.3390/molecules27206861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 11/17/2022] Open
Abstract
The functional structure of proteins results from marginally stable folded conformations. Reversible unfolding, irreversible denaturation, and deterioration can be caused by chemical and physical agents due to changes in the physicochemical conditions of pH, ionic strength, temperature, pressure, and electric field or due to the presence of a cosolvent that perturbs the delicate balance between stabilizing and destabilizing interactions and eventually induces chemical modifications. For most proteins, denaturation is a complex process involving transient intermediates in several reversible and eventually irreversible steps. Knowledge of protein stability and denaturation processes is mandatory for the development of enzymes as industrial catalysts, biopharmaceuticals, analytical and medical bioreagents, and safe industrial food. Electrophoresis techniques operating under extreme conditions are convenient tools for analyzing unfolding transitions, trapping transient intermediates, and gaining insight into the mechanisms of denaturation processes. Moreover, quantitative analysis of electrophoretic mobility transition curves allows the estimation of the conformational stability of proteins. These approaches include polyacrylamide gel electrophoresis and capillary zone electrophoresis under cold, heat, and hydrostatic pressure and in the presence of non-ionic denaturing agents or stabilizers such as polyols and heavy water. Lastly, after exposure to extremes of physical conditions, electrophoresis under standard conditions provides information on irreversible processes, slow conformational drifts, and slow renaturation processes. The impressive developments of enzyme technology with multiple applications in fine chemistry, biopharmaceutics, and nanomedicine prompted us to revisit the potentialities of these electrophoretic approaches. This feature review is illustrated with published and unpublished results obtained by the authors on cholinesterases and paraoxonase, two physiologically and toxicologically important enzymes.
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27
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Liu X, Feng H, Wu J, Xia K. Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation. J Chem Inf Model 2022; 62:3961-3969. [PMID: 36040839 DOI: 10.1021/acs.jcim.2c00580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G1, G) can be generated for the graph representation G of protein-protein complex by using different graphs G1, which reveal G1-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G1, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.
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Affiliation(s)
- Xiang Liu
- Chern Institute of Mathematics and LPMC, Nankai University, Tianjin, China, 300071.,Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
| | - Huitao Feng
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371.,Mathematical Science Research Center, Chongqing University of Technology, Chongqing, China, 400054
| | - Jie Wu
- Yanqi Lake Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing, China,101408
| | - Kelin Xia
- Division of Mathematical Sciences, School of Physical and Mathematical Sciences Nanyang Technological University, Singapore 637371
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28
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Abstract
Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.
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29
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Conti S, Ovchinnikov V, Karplus M. ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning. J Comput Chem 2022; 43:1747-1757. [PMID: 35930347 DOI: 10.1002/jcc.26974] [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: 03/07/2022] [Revised: 07/01/2022] [Accepted: 07/13/2022] [Indexed: 11/07/2022]
Abstract
This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC50 ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC50 of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.
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Affiliation(s)
- Simone Conti
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Victor Ovchinnikov
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Martin Karplus
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts, USA.,Laboratoire de Chimie Biophysique, Institut de Science et d'Ingénierie Supramoléculaires, Université de Strasbourg, Strasbourg, France
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30
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Beshnova D, Fang Y, Du M, Sun Y, Du F, Ye J, Chen ZJ, Li B. Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies. Comput Struct Biotechnol J 2022; 20:2212-2222. [PMID: 35530743 PMCID: PMC9059344 DOI: 10.1016/j.csbj.2022.04.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 12/03/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide as a severe pandemic and caused enormous global health and economical damage. Since December 2019, more than 197 million cases have been reported, causing 4.2 million deaths. In the settings of pandemic it is an urgent necessity for the development of an effective COVID-19 treatment. While in-vitro screening of hundreds of antibodies isolated from convalescent patients is challenging due to its high cost, use of computational methods may provide an attractive solution in selecting the top candidates. Here, we developed a computational approach (SARS-AB) for binding prediction of spike protein SARS-CoV-2 with monoclonal antibodies. We validated our approach using existing structures in the protein data bank (PDB), and demonstrated its prediction power in antibody-spike protein binding prediction. We further tested its performance using antibody sequences from the literature where crystal structure is not available, and observed a high prediction accuracy (AUC = 99.6%). Finally, we demonstrated that SARS-AB can be used to design effective antibodies against novel SARS-CoV-2 mutants that might escape the current antibody protections. We believe that SARS-AB can significantly accelerate the discovery of neutralizing antibodies against SARS-CoV-2 and its mutants.
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Affiliation(s)
- Daria Beshnova
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yan Fang
- Department of Molecular Biology, USA
| | | | - Yehui Sun
- Department of Molecular Biology, USA
| | - Fenghe Du
- Department of Molecular Biology, USA
| | - Jianfeng Ye
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | | | - Bo Li
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA
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31
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Ghadie MA, Xia Y. Are transient protein-protein interactions more dispensable? PLoS Comput Biol 2022; 18:e1010013. [PMID: 35404956 PMCID: PMC9000134 DOI: 10.1371/journal.pcbi.1010013] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 03/11/2022] [Indexed: 12/12/2022] Open
Abstract
Protein-protein interactions (PPIs) are key drivers of cell function and evolution. While it is widely assumed that most permanent PPIs are important for cellular function, it remains unclear whether transient PPIs are equally important. Here, we estimate and compare dispensable content among transient PPIs and permanent PPIs in human. Starting with a human reference interactome mapped by experiments, we construct a human structural interactome by building three-dimensional structural models for PPIs, and then distinguish transient PPIs from permanent PPIs using several structural and biophysical properties. We map common mutations from healthy individuals and disease-causing mutations onto the structural interactome, and perform structure-based calculations of the probabilities for common mutations (assumed to be neutral) and disease mutations (assumed to be mildly deleterious) to disrupt transient PPIs and permanent PPIs. Using Bayes' theorem we estimate that a similarly small fraction (<~20%) of both transient and permanent PPIs are completely dispensable, i.e., effectively neutral upon disruption. Hence, transient and permanent interactions are subject to similarly strong selective constraints in the human interactome.
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Affiliation(s)
| | - Yu Xia
- Department of Bioengineering, McGill University, Montreal, Canada
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32
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Tiberti M, Terkelsen T, Degn K, Beltrame L, Cremers TC, da Piedade I, Di Marco M, Maiani E, Papaleo E. MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles. Brief Bioinform 2022; 23:6552273. [PMID: 35323860 DOI: 10.1093/bib/bbac074] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/28/2022] [Accepted: 02/16/2022] [Indexed: 12/26/2022] Open
Abstract
Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.
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Affiliation(s)
- Matteo Tiberti
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Thilde Terkelsen
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Kristine Degn
- Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800, Lyngby, Denmark
| | - Ludovica Beltrame
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Tycho Canter Cremers
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Isabelle da Piedade
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Miriam Di Marco
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Emiliano Maiani
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark
| | - Elena Papaleo
- Cancer Structural Biology, Danish Cancer Society Research Center, 2100, Copenhagen, Denmark.,Cancer Systems Biology, Section for Bioinformatics, Department of Health and Technology, Technical University of Denmark, 2800, Lyngby, Denmark.,Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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33
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Xiong D, Lee D, Li L, Zhao Q, Yu H. Implications of disease-related mutations at protein-protein interfaces. Curr Opin Struct Biol 2022; 72:219-225. [PMID: 34959033 PMCID: PMC8863207 DOI: 10.1016/j.sbi.2021.11.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 02/03/2023]
Abstract
Protein-protein interfaces have been attracting great attention owing to their critical roles in protein-protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations. Besides, as more large-scale experimental data become available, various computational approaches have been proposed to advance our understanding of disease mutations from the data. Here, we overview recent advances in characteristics of disease-related mutations at protein-protein interfaces, mutation effects on protein interactions, and investigation of mutations on specific diseases.
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Affiliation(s)
- Dapeng Xiong
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Dongjin Lee
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Le Li
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Qiuye Zhao
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Haiyuan Yu
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA,Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
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Bozdaganyan ME, Shaitan KV, Kirpichnikov MP, Sokolova OS, Orekhov PS. Computational Analysis of Mutations in the Receptor-Binding Domain of SARS-CoV-2 Spike and Their Effects on Antibody Binding. Viruses 2022; 14:v14020295. [PMID: 35215888 PMCID: PMC8874930 DOI: 10.3390/v14020295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Currently, SARS-CoV-2 causing coronavirus disease 2019 (COVID-19) is responsible for one of the most deleterious pandemics of our time. The interaction between the ACE2 receptors at the surface of human cells and the viral Spike (S) protein triggers the infection, making the receptor-binding domain (RBD) of the SARS-CoV-2 S-protein a focal target for the neutralizing antibodies (Abs). Despite the recent progress in the development and deployment of vaccines, the emergence of novel variants of SARS-CoV-2 insensitive to Abs produced in response to the vaccine administration and/or monoclonal ones represent a potential danger. Here, we analyzed the diversity of neutralizing Ab epitopes and assessed the possible effects of single and multiple mutations in the RBD of SARS-CoV-2 S-protein on its binding affinity to various antibodies and the human ACE2 receptor using bioinformatics approaches. The RBD-Ab complexes with experimentally resolved structures were grouped into four clusters with distinct features at sequence and structure level. The performed computational analysis indicates that while single amino acid replacements in RBD may only cause partial impairment of the Abs binding, moreover, limited to specific epitopes, the variants of SARS-CoV-2 with multiple mutations, including some which were already detected in the population, may potentially result in a much broader antigenic escape. Further analysis of the existing RBD variants pointed to the trade-off between ACE2 binding and antigenic escape as a key limiting factor for the emergence of novel SAR-CoV-2 strains, as the naturally occurring mutations in RBD tend to reduce its binding affinity to Abs but not to ACE2. The results provide guidelines for further experimental studies aiming to identify high-risk RBD mutations that allow for an antigenic escape.
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Affiliation(s)
- Marine E. Bozdaganyan
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (M.E.B.); (K.V.S.); (M.P.K.)
- N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119991 Moscow, Russia
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen 518172, China
| | - Konstantin V. Shaitan
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (M.E.B.); (K.V.S.); (M.P.K.)
- N.N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119991 Moscow, Russia
| | - Mikhail P. Kirpichnikov
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (M.E.B.); (K.V.S.); (M.P.K.)
| | - Olga S. Sokolova
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (M.E.B.); (K.V.S.); (M.P.K.)
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen 518172, China
- Correspondence: (O.S.S.); (P.S.O.)
| | - Philipp S. Orekhov
- Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia; (M.E.B.); (K.V.S.); (M.P.K.)
- Faculty of Biology, Shenzhen MSU-BIT University, Shenzhen 518172, China
- Institute of Personalized Medicine, Sechenov University, 119146 Moscow, Russia
- Correspondence: (O.S.S.); (P.S.O.)
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35
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Zacharias M. Match_Motif: A rapid computational tool to assist in protein-protein interaction design. Protein Sci 2022; 31:147-157. [PMID: 34648221 PMCID: PMC8740833 DOI: 10.1002/pro.4208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 11/12/2022]
Abstract
In order to generate protein assemblies with a desired function, the rational design of protein-protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein-protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.
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Affiliation(s)
- Martin Zacharias
- Center of Functional Protein AssembliesTechnical University of MunichGarchingGermany
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36
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Renaud N, Geng C, Georgievska S, Ambrosetti F, Ridder L, Marzella DF, Réau MF, Bonvin AMJJ, Xue LC. DeepRank: a deep learning framework for data mining 3D protein-protein interfaces. Nat Commun 2021; 12:7068. [PMID: 34862392 PMCID: PMC8642403 DOI: 10.1038/s41467-021-27396-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 11/12/2021] [Indexed: 11/08/2022] Open
Abstract
Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology.
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Affiliation(s)
- Nicolas Renaud
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Cunliang Geng
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Sonja Georgievska
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Francesco Ambrosetti
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Lars Ridder
- Netherlands eScience Center, Science Park 140, 1098 XG, Amsterdam, The Netherlands
| | - Dario F Marzella
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525, Nijmegen, GA, The Netherlands
| | - Manon F Réau
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands.
| | - Li C Xue
- Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Padualaan 8, 3584, Utrecht, CH, The Netherlands.
- Center for Molecular and Biomolecular Informatics, Radboudumc, Greet Grooteplein 26-28, 6525, Nijmegen, GA, The Netherlands.
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37
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Structural Modelling of KCNQ1 and KCNH2 Double Mutant Proteins, Identified in Two Severe Long QT Syndrome Cases, Reveals New Insights into Cardiac Channelopathies. Int J Mol Sci 2021; 22:ijms222312861. [PMID: 34884666 PMCID: PMC8657475 DOI: 10.3390/ijms222312861] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 10/13/2021] [Accepted: 10/14/2021] [Indexed: 11/17/2022] Open
Abstract
Congenital long QT syndrome (LQTS) is a cardiac channelopathy characterized by a prolongation of the QT interval and T-wave abnormalities, caused, in most cases, by mutations in KCNQ1, KCNH2, and SCN5A. Although the predominant pattern of LQTS inheritance is autosomal dominant, compound heterozygous mutations in genes encoding potassium channels have been reported, often with early disease onset and more severe phenotypes. Since the molecular mechanisms underlying severe phenotypes in carriers of compound heterozygous mutations are unknown, it is possible that these compound mutations lead to synergistic or additive alterations to channel structure and function. In this study, all-atom molecular dynamic simulations of KCNQ1 and hERG channels were carried out, including wild-type and channels with compound mutations found in two patients with severe LQTS phenotypes and limited family history of the disease. Because channels can likely incorporate different subunit combinations from different alleles, there are multiple possible configurations of ion channels in LQTS patients. This analysis allowed us to establish the structural impact of different configurations of mutant channels in the activated/open state. Our data suggest that channels with these mutations show moderate changes in folding energy (in most cases of stabilizing character) and changes in channel mobility and volume, differentiating them from each other and from WT. This would indicate possible alterations in K+ ion flow. Hetero-tetrameric mutant channels showed intermediate structural and volume alterations vis-à-vis homo-tetrameric channels. These findings support the hypothesis that hetero-tetrameric channels in patients with compound heterozygous mutations do not necessarily lead to synergistic structural alterations.
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38
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Zahradník J, Schreiber G. Protein Engineering in the Design of Protein-Protein Interactions: SARS-CoV-2 Inhibitors as a Test Case. Biochemistry 2021; 60:3429-3435. [PMID: 34196543 PMCID: PMC8613841 DOI: 10.1021/acs.biochem.1c00356] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/01/2021] [Indexed: 11/28/2022]
Abstract
The formation of specific protein-protein interactions (PPIs) drive most biological processes. Malfunction of such interactions is the molecular driver of many diseases. Our ability to engineer existing PPIs or create new ones has become a vital research tool. In addition, engineered proteins with new or altered interactions are among the most critical drugs that have been developed in recent years. These include antibodies, cytokines, inhibitors, and others. Here, we provide a perspective on the current status of the methods used to engineer new or altered PPIs. The emergence of the COVID-19 pandemic, which resulted in a worldwide quest to develop specific PPI inhibitors as drugs, provided an up-to-date and state-of-the-art status report on the methodologies for engineering PPIs targeting the interaction of the viral spike protein with its cellular target, ACE2. Multiple, very high affinity binders were generated within a few months using in vitro evolution by itself, or in combination with computational design. The different experimental and computational methods used to block this interaction provide a road map for the future of PPI engineering.
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Affiliation(s)
- Jiří Zahradník
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Gideon Schreiber
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
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39
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Chen C, Boorla VS, Banerjee D, Chowdhury R, Cavener VS, Nissly RH, Gontu A, Boyle NR, Vandegrift K, Nair MS, Kuchipudi SV, Maranas CD. Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2. Proc Natl Acad Sci U S A 2021; 118:e2106480118. [PMID: 34588290 PMCID: PMC8594574 DOI: 10.1073/pnas.2106480118] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2021] [Indexed: 01/22/2023] Open
Abstract
The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π-π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single-amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single- and multiple-amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.
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Affiliation(s)
- Chen Chen
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Veda Sheersh Boorla
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Deepro Banerjee
- The Bioinformatics and Genomics Program, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Ratul Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802
| | - Victoria S Cavener
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Ruth H Nissly
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Abhinay Gontu
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Nina R Boyle
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Kurt Vandegrift
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802
| | - Meera Surendran Nair
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
| | - Suresh V Kuchipudi
- Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802;
- Animal Diagnostic Laboratory, Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA 16802
- Center for Infectious Disease Dynamics, The Pennsylvania State University, University Park, PA 16802
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802;
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40
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Queiroz MCV, Douin M, Sato ME, Tixier MS. Molecular variation of the cytochrome b DNA and protein sequences in Phytoseiulus macropilis and P. persimilis (Acari: Phytoseiidae) reflect population differentiation. EXPERIMENTAL & APPLIED ACAROLOGY 2021; 84:687-701. [PMID: 34324135 DOI: 10.1007/s10493-021-00648-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
Several phytoseiid mite species are important natural enemies used in biological control strategies. In the present study, Cytb mtDNA sequences of various populations of two species, Phytoseiulus macropolis and P. persimilis, were compared to determine whether the specimens collected in Brazil could belong to P. persimilis as this latter species is reported in South America but not in Brazil. The Cytb marker was used because of its high evolution rate, assumed to capture intraspecific variation. No overlap between intra- and interspecific distances was observed but the distances were quite low for interspecific variation. This can be due to the particular biology of Phytoseiulus species and this shows the difficulty to apply a universal threshold in genetic distances to conclude about the existence of one or several species. Cytb mtDNA sequences were also considered to assess intraspecific variation. The DNA sequences of P. persimilis populations were very similar, probably because they all originated from the West Palearctic region or because of a prevalence of commercialized specimens in natura. For P. macropilis, higher genetic distances were observed and differentiation was noted according to geographic location and, to a smaller extent, pyrethroid resistance. To determine how DNA variation might impact the protein function (CytB fragment considered), the amino acid compositions of the populations studied were compared. No diagnostic mutation was observed between pyrethroid resistant and susceptible populations, whereas four mutations were identified between populations of P. macropilis separated by 1300 km (different climatic conditions). The impact of such mutations is discussed but knowledge is scarce, which makes it difficult to root testable hypotheses. The protein analysis clearly opens new perspectives in Phytoseiidae studies.
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Affiliation(s)
| | - Martial Douin
- CBGP, Montpellier SupAgro, INRA, CIRAD, IRD, Univ. Montpellier, Campus International de Baillarguet, CS 30016, Montferrier-sur-Lez cedex, 34988, Montpellier, France
| | - Mario Eidi Sato
- Instituto Biológico, APTA, Caixa Postal 70, Campinas, SP, 13001-970, Brazil
| | - Marie-Stéphane Tixier
- CBGP, Montpellier SupAgro, INRA, CIRAD, IRD, Univ. Montpellier, Campus International de Baillarguet, CS 30016, Montferrier-sur-Lez cedex, 34988, Montpellier, France.
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41
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Sarfati H, Naftaly S, Papo N, Keasar C. Predicting mutant outcome by combining deep mutational scanning and machine learning. Proteins 2021; 90:45-57. [PMID: 34293212 DOI: 10.1002/prot.26184] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 06/01/2021] [Accepted: 07/11/2021] [Indexed: 02/02/2023]
Abstract
Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.
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Affiliation(s)
- Hagit Sarfati
- Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Si Naftaly
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Niv Papo
- Avram and Stella Goldstein-Goren Department of Biotechnology Engineering and the National Institute of Biotechnology in the Negev, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Chen Keasar
- Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva, Israel
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42
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Lindquist P, Madsen JS, Bräuner-Osborne H, Rosenkilde MM, Hauser AS. Mutational Landscape of the Proglucagon-Derived Peptides. Front Endocrinol (Lausanne) 2021; 12:698511. [PMID: 34220721 PMCID: PMC8248487 DOI: 10.3389/fendo.2021.698511] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 05/24/2021] [Indexed: 12/18/2022] Open
Abstract
Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.
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Affiliation(s)
- Peter Lindquist
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jakob S. Madsen
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hans Bräuner-Osborne
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette M. Rosenkilde
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alexander S. Hauser
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Wilson CJ, Chang M, Karttunen M, Choy WY. KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability. Int J Mol Sci 2021; 22:5408. [PMID: 34065616 PMCID: PMC8161161 DOI: 10.3390/ijms22105408] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 05/11/2021] [Accepted: 05/13/2021] [Indexed: 12/30/2022] Open
Abstract
We have performed 280 μs of unbiased molecular dynamics (MD) simulations to investigate the effects of 12 different cancer mutations on Kelch-like ECH-associated protein 1 (KEAP1) (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C, R470C, R470H, R470S and G476R), one of the frequently mutated proteins in lung cancer. The aim was to provide structural insight into the effects of these mutants, including a new class of ANCHOR (additionally NRF2-complexed hypomorph) mutant variants. Our work provides additional insight into the structural dynamics of mutants that could not be analyzed experimentally, painting a more complete picture of their mutagenic effects. Notably, blade-wise analysis of the Kelch domain points to stability as a possible target of cancer in KEAP1. Interestingly, structural analysis of the R470C ANCHOR mutant, the most prevalent missense mutation in KEAP1, revealed no significant change in structural stability or NRF2 binding site dynamics, possibly indicating an covalent modification as this mutant's mode of action.
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Affiliation(s)
- Carter J. Wilson
- Department of Biochemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5C1, Canada; (C.J.W.); (M.C.)
- Department of Applied Mathematics, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Megan Chang
- Department of Biochemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5C1, Canada; (C.J.W.); (M.C.)
| | - Mikko Karttunen
- Department of Applied Mathematics, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
- Department of Chemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada
- Centre for Advanced Materials and Biomaterials Research, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5B7, Canada
| | - Wing-Yiu Choy
- Department of Biochemistry, The University of Western Ontario, 1151 Richmond Street, London, ON N6A 5C1, Canada; (C.J.W.); (M.C.)
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Di Rienzo L, Monti M, Milanetti E, Miotto M, Boffi A, Tartaglia GG, Ruocco G. Computational optimization of angiotensin-converting enzyme 2 for SARS-CoV-2 Spike molecular recognition. Comput Struct Biotechnol J 2021; 19:3006-3014. [PMID: 34002118 PMCID: PMC8116125 DOI: 10.1016/j.csbj.2021.05.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 05/08/2021] [Accepted: 05/09/2021] [Indexed: 12/13/2022] Open
Abstract
Since the beginning of the Covid19 pandemic, many efforts have been devoted to identifying approaches to neutralize SARS-CoV-2 replication within the host cell. A promising strategy to block the infection consists of using a mutant of the human receptor angiotensin-converting enzyme 2 (ACE2) as a decoy to compete with endogenous ACE2 for the binding to the SARS-CoV-2 Spike protein, which decreases the ability of the virus to enter the host cell. Here, using a computational framework based on the 2D Zernike formalism we investigate details of the molecular binding and evaluate the changes in ACE2-Spike binding compatibility upon mutations occurring in the ACE2 side of the molecular interface. We demonstrate the efficacy of our method by comparing our results with experimental binding affinities changes upon ACE2 mutations, separating ones that increase or decrease binding affinity with an Area Under the ROC curve ranging from 0.66 to 0.93, depending on the magnitude of the effects analyzed. Importantly, the iteration of our approach leads to the identification of a set of ACE2 mutants characterized by an increased shape complementarity with Spike. We investigated the physico-chemical properties of these ACE2 mutants and propose them as bona fide candidates for Spike recognition.
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Affiliation(s)
- Lorenzo Di Rienzo
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
| | - Michele Monti
- RNA System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
| | - Edoardo Milanetti
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Mattia Miotto
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Alberto Boffi
- Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Gian Gaetano Tartaglia
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- RNA System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genoa, Italy
- Department of Biology and Biotechnology “Charles Darwin”, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nanoscience, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy
- Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00185 Rome, Italy
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Amengual-Rigo P, Fernández-Recio J, Guallar V. UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes. Bioinformatics 2021; 37:334-341. [PMID: 32761082 DOI: 10.1093/bioinformatics/btaa708] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 07/23/2020] [Accepted: 07/31/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data. RESULTS Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code. AVAILABILITY AND IMPLEMENTATION UEP algorithm can be found at: https://github.com/pepamengual/UEP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Pep Amengual-Rigo
- Department of Life Sciences, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain
| | - Juan Fernández-Recio
- Department of Life Sciences, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC-Universidad de la Rioja-Gobierno de la Rioja, 26007 Logroño, Spain
| | - Victor Guallar
- Department of Life Sciences, Barcelona Supercomputing Center (BSC), 08034 Barcelona, Spain.,ICREA: Institució Catalana de Recerca i Estudis Avançats, 08010 Barcelona, Spain
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Blake S, Hemming I, Heng JIT, Agostino M. Structure-Based Approaches to Classify the Functional Impact of ZBTB18 Missense Variants in Health and Disease. ACS Chem Neurosci 2021; 12:979-989. [PMID: 33621064 DOI: 10.1021/acschemneuro.0c00758] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
The Cys2His2 type zinc finger is a motif found in many eukaryotic transcription factor proteins that facilitates binding to genomic DNA so as to influence cellular gene expression. One such transcription factor is ZBTB18, characterized as a repressor that orchestrates the development of mammalian tissues including skeletal muscle and brain during embryogenesis. In humans, it has been recognized that disease-associated ZBTB18 missense variants mapping to the coding sequence of the zinc finger domain influence sequence-specific DNA binding, disrupt transcriptional regulation, and impair neural circuit formation in the brain. Furthermore, general population ZBTB18 missense variants that influence DNA binding and transcriptional regulation have also been documented within this domain; however, the molecular traits that explain why some variants cause disease while others do not are poorly understood. Here, we have applied five structure-based approaches to evaluate their ability to discriminate between disease-associated and general population ZBTB18 missense variants. We found that thermodynamic integration and Residue Scanning in the Schrodinger Biologics Suite were the best approaches for distinguishing disease-associated variants from general population variants. Our results demonstrate the effectiveness of structure-based approaches for the functional characterization of missense alleles to DNA binding, zinc finger transcription factor protein-coding genes that underlie human health and disease.
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Affiliation(s)
- Steven Blake
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
| | - Isabel Hemming
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
- The Faculty of Health and Medical Sciences, Medical School, The University of Western Australia, Crawley, Western Australia 6009, Australia
| | - Julian Ik-Tsen Heng
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- Ralph and Patricia Sarich Neuroscience Research Institute, Nedlands, Western Australia 6009, Australia
| | - Mark Agostino
- Curtin Health Innovation Research Institute, Curtin University, Bentley, Western Australia 6102, Australia
- School of Pharmacy and Biomedical Sciences, Curtin University, Bentley, Western Australia 6845, Australia
- Curtin Institute for Computation, Curtin University, Bentley, Western Australia, Australia
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Gonzalez TR, Martin KP, Barnes JE, Patel JS, Ytreberg FM. Assessment of software methods for estimating protein-protein relative binding affinities. PLoS One 2020; 15:e0240573. [PMID: 33347442 PMCID: PMC7751979 DOI: 10.1371/journal.pone.0240573] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 12/07/2020] [Indexed: 11/19/2022] Open
Abstract
A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83-98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.
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Affiliation(s)
- Tawny R. Gonzalez
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
| | - Kyle P. Martin
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jonathan E. Barnes
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
| | - Jagdish Suresh Patel
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Biological Sciences, University of Idaho, Moscow, Idaho, United States of America
| | - F. Marty Ytreberg
- Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho, United States of America
- Department of Physics, University of Idaho, Moscow, Idaho, United States of America
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Integrative modeling of membrane-associated protein assemblies. Nat Commun 2020; 11:6210. [PMID: 33277503 PMCID: PMC7718903 DOI: 10.1038/s41467-020-20076-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/13/2020] [Indexed: 01/03/2023] Open
Abstract
Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins.
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Predicting antibody affinity changes upon mutations by combining multiple predictors. Sci Rep 2020; 10:19533. [PMID: 33177627 PMCID: PMC7658247 DOI: 10.1038/s41598-020-76369-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023] Open
Abstract
Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biotherapeutic and bioengineering applications. The prediction of antibody affinity changes upon mutations (\documentclass[12pt]{minimal}
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\begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding) is important for antibody engineering. Numerous computational methods have been proposed based on different approaches including molecular mechanics and machine learning. However, the accuracy by each individual predictor is not enough for efficient antibody development. In this study, we develop a new prediction method by combining multiple predictors based on machine learning. Our method was tested on the SiPMAB database, evaluating the Pearson’s correlation coefficient between predicted and experimental \documentclass[12pt]{minimal}
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\begin{document}$${{\Delta \Delta {\mathrm{G}}}}_{\mathrm{binding}}$$\end{document}ΔΔGbinding. Our method achieved higher accuracy (R = 0.69) than previous molecular mechanics or machine-learning based methods (R = 0.59) and the previous method using the average of multiple predictors (R = 0.64). Feature importance analysis indicated that the improved accuracy was obtained by combining predictors with different importance, which have different protocols for calculating energies and for generating mutant and unbound state structures. This study demonstrates that machine learning is a powerful framework for combining different approaches to predict antibody affinity changes.
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50
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Harshbarger W, Tian S, Wahome N, Balsaraf A, Bhattacharya D, Jiang D, Pandey R, Tungare K, Friedrich K, Mehzabeen N, Biancucci M, Chinchilla-Olszar D, Mallett CP, Huang Y, Wang Z, Bottomley MJ, Malito E, Chandramouli S. Convergent structural features of respiratory syncytial virus neutralizing antibodies and plasticity of the site V epitope on prefusion F. PLoS Pathog 2020; 16:e1008943. [PMID: 33137810 PMCID: PMC7660905 DOI: 10.1371/journal.ppat.1008943] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 11/12/2020] [Accepted: 08/28/2020] [Indexed: 11/21/2022] Open
Abstract
Respiratory syncytial virus (RSV) is a global public health burden for which no licensed vaccine exists. To aid vaccine development via increased understanding of the protective antibody response to RSV prefusion glycoprotein F (PreF), we performed structural and functional studies using the human neutralizing antibody (nAb) RSB1. The crystal structure of PreF complexed with RSB1 reveals a conformational, pre-fusion specific site V epitope with a unique cross-protomer binding mechanism. We identify shared structural features between nAbs RSB1 and CR9501, elucidating for the first time how diverse germlines obtained from different subjects can develop convergent molecular mechanisms for recognition of the same PreF site of vulnerability. Importantly, RSB1-like nAbs were induced upon immunization with PreF in naturally-primed cattle. Together, this work reveals new details underlying the immunogenicity of site V and further supports PreF-based vaccine development efforts. Respiratory syncytial virus (RSV) is a persistent, contagious seasonal pathogen and a serious public health threat. While infants are the most at-risk population, with infections potentially leading to bronchiolitis, adults, especially the elderly, are also burdened by RSV-induced respiratory infections. The only treatment currently available for RSV is passive immunization for high-risk infants. Thus, there is a critical need to develop a vaccine for the vast majority of the vulnerable population for which there is no preventative treatment. The RSV fusion protein in its prefusion form (PreF) is the target of the majority of naturally-induced neutralizing antibodies, and several clinical trials are currently evaluating PreF as a promising vaccine candidate. In this study, we solved the X-ray structure of PreF bound to the Fab fragment of a human neutralizing antibody. The structure reveals plasticity of the epitope, as well as a unique molecular signature for antibodies elicited towards this region of PreF. We also find that similar antibodies are induced upon immunization of naturally-primed cattle with a PreF vaccine antigen, suggesting that this epitope is highly immunogenic. These results will help us better understand the human immune response to RSV infection and vaccination, and guide future vaccine-design efforts.
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Affiliation(s)
| | - Sai Tian
- GSK, Rockville, MD, United States of America
| | | | | | | | | | | | | | | | | | | | | | | | - Ying Huang
- GSK, Rockville, MD, United States of America
| | - Zihao Wang
- GSK, Rockville, MD, United States of America
| | | | - Enrico Malito
- GSK, Rockville, MD, United States of America
- * E-mail: (EM); (SC)
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