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Soler MA, Minovski N, Rocchia W, Fortuna S. Replica-exchange optimization of antibody fragments. Comput Biol Chem 2023; 103:107819. [PMID: 36657284 DOI: 10.1016/j.compbiolchem.2023.107819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/16/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
In the framework of the rational design of macromolecules capable of binding to a specific target for biosensing applications, we here further develop an evolutionary protocol designed to optimize the binding affinity of protein binders. In particular we focus on the optimization of the binding portion of small antibody fragments known as nanobodies (or VHH) and choose the hen egg white lysozyme (HEWL) as our target. By implementing a replica exchange scheme for this optimization, we show that an initial hit is not needed and similar solutions can be found by either optimizing an already known anti-HEWL VHH or a randomly selected binder (here a VHH selective towards another macromolecule). While we believe that exhaustive searches of the mutation space are most appropriate when only few key residues have to be optimized, in case a lead binder is not available the proposed evolutionary algorithm should be instead the method of choice.
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Affiliation(s)
- Miguel A Soler
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze 206, Udine, Italy
| | - Nikola Minovski
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy
| | - Walter Rocchia
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy
| | - Sara Fortuna
- Italian Institute of Technology (IIT), Via Melen 83, B Block, Genova, Italy; Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, Trieste, Italy.
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2
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Medagli B, Soler MA, De Zorzi R, Fortuna S. Antibody Affinity Maturation Using Computational Methods: From an Initial Hit to Small-Scale Expression of Optimized Binders. Methods Mol Biol 2023; 2552:333-359. [PMID: 36346602 DOI: 10.1007/978-1-0716-2609-2_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Nanobodies (VHHs) are engineered fragments of the camelid single-chain immunoglobulins. The VHH domain contains the highly variable segments responsible for antigen recognition. VHHs can be easily produced as recombinant proteins. Their small size is a good advantage for in silico approaches. Computer methods represent a valuable strategy for the optimization and improvement of their binding affinity. They also allow for epitope selection offering the possibility to design new VHHs for regions of a target protein that are not naturally immunogenic. Here we present an in silico mutagenic protocol developed to improve the binding affinity of nanobodies together with the first step of their in vitro production. The method, already proven successful in improving the low Kd of a nanobody hit obtained by panning, can be employed for the ex novo design of antibody fragments against selected protein target epitopes.
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Affiliation(s)
- Barbara Medagli
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy.
| | - Miguel A Soler
- CONCEPT Lab, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
| | - Rita De Zorzi
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy
| | - Sara Fortuna
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy.
- CONCEPT Lab, Istituto Italiano di Tecnologia, Genova, Italy.
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3
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Ochoa R, Cossio P, Fox T. Protocol for iterative optimization of modified peptides bound to protein targets. J Comput Aided Mol Des 2022; 36:825-835. [PMID: 36258137 PMCID: PMC9640467 DOI: 10.1007/s10822-022-00482-1] [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: 07/20/2022] [Accepted: 10/03/2022] [Indexed: 12/02/2022]
Abstract
Peptides are commonly used as therapeutic agents. However, they suffer from easy degradation and instability. Replacing natural by non-natural amino acids can avoid these problems, and potentially improve the affinity towards the target protein. Here, we present a computational pipeline to optimize peptides based on adding non-natural amino acids while improving their binding affinity. The workflow is an iterative computational evolution algorithm, inspired by the PARCE protocol, that performs single-point mutations on the peptide sequence using modules from the Rosetta framework. The modifications can be guided based on the structural properties or previous knowledge of the biological system. At each mutation step, the affinity to the protein is estimated by sampling the complex conformations and applying a consensus metric using various open protein-ligand scoring functions. The mutations are accepted based on the score differences, allowing for an iterative optimization of the initial peptide. The sampling/scoring scheme was benchmarked with a set of protein-peptide complexes where experimental affinity values have been reported. In addition, a basic application using a known protein-peptide complex is also provided. The structure- and dynamic-based approach allows users to optimize bound peptides, with the option to personalize the code for further applications. The protocol, called mPARCE, is available at: https://github.com/rochoa85/mPARCE/.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, 050010, Colombia. .,Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach/Riss, Germany.
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, 050010, Colombia.,Center for Computational Mathematics, Flatiron Institute, New York, 10010, USA.,Center for Computational Biology, Flatiron Institute, New York, 10010, USA
| | - Thomas Fox
- Medicinal Chemistry, Boehringer Ingelheim Pharma GmbH & Co KG, 88397, Biberach/Riss, Germany
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4
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Moro G, Severin Sfragano P, Ghirardo J, Mazzocato Y, Angelini A, Palchetti I, Polo F. Bicyclic peptide-based assay for uPA cancer biomarker. Biosens Bioelectron 2022; 213:114477. [PMID: 35751954 DOI: 10.1016/j.bios.2022.114477] [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: 04/16/2022] [Revised: 06/02/2022] [Accepted: 06/08/2022] [Indexed: 11/02/2022]
Abstract
The use of synthetic bioreceptors to develop biosensing platforms has been recently gaining momentum. This case study compares the performance of a biosensing platform for the human biomarker urokinase-type plasminogen activator (h-uPA) when using two bicyclic peptides (P1 and P2) with different affinities for the target protein. The bioreceptors P1 and P2 were immobilized on magnetic microbeads and tested within a sandwich-type affinity electrochemical assay. Apart from enabling h-uPA quantification at nanomolar levels (105.8 ng/mL for P1 and 32.5 ng/mL for P2), this case study showed the potential of synthetic bicyclic peptides applicability and how bioreceptor affinity can influence the performance of the final sensing platform.
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Affiliation(s)
- Giulia Moro
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy
| | - Patrick Severin Sfragano
- Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino (FI), Italy
| | - Jessica Ghirardo
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy
| | - Ylenia Mazzocato
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy
| | - Alessandro Angelini
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy; European Centre for Living Technology (ECLT), Ca' Bottacin, Dorsoduro 3911, Calle Crosera, Venice, 30124, Italy
| | - Ilaria Palchetti
- Department of Chemistry "Ugo Schiff", University of Florence, Via della Lastruccia 3, 50019 Sesto Fiorentino (FI), Italy.
| | - Federico Polo
- Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy; European Centre for Living Technology (ECLT), Ca' Bottacin, Dorsoduro 3911, Calle Crosera, Venice, 30124, Italy.
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5
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Ochoa R, Soler MA, Gladich I, Battisti A, Minovski N, Rodriguez A, Fortuna S, Cossio P, Laio A. Computational Evolution Protocol for Peptide Design. Methods Mol Biol 2022; 2405:335-359. [PMID: 35298821 DOI: 10.1007/978-1-0716-1855-4_16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin, Colombia
| | | | - Ivan Gladich
- Qatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Doha, Qatar
- SISSA, Trieste, Italy
| | | | - Nikola Minovski
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy
- Theory Department, Laboratory for Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Alex Rodriguez
- The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
| | - Sara Fortuna
- Italian Institute of Technology (IIT), Genova, Italy
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, Italy
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin, Colombia
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
| | - Alessandro Laio
- The Abdus Salam International Centre for Theoretical Physics, Trieste, Italy
- SISSA, Trieste, Italy
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6
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Lv E, Li Y, Ding J, Qin W. Magnetic‐Field‐Driven Extraction of Bioreceptors into Polymeric Membranes for Label‐Free Potentiometric Biosensing. Angew Chem Int Ed Engl 2021. [DOI: 10.1002/ange.202011331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Enguang Lv
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation Yantai Institute of Coastal Zone Research (YIC) Chinese Academy of Sciences (CAS) Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS Yantai Shandong 264003 P. R. China
- University of Chinese Academy of Sciences Beijing 100049 P. R. China
| | - Yanhong Li
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation Yantai Institute of Coastal Zone Research (YIC) Chinese Academy of Sciences (CAS) Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS Yantai Shandong 264003 P. R. China
- University of Chinese Academy of Sciences Beijing 100049 P. R. China
| | - Jiawang Ding
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation Yantai Institute of Coastal Zone Research (YIC) Chinese Academy of Sciences (CAS) Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS Yantai Shandong 264003 P. R. China
- Laboratory for Marine Biology and Biotechnology Pilot National Laboratory for Marine Science and Technology (Qingdao) Qingdao Shandong 266237 P. R. China
- Center for Ocean Mega-Science Chinese Academy of Sciences Qingdao Shandong 266071 P. R. China
| | - Wei Qin
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation Yantai Institute of Coastal Zone Research (YIC) Chinese Academy of Sciences (CAS) Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS Yantai Shandong 264003 P. R. China
- Laboratory for Marine Biology and Biotechnology Pilot National Laboratory for Marine Science and Technology (Qingdao) Qingdao Shandong 266237 P. R. China
- Center for Ocean Mega-Science Chinese Academy of Sciences Qingdao Shandong 266071 P. R. China
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7
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Lv E, Li Y, Ding J, Qin W. Magnetic-Field-Driven Extraction of Bioreceptors into Polymeric Membranes for Label-Free Potentiometric Biosensing. Angew Chem Int Ed Engl 2021; 60:2609-2613. [PMID: 33021005 DOI: 10.1002/anie.202011331] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 09/30/2020] [Indexed: 01/25/2023]
Abstract
We report here the concept of a magnetically controlled extraction of hydrophilic bioreceptors into polymeric membranes for bioassays. The potentiometric assay relies on the intrinsic charges of an antimicrobial peptide and its unique recognition abilities, which can eliminate the probe labeling and indicator addition. The target binding event could effectively prevent the extraction of the peptide into the polymeric membrane doped with an ion exchanger, thus resulting in a potential change. The potentiometric response properties of the peptide assembled on magnetic beads can be dynamically controlled and modulated by applying a magnetic field. Staphylococcus aureus, as a model of food-borne pathogens, was measured at levels down to 10 CFU mL-1 . Based on this sensing strategy, a potentiometric array was developed for the pattern recognition of bacteria. The proposed general platform can be used for potentiometric biosensing using other hydrophilic bioreceptors.
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Affiliation(s)
- Enguang Lv
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, 264003, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yanhong Li
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, 264003, P. R. China.,University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Jiawang Ding
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, 264003, P. R. China.,Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, Shandong, 266237, P. R. China.,Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, Shandong, 266071, P. R. China
| | - Wei Qin
- Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Provincial Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai, Shandong, 264003, P. R. China.,Laboratory for Marine Biology and Biotechnology, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao, Shandong, 266237, P. R. China.,Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, Shandong, 266071, P. R. China
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8
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Computational Evolution of Beta-2-Microglobulin Binding Peptides for Nanopatterned Surface Sensors. Int J Mol Sci 2021; 22:ijms22020812. [PMID: 33467468 PMCID: PMC7831021 DOI: 10.3390/ijms22020812] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/24/2020] [Accepted: 01/04/2021] [Indexed: 11/17/2022] Open
Abstract
The bottom-up design of smart nanodevices largely depends on the accuracy by which each of the inherent nanometric components can be functionally designed with predictive methods. Here, we present a rationally designed, self-assembled nanochip capable of capturing a target protein by means of pre-selected binding sites. The sensing elements comprise computationally evolved peptides, designed to target an arbitrarily selected binding site on the surface of beta-2-Microglobulin (β2m), a globular protein that lacks well-defined pockets. The nanopatterned surface was generated by an atomic force microscopy (AFM)-based, tip force-driven nanolithography technique termed nanografting to construct laterally confined self-assembled nanopatches of single stranded (ss)DNA. These were subsequently associated with an ssDNA-peptide conjugate by means of DNA-directed immobilization, therefore allowing control of the peptide's spatial orientation. We characterized the sensitivity of such peptide-containing systems against β2m in solution by means of AFM-based differential topographic imaging and surface plasmon resonance (SPR) spectroscopy. Our results show that the confined peptides are capable of specifically capturing β2m from the surface-liquid interface with micromolar affinity, hence providing a viable proof-of-concept for our approach to peptide design.
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9
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Khoshbin Z, Housaindokht MR, Izadyar M, Bozorgmehr MR, Verdian A. Recent advances in computational methods for biosensor design. Biotechnol Bioeng 2020; 118:555-578. [PMID: 33135778 DOI: 10.1002/bit.27618] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 09/25/2020] [Accepted: 10/29/2020] [Indexed: 01/20/2023]
Abstract
Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost-effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast-growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.
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Affiliation(s)
- Zahra Khoshbin
- Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Mohammad Izadyar
- Department of Chemistry, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran
| | | | - Asma Verdian
- Department of Food Safety and Quality Control, Research Institute of Food Science and Technology (RIFST), Mashhad, Iran
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10
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Chi LA, Vargas MC. In silico design of peptides as potential ligands to resistin. J Mol Model 2020; 26:101. [PMID: 32297015 DOI: 10.1007/s00894-020-4338-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 03/04/2020] [Indexed: 12/28/2022]
Abstract
Resistin is a hormone of biological interest due to its connection with several diseases of worldwide concern. This work aims to design a series of cyclic peptides as "lead compounds" to identify potential ligands to resistin. To this end, we propose an approach based on a peptide design algorithm plus a two-stage selection which accounts for selectivity, one of the most forgotten steps in the design of ligands. Following this approach, we have been able to identify several peptides as strong candidates for the design of elements of bio-recognition. Those peptides present low scoring binding energy to albumin, good water solubility, stability in water at 300 K, and high scoring binding energy to resistin. Among those peptides, two were chosen, to perform a more rigorous calculation of binding free energy based on the Alchemical Absolute Binding Free Energy method. We were able to establish a methodological route for the development of strong candidates for the design of ligands to resistin. Graphical Abstract Combined MD + MC + AABFE approach to design and screening of high-affinity binders to resistin.
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Affiliation(s)
- L América Chi
- Departamento de Física Aplicada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, Apartado Postal 73 "Cordemex", 97310, Mérida, Mexico.
| | - M Cristina Vargas
- Departamento de Física Aplicada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Mérida, Apartado Postal 73 "Cordemex", 97310, Mérida, Mexico
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11
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Moghimi S, Morsali A, Heravi MM, Beyramabadi SA. Quantum‐Chemical Modeling of Cyclic Peptide‐Selenium Nanoparticle as an Anticancer Drug Nanocarrier. B KOREAN CHEM SOC 2019. [DOI: 10.1002/bkcs.11912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Sara Moghimi
- Department of Chemistry, Mashhad BranchIslamic Azad University Mashhad Iran
| | - Ali Morsali
- Department of Chemistry, Mashhad BranchIslamic Azad University Mashhad Iran
- Research Center for Animal Development Applied Biology, Mashhad BranchIslamic Azad University Mashhad 917568 Iran
| | - Mohammad M. Heravi
- Department of Chemistry, Mashhad BranchIslamic Azad University Mashhad Iran
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12
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Soler MA, Medagli B, Semrau MS, Storici P, Bajc G, de Marco A, Laio A, Fortuna S. A consensus protocol for the in silico optimisation of antibody fragments. Chem Commun (Camb) 2019; 55:14043-14046. [PMID: 31690899 DOI: 10.1039/c9cc06182g] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We present an in silico mutagenetic protocol for improving the binding affinity of single domain antibodies (or nanobodies, VHHs). The method iteratively attempts random mutations in the interacting region of the protein and evaluates the resulting binding affinity towards the target by scoring, with a collection of scoring functions, short explicit solvent molecular dynamics trajectories of the binder-target complexes. The acceptance/rejection of each attempted mutation is carried out by a consensus decision-making algorithm, which considers all individual assessments derived from each scoring function. The method was benchmarked by evolving a single complementary determining region (CDR) of an anti-HER2 VHH hit obtained by direct panning of a phage display library. The optimised VHH mutant showed significantly enhanced experimental affinity with respect to the original VHH it matured from. The protocol can be employed as it is for the optimization of peptides, antibody fragments, and (given enough computational power) larger antibodies.
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Affiliation(s)
- Miguel A Soler
- International School for Advanced Studies (SISSA), Via Bonomea 265, 34136, Trieste, Italy.
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13
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Ochoa R, Laio A, Cossio P. Predicting the Affinity of Peptides to Major Histocompatibility Complex Class II by Scoring Molecular Dynamics Simulations. J Chem Inf Model 2019; 59:3464-3473. [PMID: 31290667 DOI: 10.1021/acs.jcim.9b00403] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Predicting the binding affinity of peptides able to interact with major histocompatibility complex (MHC) molecules is a priority for researchers working in the identification of novel vaccines candidates. Most available approaches are based on the analysis of the sequence of peptides of known experimental affinity. However, for MHC class II receptors, these approaches are not very accurate, due to the intrinsic flexibility of the complex. To overcome these limitations, we propose to estimate the binding affinity of peptides bound to an MHC class II by averaging the score of the configurations from finite-temperature molecular dynamics simulations. The score is estimated for 18 different scoring functions, and we explored the optimal manner for combining them. To test the predictions, we considered eight peptides of known binding affinity. We found that six scoring functions correlate with the experimental ranking of the peptides significantly better than the others. We then assessed a set of techniques for combining the scoring functions by linear regression and logistic regression. We obtained a maximum accuracy of 82% for the predicted sign of the binding affinity using a logistic regression with optimized weights. These results are potentially useful to improve the reliability of in silico protocols to design high-affinity binding peptides for MHC class II receptors.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group , University of Antioquia , 050010 Medellin , Colombia
| | - Alessandro Laio
- International School for Advanced Studies (SISSA) , Via Bonomea 265 , 34136 Trieste , Italy.,The Abdus Salam International Centre for Theoretical Physics (ICTP) , Strada Costiera 11 , 34151 Trieste , Italy
| | - Pilar Cossio
- Biophysics of Tropical Diseases, Max Planck Tandem Group , University of Antioquia , 050010 Medellin , Colombia.,Department of Theoretical Biophysics , Max Planck Institute of Biophysics , 60438 Frankfurt am Main , Germany
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14
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Panyayai T, Ngamphiw C, Tongsima S, Mhuantong W, Limsripraphan W, Choowongkomon K, Sawatdichaikul O. FeptideDB: A web application for new bioactive peptides from food protein. Heliyon 2019; 5:e02076. [PMID: 31372542 PMCID: PMC6656964 DOI: 10.1016/j.heliyon.2019.e02076] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 06/11/2019] [Accepted: 07/08/2019] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Bioactive peptides derived from food are important sources for alternative medicine and possess therapeutic activity. Several biochemical methods have been achieved to isolate bioactive peptides from food, which are tedious and time consuming. In silico methods are an alternative process to reduce cost and time with respect to bioactive peptide production. In this paper, FeptideDB was used to collect bioactive peptide (BP) data from both published research articles and available bioactive peptide databases. FeptideDB was developed to assist in forecasting bioactive peptides from food by combining peptide cleavage tools and database matching. Furthermore, this application was able to predict the potential of cleaved peptides from 'enzyme digestion module' to identify new ACE (angiotensin converting enzyme) inhibitors using an automatic molecular docking approach. RESULTS The FeptideDB web application contains tools for generating all possible peptides cleaved from input protein by various available enzymes. This database was also used for analysis and visualization to assist in bioactive peptide discovery. One module of FeptideDB has the ability to create 3-dimensional peptide structures to further predict inhibitors for the target protein, ACE (angiotensin converting enzyme). CONCLUSIONS FeptideDB is freely available to researchers who are interested in exploring bioactive peptides. The FeptideDB interface is easy to use, allowing users to rapidly retrieve data based on desired search criteria. FeptideDB is freely available at http://www4g.biotec.or.th/FeptideDB/. Ultimately, FeptideDB is a computational aid for assessing peptide bioactivities.
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Affiliation(s)
- Thitima Panyayai
- Genetic Engineering Interdisciplinary Program, Graduate School, Kasetsart University, 50 Ngam Wong Wan Rd, Bangkok, Chatuchak, 10900, Thailand
- Department of Research and Development, Betagro Science Center Co. Ltd., Klong Luang, Pathumthani, 12120, Thailand
| | - Chumpol Ngamphiw
- National Biobank of Thailand, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Sissades Tongsima
- National Biobank of Thailand, National Center for Genetic Engineering and Biotechnology (BIOTEC), Thailand Science Park, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Wuttichai Mhuantong
- Enzyme Technology Laboratory, National Center for Genetic Engineering and Biotechnology (BIOTEC), 113 Thailand Science Park, Phahonyothin Road Khlong Nueng, Khlong Luang, Pathum Thani, 12120, Thailand
| | - Wachira Limsripraphan
- Department of Computer Engineering, Faculty of Industrial Technology, Pibulsongkram Rajabhat University, 156 Mu 5 Plaichumpol Sub-district, Muang District, Phitsanulok, 65000, Thailand
| | - Kiattawee Choowongkomon
- Department of Biochemistry, Faculty of Science, Kasetsart University, 50 Ngam, Wong Wan Rd, Bangkok, Chatuchak, 10900, Thailand
- Center for Advanced Studies in Nanotechnology for Chemical, Food and Agricultural Industries, KU Institute for Advanced Studies, Kasetsart University, Bangkok, 10900, Thailand
| | - Orathai Sawatdichaikul
- Department of Nutrition and Health, Institute of Food Research and Product Development, Kasetsart University, 50 Ngam Wong Wan Rd, Ladyaow, Chatuchak, Bangkok, 10900, Thailand
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