1
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes in and on the Membrane with Predicted Spatial Arrangement Constraints. J Mol Biol 2024; 436:168486. [PMID: 38336197 PMCID: PMC10942765 DOI: 10.1016/j.jmb.2024.168486] [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: 11/08/2023] [Revised: 01/17/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA; Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN 47907, USA.
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2
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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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3
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Asim A. Approaches to Backbone Flexibility in Protein-Protein Docking. Methods Mol Biol 2024; 2780:45-68. [PMID: 38987463 DOI: 10.1007/978-1-0716-3985-6_4] [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
Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.
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Affiliation(s)
- Ayesha Asim
- Department of Synthesis and Chemical Technology of Pharmaceutical Substances with Computer Modeling Laboratory, Faculty of Pharmacy, Medical University of Lublin, Lublin, Poland
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4
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Christoffer C, Harini K, Archit G, Kihara D. Assembly of Protein Complexes In and On the Membrane with Predicted Spatial Arrangement Constraints. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563303. [PMID: 37961264 PMCID: PMC10634698 DOI: 10.1101/2023.10.20.563303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Membrane proteins play crucial roles in various cellular processes, and their interactions with other proteins in and on the membrane are essential for their proper functioning. While an increasing number of structures of more membrane proteins are being determined, the available structure data is still sparse. To gain insights into the mechanisms of membrane protein complexes, computational docking methods are necessary due to the challenge of experimental determination. Here, we introduce Mem-LZerD, a rigid-body membrane docking algorithm designed to take advantage of modern membrane modeling and protein docking techniques to facilitate the docking of membrane protein complexes. Mem-LZerD is based on the LZerD protein docking algorithm, which has been constantly among the top servers in many rounds of CAPRI protein docking assessment. By employing a combination of geometric hashing, newly constrained by the predicted membrane height and tilt angle, and model scoring accounting for the energy of membrane insertion, we demonstrate the capability of Mem-LZerD to model diverse membrane protein-protein complexes. Mem-LZerD successfully performed unbound docking on 13 of 21 (61.9%) transmembrane complexes in an established benchmark, more than shown by previous approaches. It was additionally tested on new datasets of 44 transmembrane complexes and 92 peripheral membrane protein complexes, of which it successfully modeled 35 (79.5%) and 15 (16.3%) complexes respectively. When non-blind orientations of peripheral targets were included, the number of successes increased to 54 (58.7%). We further demonstrate that Mem-LZerD produces complex models which are suitable for molecular dynamics simulation. Mem-LZerD is made available at https://lzerd.kiharalab.org.
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Affiliation(s)
- Charles Christoffer
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Kannan Harini
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Gupta Archit
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Genetic Engineering, SRM Institute of Science and Technology, Kattankulathur 603203, India
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA
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5
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DOCKGROUND membrane protein-protein set. PLoS One 2022; 17:e0267531. [PMID: 35580077 PMCID: PMC9113569 DOI: 10.1371/journal.pone.0267531] [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: 12/23/2021] [Accepted: 04/10/2022] [Indexed: 11/19/2022] Open
Abstract
Membrane proteins are significantly underrepresented in Protein Data Bank despite their essential role in cellular mechanisms and the major progress in experimental protein structure determination. Thus, computational approaches are especially valuable in the case of membrane proteins and their assemblies. The main focus in developing structure prediction techniques has been on soluble proteins, in part due to much greater availability of the structural data. Currently, structure prediction of protein complexes (protein docking) is a well-developed field of study. However, the generic protein docking approaches are not optimal for the membrane proteins because of the differences in physicochemical environment and the spatial constraints imposed by the membranes. Thus, docking of the membrane proteins requires specialized computational methods. Development and benchmarking of the membrane protein docking approaches has to be based on high-quality sets of membrane protein complexes. In this study we present a new dataset of 456 non-redundant alpha helical binary interfaces. The set is significantly larger and more representative than the previously developed sets. In the future, it will become the basis for the development of docking and scoring benchmarks, similar to the ones for soluble proteins in the Dockground resource http://dockground.compbio.ku.edu.
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6
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Puthenveetil R, Christenson ET, Vinogradova O. New Horizons in Structural Biology of Membrane Proteins: Experimental Evaluation of the Role of Conformational Dynamics and Intrinsic Flexibility. MEMBRANES 2022; 12:227. [PMID: 35207148 PMCID: PMC8877495 DOI: 10.3390/membranes12020227] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/08/2023]
Abstract
A plethora of membrane proteins are found along the cell surface and on the convoluted labyrinth of membranes surrounding organelles. Since the advent of various structural biology techniques, a sub-population of these proteins has become accessible to investigation at near-atomic resolutions. The predominant bona fide methods for structure solution, X-ray crystallography and cryo-EM, provide high resolution in three-dimensional space at the cost of neglecting protein motions through time. Though structures provide various rigid snapshots, only an amorphous mechanistic understanding can be inferred from interpolations between these different static states. In this review, we discuss various techniques that have been utilized in observing dynamic conformational intermediaries that remain elusive from rigid structures. More specifically we discuss the application of structural techniques such as NMR, cryo-EM and X-ray crystallography in studying protein dynamics along with complementation by conformational trapping by specific binders such as antibodies. We finally showcase the strength of various biophysical techniques including FRET, EPR and computational approaches using a multitude of succinct examples from GPCRs, transporters and ion channels.
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Affiliation(s)
- Robbins Puthenveetil
- Section on Structural and Chemical Biology of Membrane Proteins, Neurosciences and Cellular and Structural Biology Division, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 35A Convent Dr., Bethesda, MD 20892, USA
| | | | - Olga Vinogradova
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Connecticut, Storrs, CT 06269, USA
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7
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Mias-Lucquin D, Chauvot de Beauchene I. Conformational variability in proteins bound to single-stranded DNA: A new benchmark for new docking perspectives. Proteins 2021; 90:625-631. [PMID: 34617336 PMCID: PMC9292434 DOI: 10.1002/prot.26258] [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: 03/25/2021] [Revised: 09/15/2021] [Accepted: 09/27/2021] [Indexed: 12/19/2022]
Abstract
We explored the Protein Data Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions and compared it to the supposedly binding-induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software. This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA-protein interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.
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8
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Khazen G, Gyulkhandanian A, Issa T, Maroun RC. Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes. Comput Struct Biotechnol J 2021; 19:5184-5197. [PMID: 34630938 PMCID: PMC8476896 DOI: 10.1016/j.csbj.2021.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/23/2021] [Accepted: 09/12/2021] [Indexed: 02/03/2023] Open
Abstract
Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.
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Affiliation(s)
- Georges Khazen
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Aram Gyulkhandanian
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
| | - Tina Issa
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Rachid C Maroun
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
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9
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Alford RF, Samanta R, Gray JJ. Diverse Scientific Benchmarks for Implicit Membrane Energy Functions. J Chem Theory Comput 2021; 17:5248-5261. [PMID: 34310137 DOI: 10.1021/acs.jctc.0c00646] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Energy functions are fundamental to biomolecular modeling. Their success depends on robust physical formalisms, efficient optimization, and high-resolution data for training and validation. Over the past 20 years, progress in each area has advanced soluble protein energy functions. Yet, energy functions for membrane proteins lag behind due to sparse and low-quality data, leading to overfit tools. To overcome this challenge, we assembled a suite of 12 tests on independent data sets varying in size, diversity, and resolution. The tests probe an energy function's ability to capture membrane protein orientation, stability, sequence, and structure. Here, we present the tests and use the franklin2019 energy function to demonstrate them. We then identify areas for energy function improvement and discuss potential future integration with machine-learning-based optimization methods. The tests are available through the Rosetta Benchmark Server (https://benchmark.graylab.jhu.edu/) and GitHub (https://github.com/rfalford12/Implicit-Membrane-Energy-Function-Benchmark).
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Affiliation(s)
- Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Rituparna Samanta
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, United States
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10
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Ozkan A, Sitharam M, Flores-Canales JC, Prabhu R, Kurnikova M. Baseline Comparisons of Complementary Sampling Methods for Assembly Driven by Short-Ranged Pair Potentials toward Fast and Flexible Hybridization. J Chem Theory Comput 2021; 17:1967-1987. [PMID: 33576635 DOI: 10.1021/acs.jctc.0c00945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
This work measures baseline sampling characteristics that highlight fundamental differences between sampling methods for assembly driven by short-ranged pair potentials. Such granular comparison is essential for fast, flexible, and accurate hybridization of complementary methods. Besides sampling speed, efficiency, and accuracy of uniform grid coverage, other sampling characteristics measured are (i) accuracy of covering narrow low energy regions that have low effective dimension (ii) ability to localize sampling to specific basins, and (iii) flexibility in sampling distributions. As a proof of concept, we compare a recently developed geometric methodology EASAL (Efficient Atlasing and Search of Assembly Landscapes) and the traditional Monte Carlo (MC) method for sampling the energy landscape of two assembling trans-membrane helices, driven by short-range pair potentials. By measuring the above-mentioned sampling characteristics, we demonstrate that EASAL provides localized and accurate coverage of crucial regions of the energy landscape of low effective dimension, under flexible sampling distributions, with much fewer samples and computational resources than MC sampling. EASAL's empirically validated theoretical guarantees permit credible extrapolation of these measurements and comparisons to arbitrary number and size of assembling units. Promising avenues for hybridizing the complementary advantages of the two methods are discussed.
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Affiliation(s)
- Aysegul Ozkan
- CISE Department, University of Florida, Gainesville, Florida 32611-6120, United States
| | - Meera Sitharam
- CISE Department, University of Florida, Gainesville, Florida 32611-6120, United States
| | | | - Rahul Prabhu
- CISE Department, University of Florida, Gainesville, Florida 32611-6120, United States
| | - Maria Kurnikova
- Chemistry Department, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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11
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Abstract
Transmembrane proteins act as an intermediary for a broad range of biological process. Making up 20% to 30% of the proteome, their ubiquitous nature has resulted in them comprising 50% of all targets in drug design. Despite their importance, they make up only 4% of all structures in the PDB database, primarily owing to difficulties associated with isolating and characterizing them. Membrane protein docking algorithms could help to fill this knowledge gap, yet only few exist. Moreover, these existing methods achieve success rates lower than the current best soluble proteins docking software. We present and test a pipeline using our software, JabberDock, to dock membrane proteins. JabberDock docks shapes representative of membrane protein structure and dynamics in their biphasic environment. We verify JabberDock's ability to yield accurate predictions by applying it to a benchmark of 20 transmembrane dimers, returning a success rate of 75.0%. This makes our software very competitive among available membrane protein-protein docking tools.
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Affiliation(s)
- Lucas S P Rudden
- Department of Physics, Durham University, South Road, DH1 3LE Durham, United Kingdom
| | - Matteo T Degiacomi
- Department of Physics, Durham University, South Road, DH1 3LE Durham, United Kingdom
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12
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Rosário-Ferreira N, Marques-Pereira C, Gouveia RP, Mourão J, Moreira IS. Guardians of the Cell: State-of-the-Art of Membrane Proteins from a Computational Point-of-View. Methods Mol Biol 2021; 2315:3-28. [PMID: 34302667 DOI: 10.1007/978-1-0716-1468-6_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Membrane proteins (MPs) encompass a large family of proteins with distinct cellular functions, and although representing over 50% of existing pharmaceutical drug targets, their structural and functional information is still very scarce. Over the last years, in silico analysis and algorithm development were essential to characterize MPs and overcome some limitations of experimental approaches. The optimization and improvement of these methods remain an ongoing process, with key advances in MPs' structure, folding, and interface prediction being continuously tackled. Herein, we discuss the latest trends in computational methods toward a deeper understanding of the atomistic and mechanistic details of MPs.
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Affiliation(s)
- Nícia Rosário-Ferreira
- Coimbra Chemistry Center, Department of Chemistry, University of Coimbra, Coimbra, Portugal.,Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Catarina Marques-Pereira
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal.,PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Coimbra, Portugal
| | - Raquel P Gouveia
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Coimbra, Portugal.
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13
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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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14
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Aderinwale T, Christoffer CW, Sarkar D, Alnabati E, Kihara D. Computational structure modeling for diverse categories of macromolecular interactions. Curr Opin Struct Biol 2020; 64:1-8. [PMID: 32599506 PMCID: PMC7665979 DOI: 10.1016/j.sbi.2020.05.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 05/06/2020] [Accepted: 05/21/2020] [Indexed: 01/23/2023]
Abstract
Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.
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Affiliation(s)
- Tunde Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | | | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA; Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA.
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15
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Vakser IA. Challenges in protein docking. Curr Opin Struct Biol 2020; 64:160-165. [PMID: 32836051 DOI: 10.1016/j.sbi.2020.07.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/19/2020] [Accepted: 07/11/2020] [Indexed: 11/30/2022]
Abstract
Current developments in protein docking aim at improvement of applicability, accuracy and utility of modeling macromolecular complexes. The challenges include the need for greater emphasis on protein docking to molecules of different types, proper accounting for conformational flexibility upon binding, new promising methodologies based on residue co-evolution and deep learning, affinity prediction, and further development of fully automated docking servers. Importantly, new developments increasingly focus on realistic modeling of protein interactions in vivo, including crowded environment inside a cell, which involves multiple transient encounters, and propagating the system in time. This opinion paper offers the author's perspective on these challenges in structural modeling of protein interactions and the future of protein docking.
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Affiliation(s)
- Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA.
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16
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Koukos P, Bonvin A. Integrative Modelling of Biomolecular Complexes. J Mol Biol 2020; 432:2861-2881. [DOI: 10.1016/j.jmb.2019.11.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022]
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17
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Kyrilis FL, Meister A, Kastritis PL. Integrative biology of native cell extracts: a new era for structural characterization of life processes. Biol Chem 2020; 400:831-846. [PMID: 31091193 DOI: 10.1515/hsz-2018-0445] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 03/29/2019] [Indexed: 01/04/2023]
Abstract
Advances in electron microscopy have provided unprecedented access to the structural characterization of large, flexible and heterogeneous complexes. Until recently, cryo-electron microscopy (cryo-EM) has been applied to understand molecular organization in either highly purified, isolated biomolecules or in situ. An emerging field is developing, bridging the gap between the two approaches, and focuses on studying molecular organization in native cell extracts. This field has demonstrated its potential by resolving the structure of fungal fatty acid synthase (FAS) at 4.7 Å [Fourier shell correlation (FSC) = 0.143]; FAS was not only less than 50% enriched, but also retained higher-order binders, previously unknown. Although controversial in the sense that the lysis step might introduce artifacts, cell extracts preserve aspects of cellular function. In addition, cell extracts are accessible, besides cryo-EM, to modern proteomic methods, chemical cross-linking, network biology and biophysical modeling. We expect that automation in imaging cell extracts, along with the integration of molecular/cell biology approaches, will provide remarkable achievements in the study of closer-to-life biomolecular states of pronounced biotechnological and medical importance. Such steps will, eventually, bring us a step closer to the biophysical description of cellular processes in an integrative, holistic approach.
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Affiliation(s)
- Fotis L Kyrilis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, D-06120 Halle/Saale, Germany
| | - Annette Meister
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, D-06120 Halle/Saale, Germany.,Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, D-06120 Halle/Saale, Germany
| | - Panagiotis L Kastritis
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3a, D-06120 Halle/Saale, Germany.,Institute of Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Kurt-Mothes-Straße 3, D-06120 Halle/Saale, Germany.,Biozentrum, Martin Luther University Halle-Wittenberg, Weinbergweg 22, D-06120 Halle/Saale, Germany
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18
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Roel-Touris J, Don CG, V Honorato R, Rodrigues JPGLM, Bonvin AMJJ. Less Is More: Coarse-Grained Integrative Modeling of Large Biomolecular Assemblies with HADDOCK. J Chem Theory Comput 2019; 15:6358-6367. [PMID: 31539250 PMCID: PMC6854652 DOI: 10.1021/acs.jctc.9b00310] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing for modeling larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modeling platform. Docking and refinement are performed at the coarse-grained level, and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ∼7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.
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Affiliation(s)
- Jorge Roel-Touris
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands
| | - Charleen G Don
- Department of Pharmaceutical Sciences , University of Basel , 4056 Basel , Switzerland
| | - Rodrigo V Honorato
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands
| | - João P G L M Rodrigues
- Department of Structural Biology , Stanford University School of Medicine , Stanford , California 94305 , United States
| | - Alexandre M J J Bonvin
- Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands
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19
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Torres PHM, Sodero ACR, Jofily P, Silva-Jr FP. Key Topics in Molecular Docking for Drug Design. Int J Mol Sci 2019; 20:E4574. [PMID: 31540192 PMCID: PMC6769580 DOI: 10.3390/ijms20184574] [Citation(s) in RCA: 176] [Impact Index Per Article: 35.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/09/2019] [Accepted: 07/10/2019] [Indexed: 12/18/2022] Open
Abstract
Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.
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Affiliation(s)
- Pedro H M Torres
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.
| | - Ana C R Sodero
- Department of Drugs and Medicines; School of Pharmacy; Federal University of Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Paula Jofily
- Laboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil.
| | - Floriano P Silva-Jr
- Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21949-900, RJ, Brazil.
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20
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Marrink SJ, Corradi V, Souza PC, Ingólfsson HI, Tieleman DP, Sansom MS. Computational Modeling of Realistic Cell Membranes. Chem Rev 2019; 119:6184-6226. [PMID: 30623647 PMCID: PMC6509646 DOI: 10.1021/acs.chemrev.8b00460] [Citation(s) in RCA: 419] [Impact Index Per Article: 83.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Indexed: 12/15/2022]
Abstract
Cell membranes contain a large variety of lipid types and are crowded with proteins, endowing them with the plasticity needed to fulfill their key roles in cell functioning. The compositional complexity of cellular membranes gives rise to a heterogeneous lateral organization, which is still poorly understood. Computational models, in particular molecular dynamics simulations and related techniques, have provided important insight into the organizational principles of cell membranes over the past decades. Now, we are witnessing a transition from simulations of simpler membrane models to multicomponent systems, culminating in realistic models of an increasing variety of cell types and organelles. Here, we review the state of the art in the field of realistic membrane simulations and discuss the current limitations and challenges ahead.
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Affiliation(s)
- Siewert J. Marrink
- Groningen
Biomolecular Sciences and Biotechnology Institute & Zernike Institute
for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Valentina Corradi
- Centre
for Molecular Simulation and Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Paulo C.T. Souza
- Groningen
Biomolecular Sciences and Biotechnology Institute & Zernike Institute
for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Helgi I. Ingólfsson
- Biosciences
and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, California 94550, United States
| | - D. Peter Tieleman
- Centre
for Molecular Simulation and Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Mark S.P. Sansom
- Department
of Biochemistry, University of Oxford, South Parks Road, Oxford OX1 3QU, U.K.
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