1
|
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.
Collapse
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.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Shin WH, Kumazawa K, Imai K, Hirokawa T, Kihara D. Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches. Front Mol Biosci 2023; 10:1110567. [PMID: 36814641 PMCID: PMC9939524 DOI: 10.3389/fmolb.2023.1110567] [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: 11/29/2022] [Accepted: 01/24/2023] [Indexed: 02/09/2023] Open
Abstract
Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.
Collapse
Affiliation(s)
- Woong-Hee Shin
- Department of Chemistry Education, Sunchon National University, Suncheon, South Korea,Department of Advanced Components and Materials Engineering, Sunchon National University, Suncheon, South Korea
| | - Keiko Kumazawa
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Tokyo, Japan
| | - Kenichiro Imai
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan
| | - Takatsugu Hirokawa
- Division of Biomedical Science, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan,Transborder Medical Research Center, University of Tsukuba, Tsukuba, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States,Department of Computer Science, Purdue University, West Lafayette, IN, United States,Center for Cancer Research, Purdue University, West Lafayette, IN, United States,*Correspondence: Daisuke Kihara,
| |
Collapse
|
4
|
Kumar A, Zhang KYJ. Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery. Front Chem 2018; 6:315. [PMID: 30090808 PMCID: PMC6068280 DOI: 10.3389/fchem.2018.00315] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 07/09/2018] [Indexed: 12/21/2022] Open
Abstract
Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery.
Collapse
Affiliation(s)
| | - Kam Y. J. Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Japan
| |
Collapse
|
5
|
Passeri GI, Trisciuzzi D, Alberga D, Siragusa L, Leonetti F, Mangiatordi GF, Nicolotti O. Strategies of Virtual Screening in Medicinal Chemistry. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.
Collapse
Affiliation(s)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Lydia Siragusa
- Molecular Discovery Ltd., Pinner, Middlesex, London, United Kingdom
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | | |
Collapse
|
6
|
Han X, Wei Q, Kihara D. Protein 3D Structure and Electron Microscopy Map Retrieval Using 3D-SURFER2.0 and EM-SURFER. ACTA ACUST UNITED AC 2017; 60:3.14.1-3.14.15. [PMID: 29220075 DOI: 10.1002/cpbi.37] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
With the rapid growth in the number of solved protein structures stored in the Protein Data Bank (PDB) and the Electron Microscopy Data Bank (EMDB), it is essential to develop tools to perform real-time structure similarity searches against the entire structure database. Since conventional structure alignment methods need to sample different orientations of proteins in the three-dimensional space, they are time consuming and unsuitable for rapid, real-time database searches. To this end, we have developed 3D-SURFER and EM-SURFER, which utilize 3D Zernike descriptors (3DZD) to conduct high-throughput protein structure comparison, visualization, and analysis. Taking an atomic structure or an electron microscopy map of a protein or a protein complex as input, the 3DZD of a query protein is computed and compared with the 3DZD of all other proteins in PDB or EMDB. In addition, local geometrical characteristics of a query protein can be analyzed using VisGrid and LIGSITECSC in 3D-SURFER. This article describes how to use 3D-SURFER and EM-SURFER to carry out protein surface shape similarity searches, local geometric feature analysis, and interpretation of the search results. © 2017 by John Wiley & Sons, Inc.
Collapse
Affiliation(s)
- Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana
| | - Qing Wei
- Department of Computer Science, Purdue University, West Lafayette, Indiana
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana.,Department of Computer Science, Purdue University, West Lafayette, Indiana
| |
Collapse
|
7
|
Kumar A, Kumar S, Kumar U, Suravajhala P, Gajula MP. Functional and structural insights into novel DREB1A transcription factors in common wheat (Triticum aestivum L.): A molecular modeling approach. Comput Biol Chem 2016; 64:217-226. [DOI: 10.1016/j.compbiolchem.2016.07.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 06/23/2016] [Accepted: 07/17/2016] [Indexed: 10/21/2022]
|
8
|
Shin WH, Christoffer CW, Wang J, Kihara D. PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation. J Chem Inf Model 2016; 56:1676-91. [PMID: 27500657 PMCID: PMC5037053 DOI: 10.1021/acs.jcim.6b00163] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .
Collapse
Affiliation(s)
- Woong-Hee Shin
- Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
| | - Charles W. Christoffer
- Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
| | - Jibo Wang
- Discovery Chemistry Research and Technologies, Eli Lilly and Company, 893 S. Delaware Street, Indianapolis, IN, USA
| | - Daisuke Kihara
- Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
- Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
| |
Collapse
|