1
|
Ahmed S, Chattopadhyay G, Manjunath K, Bhasin M, Singh N, Rasool M, Das S, Rana V, Khan N, Mitra D, Asok A, Singh R, Varadarajan R. Combining cysteine scanning with chemical labeling to map protein-protein interactions and infer bound structure in an intrinsically disordered region. Front Mol Biosci 2022; 9:997653. [PMID: 36275627 PMCID: PMC9585320 DOI: 10.3389/fmolb.2022.997653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
The Mycobacterium tuberculosis genome harbours nine toxin-antitoxin (TA) systems of the mazEF family. These consist of two proteins, a toxin and an antitoxin, encoded in an operon. While the toxin has a conserved fold, the antitoxins are structurally diverse and the toxin binding region is typically intrinsically disordered before binding. We describe high throughput methodology for accurate mapping of interfacial residues and apply it to three MazEF complexes. The method involves screening one partner protein against a panel of chemically masked single cysteine mutants of its interacting partner, displayed on the surface of yeast cells. Such libraries have much lower diversity than those generated by saturation mutagenesis, simplifying library generation and data analysis. Further, because of the steric bulk of the masking reagent, labeling of virtually all exposed epitope residues should result in loss of binding, and buried residues are inaccessible to the labeling reagent. The binding residues are deciphered by probing the loss of binding to the labeled cognate partner by flow cytometry. Using this methodology, we have identified the interfacial residues for MazEF3, MazEF6 and MazEF9 TA systems of M. tuberculosis. In the case of MazEF9, where a crystal structure was available, there was excellent agreement between our predictions and the crystal structure, superior to those with AlphaFold2. We also report detailed biophysical characterization of the MazEF3 and MazEF9 TA systems and measured the relative affinities between cognate and non-cognate toxin–antitoxin partners in order to probe possible cross-talk between these systems.
Collapse
Affiliation(s)
- Shahbaz Ahmed
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | | | | | - Munmun Bhasin
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Neelam Singh
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Mubashir Rasool
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Sayan Das
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Varsha Rana
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Neha Khan
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Debarghya Mitra
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Aparna Asok
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Ramandeep Singh
- Tuberculosis Research Laboratory, Translational Health Science and Technology Institute, Faridabad, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- *Correspondence: Raghavan Varadarajan,
| |
Collapse
|
2
|
Coevolutive, evolutive and stochastic information in protein-protein interactions. Comput Struct Biotechnol J 2019; 17:1429-1435. [PMID: 31871588 PMCID: PMC6906720 DOI: 10.1016/j.csbj.2019.10.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 10/19/2019] [Accepted: 10/22/2019] [Indexed: 11/24/2022] Open
Abstract
Here, we investigate the contributions of coevolutive, evolutive and stochastic information in determining protein-protein interactions (PPIs) based on primary sequences of two interacting protein families A and B. Specifically, under the assumption that coevolutive information is imprinted on the interacting amino acids of two proteins in contrast to other (evolutive and stochastic) sources spread over their sequences, we dissect those contributions in terms of compensatory mutations at physically-coupled and uncoupled amino acids of A and B. We find that physically-coupled amino-acids at short range distances store the largest per-contact mutual information content, with a significant fraction of that content resulting from coevolutive sources alone. The information stored in coupled amino acids is shown further to discriminate multi-sequence alignments (MSAs) with the largest expectation fraction of PPI matches – a conclusion that holds against various definitions of intermolecular contacts and binding modes. When compared to the informational content resulting from evolution at long-range interactions, the mutual information in physically-coupled amino-acids is the strongest signal to distinguish PPIs derived from cospeciation and likely, the unique indication in case of molecular coevolution in independent genomes as the evolutive information must vanish for uncorrelated proteins.
Collapse
|
3
|
Li Y, Li LP, Wang L, Yu CQ, Wang Z, You ZH. An Ensemble Classifier to Predict Protein-Protein Interactions by Combining PSSM-based Evolutionary Information with Local Binary Pattern Model. Int J Mol Sci 2019; 20:E3511. [PMID: 31319578 PMCID: PMC6679202 DOI: 10.3390/ijms20143511] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 07/04/2019] [Accepted: 07/15/2019] [Indexed: 01/03/2023] Open
Abstract
Protein plays a critical role in the regulation of biological cell functions. Among them, whether proteins interact with each other has become a fundamental problem, because proteins usually perform their functions by interacting with other proteins. Although a large amount of protein-protein interactions (PPIs) data has been produced by high-throughput biotechnology, the disadvantage of biological experimental technique is time-consuming and costly. Thus, computational methods for predicting protein interactions have become a research hot spot. In this research, we propose an efficient computational method that combines Rotation Forest (RF) classifier with Local Binary Pattern (LBP) feature extraction method to predict PPIs from the perspective of Position-Specific Scoring Matrix (PSSM). The proposed method has achieved superior performance in predicting Yeast, Human, and H. pylori datasets with average accuracies of 92.12%, 96.21%, and 86.59%, respectively. In addition, we also evaluated the performance of the proposed method on the four independent datasets of C. elegans, H. pylori, H. sapiens, and M. musculus datasets. These obtained experimental results fully prove that our model has good feasibility and robustness in predicting PPIs.
Collapse
Affiliation(s)
- Yang Li
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China.
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123, China.
| | - Zheng Wang
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Zhu-Hong You
- School of Information Engineering, Xijing University, Xi'an 710123, China
| |
Collapse
|
4
|
Schmidt M, Hamacher K. Three-body interactions improve contact prediction within direct-coupling analysis. Phys Rev E 2017; 96:052405. [PMID: 29347718 DOI: 10.1103/physreve.96.052405] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Indexed: 11/07/2022]
Abstract
The prediction of residue contacts in a protein solely from sequence information is a promising approach to computational structure prediction. Recent developments use statistical or information theoretic methods to extract contact information from a multiple sequence alignment. Despite good results, accuracy is limited due to usage of two-body interactions within a Potts model. In this paper we generalize this approach and propose a Hamiltonian with an additional three-body interaction term. We derive a mean-field approximation for inference of three-body couplings within a Potts model which is fast enough on modern computers. Finally, we show that our model has a higher accuracy in predicting residue contacts in comparison with the plain two-body-interaction model.
Collapse
Affiliation(s)
- Michael Schmidt
- Department of Physics, TU Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany
| | - Kay Hamacher
- Department of Biology and Department of Computer Science and Department of Physics, TU Darmstadt, Karolinenpl. 5, 64289 Darmstadt, Germany
| |
Collapse
|