Dataset Information

AlphaLink: bringing protein structure prediction in situ with in-cell photo-crosslinking mass spectrometry, crosslinking mass spectrometry, XL-MS,

ABSTRACT: The protein structure prediction problem has been revolutionised by AlphaFold2, an algorithm that uses neural networks and evolutionary information to predict accurate models from the primary sequence. However, some proteins remain difficult to predict. Moreover, proteins are dynamic entities that exist in complex environments in vivo. Here, we use the noncanonical amino acid Photo-Leucine to obtain information on residue-residue contacts inside cells by crosslinking mass spectrometry. We then introduce AlphaLink, a modified version of the AlphaFold2 algorithm that synergistically incorporates experimental distance restraint information into its network architecture. AlphaLink improves on the performance of AlphaFold2 in predicting challenging targets in both synthetic and real-world scenarios by employing sparse experimental contacts as anchor points. The program can predict individual conformations of proteins based on the distance restraints provided. The noise-tolerant framework presented here for integrating data in protein structure prediction opens a path to accurate characterisation of protein structures from in-cell data.

ORGANISM(S): Escherichia Coli

SUBMITTER: Juri Rappsilber

PROVIDER: PXD036833 | JPOST Repository | Wed Feb 22 00:00:00 GMT 2023

REPOSITORIES: jPOST

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			Action	DRS
	recal_B190214_02_Lumos_TD_IN_120_Ecoli_photoAA_membrane_SCX16_SEC6.mgf	Mgf
	recal_B190214_03_Lumos_TD_IN_120_Ecoli_photoAA_membrane_SCX16_SEC7.mgf	Mgf
	recal_B190214_04_Lumos_TD_IN_120_Ecoli_photoAA_membrane_SCX16_SEC8.mgf	Mgf
	recal_B190214_05_Lumos_TD_IN_120_Ecoli_photoAA_membrane_SCX16_SEC9.mgf	Mgf
	recal_B190214_06_Lumos_TD_IN_120_Ecoli_photoAA_membrane_SCX16_SEC10.mgf	Mgf

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Project description:Abstract: Crosslinking mass spectrometry (Crosslinking MS) has developed into a robust technique that is increasingly used to investigate the interactomes of organelles and cells. However, the incomplete and noisy information contained in spectra limits especially the identification of heteromeric protein-protein interactions (PPIs) from the many theoretically possible PPIs. We successfully leveraged here chromatographic retention time (RT) to complement the mass spectrometry-centric identification process. For this, we first made crosslinked peptides amenable to RT prediction, through a Siamese neural network, and then added RT information to the identification process. Our multi-task machine learning model xiRT achieved highly accurate predictions in a multi-dimensional separation experiment of crosslinked E. coli lysate conducted for this study. We combined strong cation exchange (SCX), hydrophilic strong anion exchange (hSAX) and reversed-phase (RP) chromatography and reached R^2 0.94 in RP and a margin of error of 1 fraction for hSAX in 94%, and SCX in 85% of the cases. Importantly, supplementing the search engine score with retention time features led to a 1.4-fold increase in PPIs, at 1% PPI false discovery rate (FDR). We also demonstrated the value of this approach for the more routine analysis of multiprotein complexes. In the Fanconi anaemia monoubiquitin ligase complex, an increase of 1.7-fold in heteromeric residue-pairs was achieved at 1% residue-pair FDR, solely using reversed-phase RT. Retention times therefore proved to be a powerful complement to mass spectrometric information to improve the identification of crosslinked peptides. We envision xiRT to supplement search engines in their scoring routines to increase the sensitivity of Crosslinking MS analyses especially for protein-protein interactions. Conclusion: Using a Siamese network architecture, we succeeded in bringing RT prediction into the Crosslinking MS field, independent of separation setup and search software. Our open source application xiRT introduces the concept of multi-task learning to achieve multi-dimensional chromatographic retention time prediction, and may use any peptide sequence-dependent measure including for example collision cross section or isoelectric point. The black-box character of the neural network was reduced by means of interpretable machine learning that revealed individual amino acid contributions towards the separation behavior. The RT predictions – even when using only the RP dimension – complement mass spectrometric information to enhance the identification of heteromeric crosslinks in multiprotein complex and proteome-wide studies. Overfitting does not account for this gain as known false target matches from an entrapment database did not increase. Leveraging additional information sources may help to address the mass-spectrometric identification challenge of heteromeric crosslinks.

Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT, RDX and HMX. One important goal of microarray experiments is to discover novel biomarker genes for quantitative phenotypic prediction. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can be used to quantitatively predict earthworm tissue residues of the explosives compounds that they were exposed to and took in from the HMX-spiked soil. Results: We collected a large microarray gene expression and earthworm tissue residue dataset. First, differentially expressed genes were identified for each exposure duration (4, 14 and 28 days). These genes were used in multivariate regression modeling for HMX residue prediction. Eighteen different regression models were tested and compared. The best performing model was able to achieve very high prediction accuracies with R2 values of 0.715, 0.728 and 0.822 for 4 days, 14 days and 28 days exposures, separately. Conclusions: This study demonstrated that multivariate regression coupled with high throughput microarray gene expression was a promising approach to quantitative phenotypic prediction. Adult earthworms (Eisenia fetida) were exposed in soil spiked with HMX (0, 8, 16, 32, 64, or 128 mg/kg) for 4, 14 or 28 days. Each treatment originally had 10 replicate worms with all 10 worms survived at the end of exposure. Total RNA was isolated from the surviving worms. A total of 120 worm RNA samples were hybridized to a custom-designed oligo array using AgilentM-bM-^@M-^Ys one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript. After hybridization and scanning, gene expression data were acquired using AgilentM-bM-^@M-^Ys Feature Extraction Software (v.9.1.3). The 120-array dataset consists of three exposure groups (4 day, 14 day and 28 day) with each group having the following 8 treatments: day 0 control, blank control, solvent control, 8, 16, 32, 64, and 128 mg HMX/g soil. Each treatment had 5 biological replicates (i.e., five individual worms).

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AlphaLink: bringing protein structure prediction in situ with in-cell photo-crosslinking mass spectrometry, crosslinking mass spectrometry, XL-MS,

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