CATHER: a novel threading algorithm with predicted contacts

Roche

2020

Preprint

Motivation: Threading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence-based predicted contact or distance information is used. Contact-or distance-assisted threading methods utilize only the residue pairs predicted to be spatially close for template selection and alignment, ignoring the neighborhood effect induced by the interacting pairs. Results: We present a new distance-based covariational threading method called DisCovER by effectively integrating information from distance maps and their topological network neighborhood. Our method first selects a subset of templates using standard profile-based threading to perform distancebased query-template alignment, and then iteratively improves the sensitivity of the alignment through double dynamic programming by incorporating topological network similarity terms induced by distance-derived inter-residue interaction network to account for the neighborhood effect. Multiple largescale benchmarking results on query proteins classified in current literature as hard threading targets show that our method greatly outperforms not only the traditional threading methods including SparkX, HHpred, CNFpred, MUSTER, PPAS, and pGenThreader; but also the state-of-the-art contact-assisted threading approaches such as CEthreader, map_align, EigenTHREADER, and CATHER. As threading gets harder and harder by the exclusion of more and more structurally similar templates, the performance of our method gets better and better compared to the contact-assisted approaches CEthreader, map_align, and EigenTHREADER. At one of the hardest threading scenarios, the average performance of DisCovER is much better than the reported average accuracy of the only other distance-based threading method DeepThreader, which is not publicly available. Controlled experiments reveal that integration of distance information significantly improves threading template selection and alignment and is one of the key factors behind the improved performance of DisCovER.

Section: Benchmark Datasets Methods To Compare Template Libraries Umentioning

confidence: 99%

“…of the template is within the distance threshold of kÅ and 0 otherwise; ! is the corresponding weight parameter adapted from the literature (Du et al, 2020) with ! = !…”

Section: Stage 2 Scoring Distance-and Orientation-based Alignmentmentioning

confidence: 99%

Section: Performance On 131 Hard Targets From Cathermentioning

confidence: 99%

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confidence: 99%

See 2 more Smart Citations

DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins

Roche

2020

Preprint

“…Fueled by this, several efforts have been made in the recent past to integrate interaction maps into threading. For instance, EigenTHREADER ( Buchan and Jones, 2017 ), map_align ( Ovchinnikov et al, 2017 ), CEthreader ( Zheng et al, 2019a ), CATHER ( Du et al, 2020 ), and ThreaderAI ( Zhang and Shen, 2020 ) have utilized predicted contact maps in protein threading. DeepThreader ( Zhu et al, 2018 ) has exploited finer-grained distance maps for query proteins instead of using binary contacts to improve threading template selection and alignment.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

Roche

Shuvo

et al. 2021

Front. Mol. Biosci.

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates

Wang

et al. 2021

Advanced Science

Self Cite

The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi‐scale network, i.e., Res2Net, for improved prediction of inter‐residue geometries, including distance and orientations. The second is an attention‐based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM‐score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi‐scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.