A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction

By Renxiang Yan, Dong Xu, Jianyi Yang, S. Walker, Yang Zhang. 2013

203 citations recorded by the source corpus.

Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions.

Subfields: Molecular Biology

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In Molecular Biology

1. Yang Zhang
2. Robin Pearce
3. Chengxin Zhang
4. Yang Li
5. Eric W. Bell
6. Wei Zheng
7. Nick V. Grishin
8. Lisa N. Kinch

Citations in the corpus

• Using iterative fragment assembly and progressive sequence truncation to facilitate phasing and crystal structure determination of distantly related proteins. (2016, unknown): “I-TASSER The query sequence is first threaded through a nonredundant structure library derived from the Protein Data Bank (PDB) to search for structural templates and supersecondary-structure motifs using LOMETS (Wu & Zhang, 2007), a meta-threading algorithm consisting of eight distinct programs: FF”
• Deep-learning contact-map guided protein structure prediction in CASP13 (2019, methodology): “The wrong prediction of the local topology stemmed from incorrect secondary structure prediction by PSIPRED 51 and PSSpred 52 in that region.”
• I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction (2022, methodology): “Then the secondary structure is predicted by PSSpred 55 , and the contact map is generated by the deep-learning-based predictor ResPRE 10 , which has been included in DeepPotential, using features directly extracted from the MSA.”
• Mutation severity spectrum of rare alleles in the human genome is predictive of disease type (2019, methodology): “Prediction of 3-state secondary structures (helix, strand, and coil) were made by three programs (PSIPRED [94], SPIDER [95], and PSSpred [96]), resulting in nine features.”
• Automatic Prediction of Protein 3D Structures by Probabilistic Multi-template Homology Modeling (2015, methodology): “This is mainly owed to the development of more sensitive and accurate methods for sequence searching and alignment that compare sequence profiles or profile hidden Markov models (HMMs) with each other [4–6].”