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Abstract We report the results of the “UM‐TBM” and “Zheng” groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D‐I‐TASSER and DMFold‐Multimer algorithms, respectively. For monomer structure prediction, D‐I‐TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi‐source MSA searching and a structural modeling‐based MSA ranker; (ii) attention‐network based spatial restraints; (iii) a multi‐domain module containing domain partition and arrangement for domain‐level templates and spatial restraints; (iv) an optimized I‐TASSER‐based folding simulation system for full‐length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge‐based potentials. For 47 free modeling targets in CASP15, the final models predicted by D‐I‐TASSER showed average TM‐score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo‐based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end‐to‐end deep learning methods alone. For protein complex structure prediction, DMFold‐Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end‐to‐end modeling module from AlphaFold2‐Multimer. For the 38 complex targets, DMFold‐Multimer generated models with an average TM‐score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.
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LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation
Abstract Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)–(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.
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- Award ID(s):
- 2025426
- PAR ID:
- 10368557
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Nucleic Acids Research
- Volume:
- 50
- Issue:
- W1
- ISSN:
- 0305-1048
- Format(s):
- Medium: X Size: p. W454-W464
- Size(s):
- p. W454-W464
- Sponsoring Org:
- National Science Foundation
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