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1. Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15

   https://doi.org/10.1038/s42004-023-00991-6  

   Liu, Jian; Guo, Zhiye; Wu, Tianqi; Roy, Raj S.; Chen, Chen; Cheng, Jianlin (December 2023, Communications Chemistry)

   Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively. 

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2. Integrating deep learning, threading alignments, and a multi‐MSA strategy for high‐quality protein monomer and complex structure prediction in CASP15

   https://doi.org/10.1002/prot.26585  

   Zheng, Wei; Wuyun, Qiqige; Freddolino, Peter L.; Zhang, Yang (December 2023, Proteins: Structure, Function, and Bioinformatics)

   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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3. Assessment of Protein Complex Predictions in CASP16 : Are We Making Progress?

   https://doi.org/10.1002/prot.70068  

   Zhang, Jing; Yuan, Rongqing; Kryshtafovych, Andriy; Pei, Jimin; Kretsch, Rachael C; Schaeffer, R Dustin; Zhou, Jian; Das, Rhiju; Grishin, Nick V; Cong, Qian (January 2026, Proteins: Structure, Function, and Bioinformatics)

   ABSTRACT The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. Even the leading groups can only predict slightly more than half of the targets to high accuracy. Most CASP16 groups relied on AlphaFold‐Multimer (AFM) or AlphaFold3 (AF3) as their core modeling engines. By optimizing input MSAs, refining modeling constructs (using partial rather than full sequences), and employing massive model sampling and selection, top‐performing groups were able to significantly outperform the default AFM/AF3 predictions. CASP16 also introduced two additional challenges: Phase 0, which required predictions without stoichiometry information, and Phase 2, which provided participants with thousands of models generated by MassiveFold (MF) to enable large‐scale sampling for resource‐limited groups. Across all phases, the MULTICOM series and Kiharalab emerged as top performers based on the quality of their best models. However, these groups did not have a strong advantage in model ranking, and thus their lead over other teams, such as Yang‐Multimer and kozakovvajda, was less pronounced when evaluating only the first submitted models. Compared to CASP15, CASP16 showed moderate overall improvement, likely driven by the release of AF3 and the extensive model sampling employed by top groups. Several notable trends highlight frontiers for future development. First, the kozakovvajda group significantly outperformed others on antibody–antigen targets, achieving over a 60% success rate without relying on AFM or AF3 as their primary modeling framework, suggesting that alternative approaches may offer promising solutions for these difficult targets. Second, model ranking and selection continue to be major bottlenecks. The PEZYFoldings group demonstrated a notable advantage in selecting their best models as first models, suggesting that their pipeline for model ranking may offer important insights for the field. Finally, the Phase 0 experiment indicated moderate success in stoichiometry prediction; however, stoichiometry prediction remains challenging for high‐order assemblies and targets that differ from available homologous templates. Overall, CASP16 demonstrated steady progress in multimer prediction while emphasizing the need for more effective model ranking strategies, improved stoichiometry prediction, and new modeling methods that extend beyond the current AF‐based paradigm. 

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4. Estimating protein complex model accuracy using graph transformers and pairwise similarity graphs

   https://doi.org/10.1093/bioadv/vbaf180  

   Liu, Jian; Neupane, Pawan; Cheng, Jianlin (July 2025, Bioinformatics Advances)

   Tunçbağ, Nurcan (Ed.)

   Abstract MotivationEstimation of protein complex structure accuracy is essential for effective structural model selection in structural biology applications such as protein function analysis and drug design. Despite the success of structure prediction methods such as AlphaFold2 and AlphaFold3, selecting top-quality structural models from large model pools remains challenging. ResultsWe present GATE, a novel method that uses graph transformers on pairwise model similarity graphs to predict the quality (accuracy) of complex structural models. By integrating single-model and multimodel quality features, GATE captures intrinsic model characteristics and intermodel geometric similarities to make robust predictions. On the dataset of the 15th Critical Assessment of Protein Structure Prediction (CASP15), GATE achieved the highest Pearson’s correlation (0.748) and the lowest ranking loss (0.1191) compared with existing methods. In the blind CASP16 experiment, GATE ranked fifth based on the sum of z-scores, with a Pearson’s correlation of 0.7076 (first), a Spearman’s correlation of 0.4514 (fourth), a ranking loss of 0.1221 (third), and an area under the curve score of 0.6680 (third) on per-target TM-score-based metrics. Additionally, GATE also performed consistently on large in-house datasets generated by extensive AlphaFold-based sampling with MULTICOM4, confirming its robustness and practical applicability in real-world model selection scenarios. Availability and implementationGATE is available at https://github.com/BioinfoMachineLearning/GATE. 

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5. MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction

   https://doi.org/10.1038/s41598-021-92395-6  

   Wu, Tianqi; Liu, Jian; Guo, Zhiye; Hou, Jie; Cheng, Jianlin (December 2021, Scientific Reports)

   Abstract Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system—MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors (MULTICOM-HYBRID, MULTICOM-DEEP, MULTICOM-DIST) were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 . 

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