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  1. Article ; Online: To split or not to split: CASP15 targets and their processing into tertiary structure evaluation units.

    Kryshtafovych, Andriy / Rigden, Daniel J

    Proteins

    2023  Volume 91, Issue 12, Page(s) 1558–1570

    Abstract: Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. ... ...

    Abstract Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. Models were then evaluated against these domains and their combinations. The domains were joined into larger EUs if predictors' performance on the combined units was similar to that on individual domains. Alternatively, if most predictors performed better on the individual domains, then they were retained as EUs. As a result, 112 evaluation units were created from 77 tertiary structure prediction targets. The EUs were assigned to four prediction classes roughly corresponding to target difficulty categories in previous CASPs: TBM (template-based modeling, easy or hard), FM (free modeling), and the TBM/FM overlap category. More than a third of CASP15 EUs were attributed to the historically most challenging FM class, where homology or structural analogy to proteins of known fold cannot be detected.
    MeSH term(s) Protein Folding ; Models, Molecular ; Computational Biology ; Databases, Protein ; Proteins/chemistry
    Chemical Substances Proteins
    Language English
    Publishing date 2023-05-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26533
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  2. Article ; Online: The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

    Ozden, Burcu / Kryshtafovych, Andriy / Karaca, Ezgi

    Proteins

    2023  Volume 91, Issue 12, Page(s) 1636–1657

    Abstract: In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). ...

    Abstract In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.
    MeSH term(s) Furylfuramide ; Algorithms ; Models, Molecular ; Proteins/chemistry ; Artificial Intelligence ; Protein Conformation ; Computational Biology/methods
    Chemical Substances Furylfuramide (054NR2135Y) ; Proteins
    Language English
    Publishing date 2023-10-20
    Publishing country United States
    Document type Journal Article
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26598
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.

    Ozden, Burcu / Kryshtafovych, Andriy / Karaca, Ezgi

    bioRxiv : the preprint server for biology

    2023  

    Abstract: In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). ... ...

    Abstract In CASP15, 87 predictors submitted around 11,000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact prediction, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes remains also challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved the 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.
    Language English
    Publishing date 2023-09-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.07.10.548341
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Critical assessment of methods of protein structure prediction (CASP)-Round XV.

    Kryshtafovych, Andriy / Schwede, Torsten / Topf, Maya / Fidelis, Krzysztof / Moult, John

    Proteins

    2023  Volume 91, Issue 12, Page(s) 1539–1549

    Abstract: Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. ... ...

    Abstract Computing protein structure from amino acid sequence information has been a long-standing grand challenge. Critical assessment of structure prediction (CASP) conducts community experiments aimed at advancing solutions to this and related problems. Experiments are conducted every 2 years. The 2020 experiment (CASP14) saw major progress, with the second generation of deep learning methods delivering accuracy comparable with experiment for many single proteins. There is an expectation that these methods will have much wider application in computational structural biology. Here we summarize results from the most recent experiment, CASP15, in 2022, with an emphasis on new deep learning-driven progress. Other papers in this special issue of proteins provide more detailed analysis. For single protein structures, the AlphaFold2 deep learning method is still superior to other approaches, but there are two points of note. First, although AlphaFold2 was the core of all the most successful methods, there was a wide variety of implementation and combination with other methods. Second, using the standard AlphaFold2 protocol and default parameters only produces the highest quality result for about two thirds of the targets, and more extensive sampling is required for the others. The major advance in this CASP is the enormous increase in the accuracy of computed protein complexes, achieved by the use of deep learning methods, although overall these do not fully match the performance for single proteins. Here too, AlphaFold2 based method perform best, and again more extensive sampling than the defaults is often required. Also of note are the encouraging early results on the use of deep learning to compute ensembles of macromolecular structures. Critically for the usability of computed structures, for both single proteins and protein complexes, deep learning derived estimates of both local and global accuracy are of high quality, however the estimates in interface regions are slightly less reliable. CASP15 also included computation of RNA structures for the first time. Here, the classical approaches produced better agreement with experiment than the new deep learning ones, and accuracy is limited. Also, for the first time, CASP included the computation of protein-ligand complexes, an area of special interest for drug design. Here too, classical methods were still superior to deep learning ones. Many new approaches were discussed at the CASP conference, and it is clear methods will continue to advance.
    MeSH term(s) Protein Conformation ; Models, Molecular ; Proteins/chemistry ; Amino Acid Sequence ; Computational Biology/methods
    Chemical Substances Proteins
    Language English
    Publishing date 2023-11-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26617
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  5. Article ; Online: Petabase-Scale Homology Search for Structure Prediction.

    Lee, Sewon / Kim, Gyuri / Karin, Eli Levy / Mirdita, Milot / Park, Sukhwan / Chikhi, Rayan / Babaian, Artem / Kryshtafovych, Andriy / Steinegger, Martin

    Cold Spring Harbor perspectives in biology

    2024  

    Abstract: The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, ... ...

    Abstract The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.
    Language English
    Publishing date 2024-02-05
    Publishing country United States
    Document type Journal Article
    ISSN 1943-0264
    ISSN (online) 1943-0264
    DOI 10.1101/cshperspect.a041465
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  6. Article ; Online: Breaking the conformational ensemble barrier: Ensemble structure modeling challenges in CASP15.

    Kryshtafovych, Andriy / Montelione, Gaetano T / Rigden, Daniel J / Mesdaghi, Shahram / Karaca, Ezgi / Moult, John

    Proteins

    2023  Volume 91, Issue 12, Page(s) 1903–1911

    Abstract: For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for ...

    Abstract For the first time, the 2022 CASP (Critical Assessment of Structure Prediction) community experiment included a section on computing multiple conformations for protein and RNA structures. There was full or partial success in reproducing the ensembles for four of the nine targets, an encouraging result. For protein structures, enhanced sampling with variations of the AlphaFold2 deep learning method was by far the most effective approach. One substantial conformational change caused by a single mutation across a complex interface was accurately reproduced. In two other assembly modeling cases, methods succeeded in sampling conformations near to the experimental ones even though environmental factors were not included in the calculations. An experimentally derived flexibility ensemble allowed a single accurate RNA structure model to be identified. Difficulties included how to handle sparse or low-resolution experimental data and the current lack of effective methods for modeling RNA/protein complexes. However, these and other obstacles appear addressable.
    MeSH term(s) Protein Conformation ; Proteins/chemistry ; Mutation ; RNA
    Chemical Substances Proteins ; RNA (63231-63-0)
    Language English
    Publishing date 2023-10-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26584
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  7. Article ; Online: Assessment of protein model structure accuracy estimation in CASP14: Old and new challenges.

    Kwon, Sohee / Won, Jonghun / Kryshtafovych, Andriy / Seok, Chaok

    Proteins

    2021  Volume 89, Issue 12, Page(s) 1940–1948

    Abstract: In CASP, blind testing of model accuracy estimation methods has been conducted on models submitted by tertiary structure prediction servers. In CASP14, model accuracy estimation results were evaluated in terms of both global and local structure accuracy, ...

    Abstract In CASP, blind testing of model accuracy estimation methods has been conducted on models submitted by tertiary structure prediction servers. In CASP14, model accuracy estimation results were evaluated in terms of both global and local structure accuracy, as in the previous CASPs. Unlike the previous CASPs that did not show pronounced improvements in performance, the best single-model method (from the Baker group) showed an improved performance in CASP14, particularly in evaluating global structure accuracy when compared to both the best single-model methods in previous CASPs and the best multi-model methods in the current CASP. Although the CASP14 experiment on model accuracy estimation did not deal with the structures generated by AlphaFold2, new challenges that have arisen due to the success of AlphaFold2 are discussed.
    MeSH term(s) Computational Biology ; Models, Molecular ; Protein Conformation ; Proteins/chemistry ; Proteins/metabolism ; Reproducibility of Results ; Sequence Analysis, Protein/methods ; Software
    Chemical Substances Proteins
    Language English
    Publishing date 2021-08-05
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26192
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  8. Article ; Online: Cryo-EM targets in CASP14.

    Cragnolini, Tristan / Kryshtafovych, Andriy / Topf, Maya

    Proteins

    2021  Volume 89, Issue 12, Page(s) 1949–1958

    Abstract: Structures of seven CASP14 targets were determined using cryo-electron microscopy (cryo-EM) technique with resolution between 2.1 and 3.8 Å. We provide an evaluation of the submitted models versus the experimental data (cryo-EM density maps) and ... ...

    Abstract Structures of seven CASP14 targets were determined using cryo-electron microscopy (cryo-EM) technique with resolution between 2.1 and 3.8 Å. We provide an evaluation of the submitted models versus the experimental data (cryo-EM density maps) and experimental reference structures built into the maps. The accuracy of models is measured in terms of coordinate-to-density and coordinate-to-coordinate fit. A-posteriori refinement of the most accurate models in their corresponding cryo-EM density resulted in structures that are close to the reference structure, including some regions with better fit to the density. Regions that were found to be less "refineable" correlate well with regions of high diversity between the CASP models and low goodness-of-fit to density in the reference structure.
    MeSH term(s) Computational Biology ; Cryoelectron Microscopy/methods ; Models, Molecular ; Protein Conformation ; Proteins/chemistry ; Proteins/metabolism ; Sequence Analysis, Protein ; Software
    Chemical Substances Proteins
    Language English
    Publishing date 2021-09-16
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26216
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  9. Article ; Online: Assessment of the CASP14 assembly predictions.

    Ozden, Burcu / Kryshtafovych, Andriy / Karaca, Ezgi

    Proteins

    2021  Volume 89, Issue 12, Page(s) 1787–1799

    Abstract: In CASP14, 39 research groups submitted more than 2500 3D models on 22 protein complexes. In general, the community performed well in predicting the fold of the assemblies (for 80% of the targets), although it faced significant challenges in reproducing ... ...

    Abstract In CASP14, 39 research groups submitted more than 2500 3D models on 22 protein complexes. In general, the community performed well in predicting the fold of the assemblies (for 80% of the targets), although it faced significant challenges in reproducing the native contacts. This is especially the case for the complexes without whole-assembly templates. The leading predictor, BAKER-experimental, used a methodology combining classical techniques (template-based modeling, protein docking) with deep learning-based contact predictions and a fold-and-dock approach. The Venclovas team achieved the runner-up position with template-based modeling and docking. By analyzing the target interfaces, we showed that the complexes with depleted charged contacts or dominating hydrophobic interactions were the most challenging ones to predict. We also demonstrated that if AlphaFold2 predictions were at hand, the interface prediction challenge could be alleviated for most of the targets. All in all, it is evident that new approaches are needed for the accurate prediction of assemblies, which undoubtedly will expand on the significant improvements in the tertiary structure prediction field.
    MeSH term(s) Computational Biology ; Databases, Protein ; Models, Molecular ; Protein Conformation ; Protein Structure, Quaternary ; Proteins/chemistry ; Proteins/metabolism ; Sequence Analysis, Protein ; Software
    Chemical Substances Proteins
    Language English
    Publishing date 2021-08-31
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26199
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  10. Article ; Online: Critical assessment of methods of protein structure prediction (CASP)-Round XIV.

    Kryshtafovych, Andriy / Schwede, Torsten / Topf, Maya / Fidelis, Krzysztof / Moult, John

    Proteins

    2021  Volume 89, Issue 12, Page(s) 1607–1617

    Abstract: Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results ...

    Abstract Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14), deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein-folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance. Here, we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter-residue contacts and distances, are also described.
    MeSH term(s) Amino Acid Sequence ; Computational Biology ; Models, Statistical ; Molecular Dynamics Simulation ; Protein Conformation ; Protein Folding ; Proteins/chemistry ; Proteins/metabolism ; Sequence Analysis, Protein ; Software
    Chemical Substances Proteins
    Language English
    Publishing date 2021-10-07
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 806683-8
    ISSN 1097-0134 ; 0887-3585
    ISSN (online) 1097-0134
    ISSN 0887-3585
    DOI 10.1002/prot.26237
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