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  1. Article ; Online: More than just pattern recognition: Prediction of uncommon protein structure features by AI methods.

    Herzberg, Osnat / Moult, John

    Proceedings of the National Academy of Sciences of the United States of America

    2023  Volume 120, Issue 28, Page(s) e2221745120

    Abstract: The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI ... ...

    Abstract The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track
    MeSH term(s) Artificial Intelligence ; Amino Acid Sequence ; Proteins/chemistry ; Protein Structure, Secondary ; Neural Networks, Computer ; Protein Conformation
    Chemical Substances Proteins
    Language English
    Publishing date 2023-07-03
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2221745120
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  2. Article: Comparative Modeling of Protein Structure-Progress and Prospects.

    Moult, John

    Journal of research of the National Institute of Standards and Technology

    2016  Volume 94, Issue 1, Page(s) 79–84

    Abstract: Comparative modeling of protein structure is a process which determines the three-dimensional structure of protein molecules on the basis of amino acid sequence similarity to experimentally linown structures. The procedure is facilitated by the growing ... ...

    Abstract Comparative modeling of protein structure is a process which determines the three-dimensional structure of protein molecules on the basis of amino acid sequence similarity to experimentally linown structures. The procedure is facilitated by the growing database of protein structures obtained from crystallography. In this review a series of stages in the modeling process are identified and discussed. These are: (i) obtaining a reliable amino acid sequence of the structure of interest, (ii) producing a structurally correct sequence alignment, (iii) identifying which structural features are conserved between target and parent structures, (iv) modeling the new pieces of structure, and (v) tests of reliability.
    Language English
    Publishing date 2016-12-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1473724-3
    ISSN 1044-677X ; 0160-1741
    ISSN 1044-677X ; 0160-1741
    DOI 10.6028/jres.094.010
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  3. 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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  4. 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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  5. Article ; Online: MecCog: a knowledge representation framework for genetic disease mechanism.

    Kundu, Kunal / Darden, Lindley / Moult, John

    Bioinformatics (Oxford, England)

    2021  Volume 37, Issue 22, Page(s) 4180–4186

    Abstract: Motivation: Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams and network graphs. Integration and structuring of such ... ...

    Abstract Motivation: Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams and network graphs. Integration and structuring of such mechanistic information greatly enhances its utility.
    Results: MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology and depiction of knowledge gaps, ambiguities and uncertainties. The web platform enables a user to construct, store, publish, browse, query and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites and critical future experiments.
    Availability and implementation: The MecCog framework is freely available at http://www.meccog.org.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Phenotype ; Genetic Diseases, Inborn ; Computational Biology
    Language English
    Publishing date 2021-05-18
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btab432
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  6. 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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  7. Article: Comparative modeling in structural genomics.

    Moult, John

    Structure (London, England : 1993)

    2008  Volume 16, Issue 1, Page(s) 14–16

    MeSH term(s) Genomics ; Models, Molecular ; Models, Structural ; National Institutes of Health (U.S.) ; Proteins/chemistry ; Proteins/genetics ; United States
    Chemical Substances Proteins
    Language English
    Publishing date 2008-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1213087-4
    ISSN 1878-4186 ; 0969-2126
    ISSN (online) 1878-4186
    ISSN 0969-2126
    DOI 10.1016/j.str.2007.12.001
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  8. Article ; Online: A tribute to Anna Tramontano (1957-2017).

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

    Proteins

    2017  Volume 86 Suppl 1, Page(s) 5–6

    Language English
    Publishing date 2017-11-20
    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.25406
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  9. Article ; Online: MecCog: A knowledge representation framework for genetic disease mechanism

    Kundu, Kunal / Darden, Lindley / Moult, John

    bioRxiv

    Abstract: Motivation Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams, and network graphs. Integration and structuring of such ... ...

    Abstract Motivation Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams, and network graphs. Integration and structuring of such mechanistic information will greatly enhance its utility. Results MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology, and depiction of knowledge gaps, ambiguities, and uncertainties. The web platform enables a user to construct, store, publish, browse, query, and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites, and critical future experiments. Availability and Implementation The MecCog framework is freely available at http://www.meccog.org. Contact jmoult@umd.edu Supplementary information Supplementary material is available at Bioinformatics online.
    Keywords covid19
    Publisher BioRxiv
    Document type Article ; Online
    DOI 10.1101/2020.09.03.282012
    Database COVID19

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  10. Article ; Online: Genetic Basis of Common Human Disease: Insight into the Role of Missense SNPs from Genome-Wide Association Studies.

    Pal, Lipika R / Moult, John

    Journal of molecular biology

    2015  Volume 427, Issue 13, Page(s) 2271–2289

    Abstract: Recent genome-wide association studies (GWAS) have led to the reliable identification of single nucleotide polymorphisms (SNPs) at a number of loci associated with increased risk of specific common human diseases. Each such locus implicates multiple ... ...

    Abstract Recent genome-wide association studies (GWAS) have led to the reliable identification of single nucleotide polymorphisms (SNPs) at a number of loci associated with increased risk of specific common human diseases. Each such locus implicates multiple possible candidate SNPs for involvement in disease mechanism. A variety of mechanisms may link the presence of an SNP to altered in vivo gene product function and hence contribute to disease risk. Here, we report an analysis of the role of one of these mechanisms, missense SNPs (msSNPs) in proteins in seven complex trait diseases. Linkage disequilibrium information was used to identify possible candidate msSNPs associated with increased disease risk at each of 356 loci for the seven diseases. Two computational methods were used to estimate which of these SNPs has a significant impact on in vivo protein function. 69% of the loci have at least one candidate msSNP and 33% have at least one predicted high-impact msSNP. In some cases, these SNPs are in well-established disease-related proteins, such as MST1 (macrophage stimulating 1) for Crohn's disease. In others, they are in proteins identified by GWAS as likely candidates for disease relevance, but previously without known mechanism, such as ADAMTS13 (ADAM metallopeptidase with thrombospondin type 1 motif, 13) for coronary artery disease. In still other cases, the missense SNPs are in proteins not previously suggested as disease candidates, such as TUBB1 (tubulin, beta 1, class VI) for hypertension. Together, these data support a substantial role for this class of SNPs in susceptibility to common human disease.
    MeSH term(s) ADAM Proteins/genetics ; ADAMTS13 Protein ; Alleles ; Arthritis, Rheumatoid/genetics ; Coronary Artery Disease/genetics ; Crohn Disease/genetics ; Diabetes Mellitus, Type 1/genetics ; Genetic Predisposition to Disease ; Genome-Wide Association Study/methods ; Hepatocyte Growth Factor/genetics ; Humans ; Linkage Disequilibrium ; Polymorphism, Single Nucleotide ; Protein Conformation ; Proteins/chemistry ; Proteins/genetics ; Proteins/metabolism ; Proto-Oncogene Proteins/genetics
    Chemical Substances Proteins ; Proto-Oncogene Proteins ; macrophage stimulating protein ; Hepatocyte Growth Factor (67256-21-7) ; ADAM Proteins (EC 3.4.24.-) ; ADAMTS13 Protein (EC 3.4.24.87) ; ADAMTS13 protein, human (EC 3.4.24.87)
    Language English
    Publishing date 2015-05-01
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 80229-3
    ISSN 1089-8638 ; 0022-2836
    ISSN (online) 1089-8638
    ISSN 0022-2836
    DOI 10.1016/j.jmb.2015.04.014
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