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Garg A, Pal D. Inferring metal binding sites in flexible regions of proteins. Proteins 2021; 89:1125-1133. [PMID: 33864411 DOI: 10.1002/prot.26085] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 03/15/2021] [Accepted: 04/12/2021] [Indexed: 12/20/2022]
Abstract
Metal ions are central to the molecular function of many proteins. Thus their knowledge in experimentally determined structure is important; however, such structures often lose bound metal ions during sample preparation. Identification of these metal-binding site(s) becomes difficult when the receptor is novel and/or their conformations differ in the bound/unbound states. Locating such sites in theoretical models also poses a challenge due to the uncertainties with side-chain modeling. We address the problem by employing the Geometric Hashing algorithm to create a template library of functionally important binding sites and match query structures with the available templates. The matching is done on the structure ensemble obtained from coarse-grained molecular dynamics simulation, where metal-specific amino acids are screened to infer the true site. Test on 1347 non-redundant monomer protein structures show that Ca2+ , Zn2+ , Mg2+ , Cu2+ , and Fe3+ binding site residues can be classified at 0.92, 0.95, 0.80, 0.90, and 0.92 aggregate performance (out of 1) across all possible thresholds. The performance for Ca2+ and Zn2+ is notably superior in comparison to state-of-the-art methods like IonCom and MIB. Specific case studies show that additionally predicted metal-binding site residues in proteins have features necessary for ion binding. These include new sites not predicted by other methods. The use of coarse-grained dynamics thus provides a generalized approach to improve metal-binding site prediction. The work is expected to contribute to improving our ability to correctly predict protein molecular function where knowledge of metal binding is a key requirement.
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Affiliation(s)
- Aditi Garg
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
| | - Debnath Pal
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, India
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2
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Yang L, Jiao X. Distinguishing Enzymes and Non-enzymes Based on Structural Information with an Alignment Free Approach. Curr Bioinform 2021. [DOI: 10.2174/1574893615666200324134037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Knowledge of protein functions is very crucial for the understanding of biological processes. Experimental methods for protein function prediction are powerless to treat the growing amount of protein sequence and structure data.
Objective:
To develop some computational techniques for the protein function prediction.
Method:
Based on the residue interaction network features and the motion mode information, an
SVM model was constructed and used as the predictor. The role of these features was analyzed
and some interesting results were obtained.
Results:
An alignment-free method for the classification of enzyme and non-enzyme is developed in this work. There is not any single feature that occupies a dominant position in the prediction process. The topological and the information-theoretic residue interaction network features have a better performance. The combination of the fast mode and the slow mode can get a better explanation for the classification result.
Conclusion:
The method proposed in this paper can act as a classifier for the enzymes and nonenzymes.
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Affiliation(s)
- Lifeng Yang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600,China
| | - Xiong Jiao
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, 030600,China
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4
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Voskanian A, Katsonis P, Lichtarge O, Pejaver V, Radivojac P, Mooney SD, Capriotti E, Bromberg Y, Wang Y, Miller M, Martelli PL, Savojardo C, Babbi G, Casadio R, Cao Y, Sun Y, Shen Y, Garg A, Pal D, Yu Y, Huff CD, Tavtigian SV, Young E, Neuhausen SL, Ziv E, Pal LR, Andreoletti G, Brenner S, Kann MG. Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer. Hum Mutat 2019; 40:1612-1622. [PMID: 31241222 PMCID: PMC6744287 DOI: 10.1002/humu.23849] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/23/2019] [Accepted: 06/21/2019] [Indexed: 01/22/2023]
Abstract
The availability of disease-specific genomic data is critical for developing new computational methods that predict the pathogenicity of human variants and advance the field of precision medicine. However, the lack of gold standards to properly train and benchmark such methods is one of the greatest challenges in the field. In response to this challenge, the scientific community is invited to participate in the Critical Assessment for Genome Interpretation (CAGI), where unpublished disease variants are available for classification by in silico methods. As part of the CAGI-5 challenge, we evaluated the performance of 18 submissions and three additional methods in predicting the pathogenicity of single nucleotide variants (SNVs) in checkpoint kinase 2 (CHEK2) for cases of breast cancer in Hispanic females. As part of the assessment, the efficacy of the analysis method and the setup of the challenge were also considered. The results indicated that though the challenge could benefit from additional participant data, the combined generalized linear model analysis and odds of pathogenicity analysis provided a framework to evaluate the methods submitted for SNV pathogenicity identification and for comparison to other available methods. The outcome of this challenge and the approaches used can help guide further advancements in identifying SNV-disease relationships.
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Affiliation(s)
- Alin Voskanian
- Department of Biological Sciences, University of Maryland, Baltimore County, MD, U.S.A
| | - Panagiotis Katsonis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, U.S.A
| | - Olivier Lichtarge
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, U.S.A
- Department of Pharmacology, Computational and Integrative Biomedical Research Center, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Vikas Pejaver
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, U.S.A
- The eScience Institute, University of Washington, Seattle, Washington, U.S.A
| | - Predrag Radivojac
- Khoury College of Computer and Information Sciences, Northeastern University, Boston, Massachusetts, U.S.A
| | - Sean D. Mooney
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, U.S.A
| | - Emidio Capriotti
- BioFolD Unit, Department of Pharmacy and Biotechnology (FaBiT), University of Bologna, Via Selmi 3, 40126 Bologna, Italy
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, U.S.A
- Department of Genetics, Rutgers University, New Brunswick, New Jersey, U.S.A
- Technical University of Munich Institute for Advanced Study, (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, U.S.A
| | - Max Miller
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, U.S.A
| | - Pier Luigi Martelli
- Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Via F. Selmi 3, Bologna, 40126, Italy
| | - Castrense Savojardo
- Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Via F. Selmi 3, Bologna, 40126, Italy
| | - Giulia Babbi
- Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Via F. Selmi 3, Bologna, 40126, Italy
| | - Rita Casadio
- Biocomputing Group, BiGeA/Giorgio Prodi Interdepartmental Center for Cancer Research, University of Bologna, Via F. Selmi 3, Bologna, 40126, Italy
| | - Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, U.S.A
| | - Yuanfei Sun
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, U.S.A
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, U.S.A
| | - Aditi Garg
- Department of Computational and Data Sciences Indian Institute of Science, Bengaluru 560 012, India
| | - Debnath Pal
- Department of Computational and Data Sciences Indian Institute of Science, Bengaluru 560 012, India
| | - Yao Yu
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, U.S.A
| | - Chad D. Huff
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, U.S.A
| | - Sean V. Tavtigian
- Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84132, U.S.A
| | - Erin Young
- Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, UT 84132, U.S.A
| | - Susan L. Neuhausen
- Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, CA, 91010 U.S.A
| | - Elad Ziv
- Division of General Internal Medicine, Department of Medicine, Institute of Human Genetics, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA,U.S.A
| | - Lipika R. Pal
- Institute for Bioscience and Biotechnology Research, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Steven Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720, USA
| | - Maricel G. Kann
- Department of Biological Sciences, University of Maryland, Baltimore County, MD, U.S.A
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5
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Das S, Bhadra P, Ramakumar S, Pal D. Molecular Dynamics Information Improves cis-Peptide-Based Function Annotation of Proteins. J Proteome Res 2017. [PMID: 28633522 DOI: 10.1021/acs.jproteome.7b00217] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
cis-Peptide bonds, whose occurrence in proteins is rare but evolutionarily conserved, are implicated to play an important role in protein function. This has led to their previous use in a homology-independent, fragment-match-based protein function annotation method. However, proteins are not static molecules; dynamics is integral to their activity. This is nicely epitomized by the geometric isomerization of cis-peptide to trans form for molecular activity. Hence we have incorporated both static (cis-peptide) and dynamics information to improve the prediction of protein molecular function. Our results show that cis-peptide information alone cannot detect functional matches in cases where cis-trans isomerization exists but 3D coordinates have been obtained for only the trans isomer or when the cis-peptide bond is incorrectly assigned as trans. On the contrary, use of dynamics information alone includes false-positive matches for cases where fragments with similar secondary structure show similar dynamics, but the proteins do not share a common function. Combining the two methods reduces errors while detecting the true matches, thereby enhancing the utility of our method in function annotation. A combined approach, therefore, opens up new avenues of improving existing automated function annotation methodologies.
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Affiliation(s)
- Sreetama Das
- Department of Physics and ‡Department of Computational and Data Sciences, Indian Institute of Science , Bangalore 560012, India
| | - Pratiti Bhadra
- Department of Physics and ‡Department of Computational and Data Sciences, Indian Institute of Science , Bangalore 560012, India
| | - Suryanarayanarao Ramakumar
- Department of Physics and ‡Department of Computational and Data Sciences, Indian Institute of Science , Bangalore 560012, India
| | - Debnath Pal
- Department of Physics and ‡Department of Computational and Data Sciences, Indian Institute of Science , Bangalore 560012, India
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6
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Bhadra P, Pal D. Pipeline for inferring protein function from dynamics using coarse-grained molecular mechanics forcefield. Comput Biol Med 2017; 83:134-142. [PMID: 28279862 DOI: 10.1016/j.compbiomed.2017.02.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2016] [Revised: 02/18/2017] [Accepted: 02/22/2017] [Indexed: 11/28/2022]
Abstract
Dynamics is integral to the function of proteins, yet the use of molecular dynamics (MD) simulation as a technique remains under-explored for molecular function inference. This is more important in the context of genomics projects where novel proteins are determined with limited evolutionary information. Recently we developed a method to match the query protein's flexible segments to infer function using a novel approach combining analysis of residue fluctuation-graphs and auto-correlation vectors derived from coarse-grained (CG) MD trajectory. The method was validated on a diverse dataset with sequence identity between proteins as low as 3%, with high function-recall rates. Here we share its implementation as a publicly accessible web service, named DynFunc (Dynamics Match for Function) to query protein function from ≥1 µs long CG dynamics trajectory information of protein subunits. Users are provided with the custom-developed coarse-grained molecular mechanics (CGMM) forcefield to generate the MD trajectories for their protein of interest. On upload of trajectory information, the DynFunc web server identifies specific flexible regions of the protein linked to putative molecular function. Our unique application does not use evolutionary information to infer molecular function from MD information and can, therefore, work for all proteins, including moonlighting and the novel ones, whenever structural information is available. Our pipeline is expected to be of utility to all structural biologists working with novel proteins and interested in moonlighting functions.
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Affiliation(s)
- Pratiti Bhadra
- Institute Mathematics Initiative, Indian Institute of Science, Bengaluru 560012, India
| | - Debnath Pal
- Institute Mathematics Initiative, Indian Institute of Science, Bengaluru 560012, India; Computational and Data Sciences, Indian Institute of Science, Bengaluru 560012, India.
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7
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Das S, Ramakumar S, Pal D. Identifying functionally important cis-peptide containing segments in proteins and their utility in molecular function annotation. FEBS J 2014; 281:5602-21. [PMID: 25291238 DOI: 10.1111/febs.13100] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 09/21/2014] [Accepted: 10/03/2014] [Indexed: 01/09/2023]
Abstract
Cis-peptide embedded segments are rare in proteins but often highlight their important role in molecular function when they do occur. The high evolutionary conservation of these segments illustrates this observation almost universally, although no attempt has been made to systematically use this information for the purpose of function annotation. In the present study, we demonstrate how geometric clustering and level-specific Gene Ontology molecular-function terms (also known as annotations) can be used in a statistically significant manner to identify cis-embedded segments in a protein linked to its molecular function. The present study identifies novel cis-peptide fragments, which are subsequently used for fragment-based function annotation. Annotation recall benchmarks interpreted using the receiver-operator characteristic plot returned an area-under-curve > 0.9, corroborating the utility of the annotation method. In addition, we identified cis-peptide fragments occurring in conjunction with functionally important trans-peptide fragments, providing additional insights into molecular function. We further illustrate the applicability of our method in function annotation where homology-based annotation transfer is not possible. The findings of the present study add to the repertoire of function annotation approaches and also facilitate engineering, design and allied studies around the cis-peptide neighborhood of proteins.
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Affiliation(s)
- Sreetama Das
- Department of Physics, Indian Institute of Science, Bangalore, India
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