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Pandey M, Shah SK, Gromiha MM. Computational approaches for identifying disease-causing mutations in proteins. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2023; 139:141-171. [PMID: 38448134 DOI: 10.1016/bs.apcsb.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.
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Affiliation(s)
- Medha Pandey
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - Suraj Kumar Shah
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India; International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama, Japan.
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Matos-Filipe P, Preto AJ, Koukos PI, Mourão J, Bonvin AMJJ, Moreira IS. MENSAdb: a thorough structural analysis of membrane protein dimers. Database (Oxford) 2021; 2021:baab013. [PMID: 33822911 PMCID: PMC8023553 DOI: 10.1093/database/baab013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 01/19/2021] [Accepted: 03/01/2021] [Indexed: 11/14/2022]
Abstract
Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.
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Affiliation(s)
- Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
| | - António J Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
- PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research, University of Coimbra, Coimbra, 3030-789, Portugal
| | - Panagiotis I Koukos
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra 3005-504, Portugal
| | - Alexandre M J J Bonvin
- Bijvoet Centre for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, 3584, CH, Netherlands
| | - Irina S Moreira
- Department of Life Sciences, University of Coimbra, Coimbra, 3000-456, Portugal
- Center for Neuroscience and Cell Biology, Center for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
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Jia K, Jernigan RL. New amino acid substitution matrix brings sequence alignments into agreement with structure matches. Proteins 2021; 89:671-682. [PMID: 33469973 PMCID: PMC8641535 DOI: 10.1002/prot.26050] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 01/08/2021] [Accepted: 01/12/2021] [Indexed: 12/27/2022]
Abstract
Protein sequence matching presently fails to identify many structures that are highly similar, even when they are known to have the same function. The high packing densities in globular proteins lead to interdependent substitutions, which have not previously been considered for amino acid similarities. At present, sequence matching compares sequences based only upon the similarities of single amino acids, ignoring the fact that in densely packed protein, there are additional conservative substitutions representing exchanges between two interacting amino acids, such as a small-large pair changing to a large-small pair substitutions that are not individually so conservative. Here we show that including information for such pairs of substitutions yields improved sequence matches, and that these yield significant gains in the agreements between sequence alignments and structure matches of the same protein pair. The result shows sequence segments matched where structure segments are aligned. There are gains for all 2002 collected cases where the sequence alignments that were not previously congruent with the structure matches. Our results also demonstrate a significant gain in detecting homology for “twilight zone” protein sequences. The amino acid substitution metrics derived have many other potential applications, for annotations, protein design, mutagenesis design, and empirical potential derivation.
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Affiliation(s)
- Kejue Jia
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
| | - Robert L Jernigan
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
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Trivedi R, Nagarajaram HA. Substitution scoring matrices for proteins - An overview. Protein Sci 2020; 29:2150-2163. [PMID: 32954566 DOI: 10.1002/pro.3954] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 01/17/2023]
Abstract
Sequence analysis is the primary and simplest approach to discover structural, functional and evolutionary details of related proteins. All the alignment based approaches of sequence analysis make use of amino acid substitution matrices, and the accuracy of the results largely depends on the type of scoring matrices used to perform alignment tasks. An amino acid substitution matrix is a 20 × 20 matrix in which the individual elements encapsulate the rates at which each of the 20 amino acid residues in proteins are substituted by other amino acid residues over time. In contrast to most globular/ordered proteins whose amino acids composition is considered as standard, there are several classes of proteins (e.g., transmembrane proteins) in which certain types of amino acid (e.g., hydrophobic residues) are enriched. These compositional differences among various classes of proteins are manifested in their underlying residue substitution frequencies. Therefore, each of the compositionally distinct class of proteins or protein segments should be studied using specific scoring matrices that reflect their distinct residue substitution pattern. In this review, we describe the development and application of various substitution scoring matrices peculiar to proteins with standard and biased compositions. Along with most commonly used standard matrices (PAM, BLOSUM, MD and VTML) that act as default parameters in various homologs search and alignment tools, different substitution scoring matrices specific to compositionally distinct class of proteins are discussed in detail.
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Affiliation(s)
- Rakesh Trivedi
- Laboratory of Computational Biology, Centre for DNA Fingerprinting and Diagnostics, Uppal, Hyderabad, Telangana, India.,Graduate School, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Hampapathalu Adimurthy Nagarajaram
- Laboratory of Computational Biology, Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India.,Centre for Modelling, Simulation and Design, University of Hyderabad, Hyderabad, Telangana, India
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Barlowe S, Coan HB, Youker RT. SubVis: an interactive R package for exploring the effects of multiple substitution matrices on pairwise sequence alignment. PeerJ 2017; 5:e3492. [PMID: 28674656 PMCID: PMC5490468 DOI: 10.7717/peerj.3492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2017] [Accepted: 05/27/2017] [Indexed: 01/13/2023] Open
Abstract
Understanding how proteins mutate is critical to solving a host of biological problems. Mutations occur when an amino acid is substituted for another in a protein sequence. The set of likelihoods for amino acid substitutions is stored in a matrix and input to alignment algorithms. The quality of the resulting alignment is used to assess the similarity of two or more sequences and can vary according to assumptions modeled by the substitution matrix. Substitution strategies with minor parameter variations are often grouped together in families. For example, the BLOSUM and PAM matrix families are commonly used because they provide a standard, predefined way of modeling substitutions. However, researchers often do not know if a given matrix family or any individual matrix within a family is the most suitable. Furthermore, predefined matrix families may inaccurately reflect a particular hypothesis that a researcher wishes to model or otherwise result in unsatisfactory alignments. In these cases, the ability to compare the effects of one or more custom matrices may be needed. This laborious process is often performed manually because the ability to simultaneously load multiple matrices and then compare their effects on alignments is not readily available in current software tools. This paper presents SubVis, an interactive R package for loading and applying multiple substitution matrices to pairwise alignments. Users can simultaneously explore alignments resulting from multiple predefined and custom substitution matrices. SubVis utilizes several of the alignment functions found in R, a common language among protein scientists. Functions are tied together with the Shiny platform which allows the modification of input parameters. Information regarding alignment quality and individual amino acid substitutions is displayed with the JavaScript language which provides interactive visualizations for revealing both high-level and low-level alignment information.
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Affiliation(s)
- Scott Barlowe
- Department of Mathematics and Computer Science, Western Carolina University, Cullowhee, NC, United States of America
| | - Heather B Coan
- Department of Biology, Western Carolina University, Cullowhee, NC, United States of America
| | - Robert T Youker
- Department of Biology, Western Carolina University, Cullowhee, NC, United States of America
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Yamada K, Tomii K. Revisiting amino acid substitution matrices for identifying distantly related proteins. ACTA ACUST UNITED AC 2013; 30:317-25. [PMID: 24281694 PMCID: PMC3904525 DOI: 10.1093/bioinformatics/btt694] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Motivation: Although many amino acid substitution matrices have been developed, it has not been well understood which is the best for similarity searches, especially for remote homology detection. Therefore, we collected information related to existing matrices, condensed it and derived a novel matrix that can detect more remote homology than ever. Results: Using principal component analysis with existing matrices and benchmarks, we developed a novel matrix, which we designate as MIQS. The detection performance of MIQS is validated and compared with that of existing general purpose matrices using SSEARCH with optimized gap penalties for each matrix. Results show that MIQS is able to detect more remote homology than the existing matrices on an independent dataset. In addition, the performance of our developed matrix was superior to that of CS-BLAST, which was a novel similarity search method with no amino acid matrix. We also evaluated the alignment quality of matrices and methods, which revealed that MIQS shows higher alignment sensitivity than that with the existing matrix series and CS-BLAST. Fundamentally, these results are expected to constitute good proof of the availability and/or importance of amino acid matrices in sequence analysis. Moreover, with our developed matrix, sophisticated similarity search methods such as sequence–profile and profile–profile comparison methods can be improved further. Availability and implementation: Newly developed matrices and datasets used for this study are available at http://csas.cbrc.jp/Ssearch/. Contact:k-tomii@aist.go.jp Supplementary information:Supplementary data are available at Bioinformatics online
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Affiliation(s)
- Kazunori Yamada
- Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
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Stamm M, Staritzbichler R, Khafizov K, Forrest LR. Alignment of helical membrane protein sequences using AlignMe. PLoS One 2013; 8:e57731. [PMID: 23469223 PMCID: PMC3587630 DOI: 10.1371/journal.pone.0057731] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Accepted: 01/24/2013] [Indexed: 12/20/2022] Open
Abstract
Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments. Existing approaches typically consider membrane-related information either by using membrane-specific substitution matrices or by assigning distinct penalties for gap creation in transmembrane and non-transmembrane regions. Here, we ask whether favoring matching of predicted transmembrane segments within a standard dynamic programming algorithm can improve the accuracy of pairwise membrane protein sequence alignments. We tested various strategies using a specifically designed program called AlignMe. An updated set of homologous membrane protein structures, called HOMEP2, was used as a reference for optimizing the gap penalties. The best of the membrane-protein optimized approaches were then tested on an independent reference set of membrane protein sequence alignments from the BAliBASE collection. When secondary structure (S) matching was combined with evolutionary information (using a position-specific substitution matrix (P)), in an approach we called AlignMePS, the resultant pairwise alignments were typically among the most accurate over a broad range of sequence similarities when compared to available methods. Matching transmembrane predictions (T), in addition to evolutionary information, and secondary-structure predictions, in an approach called AlignMePST, generally reduces the accuracy of the alignments of closely-related proteins in the BAliBASE set relative to AlignMePS, but may be useful in cases of extremely distantly related proteins for which sequence information is less informative. The open source AlignMe code is available at https://sourceforge.net/projects/alignme/, and at http://www.forrestlab.org, along with an online server and the HOMEP2 data set.
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Affiliation(s)
- Marcus Stamm
- Computational Structural Biology Group, Max Planck Institute of Biophysics, Frankfurt am Main, Germany.
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Boratyn GM, Schäffer AA, Agarwala R, Altschul SF, Lipman DJ, Madden TL. Domain enhanced lookup time accelerated BLAST. Biol Direct 2012; 7:12. [PMID: 22510480 PMCID: PMC3438057 DOI: 10.1186/1745-6150-7-12] [Citation(s) in RCA: 555] [Impact Index Per Article: 46.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Accepted: 04/17/2012] [Indexed: 11/10/2022] Open
Abstract
Background BLAST is a commonly-used software package for comparing a query sequence to a database of known sequences; in this study, we focus on protein sequences. Position-specific-iterated BLAST (PSI-BLAST) iteratively searches a protein sequence database, using the matches in round i to construct a position-specific score matrix (PSSM) for searching the database in round i + 1. Biegert and Söding developed Context-sensitive BLAST (CS-BLAST), which combines information from searching the sequence database with information derived from a library of short protein profiles to achieve better homology detection than PSI-BLAST, which builds its PSSMs from scratch. Results We describe a new method, called domain enhanced lookup time accelerated BLAST (DELTA-BLAST), which searches a database of pre-constructed PSSMs before searching a protein-sequence database, to yield better homology detection. For its PSSMs, DELTA-BLAST employs a subset of NCBI’s Conserved Domain Database (CDD). On a test set derived from ASTRAL, with one round of searching, DELTA-BLAST achieves a ROC5000 of 0.270 vs. 0.116 for CS-BLAST. The performance advantage diminishes in iterated searches, but DELTA-BLAST continues to achieve better ROC scores than CS-BLAST. Conclusions DELTA-BLAST is a useful program for the detection of remote protein homologs. It is available under the “Protein BLAST” link at http://blast.ncbi.nlm.nih.gov. Reviewers This article was reviewed by Arcady Mushegian, Nick V. Grishin, and Frank Eisenhaber.
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Affiliation(s)
- Grzegorz M Boratyn
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
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Naveed H, Xu Y, Jackups R, Liang J. Predicting three-dimensional structures of transmembrane domains of β-barrel membrane proteins. J Am Chem Soc 2012; 134:1775-81. [PMID: 22148174 DOI: 10.1021/ja209895m] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
β-Barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank. We have developed a computational method for predicting structures of the transmembrane (TM) domains of β-barrel membrane proteins. Based on physical principles, our method can predict structures of the TM domain of β-barrel membrane proteins of novel topology, including those from eukaryotic mitochondria. Our method is based on a model of physical interactions, a discrete conformational state space, an empirical potential function, as well as a model to account for interstrand loop entropy. We are able to construct three-dimensional atomic structure of the TM domains from sequences for a set of 23 nonhomologous proteins (resolution 1.8-3.0 Å). The median rmsd of TM domains containing 75-222 residues between predicted and measured structures is 3.9 Å for main chain atoms. In addition, stability determinants and protein-protein interaction sites can be predicted. Such predictions on eukaryotic mitochondria outer membrane protein Tom40 and VDAC are confirmed by independent mutagenesis and chemical cross-linking studies. These results suggest that our model captures key components of the organization principles of β-barrel membrane protein assembly.
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Affiliation(s)
- Hammad Naveed
- Department of Bioengineering, University of Illinois at Chicago, 835 South Wolcott Avenue, Chicago, Illinois 60607, USA
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Computational studies of membrane proteins: models and predictions for biological understanding. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2011; 1818:927-41. [PMID: 22051023 DOI: 10.1016/j.bbamem.2011.09.026] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2011] [Revised: 09/22/2011] [Accepted: 09/26/2011] [Indexed: 01/26/2023]
Abstract
We discuss recent progresses in computational studies of membrane proteins based on physical models with parameters derived from bioinformatics analysis. We describe computational identification of membrane proteins and prediction of their topology from sequence, discovery of sequence and spatial motifs, and implications of these discoveries. The detection of evolutionary signal for understanding the substitution pattern of residues in the TM segments and for sequence alignment is also discussed. We further discuss empirical potential functions for energetics of inserting residues in the TM domain, for interactions between TM helices or strands, and their applications in predicting lipid-facing surfaces of the TM domain. Recent progresses in structure predictions of membrane proteins are also reviewed, with further discussions on calculation of ensemble properties such as melting temperature based on simplified state space model. Additional topics include prediction of oligomerization state of membrane proteins, identification of the interfaces for protein-protein interactions, and design of membrane proteins. This article is part of a Special Issue entitled: Protein Folding in Membranes.
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