1
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Planas-Iglesias J, Marques SM, Pinto GP, Musil M, Stourac J, Damborsky J, Bednar D. Computational design of enzymes for biotechnological applications. Biotechnol Adv 2021; 47:107696. [PMID: 33513434 DOI: 10.1016/j.biotechadv.2021.107696] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/12/2021] [Accepted: 01/13/2021] [Indexed: 12/14/2022]
Abstract
Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.
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Affiliation(s)
- Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Sérgio M Marques
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Gaspar P Pinto
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Milos Musil
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic; IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 61266 Brno, Czech Republic
| | - Jan Stourac
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic.
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, Kamenice 5/A13, 625 00 Brno, Czech Republic.
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2
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Abstract
Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models and methods. Potential function parameters can be easily extracted; however, it is usually hard to directly obtain the calculated atom pairwise energies from scoring functions. Amber, as one of the most popular suites of modeling programs, has an extensive history and library of force field potential functions. In this work, we directly used the force field parameters in ff94 and ff14SB from Amber and encoded them to calculate atom pairwise energies for different interactions. Two sets of structures (single amino acid set and a dipeptide set) were used to evaluate the performance of our encoded Amber potentials. From the comparison results between energy terms obtained from our encoding and Amber, we find energy difference within ±0.06 kcal/mol for all tested structures. Previously we have shown that the Random Forest (RF) model can help to emphasize more important atom pairwise interactions and ignore insignificant ones [Pei, J.; Zheng, Z.; Merz, K. M. J. Chem. Inf. Model. 2019, 59, 1919-1929]. Here, as an example of combining ML methods with traditional potential functions, we followed the same work flow to combine the RF models with force field potential functions from Amber. To determine the performance of our RF models with force field potential functions, 224 different protein native-decoy systems were used as our training and testing sets We find that the RF models with ff94 and ff14SB force field parameters outperformed all other scoring functions (RF models with KECSA2, RWplus, DFIRE, dDFIRE, and GOAP) considered in this work for native structure detection, and they performed similarly in detecting the best decoy. Through inclusion of best decoy to decoy comparisons in building our RF models, we were able to generate models that outperformed the score functions tested herein both on accuracy and best decoy detection, again showing the performance and flexibility of our RF models to tackle this problem. Finally, the importance of the RF algorithm and force field parameters were also tested and the comparison results suggest that both the RF algorithm and force field potentials are important with the ML scoring function achieving its best performance only by combining them together. All code and data used in this work are available at https://github.com/JunPei000/FFENCODER_for_Protein_Folding_Pose_Selection.
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Affiliation(s)
- Jun Pei
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lin Frank Song
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry and the Department of Biochemistry and Molecular Biology, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
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3
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Zaucha J, Heinzinger M, Kulandaisamy A, Kataka E, Salvádor ÓL, Popov P, Rost B, Gromiha MM, Zhorov BS, Frishman D. Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins. Brief Bioinform 2020; 22:5872174. [PMID: 32672331 DOI: 10.1093/bib/bbaa132] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/26/2020] [Accepted: 05/28/2020] [Indexed: 12/18/2022] Open
Abstract
Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.
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Affiliation(s)
- Jan Zaucha
- Department of Bioinformatics of the TUM School of Life Sciences Weihenstephan in Freising, Germany
| | - Michael Heinzinger
- Department of Informatics, Bioinformatics and Computational Biology of the TUM Faculty of Informatics in Garching, Germany
| | - A Kulandaisamy
- Department of Biotechnology of the IIT Bhupat and Jyoti Mehta School of BioSciences in Madras, India
| | - Evans Kataka
- Department of Bioinformatics of the TUM School of Life Sciences Weihenstephan in Freising, Germany
| | - Óscar Llorian Salvádor
- Department of Informatics, Bioinformatics and Computational Biology of the TUM Faculty of Informatics in Garching, Germany
| | - Petr Popov
- Center for Computational and Data-Intensive Science and Engineering of the Skolkovo Institute of Science and Technology in Moscow, Russia
| | - Burkhard Rost
- Department of Informatics, Bioinformatics and Computational Biology at the TUM Faculty of Informatics in Garching, Germany
| | | | - Boris S Zhorov
- Department of Biochemistry and Biomedical Sciences, McMaster University in Hamilton, Canada
| | - Dmitrij Frishman
- Department of Bioinformatics at the TUM School of Life Sciences Weihenstephan in Freising, Germany
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4
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Marabotti A, Scafuri B, Facchiano A. Predicting the stability of mutant proteins by computational approaches: an overview. Brief Bioinform 2020; 22:5850907. [PMID: 32496523 DOI: 10.1093/bib/bbaa074] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/07/2020] [Accepted: 04/10/2020] [Indexed: 01/06/2023] Open
Abstract
A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.
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5
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Pandurangan AP, Blundell TL. Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning. Protein Sci 2020; 29:247-257. [PMID: 31693276 PMCID: PMC6933854 DOI: 10.1002/pro.3774] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/31/2019] [Accepted: 10/31/2019] [Indexed: 02/02/2023]
Abstract
Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.
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Affiliation(s)
- Arun Prasad Pandurangan
- Department of BiochemistryUniversity of CambridgeCambridgeUK
- MRC Laboratory of Molecular BiologyCambridgeUK
| | - Tom L. Blundell
- Department of BiochemistryUniversity of CambridgeCambridgeUK
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6
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Xu G, Ma T, Wang Q, Ma J. OPUS-SSF: A side-chain-inclusive scoring function for ranking protein structural models. Protein Sci 2019; 28:1157-1162. [PMID: 30919509 DOI: 10.1002/pro.3608] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 03/21/2019] [Accepted: 03/27/2019] [Indexed: 12/21/2022]
Abstract
We introduce a side-chain-inclusive scoring function, named OPUS-SSF, for ranking protein structural models. The method builds a scoring function based on the native distributions of the coordinate components of certain anchoring points in a local molecular system for peptide segments of 5, 7, 9, and 11 residues in length. Differing from our previous OPUS-CSF [Xu et al., Protein Sci. 2018; 27: 286-292], which exclusively uses main chain information, OPUS-SSF employs anchoring points on side chains so that the effect of side chains is taken into account. The performance of OPUS-SSF was tested on 15 decoy sets containing totally 603 proteins, and 571 of them had their native structures recognized from their decoys. Similar to OPUS-CSF, OPUS-SSF does not employ the Boltzmann formula in constructing scoring functions. The results indicate that OPUS-SSF has achieved a significant improvement on decoy recognition and it should be a very useful tool for protein structural prediction and modeling.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, Texas 77005.,Department of Bioengineering, Rice University, Houston, Texas 77005
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing 100084, People's Republic of China.,Applied Physics Program, Rice University, Houston, Texas 77005.,Department of Bioengineering, Rice University, Houston, Texas 77005.,Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, Texas 77030
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7
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Pei J, Zheng Z, Merz KM. Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection. J Chem Inf Model 2019; 59:1919-1929. [DOI: 10.1021/acs.jcim.8b00734] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jun Pei
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Zheng Zheng
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M. Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
- Institute for Cyber Enabled Research, Michigan State University, 567 Wilson Road, East Lansing, Michigan 48824, United States
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8
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Musil M, Konegger H, Hon J, Bednar D, Damborsky J. Computational Design of Stable and Soluble Biocatalysts. ACS Catal 2018. [DOI: 10.1021/acscatal.8b03613] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Milos Musil
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - Hannes Konegger
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Hon
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- IT4Innovations Centre of Excellence, Faculty of Information Technology, Brno University of Technology, 612 66 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Centre for Toxic Compounds in the Environment (RECETOX), and Department of Experimental Biology, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 53, 656 91 Brno, Czech Republic
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9
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Panja AS, Nag A, Bandopadhyay B, Maiti S. Protein Stability Determination (PSD): A Tool for Proteomics Analysis. Curr Bioinform 2018. [DOI: 10.2174/1574893613666180315121614] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Protein Stability Determination (PSD) is a sequence-based bioinformatics tool which was developed by utilizing a large input of datasets of protein sequences in FASTA format. The PSD can be used to analyze the meta-proteomics data which will help to predict and design thermozyme and mesozyme for academic and industrial purposes. The PSD also can be utilized to analyze the protein sequence and to predict whether it will be stable in thermophilic or in the mesophilic environment. </P><P> Method and Results: This tool which is supported by any operating system is designed in Java and it provides a user-friendly graphical interface. It is a simple programme and can predict the thermostability nature of proteins with >90% accuracy. The PSD can also predict the nature of constituent amino acids i.e. acidic or basic and polar or nonpolar etc.Conclusion:PSD is highly capable to determine the thermostability status of a protein of hypothetical or unknown peptides as well as meta-proteomics data from any established database. The utilities of the PSD driven analyses include predictions on the functional assignment to a protein. The PSD also helps in designing peptides having flexible combinations of amino acids for functional stability. PSD is freely available at https://sourceforge.net/projects/protein-sequence-determination.
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Affiliation(s)
- Anindya Sundar Panja
- Post Graduate Department of Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India
| | - Akash Nag
- Department of Computer science, University of Burdwan, India
| | - Bidyut Bandopadhyay
- Post Graduate Department of Biotechnology, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India
| | - Smarajit Maiti
- Post Graduate Department of Biochemistry and Biotechnology, Cell and Molecular Therapeutics Laboratory, Oriental Institute of Science and Technology, Vidyasagar University, Midnapore-721102, West Bengal, India
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10
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Sutiono S, Carsten J, Sieber V. Structure-Guided Engineering of α-Keto Acid Decarboxylase for the Production of Higher Alcohols at Elevated Temperature. CHEMSUSCHEM 2018; 11:3335-3344. [PMID: 29953730 DOI: 10.1002/cssc.201800944] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 06/15/2018] [Indexed: 06/08/2023]
Abstract
Branched-chain keto acid decarboxylases (KDCs) are a class of enzymes that catalyze the decarboxylation of α-keto acids. They are key enzymes for production of higher alcohols in vivo and in vitro. However, the two most active KDCs (KivD and KdcA) have only moderate thermostability (<55 °C), which hinders the production of alcohols at high temperatures. Herein, structure-guided engineering toward improved thermostability of KdcA is outlined. Strategies such as stabilization of the catalytic center, surface engineering, and optimization of dimer interactions were applied. With seven amino acid substitutions, variant 7M.D showed an increase of the temperature at which 50 % of activity remains after one-hour incubation T1h50 by 14.8 °C without compromising its substrate specificity. 7M.D exhibited greater than 400-fold improvement of half-life at 70 °C and greater than 600-fold increase in process stability in the presence of 4 % isobutanol at 50 °C. 7M.D is more promising for the production of higher alcohols in thermophiles (>65 °C) and in cell-free applications.
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Affiliation(s)
- Samuel Sutiono
- Chair of Chemistry of Biogenic Resources, TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Schulgasse 16, 94315, Straubing, Germany
| | - Jörg Carsten
- Catalytic Research Center, Technical University of Munich, Ernst-Otto-Fischer-Straße 1, 85748, Garching, Germany
| | - Volker Sieber
- Chair of Chemistry of Biogenic Resources, TUM Campus Straubing for Biotechnology and Sustainability, Technical University of Munich, Schulgasse 16, 94315, Straubing, Germany
- Catalytic Research Center, Technical University of Munich, Ernst-Otto-Fischer-Straße 1, 85748, Garching, Germany
- Straubing Branch BioCat, Fraunhofer IGB, Schulgasse 11a, 94315, Straubing, Germany
- School of Chemistry and Molecular Biosciences, The University of Queensland, 68 Copper Road, St., Lucia, 4072, Australia
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11
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Xu G, Ma T, Zang T, Wang Q, Ma J. OPUS-CSF: A C-atom-based scoring function for ranking protein structural models. Protein Sci 2017; 27:286-292. [PMID: 29047165 PMCID: PMC5734313 DOI: 10.1002/pro.3327] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 10/14/2017] [Accepted: 10/16/2017] [Indexed: 12/12/2022]
Abstract
We report a C‐atom‐based scoring function, named OPUS‐CSF, for ranking protein structural models. Rather than using traditional Boltzmann formula, we built a scoring function (CSF score) based on the native distributions (derived from the entire PDB) of coordinate components of mainchain C (carbonyl) atoms on selected residues of peptide segments of 5, 7, 9, and 11 residues in length. In testing OPUS‐CSF on decoy recognition, it maximally recognized 257 native structures out of 278 targets in 11 commonly used decoy sets, significantly outperforming other popular all‐atom empirical potentials. The average correlation coefficient with TM‐score was also comparable with those of other potentials. OPUS‐CSF is a highly coarse‐grained scoring function, which only requires input of partial mainchain information, and very fast. Thus, it is suitable for applications at early stage of structural building.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, Texas.,Department of Bioengineering, Rice University, Houston, Texas
| | - Tianwu Zang
- Applied Physics Program, Rice University, Houston, Texas.,Department of Bioengineering, Rice University, Houston, Texas
| | - Qinghua Wang
- Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing, China.,Applied Physics Program, Rice University, Houston, Texas.,Department of Bioengineering, Rice University, Houston, Texas.,Verna and Marrs McLean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, One Baylor Plaza, BCM-125, Houston, Texas
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12
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Xu G, Ma T, Zang T, Sun W, Wang Q, Ma J. OPUS-DOSP: A Distance- and Orientation-Dependent All-Atom Potential Derived from Side-Chain Packing. J Mol Biol 2017; 429:3113-3120. [PMID: 28864201 DOI: 10.1016/j.jmb.2017.08.013] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/27/2017] [Accepted: 08/22/2017] [Indexed: 01/18/2023]
Abstract
We report a new distance- and orientation-dependent, all-atom statistical potential derived from side-chain packing, named OPUS-DOSP, for protein structure modeling. The framework of OPUS-DOSP is based on OPUS-PSP, previously developed by us [JMB (2008), 376, 288-301], with refinement and new features. In particular, distance or orientation contribution is considered depending on the range of contact distance. A new auxiliary function in energy function is also introduced, in addition to the traditional Boltzmann term, in order to adjust the contributions of extreme cases. OPUS-DOSP was tested on 11 decoy sets commonly used for statistical potential benchmarking. Among 278 native structures, 239 and 249 native structures were recognized by OPUS-DOSP without and with the auxiliary function, respectively. The results show that OPUS-DOSP has an increased decoy recognition capability comparing with those of other relevant potentials to date.
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Affiliation(s)
- Gang Xu
- School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Tianqi Ma
- Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States
| | - Tianwu Zang
- Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States
| | - Weitao Sun
- Zhou Pei-Yuan Center for Applied Mathematics, Tsinghua University, Beijing 100084, China
| | - Qinghua Wang
- Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, United States
| | - Jianpeng Ma
- School of Life Sciences, Tsinghua University, Beijing 100084, China; Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States; Verna and Marrs Mclean Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, United States.
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13
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Hoque MT, Yang Y, Mishra A, Zhou Y. s
DFIRE
: Sequence‐specific statistical energy function for protein structure prediction by decoy selections. J Comput Chem 2016; 37:1119-24. [DOI: 10.1002/jcc.24298] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Revised: 12/06/2015] [Accepted: 12/13/2015] [Indexed: 12/15/2022]
Affiliation(s)
- Md Tamjidul Hoque
- Computer Science, University of New Orleans, New OrleansLouisiana70148
| | - Yuedong Yang
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith UniversityQueensland4222 Australia
| | - Avdesh Mishra
- Computer Science, University of New Orleans, New OrleansLouisiana70148
| | - Yaoqi Zhou
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith UniversityQueensland4222 Australia
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14
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Gromiha MM, Anoosha P, Huang LT. Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants. Methods Mol Biol 2016; 1415:71-89. [PMID: 27115628 DOI: 10.1007/978-1-4939-3572-7_4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.
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Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600 036, India.
| | - P Anoosha
- Department of Biotechnology, Bhupat & Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, 600 036, India
| | - Liang-Tsung Huang
- Department of Medical Informatics, Tzu Chi University, Hualien, 970, Taiwan
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15
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Chae MH, Krull F, Knapp EW. Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction. Proteins 2015; 83:881-90. [PMID: 25693513 DOI: 10.1002/prot.24782] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2014] [Revised: 02/06/2015] [Accepted: 02/10/2015] [Indexed: 12/20/2022]
Abstract
The DOcking decoy-based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance-dependent atom-pair interactions. To optimize the atom-pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand-receptor systems (or just pairs). Thus, a total of 8609 ligand-receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand-receptor systems, 1000 evenly sampled docking decoys with 0-10 Å interface root-mean-square-deviation (iRMSD) were generated with a method used before for protein-protein docking. A neural network-based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel-like energy landscape for the interaction between these hypothetical ligand-receptor systems. Thus, our method hierarchically models the overall funnel-like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom-pair-based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation-dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand-receptor systems and their decoys are freely available at http://agknapp.chemie.fu-berlin.de/doop/.
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Affiliation(s)
- Myong-Ho Chae
- Department of Biology, University of Science, Unjong-District, Pyongyang, DPR Korea
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16
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Bhattacharya A, Pletschke BI. Review of the enzymatic machinery of Halothermothrix orenii with special reference to industrial applications. Enzyme Microb Technol 2013; 55:159-69. [PMID: 24411459 DOI: 10.1016/j.enzmictec.2013.10.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Revised: 10/23/2013] [Accepted: 10/25/2013] [Indexed: 11/20/2022]
Abstract
Over the past few decades the extremes at which life thrives has continued to challenge our understanding of physiology, biochemistry, microbial ecology and evolution. Innovative culturing approaches, environmental genome sequencing, and whole genome sequencing have provided new opportunities for the biotechnological exploration of extremophiles. The whole genome sequencing of H. orenii has provided valuable insights not only into the survival and adaptation strategies of thermohalophiles but has also led to the identification of genes encoding biotechnologically relevant enzymes. The present review focuses on the purified and characterized enzymes from H. orenii including amylases, β-glucosidase, fructokinase, and ribokinase--along with uncharacterized but industrially important enzymes encoded by the genes identified in the genome such as β-galactosidases, mannosidases, pullulanases, chitinases, α-L-arabinofuranosidases and other glycosyl hydrolases of commercial interest. This review highlights the importance of the enzymes and their applications in different sectors and why future research for exploring the enzymatic machinery of H. orenii should focus on the expression, purification, and characterization of the novel proteins in H. orenii and their feasible application to pertinent industrial sectors. H. orenii is an anaerobe; genome sequencing studies have also revealed the presence of enzymes for gluconeogenesis and fermentation to ethanol and acetate, making H. orenii an attractive strain for the conversion of starch into bioethanol.
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Affiliation(s)
- Abhishek Bhattacharya
- Department of Biochemistry, Microbiology and Biotechnology, Rhodes University, PO Box 94, Grahamstown 6140, South Africa
| | - Brett I Pletschke
- Department of Biochemistry, Microbiology and Biotechnology, Rhodes University, PO Box 94, Grahamstown 6140, South Africa.
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17
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Mutation induced structural variation in membrane proteins. Chem Res Chin Univ 2013. [DOI: 10.1007/s40242-013-2427-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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18
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Moal IH, Fernandez-Recio J. Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation. J Chem Theory Comput 2013; 9:3715-27. [PMID: 26584123 DOI: 10.1021/ct400295z] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center , C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Juan Fernandez-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center , C/Jordi Girona 29, 08034 Barcelona, Spain
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19
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Structure-based mutant stability predictions on proteins of unknown structure. J Biotechnol 2012; 161:287-93. [DOI: 10.1016/j.jbiotec.2012.06.020] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2012] [Revised: 06/19/2012] [Accepted: 06/22/2012] [Indexed: 11/23/2022]
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20
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Analyzing effects of naturally occurring missense mutations. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2012; 2012:805827. [PMID: 22577471 PMCID: PMC3346971 DOI: 10.1155/2012/805827] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2011] [Revised: 02/01/2012] [Accepted: 02/01/2012] [Indexed: 11/17/2022]
Abstract
Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well.
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21
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Zhou H, Skolnick J. GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. Biophys J 2012; 101:2043-52. [PMID: 22004759 DOI: 10.1016/j.bpj.2011.09.012] [Citation(s) in RCA: 197] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 09/07/2011] [Accepted: 09/09/2011] [Indexed: 12/18/2022] Open
Abstract
An accurate scoring function is a key component for successful protein structure prediction. To address this important unsolved problem, we develop a generalized orientation and distance-dependent all-atom statistical potential. The new statistical potential, generalized orientation-dependent all-atom potential (GOAP), depends on the relative orientation of the planes associated with each heavy atom in interacting pairs. GOAP is a generalization of previous orientation-dependent potentials that consider only representative atoms or blocks of side-chain or polar atoms. GOAP is decomposed into distance- and angle-dependent contributions. The DFIRE distance-scaled finite ideal gas reference state is employed for the distance-dependent component of GOAP. GOAP was tested on 11 commonly used decoy sets containing 278 targets, and recognized 226 native structures as best from the decoys, whereas DFIRE recognized 127 targets. The major improvement comes from decoy sets that have homology-modeled structures that are close to native (all within ∼4.0 Å) or from the ROSETTA ab initio decoy set. For these two kinds of decoys, orientation-independent DFIRE or only side-chain orientation-dependent RWplus performed poorly. Although the OPUS-PSP block-based orientation-dependent, side-chain atom contact potential performs much better (recognizing 196 targets) than DFIRE, RWplus, and dDFIRE, it is still ∼15% worse than GOAP. Thus, GOAP is a promising advance in knowledge-based, all-atom statistical potentials. GOAP is available for download at http://cssb.biology.gatech.edu/GOAP.
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Affiliation(s)
- Hongyi Zhou
- Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta, Georgia, USA
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22
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Zhang Z, Wang L, Gao Y, Zhang J, Zhenirovskyy M, Alexov E. Predicting folding free energy changes upon single point mutations. ACTA ACUST UNITED AC 2012; 28:664-71. [PMID: 22238268 DOI: 10.1093/bioinformatics/bts005] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
MOTIVATION The folding free energy is an important characteristic of proteins stability and is directly related to protein's wild-type function. The changes of protein's stability due to naturally occurring mutations, missense mutations, are typically causing diseases. Single point mutations made in vitro are frequently used to assess the contribution of given amino acid to the stability of the protein. In both cases, it is desirable to predict the change of the folding free energy upon single point mutations in order to either provide insights of the molecular mechanism of the change or to design new experimental studies. RESULTS We report an approach that predicts the free energy change upon single point mutation by utilizing the 3D structure of the wild-type protein. It is based on variation of the molecular mechanics Generalized Born (MMGB) method, scaled with optimized parameters (sMMGB) and utilizing specific model of unfolded state. The corresponding mutations are built in silico and the predictions are tested against large dataset of 1109 mutations with experimentally measured changes of the folding free energy. Benchmarking resulted in root mean square deviation = 1.78 kcal/mol and slope of the linear regression fit between the experimental data and the calculations was 1.04. The sMMGB is compared with other leading methods of predicting folding free energy changes upon single mutations and results discussed with respect to various parameters. AVAILABILITY All the pdb files we used in this article can be downloaded from http://compbio.clemson.edu/downloadDir/mentaldisorders/sMMGB_pdb.rar. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zhe Zhang
- Computational Biophysics and Bioinformatics, Department of Physics, Clemson University, Clemson, SC 29634, USA
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23
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Zhou Y, Duan Y, Yang Y, Faraggi E, Lei H. Trends in template/fragment-free protein structure prediction. Theor Chem Acc 2011; 128:3-16. [PMID: 21423322 PMCID: PMC3030773 DOI: 10.1007/s00214-010-0799-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2010] [Accepted: 08/15/2010] [Indexed: 12/13/2022]
Abstract
Predicting the structure of a protein from its amino acid sequence is a long-standing unsolved problem in computational biology. Its solution would be of both fundamental and practical importance as the gap between the number of known sequences and the number of experimentally solved structures widens rapidly. Currently, the most successful approaches are based on fragment/template reassembly. Lacking progress in template-free structure prediction calls for novel ideas and approaches. This article reviews trends in the development of physical and specific knowledge-based energy functions as well as sampling techniques for fragment-free structure prediction. Recent physical- and knowledge-based studies demonstrated that it is possible to sample and predict highly accurate protein structures without borrowing native fragments from known protein structures. These emerging approaches with fully flexible sampling have the potential to move the field forward.
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Affiliation(s)
- Yaoqi Zhou
- School of Informatics, Indiana Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 719 Indiana Ave #319, Walker Plaza Building, Indianapolis, IN 46202 USA
| | - Yong Duan
- UC Davis Genome Center and Department of Applied Science, University of California, One Shields Avenue, Davis, CA USA
- College of Physics, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074 Wuhan, China
| | - Yuedong Yang
- School of Informatics, Indiana Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 719 Indiana Ave #319, Walker Plaza Building, Indianapolis, IN 46202 USA
| | - Eshel Faraggi
- School of Informatics, Indiana Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana University Purdue University, 719 Indiana Ave #319, Walker Plaza Building, Indianapolis, IN 46202 USA
| | - Hongxing Lei
- UC Davis Genome Center and Department of Applied Science, University of California, One Shields Avenue, Davis, CA USA
- Beijing Institute of Genomics, Chinese Academy of Sciences, 100029 Beijing, China
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24
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Distance-dependent statistical potentials for discriminating thermophilic and mesophilic proteins. Biochem Biophys Res Commun 2010; 396:736-41. [PMID: 20451495 DOI: 10.1016/j.bbrc.2010.05.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2010] [Accepted: 05/02/2010] [Indexed: 11/22/2022]
Abstract
Identification of the characteristic structural patterns responsible for protein thermostability is theoretically important and practically useful but largely remains an open problem. These patterns may be revealed through comparative study on thermophilic and mesophilic proteins that have distinct thermostability. In this study, we constructed several distance-dependant potentials from thermophilic and mesophilic proteins. These potentials were then used to evaluate the structural difference between thermophilic and mesophilic proteins. We found that using the subtraction or division of the potentials derived from thermophilic and mesophilic proteins can dramatically increase the discriminatory ability. This approach revealed that the ability to distinct the subtle structural features responsible for protein thermostability may be effectively enhanced through rationally designed comparative study.
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25
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Su Y, Zhou A, Xia X, Li W, Sun Z. Quantitative prediction of protein-protein binding affinity with a potential of mean force considering volume correction. Protein Sci 2010; 18:2550-8. [PMID: 19798743 DOI: 10.1002/pro.257] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Quantitative prediction of protein-protein binding affinity is essential for understanding protein-protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein-protein binding affinity. The potential is obtained by statistically analyzing X-ray structures of protein-protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard-Jones potential. To evaluate the prediction ability for protein-protein binding affinity, six test sets are examined. Sets 1-5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1-5, with a total of 86 protein-protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1-5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.
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Affiliation(s)
- Yu Su
- MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Department of Biological Sciences and Biotechnology, Tsinghua University, Beijing 100084, China
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26
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Abstract
When designing a mutagenesis experiment, it is often crucial to estimate the stability change of proteins induced by mutations (Delta DG). Despite the recent advances in computational methods, it is still challenging to estimate D DG quickly and accurately. We recently developed the Eris protocols for in silico evaluation of the Delta DG. Starting from the tertiary structure of the wide-type protein, the Eris protocols can model the structure of the mutant protein and estimate Delta DG using the structure models. The Eris protocols not only efficiently optimize the side chains conformations, taking advantage of a fast rotamer-based searching algorithm, but also allow protein backbone flexibility during the modeling. As a result, the Eris protocols effectively resolve steric clashes induced by certain mutations and have more accurate Delta DG predictions than a fixed-backbone approach. We discuss the general aspects of computational Delta DG estimations and discuss in detail the principles and methodologies of the Eris protocols.
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27
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Kutchukian PS, Yang JS, Verdine GL, Shakhnovich EI. All-atom model for stabilization of alpha-helical structure in peptides by hydrocarbon staples. J Am Chem Soc 2009; 131:4622-7. [PMID: 19334772 DOI: 10.1021/ja805037p] [Citation(s) in RCA: 92] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Recent work has shown that the incorporation of an all-hydrocarbon "staple" into peptides can greatly increase their alpha-helix propensity, leading to an improvement in pharmaceutical properties such as proteolytic stability, receptor affinity, and cell permeability. Stapled peptides thus show promise as a new class of drugs capable of accessing intractable targets such as those that engage in intracellular protein-protein interactions. The extent of alpha-helix stabilization provided by stapling has proven to be substantially context dependent, requiring cumbersome screening to identify the optimal site for staple incorporation. In certain cases, a staple encompassing one turn of the helix (attached at residues i and i+4) furnishes greater helix stabilization than one encompassing two turns (i,i+7 staple), which runs counter to expectation based on polymer theory. These findings highlight the need for a more thorough understanding of the forces that underlie helix stabilization by hydrocarbon staples. Here we report all-atom Monte Carlo folding simulations comparing unmodified peptides derived from RNase A and BID BH3 with various i,i+4 and i,i+7 stapled versions thereof. The results of these simulations were found to be in quantitative agreement with experimentally determined helix propensities. We also discovered that staples can stabilize quasi-stable decoy conformations, and that the removal of these states plays a major role in determining the helix stability of stapled peptides. Finally, we critically investigate why our method works, exposing the underlying physical forces that stabilize stapled peptides.
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Affiliation(s)
- Peter S Kutchukian
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, Massachusetts 02138, USA
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28
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Montanucci L, Fariselli P, Martelli PL, Casadio R. Predicting protein thermostability changes from sequence upon multiple mutations. Bioinformatics 2008; 24:i190-5. [PMID: 18586713 PMCID: PMC2718644 DOI: 10.1093/bioinformatics/btn166] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Motivation: A basic question in protein science is to which extent mutations affect protein thermostability. This knowledge would be particularly relevant for engineering thermostable enzymes. In several experimental approaches, this issue has been serendipitously addressed. It would be therefore convenient providing a computational method that predicts when a given protein mutant is more thermostable than its corresponding wild-type. Results: We present a new method based on support vector machines that is able to predict whether a set of mutations (including insertion and deletions) can enhance the thermostability of a given protein sequence. When trained and tested on a redundancy-reduced dataset, our predictor achieves 88% accuracy and a correlation coefficient equal to 0.75. Our predictor also correctly classifies 12 out of 14 experimentally characterized protein mutants with enhanced thermostability. Finally, it correctly detects all the 11 mutated proteins whose increase in stability temperature is >10°C. Availability: The dataset and the list of protein clusters adopted for the SVM cross-validation are available at the web site http://lipid.biocomp.unibo.it/~ludovica/thermo-meso-MUT. Contact:casadio@alma.unibo.it
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Affiliation(s)
- Ludovica Montanucci
- Department of Biology, University of Bologna, via Irnerio 42, 40126 Bologna, Italy
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29
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Masso M, Vaisman II. Accurate prediction of stability changes in protein mutants by combining machine learning with structure based computational mutagenesis. Bioinformatics 2008; 24:2002-9. [PMID: 18632749 DOI: 10.1093/bioinformatics/btn353] [Citation(s) in RCA: 132] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Majid Masso
- Department of Bioinformatics and Computational Biology, Laboratory for Structural Bioinformatics, George Mason University, 10900 University Blvd, MSN 5B3, Manassas, VA 20110, USA
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30
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Benkert P, Tosatto SCE, Schomburg D. QMEAN: A comprehensive scoring function for model quality assessment. Proteins 2008; 71:261-77. [PMID: 17932912 DOI: 10.1002/prot.21715] [Citation(s) in RCA: 726] [Impact Index Per Article: 45.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In protein structure prediction, a considerable number of alternative models are usually produced from which subsequently the final model has to be selected. Thus, a scoring function for the identification of the best model within an ensemble of alternative models is a key component of most protein structure prediction pipelines. QMEAN, which stands for Qualitative Model Energy ANalysis, is a composite scoring function describing the major geometrical aspects of protein structures. Five different structural descriptors are used. The local geometry is analyzed by a new kind of torsion angle potential over three consecutive amino acids. A secondary structure-specific distance-dependent pairwise residue-level potential is used to assess long-range interactions. A solvation potential describes the burial status of the residues. Two simple terms describing the agreement of predicted and calculated secondary structure and solvent accessibility, respectively, are also included. A variety of different implementations are investigated and several approaches to combine and optimize them are discussed. QMEAN was tested on several standard decoy sets including a molecular dynamics simulation decoy set as well as on a comprehensive data set of totally 22,420 models from server predictions for the 95 targets of CASP7. In a comparison to five well-established model quality assessment programs, QMEAN shows a statistically significant improvement over nearly all quality measures describing the ability of the scoring function to identify the native structure and to discriminate good from bad models. The three-residue torsion angle potential turned out to be very effective in recognizing the native fold.
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Affiliation(s)
- Pascal Benkert
- Institute for Biochemistry, University of Cologne, 50674 Cologne, Germany
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31
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An improved method of potential of mean force for protein-protein interactions. Sci Bull (Beijing) 2008. [DOI: 10.1007/s11434-008-0036-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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32
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Yang Y, Zhou Y. Specific interactions for ab initio folding of protein terminal regions with secondary structures. Proteins 2008; 72:793-803. [PMID: 18260109 DOI: 10.1002/prot.21968] [Citation(s) in RCA: 202] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Yuedong Yang
- Indiana University School of Informatics, Indianapolis, Indiana 46202, USA
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33
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Chen Y, Ding F, Nie H, Serohijos AW, Sharma S, Wilcox KC, Yin S, Dokholyan NV. Protein folding: then and now. Arch Biochem Biophys 2008; 469:4-19. [PMID: 17585870 PMCID: PMC2173875 DOI: 10.1016/j.abb.2007.05.014] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2007] [Revised: 05/11/2007] [Accepted: 05/21/2007] [Indexed: 01/19/2023]
Abstract
Over the past three decades the protein folding field has undergone monumental changes. Originally a purely academic question, how a protein folds has now become vital in understanding diseases and our abilities to rationally manipulate cellular life by engineering protein folding pathways. We review and contrast past and recent developments in the protein folding field. Specifically, we discuss the progress in our understanding of protein folding thermodynamics and kinetics, the properties of evasive intermediates, and unfolded states. We also discuss how some abnormalities in protein folding lead to protein aggregation and human diseases.
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Affiliation(s)
| | | | | | | | | | | | | | - Nikolay V. Dokholyan
- † To whom correspondence should be addressed: Nikolay V. Dokholyan, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, North Carolina 27599. Fax: 919-966-2852.
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34
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Yin S, Ding F, Dokholyan NV. Modeling Backbone Flexibility Improves Protein Stability Estimation. Structure 2007; 15:1567-76. [DOI: 10.1016/j.str.2007.09.024] [Citation(s) in RCA: 133] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2007] [Revised: 09/06/2007] [Accepted: 09/26/2007] [Indexed: 11/16/2022]
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Parthiban V, Gromiha MM, Abhinandan M, Schomburg D. Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development. BMC STRUCTURAL BIOLOGY 2007; 7:54. [PMID: 17705837 PMCID: PMC2000882 DOI: 10.1186/1472-6807-7-54] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2007] [Accepted: 08/16/2007] [Indexed: 02/02/2023]
Abstract
Background Understanding and predicting protein stability upon point mutations has wide-spread importance in molecular biology. Several prediction models have been developed in the past with various algorithms. Statistical potentials are one of the widely used algorithms for the prediction of changes in stability upon point mutations. Although the methods provide flexibility and the capability to develop an accurate and reliable prediction model, it can be achieved only by the right selection of the structural factors and optimization of their parameters for the statistical potentials. In this work, we have selected five atom classification systems and compared their efficiency for the development of amino acid atom potentials. Additionally, torsion angle potentials have been optimized to include the orientation of amino acids in such a way that altered backbone conformation in different secondary structural regions can be included for the prediction model. This study also elaborates the importance of classifying the mutations according to their solvent accessibility and secondary structure specificity. The prediction efficiency has been calculated individually for the mutations in different secondary structural regions and compared. Results Results show that, in addition to using an advanced atom description, stepwise regression and selection of atoms are necessary to avoid the redundancy in atom distribution and improve the reliability of the prediction model validation. Comparing to other atom classification models, Melo-Feytmans model shows better prediction efficiency by giving a high correlation of 0.85 between experimental and theoretical ΔΔG with 84.06% of the mutations correctly predicted out of 1538 mutations. The theoretical ΔΔG values for the mutations in partially buried β-strands generated by the structural training dataset from PISCES gave a correlation of 0.84 without performing the Gaussian apodization of the torsion angle distribution. After the Gaussian apodization, the correlation increased to 0.92 and prediction accuracy increased from 80% to 88.89% respectively. Conclusion These findings were useful for the optimization of the Melo-Feytmans atom classification system and implementing them to develop the statistical potentials. It was also significant that the prediction efficiency of mutations in the partially buried β-strands improves with the help of Gaussian apodization of the torsion angle distribution. All these comparisons and optimization techniques demonstrate their advantages as well as the restrictions for the development of the prediction model. These findings will be quite helpful not only for the protein stability prediction, but also for various structure solutions in future.
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Affiliation(s)
- Vijaya Parthiban
- Cologne University Bioinformatics Center, International Max Planck Research School, Cologne, Germany
| | - M Michael Gromiha
- Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Japan
| | - Madenhalli Abhinandan
- Cologne University Bioinformatics Center, International Max Planck Research School, Cologne, Germany
| | - Dietmar Schomburg
- Cologne University Bioinformatics Center, International Max Planck Research School, Cologne, Germany
- Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Japan
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Bueno M, Camacho CJ, Sancho J. SIMPLE estimate of the free energy change due to aliphatic mutations: Superior predictions based on first principles. Proteins 2007; 68:850-62. [PMID: 17523191 DOI: 10.1002/prot.21453] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The bioinformatics revolution of the last decade has been instrumental in the development of empirical potentials to quantitatively estimate protein interactions for modeling and design. Although computationally efficient, these potentials hide most of the relevant thermodynamics in 5-to-40 parameters that are fitted against a large experimental database. Here, we revisit this longstanding problem and show that a careful consideration of the change in hydrophobicity, electrostatics, and configurational entropy between the folded and unfolded state of aliphatic point mutations predicts 20-30% less false positives and yields more accurate predictions than any published empirical energy function. This significant improvement is achieved with essentially no free parameters, validating past theoretical and experimental efforts to understand the thermodynamics of protein folding. Our first principle analysis strongly suggests that both the solute-solute van der Waals interactions in the folded state and the electrostatics free energy change of exposed aliphatic mutations are almost completely compensated by similar interactions operating in the unfolded ensemble. Not surprisingly, the problem of properly accounting for the solvent contribution to the free energy of polar and charged group mutations, as well as of mutations that disrupt the protein backbone remains open.
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Affiliation(s)
- Marta Bueno
- Department of Computational Biology, University of Pittsburgh, Pennsylvania, USA
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Podar M, Reysenbach AL. New opportunities revealed by biotechnological explorations of extremophiles. Curr Opin Biotechnol 2006; 17:250-5. [PMID: 16701993 DOI: 10.1016/j.copbio.2006.05.002] [Citation(s) in RCA: 108] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2006] [Revised: 04/25/2006] [Accepted: 05/04/2006] [Indexed: 11/16/2022]
Abstract
Over the past few decades the extremes at which life thrives has continued to challenge our understanding of biochemistry, biology and evolution. As more new extremophiles are brought into laboratory culture, they have provided a multitude of potential applications for biotechnology. More recently, innovative culturing approaches, environmental genome sequencing and whole genome sequencing have provided new opportunities for the biotechnological exploration of extremophiles.
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