1
|
Qiao F, Binknowski TA, Broughan I, Chen W, Natarajan A, Schiltz GE, Scheidt KA, Anderson WF, Bergan R. Protein Structure Inspired Drug Discovery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594634. [PMID: 38826221 PMCID: PMC11142055 DOI: 10.1101/2024.05.17.594634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Drug discovery starts with known function, either of a compound or a protein, in-turn prompting investigations to probe 3D structure of the compound-protein interface. As protein structure determines function, we hypothesized that unique 3D structural motifs represent primary information denoting unique function that can drive discovery of novel agents. Using a physics-based protein structure analysis platform developed by us, designed to conduct computationally intensive analysis at supercomputing speeds, we probed a high-resolution protein x-ray crystallographic library developed by us. We selected 3D structural motifs whose function was not otherwise established, that offered environments supporting binding of drug-like chemicals and were present on proteins that were not established therapeutic targets. For each of eight potential binding pockets on six different proteins we accessed a 60 million compound library and used our analysis platform to evaluate binding. Using eight-day colony formation assays acquired compounds were screened for efficacy against human breast, prostate, colon and lung cancer cells and toxicity against human bone marrow stem cells. Compounds selectively inhibiting cancer growth segregated to two pockets on separate proteins. The compound, Dxr2-017, exhibited selective activity against human melanoma cells in the NCI-60 cell line screen, had an IC50 of 19 nM against human melanoma M14 cells in our eight-day assay, while over 2100-fold higher concentrations inhibited stem cells by less than 30%. We show that Dxr2-017 induces anoikis, a unique form of programmed cell death in need of targeted therapeutics. The predicted target protein for Dxr2-017 is expressed in bacteria, not in humans. This supports our strategy of focusing on unique 3D structural motifs. It is known that functionally important 3D structures are evolutionarily conserved. Here we demonstrate proof-of-concept that protein structure represents high value primary data to support discovery of novel therapeutics. This approach is widely applicable.
Collapse
Affiliation(s)
- Fangfang Qiao
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| | | | - Irene Broughan
- Department of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Weining Chen
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| | - Amarnath Natarajan
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| | - Gary E. Schiltz
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Karl A. Scheidt
- Department of Chemistry, Northwestern University, Evanston, IL 60208, USA
| | - Wayne F. Anderson
- Department of Biochemistry and Molecular Genetics, Northwestern University, Chicago, IL 60611, USA
| | - Raymond Bergan
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68105, USA
| |
Collapse
|
2
|
Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
Collapse
Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| |
Collapse
|
3
|
Li G, Dai QQ, Li GB. MeCOM: A Method for Comparing Three-Dimensional Metalloenzyme Active Sites. J Chem Inf Model 2022; 62:730-739. [PMID: 35044164 DOI: 10.1021/acs.jcim.1c01335] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Since metalloenzymes are a large collection of metal ion(s) dependent enzymes, comparison analyses of metalloenzyme active sites are critical for metalloenzyme de novo design, function investigation, and inhibitor development. Here, we report a method named MeCOM for comparing metalloenzyme active sites. It is characterized by metal ion(s) centric active site recognition and three-dimensional superimposition using α-carbon or pharmacophore features. The test results revealed that for the given metalloenzymes, MeCOM could effectively recognize the active sites, extract active site features, and superimpose the active sites; it also could correctly identify similar active sites, differentiate dissimilar active sites, and evaluate the similarity degree. Moreover, MeCOM showed potential to establish new associations between structurally distinct metalloenzymes by active site comparison. MeCOM is freely available at https://mecom.ddtmlab.org.
Collapse
Affiliation(s)
- Gen Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Qing-Qing Dai
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Department of Medicinal Chemistry, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| |
Collapse
|
4
|
Chemometric Models of Differential Amino Acids at the Na vα and Na vβ Interface of Mammalian Sodium Channel Isoforms. Molecules 2020; 25:molecules25153551. [PMID: 32756517 PMCID: PMC7435598 DOI: 10.3390/molecules25153551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/21/2020] [Accepted: 07/22/2020] [Indexed: 12/19/2022] Open
Abstract
(1) Background: voltage-gated sodium channels (Navs) are integral membrane proteins that allow the sodium ion flux into the excitable cells and initiate the action potential. They comprise an α (Navα) subunit that forms the channel pore and are coupled to one or more auxiliary β (Navβ) subunits that modulate the gating to a variable extent. (2) Methods: after performing homology in silico modeling for all nine isoforms (Nav1.1α to Nav1.9α), the Navα and Navβ protein-protein interaction (PPI) was analyzed chemometrically based on the primary and secondary structures as well as topological or spatial mapping. (3) Results: our findings reveal a unique isoform-specific correspondence between certain segments of the extracellular loops of the Navα subunits. Precisely, loop S5 in domain I forms part of the PPI and assists Navβ1 or Navβ3 on all nine mammalian isoforms. The implied molecular movements resemble macroscopic springs, all of which explains published voltage sensor effects on sodium channel fast inactivation in gating. (4) Conclusions: currently, the specific functions exerted by the Navβ1 or Navβ3 subunits on the modulation of Navα gating remain unknown. Our work determined functional interaction in the extracellular domains on theoretical grounds and we propose a schematic model of the gating mechanism of fast channel sodium current inactivation by educated guessing.
Collapse
|
5
|
Mazmanian K, Sargsyan K, Lim C. How the Local Environment of Functional Sites Regulates Protein Function. J Am Chem Soc 2020; 142:9861-9871. [PMID: 32407086 DOI: 10.1021/jacs.0c02430] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proteins form complex biological machineries whose functions in the cell are highly regulated at both the cellular and molecular levels. Cellular regulation of protein functions involves differential gene expressions, post-translation modifications, and signaling cascades. Molecular regulation, on the other hand, involves tuning an optimal local protein environment for the functional site. Precisely how a protein achieves such an optimal environment around a given functional site is not well understood. Herein, by surveying the literature, we first summarize the various reported strategies used by certain proteins to ensure their correct functioning. We then formulate three key physicochemical factors for regulating a protein's functional site, namely, (i) its immediate interactions, (ii) its solvent accessibility, and (iii) its conformational flexibility. We illustrate how these factors are applied to regulate the functions of free/metal-bound Cys and Zn sites in proteins.
Collapse
Affiliation(s)
- Karine Mazmanian
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Karen Sargsyan
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Carmay Lim
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan.,Department of Chemistry, National Tsing Hua University, Hsinchu 300, Taiwan
| |
Collapse
|
6
|
Andrade CH, Neves BJ, Melo-Filho CC, Rodrigues J, Silva DC, Braga RC, Cravo PVL. In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases. Curr Med Chem 2019. [DOI: 10.2174/0929867325666180309114824] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs)
have reached clinical trials in the last decades, underscoring the need for new, safe and effective
treatments. In such context, drug repositioning, which allows finding novel indications
for approved drugs whose pharmacokinetic and safety profiles are already known,
emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent
of the typical drug discovery process that involves the systematic screening of chemical
compounds against drug targets in high-throughput screening (HTS) efforts, for the identification
of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics
attempts to identify all potential ligands for all possible targets and diseases. In
this review, we summarize current methodological development efforts in drug repositioning
that use state-of-the-art computational ligand- and structure-based chemogenomics approaches.
Furthermore, we highlighted the recent progress in computational drug repositioning
for some NTDs, based on curation and modeling of genomic, biological, and chemical data.
Additionally, we also present in-house and other successful examples and suggest possible solutions
to existing pitfalls.
Collapse
Affiliation(s)
- Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Bruno Junior Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Cleber Camilo Melo-Filho
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Juliana Rodrigues
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Diego Cabral Silva
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Rodolpho Campos Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Pedro Vitor Lemos Cravo
- Laboratory of Cheminformatics, Centro Universitario de Anapolis (UniEVANGELICA), Anapolis, GO, 75083-515, Brazil
| |
Collapse
|
7
|
Xu L, Gordon R, Farmer R, Pattanayak A, Binkowski A, Huang X, Avram M, Krishna S, Voll E, Pavese J, Chavez J, Bruce J, Mazar A, Nibbs A, Anderson W, Li L, Jovanovic B, Pruell S, Valsecchi M, Francia G, Betori R, Scheidt K, Bergan R. Precision therapeutic targeting of human cancer cell motility. Nat Commun 2018; 9:2454. [PMID: 29934502 PMCID: PMC6014988 DOI: 10.1038/s41467-018-04465-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Accepted: 05/02/2018] [Indexed: 12/12/2022] Open
Abstract
Increased cancer cell motility constitutes a root cause of end organ destruction and mortality, but its complex regulation represents a barrier to precision targeting. We use the unique characteristics of small molecules to probe and selectively modulate cell motility. By coupling efficient chemical synthesis routes to multiple upfront in parallel phenotypic screens, we identify that KBU2046 inhibits cell motility and cell invasion in vitro. Across three different murine models of human prostate and breast cancer, KBU2046 inhibits metastasis, decreases bone destruction, and prolongs survival at nanomolar blood concentrations after oral administration. Comprehensive molecular, cellular and systemic-level assays all support a high level of selectivity. KBU2046 binds chaperone heterocomplexes, selectively alters binding of client proteins that regulate motility, and lacks all the hallmarks of classical chaperone inhibitors, including toxicity. We identify a unique cell motility regulatory mechanism and synthesize a targeted therapeutic, providing a platform to pursue studies in humans. In this study, the authors identify and validate a halogen-substituted isoflavanone able to inhibit prostate cancer cell motility, invasion and metastasis in vitro and in vivo. They demonstrate its ability to selectively inhibit activation of client proteins that stimulate cell motility.
Collapse
Affiliation(s)
- Li Xu
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA.,Department of Gastroenterology, Xiang'an Hospital of Xiamen University, Fujian, 361101, Xiamen, China
| | - Ryan Gordon
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Rebecca Farmer
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
| | - Abhinandan Pattanayak
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Andrew Binkowski
- Department of Computer Science, University of Chicago, Chicago, IL, 60637, USA
| | - Xiaoke Huang
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Michael Avram
- Department of Anesthesiology, Northwestern University, Chicago, IL, 60611, USA
| | - Sankar Krishna
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Eric Voll
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Janet Pavese
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Juan Chavez
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - James Bruce
- Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA
| | - Andrew Mazar
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
| | - Antoinette Nibbs
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
| | - Wayne Anderson
- Department of Molecular Pharmacology and Biological Chemistry, Northwestern University, Chicago, IL, 60611, USA
| | - Lin Li
- Department of Pathology, Northwestern University, Chicago, IL, 60611, USA
| | - Borko Jovanovic
- Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Sean Pruell
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Matias Valsecchi
- Department of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Giulio Francia
- Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX, 79968, USA
| | - Rick Betori
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
| | - Karl Scheidt
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
| | - Raymond Bergan
- Division of Hematology/Oncology, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, 97239, USA.
| |
Collapse
|
8
|
Yadav U, Arya R, Kundu S, Sundd M. The “Recognition Helix” of the Type II Acyl Carrier Protein (ACP) Utilizes a “Ubiquitin Interacting Motif (UIM)”-like Surface To Bind Its Partners. Biochemistry 2018; 57:3690-3701. [DOI: 10.1021/acs.biochem.8b00220] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Usha Yadav
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110 067, India
| | - Richa Arya
- Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India
| | - Suman Kundu
- Department of Biochemistry, University of Delhi South Campus, Benito Juarez Road, New Delhi 110 021, India
| | - Monica Sundd
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110 067, India
| |
Collapse
|
9
|
Waldner BJ, Kraml J, Kahler U, Spinn A, Schauperl M, Podewitz M, Fuchs JE, Cruciani G, Liedl KR. Electrostatic recognition in substrate binding to serine proteases. J Mol Recognit 2018; 31:e2727. [PMID: 29785722 PMCID: PMC6175425 DOI: 10.1002/jmr.2727] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 04/11/2018] [Accepted: 04/11/2018] [Indexed: 12/16/2022]
Abstract
Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.
Collapse
Affiliation(s)
- Birgit J Waldner
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Johannes Kraml
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Ursula Kahler
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Alexander Spinn
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Michael Schauperl
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Maren Podewitz
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Julian E Fuchs
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| | - Gabriele Cruciani
- Laboratory of Chemometrics, Department of Chemistry, University of Perugia, Perugia, Italy
| | - Klaus R Liedl
- Institute of General, Inorganic and Theoretical Chemistry, and Center for Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innsbruck, Austria
| |
Collapse
|
10
|
Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
Collapse
Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| |
Collapse
|
11
|
Pang B, Schlessman D, Kuang X, Zhao N, Shyu D, Korkin D, Shyu CR. An Integrated Approach to Sequence-Independent Local Alignment of Protein Binding Sites. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:298-308. [PMID: 26357218 DOI: 10.1109/tcbb.2014.2355208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Accurate alignment of protein-protein binding sites can aid in protein docking studies and constructing templates for predicting structure of protein complexes, along with in-depth understanding of evolutionary and functional relationships. However, over the past three decades, structural alignment algorithms have focused predominantly on global alignments with little effort on the alignment of local interfaces. In this paper, we introduce the PBSalign (Protein-protein Binding Site alignment) method, which integrates techniques in graph theory, 3D localized shape analysis, geometric scoring, and utilization of physicochemical and geometrical properties. Computational results demonstrate that PBSalign is capable of identifying similar homologous and analogous binding sites accurately and performing alignments with better geometric match measures than existing protein-protein interface comparison tools. The proportion of better alignment quality generated by PBSalign is 46, 56, and 70 percent more than iAlign as judged by the average match index (MI), similarity index (SI), and structural alignment score (SAS), respectively. PBSalign provides the life science community an efficient and accurate solution to binding-site alignment while striking the balance between topological details and computational complexity.
Collapse
|
12
|
Krotzky T, Grunwald C, Egerland U, Klebe G. Large-scale mining for similar protein binding pockets: with RAPMAD retrieval on the fly becomes real. J Chem Inf Model 2014; 55:165-79. [PMID: 25474400 DOI: 10.1021/ci5005898] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Determination of structural similarities between protein binding pockets is an important challenge in in silico drug design. It can help to understand selectivity considerations, predict unexpected ligand cross-reactivity, and support the putative annotation of function to orphan proteins. To this end, Cavbase was developed as a tool for the automated detection, storage, and classification of putative protein binding sites. In this context, binding sites are characterized as sets of pseudocenters, which denote surface-exposed physicochemical properties, and can be used to enable mutual binding site comparisons. However, these comparisons tend to be computationally very demanding and often lead to very slow computations of the similarity measures. In this study, we propose RAPMAD (RApid Pocket MAtching using Distances), a new evaluation formalism for Cavbase entries that allows for ultrafast similarity comparisons. Protein binding sites are represented by sets of distance histograms that are both generated and compared with linear complexity. Attaining a speed of more than 20 000 comparisons per second, screenings across large data sets and even entire databases become easily feasible. We demonstrate the discriminative power and the short runtime by performing several classification and retrieval experiments. RAPMAD attains better success rates than the comparison formalism originally implemented into Cavbase or several alternative approaches developed in recent time, while requiring only a fraction of their runtime. The pratical use of our method is finally proven by a successful prospective virtual screening study that aims for the identification of novel inhibitors of the NMDA receptor.
Collapse
Affiliation(s)
- Timo Krotzky
- Department of Pharmaceutical Chemistry, Philipps-Universität Marburg , Marbacher Weg 6-10, 35032 Marburg, Germany
| | | | | | | |
Collapse
|
13
|
Krotzky T, Rickmeyer T, Fober T, Klebe G. Extraction of protein binding pockets in close neighborhood of bound ligands makes comparisons simple due to inherent shape similarity. J Chem Inf Model 2014; 54:3229-37. [PMID: 25345905 DOI: 10.1021/ci500553a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Methods for comparing protein binding sites are frequently validated on data sets of pockets that were obtained simply by extracting the protein area next to the bound ligands. With this strategy, any unoccupied pocket will remain unconsidered. Furthermore, a large amount of ligand-biased intrinsic shape information is predefined, inclining the subsequent comparisons as rather trivial even in data sets that hardly contain redundancies in sequence information. In this study, we present the results of a very simplistic and shape-biased comparison approach, which stress that unrestricted cavity extraction is essential to enable unexpected cross-reactivity predictions among proteins and function annotations of orphan proteins.
Collapse
Affiliation(s)
- Timo Krotzky
- Institute of Pharmaceutical Chemistry, University of Marburg , Marbacher Weg 6-10, 35032 Marburg, Germany
| | | | | | | |
Collapse
|
14
|
Binkowski TA, Jiang W, Roux B, Anderson WF, Joachimiak A. Virtual high-throughput ligand screening. Methods Mol Biol 2014; 1140:251-61. [PMID: 24590723 DOI: 10.1007/978-1-4939-0354-2_19] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
In Structural Genomics projects, virtual high-throughput ligand screening can be utilized to provide important functional details for newly determined protein structures. Using a variety of publicly available software tools, it is possible to computationally model, predict, and evaluate how different ligands interact with a given protein. At the Center for Structural Genomics of Infectious Diseases (CSGID) a series of protein analysis, docking and molecular dynamics software is scripted into a single hierarchical pipeline allowing for an exhaustive investigation of protein-ligand interactions. The ability to conduct accurate computational predictions of protein-ligand binding is a vital component in improving both the efficiency and economics of drug discovery. Computational simulations can minimize experimental efforts, the slowest and most cost prohibitive aspect of identifying new therapeutics.
Collapse
Affiliation(s)
- T Andrew Binkowski
- Center for Structural Genomics of Infectious Diseases, Computation Institute, University of Chicago, Chicago, IL, USA,
| | | | | | | | | |
Collapse
|
15
|
Gallo Cassarino T, Bordoli L, Schwede T. Assessment of ligand binding site predictions in CASP10. Proteins 2014; 82 Suppl 2:154-63. [PMID: 24339001 DOI: 10.1002/prot.24495] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2013] [Revised: 12/04/2013] [Accepted: 12/09/2013] [Indexed: 12/27/2022]
Abstract
The identification of amino acid residues in proteins involved in binding small molecule ligands is an important step for their functional characterization, as the function of a protein often depends on specific interactions with other molecules. The accuracy of computational methods aiming to predict such binding residues was evaluated within the "function prediction (prediction of binding sites, FN)" category of the critical assessment of protein structure prediction (CASP) experiment. In the last edition of the experiment (CASP10), 17 research groups participated in this category, and their predictions were evaluated on 13 prediction targets containing biologically relevant ligands. The results of this experiment indicate that several methods achieved an overall good performance, showing the usefulness of such methods in predicting ligand binding residues. As in previous years, methods based on a homology transfer approach were dominating. In comparison to CASP9, a larger fraction of the top predictors are automated servers. However, due to the small number of targets and the characteristics of the prediction format, the differences observed among the first ten methods were not statistically significant and it was also not possible to analyze differences in accuracy for different ligand types or overall structure, difficulty. To overcome these limitations and to allow for a more detailed evaluation, in future editions of CASP, methods in the FN category will no longer be evaluated on the "normal" CASP targets, but assessed continuously by CAMEO (continuous automated model evaluation) based on weekly prereleased sequences from the PDB.
Collapse
Affiliation(s)
- Tiziano Gallo Cassarino
- Biozentrum, University of Basel, Basel, Switzerland; SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | | |
Collapse
|
16
|
Chen BY. VASP-E: specificity annotation with a volumetric analysis of electrostatic isopotentials. PLoS Comput Biol 2014; 10:e1003792. [PMID: 25166865 PMCID: PMC4148194 DOI: 10.1371/journal.pcbi.1003792] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 06/17/2014] [Indexed: 12/01/2022] Open
Abstract
Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins. Proteins, the ubiquitous worker molecules of the cell, are a diverse class of molecules that perform very specific tasks. Understanding how proteins achieve specificity is a critical step towards understanding biological systems and a key prerequisite for rationally engineering new proteins. To examine electrostatic influences on specificity in proteins, this paper presents VASP-E, a software tool that generates solid representations of the electrostatic potential fields that surround proteins. VASP-E compares solids with constructive solid geometry, a class of techniques developed first for modeling complex machine parts. We observed that solid representations could quantify the degree of charge complementarity in protein-protein interactions and identify key residues that strengthen or weaken them. VASP-E correctly identified amino acids with established experimental influences on protein-protein binding specificity. We also observed that solid representations of electrostatic fields could identify electrostatic conservations and variations that relate to similarities and differences in binding specificity between proteins and small molecules.
Collapse
Affiliation(s)
- Brian Y. Chen
- Department of Computer Science and Engineering, P.C. Rossin College of Engineering and Applied Sciences, Lehigh University, Bethlehem, Pennsylvania, United States of America
- * E-mail:
| |
Collapse
|
17
|
Koromyslova AD, Chugunov AO, Efremov RG. Deciphering fine molecular details of proteins' structure and function with a Protein Surface Topography (PST) method. J Chem Inf Model 2014; 54:1189-99. [PMID: 24689707 DOI: 10.1021/ci500158y] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Molecular surfaces are the key players in biomolecular recognition and interactions. Nowadays, it is trivial to visualize a molecular surface and surface-distributed properties in three-dimensional space. However, such a representation trends to be biased and ambiguous in case of thorough analysis. We present a new method to create 2D spherical projection maps of entire protein surfaces and manipulate with them--protein surface topography (PST). It permits visualization and thoughtful analysis of surface properties. PST helps to easily portray conformational transitions, analyze proteins' properties and their dynamic behavior, improve docking performance, and reveal common patterns and dissimilarities in molecular surfaces of related bioactive peptides. This paper describes basic usage of PST with an example of small G-proteins conformational transitions, mapping of caspase-1 intersubunit interface, and intrinsic "complementarity" in the conotoxin-acetylcholine binding protein complex. We suggest that PST is a beneficial approach for structure-function studies of bioactive peptides and small proteins.
Collapse
Affiliation(s)
- Anna D Koromyslova
- M. M. Shemyakin and Yu. A. Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences , 117997, Moscow, Russia
| | | | | |
Collapse
|
18
|
Schumann M, Armen RS. Identification of distant drug off-targets by direct superposition of binding pocket surfaces. PLoS One 2013; 8:e83533. [PMID: 24391782 PMCID: PMC3877058 DOI: 10.1371/journal.pone.0083533] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 11/04/2013] [Indexed: 01/23/2023] Open
Abstract
Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target ("distant off-targets"). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target ("distant off-target").
Collapse
Affiliation(s)
- Marcel Schumann
- Department of Pharmaceutical Sciences, School of Pharmacy, Thomas Jefferson University, Philadelphia, Pennsylvania, United States of America
| | - Roger S. Armen
- Department of Pharmaceutical Sciences, School of Pharmacy, Thomas Jefferson University, Philadelphia, Pennsylvania, United States of America
| |
Collapse
|
19
|
Jalencas X, Mestres J. Identification of Similar Binding Sites to Detect Distant Polypharmacology. Mol Inform 2013; 32:976-90. [PMID: 27481143 DOI: 10.1002/minf.201300082] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/29/2013] [Indexed: 01/19/2023]
Abstract
The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.
Collapse
Affiliation(s)
- Xavier Jalencas
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550
| | - Jordi Mestres
- Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Research Institute & University Pompeu Fabra, Parc de Recerca Biomèdica, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain fax: +34 93 3160550.
| |
Collapse
|
20
|
Blumenthal S, Tang Y, Yang W, Chen BY. Isolating influential regions of electrostatic focusing in protein and DNA structure. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:1188-1198. [PMID: 24384707 DOI: 10.1109/tcbb.2013.124] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Electrostatic focusing is a general phenomenon that occurs in cavities and grooves on the molecular surface of biomolecules. Narrow surface features can partially shield charged atoms from the high-dielectric solvent, enhancing electrostatic potentials inside the cavity and projecting electric field lines outward into the solvent. This effect has been observed in many instances and is widely considered in the human examination of molecular structure, but it is rarely integrated into the digital representations used in protein structure comparison software. To create a computational representation of electrostatic focusing, that is compatible with structure comparison algorithms, this paper presents an approach that generates three-dimensional solids that approximate regions where focusing occurs. We verify the accuracy of this representation against instances of focusing in proteins and DNA. Noting that this representation also identifies thin focusing regions on the molecular surface that are unlikely to affect binding, we describe a second algorithm that conservatively isolates larger focusing regions. The resulting 3D solids can be compared with Boolean set operations, permitting a new range of analyses on the regions where electrostatic focusing occurs. They also represent a novel integration of molecular shape and electrostatic focusing into the same structure comparison framework.
Collapse
|
21
|
von Behren MM, Volkamer A, Henzler AM, Schomburg KT, Urbaczek S, Rarey M. Fast protein binding site comparison via an index-based screening technology. J Chem Inf Model 2013; 53:411-22. [PMID: 23390978 DOI: 10.1021/ci300469h] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
We present TrixP, a new index-based method for fast protein binding site comparison and function prediction. TrixP determines binding site similarities based on the comparison of descriptors that encode pharmacophoric and spatial features. Therefore, it adopts the efficient core components of TrixX, a structure-based virtual screening technology for large compound libraries. TrixP expands this technology by new components in order to allow a screening of protein libraries. TrixP accounts for the inherent flexibility of proteins employing a partial shape matching routine. After the identification of structures with matching pharmacophoric features and geometric shape, TrixP superimposes the binding sites and, finally, assesses their similarity according to the fit of pharmacophoric properties. TrixP is able to find analogies between closely and distantly related binding sites. Recovery rates of 81.8% for similar binding site pairs, assisted by rejecting rates of 99.5% for dissimilar pairs on a test data set containing 1331 pairs, confirm this ability. TrixP exclusively identifies members of the same protein family on top ranking positions out of a library consisting of 9802 binding sites. Furthermore, 30 predicted kinase binding sites can almost perfectly be classified into their known subfamilies.
Collapse
Affiliation(s)
- Mathias M von Behren
- Center for Bioinformatics, University of Hamburg, Bundesstrasse 43, 20146 Hamburg, Germany
| | | | | | | | | | | |
Collapse
|
22
|
Volkamer A, Kuhn D, Rippmann F, Rarey M. Predicting enzymatic function from global binding site descriptors. Proteins 2012; 81:479-89. [DOI: 10.1002/prot.24205] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Revised: 09/21/2012] [Accepted: 10/11/2012] [Indexed: 11/09/2022]
|
23
|
Local functional descriptors for surface comparison based binding prediction. BMC Bioinformatics 2012; 13:314. [PMID: 23176080 PMCID: PMC3585919 DOI: 10.1186/1471-2105-13-314] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2012] [Accepted: 10/10/2012] [Indexed: 11/10/2022] Open
Abstract
Background Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface. Results We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods. Conclusions Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications.
Collapse
|
24
|
Chen BY, Bandyopadhyay S. A regionalizable statistical model of intersecting regions in protein-ligand binding cavities. J Bioinform Comput Biol 2012; 10:1242004. [PMID: 22809380 DOI: 10.1142/s0219720012420048] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Finding elements of proteins that influence ligand binding specificity is an essential aspect of research in many fields. To assist in this effort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The first model specializes in the "unified" comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding specificity. The second model enables a "regionalized" comparison of cavities within a user-defined region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our unified model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-defined region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify influential structural similarities within binding site structure in order to better detect influences on protein-ligand binding specificity.
Collapse
Affiliation(s)
- Brian Y Chen
- Department of Computer Science and Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA 18015, USA.
| | | |
Collapse
|
25
|
Chen BY, Bandyopadhyay S. Modeling regionalized volumetric differences in protein-ligand binding cavities. Proteome Sci 2012; 10 Suppl 1:S6. [PMID: 22759583 PMCID: PMC3390949 DOI: 10.1186/1477-5956-10-s1-s6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023] Open
Abstract
Identifying elements of protein structures that create differences in protein-ligand
binding specificity is an essential method for explaining the molecular mechanisms
underlying preferential binding. In some cases, influential mechanisms can be
visually identified by experts in structural biology, but subtler mechanisms, whose
significance may only be apparent from the analysis of many structures, are harder to
find. To assist this process, we present a geometric algorithm and two statistical
models for identifying significant structural differences in protein-ligand binding
cavities. We demonstrate these methods in an analysis of sequentially nonredundant
structural representatives of the canonical serine proteases and the enolase
superfamily. Here, we observed that statistically significant structural variations
identified experimentally established determinants of specificity. We also observed
that an analysis of individual regions inside cavities can reveal areas where small
differences in shape can correspond to differences in specificity.
Collapse
Affiliation(s)
- Brian Y Chen
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA, USA.
| | | |
Collapse
|
26
|
Sael L, Chitale M, Kihara D. Structure- and sequence-based function prediction for non-homologous proteins. ACTA ACUST UNITED AC 2012; 13:111-23. [PMID: 22270458 DOI: 10.1007/s10969-012-9126-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2011] [Accepted: 01/10/2012] [Indexed: 01/14/2023]
Abstract
The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recent developments of local structure-based and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, protein function prediction and extended similarity group, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures.
Collapse
Affiliation(s)
- Lee Sael
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | | | | |
Collapse
|
27
|
Vlachakis D, Tsiliki G, Tsagkrasoulis D, Carvalho CS, Megalooikonomou V, Kossida S. Speeding up the drug discovery process: structural similarity searches using molecular surfaces. ACTA ACUST UNITED AC 2012; 18:6-9. [PMID: 31440460 DOI: 10.14806/ej.18.1.501] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Dimitrios Vlachakis
- Bioinformatics & Medical Informatics Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens
| | - Georgia Tsiliki
- Bioinformatics & Medical Informatics Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens
| | - Dimosthenis Tsagkrasoulis
- Bioinformatics & Medical Informatics Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens
| | - Carla Sofia Carvalho
- Bioinformatics & Medical Informatics Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens
| | - Vasileios Megalooikonomou
- Bioinformatics & Medical Informatics Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens
| | - Sofia Kossida
- Bioinformatics & Medical Informatics Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens
| |
Collapse
|
28
|
Protein surface characterization using an invariant descriptor. Int J Biomed Imaging 2011; 2011:918978. [PMID: 22144981 PMCID: PMC3227456 DOI: 10.1155/2011/918978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2011] [Accepted: 08/14/2011] [Indexed: 11/17/2022] Open
Abstract
Aim. To develop a new invariant descriptor for the characterization of protein surfaces, suitable for various analysis tasks, such as protein functional classification, and search and retrieval of protein surfaces over a large database. Methods. We start with a local descriptor of selected circular patches on the protein surface. The descriptor records the distance distribution between the central residue and the residues within the patch, keeping track of the number of particular pairwise residue cooccurrences in the patch. A global descriptor for the entire protein surface is then constructed by combining information from the local descriptors. Our method is novel in its focus on residue-specific distance distributions, and the use of residue-distance co-occurrences as the basis for the proposed protein surface descriptors. Results. Results are presented for protein classification and for retrieval for three protein families. For the three families, we obtained an area under the curve for precision and recall ranging from 0.6494 (without residue co-occurrences) to 0.6683 (with residue co-occurrences). Large-scale screening using two other protein families placed related family members at the top of the rank, with a number of uncharacterized proteins also retrieved. Comparative results with other proposed methods are included.
Collapse
|
29
|
Structure of transcription factor HetR required for heterocyst differentiation in cyanobacteria. Proc Natl Acad Sci U S A 2011; 108:10109-14. [PMID: 21628585 DOI: 10.1073/pnas.1106840108] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
HetR is an essential regulator of heterocyst development in cyanobacteria. HetR binds to a DNA palindrome upstream of the hetP gene. We report the crystal structure of HetR from Fischerella at 3.0 Å. The protein is a dimer comprised of a central DNA-binding unit containing the N-terminal regions of the two subunits organized with two helix-turn-helix motifs; two globular flaps extending in opposite directions; and a hood over the central core formed from the C-terminal subdomains. The flaps and hood have no structural precedent in the protein database, therefore representing new folds. The structural assignments are supported by site-directed mutagenesis and DNA-binding studies. We suggest that HetR serves as a scaffold for assembly of transcription components critical for heterocyst development.
Collapse
|
30
|
Totrov M. Ligand binding site superposition and comparison based on Atomic Property Fields: identification of distant homologues, convergent evolution and PDB-wide clustering of binding sites. BMC Bioinformatics 2011; 12 Suppl 1:S35. [PMID: 21342566 PMCID: PMC3044291 DOI: 10.1186/1471-2105-12-s1-s35] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
A new binding site comparison algorithm using optimal superposition of the continuous pharmacophoric property distributions is reported. The method demonstrates high sensitivity in discovering both, distantly homologous and convergent binding sites. Good quality of superposition is also observed on multiple examples. Using the new approach, a measure of site similarity is derived and applied to clustering of ligand binding pockets in PDB.
Collapse
Affiliation(s)
- Maxim Totrov
- Molsoft LLC,3366 N Torrey Pines Ct, La Jolla, CA 92037, USA.
| |
Collapse
|
31
|
Babu M, Beloglazova N, Flick R, Graham C, Skarina T, Nocek B, Gagarinova A, Pogoutse O, Brown G, Binkowski A, Phanse S, Joachimiak A, Koonin EV, Savchenko A, Emili A, Greenblatt J, Edwards AM, Yakunin AF. A dual function of the CRISPR-Cas system in bacterial antivirus immunity and DNA repair. Mol Microbiol 2011; 79:484-502. [PMID: 21219465 PMCID: PMC3071548 DOI: 10.1111/j.1365-2958.2010.07465.x] [Citation(s) in RCA: 214] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) and the associated proteins (Cas) comprise a system of adaptive immunity against viruses and plasmids in prokaryotes. Cas1 is a CRISPR-associated protein that is common to all CRISPR-containing prokaryotes but its function remains obscure. Here we show that the purified Cas1 protein of Escherichia coli (YgbT) exhibits nuclease activity against single-stranded and branched DNAs including Holliday junctions, replication forks and 5'-flaps. The crystal structure of YgbT and site-directed mutagenesis have revealed the potential active site. Genome-wide screens show that YgbT physically and genetically interacts with key components of DNA repair systems, including recB, recC and ruvB. Consistent with these findings, the ygbT deletion strain showed increased sensitivity to DNA damage and impaired chromosomal segregation. Similar phenotypes were observed in strains with deletion of CRISPR clusters, suggesting that the function of YgbT in repair involves interaction with the CRISPRs. These results show that YgbT belongs to a novel, structurally distinct family of nucleases acting on branched DNAs and suggest that, in addition to antiviral immunity, at least some components of the CRISPR-Cas system have a function in DNA repair.
Collapse
Affiliation(s)
- Mohan Babu
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Natalia Beloglazova
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Robert Flick
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Chris Graham
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Tatiana Skarina
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Boguslaw Nocek
- Midwest Center for Structural Genomics and Structural Biology Center, Department of Biosciences, Argonne National Laboratory, Argonne, IL 60439
| | - Alla Gagarinova
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Oxana Pogoutse
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Greg Brown
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Andrew Binkowski
- Midwest Center for Structural Genomics and Structural Biology Center, Department of Biosciences, Argonne National Laboratory, Argonne, IL 60439
| | - Sadhna Phanse
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Andrzej Joachimiak
- Midwest Center for Structural Genomics and Structural Biology Center, Department of Biosciences, Argonne National Laboratory, Argonne, IL 60439
| | - Eugene V. Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894
| | - Alexei Savchenko
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| | - Andrew Emili
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
| | - Jack Greenblatt
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
| | - Aled M. Edwards
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
- Midwest Center for Structural Genomics and Structural Biology Center, Department of Biosciences, Argonne National Laboratory, Argonne, IL 60439
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, M5S 1A8, Canada
- Structural Genomics Consortium, University of Toronto, Toronto, Ontario, M5G 1L7, Canada
| | - Alexander F. Yakunin
- Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, M5G 1L6, Canada
| |
Collapse
|
32
|
Unmet challenges of structural genomics. Curr Opin Struct Biol 2010; 20:587-97. [PMID: 20810277 DOI: 10.1016/j.sbi.2010.08.001] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2010] [Revised: 07/30/2010] [Accepted: 08/03/2010] [Indexed: 11/22/2022]
Abstract
Structural genomics (SG) programs have developed during the last decade many novel methodologies for faster and more accurate structure determination. These new tools and approaches led to the determination of thousands of protein structures. The generation of enormous amounts of experimental data resulted in significant improvements in the understanding of many biological processes at molecular levels. However, the amount of data collected so far is so large that traditional analysis methods are limiting the rate of extraction of biological and biochemical information from 3D models. This situation has prompted us to review the challenges that remain unmet by SG, as well as the areas in which the potential impact of SG could exceed what has been achieved so far.
Collapse
|
33
|
Chen BY, Honig B. VASP: a volumetric analysis of surface properties yields insights into protein-ligand binding specificity. PLoS Comput Biol 2010; 6. [PMID: 20814581 PMCID: PMC2930297 DOI: 10.1371/journal.pcbi.1000881] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2009] [Accepted: 07/13/2010] [Indexed: 12/04/2022] Open
Abstract
Many algorithms that compare protein structures can reveal similarities that suggest related biological functions, even at great evolutionary distances. Proteins with related function often exhibit differences in binding specificity, but few algorithms identify structural variations that effect specificity. To address this problem, we describe the Volumetric Analysis of Surface Properties (VASP), a novel volumetric analysis tool for the comparison of binding sites in aligned protein structures. VASP uses solid volumes to represent protein shape and the shape of surface cavities, clefts and tunnels that are defined with other methods. Our approach, inspired by techniques from constructive solid geometry, enables the isolation of volumetrically conserved and variable regions within three dimensionally superposed volumes. We applied VASP to compute a comparative volumetric analysis of the ligand binding sites formed by members of the steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domains and the serine proteases. Within both families, VASP isolated individual amino acids that create structural differences between ligand binding cavities that are known to influence differences in binding specificity. Also, VASP isolated cavity subregions that differ between ligand binding cavities which are essential for differences in binding specificity. As such, VASP should prove a valuable tool in the study of protein-ligand binding specificity. Proteins carry out vital and specific functions by physically binding other molecules. Understanding specificity, the preferential binding of certain molecules to one another, is essential for numerous medical and industrial applications. Given the structure of a protein with unknown function, algorithms are available that suggest hypothetical functions based on structural similarities to better-studied proteins, even at vast evolutionary distances. In contrast, few algorithms identify structural differences that relate to differences in specificity among closely-related proteins. To address this problem, we present a Volumetric Analysis of Surface Properties (VASP). VASP differs from existing methods because it compares solid representations of protein structures and cavities based on principles from computer graphics and computer aided design. In our results, solid representations enabled VASP to isolate elements of protein structure that create differences in binding sites and thereby lead to differences in binding preferences. These observations point to applications for the annotation and engineering of protein specificity.
Collapse
Affiliation(s)
- Brian Y. Chen
- Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
- Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America
| | - Barry Honig
- Department of Biochemistry and Molecular Biophysics, Center for Computational Biology and Bioinformatics, Columbia University, New York, New York, United States of America
- Howard Hughes Medical Institute, Columbia University, New York, New York, United States of America
- * E-mail:
| |
Collapse
|
34
|
Ridout KE, Dixon CJ, Filatov DA. Positive selection differs between protein secondary structure elements in Drosophila. Genome Biol Evol 2010; 2:166-79. [PMID: 20624723 PMCID: PMC2997536 DOI: 10.1093/gbe/evq008] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Different protein secondary structure elements have different physicochemical properties and roles in the protein, which may determine their evolutionary flexibility. However, it is not clear to what extent protein structure affects the way Darwinian selection acts at the amino acid level. Using phylogeny-based likelihood tests for positive selection, we have examined the relationship between protein secondary structure and selection across six species of Drosophila. We find that amino acids that form disordered regions, such as random coils, are far more likely to be under positive selection than expected from their proportion in the proteins, and residues in helices and β-structures are subject to less positive selection than predicted. In addition, it appears that sites undergoing positive selection are more likely than expected to occur close to one another in the protein sequence. Finally, on a genome-wide scale, we have determined that positively selected sites are found more frequently toward the gene ends. Our results demonstrate that protein structures with a greater degree of organization and strong hydrophobicity, represented here as helices and β-structures, are less tolerant to molecular adaptation than disordered, hydrophilic regions, across a diverse set of proteins.
Collapse
Affiliation(s)
- Kate E Ridout
- Department of Plant Sciences, University of Oxford, Oxford, United Kingdom
| | | | | |
Collapse
|
35
|
Abstract
The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs.
Collapse
|
36
|
Zhang Q, Zmasek CM, Godzik A. Domain architecture evolution of pattern-recognition receptors. Immunogenetics 2010; 62:263-72. [PMID: 20195594 PMCID: PMC2858798 DOI: 10.1007/s00251-010-0428-1] [Citation(s) in RCA: 62] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2009] [Accepted: 02/03/2010] [Indexed: 12/11/2022]
Abstract
In animals, the innate immune system is the first line of defense against invading microorganisms, and the pattern-recognition receptors (PRRs) are the key components of this system, detecting microbial invasion and initiating innate immune defenses. Two families of PRRs, the intracellular NOD-like receptors (NLRs) and the transmembrane Toll-like receptors (TLRs), are of particular interest because of their roles in a number of diseases. Understanding the evolutionary history of these families and their pattern of evolutionary changes may lead to new insights into the functioning of this critical system. We found that the evolution of both NLR and TLR families included massive species-specific expansions and domain shuffling in various lineages, which resulted in the same domain architectures evolving independently within different lineages in a process that fits the definition of parallel evolution. This observation illustrates both the dynamics of the innate immune system and the effects of "combinatorially constrained" evolution, where existence of the limited numbers of functionally relevant domains constrains the choices of domain architectures for new members in the family, resulting in the emergence of independently evolved proteins with identical domain architectures, often mistaken for orthologs.
Collapse
Affiliation(s)
- Qing Zhang
- Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037 USA
| | - Christian M. Zmasek
- Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037 USA
| | - Adam Godzik
- Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, CA 92037 USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093 USA
| |
Collapse
|
37
|
Binkowski TA, Cuff M, Nocek B, Chang C, Joachimiak A. Assisted assignment of ligands corresponding to unknown electron density. ACTA ACUST UNITED AC 2010; 11:21-30. [PMID: 20091237 DOI: 10.1007/s10969-010-9078-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2009] [Accepted: 01/03/2010] [Indexed: 11/28/2022]
Abstract
A semi-automated computational procedure to assist in the identification of bound ligands from unknown electron density has been developed. The atomic surface surrounding the density blob is compared to a library of three-dimensional ligand binding surfaces extracted from the Protein Data Bank (PDB). Ligands corresponding to surfaces which share physicochemical texture and geometric shape similarities are considered for assignment. The method is benchmarked against a set of well represented ligands from the PDB, in which we show that we can identify the correct ligand based on the corresponding binding surface. Finally, we apply the method during model building and refinement stages from structural genomics targets in which unknown density blobs were discovered. A semi-automated computational method is described which aims to assist crystallographers with assigning the identity of a ligand corresponding to unknown electron density. Using shape and physicochemical similarity assessments between the protein surface surrounding the density and a database of known ligand binding surfaces, a plausible list of candidate ligands are identified for consideration. The method is validated against highly observed ligands from the Protein Data Bank and results are shown from its use in a high-throughput structural genomics pipeline.
Collapse
Affiliation(s)
- T Andrew Binkowski
- Midwest Center for Structural Genomics (MCSG), Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA.
| | | | | | | | | |
Collapse
|
38
|
Davies DR, Mamat B, Magnusson OT, Christensen J, Haraldsson MH, Mishra R, Pease B, Hansen E, Singh J, Zembower D, Kim H, Kiselyov AS, Burgin AB, Gurney ME, Stewart LJ. Discovery of leukotriene A4 hydrolase inhibitors using metabolomics biased fragment crystallography. J Med Chem 2009; 52:4694-715. [PMID: 19618939 PMCID: PMC2722745 DOI: 10.1021/jm900259h] [Citation(s) in RCA: 97] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
![]()
We describe a novel fragment library termed fragments of life (FOL) for structure-based drug discovery. The FOL library includes natural small molecules of life, derivatives thereof, and biaryl protein architecture mimetics. The choice of fragments facilitates the interrogation of protein active sites, allosteric binding sites, and protein−protein interaction surfaces for fragment binding. We screened the FOL library against leukotriene A4 hydrolase (LTA4H) by X-ray crystallography. A diverse set of fragments including derivatives of resveratrol, nicotinamide, and indole were identified as efficient ligands for LTA4H. These fragments were elaborated in a small number of synthetic cycles into potent inhibitors of LTA4H representing multiple novel chemotypes for modulating leukotriene biosynthesis. Analysis of the fragment-bound structures also showed that the fragments comprehensively recapitulated key chemical features and binding modes of several reported LTA4H inhibitors.
Collapse
Affiliation(s)
- Douglas R Davies
- deCODE biostructures, Inc., 7869 NE Day Road West, Bainbridge Island, Washington 98110, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
39
|
Joachimiak A. High-throughput crystallography for structural genomics. Curr Opin Struct Biol 2009; 19:573-84. [PMID: 19765976 DOI: 10.1016/j.sbi.2009.08.002] [Citation(s) in RCA: 96] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2009] [Revised: 08/14/2009] [Accepted: 08/20/2009] [Indexed: 11/20/2022]
Abstract
Protein X-ray crystallography recently celebrated its 50th anniversary. The structures of myoglobin and hemoglobin determined by Kendrew and Perutz provided the first glimpses into the complex protein architecture and chemistry. Since then, the field of structural molecular biology has experienced extraordinary progress and now more than 55000 protein structures have been deposited into the Protein Data Bank. In the past decade many advances in macromolecular crystallography have been driven by world-wide structural genomics efforts. This was made possible because of third-generation synchrotron sources, structure phasing approaches using anomalous signal, and cryo-crystallography. Complementary progress in molecular biology, proteomics, hardware and software for crystallographic data collection, structure determination and refinement, computer science, databases, robotics and automation improved and accelerated many processes. These advancements provide the robust foundation for structural molecular biology and assure strong contribution to science in the future. In this report we focus mainly on reviewing structural genomics high-throughput X-ray crystallography technologies and their impact.
Collapse
Affiliation(s)
- Andrzej Joachimiak
- Midwest Center for Structural Genomics, Structural Biology Center, Biosciences Division, Argonne National Laboratory, 9700 S Class Ave., Argonne, IL 60439, USA.
| |
Collapse
|
40
|
Argiriadi MA, Xiang T, Wu C, Ghayur T, Borhani DW. Unusual water-mediated antigenic recognition of the proinflammatory cytokine interleukin-18. J Biol Chem 2009; 284:24478-89. [PMID: 19553661 DOI: 10.1074/jbc.m109.023887] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The unique cytokine interleukin-18 (IL-18) acts synergistically with IL-12 to regulate T-helper 1 and 2 lymphocytes and, as such, seems to underlie the pathogenesis of various autoimmune and allergic diseases. Several anti-IL-18 agents are in clinical development, including the recombinant human antibody ABT-325, which is entering trials for autoimmune diseases. Given competing cytokine/receptor and cytokine/receptor decoy interactions, understanding the structural basis for recognition is critical for effective development of anti-cytokine therapies. Here we report three crystal structures: the murine antibody 125-2H Fab fragment bound to human IL-18, at 1.5 A resolution; the 125-2H Fab (2.3 A); and the ABT-325 Fab (1.5 A). These structures, along with human/mouse IL-18 chimera binding data, allow us to make three key observations relevant to the biology and antigenic recognition of IL-18 and related cytokines. First, several IL-18 residues shift dramatically (> 10 A) upon binding 125-2H, compared with unbound IL-18 (Kato, Z., Jee, J., Shikano, H., Mishima, M., Ohki, I., Ohnishi, H., Li, A., Hashimoto, K., Matsukuma, E., Omoya, K., Yamamoto, Y., Yoneda, T., Hara, T., Kondo, N., and Shirakawa, M. (2003) Nat. Struct. Biol. 10, 966-971). IL-18 thus exhibits plasticity that may be common to its interactions with other receptors. Related cytokines may exhibit similar plasticity. Second, ABT-325 and 125-2H differ significantly in combining site character and architecture, thus explaining their ability to bind IL-18 simultaneously at distinct epitopes. These data allow us to define the likely ABT-325 epitope and thereby explain the distinct neutralizing mechanisms of both antibodies. Third, given the high 125-2H potency, 10 well ordered water molecules are trapped upon complex formation in a cavity between two IL-18 loops and all six 125-2H complementarity-determining regions. Thus, counterintuitively, tight and specific antibody binding may in some cases be water-mediated.
Collapse
Affiliation(s)
- Maria A Argiriadi
- Department of Biochemistry, Abbott Laboratories, Worcester, Massachusetts 01605, USA.
| | | | | | | | | |
Collapse
|
41
|
Xie L, Xie L, Bourne PE. A unified statistical model to support local sequence order independent similarity searching for ligand-binding sites and its application to genome-based drug discovery. Bioinformatics 2009; 25:i305-12. [PMID: 19478004 PMCID: PMC2687974 DOI: 10.1093/bioinformatics/btp220] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Functional relationships between proteins that do not share global structure similarity can be established by detecting their ligand-binding-site similarity. For a large-scale comparison, it is critical to accurately and efficiently assess the statistical significance of this similarity. Here, we report an efficient statistical model that supports local sequence order independent ligand-binding-site similarity searching. Most existing statistical models only take into account the matching vertices between two sites that are defined by a fixed number of points. In reality, the boundary of the binding site is not known or is dependent on the bound ligand making these approaches limited. To address these shortcomings and to perform binding-site mapping on a genome-wide scale, we developed a sequence-order independent profile-profile alignment (SOIPPA) algorithm that is able to detect local similarity between unknown binding sites a priori. The SOIPPA scoring integrates geometric, evolutionary and physical information into a unified framework. However, this imposes a significant challenge in assessing the statistical significance of the similarity because the conventional probability model that is based on fixed-point matching cannot be applied. Here we find that scores for binding-site matching by SOIPPA follow an extreme value distribution (EVD). Benchmark studies show that the EVD model performs at least two-orders faster and is more accurate than the non-parametric statistical method in the previous SOIPPA version. Efficient statistical analysis makes it possible to apply SOIPPA to genome-based drug discovery. Consequently, we have applied the approach to the structural genome of Mycobacterium tuberculosis to construct a protein-ligand interaction network. The network reveals highly connected proteins, which represent suitable targets for promiscuous drugs.
Collapse
Affiliation(s)
- Lei Xie
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA.
| | | | | |
Collapse
|
42
|
Potential for protein surface shape analysis using spherical harmonics and 3D Zernike descriptors. Cell Biochem Biophys 2009; 54:23-32. [PMID: 19521674 DOI: 10.1007/s12013-009-9051-x] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2009] [Accepted: 05/22/2009] [Indexed: 10/20/2022]
Abstract
With structure databases expanding at a rapid rate, the task at hand is to provide reliable clues to their molecular function and to be able to do so on a large scale. This, however, requires suitable encodings of the molecular structure which are amenable to fast screening. To this end, moment-based representations provide a compact and nonredundant description of molecular shape and other associated properties. In this article, we present an overview of some commonly used representations with specific focus on two schemes namely spherical harmonics and their extension, the 3D Zernike descriptors. Key features and differences of the two are reviewed and selected applications are highlighted. We further discuss recent advances covering aspects of shape and property-based comparison at both global and local levels and demonstrate their applicability through some of our studies.
Collapse
|