1
|
Singh J, Khanduja KL, Avti PK. Unravelling benzazepines and aminopyrimidine as multi-target therapeutic repurposing drugs for EGFR V774M mutation in neuroglioma patients. BIOIMPACTS : BI 2023; 14:28876. [PMID: 38938756 PMCID: PMC11199933 DOI: 10.34172/bi.2023.28876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 07/03/2023] [Accepted: 08/23/2023] [Indexed: 06/29/2024]
Abstract
Introduction Neuroglioma, a classification encompassing tumors arising from glial cells, exhibits variable aggressiveness and depends on tumor grade and stage. Unraveling the EGFR gene alterations, including amplifications (unaltered), deletions, and missense mutations (altered), is emerging in glioma. However, the precise understanding of emerging EGFR mutations and their role in neuroglioma remains limited. This study aims to identify specific EGFR mutations prevalent in neuroglioma patients and investigate their potential as therapeutic targets using FDA-approved drugs for repurposing approach. Methods Neuroglioma patient's data were analyzed to identify the various mutations and survival rates. High throughput virtual screening (HTVS) of FDA-approved (1615) drugs using molecular docking and simulation was executed to determine the potential hits. Results Neuroglioma patient samples (n=4251) analysis reveals 19% EGFR alterations with most missense mutations at V774M in exon 19. The Kaplan-Meier plots show that the overall survival rate was higher in the unaltered group than in the altered group. Docking studies resulted the best hits based on each target's higher docking score, minimum free energy (MMGBSA), minimum kd, ki, and IC50 values. MD simulations and their trajectories show that compounds ZINC000011679756 target unaltered EGFR and ZINC000003978005 targets altered EGFR, whereas ZINC000012503187 (Conivaptan, Benzazepine) and ZINC000068153186 (Dabrafenib, aminopyrimidine) target both the EGFRs. The shortlisted compounds demonstrate favorable residual interactions with their respective targets, forming highly stable complexes. Moreover, these shortlisted compounds have drug- like properties as assessed by ADMET profiling. Conclusion Therefore, compounds (ZINC000012503187 and ZINC000068153186) can effectively target both the unaltered/altered EGFRs as multi-target therapeutic repurposing drugs towards neuroglioma.
Collapse
Affiliation(s)
- Jitender Singh
- Department of Biophysics, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India – 160012
| | - Krishan L Khanduja
- Department of Biophysics, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India – 160012
| | - Pramod K Avti
- Department of Biophysics, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India – 160012
| |
Collapse
|
2
|
Nagar N, Tubiana J, Loewenthal G, Wolfson HJ, Ben Tal N, Pupko T. EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning. J Mol Biol 2023; 435:168155. [PMID: 37356902 DOI: 10.1016/j.jmb.2023.168155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 05/13/2023] [Accepted: 05/17/2023] [Indexed: 06/27/2023]
Abstract
Multiple sequence alignments (MSAs) are the workhorse of molecular evolution and structural biology research. From MSAs, the amino acids that are tolerated at each site during protein evolution can be inferred. However, little is known regarding the repertoire of tolerated amino acids in proteins when only a few or no sequence homologs are available, such as orphan and de novo designed proteins. Here we present EvoRator2, a deep-learning algorithm trained on over 15,000 protein structures that can predict which amino acids are tolerated at any given site, based exclusively on protein structural information mined from atomic coordinate files. We show that EvoRator2 obtained satisfying results for the prediction of position-weighted scoring matrices (PSSM). We further show that EvoRator2 obtained near state-of-the-art performance on proteins with high quality structures in predicting the effect of mutations in deep mutation scanning (DMS) experiments and that for certain DMS targets, EvoRator2 outperformed state-of-the-art methods. We also show that by combining EvoRator2's predictions with those obtained by a state-of-the-art deep-learning method that accounts for the information in the MSA, the prediction of the effect of mutation in DMS experiments was improved in terms of both accuracy and stability. EvoRator2 is designed to predict which amino-acid substitutions are tolerated in such proteins without many homologous sequences, including orphan or de novo designed proteins. We implemented our approach in the EvoRator web server (https://evorator.tau.ac.il).
Collapse
Affiliation(s)
- Natan Nagar
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Jérôme Tubiana
- Blavatnik School of Computer Science, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Gil Loewenthal
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Nir Ben Tal
- School of Neurobiology, Biochemistry & Biophysics, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tal Pupko
- The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
| |
Collapse
|
3
|
Wu C, Guo D. Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants. Int J Mol Sci 2022; 23:ijms232012688. [PMID: 36293547 PMCID: PMC9604239 DOI: 10.3390/ijms232012688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 11/16/2022] Open
Abstract
Proteins are modular functionalities regulating multiple cellular activities in prokaryotes and eukaryotes. As a consequence of higher plants adapting to arid and thermal conditions, C4 photosynthesis is the carbon fixation process involving multi-enzymes working in a coordinated fashion. However, how these enzymes interact with each other and whether they co-evolve in parallel to maintain interactions in different plants remain elusive to date. Here, we report our findings on the global protein co-evolution relationship and local dynamics of co-varying site shifts in key C4 photosynthetic enzymes. We found that in most of the selected key C4 photosynthetic enzymes, global pairwise co-evolution events exist to form functional couplings. Besides, protein-protein interactions between these enzymes may suggest their unknown functionalities in the carbon delivery process. For PEPC and PPCK regulation pairs, pocket formation at the interactive interface are not necessary for their function. This feature is distinct from another well-known regulation pair in C4 photosynthesis, namely, PPDK and PPDK-RP, where the pockets are necessary. Our findings facilitate the discovery of novel protein regulation types and contribute to expanding our knowledge about C4 photosynthesis.
Collapse
|
4
|
Triveri A, Sanchez-Martin C, Torielli L, Serapian SA, Marchetti F, D'Acerno G, Pirota V, Castelli M, Moroni E, Ferraro M, Quadrelli P, Rasola A, Colombo G. Protein allostery and ligand design: Computational design meets experiments to discover novel chemical probes. J Mol Biol 2022; 434:167468. [DOI: 10.1016/j.jmb.2022.167468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/18/2022] [Accepted: 01/21/2022] [Indexed: 10/19/2022]
|
5
|
Fan J, Liu Y, Kong R, Ni D, Yu Z, Lu S, Zhang J. Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery. J Med Chem 2021; 64:17728-17743. [PMID: 34878270 DOI: 10.1021/acs.jmedchem.1c01695] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Allostery is a fundamental and extensive mechanism of intramolecular signal transmission. Allosteric drugs possess several unique pharmacological advantages over traditional orthosteric drugs, including greater selectivity, better physicochemical properties, and lower off-target toxicity. However, owing to the complexity of allosteric regulation, experimental approaches for the development of allosteric modulators are traditionally serendipitous. Recently, the reversed allosteric communication theory has been proposed, providing a feasible tool for the unbiased detection of allosteric sites. Herein, we review the latest research on the reversed allosteric communication effect using the examples of sirtuin 6, epidermal growth factor receptor, 3-phosphoinositide-dependent protein kinase 1, and Related to A and C kinases (RAC) serine/threonine protein kinase B and recapitulate the methodologies of reversed allosteric communication strategy. The novel reversed allosteric communication strategy greatly expands the horizon of allosteric site identification and allosteric mechanism exploration and is expected to accelerate an end-to-end framework for drug discovery.
Collapse
Affiliation(s)
- Jigang Fan
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Zhiyuan Innovative Research Center, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yaqin Liu
- Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Ren Kong
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, China
| | - Duan Ni
- The Charles Perkins Centre, University of Sydney, Sydney, New South Wales 2006, Australia
| | | | - Shaoyong Lu
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China
| | - Jian Zhang
- College of Pharmacy, Ningxia Medical University, Yinchuan, Ningxia Hui Autonomous Region 750004, China.,State Key Laboratory of Oncogenes and Related Genes, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University, School of Medicine, Shanghai 200025, China.,School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450001, China
| |
Collapse
|
6
|
Chatzigoulas A, Cournia Z. Rational design of allosteric modulators: Challenges and successes. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1529] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alexios Chatzigoulas
- Biomedical Research Foundation Academy of Athens Athens Greece
- Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece
| | - Zoe Cournia
- Biomedical Research Foundation Academy of Athens Athens Greece
| |
Collapse
|
7
|
Garlick JM, Mapp AK. Selective Modulation of Dynamic Protein Complexes. Cell Chem Biol 2020; 27:986-997. [PMID: 32783965 PMCID: PMC7469457 DOI: 10.1016/j.chembiol.2020.07.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/07/2020] [Accepted: 07/22/2020] [Indexed: 12/11/2022]
Abstract
Dynamic proteins perform critical roles in cellular machines, including those that control proteostasis, transcription, translation, and signaling. Thus, dynamic proteins are prime candidates for chemical probe and drug discovery but difficult targets because they do not conform to classical rules of design and screening. Selectivity is pivotal for candidate probe molecules due to the extensive interaction network of these dynamic hubs. Recognition that the traditional rules of probe discovery are not necessarily applicable to dynamic proteins and their complexes, as well as technological advances in screening, have produced remarkable results in the last 2-4 years. Particularly notable are the improvements in target selectivity for small-molecule modulators of dynamic proteins, especially with techniques that increase the discovery likelihood of allosteric regulatory mechanisms. We focus on approaches to small-molecule screening that appear to be more suitable for highly dynamic targets and have the potential to streamline identification of selective modulators.
Collapse
Affiliation(s)
- Julie M Garlick
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anna K Mapp
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA; Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48109, USA.
| |
Collapse
|
8
|
Serapian SA, Colombo G. Designing Molecular Spanners to Throw in the Protein Networks. Chemistry 2020; 26:4656-4670. [DOI: 10.1002/chem.201904523] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/18/2019] [Indexed: 12/18/2022]
Affiliation(s)
- Stefano A. Serapian
- Department of ChemistryUniversity of Pavia Via Taramelli 12 27100 Pavia Italy
| | - Giorgio Colombo
- Department of ChemistryUniversity of Pavia Via Taramelli 12, 27 100 Pavia Italy
- SCITEC-CNR Via Mario Bianco 9 20131 Milano Italy
| |
Collapse
|
9
|
Trosset JY, Cavé C. In Silico Target Druggability Assessment: From Structural to Systemic Approaches. Methods Mol Biol 2019; 1953:63-88. [PMID: 30912016 DOI: 10.1007/978-1-4939-9145-7_5] [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] [Indexed: 01/10/2023]
Abstract
This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.
Collapse
Affiliation(s)
| | - Christian Cavé
- BioCIS UFR Pharmacie UMR CNRS 8076, Université Paris Saclay, Orsay, France
| |
Collapse
|
10
|
Daura X. Advances in the Computational Identification of Allosteric Sites and Pathways in Proteins. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1163:141-169. [PMID: 31707703 DOI: 10.1007/978-981-13-8719-7_7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
With the increasing difficulty to develop new drugs and the emergence of resistance to traditional orthosteric-site inhibitors, the search for alternatives is finally approaching the focus on allosteric sites. Allosteric sites offer opportunities to regulate many pharmacologically targeted pathways by inhibition or activation. In addition, allosteric sites tend to be less conserved than the functional site, which may facilitate the design of specific effectors in the protein families for which specific orthosteric inhibitors have proved difficult to design. Furthermore, recent evidence suggests that all proteins might be susceptible of allosteric regulation, increasing the space of druggable targets. Computational identification of allosteric sites has therefore become an active field of research. The problem can be approached from two sides: (1) the identification of allosteric-communication pathways between the functional site and potential allosteric sites and (2) the functional-site-independent identification of allosteric sites. While the first approach tends to be more laborious and thus restricted to a single protein, the second tends to be more amenable to larger-scale analysis, thus providing tools for the two drug discovery scenarios: the analysis of known targets and the screening for new potential targets. Here, I show some basic concepts and methods useful to the identification of allosteric sites and pathways, in line with these two approaches. I describe them in some detail to build a clear framework, at the risk of losing the interest of experts. Examples of recent studies involving these methods are also illustrated, focusing on the techniques rather than on their findings on allosterism.
Collapse
Affiliation(s)
- Xavier Daura
- Catalan Institution for Research and Advanced Studies (ICREA) and Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
| |
Collapse
|
11
|
Ferraro M, D’Annessa I, Moroni E, Morra G, Paladino A, Rinaldi S, Compostella F, Colombo G. Allosteric Modulators of HSP90 and HSP70: Dynamics Meets Function through Structure-Based Drug Design. J Med Chem 2018; 62:60-87. [DOI: 10.1021/acs.jmedchem.8b00825] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Mariarosaria Ferraro
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
| | - Ilda D’Annessa
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
| | | | - Giulia Morra
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
| | - Antonella Paladino
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
| | - Silvia Rinaldi
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
| | - Federica Compostella
- Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università degli Studi di Milano, Via Saldini, 50, 20133 Milano, Italy
| | - Giorgio Colombo
- Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
- Dipartimento di Chimica, Università di Pavia, V.le Taramelli 12, 27100 Pavia, Italy
| |
Collapse
|
12
|
Kumar AP, Lukman S. Allosteric binding sites in Rab11 for potential drug candidates. PLoS One 2018; 13:e0198632. [PMID: 29874286 PMCID: PMC5991966 DOI: 10.1371/journal.pone.0198632] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 05/22/2018] [Indexed: 12/19/2022] Open
Abstract
Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes. Although they are medically important, no previous study has found Rab11 allosteric binding sites where potential drug candidates can bind to. In this study, by employing multiple clustering approaches integrating principal component analysis, independent component analysis and locally linear embedding, we performed structural analyses of Rab11 and identified eight representative structures. Using these representatives to perform binding site mapping and virtual screening, we identified two novel binding sites in Rab11 and small molecules that can preferentially bind to different conformations of these sites with high affinities. After identifying the binding sites and the residue interaction networks in the representatives, we computationally showed that these binding sites may allosterically regulate Rab11, as these sites communicate with switch 2 region that binds to GTP/GDP. These two allosteric binding sites in Rab11 are also similar to two allosteric pockets in Ras that we discovered previously.
Collapse
Affiliation(s)
- Ammu Prasanna Kumar
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Suryani Lukman
- Department of Chemistry, College of Arts and Sciences, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| |
Collapse
|
13
|
Srinivasan B, Rodrigues JV, Tonddast-Navaei S, Shakhnovich E, Skolnick J. Rational Design of Novel Allosteric Dihydrofolate Reductase Inhibitors Showing Antibacterial Effects on Drug-Resistant Escherichia coli Escape Variants. ACS Chem Biol 2017; 12:1848-1857. [PMID: 28525268 DOI: 10.1021/acschembio.7b00175] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In drug discovery, systematic variations of substituents on a common scaffold and bioisosteric replacements are often used to generate diversity and obtain molecules with better biological effects. However, this could saturate the small-molecule diversity pool resulting in drug resistance. On the other hand, conventional drug discovery relies on targeting known pockets on protein surfaces leading to drug resistance by mutations of critical pocket residues. Here, we present a two-pronged strategy of designing novel drugs that target unique pockets on a protein's surface to overcome the above problems. Dihydrofolate reductase, DHFR, is a critical enzyme involved in thymidine and purine nucleotide biosynthesis. Several classes of compounds that are structural analogues of the substrate dihydrofolate have been explored for their antifolate activity. Here, we describe 10 novel small-molecule inhibitors of Escherichia coli DHFR, EcDHFR, belonging to the stilbenoid, deoxybenzoin, and chalcone family of compounds discovered by a combination of pocket-based virtual ligand screening and systematic scaffold hopping. These inhibitors show a unique uncompetitive or noncompetitive inhibition mechanism, distinct from those reported for all known inhibitors of DHFR, indicative of binding to a unique pocket distinct from either substrate or cofactor-binding pockets. Furthermore, we demonstrate that rescue mutants of EcDHFR, with reduced affinity to all known classes of DHFR inhibitors, are inhibited at the same concentration as the wild-type. These compounds also exhibit antibacterial activity against E. coli harboring the drug-resistant variant of DHFR. This discovery is the first report on a novel class of inhibitors targeting a unique pocket on EcDHFR.
Collapse
Affiliation(s)
- Bharath Srinivasan
- Center
for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, Georgia 30332, United States
| | - João V. Rodrigues
- Department
of Chemistry and Chemical Biology, Harvard University, 12 Oxford
Street, Cambridge, Massachusetts 02138, United States
| | - Sam Tonddast-Navaei
- Center
for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, Georgia 30332, United States
| | - Eugene Shakhnovich
- Department
of Chemistry and Chemical Biology, Harvard University, 12 Oxford
Street, Cambridge, Massachusetts 02138, United States
| | - Jeffrey Skolnick
- Center
for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, 950 Atlantic Drive, Atlanta, Georgia 30332, United States
| |
Collapse
|
14
|
Greener JG, Filippis I, Sternberg MJE. Predicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints. Structure 2017; 25:546-558. [PMID: 28190781 PMCID: PMC5343748 DOI: 10.1016/j.str.2017.01.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Revised: 11/24/2016] [Accepted: 01/19/2017] [Indexed: 11/16/2022]
Abstract
The related concepts of protein dynamics, conformational ensembles and allostery are often difficult to study with molecular dynamics (MD) due to the timescales involved. We present ExProSE (Exploration of Protein Structural Ensembles), a distance geometry-based method that generates an ensemble of protein structures from two input structures. ExProSE provides a unified framework for the exploration of protein structure and dynamics in a fast and accessible way. Using a dataset of apo/holo pairs it is shown that existing coarse-grained methods often cannot span large conformational changes. For T4-lysozyme, ExProSE is able to generate ensembles that are more native-like than tCONCOORD and NMSim, and comparable with targeted MD. By adding additional constraints representing potential modulators, ExProSE can predict allosteric sites. ExProSE ranks an allosteric pocket first or second for 27 out of 58 allosteric proteins, which is similar and complementary to existing methods. The ExProSE source code is freely available.
Collapse
Affiliation(s)
- Joe G Greener
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
| | - Ioannis Filippis
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK
| |
Collapse
|
15
|
Clarke D, Sethi A, Li S, Kumar S, Chang RWF, Chen J, Gerstein M. Identifying Allosteric Hotspots with Dynamics: Application to Inter- and Intra-species Conservation. Structure 2016; 24:826-837. [PMID: 27066750 PMCID: PMC4883016 DOI: 10.1016/j.str.2016.03.008] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Revised: 01/01/2016] [Accepted: 03/04/2016] [Indexed: 01/17/2023]
Abstract
The rapidly growing volume of data being produced by next-generation sequencing initiatives is enabling more in-depth analyses of conservation than previously possible. Deep sequencing is uncovering disease loci and regions under selective constraint, despite the fact that intuitive biophysical reasons for such constraint are sometimes absent. Allostery may often provide the missing explanatory link. We use models of protein conformational change to identify allosteric residues by finding essential surface pockets and information-flow bottlenecks, and we develop a software tool that enables users to perform this analysis on their own proteins of interest. Though fundamentally 3D-structural in nature, our analysis is computationally fast, thereby allowing us to run it across the PDB and to evaluate general properties of predicted allosteric residues. We find that these tend to be conserved over diverse evolutionary time scales. Finally, we highlight examples of allosteric residues that help explain poorly understood disease-associated variants.
Collapse
Affiliation(s)
- Declan Clarke
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, CT 06520, USA
| | - Anurag Sethi
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Computer Science, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Richard W F Chang
- Yale College, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Jieming Chen
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Integrated Graduate Program in Physical and Engineering Biology, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA; Department of Computer Science, Yale University, 260/266 Whitney Avenue, PO Box 208114, New Haven, CT 06520, USA.
| |
Collapse
|
16
|
Greener JG, Sternberg MJE. AlloPred: prediction of allosteric pockets on proteins using normal mode perturbation analysis. BMC Bioinformatics 2015; 16:335. [PMID: 26493317 PMCID: PMC4619270 DOI: 10.1186/s12859-015-0771-1] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Accepted: 10/13/2015] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Despite being hugely important in biological processes, allostery is poorly understood and no universal mechanism has been discovered. Allosteric drugs are a largely unexplored prospect with many potential advantages over orthosteric drugs. Computational methods to predict allosteric sites on proteins are needed to aid the discovery of allosteric drugs, as well as to advance our fundamental understanding of allostery. RESULTS AlloPred, a novel method to predict allosteric pockets on proteins, was developed. AlloPred uses perturbation of normal modes alongside pocket descriptors in a machine learning approach that ranks the pockets on a protein. AlloPred ranked an allosteric pocket top for 23 out of 40 known allosteric proteins, showing comparable and complementary performance to two existing methods. In 28 of 40 cases an allosteric pocket was ranked first or second. The AlloPred web server, freely available at http://www.sbg.bio.ic.ac.uk/allopred/home, allows visualisation and analysis of predictions. The source code and dataset information are also available from this site. CONCLUSIONS Perturbation of normal modes can enhance our ability to predict allosteric sites on proteins. Computational methods such as AlloPred assist drug discovery efforts by suggesting sites on proteins for further experimental study.
Collapse
Affiliation(s)
- Joe G Greener
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK.
| | - Michael J E Sternberg
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, SW7 2AZ, UK
| |
Collapse
|
17
|
Barbany M, Meyer T, Hospital A, Faustino I, D'Abramo M, Morata J, Orozco M, de la Cruz X. Molecular dynamics study of naturally existing cavity couplings in proteins. PLoS One 2015; 10:e0119978. [PMID: 25816327 PMCID: PMC4376744 DOI: 10.1371/journal.pone.0119978] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 01/26/2015] [Indexed: 11/18/2022] Open
Abstract
Couplings between protein sub-structures are a common property of protein dynamics. Some of these couplings are especially interesting since they relate to function and its regulation. In this article we have studied the case of cavity couplings because cavities can host functional sites, allosteric sites, and are the locus of interactions with the cell milieu. We have divided this problem into two parts. In the first part, we have explored the presence of cavity couplings in the natural dynamics of 75 proteins, using 20 ns molecular dynamics simulations. For each of these proteins, we have obtained two trajectories around their native state. After applying a stringent filtering procedure, we found significant cavity correlations in 60% of the proteins. We analyze and discuss the structure origins of these correlations, including neighbourhood, cavity distance, etc. In the second part of our study, we have used longer simulations (≥100 ns) from the MoDEL project, to obtain a broader view of cavity couplings, particularly about their dependence on time. Using moving window computations we explored the fluctuations of cavity couplings along time, finding that these couplings could fluctuate substantially during the trajectory, reaching in several cases correlations above 0.25/0.5. In summary, we describe the structural origin and the variations with time of cavity couplings. We complete our work with a brief discussion of the biological implications of these results.
Collapse
Affiliation(s)
- Montserrat Barbany
- Translational Bioinformatics in Neurosciences, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Tim Meyer
- Theoretische und computergestützte Biophysik, Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany
| | - Adam Hospital
- Joint IRB (Institute for Research in Biomedicine)—BSC (Barcelona Supercomputing Center) Program on Computational Biology, Barcelona, Spain
| | - Ignacio Faustino
- Joint IRB (Institute for Research in Biomedicine)—BSC (Barcelona Supercomputing Center) Program on Computational Biology, Barcelona, Spain
| | - Marco D'Abramo
- Department of Chemistry, Università degli Studi di Roma "La Sapienza", Roma, Italy
| | - Jordi Morata
- Centre for Research in Agricultural Genomics (CRAG), Barcelona, Spain
| | - Modesto Orozco
- Joint IRB (Institute for Research in Biomedicine)—BSC (Barcelona Supercomputing Center) Program on Computational Biology, Barcelona, Spain
- Departament de Bioquímica i Biologia Molecular, Facultat de Biologia, Universitat de Barcelona, Barcelona, Spain
| | - Xavier de la Cruz
- Translational Bioinformatics in Neurosciences, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- * E-mail:
| |
Collapse
|
18
|
Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models. Amino Acids 2014; 46:2665-80. [DOI: 10.1007/s00726-014-1817-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Accepted: 07/21/2014] [Indexed: 11/26/2022]
|
19
|
Panjkovich A, Gibert I, Daura X. antibacTR: dynamic antibacterial-drug-target ranking integrating comparative genomics, structural analysis and experimental annotation. BMC Genomics 2014; 15:36. [PMID: 24438389 PMCID: PMC3932961 DOI: 10.1186/1471-2164-15-36] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2013] [Accepted: 01/11/2014] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Development of novel antibacterial drugs is both an urgent healthcare necessity and a partially neglected field. The last decades have seen a substantial decrease in the discovery of novel antibiotics, which combined with the recent thrive of multi-drug-resistant pathogens have generated a scenario of general concern. The procedures involved in the discovery and development of novel antibiotics are economically challenging, time consuming and lack any warranty of success. Furthermore, the return-on-investment for an antibacterial drug is usually marginal when compared to other therapeutics, which in part explains the decrease of private investment. RESULTS In this work we present antibacTR, a computational pipeline designed to aid researchers in the selection of potential drug targets, one of the initial steps in antibacterial-drug discovery. The approach was designed and implemented as part of two publicly funded initiatives aimed at discovering novel antibacterial targets, mechanisms and drugs for a priority list of Gram-negative pathogens: Acinetobacter baumannii, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. However, at present this list has been extended to cover a total of 74 fully sequenced Gram-negative pathogens. antibacTR is based on sequence comparisons and queries to multiple databases (e.g. gene essentiality, virulence factors) to rank proteins according to their potential as antibacterial targets. The dynamic ranking of potential drug targets can easily be executed, customized and accessed by the user through a web interface which also integrates computational analyses performed in-house and visualizable on-site. These include three-dimensional modeling of protein structures and prediction of active sites among other functionally relevant ligand-binding sites. CONCLUSIONS Given its versatility and ease-of-use at integrating both experimental annotation and computational analyses, antibacTR may effectively assist microbiologists, medicinal-chemists and other researchers working in the field of antibacterial drug-discovery. The public web-interface for antibacTR is available at 'http://bioinf.uab.cat/antibactr'.
Collapse
Affiliation(s)
| | | | - Xavier Daura
- Institute of Biotechnology and Biomedicine (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain.
| |
Collapse
|
20
|
Panjkovich A, Daura X. PARS: a web server for the prediction of Protein Allosteric and Regulatory Sites. Bioinformatics 2014; 30:1314-5. [DOI: 10.1093/bioinformatics/btu002] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
|
21
|
Ma B, Nussinov R. Druggable orthosteric and allosteric hot spots to target protein-protein interactions. Curr Pharm Des 2014; 20:1293-301. [PMID: 23713780 PMCID: PMC6361532 DOI: 10.2174/13816128113199990073] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2013] [Accepted: 05/21/2013] [Indexed: 11/22/2022]
Abstract
Drug designing targeting protein-protein interactions is challenging. Because structural elucidation and computational analysis have revealed the importance of hot spot residues in stabilizing these interactions, there have been on-going efforts to develop drugs which bind the hot spots and out-compete the native protein partners. The question arises as to what are the key 'druggable' properties of hot spots in protein-protein interactions and whether these mimic the general hot spot definition. Identification of orthosteric (at the protein- protein interaction site) and allosteric (elsewhere) druggable hot spots is expected to help in discovering compounds that can more effectively modulate protein-protein interactions. For example, are there any other significant features beyond their location in pockets in the interface? The interactions of protein-protein hot spots are coupled with conformational dynamics of protein complexes. Currently increasing efforts focus on the allosteric drug discovery. Allosteric drugs bind away from the native binding site and can modulate the native interactions. We propose that identification of allosteric hot spots could similarly help in more effective allosteric drug discovery. While detection of allosteric hot spots is challenging, targeting drugs to these residues has the potential of greatly increasing the hot spot and protein druggability.
Collapse
Affiliation(s)
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc. Cancer and Inflammation Program, NCIFrederick, Frederick, MD 21702.
| |
Collapse
|
22
|
Warren S, Wan XF, Conant G, Korkin D. Extreme evolutionary conservation of functionally important regions in H1N1 influenza proteome. PLoS One 2013; 8:e81027. [PMID: 24282564 PMCID: PMC3839886 DOI: 10.1371/journal.pone.0081027] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 10/08/2013] [Indexed: 12/31/2022] Open
Abstract
The H1N1 subtype of influenza A virus has caused two of the four documented pandemics and is responsible for seasonal epidemic outbreaks, presenting a continuous threat to public health. Co-circulating antigenically divergent influenza strains significantly complicates vaccine development and use. Here, by combining evolutionary, structural, functional, and population information about the H1N1 proteome, we seek to answer two questions: (1) do residues on the protein surfaces evolve faster than the protein core residues consistently across all proteins that constitute the influenza proteome? and (2) in spite of the rapid evolution of surface residues in influenza proteins, are there any protein regions on the protein surface that do not evolve? To answer these questions, we first built phylogenetically-aware models of the patterns of surface and interior substitutions. Employing these models, we found a single coherent pattern of faster evolution on the protein surfaces that characterizes all influenza proteins. The pattern is consistent with the events of inter-species reassortment, the worldwide introduction of the flu vaccine in the early 80's, as well as the differences caused by the geographic origins of the virus. Next, we developed an automated computational pipeline to comprehensively detect regions of the protein surface residues that were 100% conserved over multiple years and in multiple host species. We identified conserved regions on the surface of 10 influenza proteins spread across all avian, swine, and human strains; with the exception of a small group of isolated strains that affected the conservation of three proteins. Surprisingly, these regions were also unaffected by genetic variation in the pandemic 2009 H1N1 viral population data obtained from deep sequencing experiments. Finally, the conserved regions were intrinsically related to the intra-viral macromolecular interaction interfaces. Our study may provide further insights towards the identification of novel protein targets for influenza antivirals.
Collapse
Affiliation(s)
- Samantha Warren
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Xiu-Feng Wan
- Department of Basic Sciences, Mississippi State University, Mississippi State, Mississippi, United States of America
| | - Gavin Conant
- Division of Animal Sciences, University of Missouri, Columbia, Missouri, United States of America
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
| | - Dmitry Korkin
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
| |
Collapse
|
23
|
Hemsworth GR, Price HP, Smith DF, Wilson KS. Crystal structure of the small GTPase Arl6/BBS3 from Trypanosoma brucei. Protein Sci 2013. [PMID: 23184293 DOI: 10.1002/pro.2198] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Arl6/BBS3 is a small GTPase, mutations in which are implicated in the human ciliopathy Bardet-Biedl Syndrome (BBS). Arl6 is proposed to facilitate the recruitment of a large protein complex known as the BBSome to the base of the primary cilium, mediating specific trafficking of molecules to this important sensory organelle. Orthologues of Arl6 and the BBSome core subunits have been identified in the genomes of trypanosomes. Flagellum function and motility are crucial to the survival of Trypanosoma brucei, the causative agent of human African sleeping sickness, in the human bloodstream stage of its lifecycle and so the function of the BBSome proteins in trypanosomes warrants further study. RNAi knockdown of T. brucei Arl6 (TbArl6) has recently been shown to result in shortening of the trypanosome flagellum. Here we present the crystal structure of TbArl6 with the bound non-hydrolysable GTP analog GppNp at 2.0 Å resolution and highlight important differences between the trypanosomal and human proteins. Analysis of the TbArl6 active site confirms that it lacks the key glutamine that activates the nucleophile during GTP hydrolysis in other small GTPases. Furthermore, the trypanosomal proteins are significantly shorter at their N-termini suggesting a different method of membrane insertion compared to humans. Finally, analysis of sequence conservation suggests two surface patches that may be important for protein-protein interactions. Our structural analysis thus provides the basis for future biochemical characterisation of this important family of small GTPases.
Collapse
Affiliation(s)
- Glyn R Hemsworth
- Structural Biology Laboratory, Department of Chemistry, University of York, Heslington, York, United Kingdom
| | | | | | | |
Collapse
|
24
|
Abstract
The focus of this chapter is on the important concepts behind the in silico techniques that are used today to assess target druggability. The first step of the assessment consists of finding cavity space in the protein using 2D and/or 3D topological concepts. These concepts underlie the geometry and energy-based pocketfinder algorithms. Analysis pursues on the physico-chemical complementarity between the binding site and the drug like molecule. Geometrical and molecular flexibility aspect are also included in this assessment. The presence of hot interaction spots are shown to be particularly important for targeting protein-protein interactions. Finally, binding site promiscuity can be assessed by large scale structural comparison with other targets. Common chemical features amongst protein cavities can predict potential cross-reactivity with unwanted targets.
Collapse
|
25
|
Panjkovich A, Daura X. Exploiting protein flexibility to predict the location of allosteric sites. BMC Bioinformatics 2012; 13:273. [PMID: 23095452 PMCID: PMC3562710 DOI: 10.1186/1471-2105-13-273] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Accepted: 10/17/2012] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. RESULTS By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity), by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing that this simple coarse-grained methodology is able to capture the effects triggered by allosteric ligands already described in the literature. CONCLUSIONS We introduce a simple computational approach to predict the presence and position of allosteric sites in a protein based on the analysis of changes in protein normal modes upon the binding of a coarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newly curated non-redundant set of 91 proteins with reported allosteric properties. The software developed in this work is available upon request from the authors.
Collapse
Affiliation(s)
- Alejandro Panjkovich
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain
| | | |
Collapse
|
26
|
Qi Y, Wang Q, Tang B, Lai L. Identifying Allosteric Binding Sites in Proteins with a Two-State Go̅ Model for Novel Allosteric Effector Discovery. J Chem Theory Comput 2012; 8:2962-71. [DOI: 10.1021/ct300395h] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Yifei Qi
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Qian Wang
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Bo Tang
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Luhua Lai
- BNLMS,
State Key Laboratory for Structural Chemistry of Unstable and Stable
Species, and Peking−Tsinghua Center for Life Sciences at College
of Chemistry and Molecular Engineering and ‡Center for Quantitative Biology, Peking University, Beijing 100871, China
| |
Collapse
|
27
|
Kochańczyk M. Prediction of functionally important residues in globular proteins from unusual central distances of amino acids. BMC STRUCTURAL BIOLOGY 2011; 11:34. [PMID: 21923943 PMCID: PMC3188475 DOI: 10.1186/1472-6807-11-34] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/22/2011] [Accepted: 09/18/2011] [Indexed: 12/12/2022]
Abstract
BACKGROUND Well-performing automated protein function recognition approaches usually comprise several complementary techniques. Beside constructing better consensus, their predictive power can be improved by either adding or refining independent modules that explore orthogonal features of proteins. In this work, we demonstrated how the exploration of global atomic distributions can be used to indicate functionally important residues. RESULTS Using a set of carefully selected globular proteins, we parametrized continuous probability density functions describing preferred central distances of individual protein atoms. Relative preferred burials were estimated using mixture models of radial density functions dependent on the amino acid composition of a protein under consideration. The unexpectedness of extraordinary locations of atoms was evaluated in the information-theoretic manner and used directly for the identification of key amino acids. In the validation study, we tested capabilities of a tool built upon our approach, called SurpResi, by searching for binding sites interacting with ligands. The tool indicated multiple candidate sites achieving success rates comparable to several geometric methods. We also showed that the unexpectedness is a property of regions involved in protein-protein interactions, and thus can be used for the ranking of protein docking predictions. The computational approach implemented in this work is freely available via a Web interface at http://www.bioinformatics.org/surpresi. CONCLUSIONS Probabilistic analysis of atomic central distances in globular proteins is capable of capturing distinct orientational preferences of amino acids as resulting from different sizes, charges and hydrophobic characters of their side chains. When idealized spatial preferences can be inferred from the sole amino acid composition of a protein, residues located in hydrophobically unfavorable environments can be easily detected. Such residues turn out to be often directly involved in binding ligands or interfacing with other proteins.
Collapse
Affiliation(s)
- Marek Kochańczyk
- Faculty of Physics, Jagiellonian University, ul, Reymonta 4, 30-059 Krakow, Poland.
| |
Collapse
|
28
|
Maksay G. Allostery in pharmacology: Thermodynamics, evolution and design. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2011; 106:463-73. [DOI: 10.1016/j.pbiomolbio.2011.01.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2011] [Accepted: 01/03/2011] [Indexed: 12/13/2022]
|
29
|
Peracchi A, Mozzarelli A. Exploring and exploiting allostery: Models, evolution, and drug targeting. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2010; 1814:922-33. [PMID: 21035570 DOI: 10.1016/j.bbapap.2010.10.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2010] [Revised: 10/19/2010] [Accepted: 10/20/2010] [Indexed: 12/11/2022]
Abstract
The concept of allostery was elaborated almost 50years ago by Monod and coworkers to provide a framework for interpreting experimental studies on the regulation of protein function. In essence, binding of a ligand at an allosteric site affects the function at a distant site exploiting protein flexibility and reshaping protein energy landscape. Both monomeric and oligomeric proteins can be allosteric. In the past decades, the behavior of allosteric systems has been analyzed in many investigations while general theoretical models and variations thereof have been steadily proposed to interpret the experimental data. Allostery has been established as a fundamental mechanism of regulation in all organisms, governing a variety of processes that range from metabolic control to receptor function and from ligand transport to cell motility. A number of studies have shed light on how evolutionary pressures have favored and molded the development of allosteric features in specific macromolecular systems. The widespread occurrence of allostery has been recently exploited for the development and design of allosteric drugs that bind to either physiological or non-physiological allosteric sites leading to gain of function or loss of function. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.
Collapse
Affiliation(s)
- Alessio Peracchi
- Department of Biochemistry and Molecular Biology, University of Parma, Parma, Italy.
| | | |
Collapse
|
30
|
Brylinski M, Lee SY, Zhou H, Skolnick J. The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement. J Struct Biol 2010; 173:558-69. [PMID: 20850544 DOI: 10.1016/j.jsb.2010.09.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2010] [Revised: 09/08/2010] [Accepted: 09/10/2010] [Indexed: 01/01/2023]
Abstract
Exhaustive exploration of molecular interactions at the level of complete proteomes requires efficient and reliable computational approaches to protein function inference. Ligand docking and ranking techniques show considerable promise in their ability to quantify the interactions between proteins and small molecules. Despite the advances in the development of docking approaches and scoring functions, the genome-wide application of many ligand docking/screening algorithms is limited by the quality of the binding sites in theoretical receptor models constructed by protein structure prediction. In this study, we describe a new template-based method for the local refinement of ligand-binding regions in protein models using remotely related templates identified by threading. We designed a Support Vector Regression (SVR) model that selects correct binding site geometries in a large ensemble of multiple receptor conformations. The SVR model employs several scoring functions that impose geometrical restraints on the Cα positions, account for the specific chemical environment within a binding site and optimize the interactions with putative ligands. The SVR score is well correlated with the RMSD from the native structure; in 47% (70%) of the cases, the Pearson's correlation coefficient is >0.5 (>0.3). When applied to weakly homologous models, the average heavy atom, local RMSD from the native structure of the top-ranked (best of top five) binding site geometries is 3.1Å (2.9Å) for roughly half of the targets; this represents a 0.1 (0.3)Å average improvement over the original predicted structure. Focusing on the subset of strongly conserved residues, the average heavy atom RMSD is 2.6Å (2.3Å). Furthermore, we estimate the upper bound of template-based binding site refinement using only weakly related proteins to be ∼2.6Å RMSD. This value also corresponds to the plasticity of the ligand-binding regions in distant homologues. The Binding Site Refinement (BSR) approach is available to the scientific community as a web server that can be accessed at http://cssb.biology.gatech.edu/bsr/.
Collapse
Affiliation(s)
- Michal Brylinski
- Center for the Study of Systems Biology, Georgia Institute of Technology, Atlanta, GA 30318, USA
| | | | | | | |
Collapse
|
31
|
Schmidtke P, Barril X. Understanding and Predicting Druggability. A High-Throughput Method for Detection of Drug Binding Sites. J Med Chem 2010; 53:5858-67. [DOI: 10.1021/jm100574m] [Citation(s) in RCA: 223] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Peter Schmidtke
- Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
| | - Xavier Barril
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Spain
- Departament de Fisicoquímica, Facultat de Farmàcia, Universitat de Barcelona, Av. Joan XXIII s/n, 08028 Barcelona, Spain
| |
Collapse
|