1
|
Tam JZ, Palumbo T, Miwa JM, Chen BY. Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction. Molecules 2022; 27:molecules27196178. [PMID: 36234723 PMCID: PMC9572624 DOI: 10.3390/molecules27196178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 09/03/2022] [Accepted: 09/10/2022] [Indexed: 11/24/2022] Open
Abstract
Protein-protein interactions often involve a complex system of intermolecular interactions between residues and atoms at the binding site. A comprehensive exploration of these interactions can help reveal key residues involved in protein-protein recognition that are not obvious using other protein analysis techniques. This paper presents and extends DiffBond, a novel method for identifying and classifying intermolecular bonds while applying standard definitions of bonds in chemical literature to explain protein interactions. DiffBond predicted intermolecular bonds from four protein complexes: Barnase-Barstar, Rap1a-raf, SMAD2-SMAD4, and a subset of complexes formed from three-finger toxins and nAChRs. Based on validation through manual literature search and through comparison of two protein complexes from the SKEMPI dataset, DiffBond was able to identify intermolecular ionic bonds and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. DiffBond predictions on bond existence were also strongly correlated with observations of Gibbs free energy change and electrostatic complementarity in mutational experiments. DiffBond can be a powerful tool for predicting and characterizing influential residues in protein-protein interactions, and its predictions can support research in mutational experiments and drug design.
Collapse
Affiliation(s)
- Justin Z. Tam
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Talulla Palumbo
- Department of Biological Sciences, Lehigh University, Bethlehem, PA 18015, USA
| | - Julie M. Miwa
- Department of Biological Sciences, Lehigh University, Bethlehem, PA 18015, USA
| | - Brian Y. Chen
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
- Correspondence:
| |
Collapse
|
2
|
Quintana FM, Kong Z, He L, Chen BY. DeepVASP-E: A Flexible Analysis of Electrostatic Isopotentials for Finding and Explaining Mechanisms that Control Binding Specificity. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:56-67. [PMID: 34890136 PMCID: PMC9174418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Amino acids that play a role in binding specificity can be identified with many methods, but few techniques identify the biochemical mechanisms by which they act. To address a part of this problem, we present DeepVASP-E, an algorithm that can suggest electrostatic mechanisms that influence specificity. DeepVASP-E uses convolutional neural networks to classify an electrostatic representation of ligand binding sites into specificity categories. It also uses class activation mapping to identify regions of electrostatic potential that are salient for classification. We hypothesize that electrostatic regions that are salient for classification are also likely to play a biochemical role in achieving specificity. Our findings, on two families of proteins with electrostatic influences on specificity, suggest that large salient regions can identify amino acids that have an electrostatic role in binding, and that DeepVASP-E is an effective classifier of ligand binding sites.
Collapse
|
3
|
Tam J, Palumbo T, Miwa JM, Chen BY. DiffBond: A Method for Predicting Intermolecular Bond Formation. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:2574-2586. [PMID: 35378834 DOI: 10.1109/bibm52615.2021.9669850] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Many tools that explore models of protein complexes are also able to analyze interactions between specific residues and atoms. A comprehensive exploration of these interactions can often uncover aspects of protein-protein recognition that are not obvious using other protein analysis techniques. This paper describes DiffBond, a novel method for searching for intermolecular interactions between protein complexes while differentiating between three different types of interaction: hydrogen bonds, ionic bonds, and salt bridges. DiffBond incorporates textbook definitions of these three interactions while contending with uncertainties that are inherent in computational models of interacting proteins. We used it to examine the barnase-barstar, Rap1a-raf, and Smad2-Smad4 complexes, as well as a subset of protein complexes formed between three-finger toxins and nAChRs. Based on electrostatic interactions established by previous experimental studies, DiffBond was able to identify ionic and hydrogen bonds with high precision and recall, and identify salt bridges with high precision. In combination with other electrostatic analysis methods, DiffBond can be a useful tool in helping predict influential amino acids in protein-protein interactions and characterizing the type of interaction.
Collapse
Affiliation(s)
- Justin Tam
- Dept. Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| | - Talulla Palumbo
- Dept. Biological Sciences, Lehigh University, Bethlehem, PA 18015, USA
| | - Julie M Miwa
- Dept. Biological Sciences, Lehigh University, Bethlehem, PA 18015, USA
| | - Brian Y Chen
- Dept. Computer Science and Engineering, Lehigh University, Bethlehem, PA 18015, USA
| |
Collapse
|
4
|
Georgiev GD, Dodd KF, Chen BY. Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials. Algorithms Mol Biol 2020; 15:11. [PMID: 32489400 PMCID: PMC7247173 DOI: 10.1186/s13015-020-00168-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 04/15/2020] [Indexed: 11/10/2022] Open
Abstract
Geometric comparisons of binding sites and their electrostatic properties can identify subtle variations that select different binding partners and subtle similarities that accommodate similar partners. Because subtle features are central for explaining how proteins achieve specificity, algorithmic efficiency and geometric precision are central to algorithmic design. To address these concerns, this paper presents pClay, the first algorithm to perform parallel and arbitrarily precise comparisons of molecular surfaces and electrostatic isopotentials as geometric solids. pClay was presented at the 2019 Workshop on Algorithms in Bioinformatics (WABI 2019) and is described in expanded detail here, especially with regard to the comparison of electrostatic isopotentials. Earlier methods have generally used parallelism to enhance computational throughput, pClay is the first algorithm to use parallelism to make arbitrarily high precision comparisons practical. It is also the first method to demonstrate that high precision comparisons of geometric solids can yield more precise structural inferences than algorithms that use existing standards of precision. One advantage of added precision is that statistical models can be trained with more accurate data. Using structural data from an existing method, a model of steric variations between binding cavities can overlook 53% of authentic steric influences on specificity, whereas a model trained with data from pClay overlooks none. Our results also demonstrate the parallel performance of pClay on both workstation CPUs and a 61-core Xeon Phi. While slower on one core, additional processor cores rapidly outpaced single core performance and existing methods. Based on these results, it is clear that pClay has applications in the automatic explanation of binding mechanisms and in the rational design of protein binding preferences.
Collapse
Affiliation(s)
- Georgi D. Georgiev
- Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA
| | - Kevin F. Dodd
- Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA
| | - Brian Y. Chen
- Department of Computer Science and Engineering, Lehigh University, 113 Research Drive, Bethlehem, PA USA
| |
Collapse
|
5
|
Nussinov R, Tsai CJ, Shehu A, Jang H. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019; 24:molecules24030637. [PMID: 30759724 PMCID: PMC6384756 DOI: 10.3390/molecules24030637] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/05/2019] [Accepted: 02/10/2019] [Indexed: 02/06/2023] Open
Abstract
Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| | - Amarda Shehu
- Departments of Computer Science, Department of Bioengineering, and School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| |
Collapse
|
6
|
Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia. BMC Bioinformatics 2018; 19:414. [PMID: 30453883 PMCID: PMC6245605 DOI: 10.1186/s12859-018-2381-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Background Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. Results Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. Conclusions The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.
Collapse
|
7
|
Mason S, Chen BY, Jagodzinski F. Exploring Protein Cavities through Rigidity Analysis. Molecules 2018; 23:molecules23020351. [PMID: 29414909 PMCID: PMC6017401 DOI: 10.3390/molecules23020351] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 01/25/2018] [Accepted: 01/31/2018] [Indexed: 12/23/2022] Open
Abstract
The geometry of cavities in the surfaces of proteins facilitates a variety of biochemical functions. To better understand the biochemical nature of protein cavities, the shape, size, chemical properties, and evolutionary nature of functional and nonfunctional surface cavities have been exhaustively surveyed in protein structures. The rigidity of surface cavities, however, is not immediately available as a characteristic of structure data, and is thus more difficult to examine. Using rigidity analysis for assessing and analyzing molecular rigidity, this paper performs the first survey of the relationships between cavity properties, such as size and residue content, and how they correspond to cavity rigidity. Our survey measured a variety of rigidity metrics on 120,323 cavities from 12,785 sequentially non-redundant protein chains. We used VASP-E, a volume-based algorithm for analyzing cavity geometry. Our results suggest that rigidity properties of protein cavities are dependent on cavity surface area.
Collapse
Affiliation(s)
- Stephanie Mason
- Department of Computer Science, Western Washington University, 516 High Street, Bellingham, WA 98225, USA.
| | - Brian Y Chen
- Department of Computer Science and Engineering, Lehigh University, 19 Memorial Drive West, Bethlehem, PA 18015, USA.
| | - Filip Jagodzinski
- Department of Computer Science, Western Washington University, 516 High Street, Bellingham, WA 98225, USA.
| |
Collapse
|
8
|
Nolan BE, Levenson E, Chen BY. Influential Mutations in the SMAD4 Trimer Complex Can Be Detected from Disruptions of Electrostatic Complementarity. J Comput Biol 2018; 24:68-78. [PMID: 28051901 DOI: 10.1089/cmb.2016.0162] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
This article examines three techniques for rapidly assessing the electrostatic contribution of individual amino acids to the stability of protein-protein complexes. Whereas the energetic minimization of modeled oligomers may yield more accurate complexes, we examined the possibility that simple modeling may be sufficient to identify amino acids that add to or detract from electrostatic complementarity. The three methods evaluated were (a) the elimination of entire side chains (e.g., glycine scanning), (b) the elimination of the electrostatic contribution from the atoms of a side chain, called nullification, and (c) side chain structure prediction using SCWRL4. These techniques generate models in seconds, enabling large-scale mutational scanning. We evaluated these techniques on the SMAD2/SMAD4 heterotrimer, whose formation plays a crucial role in antitumor pathways. Many studies have documented the clinical and structural effect of specific mutations on trimer formation. Our results describe how glycine scanning yields more specific predictions, although nullification may be more sensitive, and how side chain structure prediction enables the identification of uncharged-to-charge mutations.
Collapse
Affiliation(s)
- Bridget E Nolan
- Department of Computer Science and Engineering, Lehigh University , Bethlehem, Pennsylvania
| | - Emily Levenson
- Department of Computer Science and Engineering, Lehigh University , Bethlehem, Pennsylvania
| | - Brian Y Chen
- Department of Computer Science and Engineering, Lehigh University , Bethlehem, Pennsylvania
| |
Collapse
|
9
|
Zhou Y, Li XP, Chen BY, Tumer NE. Ricin uses arginine 235 as an anchor residue to bind to P-proteins of the ribosomal stalk. Sci Rep 2017; 7:42912. [PMID: 28230053 PMCID: PMC5322317 DOI: 10.1038/srep42912] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 01/17/2017] [Indexed: 01/24/2023] Open
Abstract
Ricin toxin A chain (RTA) binds to stalk P-proteins to reach the α-sarcin/ricin loop (SRL) where it cleaves a conserved adenine. Arginine residues at the RTA/RTB interface are involved in this interaction. To investigate the individual contribution of each arginine, we generated single, double and triple arginine mutations in RTA. The R235A mutation reduced toxicity and depurination activity more than any other single arginine mutation in yeast. Further reduction in toxicity, depurination activity and ribosome binding was observed when R235A was combined with a mutation in a nearby arginine. RTA interacts with the ribosome via a two-step process, which involves slow and fast interactions. Single arginine mutations eliminated the fast interactions with the ribosome, indicating that they increase the binding rate of RTA. Arginine residues form a positively charged patch to bind to negatively charged residues at the C-termini of P-proteins. When electrostatic interactions conferred by the arginines are lost, hydrophobic interactions are also abolished, suggesting that the hydrophobic interactions alone are insufficient to allow binding. We propose that Arg235 serves as an anchor residue and cooperates with nearby arginines and the hydrophobic interactions to provide the binding specificity and strength in ribosome targeting of RTA.
Collapse
Affiliation(s)
- Yijun Zhou
- Department of Plant Biology and Pathology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, New Jersey 08901-8520, USA
| | - Xiao-Ping Li
- Department of Plant Biology and Pathology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, New Jersey 08901-8520, USA
| | - Brian Y Chen
- Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015-3084, USA
| | - Nilgun E Tumer
- Department of Plant Biology and Pathology, School of Environmental and Biological Sciences, Rutgers University, New Brunswick, New Jersey 08901-8520, USA
| |
Collapse
|
10
|
Axenopoulos A, Rafailidis D, Papadopoulos G, Houstis EN, Daras P. Similarity Search of Flexible 3D Molecules Combining Local and Global Shape Descriptors. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:954-970. [PMID: 26561479 DOI: 10.1109/tcbb.2015.2498553] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this paper, a framework for shape-based similarity search of 3D molecular structures is presented. The proposed framework exploits simultaneously the discriminative capabilities of a global, a local, and a hybrid local-global shape feature to produce a geometric descriptor that achieves higher retrieval accuracy than each feature does separately. Global and hybrid features are extracted using pairwise computations of diffusion distances between the points of the molecular surface, while the local feature is based on accumulating pairwise relations among oriented surface points into local histograms. The local features are integrated into a global descriptor vector using the bag-of-features approach. Due to the intrinsic property of its constituting shape features to be invariant to articulations of the 3D objects, the framework is appropriate for similarity search of flexible 3D molecules, while at the same time it is also accurate in retrieving rigid 3D molecules. The proposed framework is evaluated in flexible and rigid shape matching of 3D protein structures as well as in shape-based virtual screening of large ligand databases with quite promising results.
Collapse
|