Protein Structural Statistics with PSS

BackgroundA central tenet of structural biology is that related proteins of common function share structural similarity. This has key practical consequences for the derivation and analysis of protein structures, and is exploited by the process of “molecular sieving” whereby a common core is progressively distilled from a comparison of two or more protein structures. This paper reports a novel web server for “sieving” of protein structures, based on the multiple structural alignment program MUSTANG.Methodology/Principal Findings“Sieved” models are generated from MUSTANG-generated multiple alignment and superpositions by iteratively filtering out noisy residue-residue correspondences, until the resultant correspondences in the models are optimally “superposable” under a threshold of RMSD. This residue-level sieving is also accompanied by iterative elimination of the poorly fitting structures from the input ensemble. Therefore, by varying the thresholds of RMSD and the cardinality of the ensemble, multiple sieved models are generated for a given multiple alignment and superposition from MUSTANG. To aid the identification of structurally conserved regions of functional importance in an ensemble of protein structures, Lesk-Hubbard graphs are generated, plotting the number of residue correspondences in a superposition as a function of its corresponding RMSD. The conserved “core” (or typically active site) shows a linear trend, which becomes exponential as divergent parts of the structure are included into the superposition.ConclusionsThe application addresses two fundamental problems in structural biology: First, the identification of common substructures among structurally related proteins—an important problem in characterization and prediction of function; second, generation of sieved models with demonstrated uses in protein crystallographic structure determination using the technique of Molecular Replacement.

While a number of approaches have been geared toward multiple sequence alignments, to date there have been very few approaches to multiple structure alignment and detection of a recurring substructural motif. Among these, none performs both multiple structure comparison and motif detection simultaneously. Further, none considers all structures at the same time, rather than initiating from pairwise molecular comparisons. We present such a multiple structural alignment algorithm. Given an ensemble of protein structures, the algorithm automatically finds the largest common substructure (core) of C(alpha) atoms that appears in all the molecules in the ensemble. The detection of the core and the structural alignment are done simultaneously. Additional structural alignments also are obtained and are ranked by the sizes of the substructural motifs, which are present in the entire ensemble. The method is based on the geometric hashing paradigm. As in our previous structural comparison algorithms, it compares the structures in an amino acid sequence order-independent way, and hence the resulting alignment is unaffected by insertions, deletions and protein chain directionality. As such, it can be applied to protein surfaces, protein-protein interfaces and protein cores to find the optimally, and suboptimally spatially recurring substructural motifs. There is no predefinition of the motif. We describe the algorithm, demonstrating its efficiency. In particular, we present a range of results for several protein ensembles, with different folds and belonging to the same, or to different, families. Since the algorithm treats molecules as collections of points in three-dimensional space, it can also be applied to other molecules, such as RNA, or drugs.

Ensemble-function relationships: From qualitative to quantitative relationships between protein structure and function

Databases are the computerized platform where information is stored and can be retrieved easily by public users. Biological databases are the repositories of biological data. These biological data libraries contain facts and figures related to various disciplines of research including genomics, proteomics, microarray technology, metabolomics and phylogenetics. By using biological databases, a broad collection of essential biological information can be exploited ranging from function, structure and localization of gene, clinical consequences of mutation to similarity index among biological sequences and structures. Nowadays, different kinds of biological databases are available on the web. The present write up focuses on biological databases and bioinformatics tools for protein structure analysis. This review also aims to elaborate the searching schemes, available in different structural databases. The wide variety of different levels and types of information content related to 3D protein structures are available on web-based databases. Regarding the biological functions and 3D structures of various proteins, these databases provide a huge range of useful links, schematic diagrams as well as strategies for detailed analysis of proteins and other macromolecules structures. 3D structural illustration of proteins stored in structural databases is determined and visualized by X-ray crystallography, electron microscopy and NMR spectroscopy. On regular basis, a large number of protein structures are submitted by structural biologists, updated and curated by subject experts. Most familiar biological databases that store 3D protein and other macromolecules structures include, PDB, 3D Genomics, CATH, & SCOP. These databases contain valuable information of overall protein structures, domains and motif structures, protein-protein complex systems and complex of protein with other biomolecules.

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C(alpha) pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.

Although many studies have addressed the significance of interactions in the dimeric structures of proteins, there is no dedicated database of these interactions. To this end, the Molecular Interactions in Protein Dimer Structure (MIPDS) database has been developed; it is an open-access repository containing 60 298 3D structures of dimeric proteins sourced from the Protein Data Bank. This helps researchers comprehend the types of interaction, which include those mediated by water, small molecules or ligands and direct interactions, in 3D structures at the molecular level. The database is accessible through a user-friendly interface, where users can conduct searches based on PDB accession number, interaction type and geometric parameters. It can be viewed in textual and graphical formats using the plug-in JSmol. MIPDS is updated weekly using programmed scripts to incorporate newly released dimeric structures and analyses of their interaction types. The database is intended for the scientific community working in structural biology, structural bioinformatics, drug discovery and development. MIPDS is freely accessible to users worldwide at http://dicsoft1.physics.iisc.ac.in/mipds.

Present at the flood: How structural biology came about, by Dickerson, Richard E.. 2005. Sinauer Associates, Sunderland, MA. 307 pp. $34.95 (paper) Robert M. Stroud*, * Department of Biochemistry, University of California at San Francisco, San Francisco, CA 94143, USA Present at the Flood: How Structural Biology Came About is an essential book for every structural biologist and a must for everyone who reflects on the origins of molecular biology. Present at the Flood, authored by Richard E. Dickerson, enchantingly traces the critical path to the discovery of the underlying atomic structures of proteins and DNA. These discoveries became the foundations of modern molecular biology. Present at the Flood interprets the 30 years that revolutionized structural and molecular biology, from 1933 to 1963, and is dedicated to Francis Crick. Francis also helped Dickerson to unearth some of the unpublished materials that illustrate the true history, such as the draft of the first erroneous DNA model. The moments of critical insight and of erroneous ideas are brilliantly captured by Dickerson, who was himself an essential, key ingredient in the discovery of the first protein structure (he was indeed “present at the flood”), and at the same institution where both the myoglobin and hemoglobin protein structures and the structure of DNA were discovered. These discoveries totally revolutionized biology. They were clearly synergistic with each other in a way that depended on the interplay between the personalities just as much as between structural thinkers. Present at the Flood reflects Dickerson's own total engagement and thrill in the process of discovery. It also reflects his keen recollection of the nuance, the slight remark that triggered a cascade of thought—the essence of some of the most important discoveries in all of biology. Like a detective novel, the book draws the reader into the fabric of discovery. It became a reality after the author presented a course of the same title in 2004 at UCLA. Dickerson retreads the pathway that he and colleagues in structural biology walked, carefully reasoning from the knowledge at the time to retrace the steps and ask precisely how insight came about. The book is very different from, for example, James Watson's personal perspective in The Double Helix; rather, it is a historical analysis of the ideas in the progression to discovery and how personalities augmented or bumped the process forward, backward, or sideways. Thus, he recounts mistakes in the early interpretations of Pauling and Corey and Crick and Watson, and recalls Erwin Chargaff's indignation after the DNA structure was determined. This all makes the historical analysis a personal encounter with the players themselves and a delight to read. Each of the first seven chapters begins with a hand-crafted Dickersonian pun! “In this life one should Perutz high goals, but ultimately settle for what one Kendrew”! Then, there follows a list of key publications, some of which are reprinted at the end. In total, over 40 of the original publications are reprinted, an invaluable record in itself. The text focuses onto Dickerson's interpretation of the meaning of research results, the flash of brilliance, the chance encounter, the stumbles, and communications that became the process of scientific discovery. Dickerson reflects colloquially on personalities, and their quirks that coupled to their contributions. The book reiterates the very words of the cast, including the sometimes sarcastic or even mean-spirited responses to competitors. Thus, the reader comes away knowing a lot about the people involved and the interplay of personality in discovery. Dickerson also questions the missed discoveries that could, or even should, have been made earlier—sometimes errors in logic, and happy accidents. The appendices present an invaluable guide to “Highly Recommended Readings,” and identifies those of key contributions, each with commentary as to why they are important milestones. Appendix 3 reflects on Irving Geis, the first artist and illustrator of the meaning of molecular structure who, with Dickerson, illustrated so many of the discoveries throughout the birth of structural biology and with whom Dickerson had a long very productive interaction. One of their books, The Proteins, coauthored by Dickerson and Geis (published in 1969), represented the first picture “from within” of what structural molecular biology revealed. It was also the beginning of the field of “computer graphic” displays of molecular structures. The Flood is dedicated to Francis H.C. Crick, whose contributions, like Dickerson's, were seminal both to the first discovery of the structure of proteins and to the discovery of the structure of DNA. While Crick worked with Perutz on the hemoglobin structure, Dickerson, in 1957, developed the mathematical basis and the key process to refine the “phases” of myoglobin, and thus, to determine its structure. As Crick realized the structure of DNA with Watson and Franklin's diffraction patterns in 1953, Dickerson, in 1980, was the first to determine the atomic structure of B-form DNA at atomic resolution. He recounts Crick's response on seeing Dickerson's atomic “B-form” structure: “So that's what it looks like!” Books written by investigators who shared in the discovery process often have a special intimacy to their story, and this book certainly is rich in this. Dickerson comments on Max Perutz, “Perutz turned out to be a gifted writer and essayist, which is more than many scientists can say.” Even more could be said of Dickerson's consummate ability to engage his readership! Few writers from the inside have been able to capture and analyze the process of discovery as well as Dickerson; he is scholarly, exact, balanced, and selfless. Dickerson's keen humor and insight draws the reader into the fun and excitement at the birth of molecular biology. Present at the Flood is important for the scientific record, as perhaps the only book that focuses on the discovery of protein structure seen in three-dimensional maps of linked atoms alongside and intertwined with the deduction of the structure of DNA. As such, it is a must for all who may reflect on the impact that structural biology has on molecular and cell biology, genetics, proteomics, biochemistry, or drug discovery.

Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein-protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a "big data" science requiring more efficient analysis tools. This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Editorial overview: New protein production tools for structural biology

NIGMS PSI:Biology Initiative -- High-Throughput-Enabled Structural Biology

Intrinsically disordered proteins (IDPs) and multidomain proteins with flexible linkers show a high level of structural heterogeneity and are best described by ensembles consisting of multiple conformations with associated thermodynamic weights. Determining conformational ensembles usually involves the integration of biophysical experiments and computational models. In this review, we discuss current approaches to determine conformational ensembles of IDPs and multidomain proteins, including the choice of biophysical experiments, computational models used to sample protein conformations, models to calculate experimental observables from protein structure, and methods to refine ensembles against experimental data. We also provide examples of recent applications of integrative conformational ensemble determination to study IDPs and multidomain proteins and suggest future directions for research in the field.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

The HIV-1 gp120 exterior envelope glycoprotein undergoes a series of conformational rearrangements while sequentially interacting with the receptor CD4 and the coreceptor CCR5 or CXCR4 on the surface of host cells to initiate virus entry. Both the crystal structures of the HIV-1 gp120 core bound by CD4 and antigen 17b, and the SIV gp120 core pre-bound by CD4 are known. Despite the wealth of knowledge on these static snapshots of molecular conformations, the details of molecular motions crucial to intervention remain elusive. We presented a comprehensive comparative analysis of dynamic behavior of gp120 in its CD4-complexed, CD4-free and CD4-unliganded states based on the homology models with modeled V3 and V4 loops. CONCOORD computer simulation was utilized to generate ensembles of feasible protein structures, which were subsequently analyzed by essential dynamics technique to identify preferred concerted motions. The revealed collective fluctuations are dominated by complex motional modes such as rotation/twisting, flexing/closing, and shortness/elongation between or within the inner, outer, and bridging-sheet domains. An attempt has been made to relate these modes to receptor/coreceptor association and neutralization avoidance. Covariance web analysis revealed four subdomains that undergo concerted motion in gp120. The structural components in gp120 that move in concert with CD4 were also identified, which may be the suitable target for inhibitor design to interrupt CD4-gp120 interaction. The differences in B-factors between the three gp120 states revealed certain structural regions that could be related either to CD4 association or to subsequent dissociation of gp120 from gp41. These dynamics data provide new insights into the structure-function relationship of gp120 and may aid in structure-based anti-HIV vaccine design.

Water molecules are very important for stabilizing the protein structures. The influence of hydration (or) dehydration on protein structures leads to the changes in the activity of proteins as well their conformation. In our present study, we madde an attempt to understand the various RecA protein sequence similarity with respect to E. coli RecA Protein and comparative structure and functional conformation studies between MsRecA protein (dehydrated state) and EcRecA + DNA bound structures (active state) as well ATP-complexed DrRecA protein structure. From the sequence similarity studies, it was identified that the E.coli RecA protein exhibits the highest sequence similarity (100%) with the Shigella dysenteriae serotype 1 (strain Sd197), Shigella flexneri and the lowest sequence similarity (46%) with the Mycoplasma genitalium. From the structural alignment and superposition studies, it can be inferred that the low hydrated MsRecA protein conformation is very closely similar to the active conformation of the EcRecA protein. This confers that the lower less water molecules favors the active conformational state of the RecA protein in both M. smegmatis and E. coli whereas the ATP-bound complex of RecA protein of D. radiodurans is exhibiting different conformation from the active state conformation.

BackgroundSearching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.ResultsWe developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).ConclusionsThe GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.