Protein Structure Search Research Articles

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign’s high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

TM-search: An Efficient and Effective Tool for Protein Structure Database Search.

The quickly increasing size of the Protein Data Bank is challenging biologists to develop a more scalable protein structure alignment tool for fast structure database search. Although many protein structure search algorithms and programs have been designed and implemented for this purpose, most require a large amount of computational time. We propose a novel protein structure search approach, TM-search, which is based on the pairwise structure alignment program TM-align and a new iterative clustering algorithm. Benchmark tests demonstrate that TM-search is 27 times faster than a TM-align full database search while still being able to identify ∼90% of all high TM-score hits, which is 2-10 times more than other existing programs such as Foldseek, Dali, and PSI-BLAST.

Fast and accurate protein structure search with Foldseek

As structure prediction methods are generating millions of publicly available protein structures, searching these databases is becoming a bottleneck. Foldseek aligns the structure of a query protein against a database by describing tertiary amino acid interactions within proteins as sequences over a structural alphabet. Foldseek decreases computation times by four to five orders of magnitude with 86%, 88% and 133% of the sensitivities of Dali, TM-align and CE, respectively.

AHoJ: rapid, tailored search and retrieval of apo and holo protein structures for user-defined ligands.

Understanding the mechanism of action of a protein or designing better ligands for it, often requires access to a bound (holo) and an unbound (apo) state of the protein. Resources for the quick and easy retrieval of such conformations are severely limited. Apo-Holo Juxtaposition (AHoJ), is a web application for retrieving apo-holo structure pairs for user-defined ligands. Given a query structure and one or more user-specified ligands, it retrieves all other structures of the same protein that feature the same binding site(s), aligns them, and examines the superimposed binding sites to determine whether each structure is apo or holo, in reference to the query. The resulting superimposed datasets of apo-holo pairs can be visualized and downloaded for further analysis. AHoJ accepts multiple input queries, allowing the creation of customized apo-holo datasets. Freely available for non-commercial use at http://apoholo.cz. Source code available at https://github.com/cusbg/AHoJ-project. Supplementary data are available at Bioinformatics online.

RCSB Protein Data Bank: improved annotation, search and visualization of membrane protein structures archived in the PDB.

MotivationMembrane proteins are encoded by approximately one fifth of human genes but account for more than half of all US FDA approved drug targets. Thanks to new technological advances, the number of membrane proteins archived in the PDB is growing rapidly. However, automatic identification of membrane proteins or inference of membrane location is not a trivial task.ResultsWe present recent improvements to the RCSB Protein Data Bank web portal (RCSB PDB, rcsb.org) that provide a wealth of new membrane protein annotations integrated from four external resources: OPM, PDBTM, MemProtMD and mpstruc. We have substantially enhanced the presentation of data on membrane proteins. The number of membrane proteins with annotations available on rcsb.org was increased by ∼80%. Users can search for these annotations, explore corresponding tree hierarchies, display membrane segments at the 1D amino acid sequence level, and visualize the predicted location of the membrane layer in 3D.Availability and implementationAnnotations, search, tree data and visualization are available at our rcsb.org web portal. Membrane visualization is supported by the open-source Mol* viewer (molstar.org and github.com/molstar/molstar).Supplementary information Supplementary data are available at Bioinformatics online.

Protein structure search to support the development of protein structure prediction methods.

Protein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction methods, researchers may want to identify the most similar known structures to their predicted structures. These predicted structures often have low sequence and structure similarity to known structures. We show how RUPEE, a purely geometric protein structure search, is able to identify the structures most similar to structure predictions, regardless of how they vary from known structures, something existing protein structure searches struggle with. RUPEE accomplishes this through the use of a novel linear encoding of protein structures as a sequence of residue descriptors. Using a fast Needleman-Wunsch algorithm, RUPEE is able to perform alignments on the sequences of residue descriptors for every available structure. This is followed by a series of increasingly accurate structure alignments from TM-align alignments initialized with the Needleman-Wunsch residue descriptor alignments to standard TM-align alignments of the final results. By using alignment normalization effectively at each stage, RUPEE also can execute containment searches in addition to full-length searches to identify structural motifs within proteins. We compare the results of RUPEE to the protein structure searches mTM-align, SSM, CATHEDRAL, and VAST using a benchmark derived from the protein structure predictions submitted to CASP13. RUPEE identifies better alignments on average with respect to TM-score as well as scores specific to SSM and CATHEDRAL, Q-score and SSAP-score, respectively.

Link Your Sites (LYS) Scripts: Automated Search of Protein Structures and Mapping of Sites Under Positive Selection Detected by PAML

The visualization of the molecular context of an amino acid mutation in a protein structure is crucial for the assessment of its functional impact and the understanding of its evolutionary implications. Currently, searches for fast evolving amino acid positions using codon substitution models like those implemented in PAML (Yang and Nielsen in Estimating synonymous and nonsynonymous substitution rates under realistic evolutionary models. Mol Biol Evol 17(1):32–43, 2000; Zhang et al. in Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol 22(12):2472–2479, 2005) are done in almost complete proteomes, generating large numbers of candidate proteins making the analysis of individual protein structures and models very time-consuming. Here we present the package Link Your Sites (LYS) that can be used to reduce the number of analysed targets to those for which structural information can be retrieved. LYS consists of two python wrapper scripts, where the first one (i) mines the RCSB database (Berman et al. in The protein data bank. Nucleic Acids Res 28(1):235–242, 2000) using the BLAST alignment tool to find the best matching homologous sequences, (ii) fetches their domain positions by using Prosites (Hamelryck and Manderick in Pdb file parser and structure class implemented in python. Bioinformatics 19(17):2308–2310, 2003; Sigrist et al. in Prosite: a documented database using patterns and profiles as motif descriptors. Brief Bioinf 3(3):265–274, 2002; Sigrist et al. in New and continuing developments at prosite. Nucleic Acids Res 41(D1):D344–D347, 2012), (iii) parses the output of PAML extracting the positional information of fast-evolving sites and transforms them into the coordinate system of the protein structure, (iv) outputs one file per gene with the equivalence among the positions in the input sequence and homologous structure. The second script produces figures to be used in publications highlighting the positively selected sites mapped on regions that are known to have functional relevance.

Molecular docking of the active compound Garcinia mangostana on the RANKL/RANK/OPG system

This study aims to analyze the molecular docking between the active compounds of the Garcinia mangostana against the RANKL/RANK/OPG system and its potential as an antiosteoporosis. The research protocol includes the search and modeling of protein and ligand structures and their docking. Software used includes OpenBabel, HEX 8.0, Chimera 1.6.2, Discovery Studio 4.1, LigPlot + and LigandScout 3.1. Tovophillin has the most negative interaction energy with RANKL-OPG (−332.8 Kj/mol) and RANKL-RANK (−298.1 Kj/mol). It was concluded that fourteen active compounds of Garcinia mangostana did not interfere with the physiological function of RANKL against RANK. In addition, the active compound will not affect the RANKL-OPG complex. The antiosteoporosis mechanism of Garcinia mangostana does not by inhibiting RANKL-RANK interactions.

RUPEE: A fast and accurate purely geometric protein structure search.

Given the close relationship between protein structure and function, protein structure searches have long played an established role in bioinformatics. Despite their maturity, existing protein structure searches either use simplifying assumptions or compromise between fast response times and quality of results. These limitations can prevent the easy and efficient exploration of relationships between protein structures, which is the norm in other areas of inquiry. To address these limitations we have developed RUPEE, a fast and accurate purely geometric structure search combining techniques from information retrieval and big data with a novel approach to encoding sequences of torsion angles. Comparing our results to the output of mTM, SSM, and the CATHEDRAL structural scan, it is clear that RUPEE has set a new bar for purely geometric big data approaches to protein structure searches. RUPEE in top-aligned mode produces equal or better results than the best available protein structure searches, and RUPEE in fast mode demonstrates the fastest response times coupled with high quality results. The RUPEE protein structure search is available at https://ayoubresearch.com. Code and data are available at https://github.com/rayoub/rupee.

Learning structural motif representations for efficient protein structure search

MotivationGiven a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a ‘bag of fragments’, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library. Despite being efficient, the accuracy of FragBag is unsatisfactory because its backbone fragment library may not be optimally constructed and long-range interacting patterns are omitted.ResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.Availability and implementation https://github.com/largelymfs/DeepFold

RepeatsDB 2.0: improved annotation, classification, search and visualization of repeat protein structures.

RepeatsDB 2.0 (URL: http://repeatsdb.bio.unipd.it/) is an update of the database of annotated tandem repeat protein structures. Repeat proteins are a widespread class of non-globular proteins carrying heterogeneous functions involved in several diseases. Here we provide a new version of RepeatsDB with an improved classification schema including high quality annotations for ∼5400 protein structures. RepeatsDB 2.0 features information on start and end positions for the repeat regions and units for all entries. The extensive growth of repeat unit characterization was possible by applying the novel ReUPred annotation method over the entire Protein Data Bank, with data quality is guaranteed by an extensive manual validation for >60% of the entries. The updated web interface includes a new search engine for complex queries and a fully re-designed entry page for a better overview of structural data. It is now possible to compare unit positions, together with secondary structure, fold information and Pfam domains. Moreover, a new classification level has been introduced on top of the existing scheme as an independent layer for sequence similarity relationships at 40%, 60% and 90% identity.

RepeatsDB 2.0: improved annotation, classification, search and visualization of repeat protein structures.

RepeatsDB 2.0 (URL: http://repeatsdb.bio.unipd.it/) is an update of the database of annotated tandem repeat protein structures. Repeat proteins are a widespread class of non-globular proteins carrying heterogeneous functions involved in several diseases. Here we provide a new version of RepeatsDB with an improved classification schema including high quality annotations for ∼5400 protein structures. RepeatsDB 2.0 features information on start and end positions for the repeat regions and units for all entries. The extensive growth of repeat unit characterization was possible by applying the novel ReUPred annotation method over the entire Protein Data Bank, with data quality is guaranteed by an extensive manual validation for >60% of the entries. The updated web interface includes a new search engine for complex queries and a fully re-designed entry page for a better overview of structural data. It is now possible to compare unit positions, together with secondary structure, fold information and Pfam domains. Moreover, a new classification level has been introduced on top of the existing scheme as an independent layer for sequence similarity relationships at 40%, 60% and 90% identity.

내부 알파탄소간 거리와 비네-코시 거리를 사용한 대규모 단백질 조각 라이브러리 구성

Representing protein three-dimensional structure by concatenating a sequence of protein fragments gives an efficient application in analysis, modeling, search, and prediction of protein structures. This paper investigated the effective combination of distance measures, which can exploit large protein structure database, in order to construct a protein fragment library representing native protein structures accurately. Clustering method was used to construct a protein fragment library. Initial clustering stage used inter alpha-carbon distance having low time complexity, and cluster extension stage used the combination of inter alpha-carbon distance, Binet-Cauchy distance, and root mean square deviation. Protein fragment library was constructed by leveraging large protein structure database using the proposed combination of distance measures. This library gives low root mean square deviation in the experiments representing protein structures with protein fragments.

Entropy-Scaling Search of Massive Biological Data

Many data sets exhibit well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here we introduce a framework for similarity search based on characterizing a data set's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the data set is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains-high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND (3700x BLASTX)), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve 'compressive omics,' and the general theory can be readily applied to data science problems outside of biology. Source code: http://gems.csail.mit.edu.

Rapid search for tertiary fragments reveals protein sequence-structure relationships.

Finding backbone substructures from the Protein Data Bank that match an arbitrary query structural motif, composed of multiple disjoint segments, is a problem of growing relevance in structure prediction and protein design. Although numerous protein structure search approaches have been proposed, methods that address this specific task without additional restrictions and on practical time scales are generally lacking. Here, we propose a solution, dubbed MASTER, that is both rapid, enabling searches over the Protein Data Bank in a matter of seconds, and provably correct, finding all matches below a user-specified root-mean-square deviation cutoff. We show that despite the potentially exponential time complexity of the problem, running times in practice are modest even for queries with many segments. The ability to explore naturally plausible structural and sequence variations around a given motif has the potential to synthesize its design principles in an automated manner; so we go on to illustrate the utility of MASTER to protein structural biology. We demonstrate its capacity to rapidly establish structure-sequence relationships, uncover the native designability landscapes of tertiary structural motifs, identify structural signatures of binding, and automatically rewire protein topologies. Given the broad utility of protein tertiary fragment searches, we hope that providing MASTER in an open-source format will enable novel advances in understanding, predicting, and designing protein structure.

Deciphering the Protein Folding Code through Zipping Steps

Earlier experiments suggest that the evolutionary information (conservation of amino acids and coevolution between amino acids) encoded in protein sequences is necessary and sufficient to specify the fold of a protein family. However, there is no computational work to quantify the effect of such evolutionary information on the folding process. Here we simulate a repertoire of native and artificial WW domain sequences using a physics-based protein structure search method called ZAM (Zipping and Assembly method), which samples conformational space effectively towards native-like conformations through zipping and assembly search mechanism. We explore the sequence-structure relationship for WW domains and find that the coevolution information has a remarkable influence on local contacts of N-terminal β-turn of WW domains. This turn would not form correctly in the absence of such information. Moreover, through maximum likelihood approach, we identify five local contacts that play a critical role in folding. using the contact probability of those five local contacts at the early stage of folding, we built a classification model. This enables us to predict the foldability of a WW sequence with 81% accuracy. Based on this classification model, we re-design the unfoldable WW domain sequences and make them foldable by introducing a few mutations that lead to stabilization of these critical contacts.

A Complex Network Approach for the Analysis of Protein Units Similarity Using Structural Alphabet

In this paper we present a network approach based on the recent developed 3D-BLAST method of rapid protein structure search. We defined new local segments that represent structural feature of proteins named units of structural alphabet (USA). Each USA is composed of two protein secondary structures, and one loop located between these two secondary structures. We performed all-against-all structural comparison of USA and recognized the USA-based similarity network. The analytical result shows that the network with a power degree distribution is called scale free. These results not only suggest the existence of organizing principles in the local protein structure but also allow us to identify potential key fragments that could be useful for future new drug development and design.

GIRAF: a method for fast search and flexible alignment of ligand binding interfaces in proteins at atomic resolution.

Comparison and classification of protein structures are fundamental means to understand protein functions. Due to the computational difficulty and the ever-increasing amount of structural data, however, it is in general not feasible to perform exhaustive all-against-all structure comparisons necessary for comprehensive classifications. To efficiently handle such situations, we have previously proposed a method, now called GIRAF. We herein describe further improvements in the GIRAF protein structure search and alignment method. The GIRAF method achieves extremely efficient search of similar structures of ligand binding sites of proteins by exploiting database indexing of structural features of local coordinate frames. In addition, it produces refined atom-wise alignments by iterative applications of the Hungarian method to the bipartite graph defined for a pair of superimposed structures. By combining the refined alignments based on different local coordinate frames, it is made possible to align structures involving domain movements. We provide detailed accounts for the database design, the search and alignment algorithms as well as some benchmark results.

Fast and accurate protein substructure searching with simulated annealing and GPUs.

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.

Alignment-free local structural search by writhe decomposition

Motivation: Rapid methods for protein structure search enable biological discoveries based on flexibly defined structural similarity, unleashing the power of the ever greater number of solved protein structures. Projection methods show promise for the development of fast structural database search solutions. Projection methods map a structure to a point in a high-dimensional space and compare two structures by measuring distance between their projected points. These methods offer a tremendous increase in speed over residue-level structural alignment methods. However, current projection methods are not practical, partly because they are unable to identify local similarities.Results: We propose a new projection-based approach that can rapidly detect global as well as local structural similarities. Local structural search is enabled by a topology-inspired writhe decomposition protocol that produces a small number of fragments while ensuring that similar structures are cut in a similar manner. In benchmark tests, we show that our method, writher, improves accuracy over existing projection methods in terms of recognizing scop domains out of multi-domain proteins, while maintaining accuracy comparable with existing projection methods in a standard single-domain benchmark test.Availability: The source code is available at the following website: http://compbio.berkeley.edu/proj/writher/Contact: dzhi@compbio.berkeley.eduSupplementary information: Supplementary data are available at Bioinformatics online.