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Benchmarking Methods of protein structure alignment

JOURNAL OF MOLECULAR EVOLUTION 2020年第7期88卷 575-597页

作者： Sykes, Janan Holland, Barbara R. Charleston, Michael A. Univ Tasmania Sch Nat Sci Hobart Tas Australia

The function of a protein is primarily determined by its structure and amino acid sequence. Many biological questions of interest rely on being able to accurately determine the group of structures to which domains of a protein belong;this can be done through alignment and comparison of protein structures. Dozens of different methods for protein structure alignment (PSA) have been proposed that use a wide range of techniques. The aim of this study is to determine the ability of PSA methods to identify pairs of protein domains known to share differing levels of structural similarity, and to assess their utility for clustering domains from several different folds into known groups. We present the results of a comprehensive investigation into eighteen PSA methods, to our knowledge the largest piece of independent research on this topic. Overall, SP-AlignNS (non-sequential) was found to be the best method for classification, and among the best performing methods for clustering. Methods (where possible) were split into the algorithm used to find the optimal alignment and the score used to assess similarity. This allowed us to largely separate the algorithm from the score it maximizes and thus, to assess their effectiveness independently of each other. Surprisingly, we found that some hybrids of mismatched scores and algorithms performed better than either of the native methods at classification and, in some cases, clustering as well. It is hoped that this investigation and the accompanying discussion will be useful for researchers selecting or designing methods to align protein structures.

关键词： protein structure alignment Classification Clustering Benchmarking

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Bayesian protein sequence and structure alignment

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS 2020年第2期69卷 301-325页

作者： Fallaize, Christopher J. Green, Peter J. Mardia, Kanti, V Barber, Stuart Univ Nottingham Nottingham England Univ Bristol Bristol Avon England Univ Technol Sydney Sydney NSW Australia Univ Leeds Leeds W Yorkshire England Univ Oxford Oxford England

The structure of a protein is crucial in determining its functionality and is much more conserved than sequence during evolution. A key task in structural biology is to compare protein structures to determine evolutionary relationships, to estimate the function of newly discovered structures and to predict unknown structures. We propose a Bayesian method for protein structure alignment, with the prior on alignments based on functions which penalize 'gaps' in the aligned sequences. We show how a broad class of penalty functions fits into this framework, and how the resulting posterior distribution can be efficiently sampled. A commonly used gap penalty function is shown to be a special case, and we propose a new penalty function which alleviates an undesirable feature of the commonly used penalty. We illustrate our method on benchmark data sets and find that it competes well with popular tools from computational biology. Our method has the benefit of being able potentially to explore multiple competing alignments and to quantify their merits probabilistically. The framework naturally enables further information such as amino acid sequence to be included and could be adapted to other situations such as flexible proteins or domain swaps.

关键词： Gap penalty prior Markov chain Monte Carlo sampling protein structure alignment Structural bioinformatics Unlabelled shape analysis

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NAHAL-Flex: A Numerical and Alphabetical Hinge Detection Algorithm for Flexible protein structure alignment

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 934-943页

作者： Fotoohifiroozabadi, Samira Mohamad, Mohd Saberi Deris, Safaai Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Univ Malaysia Kelantan Fac Creat Technol & Heritage Locked Bag 01 Kota Baharu 16300 Kelantan Malaysia

Flexible proteins are proteins that have conformational changes in their structures. protein flexibility analysis is critical for classifying and understanding protein functionality. For that analysis, the hinge areas where proteins show flexibility must be detected. To detect the location of the hinges, previous methods have utilized the three-dimensional (3D) structure of proteins, which is highly computational. To reduce the computational complexity, this study proposes a novel text-based method using structural alphabets (SAs) for detecting the hinge position, called NAHAL-Flex. protein structures were encoded to a particular type of SA called the protein folding shape code (PFSC), which remains unaffected by location, scale, and rotation. The flexible regions of the proteins are the only places in which letter sequences can be distorted. With this knowledge, it is possible to find the longest alignment path of two letter sequences using a dynamic programming (DP) algorithm. Then, the proposed method looks for regions where the alphabet sequence is distorted to find the most probable hinge positions. In order to reduce the number of hinge positions, a genetic algorithm (GA) was utilized to find the best candidate hinge points. To evaluate the method's effectiveness, four different flexible and rigid protein databases, including two small datasets and two large datasets, were utilized. For the small dataset, the NAHAL-Flex method was comparable to state-of-the-art structural flexible alignment methods. The result for the large datasets show that NAHAL-Flex outperforms some well-known alignment methods, e.g., DaliLite, Matt, DeepAlign, and TM-align;the speed of NAHAL-Flex was faster and its result was more accurate than the other methods.

关键词： protein structure alignment protein flexibility linear alignment structure comparison

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Flexible protein structure alignment by Variable-length Aligned Fragment Pairs

Flexible Protein Structure Alignment by Variable-length Alig...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Cao, Hu Lu, Yonggang Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China

ISBN: (纸本)9781467367981

With the growing number of known protein 3D structures, how to efficiently compare protein structures becomes an important and challenging problem in computational structural biology. So, many protein structure alignment methods have been developed in recent years. Flexible structure alignment methods are shown to be superior to rigid structure alignment methods in identifying the structure similarities between proteins which have gone through conformational changes. It is also found that the methods based on Aligned Fragment Pairs (AFP) own special advantages in balancing global structure similarities and local structure similarities. In this work, we propose a new flexible protein 3D structure alignment method based on variable-length AFPs. Different from other methods, our method owns three special features: firstly, it uses a new AFP identification algorithm based on a local coordinate system;secondly, it allows different AFPs separated by other AFPs to share the same transformation during the concatenation;thirdly, it allows different AFPs to have different lengths, which can not only reduce the total number of AFPs, and also can improve the representation of the local structure similarities. The experiments show that, compared to three other flexible structure alignment methods, FlexProt, FATCAT and FlexSnap, the proposed method can achieve similar or better results by introducing fewer twists and in much less running time due to the reduced number of AFPs.

关键词： protein structure alignment Aligned Fragment Pairs Dynamic Programming

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Flexible protein structure alignment by Variable-length Aligned Fragment Pairs

Flexible Protein Structure Alignment by Variable-length Alig...

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IEEE International Conference on Bioinformatics and Biomedicine

作者： HU CAO YONGGANG LU School of Information Science and Engineering Lanzhou University

ISBN: (纸本)9781467368001

关键词： protein structure alignment Aligned Fragment Pairs Dynamic Programming protein structure dynamic programming variable length alignment AFP alpha-Fetoproteins Flexible structures

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Bayesian Multiple protein structure alignment

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18th Annual International Conference on Research in Computational Molecular Biology (RECOMB)

作者： Wang, Rui Schmidler, Scott C. Duke Univ Dept Stat Sci Program Computat Biol & Bioinformat Durham NC 27708 USA

ISBN: (纸本)9783319052694;9783319052687

Multiple protein structure alignment is an important tool in computational biology, with numerous algorithms published in the past two decades. However, recently literature highlights a growing recognition of the inconsistencies among alignments from different algorithms, and the instability of alignments obtained by individual algorithms under small fluctuations of the input structures. Here we present a probabilistic model-based approach to the problem of multiple structure alignment, using an explicit statistical model. The resulting algorithm produces a Bayesian posterior distribution over alignments which accounts for alignment uncertainty arising from evolutionary variability, experimental noise, and thermal fluctuation, as well as sensitivity to alignment algorithm parameters. We demonstrate the robustness of this approach on alignments identified previously in the literature as "difficult" for existing algorithms. We also show the potential for significant stabilization of tree reconstruction in structural phylogenetics.

关键词： protein structure alignment Bayesian statistics MCMC

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ROSS : A Rapid protein structure alignment Algorithm

ROSS : A Rapid Protein Structure Alignment Algorithm

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IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology

作者： Fadel, Ahmed S. Belal, Mohamed Mostafa, Mostafa-Sami M. Helwan Univ Fac Comp & Informat Cairo Egypt

ISBN: (纸本)9781479945351

protein structure comparison is very important in bioinformatics as the function of a protein is determined by its structure. Finding relationships between proteins is very necessary. The databases for proteins are growing rapidly. Therefore there is a strong need for rapid algorithms to handle large number of requests made by biologists. This means that protein structure comparison is an active research area. Relative Orientation of Secondary structure (ROSS) algorithm for protein structure alignment is proposed. ROSS is a sequential (preserving order) sequence-independent structure alignment algorithm. This work proposes a data reduction and a hierarchical approach for the structure alignment of proteins. The data reduction is done in the pre-processing stage to compute small descriptors for proteins and store them in a repository. This reduction speeds up the process of aligning the structure of a query protein to a large number of proteins. The comparison process is performed hierarchically. The reduced representation depends on the vectors representing the axes of the Secondary structure Elements (SSE's) and the inter-direction between their centroids. The accuracy of the algorithm is similar to the well known algorithms Secondary-structure Matching (SSM) and Vector alignment Search Tool (VAST), used through their websites. The speed of the algorithm is around fifty times that of TM Align algorithm. This speed enables searching for similarities within large datasets instantly. Moreover, the algorithm achieves reduction ratio around 30:1.

关键词： protein representation protein structure comparison protein structure alignment

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Detection of circular permutations by protein Language Models

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2025年 27卷 214-220页

作者： Hu, Yue Huang, Bin Zang, Chun Zi Xu, Jia Jie Qilu Univ Technol Shandong Acad Sci Sch Bioengn Jinan 250300 Shandong Peoples R China Yunnan Normal Univ Sch Life Sci Kunming 650500 Yunnan Peoples R China Qilu Univ Technol Shandong Acad Sci Kyiv Coll Jinan 250300 Shandong Peoples R China

protein circular permutations are crucial for understanding protein evolution and functionality. Traditional detection methods face challenges: sequence-based approaches struggle with detecting distant homologs, while structure-based approaches are limited by the need for structure generation and often treat proteins as rigid bodies. protein Language Model-based alignment tools have shown advantages in utilizing sequence information to overcome the challenges of detecting distant homologs without requiring structural input. However, many current protein Language Model-based alignment methods, which rely on sequence alignment algorithms like the Smith-Waterman algorithm, face significant difficulties when dealing with circular permutation (CP) due to their dependency on linear sequence order. This sequence order dependency makes them unsuitable for accurately detecting CP. Our approach, named plmCP, combines classical genetic principles with modern alignment techniques leveraging protein Language Models to address these limitations. By integrating genetic knowledge, the plmCP method avoids the sequence order dependency, allowing for effective detection of circular permutations and contributing significantly to protein research and engineering by embracing structural flexibility.

关键词： Circular Permutation protein Language Models protein structure alignment

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

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proteinS-structure FUNCTION AND BIOINFORMATICS 2021年第6期89卷 648-658页

作者： Ayoub, Ronald Lee, Yugyung Univ Missouri Sch Comp & Engn Kansas City MO 64110 USA

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.

关键词： bioinformatics protein structure alignment protein structure databases protein structure prediction protein structure search

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Optimal protein structure alignment using Modified Extremal Optimization

Optimal Protein Structure Alignment using Modified Extremal ...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Nakada, Akihiro Tamura, Keiichi Kitakami, Hajime Hiroshima City Univ Grad Sch Informat Sci Asa Minami Ku Hiroshima 7313194 Japan

ISBN: (纸本)9781467317146

proteins are important biochemical compounds that have biogenic functions for biological activities. The three-dimensional structures of proteins are closely related to its biological functions, and therefore, techniques for comparing them have been studied. Many of these techniques for comparing protein structures are based on protein structure alignment, which is one of the most effective methods. CMO (Contact Map Overlap) is formulated as combinatorial optimization to find the optimal structure alignments. In this paper, we propose a novel heuristic using Modified Extremal Optimization (MEO) for CMO. Our MEO-based heuristic is characterized by three features. First, the proposed heuristic uses MEO for alternation generations. Second, an initial solution is created by dynamic programming (DP). Third, state transition is executed using the best admissible move strategy.

关键词： protein structure alignment contact map overlap problem extremal optimization bioinformatics evolutionary computation

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