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Article ; Online: RNA threading with secondary structure and sequence profile.

Du, Zongyang / Peng, Zhenling / Yang, Jianyi

2024 Volume 40, Issue 2

Abstract: Motivation: RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale ...

Abstract	Motivation: RNA threading aims to identify remote homologies for template-based modeling of RNA 3D structure. Existing RNA alignment methods primarily rely on secondary structure alignment. They are often time- and memory-consuming, limiting large-scale applications. In addition, the accuracy is far from satisfactory. Results: Using RNA secondary structure and sequence profile, we developed a novel RNA threading algorithm, named RNAthreader. To enhance the alignment process and minimize memory usage, a novel approach has been introduced to simplify RNA secondary structures into compact diagrams. RNAthreader employs a two-step methodology. Initially, integer programming and dynamic programming are combined to create an initial alignment for the simplified diagram. Subsequently, the final alignment is obtained using dynamic programming, taking into account the initial alignment derived from the previous step. The benchmark test on 80 RNAs illustrates that RNAthreader generates more accurate alignments than other methods, especially for RNAs with pseudoknots. Another benchmark, involving 30 RNAs from the RNA-Puzzles experiments, exhibits that the models constructed using RNAthreader templates have a lower average RMSD than those created by alternative methods. Remarkably, RNAthreader takes less than two hours to complete alignments with ∼5000 RNAs, which is 3-40 times faster than other methods. These compelling results suggest that RNAthreader is a promising algorithm for RNA template detection. Availability and implementation: https://yanglab.qd.sdu.edu.cn/RNAthreader.
MeSH term(s)	RNA/chemistry ; Sequence Alignment ; Software ; Algorithms ; Protein Structure, Secondary
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2024-02-06
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btae080
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Article ; Online: A unified approach to protein domain parsing with inter-residue distance matrix.

Zhu, Kun / Su, Hong / Peng, Zhenling / Yang, Jianyi

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 2

Abstract: Motivation: It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different ... ...

Abstract	Motivation: It is fundamental to cut multi-domain proteins into individual domains, for precise domain-based structural and functional studies. In the past, sequence-based and structure-based domain parsing was carried out independently with different methodologies. The recent progress in deep learning-based protein structure prediction provides the opportunity to unify sequence-based and structure-based domain parsing. Results: Based on the inter-residue distance matrix, which can be either derived from the input structure or predicted by trRosettaX, we can decode the domain boundaries under a unified framework. We name the proposed method UniDoc. The principle of UniDoc is based on the well-accepted physical concept of maximizing intra-domain interaction while minimizing inter-domain interaction. Comprehensive tests on five benchmark datasets indicate that UniDoc outperforms other state-of-the-art methods in terms of both accuracy and speed, for both sequence-based and structure-based domain parsing. The major contribution of UniDoc is providing a unified framework for structure-based and sequence-based domain parsing. We hope that UniDoc would be a convenient tool for protein domain analysis. Availability and implementation: https://yanglab.nankai.edu.cn/UniDoc/. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Protein Domains ; Computational Biology/methods ; Proteins/chemistry
Chemical Substances	Proteins
Language	English
Publishing date	2023-02-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad070
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Article ; Online: CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information.

Peng, Zhenling / Li, Zixia / Meng, Qiaozhen / Zhao, Bi / Kurgan, Lukasz

Briefings in bioinformatics

2023 Volume 24, Issue 1

Abstract: One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and ... ...

Abstract	One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.
MeSH term(s)	Intrinsically Disordered Proteins/chemistry ; Computational Biology/methods ; Amino Acid Sequence ; Peptides/metabolism ; Protein Domains ; Databases, Protein ; Protein Binding
Chemical Substances	Intrinsically Disordered Proteins ; Peptides
Language	English
Publishing date	2023-01-31
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbac502
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Article ; Online: Improved protein structure prediction with trRosettaX2, AlphaFold2, and optimized MSAs in CASP15.

Peng, Zhenling / Wang, Wenkai / Wei, Hong / Li, Xiaoge / Yang, Jianyi

Proteins

2023 Volume 91, Issue 12, Page(s) 1704–1711

Abstract: We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality ... ...

Abstract	We present the monomer and multimer structure prediction results of our methods in CASP15. We first designed an elaborate pipeline that leverages complementary sequence databases and advanced database searching algorithms to generate high-quality multiple sequence alignments (MSAs). Top MSAs were then selected for the subsequent step of structure prediction. We utilized trRosettaX2 and AlphaFold2 for monomer structure prediction (group name Yang-Server), and AlphaFold-Multimer for multimer structure prediction (group name Yang-Multimer). Yang-Server and Yang-Multimer are ranked at the top and the fourth, respectively, for monomer and multimer structure prediction. For 94 monomers, the average TM-score of the predicted structure models by Yang-Server is 0.876, compared to 0.798 by the default AlphaFold2 (i.e., the group NBIS-AF2-standard). For 42 multimers, the average DockQ score of the predicted structure models by Yang-Multimer is 0.464, compared to 0.389 by the default AlphaFold-Multimer (i.e., the group NBIS-AF2-multimer). Detailed analysis of the results shows that several factors contribute to the improvement, including improved MSAs, iterated modeling for large targets, interplay between monomer and multimer structure prediction for intertwined structures, etc. However, the structure predictions for orphan proteins and multimers remain challenging, and breakthroughs in this area are anticipated in the future.
MeSH term(s)	Furylfuramide ; Sequence Alignment ; Algorithms ; Databases, Nucleic Acid
Chemical Substances	Furylfuramide (054NR2135Y)
Language	English
Publishing date	2023-08-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	806683-8
ISSN	1097-0134 ; 0887-3585
ISSN (online)	1097-0134
ISSN	0887-3585
DOI	10.1002/prot.26570
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Article ; Online: Single-sequence protein structure prediction using supervised transformer protein language models.

Wang, Wenkai / Peng, Zhenling / Yang, Jianyi

Nature computational science

2022 Volume 2, Issue 12, Page(s) 804–814

Abstract: Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX- ...

Abstract	Significant progress has been made in protein structure prediction in recent years. However, it remains challenging for AlphaFold2 and other deep learning-based methods to predict protein structure with single-sequence input. Here we introduce trRosettaX-Single, an automated algorithm for single-sequence protein structure prediction. It incorporates the sequence embedding from a supervised transformer protein language model into a multi-scale network enhanced by knowledge distillation to predict inter-residue two-dimensional geometry, which is then used to reconstruct three-dimensional structures via energy minimization. Benchmark tests show that trRosettaX-Single outperforms AlphaFold2 and RoseTTAFold on orphan proteins and works well on human-designed proteins (with an average template modeling score (TM-score) of 0.79). An experimental test shows that the full trRosettaX-Single pipeline is two times faster than AlphaFold2, using much fewer computing resources (<10%). On 2,000 designed proteins from network hallucination, trRosettaX-Single generates structure models with high confidence. As a demonstration, trRosettaX-Single is applied to missense mutation analysis. These data suggest that trRosettaX-Single may find potential applications in protein design and related studies.
MeSH term(s)	Humans ; Algorithms ; Benchmarking ; Distillation ; Electric Power Supplies ; Language
Language	English
Publishing date	2022-12-19
Publishing country	United States
Document type	Journal Article
ISSN	2662-8457
ISSN (online)	2662-8457
DOI	10.1038/s43588-022-00373-3
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Article ; Online: Recognition of small molecule-RNA binding sites using RNA sequence and structure.

Su, Hong / Peng, Zhenling / Yang, Jianyi

Bioinformatics (Oxford, England)

2021 Volume 37, Issue 1, Page(s) 36–42

Abstract: Motivation: RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been ... ...

Abstract	Motivation: RNA molecules become attractive small molecule drug targets to treat disease in recent years. Computer-aided drug design can be facilitated by detecting the RNA sites that bind small molecules. However, very limited progress has been reported for the prediction of small molecule-RNA binding sites. Results: We developed a novel method RNAsite to predict small molecule-RNA binding sites using sequence profile- and structure-based descriptors. RNAsite was shown to be competitive with the state-of-the-art methods on the experimental structures of two independent test sets. When predicted structure models were used, RNAsite outperforms other methods by a large margin. The possibility of improving RNAsite by geometry-based binding pocket detection was investigated. The influence of RNA structure's flexibility and the conformational changes caused by ligand binding on RNAsite were also discussed. RNAsite is anticipated to be a useful tool for the design of RNA-targeting small molecule drugs. Availability and implementation: http://yanglab.nankai.edu.cn/RNAsite. Supplementary information: Supplementary data are available at Bioinformatics online.
Language	English
Publishing date	2021-01-01
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa1092
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Article ; Online: Toward the assessment of predicted inter-residue distance.

Du, Zongyang / Peng, Zhenling / Yang, Jianyi

Bioinformatics (Oxford, England)

2021 Volume 38, Issue 4, Page(s) 962–969

Abstract: Motivation: Significant progress has been achieved in distance-based protein folding, due to improved prediction of inter-residue distance by deep learning. Many efforts are thus made to improve distance prediction in recent years. However, it remains ... ...

Abstract	Motivation: Significant progress has been achieved in distance-based protein folding, due to improved prediction of inter-residue distance by deep learning. Many efforts are thus made to improve distance prediction in recent years. However, it remains unknown what is the best way of objectively assessing the accuracy of predicted distance. Results: A total of 19 metrics were proposed to measure the accuracy of predicted distance. These metrics were discussed and compared quantitatively on three benchmark datasets, with distance and structure models predicted by the trRosetta pipeline. The experiments show that a few metrics, such as distance precision, have a high correlation with the model accuracy measure TM-score (Pearson's correlation coefficient >0.7). In addition, the metrics are applied to rank the distance prediction groups in CASP14. The ranking by our metrics coincides largely with the official version. These data suggest that the proposed metrics are effective for measuring distance prediction. We anticipate that this study paves the way for objectively monitoring the progress of inter-residue distance prediction. A web server and a standalone package are provided to implement the proposed metrics. Availability and implementation: http://yanglab.nankai.edu.cn/APD. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Algorithms ; Proteins/chemistry ; Computational Biology ; Protein Folding
Chemical Substances	Proteins
Language	English
Publishing date	2021-11-16
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab781
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Article ; Online: On Monomeric and Multimeric Structures-Based Protein-Ligand Interactions.

Dai, Yajun / Li, Yang / Wang, Liping / Peng, Zhenling / Yang, Jianyi

IEEE/ACM transactions on computational biology and bioinformatics

2022 Volume 19, Issue 1, Page(s) 569–574

Abstract: Many ligands simultaneously interact with multiple protein chains in quaternary structure (QS). However, a significant number of previous studies on template-based modeling of protein-ligand interactions were based on monomeric structure (MS), which may ... ...

Abstract	Many ligands simultaneously interact with multiple protein chains in quaternary structure (QS). However, a significant number of previous studies on template-based modeling of protein-ligand interactions were based on monomeric structure (MS), which may suffer from incomplete binding information. The defects of using MS rather than QS have not been systematically studied before. In this work, based on molecular docking experiments and binding free energy estimations, we performed a large-scale comparison of the protein-ligand interactions in both forms of structures. We found that 1) about 18.6 percent biologically relevant ligands bind multiple chains in QS simultaneously. 2) For more than 95 percent complexes with multiple chains involved in the interactions, the binding free energy is lower for the QS form than the MS form. 3) For over 70 percent complexes with multi-chain binding pockets, docking with QS yields more accurate ligand conformations than with MS. While for about 1.82 percent complexes, accurate docking conformations were obtained by MS. Based on this work, it is encouraged to make use of QS rather than MS in future studies on protein-ligand interactions.
MeSH term(s)	Binding Sites ; Ligands ; Molecular Docking Simulation ; Protein Binding ; Protein Conformation ; Proteins/metabolism
Chemical Substances	Ligands ; Proteins
Language	English
Publishing date	2022-02-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2020.3002776
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Article ; Online: Maize, wheat, and soybean root traits depend upon soil phosphorus fertility and mycorrhizal status.

Han, Jiayao / Zhang, Yali / Xi, Hao / Zeng, Jing / Peng, Zhenling / Ali, Gohar / Liu, Yongjun

Mycorrhiza

2023 Volume 33, Issue 5-6, Page(s) 359–368

Abstract: Strong effects of plant identity, soil nutrient availability or mycorrhizal fungi on root traits have been well documented, but their interactive influences on root traits are still poorly understood. Here, three crop species (maize, wheat and soybean) ... ...

Abstract	Strong effects of plant identity, soil nutrient availability or mycorrhizal fungi on root traits have been well documented, but their interactive influences on root traits are still poorly understood. Here, three crop species (maize, wheat and soybean) were grown under four phosphorus (P) addition levels (0, 20, 40 and 60 mg P kg
MeSH term(s)	Mycorrhizae/physiology ; Soil ; Glycine max ; Triticum ; Zea mays ; Phosphorus ; Plant Roots/microbiology
Chemical Substances	Soil ; Phosphorus (27YLU75U4W)
Language	English
Publishing date	2023-10-12
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1087945-6
ISSN	1432-1890 ; 0940-6360
ISSN (online)	1432-1890
ISSN	0940-6360
DOI	10.1007/s00572-023-01126-4
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Article ; Online: Protein structure prediction in the deep learning era.

Peng, Zhenling / Wang, Wenkai / Han, Renmin / Zhang, Fa / Yang, Jianyi

Current opinion in structural biology

2022 Volume 77, Page(s) 102495

Abstract: Significant advances have been achieved in protein structure prediction, especially with the recent development of the AlphaFold2 and the RoseTTAFold systems. This article reviews the progress in deep learning-based protein structure prediction methods ... ...

Abstract	Significant advances have been achieved in protein structure prediction, especially with the recent development of the AlphaFold2 and the RoseTTAFold systems. This article reviews the progress in deep learning-based protein structure prediction methods in the past two years. First, we divide the representative methods into two categories: the two-step approach and the end-to-end approach. Then, we show that the two-step approach is possible to achieve similar accuracy to the state-of-the-art end-to-end approach AlphaFold2. Compared to the end-to-end approach, the two-step approach requires fewer computing resources. We conclude that it is valuable to keep developing both approaches. Finally, a few outstanding challenges in function-orientated protein structure prediction are pointed out for future development.
Language	English
Publishing date	2022-11-10
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1068353-7
ISSN	1879-033X ; 0959-440X
ISSN (online)	1879-033X
ISSN	0959-440X
DOI	10.1016/j.sbi.2022.102495
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