Jiangsu Provincial Key Laboratory for Information Processing Technologies

Suzhou, China

Jiangsu Provincial Key Laboratory for Information Processing Technologies

Suzhou, China
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Wang H.,Soochow University of China | Liu H.,Soochow University of China | Cai L.,Soochow University of China | Wang C.,Soochow University of China | And 2 more authors.
BMC Bioinformatics | Year: 2017

Background: In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange. Results: The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies. Conclusions: Our findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein-small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand. © 2017 The Author(s).


Li L.,Soochow University of China | Xu W.,Wujiang Rural Commercial Bank | Lu Q.,Soochow University of China | Lu Q.,Jiangsu Provincial Key Laboratory for Information Processing Technologies
Journal of Molecular Modeling | Year: 2015

Computational protein-ligand docking is of great importance in drug discovery and design. Conformational changes greatly affect the results of protein-ligand docking, especially when water molecules take part in mediating protein ligand interactions or when large conformational changes are observed in the receptor backbone interface. We have developed an improved protocol, SWRosettaLigand, based on the RosettaLigand protocol. This approach incorporates the flexibility of interfacial water molecules and modeling of the interface of the receptor into the original RosettaLigand. In a coarse sampling step, SWRosettaLigand pre-optimizes the initial position of the water molecules, docks the ligand to the receptor with explicit water molecules, and minimizes the predicted structure with water molecules. The receptor backbone interface is treated as a loop and perturbed and refined by kinematic closure, or cyclic coordinate descent algorithm, with the presence of the ligand. In two cross-docking test sets, it was identified that for 8 out of 14, and 16 out of 22, test instances, the top-ranked structures by SWRosettaLigand achieved better accuracy than other protocols. © 2015, Springer-Verlag Berlin Heidelberg.


Quan L.,Soochow University of China | Lu Q.,Soochow University of China | Lu Q.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | Li H.,Soochow University of China | And 5 more authors.
BMC Bioinformatics | Year: 2014

Introduction: The accurate packing of protein side chains is important for many computational biology problems, such as ab initio protein structure prediction, homology modelling, and protein design and ligand docking applications. Many of existing solutions are modelled as a computational optimisation problem. As well as the design of search algorithms, most solutions suffer from an inaccurate energy function for judging whether a prediction is good or bad. Even if the search has found the lowest energy, there is no certainty of obtaining the protein structures with correct side chains. Methods: We present a side-chain modelling method, pacoPacker, which uses a parallel ant colony optimisation strategy based on sharing a single pheromone matrix. This parallel approach combines different sources of energy functions and generates protein side-chain conformations with the lowest energies jointly determined by the various energy functions. We further optimised the selected rotamers to construct subrotamer by rotamer minimisation, which reasonably improved the discreteness of the rotamer library. Results: We focused on improving the accuracy of side-chain conformation prediction. For a testing set of 442 proteins, 87.19% of X 1 and 77.11% of X 1 2 angles were predicted correctly within 40° of the X-ray positions. We compared the accuracy of pacoPacker with state-of-the-art methods, such as CIS-RR and SCWRL4. We analysed the results from different perspectives, in terms of protein chain and individual residues. In this comprehensive benchmark testing, 51.5% of proteins within a length of 400 amino acids predicted by pacoPacker were superior to the results of CIS-RR and SCWRL4 simultaneously. Finally, we also showed the advantage of using the subrotamers strategy. All results confirmed that our parallel approach is competitive to state-of-the-art solutions for packing side chains. Conclusions: This parallel approach combines various sources of searching intelligence and energy functions to pack protein side chains. It provides a frame-work for combining different inaccuracy/usefulness objective functions by designing parallel heuristic search algorithms. © 2014 Quan et al.; licensee BioMed Central Ltd.


Wang K.,Suzhou University of Science and Technology | Wu H.,Suzhou University of Science and Technology | Wu H.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | Lu W.,Suzhou University of Science and Technology | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95% on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location. © Springer International Publishing Switzerland 2015.


Lu W.,Suzhou University of Science and Technology | Fu B.,Suzhou University of Science and Technology | Wu H.,Suzhou University of Science and Technology | Wu H.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Transmembrane proteins are important for cell transport biology and in the treatment of disease. Understanding the helix count and locations in transmembrane proteins is a key problem for structural and functional analyses. But there is a lack of high resolution three-dimensional structures. In this study, we propose a method based on conditional random fields for predicting the helix count and locations, CRF-TM, which reflects long-range correlations in the full-length sequence as joint probabilities. Two datasets are employed in the performance validation. Our results show that CRF-TM can rank the first group better compared with other widely used TM predictors. The results obtained by CRF-TM are also used to predict the three-dimensional structures of GPCRs, which is crucial drug targets and also a subclass of transmembrane with seven spanning α-helices. © Springer International Publishing Switzerland 2015.


Lv Q.,Soochow University of China | Lv Q.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | Wu H.J.,Soochow University of China | Wu H.J.,Suzhou University of Science and Technology | And 5 more authors.
Science China Information Sciences | Year: 2013

Predicting the three-dimensional structure of proteins from amino acid sequences with only a few remote homologs, or de novo prediction, remains a major challenge in computational biology. The modeling of the protein backbone represents the initial phase of a protein structure prediction process. Using a parallel ant colony optimization based on sharing one pheromone matrix, this report proposes a parallel approach to predict the structure of a protein backbone. The parallel approach combines various sources of energy functions and generates protein backbones with the lowest energies jointly determined by the various energy functions. All free modeling targets in CASP8/9 are used to evaluate the performance of the method. For 13 targets in CASP8, two out of the predicted model1s selected by our approach are the best of the published CASP8 results, and seven out of the model1s are ranked in the top 10. For 29 targets in CASP9, 20 out of the best models from our predictions are ranked in the top 10, and 11 out of the model1s are ranked in the top 10. © 2012 Science China Press and Springer-Verlag Berlin Heidelberg.


Lu Q.,Soochow University of China | Lu Q.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | Xia X.-Y.,Soochow University of China | Xia X.-Y.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | And 5 more authors.
PLoS ONE | Year: 2012

Background: Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results: A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions: This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. © 2012 Lü et al.


Quan L.,Soochow University of China | Li H.,Soochow University of China | Xia X.,Soochow University of China | Xia X.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | And 2 more authors.
Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 | Year: 2013

Side-chains are crucial for proteins expressing their biochemical characteristics. Packing protein side-chains is then a necessary task for protein structure prediction, and critical to some descendant and important applications, such as protein design, docking and point mutation analysis. Given all possible candidate rotamers for each residue of protein backbone, packing protein side-chains can be modeled as a combinatorial optimization problem without an accurate energy function. This paper presents a parallel approach, pacoPacker, to pack protein side-chains by ant colony optimization. Each ant colony is used to pack side-chains with the guidance of an energy function. Different colonies use different energy functions. These multiple colonies are running in parallel and cooperate with each other by sharing the pheromone matrix whose role is to tune sampling the rotamer library. In this way, the intelligences embedded in different energy functions can be brought together to find out the best side-chains for the protein backbone. Experimental study has been conducted on two typical benchmarks, and the results show that pacoPacker is competitive to the state-of-art systems. © 2013 IEEE.


Huang Y.,Soochow University of China | Lu Q.,Soochow University of China | Lu Q.,Jiangsu Provincial Key Laboratory for Information Processing Technologies | Zeng J.,Soochow University of China
Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 | Year: 2013

We present an algorithm EcSymDock which optimized SymDock using Evolution couplings for better performance. SymDock is a protocol of Rosetta used to predict protein oligomer structure. Evolution Coupling (EC) is an optimized mutual information calculated by specific protein family MSA, it reveals certain contacts between residues. Today more and more researchers pay close attention to how to use ECs as constraints during protein structure prediction. We combined EC information with SymDock protocol and present the EcSymDock method to add EC as constraints into SymDock protocol. Testing on a list of dimers shows the method recovers the native dimeric configuration with better accuracy than SymDock and produces many decoys near the native structure in top 400 lowest energy. © 2013 IEEE.


Li H.,Soochow University of China | Lu L.,University of Wisconsin - Madison | Chen R.,Soochow University of China | Quan L.,Soochow University of China | And 4 more authors.
PLoS ONE | Year: 2014

Structural information related to protein-peptide complexes can be very useful for novel drug discovery and design. The computational docking of protein and peptide can supplement the structural information available on protein-peptide interactions explored by experimental ways. Protein-peptide docking of this paper can be described as three processes that occur in parallel: ab-initio peptide folding, peptide docking with its receptor, and refinement of some flexible areas of the receptor as the peptide is approaching. Several existing methods have been used to sample the degrees of freedom in the three processes, which are usually triggered in an organized sequential scheme. In this paper, we proposed a parallel approach that combines all the three processes during the docking of a folding peptide with a flexible receptor. This approach mimics the actual protein-peptide docking process in parallel way, and is expected to deliver better performance than sequential approaches. We used 22 unbound protein-peptide docking examples to evaluate our method. Our analysis of the results showed that the explicit refinement of the flexible areas of the receptor facilitated more accurate modeling of the interfaces of the complexes, while combining all of the moves in parallel helped the constructing of energy funnels for predictions. © 2014 Li et al.

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