• Publications
  • Influence
Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction
  • H. Zhou, Y. Zhou
  • Chemistry, Medicine
  • Protein science : a publication of the Protein…
  • 1 November 2002
The distance‐dependent structure‐derived potentials developed so far all employed a reference state that can be characterized as a residue (atom)‐averaged state. Here, we establish a new referenceExpand
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Protein binding site prediction using an empirical scoring function
Most biological processes are mediated by interactions between proteins and their interacting partners including proteins, nucleic acids and small molecules. This work establishes a method calledExpand
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SPINE-D: Accurate Prediction of Short and Long Disordered Regions by a Single Neural-Network Based Method
Abstract Short and long disordered regions of proteins have different preference for different amino acid residues. Different methods often have to be trained to predict them separately. In thisExpand
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SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures
TLDR
We develop a method, called SPEM, that aligns multiple sequences using pre-processed sequence profiles and predicted secondary structures, consistency-based scoring for refinement of the pairwise alignment and a progressive algorithm for final multiple alignment. Expand
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Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments
Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence‐based and structure‐based methods as well as their combinations have beenExpand
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Capturing non‐local interactions by long short‐term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and
TLDR
We showed that the application of LSTM‐BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long‐range contacts (|i‐j| >19) over a previous window‐based, deep‐learning method SPIDER2. Expand
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Single‐body residue‐level knowledge‐based energy score combined with sequence‐profile and secondary structure information for fold recognition
An elaborate knowledge‐based energy function is designed for fold recognition. It is a residue‐level single‐body potential so that highly efficient dynamic programming method can be used forExpand
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Achieving 80% ten‐fold cross‐validated accuracy for secondary structure prediction by large‐scale training
  • O. Dor, Y. Zhou
  • Computer Science, Medicine
  • Proteins
  • 18 December 2006
TLDR
An integrated system of neural networks, called SPINE, is established and optimized for predicting structural properties of proteins. Expand
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Predicting the topology of transmembrane helical proteins using mean burial propensity and a hidden‐Markov‐model‐based method
  • H. Zhou, Y. Zhou
  • Computer Science, Medicine
  • Protein science : a publication of the Protein…
  • 1 July 2003
TLDR
We introduce a method that uses a simple scale of burial propensity and a new algorithm to predict transmembrane helical (TMH) segments and a positive‐inside rule to predict amino‐terminal orientation. Expand
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Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native
TLDR
We improve a single-method fold recognition technique called SPARKS by changing the alignment scoring function and incorporating the SPINE-X techniques that make improved prediction of secondary structure, backbone torsion angle and solvent accessible surface area. Expand
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