scholarly journals DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet

2020 ◽  
Author(s):  
Yifei Qi ◽  
John Z.H. Zhang
Keyword(s):  
Neural Network ◽  
Protein Design ◽  
Protein Sequence ◽  
Protein Structures ◽  
Three Dimensional ◽  
Search Space ◽  
Computational Protein Design ◽  
Data Sets ◽  
Protein Backbone ◽  
Natural Amino Acids

<p>Computational protein design remains a challenging task despite its remarkable success in the past few decades. With the rapid progress of deep-learning techniques and the accumulation of three-dimensional protein structures, using deep neural networks to learn the relationship between protein sequences and structures and then automatically design a protein sequence for a given protein backbone structure is becoming increasingly feasible. In this study, we developed a deep neural network named DenseCPD that considers the three-dimensional density distribution of protein backbone atoms and predicts the probability of 20 natural amino acids for each residue in a protein. The accuracy of DenseCPD was 51.56±0.20% in a 5-fold cross validation on the training set and 54.45% and 50.06% on two independent test sets, which is more than 10% higher than those of previous state-of-the-art methods. Two approaches for using DenseCPD predictions in computational protein design were analyzed. The approach using the cutoff of accumulative probability had a smaller sequence search space compared to that of the approach that simply uses the top-k predictions and therefore enables higher sequence identity in redesigning three proteins with Rosetta. The network and the data sets are available on a web server at <a href="http://protein.org.cn/densecpd.html">http://protein.org.cn/densecpd.html</a>. The results of this study may benefit the further development of computational protein design methods.</p>

scholarly journals DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet

2020 ◽  
Author(s):  
Yifei Qi ◽  
John Z.H. Zhang
Keyword(s):  
Neural Network ◽  
Protein Design ◽  
Protein Sequence ◽  
Protein Structures ◽  
Three Dimensional ◽  
Search Space ◽  
Computational Protein Design ◽  
Data Sets ◽  
Protein Backbone ◽  
Natural Amino Acids

<p>Computational protein design remains a challenging task despite its remarkable success in the past few decades. With the rapid progress of deep-learning techniques and the accumulation of three-dimensional protein structures, using deep neural networks to learn the relationship between protein sequences and structures and then automatically design a protein sequence for a given protein backbone structure is becoming increasingly feasible. In this study, we developed a deep neural network named DenseCPD that considers the three-dimensional density distribution of protein backbone atoms and predicts the probability of 20 natural amino acids for each residue in a protein. The accuracy of DenseCPD was 51.56±0.20% in a 5-fold cross validation on the training set and 54.45% and 50.06% on two independent test sets, which is more than 10% higher than those of previous state-of-the-art methods. Two approaches for using DenseCPD predictions in computational protein design were analyzed. The approach using the cutoff of accumulative probability had a smaller sequence search space compared to that of the approach that simply uses the top-k predictions and therefore enables higher sequence identity in redesigning three proteins with Rosetta. The network and the data sets are available on a web server at <a href="http://protein.org.cn/densecpd.html">http://protein.org.cn/densecpd.html</a>. The results of this study may benefit the further development of computational protein design methods.</p>

scholarly journals Computational protein design with backbone plasticity

2016 ◽  
Vol 44 (5) ◽  
pp. 1523-1529 ◽  
Author(s):  
James T. MacDonald ◽  
Paul S. Freemont
Keyword(s):  
Protein Design ◽  
De Novo ◽  
Protein Structures ◽  
Search Space ◽  
Computational Protein Design ◽  
Artificial Enzymes ◽  
Backbone Flexibility ◽  
Artificial Proteins ◽  
Naturally Occurring ◽  
Backbone Structure

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

A structural homology approach for computational protein design with flexible backbone

2018 ◽  
Vol 35 (14) ◽  
pp. 2418-2426 ◽  
Author(s):  
David Simoncini ◽  
Kam Y J Zhang ◽  
Thomas Schiex ◽  
Sophie Barbe
Keyword(s):  
Amino Acid ◽  
Protein Design ◽  
Protein Sequence ◽  
Critical Role ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Computational Protein Design ◽  
Supplementary Information ◽  
Structural Homology ◽  
Homologous Proteins

Abstract Motivation Structure-based Computational Protein design (CPD) plays a critical role in advancing the field of protein engineering. Using an all-atom energy function, CPD tries to identify amino acid sequences that fold into a target structure and ultimately perform a desired function. Energy functions remain however imperfect and injecting relevant information from known structures in the design process should lead to improved designs. Results We introduce Shades, a data-driven CPD method that exploits local structural environments in known protein structures together with energy to guide sequence design, while sampling side-chain and backbone conformations to accommodate mutations. Shades (Structural Homology Algorithm for protein DESign), is based on customized libraries of non-contiguous in-contact amino acid residue motifs. We have tested Shades on a public benchmark of 40 proteins selected from different protein families. When excluding homologous proteins, Shades achieved a protein sequence recovery of 30% and a protein sequence similarity of 46% on average, compared with the PFAM protein family of the target protein. When homologous structures were added, the wild-type sequence recovery rate achieved 93%. Availability and implementation Shades source code is available at https://bitbucket.org/satsumaimo/shades as a patch for Rosetta 3.8 with a curated protein structure database and ITEM library creation software. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network

2019 ◽  
Author(s):  
Sari Sabban ◽  
Mikhail Markovsky
Keyword(s):  
Neural Network ◽  
Protein Data Bank ◽  
Protein Design ◽  
Short Term Memory ◽  
De Novo ◽  
Protein Structures ◽  
Data Bank ◽  
Protein Backbone ◽  
Helical Protein ◽  
Long Short Term Memory

AbstractThe ability to perform de novo protein design will allow researchers to expand the variety of available proteins. By designing synthetic structures computationally, they can utilise more structures than those available in the Protein Data Bank, design structures that are not found in nature, or direct the design of proteins to acquire a specific desired structure. While some researchers attempt to design proteins from first physical and thermodynamic principals, we decided to attempt to test whether it is possible to perform de novo helical protein design of just the backbone statistically using machine learning by building a model that uses a long short-term memory (LSTM) architecture. The LSTM model used only the ϕ and ψ angles of each residue from an augmented dataset of only helical protein structures. Though the network’s generated backbone structures were not perfect, they were idealised and evaluated post generation where the non-ideal structures were filtered out and the adequate structures kept. The results were successful in developing a logical, rigid, compact, helical protein backbone topology. This paper is a proof of concept that shows it is possible to generate a novel helical backbone topology using an LSTM neural network architecture using only the ϕ and ψ angles as features. The next step is to attempt to use these backbone topologies and sequence design them to form complete protein structures.Author summaryThis research project stemmed from the desire to expand the pool of protein structures that can be used as scaffolds in computational vaccine development, since the number of structures available from the Protein Data Bank was not sufficient to allow for great diversity and increase the probability of grafting a target motif onto a protein scaffold. Since a protein structure’s backbone can be defined by the ϕ and ψ angles of each amino acid in the polypeptide and can effectively translate a protein’s 3D structure into a table of numbers, and since protein structures are not random, this numerical representation of protein structures can be used to train a neural network to mathematically generalise what a protein structure is, and therefore generate new a protein backbone. Instead of using all proteins in the Protein Data Bank a curated dataset was used encompassing protein structures with specific characteristics that will, theoretically, allow them to be evaluated computationally. This paper details how a trained neural network was able to successfully generate helical protein backbones.

scholarly journals RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 298 ◽  
Author(s):  
Sari Sabban ◽  
Mikhail Markovsky
Keyword(s):  
Neural Network ◽  
Protein Design ◽  
Short Term Memory ◽  
De Novo ◽  
Protein Structures ◽  
Protein Backbone ◽  
Short Term ◽  
Term Memory ◽  
Helical Protein ◽  
Long Short Term Memory

The ability to perform de novo protein design will allow researchers to expand the variety of available proteins. By designing synthetic structures computationally, they can utilise more structures than those available in the Protein Data Bank, design structures that are not found in nature, or direct the design of proteins to acquire a specific desired structure. While some researchers attempt to design proteins from first physical and thermodynamic principals, we decided to attempt to test whether it is possible to perform de novo helical protein design of just the backbone statistically using machine learning by building a model that uses a long short-term memory (LSTM) architecture. The LSTM model used only the φ and ψ angles of each residue from an augmented dataset of only helical protein structures. Though the network’s generated backbone structures were not perfect, they were idealised and evaluated post generation where the non-ideal structures were filtered out and the adequate structures kept. The results were successful in developing a logical, rigid, compact, helical protein backbone topology. This paper is a proof of concept that shows it is possible to generate a novel helical backbone topology using an LSTM neural network architecture using only the φ and ψ angles as features. The next step is to attempt to use these backbone topologies and sequence design them to form complete protein structures.

scholarly journals RamaNet: Computational de novo helical protein backbone design using a long short-term memory generative neural network

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 298 ◽  
Author(s):  
Sari Sabban ◽  
Mikhail Markovsky
Keyword(s):  
Neural Network ◽  
Protein Design ◽  
Short Term Memory ◽  
De Novo ◽  
Protein Structures ◽  
Protein Backbone ◽  
Short Term ◽  
Term Memory ◽  
Helical Protein ◽  
Long Short Term Memory

The ability to perform de novo protein design will allow researchers to expand the variety of available proteins. By designing synthetic structures computationally, they can utilise more structures than those available in the Protein Data Bank, design structures that are not found in nature, or direct the design of proteins to acquire a specific desired structure. While some researchers attempt to design proteins from first physical and thermodynamic principals, we decided to attempt to test whether it is possible to perform de novo helical protein design of just the backbone statistically using machine learning by building a model that uses a long short-term memory (LSTM) architecture. The LSTM model used only the φ and ψ angles of each residue from an augmented dataset of only helical protein structures. Though the network’s generated backbone structures were not perfect, they were idealised and evaluated post generation where the non-ideal structures were filtered out and the adequate structures kept. The results were successful in developing a logical, rigid, compact, helical protein backbone topology. This paper is a proof of concept that shows it is possible to generate a novel helical backbone topology using an LSTM neural network architecture using only the φ and ψ angles as features. The next step is to attempt to use these backbone topologies and sequence design them to form complete protein structures.

Smart, Innovative and Intelligent Technologies Used in Drug Designing

2014 ◽  
pp. 285-301
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

scholarly journals Enhancing protein backbone angle prediction by using simpler models of deep neural networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Fereshteh Mataeimoghadam ◽  
M. A. Hakim Newton ◽  
Abdollah Dehzangi ◽  
Abdul Karim ◽  
B. Jayaram ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Structure Prediction ◽  
Protein Structures ◽  
Absolute Error ◽  
Grand Challenge ◽  
Protein Backbone ◽  
The Neural Network ◽  
Benchmark Datasets ◽  
The Neural Networks

Abstract Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6–8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap.

scholarly journals A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 278
Author(s):  
Christine Orengo ◽  
Sameer Velankar ◽  
Shoshana Wodak ◽  
Vincent Zoete ◽  
Alexandre M.J.J. Bonvin ◽  
...  
Keyword(s):  
Structural Biology ◽  
Protein Design ◽  
Sequence Data ◽  
Protein Structures ◽  
Structural Bioinformatics ◽  
Data Sets ◽  
Educational Training ◽  
Scientific Methods ◽  
Functional Sites ◽  
Common Interests

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

scholarly journals Smart, Innovative and Intelligent Technologies Used in Drug Designing

2017 ◽  
pp. 1175-1191
Author(s):  
S. Deshpande ◽  
S. K. Basu ◽  
X. Li ◽  
X. Chen
Keyword(s):  
Drug Discovery ◽  
Protein Structures ◽  
Three Dimensional ◽  
New Drugs ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Mathematical Methods ◽  
Structure Based Drug Design ◽  
Recent Developments ◽  
Therapeutic Compounds

Smart and intelligent computational methods are essential nowadays for designing, manufacturing and optimizing new drugs. New and innovative computational tools and algorithms are consistently developed and applied for the development of novel therapeutic compounds in many research projects. Rapid developments in the architecture of computers have also provided complex calculations to be performed in a smart, intelligent and timely manner for desired quality outputs. Research groups worldwide are developing drug discovery platforms and innovative tools following smart manufacturing ideas using highly advanced biophysical, statistical and mathematical methods for accelerated discovery and analysis of smaller molecules. This chapter discusses novel innovative applications in drug discovery involving use of structure-based drug design which utilizes geometrical knowledge of the three-dimensional protein structures. It discusses statistical and physics based methods such as quantum mechanics and classical molecular dynamics which can also play a major role in improving the performance and in prediction of computational drug discovery. Lastly, the authors provide insights on recent developments in cloud computing with significant increase in smart and intelligent computational power thus allowing larger data sets to be analyzed simultaneously on multi processor cloud systems. Future directions for the research are outlined.

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