The Experts below are selected from a list of 271485 Experts worldwide ranked by ideXlab platform
Stephen L. Mayo - One of the best experts on this subject based on the ideXlab platform.
-
Protein Design automation
Protein Science, 2008Co-Authors: Bassil I. Dahiyat, Stephen L. MayoAbstract:We have conceived and implemented a cyclical Protein Design strategy that couples theory, computation, and experimental testing. The combinatorially large number of possible sequences and the incomplete understanding of the factors that control Protein structure are the primary obstacles in Protein Design. Our Protein Design automation algorithm objectively predicts Protein sequences likely to achieve a desired fold. Using a rotamer description of the side chains, we implemented a fast discrete search algorithm based on the Dead-End Elimination Theorem to rapidly find the globally optimal sequence in its optimal geometry from the vast number of possible solutions. Rotamer sequences were scored for steric complementarity using a van der Waals potential. A Monte Carlo search was then executed, starting at the optimal sequence, in order to find other high-scoring sequences. As a test of the Design methodology, high-scoring sequences were found for the buried hydrophobic residues of a homodimeric coiled coil based on GCN4-p1. The corresponding peptides were synthesized and characterized by CD spectroscopy and size-exclusion chromatography. All peptides were dimeric and nearly 100% helical at 1 degree C, with melting temperatures ranging from 24 degrees C to 57 degrees C. A quantitative structure activity relation analysis was performed on the Designed peptides, and a significant correlation was found with surface area burial. Incorporation of a buried surface area potential in the scoring of sequences greatly improved the correlation between predicted and measured stabilities and demonstrated experimental feedback in a complete Design cycle.
-
Electrostatics in computational Protein Design
Current opinion in chemical biology, 2005Co-Authors: Christina L. Vizcarra, Stephen L. MayoAbstract:Catalytic activity and Protein-Protein recognition have proven to be significant challenges for computational Protein Design. Electrostatic interactions are crucial for these and other Protein functions, and therefore accurate modeling of electrostatics is necessary for successfully advancing Protein Design into the realm of Protein function. This review focuses on recent progress in modeling electrostatic interactions in computational Protein Design, with particular emphasis on continuum models.
-
A search algorithm for fixed-composition Protein Design.
Journal of computational chemistry, 2005Co-Authors: Geoffrey K. Hom, Stephen L. MayoAbstract:We present a computational Protein Design algorithm for finding low-energy sequences of fixed amino acid composition. The search algorithms used in Protein Design typically do not restrict amino acid composition. However, the random energy model of Shakhnovich suggests that the use of fixed-composition sequences may circumvent defects in the modeling of the denatured state. Our algorithm, FC_FASTER, links fixed-composition versions of Monte Carlo and the FASTER algorithm. As proof of principle, FC_FASTER was tested on an experimentally validated, full-sequence Design of the β1 domain of Protein G. For the wild-type composition, FC_FASTER found a lower energy sequence than the experimentally validated sequence. Also, for a different composition, FC_FASTER found the hypothetical lowest-energy sequence in 14 out of 32 trials.
-
Computational Protein Design.
Structure (London England : 1993), 1999Co-Authors: Arthur Street, Stephen L. MayoAbstract:A 'Protein Design cycle', involving cycling between theory and experiment, has led to recent advances in rational Protein Design. A reductionist approach, in which Protein positions are classified by their local environments, has aided development of an appropriate energy expression. The computational principles and practicalities of the Protein Design cycle are discussed.
-
Energy functions for Protein Design.
Current opinion in structural biology, 1999Co-Authors: David Benjamin Gordon, Shannon Alicia Marshall, Stephen L. MayoAbstract:Recent successes in Protein Design have illustrated the promise of computational approaches. These methods rely on energy expressions to evaluate the quality of different amino acid sequences for target Protein structures. The force fields optimized for Design differ from those typically used in molecular mechanics and molecular dynamics calculations.
Bruce R. Donald - One of the best experts on this subject based on the ideXlab platform.
-
Protein Design by Algorithm.
Communications of the ACM, 2019Co-Authors: Mark A. Hallen, Bruce R. DonaldAbstract:We review algorithms for Protein Design in general. Although these algorithms have a rich combinatorial, geometric, and mathematical structure, they are almost never covered in computer science classes. Furthermore, many of these algorithms admit provable guarantees of accuracy, soundness, complexity, completeness, optimality, and approximation bounds. The algorithms represent a delicate and beautiful balance between discrete and continuous computation and modeling, analogous to that which is seen in robotics, computational geometry, and other fields in computational science. Finally, computer scientists may be unaware of the almost direct impact of these algorithms for predicting and introducing molecular therapies that have gone in a short time from mathematics to algorithms to software to predictions to preclinical testing to clinical trials. Indeed, the overarching goal of these algorithms is to enable the development of new therapeutics that might be impossible or too expensive to discover using experimental methods. Thus the potential impact of these algorithms on individual, community, and global health has the potential to be quite significant.
-
Parallel Computational Protein Design
Methods in molecular biology (Clifton N.J.), 2016Co-Authors: Yichao Zhou, Bruce R. Donald, Jianyang ZengAbstract:Computational structure-based Protein Design (CSPD) is an important problem in computational biology, which aims to Design or improve a prescribed Protein function based on a Protein structure template. It provides a practical tool for real-world Protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational Protein Design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab (Gainza et al., Methods Enzymol 523:87, 2013) to implement a GPU-based massively parallel A* algorithm for improving Protein Design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large Protein Design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE (Gainza et al., PLoS Comput Biol 8:e1002335, 2012) and DEEPer (Hallen et al., Proteins 81:18-39, 2013) to also consider continuous backbone and side-chain flexibility.
-
cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design
Journal of computational biology : a journal of computational molecular cell biology, 2016Co-Authors: Yuchao Pan, Bruce R. Donald, Yuxi Dong, Jingtian Zhou, Mark A. Hallen, Jianyang ZengAbstract:Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational Protein Design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed Design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We Design cloud OSPREY (cOSPREY), an extension to a widely used Protein Design software OSPREY, to allow the original Design framework to scale to the commercial cloud infrastructures. We propose several novel Designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale Protein Design problems that have not been possible with previous approaches.
-
Algorithms for Protein Design.
Current opinion in structural biology, 2016Co-Authors: Pablo Gainza, Hunter M. Nisonoff, Bruce R. DonaldAbstract:Computational structure-based Protein Design programs are becoming an increasingly important tool in molecular biology. These programs compute Protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for Protein Design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational Protein Design that we believe will be especially important for the Design of therapeutic Proteins and Protein assemblies.
-
Fast search algorithms for computational Protein Design
Journal of Computational Chemistry, 2016Co-Authors: Seydou Traore, Bruce R. Donald, Thomas Schiex, Kyle E. Roberts, David Allouche, Isabelle André, Sophie BarbeAbstract:One of the main challenges in computational Protein Design (CPD) is the huge size of the Protein sequence and conformational space that has to be computationally explored. Recently, we showed that state-of-the-art combinatorial optimization technologies based on Cost Function Network (CFN) processing allow speeding up provable rigid backbone Protein Design methods by several orders of magnitudes. Building up on this, we improved and injected CFN technology into the well-established CPD package Osprey to allow all Osprey CPD algorithms to benefit from associated speedups. Because Osprey fundamentally relies on the ability of A* to produce conformations in increasing order of energy, we defined new A* strategies combining CFN lower bounds, with new side-chain positioning-based branching scheme. Beyond the speedups obtained in the new A*-CFN combination, this novel branching scheme enables a much faster enumeration of suboptimal sequences, far beyond what is reachable without it. Together with the immediate and important speedups provided by CFN technology, these developments directly benefit to all the algorithms that previously relied on the DEE/ A* combination inside Osprey* and make it possible to solve larger CPD problems with provable algorithms.
David Baker - One of the best experts on this subject based on the ideXlab platform.
-
High-Throughput Protein Design Reveals Quantitative Protein Stability Requirements
Biophysical Journal, 2017Co-Authors: G.j. Rocklin, Alex Ford, Tamuka M. Chidyausiku, Inna Goreshnik, S. Houliston, Cheryl H. Arrowsmith, David BakerAbstract:Despite two decades of progress in computational Protein Design, a large fraction of de novo Designed Proteins fail to fold as Designed due to our incomplete understanding of the principles governing Protein stability. These challenges persist in part because Design studies to date have been limited to testing at most tens of Proteins, which provides insufficient experimental data to identify causes of failure or to improve quantitative modeling. To overcome this, we combined computational Protein Design with next-generation oligo library synthesis and a high-throughput protease susceptibility assay to measure stability for thousands of unique Designed small Proteins and control sequences simultaneously. We identified hundreds of new stable Designs across four targeted topologies, and a subset of these were tested individually and found to be monomeric, fold cooperatively with high stability for their small size, and to form structures in solution closely matching their Designed structures. Iterating between high-throughput Design and testing enabled us to statistically examine hypotheses about Protein stability and led to improved Design success rates, including for topologies with no successes in the first attempt. The stabilities of the more than 10,000 Designed Proteins examined here provide the most detailed picture yet obtained of the stability requirements for small Proteins, and this approach promises to change computational Protein Design from low-throughput craft into data-driven engineering.
-
Computational Protein Design for Synthetic Biology
Synthetic Biology, 2013Co-Authors: Florian Richter, David BakerAbstract:The field of computational Protein Design (CPD) has made rapid progress over the last 10 years. Methods and algorithms to address a variety of Protein Design problems have been developed, and are beginning to be used for practical applications. In this chapter, first, a brief overview of the potential impact of CPD on synthetic biology and of commonly used general methods in CPD will be given. Next, several recent highlights in the fields of Designing Protein–Protein interactions and enzyme catalytic activity will be presented, together with an introduction to the specialized computational algorithms used to approach these problems. A brief look will be taken at thermostabilization and the Design of novel Protein structures through CPD, and finally, the relative advantages and disadvantages of CPD compared to other Protein engineering approaches will be examined.
-
RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design
PloS one, 2011Co-Authors: Po-ssu Huang, Florian Richter, Yih-en Andrew Ban, Ingemar André, Robert B. Vernon, William R. Schief, David BakerAbstract:We describe RosettaRemodel, a generalized framework for flexible Protein Design that provides a versatile and convenient interface to the Rosetta modeling suite. RosettaRemodel employs a unified interface, called a blueprint, which allows detailed control over many aspects of flexible backbone Protein Design calculations. RosettaRemodel allows the construction and elaboration of customized protocols for a wide range of Design problems ranging from loop insertion and deletion, disulfide engineering, domain assembly, loop remodeling, motif grafting, symmetrical units, to de novo structure modeling.
Bassil I. Dahiyat - One of the best experts on this subject based on the ideXlab platform.
-
Protein Design automation
Protein Science, 2008Co-Authors: Bassil I. Dahiyat, Stephen L. MayoAbstract:We have conceived and implemented a cyclical Protein Design strategy that couples theory, computation, and experimental testing. The combinatorially large number of possible sequences and the incomplete understanding of the factors that control Protein structure are the primary obstacles in Protein Design. Our Protein Design automation algorithm objectively predicts Protein sequences likely to achieve a desired fold. Using a rotamer description of the side chains, we implemented a fast discrete search algorithm based on the Dead-End Elimination Theorem to rapidly find the globally optimal sequence in its optimal geometry from the vast number of possible solutions. Rotamer sequences were scored for steric complementarity using a van der Waals potential. A Monte Carlo search was then executed, starting at the optimal sequence, in order to find other high-scoring sequences. As a test of the Design methodology, high-scoring sequences were found for the buried hydrophobic residues of a homodimeric coiled coil based on GCN4-p1. The corresponding peptides were synthesized and characterized by CD spectroscopy and size-exclusion chromatography. All peptides were dimeric and nearly 100% helical at 1 degree C, with melting temperatures ranging from 24 degrees C to 57 degrees C. A quantitative structure activity relation analysis was performed on the Designed peptides, and a significant correlation was found with surface area burial. Incorporation of a buried surface area potential in the scoring of sequences greatly improved the correlation between predicted and measured stabilities and demonstrated experimental feedback in a complete Design cycle.
-
In Silico Protein Design
Methods in molecular biology (Clifton N.J.), 2006Co-Authors: Bassil I. DahiyatAbstract:In the last 10 yr, efforts have begun to combine the goals and approaches of computational molecular Design and Protein sequence analysis to provide tools for the rational mutagenesis and functional modification of Proteins. These approaches use analysis of the three-dimensional structure of a Protein to guide the selection of appropriate amino acid sequences to create desired properties or functions. The convergence of low-cost, high-speed computers, a tremendous increase in Protein structure information, and a growing understanding of the forces that control Protein structure has resulted in dramatic advances in the ability to control Protein function and structure and to create the first truly artificial Proteins. Various academic software packages have been developed for in silico Protein Design. The methods for selecting the Protein structure, defining the portion to be Designed, and choosing the input parameters for the software are described in this chapter.
-
Protein Design automation : principles and practice
1998Co-Authors: Bassil I. DahiyatAbstract:We have conceived and implemented a cyclical Protein Design strategy that couples theory, computation and experimental testing. Our goal is an objective, quantitative Design algorithm that is based on the physical properties that determine Protein structure and stability and which is not limited to specific folds or motifs. Such a method should escape the lack of generality that has resulted from Design approaches based on system-specific heuristics and/or subjective considerations. A critical component of the development of our methods has been their experimental testing and validation. The use of a Design cycle coupling theory, computation, and experiment has improved our understanding of the physical chemistry governing Protein Design and hence enhanced the performance of the Design algorithm. Our Protein Design automation algorithm objectively predicts Protein sequences likely to achieve a desired fold by using a side-chain selection algorithm that explicitly and quantitatively considers specific side-chain to backbone and side-chain to side-chain interactions. Using a rotamer description of the side chains, we implemented a fast discrete search algorithm based on the Dead End Elimination Theorem to rapidly find the globally optimal sequence in its optimal geometry. We subdivided the sequence selection problem into regions of Proteins expected to be dominated by different factors: the tightly packed buried core, the solvent exposed surface, and the boundary between core and surface. We assessed the accuracy of a scoring function or combination of scoring functions by experimentally testing their sequence predictions. Improvements to the scoring function were derived from the experimental data and incorporated into the Design algorithm. In this manner, we developed a scoring function for the core of a Protein that considers packing interactions and hydrophobic solvation energy. In order to Design boundary residues effectively, the usually neglected effect of exposed hydrophobic surface area was addressed. Scoring functions for the Design of surface residues were developed that account for hydrogen bonding interactions and secondary structure propensities of amino acids. These potential functions were used to successfully reDesign several Proteins. The integration of these scoring functions was tested by Designing the sequence for an entire Protein and solving the NMR solution structure of the Designed Protein. This work reports the first successful automated Design and experimental validation of a novel sequence for an entire Protein.
-
Probing the role of packing specificity in Protein Design
Proceedings of the National Academy of Sciences of the United States of America, 1997Co-Authors: Bassil I. Dahiyat, Stephen L. MayoAbstract:By using a Protein-Design algorithm that quantitatively considers side-chain packing, the effect of specific steric constraints on Protein Design was assessed in the core of the streptococcal Protein G β1 domain. The strength of packing constraints used in the Design was varied, resulting in core sequences that reflected differing amounts of packing specificity. The structural flexibility and stability of several of the Designed Proteins were experimentally determined and showed a trend from well-ordered to highly mobile structures as the degree of packing specificity in the Design decreased. This trend both demonstrates that the inclusion of specific packing interactions is necessary for the Design of native-like Proteins and defines a useful range of packing specificity for the Design algorithm. In addition, an analysis of the modeled Protein structures suggested that penalizing for exposed hydrophobic surface area can improve Design performance.
Jianyang Zeng - One of the best experts on this subject based on the ideXlab platform.
-
Parallel Computational Protein Design
Methods in molecular biology (Clifton N.J.), 2016Co-Authors: Yichao Zhou, Bruce R. Donald, Jianyang ZengAbstract:Computational structure-based Protein Design (CSPD) is an important problem in computational biology, which aims to Design or improve a prescribed Protein function based on a Protein structure template. It provides a practical tool for real-world Protein engineering applications. A popular CSPD method that guarantees to find the global minimum energy solution (GMEC) is to combine both dead-end elimination (DEE) and A* tree search algorithms. However, in this framework, the A* search algorithm can run in exponential time in the worst case, which may become the computation bottleneck of large-scale computational Protein Design process. To address this issue, we extend and add a new module to the OSPREY program that was previously developed in the Donald lab (Gainza et al., Methods Enzymol 523:87, 2013) to implement a GPU-based massively parallel A* algorithm for improving Protein Design pipeline. By exploiting the modern GPU computational framework and optimizing the computation of the heuristic function for A* search, our new program, called gOSPREY, can provide up to four orders of magnitude speedups in large Protein Design cases with a small memory overhead comparing to the traditional A* search algorithm implementation, while still guaranteeing the optimality. In addition, gOSPREY can be configured to run in a bounded-memory mode to tackle the problems in which the conformation space is too large and the global optimal solution cannot be computed previously. Furthermore, the GPU-based A* algorithm implemented in the gOSPREY program can be combined with the state-of-the-art rotamer pruning algorithms such as iMinDEE (Gainza et al., PLoS Comput Biol 8:e1002335, 2012) and DEEPer (Hallen et al., Proteins 81:18-39, 2013) to also consider continuous backbone and side-chain flexibility.
-
Computational Protein Design Using AND/OR Branch-and-Bound Search.
Journal of computational biology : a journal of computational molecular cell biology, 2016Co-Authors: Yichao Zhou, Jianyang ZengAbstract:The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational Protein Design. In this paper, we propose a new Protein Design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the Design process. Tests on real Protein data show that our new Protein Design algorithm is able to solve many problems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.
-
cOSPREY: A Cloud-Based Distributed Algorithm for Large-Scale Computational Protein Design
Journal of computational biology : a journal of computational molecular cell biology, 2016Co-Authors: Yuchao Pan, Bruce R. Donald, Yuxi Dong, Jingtian Zhou, Mark A. Hallen, Jianyang ZengAbstract:Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational Protein Design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed Design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We Design cloud OSPREY (cOSPREY), an extension to a widely used Protein Design software OSPREY, to allow the original Design framework to scale to the commercial cloud infrastructures. We propose several novel Designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale Protein Design problems that have not been possible with previous approaches.
-
RECOMB - Computational Protein Design Using AND/OR Branch-and-Bound Search
Lecture Notes in Computer Science, 2015Co-Authors: Yichao Zhou, Jianyang ZengAbstract:The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational Protein Design. In this paper, we propose a new Protein Design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the Design process. Tests on real Protein data show that our new Protein Design algorithm is able to solve many problems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.
-
An efficient parallel algorithm for accelerating computational Protein Design.
Bioinformatics (Oxford England), 2014Co-Authors: Yichao Zhou, Bruce R. Donald, Jianyang ZengAbstract:Structure-based computational Protein Design (SCPR) is an important topic in Protein engineering. Under the assumption of a rigid backbone and a finite set of discrete conformations of side-chains, various methods have been proposed to address this problem. A popular method is to combine the dead-end elimination (DEE) and A* tree search algorithms, which provably finds the global minimum energy conformation (GMEC) solution. In this article, we improve the efficiency of computing A* heuristic functions for Protein Design and propose a variant of A* algorithm in which the search process can be performed on a single GPU in a massively parallel fashion. In addition, we make some efforts to address the memory exceeding problem in A* search. As a result, our enhancements can achieve a significant speedup of the A*-based Protein Design algorithm by four orders of magnitude on large-scale test data through pre-computation and parallelization, while still maintaining an acceptable memory overhead. We also show that our parallel A* search algorithm could be successfully combined with iMinDEE, a state-of-the-art DEE criterion, for rotamer pruning to further improve SCPR with the consideration of continuous side-chain flexibility. Our software is available and distributed open-source under the GNU Lesser General License Version 2.1 (GNU, February 1999). The source code can be downloaded from http://www.cs.duke.edu/donaldlab/osprey.php or http://iiis.tsinghua.edu.cn/∼compbio/software.html. © The Author 2014. Published by Oxford University Press.
