Improving computational efficiency and tractability of protein design using a piecemeal approach. A strategy for parallel and distributed protein design

Pitman, Derek J.; Schenkelberg, Christian D.; Huang, Yao-Ming; Teets, Frank D.; DiTursi, Daniel J.; Bystroff, Christopher

doi:10.1093/bioinformatics/btt735

Cited by 6 publications

(7 citation statements)

References 42 publications

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“…The sequence upon convergence of this third step is deemed the minimum energy configuration (MEC). Starting with the MEC, a library of similar low-energy sequences is generated by running MC with a gradually increasing temperature, a process called “simulated melting”, and keeping the desired number of unique sequences, typically 2000 …”

Section: Materials and Methodsmentioning

confidence: 99%

“…Computational protein design has been implemented in several laboratories and has produced, among many results, a hyper-thermophilic protein, two small molecule biosensor proteins, two novel enzymes, , and a novel protein fold . In this work, we used DEEdesign, which uses a parallel and distributed computational strategy . The energy function for DEEdesign was optimized for rotamer recovery in a large data set of high resolution structures …”

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confidence: 99%

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Toward Computationally Designed Self-Reporting Biosensors Using Leave-One-Out Green Fluorescent Protein

Huang

Banerjee²,

Crone³

et al. 2015

Biochemistry

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Leave-one-out green fluorescent protein (LOOn-GFP) is a circularly permuted and truncated GFP lacking the nth β-strand element. LOO7-GFP derived from the wild-type sequence (LOO7-WT) folds and reconstitutes fluorescence upon addition of β-strand 7 (S7) as an exogenous peptide. Computational protein design may be used to modify the sequence of LOO7-GFP to fit a different peptide sequence, while retaining the reconstitution activity. Here we present a computationally designed leave-one-out GFP in which wild-type strand 7 has been replaced by a 12-residue peptide (HA) from the H5 antigenic region of the Thailand strain of H5N1 influenza virus hemagglutinin. The DEEdesign software was used to generate a sequence library with mutations at 13 positions around the peptide, coding for approximately 3 × 105 sequence combinations. The library was coexpressed with the HA peptide in E. coli and colonies were screened for in vivo fluorescence. Glowing colonies were sequenced, and one (LOO7-HA4) with 7 mutations was purified and characterized. LOO7-HA4 folds, fluoresces in vivo and in vitro, and binds HA. However, binding results in a decrease in fluorescence instead of the expected increase, caused by the peptide-induced dissociation of a novel, glowing oligomeric complex instead of the reconstitution of the native structure. Efforts to improve binding and recover reconstitution using in vitro evolution produced colonies that glowed brighter and matured faster. Two of these were characterized. One lost all affinity for the HA peptide but glowed more brightly in the unbound oligomeric state. The other increased in affinity to the HA peptide but still did not reconstitute the fully folded state. Despite failing to fold completely, peptide binding by computational design was observed and was improved by directed evolution. The ratio of HA to S7 binding increased from 0.0 for the wild-type sequence (no binding) to 0.01 after computational design (weak binding) and to 0.48 (comparable binding) after in vitro evolution. The novel oligomeric state is composed of an open barrel.

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Section: Materials and Methodsmentioning

confidence: 99%

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confidence: 99%

Toward Computationally Designed Self-Reporting Biosensors Using Leave-One-Out Green Fluorescent Protein

Huang

Banerjee²,

Crone³

et al. 2015

Biochemistry

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“…The proposed algorithm relies on three main components: ( i ) splitting the conformational space of the interface side chains (ISC) into subspaces of overlapping ISC fragments (a “piecemeal” approach that can be traced all the way back to the Aufbau paradigm in conformational analysis), ( ii ) combination of geometric primitives (pre‐computed representations of ISC rotameric states) in constructing rotameric states from a subspace, and ( iii ) GPU acceleration of the exhaustive searches in the subspaces. Conformational splitting and the piecemeal approach have been previously successfully integrated with the dead‐end elimination and Monte Carlo methods . Employment of geometric primitives allows reduction of the allocated memory and the CPU/GPU data transfer overheads.…”

Section: Methodsmentioning

confidence: 99%

Computational Feasibility of an Exhaustive Search of Side‐Chain Conformations in Protein‐Protein Docking

2018

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Protein-protein docking procedures typically perform the global scan of the proteins relative positions, followed by the local refinement of the putative matches. Because of the size of the search space, the global scan is usually implemented as rigid-body search, using computationally inexpensive intermolecular energy approximations. An adequate refinement has to take into account structural flexibility. Since the refinement performs conformational search of the interacting proteins, it is extremely computationally challenging, given the enormous amount of the internal degrees of freedom. Different approaches limit the search space by restricting the search to the side chains, rotameric states, coarse-grained structure representation, principal normal modes, and so on. Still, even with the approximations, the refinement presents an extreme computational challenge due to the very large number of the remaining degrees of freedom. Given the complexity of the search space, the advantage of the exhaustive search is obvious. The obstacle to such search is computational feasibility. However, the growing computational power of modern computers, especially due to the increasing utilization of Graphics Processing Unit (GPU) with large amount of specialized computing cores, extends the ranges of applicability of the brute-force search methods. This proof-of-concept study demonstrates computational feasibility of an exhaustive search of side-chain conformations in protein pocking. The procedure, implemented on the GPU architecture, was used to generate the optimal conformations in a large representative set of protein-protein complexes. © 2018 Wiley Periodicals, Inc.

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“…Based on a previously designed artificial fold of TOP7 protein as target, 7 several thermophilic versions of the protein have been redesigned using a sliding window of tetrapeptide units where small regions are designed separately and then combined to a final output 22 . In the piecemeal approach, 23 the full‐length protein is divided into overlapped regions where the individual split regions are assigned to independent processors for sequence search without any inter‐process communication. The approach reduces the sequence search space complexity but may lead to conflict in the overlapped regions, which are further resolved using a second iteration of sequence search, and finally, the split regions are recombined and designed as one.…”

Section: Introductionmentioning

confidence: 99%

Modularity‐based parallel protein design algorithm with an implementation using shared memory programming

2021

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Given a target protein structure, the prime objective of protein design is to find amino acid sequences that will fold/acquire to the given three‐dimensional structure. The protein design problem belongs to the non‐deterministic polynomial‐time‐hard class as sequence search space increases exponentially with protein length. To ensure better search space exploration and faster convergence, we propose a protein modularity–based parallel protein design algorithm. The modular architecture of the protein structure is exploited by considering an intermediate structural organization between secondary structure and domain defined as protein unit (PU). Here, we have incorporated a divide‐and‐conquer approach where a protein is split into PUs and each PU region is explored in a parallel fashion. It has been further analyzed that our shared memory implementation of modularity‐based parallel sequence search leads to better search space exploration compared to the case of traditional full protein design. Sequence‐based analysis on design sequences depicts an average of 39.7% sequence similarity on the benchmark data set. Structure‐based comparison of the modeled structures of the design protein with the target structure exhibited an average root‐mean‐square deviation of 1.17 Å and an average template modeling score of 0.89. The selected modeled structures of the design protein sequences are validated using 100 ns molecular dynamics simulations where 80% of the proteins have shown better or similar stability to the respective target proteins. Our study informs that our modularity‐based protein design algorithm can be extended to protein interaction design as well.

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Improving computational efficiency and tractability of protein design using a piecemeal approach. A strategy for parallel and distributed protein design

Abstract: Programs for splitting protein design expressions are available at www.bioinfo.rpi.edu/tools/piecemeal.html CONTACT: bystrc@rpi.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Cited by 6 publications

References 42 publications

Toward Computationally Designed Self-Reporting Biosensors Using Leave-One-Out Green Fluorescent Protein

Toward Computationally Designed Self-Reporting Biosensors Using Leave-One-Out Green Fluorescent Protein

Computational Feasibility of an Exhaustive Search of Side‐Chain Conformations in Protein‐Protein Docking

Modularity‐based parallel protein design algorithm with an implementation using shared memory programming

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