Self‐organizing superimposition algorithm for conformational sampling

Zhu, Fangqiang; Agrafiotis, Dimitris

doi:10.1002/jcc.20622

Cited by 20 publications

(37 citation statements)

References 11 publications

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“…The need for energy minimization can be obviated if smaller rigid parts of each molecule are embedded in pre-minimized geometries, and rearranged at flexible regions such as rotatable bonds during embedding. Such a fragment-based embedding approach called Self-Organizing Superposition (SOS) has been developed in our group, 83 and can be combined with the method presented in this paper to produce a pharmacophore alignment that does not need to be minimized. SOS is nearly an order of magnitude faster than SPE, and produces much better geometries.…”

Section: Discussionmentioning

confidence: 98%

A self‐organizing algorithm for molecular alignment and pharmacophore development

Bandyopadhyay¹,

Agrafiotis²

2007

J Comput Chem

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We present a method for simultaneous three-dimensional (3D) structure generation and pharmacophorebased alignment using a self-organizing algorithm called Stochastic Proximity Embedding (SPE). Current flexible molecular alignment methods either start from a single low-energy structure for each molecule and tweak bonds or torsion angles, or choose from multiple conformations of each molecule. Methods that generate structures and align them iteratively (e.g., genetic algorithms) are often slow. In earlier work, we used SPE to generate good-quality 3D conformations by iteratively adjusting pairwise distances between atoms based on a set of geometric rules, and showed that it samples conformational space better and runs faster than earlier programs. In this work, we run SPE on the entire ensemble of molecules to be aligned. Additional information about which atoms or groups of atoms in each molecule correspond to points in the pharmacophore can come from an automatically generated hypothesis or be specified manually. We add distance terms to SPE to bring pharmacophore points from different molecules closer in space, and also to line up normal/direction vectors associated with these points. We also permit pharmacophore points to be constrained to lie near external coordinates from a binding site. The aligned 3D molecular structures are nearly correct if the pharmacophore hypothesis is chemically feasible; postprocessing by minimization of suitable distance and energy functions further improves the structures and weeds out infeasible hypotheses. The method can be used to test 3D pharmacophores for a diverse set of active ligands, starting from only a hypothesis about corresponding atoms or groups.

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Section: Discussionmentioning

confidence: 98%

A self‐organizing algorithm for molecular alignment and pharmacophore development

Bandyopadhyay¹,

Agrafiotis²

2007

J Comput Chem

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“…In the original SPE algorithm [11] conformer generation starts with pseudo-random atom positions, which works fine if one applies these rules to a ligand in vacuum. In contrast, in NeuroDock the ligands unfold within a protein pocket meaning they are additionally constrained.…”

Section: Socger: Structure Optimization By Stochastic Proximity Embedmentioning

confidence: 99%

“…[11] Here the target distribution is given by the spatial coordinates of the grid points in the binding pocket. portantly, in NeuroDock the connectivity of the neural gas neurons is identical to the topology of the ligand: The number of neurons n is equal to the number of ligand atoms a, and neurons n i and n j are considered to be adjacent if atom a i and a j are connected by a covalent bond.…”

Section: Initialization and Expansion Of The Neural Gasmentioning

confidence: 99%

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Automated Docking of Flexible Molecules Into Receptor Binding Sites by Ligand Self‐Organization In Situ

Klenner

Weisel

Reisen

et al. 2010

Molecular Informatics

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“…Alternatively, the loops in some proteins can be classified into structural families or canonical types, as in the antibody hypervariable regions (complementarity determining regions or CDRs) [14], [15], [16], [17], [18]. Such knowledge-based schemes utilize known structures or fragments of structures to efficiently sample loop conformations, [19], [20], [21], [22], [23], but are limited to sampling within the knowledge base. Using large databases of supersecondary structures [24], loops are successively aligned with templates based on parameters such as the stem region geometry, length, and sequence similarity [25], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

et al. 2011

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Unlike the core structural elements of a protein like regular secondary structure, template based modeling (TBM) has difficulty with loop regions due to their variability in sequence and structure as well as the sparse sampling from a limited number of homologous templates. We present a novel, knowledge-based method for loop sampling that leverages homologous torsion angle information to estimate a continuous joint backbone dihedral angle density at each loop position. The φ,ψ distributions are estimated via a Dirichlet process mixture of hidden Markov models (DPM-HMM). Models are quickly generated based on samples from these distributions and were enriched using an end-to-end distance filter. The performance of the DPM-HMM method was evaluated against a diverse test set in a leave-one-out approach. Candidates as low as 0.45 Å RMSD and with a worst case of 3.66 Å were produced. For the canonical loops like the immunoglobulin complementarity-determining regions (mean RMSD <2.0 Å), the DPM-HMM method performs as well or better than the best templates, demonstrating that our automated method recaptures these canonical loops without inclusion of any IgG specific terms or manual intervention. In cases with poor or few good templates (mean RMSD >7.0 Å), this sampling method produces a population of loop structures to around 3.66 Å for loops up to 17 residues. In a direct test of sampling to the Loopy algorithm, our method demonstrates the ability to sample nearer native structures for both the canonical CDRH1 and non-canonical CDRH3 loops. Lastly, in the realistic test conditions of the CASP9 experiment, successful application of DPM-HMM for 90 loops from 45 TBM targets shows the general applicability of our sampling method in loop modeling problem. These results demonstrate that our DPM-HMM produces an advantage by consistently sampling near native loop structure. The software used in this analysis is available for download at http://www.stat.tamu.edu/~dahl/software/cortorgles/.

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Self‐organizing superimposition algorithm for conformational sampling

Cited by 20 publications

References 11 publications

A self‐organizing algorithm for molecular alignment and pharmacophore development

A self‐organizing algorithm for molecular alignment and pharmacophore development

Automated Docking of Flexible Molecules Into Receptor Binding Sites by Ligand Self‐Organization In Situ

Near-Native Protein Loop Sampling Using Nonparametric Density Estimation Accommodating Sparcity

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