A deterministic algorithm for constrained enumeration of transmembrane protein folds

Multiple similarly effective solutions exist for biomedical feature selection and classification problems

Binary classification is a widely employed problem to facilitate the decisions on various biomedical big data questions, such as clinical drug trials between treated participants and controls, and genome-wide association studies (GWASs) between participants with or without a phenotype. A machine learning model is trained for this purpose by optimizing the power of discriminating samples from two groups. However, most of the classification algorithms tend to generate one locally optimal solution according to the input dataset and the mathematical presumptions of the dataset. Here we demonstrated from the aspects of both disease classification and feature selection that multiple different solutions may have similar classification performances. So the existing machine learning algorithms may have ignored a horde of fishes by catching only a good one. Since most of the existing machine learning algorithms generate a solution by optimizing a mathematical goal, it may be essential for understanding the biological mechanisms for the investigated classification question, by considering both the generated solution and the ignored ones.

Efficient and Unbiased Sampling of Biomolecular Systems in the Canonical Ensemble: A Review of Self-Guided Langevin Dynamics

This review provides a comprehensive description of the self-guided Langevin dynamics (SGLD) and the self-guided molecular dynamics (SGMD) methods and their applications. Example systems are included to provide guidance on optimal application of these methods in simulation studies. SGMD/SGLD has enhanced ability to overcome energy barriers and accelerate rare events to affordable time scales. It has been demonstrated that with moderate parameters, SGLD can routinely cross energy barriers of 20 kT at a rate that molecular dynamics (MD) or Langevin dynamics (LD) crosses 10 kT barriers. The core of these methods is the use of local averages of forces and momenta in a direct manner that can preserve the canonical ensemble. The use of such local averages results in methods where low frequency motion "borrows" energy from high frequency degrees of freedom when a barrier is approached and then returns that excess energy after a barrier is crossed. This self-guiding effect also results in an accelerated diffusion to enhance conformational sampling efficiency. The resulting ensemble with SGLD deviates in a small way from the canonical ensemble, and that deviation can be corrected with either an on-the-fly or a post processing reweighting procedure that provides an excellent canonical ensemble for systems with a limited number of accelerated degrees of freedom. Since reweighting procedures are generally not size extensive, a newer method, SGLDfp, uses local averages of both momenta and forces to preserve the ensemble without reweighting. The SGLDfp approach is size extensive and can be used to accelerate low frequency motion in large systems, or in systems with explicit solvent where solvent diffusion is also to be enhanced. Since these methods are direct and straightforward, they can be used in conjunction with many other sampling methods or free energy methods by simply replacing the integration of degrees of freedom that are normally sampled by MD or LD.

[Prediction of the spatial structure of proteins: emphasis on membrane targets]

Knowledge of the spatial structure of proteins is a prerequisite for both awareness of their functional mechanisms and the framework for rational drug discovery and design. Meanwhile, direct structural determination is often hampered or impractical due to the complexity, expensiveness, and limited capabilities of experimental techniques. These issues are especially pronounced for integral membrane proteins. On numerous occasions, the theoretical prediction of protein structures may facilitate the process by exploiting physical or empirical principles. This paper surveys modern techniques for the prediction of the spatial structure of proteins using computer algorithms, and the main emphasis is placed on the most "complex" targets - membrane proteins (MPs). The first part of the review describes de novo methods based on empirical physical principles; in the second part, a comparative modeling philosophy, which accounts for the structure of related proteins, is described. Special focus is made regarding pharmacologically relevant classes of G-coupled receptors, receptor tyrosine ki-nases, and other MPs. Algorithms for the assessment of the models quality and potential fields of application of computer models are discussed.

Spin-labeled analogs of CMP-NeuAc as NMR probes of the alpha-2,6-sialyltransferase ST6Gal I

Structural data on mammalian proteins are often difficult to obtain by conventional NMR approaches because of an inability to produce samples with uniform isotope labeling in bacterial expression hosts. Proteins with sparse isotope labels can be produced in eukaryotic hosts by using isotope-labeled forms of specific amino acids, but structural analysis then requires information from experiments other than nuclear Overhauser effects. One source of alternate structural information is distance-dependent perturbation of spin relaxation times by nitroxide spin-labeled analogs of natural protein ligands. Here, we introduce spin-labeled analogs of sugar nucleotide donors for sialyltransferases, specifically, CMP-TEMPO (CMP-4-O-[2,2,6,6-tetramethylpiperidine-1-oxyl]) and CMP-4carboxyTEMPO (CMP-4-O-[4-carboxy-2,2,6,6-tetramethylpiperidinine-1-oxyl]). An ability to identify resonances from active site residues and produce distance constraints is illustrated on a (15)N phenylalanine-labeled version of the structurally uncharacterized, alpha-2,6-linked sialyltransferase, ST6Gal I.

Prediction of beta-strand packing interactions using the signature product

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].