arXiv subjects

Shiyang Long

Publications and source records attributed to Shiyang Long.

5 recordsLinked to original sources

arXiv2019

A simple neural network implementation of generalized solvation free energy for assessment of protein structural models

Rapid and accurate assessment of protein structural models is essential for protein structure prediction and design. Great progress has been made in this regard, especially by recent development of ``knowledge-based'' potentials. Various machine learning based protein structural model quality assessment was also quite successful. However, performance of traditional ``physics-based'' potentials have not been as effective. Based on analysis of computational limitations of present solvation free energy formulation, which partially underlies unsatisfactory performance of ``physics-based'' potentials, we proposed a generalized sovation free energy (GSFE) framework. GSFE is intrinsically flexible for multi-scale treatments and is amenable for machine learning implementation. In this framework, each physical comprising unit of a complex molecular system has its own specific solvent environment. One distinctive feature of GSFE is that high order correlations within selected solvent environment might be captured through machine learning, in contrast to present empirical potentials (both ``knowledge-based'' and ``physics-based'') that are mainly based on pairwise interactions. Finally, we implemented a simple example of backbone and side-chain orientation based residue level protein GSFE with neural network, which was found to have competitive performance when compared with highly complex latest ``knowledge-based'' atomic potentials in distinguishing native structures from decoys.

physics.comp-ph↗

arXiv2016

Nonlinear backbone torsional pair correlations in proteins

Protein allostery requires dynamical structural correlations. Physical origin of which, however, remain elusive despite intensive studies during last two decades. Based on analysis of molecular dynamics (MD) simulation trajectories for ten proteins with different sizes and folds, we found that nonlinear backbone torsional pair (BTP) correlations, which are spatially more long-ranged and are mainly executed by loop residues, exist extensively in most analyzed proteins. Examination of torsional motion for correlated BTPs suggested that aharmonic torsional state transitions are essential for such non-linear correlations, which correspondingly occur on widely different and relatively longer time scales. In contrast, BTP correlations between backbone torsions in stable $α$ helices and $β$ strands are mainly linear and spatially more short-ranged, and are more likely to associate with intra-well torsional dynamics. Further analysis revealed that the direct cause of non-linear contributions are heterogeneous, and in extreme cases canceling, linear correlations associated with different torsional states of participating torsions. Therefore, torsional state transitions of participating torsions for a correlated BTP are only necessary but not sufficient condition for significant non-linear contributions. These findings implicate a general search strategy for novel allosteric modulation of protein activities. Meanwhile, it was suggested that ensemble averaged correlation calculation and static contact network analysis, while insightful, are not sufficient to elucidate mechanisms underlying allosteric signal transmission in general, dynamical and time scale resolved analysis are essential.

q-bio.BM↗

arXiv2016

Shot Range and High Order Correlations in Proteins

The main chain dihedral angles play an important role to decide the protein conformation. The native states of a protein can be regard as an ensemble of a lot of similar conformations, in different conformations the main chain dihedral angles vary in a certain range. Each dihedral angle value can be described as a distribution, but only using the distribution can't describe the real conformation space. The reason is that the dihedral angle has correlation with others, especially the neighbor dihedral angles in primary sequence. In our study we analysis extensive molecular dynamics (MD) simulation trajectories of eleven proteins with different sizes and folds, we found that in stable second structure the correlations only exist between the dihedrals near to each other in primary sequence, long range correlations are rare. But in unstable structures (loop) long range correlations exist. Further we observed some characteristics of the short range correlations in different second structures (α-helix, β-sheet) and we found that we can approximate good high order dihedral angle distribution good only use three order distribution in stable second structure which illustrates that high order correlations (over three order) is small in stable second structure.

q-bio.BM↗

arXiv2015

Configurational space discretization and free energy calculation in complex molecular systems

Trajectories provide dynamical information that is discarded in free energy calculations, for which we sought to design a scheme with the hope of saving cost for generating dynamical information. We first demonstrated that snapshots in a converged trajectory set are associated with implicit conformers that have invariant statistical weight distribution (ISWD). Based on the thought that infinite number of sets of implicit conformers with ISWD may be created through independent converged trajectory sets, we hypothesized that explicit conformers with ISWD may be constructed for complex molecular systems through systematic increase of conformer fineness, and tested the hypothesis in lipid molecule palmitoyloleoylphosphatidylcholine (POPC). Furthermore, when explicit conformers with ISWD were utilized as basic states to define conformational entropy, change of which between two given macrostates was found to be equivalent to change of free energy except a mere difference of a negative temperature factor, and change of enthalpy essentially cancels corresponding change of average intra-conformer entropy. These findings suggest that entropy enthalpy compensation is inherently a local phenomenon in configurational space. By implicitly taking advantage of entropy enthalpy compensation and forgoing all dynamical information, constructing explicit conformers with ISWD and counting thermally accessible number of which for interested end macrostates is likely to be an efficient and reliable alternative end point free energy calculation strategy.

physics.chem-ph↗

arXiv2015

Ideal gas behavior of rotamerically defined conformers in native globular proteins

Protein conformational transitions, which are essential for function, may be driven either by entropy or enthalpy when molecular systems comprising solute and solvent molecules are the focus. Revealing thermodynamic origin of a given molecular process is an important but difficult task, and general principles governing protein conformational distributions remain elusive. Here we demonstrate that when protein molecules are taken as thermodynamic systems and solvents being treated as the environment, conformational entropy is an excellent proxy for free energy and is sufficient to explain protein conformational distributions. Specifically, by defining each unique combination of side chain torsional state as a conformer, the population distribution (or free energy) on an arbitrarily given order parameter is approximately a linear function of conformational entropy. Additionally, span of various microscopic potential energy terms is observed to be highly correlated with both conformational entropy and free energy. Presently widely utilized free energy proxies, including minimum potential energy, average potential energy terms by themselves or in combination with vibrational entropy\cite, are found to correlate with free energy rather poorly. Therefore, our findings provide a fundamentally new theoretical base for development of significantly more reliable and efficient next generation computational tools, where the number of available conformers,rather than poential energy of microscopic configurations, is the central focus. We anticipate that many related research fields, including structure based drug design and discovery, protein design, docking and prediction of general intermolecular interactions involving proteins, are expected to benefit greatly.

q-bio.BM↗