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Biomedical subjects

Guang Song

Publications and source records attributed to Guang Song.

7 recordsLinked to original sources
PubMed2006

An enhanced elastic network model to represent the motions of domain-swapped proteins.

Domain swapping is a process where two (or more) protein molecules form a dimer (or higher oligomer) by exchanging an identical domain. In this article, based on the observation that domains are rigid and hinge loops are highly flexible, we propose a new Elastic Network Model, domain-ENM, for domain-swapped proteins. In this model, the rigidity of domains is taken into account by using a larger spring constant for intradomain contacts. The large-scale transition of domain swapping is then novelly decomposed into the relative motion between the rigid domains (only 6 degrees of freedom) plus the internal fluctuations of each domain. Consequently, this approach has the potential to produce much more meaningful transition pathways than other simulation approaches that try to find pathways in a search space of large numbers of dimensions. In this article, we also propose a new way to define the overlap measure. Past approaches used an inappropriate comparison of the large-scale conformation displacement against the computed infinitesimal motions of modes. Here, we propose an infinitesimal version of the large-scale conformation change and then compare it with the modes of motions. As a result, we obtain much better overlap values. Using this new overlap definition, we are also able for the first time to give a clear, intuitive explanation why "open" forms tend to produce better overlap values than "closed" forms with traditional ENMs. Finally, as an application, we present a simple approach to show how domain-ENM can be used to generated transition pathways for domain-swapped proteins.

Animals↗
PubMed2005

Protein folding by motion planning.

We investigate a novel approach for studying protein folding that has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs). Our focus is to study issues related to the folding process, such as the formation of secondary and tertiary structures, assuming we know the native fold. A feature of our PRM-based framework is that the large sets of folding pathways in the roadmaps it produces, in just a few hours on a desktop PC, provide global information about the protein's energy landscape. This is an advantage over other simulation methods such as molecular dynamics or Monte Carlo methods which require more computation and produce only a single trajectory in each run. In our initial studies, we obtained encouraging results for several small proteins. In this paper, we investigate more sophisticated techniques for analyzing the folding pathways in our roadmaps. In addition to more formally revalidating our previous results, we present a case study showing that our technique captures known folding differences between the structurally similar proteins G and L.

Animals↗
PubMed2003

Using motion planning to map protein folding landscapes and analyze folding kinetics of known native structures.

We investigate a novel approach for studying the kinetics of protein folding. Our framework has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMs) that have been applied in many diverse fields with great success. In our previous work, we presented our PRM-based technique and obtained encouraging results studying protein folding pathways for several small proteins. In this paper, we describe how our motion planning framework can be used to study protein folding kinetics. In particular, we present a refined version of our PRM-based framework and describe how it can be used to produce potential energy landscapes, free energy landscapes, and many folding pathways all from a single roadmap which is computed in a few hours on a desktop PC. Results are presented for 14 proteins. Our ability to produce large sets of unrelated folding pathways may potentially provide crucial insight into some aspects of folding kinetics, such as proteins that exhibit both two-state and three-state kinetics that are not captured by other theoretical techniques.

Computational Biology↗
PubMed2003

A path planning-based study of protein folding with a case study of hairpin formation in protein G and L.

We investigate a novel approach for studying protein folding that has evolved from robotics motion planning techniques called probabilistic roadmap methods (PRMS). Our focus is to study issues related to the folding process, such as the formation of secondary and tertiary structure, assuming we know the native fold. A feature of our PRM-based framework is that the large sets of folding pathways in the roadmaps it produces, in a few hours on a desktop PC, provide global information about the protein's energy landscape. This is an advantage over other simulation methods such as molecular dynamics or Monte Carlo methods which require more computation and produce only a single trajectory in each run. In our initial studies, we obtained encouraging results for several small proteins. In this paper, we investigate more sophisticated techniques for analyzing the folding pathways in our roadmaps. In addition to more formally revalidating our previous results, we present a case study showing our technique captures known folding differences between the structurally similar proteins G and L.

Bacterial Proteins↗
PubMed2002

Using motion planning to study protein folding pathways.

We present a framework for studying protein folding pathways and potential landscapes which is based on techniques recently developed in the robotics motion planning community. Our focus in this work is to study the protein folding mechanism assuming we know the native fold. That is, instead of performing fold prediction, we aim to study issues related to the folding process, such as the formation of secondary and tertiary structure, and the dependence of the folding pathway on the initial denatured conformation. Our work uses probabilistic roadmap (PRM) motion planning techniques which have proven successful for problems involving high-dimensional configuration spaces. A strength of these methods is their efficiency in rapidly covering the planning space without becoming trapped in local minima. We have applied our PRM technique to several small proteins (~60 residues) and validated the pathways computed by comparing the secondary structure formation order on our paths to known hydrogen exchange experimental results. An advantage of the PRM framework over other simulation methods is that it enables one to easily and efficiently compute folding pathways from any denatured starting state to the (known) native fold. This aspect makes our approach ideal for studying global properties of the protein's potential landscape, most of which are difficult to simulate and study with other methods. For example, in the proteins we study, the folding pathways starting from different denatured states sometimes share common portions when they are close to the native fold, and moreover, the formation order of the secondary structure appears largely independent of the starting denatured conformation. Another feature of our technique is that the distribution of the sampled conformations is correlated with the formation of secondary structure and, in particular, appears to differentiate situations in which secondary structure clearly forms first and those in which the tertiary structure is obtained more directly. Overall, our results applying PRM techniques are very encouraging and indicate the promise of our approach for studying proteins for which experimental results are not available.

Computational Biology↗
PubMedDate not supplied

Using motion planning to study RNA folding kinetics.

We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. A key feature of our method is that it provides a sparse map that captures the main features of the energy landscape which can be analyzed to compute folding kinetics. Our method is based on probabilistic roadmap motion planners that we have previously successfully applied to protein folding. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with results of other existing methods.

Computational Biology↗