Protein-DNA binding specificity: a grid-enabled computational approach applied to single and multiple protein assemblies

Dynamics and recognition within a protein-DNA complex: a molecular dynamics study of the SKN-1/DNA interaction

Molecular dynamics simulations of the Caenorhabditis elegans transcription factor SKN-1 bound to its cognate DNA site show that the protein–DNA interface undergoes significant dynamics on the microsecond timescale. A detailed analysis of the simulation shows that movements of two key arginine side chains between the major groove and the backbone of DNA generate distinct conformational sub-states that each recognize only part of the consensus binding sequence of SKN-1, while the experimentally observed binding specificity results from a time-averaged view of the dynamic recognition occurring within this complex.

Structure-based modeling of protein: DNA specificity

Protein:DNA interactions are essential to a range of processes that maintain and express the information encoded in the genome. Structural modeling is an approach that aims to understand these interactions at the physicochemical level. It has been proposed that structural modeling can lead to deeper understanding of the mechanisms of protein:DNA interactions, and that progress in this field can not only help to rationalize the observed specificities of DNA-binding proteins but also to allow researchers to engineer novel DNA site specificities. In this review we discuss recent developments in the structural description of protein:DNA interactions and specificity, as well as the challenges facing the field in the future.

Training-free atomistic prediction of nucleosome occupancy

Nucleosomes alter gene expression by preventing transcription factors from occupying binding sites along DNA. DNA methylation can affect nucleosome positioning and so alter gene expression epigenetically (without changing DNA sequence). Conventional methods to predict nucleosome occupancy are trained on observed DNA sequence patterns or known DNA oligonucleotide structures. They are statistical and lack the physics needed to predict subtle epigenetic changes due to DNA methylation. The training-free method presented here uses physical principles and state-of-the-art all-atom force fields to predict both nucleosome occupancy along genomic sequences as well as binding to known positioning sequences. Our method calculates the energy of both nucleosomal and linear DNA of the given sequence. Based on the DNA deformation energy, we accurately predict the in vitro occupancy profile observed experimentally for a 20,000-bp genomic region as well as the experimental locations of nucleosomes along 13 well-established positioning sequence elements. DNA with all C bases methylated at the 5 position shows less variation of nucleosome binding: Strong binding is weakened and weak binding is strengthened compared with normal DNA. Methylation also alters the preference of nucleosomes for some positioning sequences but not others.

Atomistic modeling of protein-DNA interaction specificity: progress and applications

An accurate, predictive understanding of protein-DNA binding specificity is crucial for the successful design and engineering of novel protein-DNA binding complexes. In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein-DNA binding specificity computationally. Although methods with limited structural flexibility have proven successful at recapitulating consensus binding sequences from wild-type complex structures, conformational flexibility is likely important for design and template-based modeling, where non-native conformations need to be sampled and accurately scored. A successful application of such computational modeling techniques in the construction of the TAL-DNA complex structure is discussed. With continued improvements in energy functions, solvation models, and conformational sampling, we are optimistic that reliable and large-scale protein-DNA binding prediction and engineering is a goal within reach.

Characterization and prediction of the binding site in DNA-binding proteins: improvement of accuracy by combining residue composition, evolutionary conservation and structural parameters

We present a set of four parameters that in combination can predict DNA-binding residues on protein structures to a high degree of accuracy. These are the number of evolutionary conserved residues (N_cons) and their spatial clustering (ρ_e), hydrogen bond donor capability (D_p) and residue propensity (R_p). We first used these parameters to characterize 130 interfaces in a set of 126 DNA-binding proteins (DBPs). The applicability of these parameters both individually and in combination, to distinguish the true binding region from the rest of the protein surface was then analyzed. R_p shows the best performance identifying the true interface with the top rank in 83% cases. Importantly, we also used the unbound-bound test cases of the protein–DNA docking benchmark to test the efficacy of our method. When applied to the unbound form of the DBPs, R_p can distinguish 86% cases. Finally, we have applied the SVM approach for recognizing the interface region using the above parameters along with the individual amino acid composition as attributes. The accuracy of prediction is 90.5% for the bound structures and 93.6% for the unbound form of the proteins.

Frontiers in molecular dynamics simulations of DNA

It has been known for decades that DNA is extremely flexible and polymorphic, but our knowledge of its accessible conformational space remains limited. Structural data, primarily from X-ray diffraction studies, is sparse in comparison to the manifold configurations possible, and direct experimental examinations of DNA's flexibility still suffer from many limitations. In the face of these shortcomings, molecular dynamics (MD) is now an essential tool in the study of DNA. It affords detailed structural and dynamical insights, which explains its recent transition from a small number of highly specialized laboratories to a large variety of groups dealing with challenging biological problems. MD is now making an irreversible journey to the mainstream of research in biology, with the attendant opportunities and challenges. But given the speed with which MD studies of DNA have spread, the roots remain somewhat shallow: in many cases, there is a lack of deep knowledge about the foundations, strengths, and limits of the technique. In this Account, we discuss how MD has become the most important source of structural and flexibility data on DNA, focusing on advances since 2007 of atomistic MD in the description of DNA under near-physiological conditions and highlighting the possibilities and shortcomings of the technique. The evolution in the field over the past four years is a prelude to the ongoing revolution. The technique has gained in robustness and predictive power, which when coupled with the spectacular improvements in software and hardware has enabled the tackling of systems of increasing complexity. Simulation times of microseconds have now been achieved, with even longer times when specialized hardware is used. As a result, we have seen the first real-time simulation of large conformational transitions, including folding and unfolding of short DNA duplexes. Noteworthy advances have also been made in the study of DNA-ligand interactions, and we predict that a global thermodynamic and kinetic picture of the binding landscape of DNA will become available in a few years. MD will become a crucial tool in areas such as biomolecular engineering and synthetic biology. MD has also been shown to be an excellent source of parameters for mesoscopic models of DNA flexibility. Such models can be refined through atomistic MD simulations on small duplexes and then applied to the study of entire chromosomes. Recent evidence suggests that MD-derived elastic models can successfully predict the position of regulatory regions in DNA and can help advance our understanding of nucleosome positioning and chromatin plasticity. If these results are confirmed, MD simulations can become the ultimate tool to decipher a physical code that can contribute to gene regulation. We are entering the golden age of MD simulations of DNA. Undoubtedly, the expectations are high, but the challenges are also enormous. These include the need for more accurate potential energy functionals and for longer and more complex simulations in more realistic systems. The joint research effort of several groups will be crucial for adapting the technique to the requirements of the coming decade.

What does physics have to do with cancer?

Large-scale cancer genomics, proteomics and RNA-sequencing efforts are currently mapping in fine detail the genetic and biochemical alterations that occur in cancer. However, it is becoming clear that it is difficult to integrate and interpret these data and to translate them into treatments. This difficulty is compounded by the recognition that cancer cells evolve, and that initiation, progression and metastasis are influenced by a wide variety of factors. To help tackle this challenge, the US National Cancer Institute Physical Sciences-Oncology Centers initiative is bringing together physicists, cancer biologists, chemists, mathematicians and engineers. How are we beginning to address cancer from the perspective of the physical sciences?

Towards computational specificity screening of DNA-binding proteins

DNA-binding proteins are key players in the regulation of gene expression and, hence, are essential for cell function. Chimeric proteins composed of DNA-binding domains and DNA modifying domains allow for precise genome manipulation. A key prerequisite is the specific recognition of a particular nucleotide sequence. Here, we quantitatively assess the binding affinity of DNA-binding proteins by molecular dynamics-based alchemical free energy simulations. A computational framework was developed to automatically set up in silico screening assays and estimate free energy differences using two independent procedures, based on equilibrium and non-equlibrium transformation pathways. The influence of simulation times on the accuracy of both procedures is presented. The binding specificity of a zinc-finger transcription factor to several sequences is calculated, and agreement with experimental data is shown. Finally we propose an in silico screening strategy aiming at the derivation of full specificity profiles for DNA-binding proteins.

Modeling DNA-bending in the nucleosome: role of AA periodicity

This paper uses atomistic molecular mechanics within the framework of the JUMNA model to study the bending properties of DNA segments, with emphasis on understanding the role of the 10 bp periodicity associated with AA repeats that has been found to dominate in nucleosomal DNA. The calculations impose a bending potential on 18 bp segments that is consistent with nucleosome structures (i.e., radius of curvature of 4.1 nm), and then determine the energies of the minimum energy structures for different values of the rotational register (a measure of the direction of bending of the DNA) subject to forces derived from the Amber force field (parm99bsc0). The results show that sequences that contain the 10 bp repeats but are otherwise random have a narrow distribution of rotational register values that minimize the energy such that it is possible to combine several minimized structures to give the 147 bp nearly planar loop structure of the nucleosome. The rotational register values that lead to minimum bending energy with 10 bp AA repeats have a narrower minor groove, which points toward the histone interior at the positions of the AA repeats, which is a result that matches the experiments. The calculations also show that these sequences have a relatively flat potential energy landscape for bending to a 4.1 nm radius of curvature. Random sequences that do not have the 10 bp AA repeats have less stable bent structures, and a flat rotational register distribution, such that low energy nearly planar loops are less likely.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.