The fundamental building blocks of living cells are biomolecules which provide the bedrock of life and now pose a research frontier for theoretical physics. All life forms can be considered to be self-organized systems assembled from these building blocks. Even though one can subdivide life into substructures like cell organelles, the entire cell, tissues, multi-cellular organisms, and even societies, biomolecules make their basic role felt across the entire hierarchy of biological order: pheromones link male and females in disperse societies, drugs treat diseases in individuals, cells are guided in their development through hormones, and every process in cells is directly linked to biomolecules. So naturally, the quest for a theory of living systems starts with the fundamental building blocks – biomolecules. But much as the physics of innate matter has derived its successes from the recognition of scales that subdivide the innate world, e.g., quarks, nuclei, molecules, so life appears to be governed by its own system of scales that provide a staircase to the ultimate goal of understanding human life. We provide an overview of the particular role that theoretical physicists/materials scientists have played and future prospects in studying life from the biomolecular perspective.
Here we describe some of the manifold roles that theory has contributed in the field of biomolecules.
Fifty years ago models of both the
double helix structure of DNA and also the first three dimensional protein
structure were determined in the Cavendish laboratory of the Physics Department
The versatile roles of biomolecules in living cells have become known with the advent of crystallography that extended the work at the Cavendish. This was first accomplished by John Kendrew and Max Perutz (Nobel Prizes in 1962) who solved the atomic level structure of the oxygen storing proteins myoglobin and hemoglobin. The role of X-ray crystallography leading to this accomplishment is well known. Less well known is the role of physics, in particular, theoretical physics in the subsequent revolutionary development of structural biology that has produced to date over ten thousand three dimensional protein structures.
Figure 1. The study of biomolecules was initiated with the double stranded structure of DNA shown on the left and the original ball and stick model of myoglobin on the right ( http://nobelprize.org/chemistry/laureates/1962/kendrew-lecture.pdf); the first 3D structure of a protein to be determined
This accomplishment was facilitated through advances in computing that permitted the extension of mathematical algorithms derived from theoretical physics of crystals and X-ray scattering to complex crystals. From the solution of the phase problem (Nobel prize in Chemistry 1985) to the application of NMR spectroscopy (Nobel Prizes 1991, 2002), to the use of refinement methods in software packages used widely by structural biologists, concepts and algorithms developed by theoretical physicists have been of fundamental and practical importance.
Naturally, biomolecules obey the same laws of physics as any other material system. However, the large number of degrees of freedom, and absence of symmetry, prohibits a complete quantum mechanical description of their dynamics. As a viable alternative, classical molecular dynamics (MD) simulations emerged as a standard approach for studying biomolecular systems. First used to investigate the condensed phase of simple mono-atomic systems, currently, as a result of spectacular developments in computational hardware, software and methodology, MD is routinely used to simulate biomolecular complexes composed of many thousands of atoms, for tens of nanoseconds. The resulting phase space trajectories are analyzed by advanced statistical mechanics methods contributed by many leading theoretical physicists.
Presently, theoretical investigations of life at the molecular level are experiencing a dramatic renewal due to the explosion in the amount of experimental data that is available at a level of precision unimaginable even few years ago. Here we provide examples from some of the most exciting research areas.
With the near completion of a number of genomes, and the subsequent extraction of genes and hence protein sequences, attention has been focused on the determination of the three dimensional structure of proteins. This is the next logical step in the chain that leads up to biomolecular function and understanding of biological processes at the supra-molecular level. Proteins are important targets for drug design and this is greatly facilitated if the three dimensional structure is known. The Protein Structure Initiative (PSI – www.structuralgenomics.org/ ) funded by the NIH is due to move into the high throughput structure determination phase in 2005. The stated aim is to produce ~ five thousand representative three-dimensional structures in 5 years, out of ~ million protein structures. Simulation will play an important continuing role in structure refinement on both the X-ray crystallographic and NMR data. The still-missing structures will have to be determined by structure prediction methods, modeling, and simulation. Theory will advise crystallographers, NMR spectroscopists, and electron microscopists in the optimal selection of structures to be solved and to render the missing structures accessible to homology modeling (looking at the folding of similar sequences). But likely, ab initio approaches to protein folding will be required to supplement homology modeling for finding new folds. The ultimate goal would be to have high confidence in structures folded computationally, as it will never be possible to determine the three dimensional structures of all proteins experimentally.
A still unsolved problem in molecular biology is to decipher how an ensemble of unfolded structures navigates rapidly through the rough free energy landscape to the unique folded structure of the native state. In the last ten years some progress has been made which has been spurred by theoretical ideas. Simulation approaches can describe protein folding in principle in great detail, but is restricted to time scales of microseconds at best. Experimentalists and theoreticians joined forces to identify fast folding proteins that open up the opportunity for computational modeling and experimental tests. Indeed, the interplay between theory and experiment is just beginning to produce a detailed characterization of the pathways, mechanisms, and transition states in the folding process. Currently, theoreticians and experimentalists are beginning to focus on understanding the folding mechanisms of larger proteins and also on the assembly of multi-protein units.
Single stranded RNA is composed of a sequence of bases which can pair up with others on the strand. Given a particular sequence and pairing energies, finding the optimal pairings (the so-called secondary structure) is an interesting and biologically important problem. Bio-statisticians had already identified a fast algorithm (polynomial in length) for finding the optimal pairing. Statistical mechanics contributions include showing that this algorithm had much in common with the summation of Hartree diagrams in many-body physics. Once more, this connection provided important insights and novel perspectives to both biology and statistical physics such as calculating force-extension curves for imputed RNA sequences.
The generation, transformation, and sustenance of mechanical forces are essential for the motion, energetics, internal transport, and stability of cells. The study of molecular motors and proteins designed for mechanical function is one of the most exciting fields of modern biophysics, guided by non-equilibrium statistical mechanics and modeling linked closely to the analysis of single molecule experiments like atomic force microscopy. Theoretical physics has rationalized the function and design of motors; while computer simulation and advanced analysis methods, e.g., using a new free energy - work relationship, have identified and explained the force-bearing and force-sensing parts of proteins in muscle and hearing. Theory has also explained the manifold adhesion properties of cells and is considered an important guidepost in planning and analyzing experiments that unravel the functioning of the many mechanical functions in cells.
Figure 2. Schematic of thermal ratchets possibly
related to molecular motors. The lateral bolts in frame (b) allow the
ratchet to move to the right. [P. Nelson, “Biological Physics” (W. H.
Figure 2. Schematic of thermal ratchets possibly
related to molecular motors. The lateral bolts in frame (b) allow the
ratchet to move to the right. [P. Nelson, “Biological Physics” (W. H.
All life on earth depends on photosynthesis, the fundamental biological process by which the energy of sun light irradiating earth is converted into an electrochemical potential, ultimately resulting in the generation of ATP, the fuel of biological cells. Recently, atomic resolution structures of several photosynthetic systems have been solved, revealing a hierarchical, modular architecture of various protein complexes that hold several hundred chromophores, chlorophylls and carotenoids, in place. This opens the exciting opportunity to understand the quantum mechanics involved in the key electronic processes in photosynthetic cells. The quantum processes in the photosynthetic membrane are exemplary for the quantum processes that arise in many fundamental biological reactions, i.e., the primary process in vision, photo-activation of biochemical reactions, electron and proton transfer reactions, or the rearrangement of covalent bonds and conformational changes involved in enzymatic reactions. The challenge to theoretical physicists derives from the need to describe complex electronic systems that are subject to static and dynamic thermal disorder. Artificial photosynthetic compounds hold out the possibility of producing electrical energy in significant quantities.
The quantum processes in the photosynthetic membrane are also exemplary for quantum processes that arise in many fundamental biological processes, i.e., photoactivation of biochemical reactions, electron and proton transfer reactions. Quantum mechanical methods have been used to identify the nature of the electronic excitations and the mechanism of excitation transfer between the pigments in the photosynthetic system of purple bacteria. The resulting description has succeeded in predicting observed spectral and kinetic properties nearly quantitatively, thereby demonstrating that complex molecular machinery like the photosynthetic apparatus involving hundreds of pigments and tens of thousands of atoms can be described with accuracy through the application of the laws of physics.
Recently, progress has been made in incorporating evolutionary aspects into theoretical studies of biomolecules. The central idea was that of hetero-polymer sequence design, which refers to the (more or less sophisticated) computational or experimental procedure which mimics the role of evolution in selecting sequences, but does not require evolutionary time scales. This allowed theorists to start, for the first time, examining many different properties, first and foremost folding, of the selected molecules in such a way that the selection criteria and mechanisms are fully under control. This approach was recently replicated in a real biological experiment, in which a de novo foldable protein was produced, with a novel fold. The next important offspring of the sequence design idea was the concept of protein structure designability. In turn, this has led to the systematic study of the network of protein folds, which is currently yielding spectacular new insights into the history of protein evolution.
With the development of picosecond time-resolved X-ray crystallography, molecular dynamics simulations can now be compared to an experiment with comparable resolution in space and time. Such a comparison shows that MD simulations reproduce with remarkable fidelity the direction, amplitude, and time scale of protein motions over the nanosecond window of the simulations, together with concomitant ligand motions and changes in the hydration structure.
Basic functions of cell-like energy transformation and protein synthesis utilize very large biomolecular assemblies involving ten of thousands to millions of atoms even in the most primitive organisms. Advances in crystallography and electron microscopy have succeeded in resolving the structures of essential molecular machines within cells. This poses great challenges for theory and modeling due to their size and multifaceted features from control switches, to large scale motion, to chemical synthesis, to force transmission. The machines need to be understood as a whole since their efficient design does not permit reduction in size, even conceptually. A prime example is the ribosome, a multi-million atom complex of RNA and protein molecules that reads genetic messages (m-RNA) and transcribes them into polypeptide strands (proteins) through faithful reading of the genetic message, recruitment of amino acid building blocks, and synthesis of peptide bonds in synchronous motion of all components.
The machines and sensors of living cells are of nanometer size and, hence, it is no surprise that the emerging technology of nanodevices provides an unsurpassed opportunity for applications that intervene in cellular processes or mimic such processes. Examples of nanodevices are DNA chips that monitor the expression level of genes in a cell, nano-fluidic devices that can handle extremely small samples for testing, or sensors that can be implanted in the body. The role of theory in the development of such devices arises in various ways, not the least through computer simulations that reached such a high level of veracity that computers play the role of microscopes imaging nanoscale structures and processes, and this meeting an extremely urgent societal demand. An example of this role is the development of nanopores manufactured in advanced, nanoscale silicon technology for single molecule electrical recording with the prospect of serving for highly cost-effective DNA sequencing. Theory and modeling are guiding the design of the nanopores and are essential for the interpretation of electrical recordings. Another example is the development of so-called nano-disks of lipid bilayers stabilized through a protein belt on its periphery. Again, simulations, tested by atomic force microscopy, image the disks and assist in designing optimal protein "belts" and use of the disks for single protein measurements or for drug delivery. Further into the future are bio-nanodevices that manipulate specific structures in living cells and assist in gene therapy.
Biomolecules tend to be large compared to classical bit structures (such as silicon devices) or qubits (such as trapped ions) and as such are often overlooked when searching for the new computational substrates. However, owing to their self-assembly properties it is worth exploring whether we can use biomolecules for information storage and manipulation. After all, the DNA/RNA system is arguably the most robust naturally occurring complex information storage and transfer system, while the human brain is the most advanced information manipulation device. Much of the DNA/RNA system's success depends on self-assembly and redundancy. If we can mimic these processes in man-made devices, we can hope to exceed both the speed and capacity of the naturally occurring systems as we learn from evolution-driven design faults and successes and skip millions of years of natural selection. The case for bioqubits is more difficult to make because of the problem of short decoherence times. There is little theoretical work on this subject beyond crude order-of-magnitude estimates of the decoherence time for a protein. In addition, it is not clear that one has to use entire protein molecules as qubits. It is possible that subparts, such as the electric dipole moment vector orientation (which depends crucially on only a few electrons in proteins such as tubulin), could be used as the physical manifestation of a qubit. Other advantages in making "living" bio(quantum) computers include ready energy sources (e.g. sunlight or 'computer food' e.g. sugar water), the possibility of self-repair (healing), and the possibility of self-advancement.
The functional relevance of features present in multiple biomolecules can sometimes be elucidated through physical studies of simpler model systems. Understanding the biological role can in turn provide the insights needed for the design and development of new functional materials and nanodevices. Simulation studies of carbon nanotubes in water, for instance, helped clarify the role of the relatively nonpolar pores frequently found in proteins conducting water and protons, where water confined into narrow pores was shown to form one-dimensionally ordered chains. Under osmotic or pressure gradients, water moves with little friction through nonpolar pores, at rates similar to those simulated for water transport in aquaporin channels in biological membranes. Moreover, protons diffuse more than one order of magnitude faster along the 1D water chains in nanopores than in the bulk fluid. Together with the phase-transition like polarity-induced emptying and filling transitions observed in the simulations, these results suggest polarity-controlled proton wires, as in cytochrome P450 and bacteriorhodopsin, and diode-like unidirectional proton conduction as a key element in the mitochondrial proton pump cytochrome c oxidase. Recent experiments and simulations suggest that the emptying transitions seen in carbon nanotubes are also relevant for ion channel gating. The insights gained from the studies of water-nanotube systems go beyond biology, and are relevant, e.g., for fuel cell design, reverse osmosis, and nanofluidics devices. Further types of biologically inspired materials, obtained by self-assembly are discussed under Supramolecular Assemblies.
Recently, the long-standing problem of which biophysical mechanism underlies the physiological compass of birds has been solved, dramatically demonstrating that molecular level calculations can be used to predict animal behavior. Physical theory yields accurate predictions about the effects of weak magnetic fields through different possible magneto-receptor molecules, in particular that very weak oscillating fields in the radiofrequency range will disrupt a compass based on a chemical reaction, but not a compass based on magnetite particles. This prediction has been tested in an experiment, whose results indicate that a chemical mechanism first postulated by theory underlies the magnetic compass.
Biological functions that involve electronic processes, e.g., light harvesting in photosynthesis or light reception in vision, are quantum mechanical in nature. Since living systems exist and function at physiological temperatures, their behavior is strongly affected by disorder (both dynamic and static) and, therefore, can not be described by ordinary (T=0) quantum mechanics. The development of novel stochastic quantum mechanics methods, capable of accounting for the effect of strong disorder in bioelectronic systems, is a major challenge in the field of biological physics. Such new methods would benefit life scientists in analyzing their experimental data and in developing a better understanding of the mechanisms underlying biological functions. For example, the effect of dynamic disorder on the absorption spectrum of chromophore aggregates can be calculated in the framework of the
Figure 5a. Molecular structure of the proton wire in gramicidin. [R. Pomes and B. Roux, Biophys. J. 82, 2304-2316 (2002)].
Figure 5b. Free-energy profile of a proton moving along a proton wire [Y. Y Sham, I Muegge, and A. Warshel, Proteins 36, 484-500 (1999)].
All biology takes place around room temperature and so it is essential to include entropic effects and hence monitor the free energy rather than the energy. Accurate calculation of the free energy difference between two distinct conformational states of a biomolecular system continues to represent one of the biggest challenges in computational biological physics. The standard method to determine the change of the free energy (also known as potential of mean force - PMF) along a reaction coordinate is the so-called umbrella sampling method. A recently derived equality, which relates the change in free energy to the statistical average of the irreversible work done by the external driving force in the considered transformation, has resulted in the development of an exciting new method of calculating the PMF through a series of steered MD simulations. This new method has been already successfully applied to solve several biologically relevant problems, for example, the ability of membrane channels to conduct matter in a highly selective, yet very fast fashion.
An important issue in biomolecules is the statistical mechanics of small systems. Even a very large biomolecule has relatively few atoms, from a thermodynamic perspective. Therefore, conventional techniques of statistical physics and condensed matter theory, which were developed for systems with approximately Avogadro’s number of atoms, must be modified to treat such small systems. In this respect, techniques which have been developed for small particles can be adapted to deal with biomolecules. A related issue is that fluctuations about the mean are likely to be much larger in biosystems than in bulk materials. Such fluctuations, even rare fluctuations, may well have profound effects on the measured properties of biomolecules. Finally, biomolecules are often studied under non-equilibrium conditions. These departures from equilibrium may be larger in biomolecules than in bulk systems. Therefore, it is very important to develop appropriate tools for treating strongly non-equilibrium and small size effects in biomolecules.
It is clear that many biological functions are carried out by large scale structures which consist of several protein domains. It is urgent to understand the dynamics of large scale structures. This requires development of suitable coarse-grained models that capture the salient properties of large assemblies. Because the functions of these structures can be triggered by induced strain, it is important to understand their elastic properties. The characterization of the dynamics of structures requires the development of novel computational and theoretical tools that deal with the heterogeneous nature of interactions as well as with the mesoscopic scale.
Figure 6. The ribosome where proteins are assembled using instructions from the genetic code is one of the largest structures ever determined by X-ray crystallography. [J.H.Cate, M.M Yusupov, G.Z. Yusupova, T.N. Earnest, H.F Noller, Science 1999;285:2095-104.]
Molecular recognition is ubiquitous in biology and is one of the bases of life. While the concept of molecular recognition occurs in chemistry, this phenomenon is more striking in biology, where molecular recognition can be triggered on and off by various co-factors. While some of the questions associated with molecular recognition belong to chemistry, others, which involve conformational changes, for example, clearly fall into the realm of physics. In biochemistry, molecular recognition is associated with ligand docking, of course, but also self-assembly of proteins. A better understanding of molecular recognition can lead to improved drug design and lessons learnt can lead to new engineering methods for self-assembly.
It is now believed that many proteins can aggregate into a beta-sheet structure instead of adopting their native state. The reason is that proteins can have a propensity both for formation of alpha-helices and beta-strands. Beta-strands attach to each other within a protein, but they can also be promiscuous and attach to beta-strands of other proteins, leading to aggregate formation. Often, these aggregates lead to pathologies, best known in case of nervous system diseases like Creutzfeldt-Jacob disease, but beta-strand cross bridging is also known to functionally join proteins in muscle and in the extra-cellular matrix. Many proteins and other biomolecules are also known to self-assemble through broad interfaces, rather then beta strands. A prominent protein aggregate occurs in the capsids of viruses that protect the genetic materials in viral DNA or RNA and release it when triggered during infection. At the molecular level, it is essential to understand the inter-protein interactions that lead to self-assembly. Since we are dealing with finite size systems, new questions arise similar to those found in atomic and molecular clusters: what are the possible metastable shapes for a given size oligomer, what is the oligomerisation pathway, and what controls the self-assembly, when this is a non-equilibrium process? These questions are discussed in more detail under Supramolecular Assemblies in the next section.
The role of theory in biological physics of biomolecules is to provide new conceptual and methodological solutions that are needed due to the complexity of biomaterials, due to the non-equilibrium nature of living cells, due to the high degree of disorder experienced at physiological temperatures, and due to ill understood properties not yet encountered in inorganic matter. In many cases, including force generation by biomolecules or biomolecular assemblies, photosynthesis, biocomputing, and aggregation, the systems are out of equilibrium. This poses further challenges for method development.
Reliable biomolecular simulations are extremely difficult because they involve complex systems such as charged macromolecules bathed in a solvent environment, long-range electrostatic interactions, correlation effects, and a high sensitivity to both temperature and dynamical effects. During the past decade, large-scale codes such as AMBER, CHARMM, and NAMD – which are primarily based on empirical, classical MD potentials, have been the mainstay of biomolecular simulations. Much has been learned, but there is a clear understanding that such an approach has inherent limitations. For instance, biomolecular simulations often involve bond breakage, charge transfer, electronic correlation effects, excited states, etc. Clearly, a treatment of these issues necessitates a quantum mechanical description, which is computationally much more demanding than a classical approach. In turn, large-scale biomolecular simulations often involve solvation effects, whose atomistic description can consume the majority of cycles in a given MD simulation. A continuum description for distant solvent molecules can substantially diminish the cost of biomolecular simulations, thereby enabling simulations of complex processes at biologically relevant length and time scales.
Based on these general considerations, we feel that some of the challenges facing the biomolecular simulation community must include the continued development of classical force fields based on physics principles, as opposed to empirical, knowledge-based potentials. It has been noted that knowledge-based potentials have had success in modeling proteins; this success is largely confined to protein systems with homologs in the Protein Data Bank. They fail to predict structures outside of this domain, and because they are based on ad hoc statistical modeling as opposed to true physics principles, it is difficult to remedy their shortcomings. Moreover, it is impossible to calculate ensemble averages with them, so that predictions of free energy changes are precluded. Force fields that are based on true physics principles, on the other hand, offer a way out of this dilemma. Unfortunately, current potentials need improvement. Currently, it is understood that the greatest loss of accuracy in the modeling of classical potentials is in the non-bonded interactions – mainly the delicate, long-range electrostatic and dispersion interactions. We note that force-field development is often a long, arduous enterprise that needs the support of the entire simulation community. A better understanding of hydrogen bonds, which have energies around room temperature, is particularly important, as most biology takes place at room temperature, and so involves the making and breaking of hydrogen bonds.
There is a need for the development of efficient and accurate computational tools at every energy scale of the problem – i.e., quantum mechanical, classical molecular dynamics, and continuum, and the ability to couple them in such a way as to achieve breakthrough simulations. We believe that the community would benefit considerably through the development of “mix-and-match” type of modules, which would enable the coupling of standard quantum chemistry and density functional theory based packages with standard classical codes such as AMBER, CHARMM, and NAMD.
Hand-in-hand with the development of better force fields and a better description of biomolecules, it is also important that attention be paid to the timescale problem. Current biomolecular simulations range up to the order of 10’s to 100’s of nanoseconds, which is too short to model many biomolecular processes. It is clear that novel and new algorithms will be needed to overcome this problem. Most likely, these will fall into two broad categories: (a) fast dynamics whose aim is to extract the long-time dynamics as accurately as possible. The remedy here will most likely involve a coarse-graining of systems at different levels – the approach will most likely be system dependent; (b) fast dynamics whose aim is to accelerate a system or explore phase-space rapidly.
Diffuse motions of biomolecules on a scale of microseconds and longer are involved in many biological functions, like enzyme activity and the operation of the ribosome. Methods that suppress the higher frequency molecular motions are useful here, where MD cannot produce results. One promising new approach is to constrain a biomolecule by imposing constraints associated with the covalent and hydrogen bonds as well as hydrophobic tethers, and then study the subsequent large scale diffusive motions. This has the advantage that it scales roughly linearly with size, so very large biomolecular complexes with ~ million atoms and larger can be handled, and where geometric and steric effects are likely to be dominant in the formation and subsequent behavior of the complex. Such methods are faster than classical MD by orders of magnitude and can give insight by locating the rigid and flexible regions in biomolecular complexes. The flexible and rigid regions can be used to identify interfaces when smaller units dock together to form stable three dimensional scaffolds, and the biological functionality can then be related to the actual motions associated with the flexible regions.
Another approach may lie in a concise representation of the conformational space of biopolymers obtained through very extensive sampling. Such sampling can be carried out in parallel computations, rather then sequentially as needed in case of following an actual motion. Obviously, a parallel approach has great benefits since it can be carried out effectively on modern computers that furnish ever higher processor counts, but only limited speed increases. The needed concise presentation could map topology-conserving networks onto the energetically accessible domains and valleys of the conformational space and guiding through its nodes and links coarse-grained trajectories, e.g., from an unfolded to a folded state of a protein.
The characterization of biological systems at the molecular level is now slowly reaching a precision that compares well with that to which the physical sciences have been accustomed. This is particularly true at the biomolecular level that ultimately is the foundation for all of life. The new data, like numerous high resolution structures of biomolecules or single molecule recordings, pose a challenge to the life sciences that can be largely met through the conceptual and methodological approaches of theoretical physics. Condensed matter physics and materials science have successfully linked experiment and theory for inorganic matter, often of macroscopic scale; their culture of the combination of experiment and theory can likely benefit living matter when the challenge posed by the inherent finite size and complexity of living matter systems is met.
Molecular level theoretical descriptions can go far in understanding the function of complex biological systems, but only if working in concert with experiments. Funding should focus initially on model systems that are (1) complex enough to warrant a systemic description and simple enough so that one can connect the behavior of the system to the description of its biomolecular components and (2) for which we have corresponding experimental data on all necessary levels. Fundamental theoretical questions should drive studies. The non-equilibrium nature of many of the problems of interest should be kept in mind.
Understanding the mechanical properties of biomolecules is fundamental to a variety of frontier problems in biology. Historically, the area of single-molecule force measurement and theory is an important example of the successes of physics and physicists in the investigation of biomolecules such as DNA and protein. Beyond the scale of single molecules, physicists have made significant progress in understanding the materials properties of large protein complexes that make up, e.g. the cytoskeleton. These materials properties and dynamics are essential to biology since:
(i) understanding the nanoscale dynamics and conformational fluctuations of proteins is necessary to elucidate their biochemistry including protein/ligand binding and protein/DNA interactions,
(ii) studying force propagation in the cytoskeleton will lead to a better understanding and perhaps control over cell adhesion, motility, and mechanosensory transduction.
One of the key functions of certain classes of biomolecules is to either induce or transfer charge carriers of various types (electrons, protons, excitons, etc). Currently the details of the mechanisms of charge separation and subsequent transport, and transduction are poorly understood. To enhance our understanding of these important classes of biomolecular problems, will require new quantum mechanical tools (e.g., advances in density functional theory, quantum chemistry, and quantum Monte Carlo techniques for understanding of charged and excited states), as well as a more detailed understanding of the coupling of the biomolecule to its immediate environment. This is an area whose implications feed back into nanotechnology. Specifically, there is considerable hope in the community for new biomolecular electronic devices and biosensors. Issues likely to be important here are the coupling of biomolecules to inorganic materials leading to new classes of Biomolecular ElectroMechanical Systems (BEMS).
The rapid advances in nano-electronics and molecular electronics opens up a new frontier for the development of biomolecular devices. Two research thrusts are immediately clear.
(i) We must increase our efforts to exploit the diverse form and functional properties of biomolecules to construct nano-devices. They have the capacity to perform complex functions that otherwise could not be achieved, or could only be achieved with much larger and complex architectures.
(ii) Organic or inorganic nanodevices can interact with biomolecules or even bio-systems to function as sensors, monitors, or to interface with living systems.
The simplest molecular electronic elements are memory, switch or logic devices, but advances using biomolecules can lead to elements that mimic processes in photosynthesis or respiration. Electrical conduction, or electron transfer, through biomolecules is not as simple to understand or model as in metals and semiconductors, and this complexity presents new challenges for theoretical techniques and technique development. For short molecules, the electron transfer process often is quantum mechanical tunneling. The quantum mechanical nature of the problem can lead to new discoveries and interesting effects such as the Coulomb blockade. The strong electron-vibrational coupling leads to important vibronic effects producing polarons and electron hopping between localized sites. The effects of water, pH, and ions in solution are fundamental topics that have a large effect of the electronic properties of biomolecules, and will surely play important roles in understanding the full potential of devices in bio-environments. There are many opportunities for the further development of the quantum mechanics and many body physics occurring in these areas. The field is just emerging, and there is a need to produce clear successful examples of theory coupled to experiment which produce guideposts for further development.