Recent developments in the theory of protein folding: searching for the global energy minimum

Exploring Conformational Preferences of Leu-enkephalin Using the Conformational Search and Double-Hybrid DFT Energy Calculations

The conformational preferences of Leu-enkephalin (Leu-Enk) were explored by the conformational search and density functional theory (DFT) calculations. By a combination of low-energy conformers of each residue, the initial structures of the neutral Leu-Enk were generated and optimized using the ECEPP3 force field in the gas phase. These structures were reoptimized at the HF/3-21G(d) and M06-2X levels of theory with 6-31G(d) and 6-31+G(d) basis functions. We finally located the 139 structures with the relative energy <10 kcal mol^-1 in the gas phase, from which the structures of the corresponding zwitterionic Leu-Enk were generated and reoptimized at the M06-2X/6-31+G(d) level of theory using the implicit solvation model based on density (SMD) in water. The conformational preferences of Leu-Enk were analyzed using Gibbs free energies corrected by single-point energies calculated at the double-hybrid DSD-PBEP86-D3BJ/def2-TZVP level of theory in the gas phase and in water. The neutral Leu-Enk dominantly adopted a folded structure in the gas phase stabilized by three H-bonds with a βII'-bend-like motif at the Gly3-Phe4 sequence and a close contact between the side chains of Phe4 and Leu5. The zwitterionic Leu-Enk exhibited a folded structure in water stabilized by three H-bonds with double β-bends such as a βII' bend at the Gly2-Gly3 sequence and a βI bend at the Gly3-Phe4 sequence. The calculated ensemble-averaged distance between C_Gly2 ^α and C_Leu5 ^α of the zwitterionic Leu-Enk in water is consistent with the value estimated from the simulated annealing using the distance constraints derived from nuclear Overhauser effect spectroscopy (NOESY) spectra in water. Interestingly, the preferred conformations of the neutral and zwitterionic Leu-Enk are new folded structures not predicted by earlier computational studies. According to the refined model of the zwitterionic Leu-Enk bound to δ-opioid receptor (δOR), there were favorable interactions of the terminal charged groups of Leu-Enk with the side chains of charged residues of δOR as well as a favorable C_Aryl···H interaction of the Phe4 residue of Leu-Enk with Trp284 of δOR. Hence, these favorable interactions would induce the folded structure of the zwitterionic Leu-Enk with double β-bends isolated in water into the "bioactive conformation" like an extended structure when binding to δOR.

Deciphering the loss of metal binding due to mutation D83G of human SOD1 protein causing FALS disease

Mutations in Cu/Zn superoxide dismutase 1 (SOD1) protein are found to be the causative factor, behind the majority of familial amyotrophic later sclerosis (FALS) cases. The mutations particularly on the metal (Zn) binding residues are found to increase the disease onset in the individuals suffering from FALS, while the presence of the metal ion (Zn) is essential for the catalytic activity and retaining the protein stability. Thus in our study, we focused on one such metal binding mutant (D83G) and assessed the impact of the mutation on protein structure and function. The influence of mutation was examined dynamically, using discrete molecular dynamics on both the native and mutant SOD1 protein respectively. Accordingly, the variation in conformational stability, residual flexibility and protein compactness along with the change in conformational free energy were monitored over the entire dynamic period. Moreover, the motion of native and mutant SOD1 was also observed via the essential dynamics. Besides, the disparity in Zn ion binding was inspected through distance analysis and steered molecular dynamics, correspondingly. Therefore, the study provides a better understanding over the profound effect of mutation on SOD1, both structurally and functionally, using computational approaches.

Structure prediction and analysis of neuraminidase sequence variants

Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the highly mutable influenza virus protein neuraminidase, which is the key target in the development of therapeutics. In light of recent pandemics, understanding how mutations confer drug resistance, which translates at the molecular level to understanding how different sequence variants differ, constitutes an area of great interest because of the ramifications in public health. This lab targets upper level undergraduate biochemistry students, and aims to introduce tools to be used to explore protein folding and protein visualization in the context of the neuraminidase case study. Students proceed to critically evaluate the folded models by comparison with crystallographic structures. When validity is established, they fold a neuraminidase sequence for which a structure is not available. Through structural alignment and visual inspection of the 150 loop, students gain molecular insight into two possible conformations of the protein, which are actively being studied. Folding the third chosen sequence mimics a true research environment in allowing students to generate a structure from a sequence for which a structure was not previously available, and to assess whether their particular variant has an open or closed loop. From this vantage, they are then challenged to speculate about the connection between loop conformation and drug susceptibility. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):361-376, 2016.

Perspective: crystal structure prediction at high pressures

Crystal structure prediction at high pressures unbiased by any prior known structure information has recently become a topic of considerable interest. We here present a short overview of recently developed structure prediction methods and propose current challenges for crystal structure prediction. We focus on first-principles crystal structure prediction at high pressures, paying particular attention to novel high pressure structures uncovered by efficient structure prediction methods. Finally, a brief perspective on the outstanding issues that remain to be solved and some directions for future structure prediction researches at high pressure are presented and discussed.

Computational modeling of RNA 3D structures, with the aid of experimental restraints

In addition to mRNAs whose primary function is transmission of genetic information from DNA to proteins, numerous other classes of RNA molecules exist, which are involved in a variety of functions, such as catalyzing biochemical reactions or performing regulatory roles. In analogy to proteins, the function of RNAs depends on their structure and dynamics, which are largely determined by the ribonucleotide sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore, the majority of known RNAs remain structurally uncharacterized. To address this problem, computational structure prediction methods were developed that simulate either the physical process of RNA structure formation ("Greek science" approach) or utilize information derived from known structures of other RNA molecules ("Babylonian science" approach). All computational methods suffer from various limitations that make them generally unreliable for structure prediction of long RNA sequences. However, in many cases, the limitations of computational and experimental methods can be overcome by combining these two complementary approaches with each other. In this work, we review computational approaches for RNA structure prediction, with emphasis on implementations (particular programs) that can utilize restraints derived from experimental analyses. We also list experimental approaches, whose results can be relatively easily used by computational methods. Finally, we describe case studies where computational and experimental analyses were successfully combined to determine RNA structures that would remain out of reach for each of these approaches applied separately.

Automated modeling of RNA 3D structure

This chapter gives an overview over the current methods for automated modeling of RNA structures, with emphasis on template-based methods. The currently used approaches to RNA modeling are presented with a side view on the protein world, where many similar ideas have been used. Two main programs for automated template-based modeling are presented: ModeRNA assembling structures from fragments and MacroMoleculeBuilder performing a simulation to satisfy spatial restraints. Both approaches have in common that they require an alignment of the target sequence to a known RNA structure that is used as a modeling template. As a way to find promising template structures and to align the target and template sequences, we propose a pipeline combining the ParAlign and Infernal programs on RNA family data from Rfam. We also briefly summarize template-free methods for RNA 3D structure prediction. Typically, RNA structures generated by automated modeling methods require local or global optimization. Thus, we also discuss methods that can be used for local or global refinement of RNA structures.

Protein aggregation and misfolding: good or evil?

Protein aggregation and misfolding have important implications in an increasing number of fields ranging from medicine to biology to nanotechnology and material science. The interest in understanding this field has accordingly increased steadily over the last two decades. During this time the number of publications that have been dedicated to protein aggregation has increased exponentially, tackling the problem from several different and sometime contradictory perspectives. This review is meant to summarize some of the highlights that come from these studies and introduce this topical issue on the subject. The factors that make a protein aggregate and the cellular strategies that defend from aggregation are discussed together with the perspectives that the accumulated knowledge may open.

Respice, adspice, and prospice

The title, "Look to the past, Look to the present, and Look to the future," the motto of City College of New York, expresses how my family life and education led me to an academic career in physical chemistry and ultimately to a study of proteins. The economic depression of the 1930s left a lasting impression on my outlook and career aspirations. With fortunate experiences at several stages in my life, I was able to participate in the great adventure of the last half of the twentieth century: the revolution in biology that advanced the field of protein chemistry to so great an extent. The future is bright and limitless, with greater understanding of biology yet to come.

RNA and protein 3D structure modeling: similarities and differences

In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been very few such methods for RNA. This review discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed “protein-like” modeling methods for RNA. We highlight analogies between many successful methods for modeling of these two types of biological macromolecules and argue that RNA 3D structure can be modeled using “protein-like” methodology. We also highlight the areas where the differences between RNA and proteins require the development of RNA-specific solutions.

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A computational pathway for bracketing native-like structures fo small alpha helical globular proteins

Impressive advances in the applications of bioinformatics for protein structure prediction coupled with growing structural databases on one hand and the insurmountable time-scale problem with ab initio computational methods on the other continue to raise doubts whether a computational solution to the protein folding problem--categorized as an NP-hard problem--is within reach in the near future. Combining some specially designed biophysical filters and vector algebra tools with ab initio methods, we present here a promising computational pathway for bracketing native-like structures of small alpha helical globular proteins departing from secondary structural information. The automated protocol is initiated by generating multiple structures around the loops between secondary structural elements. A set of knowledge-based biophysical filters namely persistence length and radius of gyration, developed and calibrated on approximately 1000 globular proteins, is introduced to screen the trial structures to filter out improbable candidates for the native and reduce the size of the library of probable structures. The ensemble so generated encompasses a few structures with native-like topology. Monte Carlo optimizations of the loop dihedrals are then carried out to remove steric clashes. The resultant structures are energy minimized and ranked according to a scoring function tested previously on a series of decoy sets vis-a-vis their corresponding natives. We find that the 100 lowest energy structures culled from the ensemble of energy optimized trial structures comprise at least a few to within 3-5 angstroms of the native. Thus the formidable "needle in a haystack" problem is narrowed down to finding an optimal solution amongst a computationally tractable number of alternatives. Encouraging results obtained on twelve small alpha helical globular proteins with the above outlined pathway are presented and discussed.

Coarse-grained free energy functions for studying protein conformational changes: a double-well network model

In this work, a double-well network model (DWNM) is presented for generating a coarse-grained free energy function that can be used to study the transition between reference conformational states of a protein molecule. Compared to earlier work that uses a single, multidimensional double-well potential to connect two conformational states, the DWNM uses a set of interconnected double-well potentials for this purpose. The DWNM free energy function has multiple intermediate states and saddle points, and is hence a "rough" free energy landscape. In this implementation of the DWNM, the free energy function is reduced to an elastic-network model representation near the two reference states. The effects of free energy function roughness on the reaction pathways of protein conformational change is demonstrated by applying the DWNM to the conformational changes of two protein systems: the coil-to-helix transition of the DB-loop in G-actin and the open-to-closed transition of adenylate kinase. In both systems, the rough free energy function of the DWNM leads to the identification of distinct minimum free energy paths connecting two conformational states. These results indicate that while the elastic-network model captures the low-frequency vibrational motions of a protein, the roughness in the free energy function introduced by the DWNM can be used to characterize the transition mechanism between protein conformations.

Protein structure prediction aided by geometrical and probabilistic constraints

Database-assisted ab initio protein structure prediction methods have exhibited considerable promise in the recent past, with several implementations being successful in community-wide experiments (CASP). We have employed combinatorial optimization techniques toward solving the protein structure prediction problem. A Monte Carlo minimization algorithm has been employed on a constrained search space to identify minimum energy configurations. The search space is constrained by using radius of gyration cutoffs, the loop backbone dihedral probability distributions, and various secondary structure packing conformations. Simulations have been carried out on several sequences and 1000 conformations have been initially generated. Of these, 50 best candidates have then been selected as probable conformations. The search for the optimum has been simplified by incorporating various geometrical constraints on secondary structural elements using distance restraint potential functions. The advantages of the reported methodology are its simplicity, and modifiability to include other geometric and probabilistic restraints.

Bhageerath: an energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins

We describe here an energy based computer software suite for narrowing down the search space of tertiary structures of small globular proteins. The protocol comprises eight different computational modules that form an automated pipeline. It combines physics based potentials with biophysical filters to arrive at 10 plausible candidate structures starting from sequence and secondary structure information. The methodology has been validated here on 50 small globular proteins consisting of 2–3 helices and strands with known tertiary structures. For each of these proteins, a structure within 3–6 Å RMSD (root mean square deviation) of the native has been obtained in the 10 lowest energy structures. The protocol has been web enabled and is accessible at .

Protein-Structure Prediction by Recombination of Fragments

The field of protein-structure prediction has been revolutionized by the application of "mix-and-match" methods both in template-based homology modeling and in template-free de novo folding. Consensus analysis and recombination of fragments copied from known protein structures is currently the only approach that allows the building of models that are closer to the native structure of the target protein than the structure of its closest homologue. It is also the most successful approach in cases in which the target protein exhibits a novel three-dimensional fold. This review summarizes the recent developments in both template-based and template-free protein structure modeling and compares the available methods for protein-structure prediction by recombination of fragments. A convergence between the "protein folding" and "protein evolution" schools of thought is postulated.

Biased fragment distribution in MC simulation of protein folding

Monte Carlo (MC) methods play an important role in simulations of protein folding. These methods rely on a random sampling of moves on a potential energy surface. To improve the efficiency of the sampling, we propose a new selection of trial moves based on an empirical distribution of three-residue (triplet) conformations. This selection is compared to random combinations of the preferred conformations of the three amino acids, and it is shown that the new trial moves lead to finding structures closer to the native conformation.

Kinetics and thermodynamics of protein adsorption: a generalized molecular theoretical approach

The thermodynamics and kinetics of protein adsorption are studied using a molecular theoretical approach. The cases studied include competitive adsorption from mixtures and the effect of conformational changes upon adsorption. The kinetic theory is based on a generalized diffusion equation in which the driving force for motion is the gradient of chemical potentials of the proteins. The time-dependent chemical potentials, as well as the equilibrium behavior of the system, are obtained using a molecular mean-field theory. The theory provides, within the same theoretical formulation, the diffusion and the kinetic (activated) controlled regimes. By separation of ideal and nonideal contributions to the chemical potential, the equation of motion shows a purely diffusive part and the motion of the particles in the potential of mean force resulting from the intermolecular interactions. The theory enables the calculation of the time-dependent surface coverage of proteins, the dynamic surface tension, and the structure of the adsorbed layer in contact with the approaching proteins. For the case of competitive adsorption from a solution containing a mixture of large and small proteins, a variety of different adsorption patterns are observed depending upon the bulk composition, the strength of the interaction between the particles, and the surface and size of the proteins. It is found that the experimentally observed Vroman sequence is predicted in the case that the bulk solution is at a composition with an excess of the small protein, and that the interaction between the large protein and the surface is much larger than that of the smaller protein. The effect of surface conformational changes of the adsorbed proteins in the time-dependent adsorption is studied in detail. The theory predicts regimes of constant density and dynamic surface tension that are long lived but are only intermediates before the final approach to equilibrium. The implications of the findings to the interpretation of experimental observations is discussed.

Stepwise assembling of polypeptide chain energy distributions

The principles and application of conformational analysis software that makes use of a new algorithm are described. It is known that the existence of a local energy minimum in the energy landscape is in general related to the clustering of polypeptide chain conformations near that energy value or, in other words, to a high density of states. A criterion based on this principle is part of an algorithm employed to select subsets of polypeptide chain conformations in broad energy ranges. Chain fragments belonging to these subsets are then combined to build larger polypeptide chains and the corresponding energy distributions. The functionality of the various operations employed in the process is described and the FORTRAN 77 source code that defines the algorithm is listed. The methodology is illustrated with a calculation involving three chain fragments belonging to the cellular prion protein (PrP(C)).

Recent improvements in prediction of protein structure by global optimization of a potential energy function

Recent improvements of a hierarchical ab initio or de novo approach for predicting both alpha and beta structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic alpha-helical protein of 70 residues was predicted to within 4.3 A alpha-carbon (C(alpha)) rms deviation (rmsd) whereas, for other alpha-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 A C(alpha) rmsd. Whereas beta structures can now be predicted with the new procedure, the success rate for alpha/beta- and beta-proteins is lower than that for alpha-proteins at present. For the beta portions of alpha/beta structures, the C(alpha) rmsd's are less than 6.0 A for contiguous fragments of 30-40 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a C(alpha) rmsd less than 6.0 A. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence.

Ab initio construction of protein tertiary structures using a hierarchical approach

We present a hierarchical method to predict protein tertiary structure models from sequence. We start with complete enumeration of conformations using a simple tetrahedral lattice model. We then build conformations with increasing detail, and at each step select a subset of conformations using empirical energy functions with increasing complexity. After enumeration on lattice, we select a subset of low energy conformations using a statistical residue-residue contact energy function, and generate all-atom models using predicted secondary structure. A combined knowledge-based atomic level energy function is then used to select subsets of the all-atom models. The final predictions are generated using a consensus distance geometry procedure. We test the feasibility of the procedure on a set of 12 small proteins covering a wide range of protein topologies. A rigorous double-blind test of our method was made under the auspices of the CASP3 experiment, where we did ab initio structure predictions for 12 proteins using this approach. The performance of our methodology at CASP3 is reasonably good and completely consistent with our initial tests.

Global optimization of clusters, crystals, and biomolecules

Finding the optimal solution to a complex optimization problem is of great importance in many fields, ranging from protein structure prediction to the design of microprocessor circuitry. Some recent progress in finding the global minima of potential energy functions is described, focusing on applications of the simple "basin-hopping" approach to atomic and molecular clusters and more complicated hypersurface deformation techniques for crystals and biomolecules. These methods have produced promising results and should enable larger and more complex systems to be treated in the future.

Protein structure prediction by global optimization of a potential energy function

An approach based exclusively on finding the global minimum of an appropriate potential energy function has been used to predict the unknown structures of five globular proteins with sizes ranging from 89 to 140 amino acid residues. Comparison of the computed lowest-energy structures of two of them (HDEA and MarA) with the crystal structures, released by the Protein Data Bank after the predictions were made, shows that large fragments (61 residues) of both proteins were predicted with rms deviations of 4.2 and 6.0 A for the Calpha atoms, for HDEA and MarA, respectively. This represents 80% and 53% of the observed structures of HDEA and MarA, respectively. Similar rms deviations were obtained for approximately 60-residue fragments of the other three proteins. These results constitute an important step toward the prediction of protein structure based solely on global optimization of a potential energy function for a given amino acid sequence.

Energy-based de novo protein folding by conformational space annealing and an off-lattice united-residue force field: application to the 10-55 fragment of staphylococcal protein A and to apo calbindin D9K

The conformational space annealing (CSA) method for global optimization has been applied to the 10-55 fragment of the B-domain of staphylococcal protein A (protein A) and to a 75-residue protein, apo calbindin D9K (PDB ID code), by using the UNRES off-lattice united-residue force field. Although the potential was not calibrated with these two proteins, the native-like structures were found among the low-energy conformations, without the use of threading or secondary-structure predictions. This is because the CSA method can find many distinct families of low-energy conformations. Starting from random conformations, the CSA method found that there are two families of low-energy conformations for each of the two proteins, the native-like fold and its mirror image. The CSA method converged to the same low-energy folds in all cases studied, as opposed to other optimization methods. It appears that the CSA method with the UNRES force field, which is based on the thermodynamic hypothesis, can be used in prediction of protein structures in real time.

Study of alpha-helix to beta-strand to beta-sheet transitions in amyloid: the role of segregated hydrophobic beta-strands

A conformational analysis including three polypeptide chains known to be amyloidogenic and neurotoxic has shown the occurrence of low probability hydrophobic beta-strands stabilized by intramolecular interactions. It is argued that by engaging in non-bonded and hydrophobic interactions these beta-strands seed the assembly of beta-sheets in amyloid fibrils following a non-cooperative mechanism dissimilar to beta-sheet folding in proteins. Molecular models of amyloid fibrils formed by such beta-strand templates were built. It is shown that the parallel alignment of beta-strands creates an extensive hydrophobic surface whereas the antiparallel alignment causes the formation of hydrophobic and hydrophilic aggregates. A comparison with experimental data and previous calculations is established.

On the thermodynamic hypothesis of protein folding

The validity of the thermodynamic hypothesis of protein folding was explored by simulating the evolution of protein sequences. Simple models of lattice proteins were allowed to evolve by random point mutations subject to the constraint that they fold into a predetermined native structure with a Monte Carlo folding algorithm. We employed a simple analytical approach to compute the probability of violation of the thermodynamic hypothesis as a function of the size of the protein, the fraction of the total number of possible conformations which are kinetically accessible, and the roughness of the free-energy landscape. It was found that even if the folding is under kinetic control, the sequence will evolve so that the native state is most often the state of minimum free energy.

Protein folding simulations with genetic algorithms and a detailed molecular description

We have explored the application of genetic algorithms (GA) to the determination of protein structure from sequence, using a full atom representation. A free energy function with point charge electrostatics and an area based solvation model is used. The method is found to be superior to previously investigated Monte Carlo algorithms. For selected fragments, up to 14 residues long, the lowest free energy structures produced by the GA are similar in conformation to the corresponding experimental structures in most cases. There are three main conclusions from these results. First, the genetic algorithm is an effective method for searching amongst the compact conformations of a polypeptide chain. Second, the free energy function is generally able to select native-like conformations. However, some deficiencies are identified, and further development is proposed. Third, the selection of native-like conformations for some protein fragments establishes that in these cases the conformation observed in the full protein structure is largely context independent. The implications for the nature of protein folding pathways are discussed.

Protein adsorption on surfaces with grafted polymers: a theoretical approach

A general theoretical framework for studying the adsorption of protein molecules on surfaces with grafted polymers is presented. The approach is a generalization of the single-chain mean-field theory, in which the grafted polymer-protein-solvent layer is assumed to be inhomogeneous in the direction perpendicular to the grafting surface. The theory enables the calculation of the adsorption isotherms of the protein as a function of the surface coverage of grafted polymers, concentration of protein in bulk, and type of solvent molecules. The potentials of mean force of the protein with the surface are calculated as a function of polymer surface coverage and amount of protein adsorbed. The theory is applied to model lysozyme on surfaces with grafted polyethylene oxide. The protein is modeled as spherical in solution, and it is assumed that the protein-polymer, protein-solvent, and polymer-solvent attractive interactions are all equal. Therefore, the interactions determining the structure of the layer (beyond the bare polymer-surface and protein-surface interactions) are purely repulsive. The bare surface-protein interaction is taken from atomistic calculations by Lee and Park. For surfaces that do not have preferential attractions with the grafted polymer segments, the adsorption isotherms of lysozyme are independent of the polymer length for chains with more than 50 ethylene oxide units. However, the potentials of mean force show strong variations with grafted polymer molecular weight. The competition between different conformations of the adsorbed protein is studied in detail. The adsorption isotherms change qualitatively for surfaces with attractive interactions with ethylene oxide monomers. The protein adsorption is a function of chain length--the longer the polymer the more effective it is in preventing protein adsorption. The structure of the layer and its deformation upon protein adsorption are very important in determining the adsorption isotherms and the potentials of mean force.

Evolutionary algorithms in computer-aided molecular design

In recent years, search and optimisation algorithms inspired by evolutionary processes have been applied with marked success to a wide variety of problems in diverse fields of study. In this review, we survey the growing application of these 'evolutionary algorithms' in one such area: computer-aided molecular design. In the course of the review, we seek to summarise the work to date and to indicate where evolutionary algorithms have met with success and where they have not fared so well. In addition to this, we also attempt to discern some future trends in both the basic research concerning these algorithms and their application to the elucidation, design and modelling of chemical and biochemical structures.