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M J Sternberg

Publications and source records attributed to M J Sternberg.

At least 73 records · Page 4Linked to original sources
PubMed1994

Identification of sequence motifs from a set of proteins with related function.

The automatic identification of motifs associated with a given function is an important challenge for molecular sequence analysis. A method is presented for the extraction of such patterns from large sets of unaligned sequences with related but general function, for example, a set of heat shock proteins. In such a set of proteins there can often be several subfamilies each characterized by one or more distinct motifs. The aim is to develop computational tools to identify these motifs. The algorithm presented locates high frequency words of length k with a given number of positions, r, fixed. Statistics for a binomial distribution are used to assess the significance of the words. The high-frequency words are clustered and highly populated clusters retained. The composition of the clusters is displayed graphically. A set of motifs associated with the sequence family can automatically be extracted. The method is benchmarked on a set of 106 heat shock sequences and a set of 257 toxin sequences. It is shown to recover previously identified motifs.

Algorithms↗
PubMed1994

Application of scaled particle theory to model the hydrophobic effect: implications for molecular association and protein stability.

The energetics of alkane dissolution and partition between water and organic solvent are described in terms of the energy of cavity formation and solute-solvent interaction using scaled particle theory. Thermodynamic arguments are proposed that allow comparison of experimental measurements of the surface area with values calculated from an all-atom representation of the solute. While the surface tension relating to the accessible surface is shape dependent, it is found that for the molecular surface it is not. This model rationalizes the change in surface tension between the microscopic (20-30 cal/mol/A2) and macroscopic (70-75 cal/mol/A2) regimes without the need to invoke Flory-Huggins theory or to apply other corrections. The difference in the values arises (i) to a small extent as a result of the curvature dependence of surface tension and (ii) to a large extent due to the difference in the molecular surface derived from the experiment and that calculated from an extended all-atom model. The model suggests that the primary driving force for alkane association in water is due to the tendency of water to reduce the solute cavity surface. It is argued that to model the energetics of alkane association, the surface tension should be related to the molecular surface (rather than the accessible surface) with a surface tension near the macroscopic limit for water. This model is compared with results from theoretical simulations of the hydrophobic effect for two well-studied systems. The implications for antibody-antigen interactions and the effect of hydrophobic amino acid deletion on protein stability are discussed. The approach can be used to model the solute cavity formation energy in solution as a first step in the continuum modelling of biomolecular interactions.

Antigen-Antibody Reactions↗
PubMed1994

Inductive logic programming used to discover topological constraints in protein structures.

This paper describes the application of the Inductive Logic Programming (ILP) program GOLEM to the discovery of constraints in the packing of beta-sheets in alpha/beta proteins. These constraints (rules) have a role in understanding the protein folding problem. Constraints were learnt for four features of beta-sheet packing: the winding direction of two sequential strands, whether two consecutive strands pack parallel or anti-parallel, whether two strands pack adjacently, and whether a beta-strand is at an edge. Investigation of the learnt constraints revealed interesting patterns, some of which were previously known, others that were novel. Novel features include the discovery: that the relationship between pairs of sequential strands is in general one of decreasing size, and that more sequential pairs of strands wind in the direction out than the direction in. We conclude that machine learning has a useful place in molecular biology as a pattern discovery tool.

Animals↗
PubMed1993

Empirical scale of side-chain conformational entropy in protein folding.

A major effect in the energetics of protein folding is the loss of conformational entropy of the side-chains. The definition of entropy as the Boltzmann sampling over all states (S = -R sigma p(i) ln p(i)) requires evaluation of the probability (p(i)) of the system being in rotameric state i. The principle of this paper is to obtain an estimate of p(i) from the observed distribution of exposed side-chain rotamers in 50 non-homologous protein crystal structures. However because of limited data we show that for all side-chains except Asn, Asp and Glu the side-chain distribution is independent of burial and accordingly all data were pooled in the calculation of p(i). For Asn, Asp and Glu side-chains with relative accessibility > 60% were used. The scale includes effects due to the symmetry of side-chains such as Phe and the free rotation of side-chain amide, carboxyl and hydroxyl groups. An empirical scale for the loss of side-chain conformational entropy during protein folding is thereby obtained. Values of the change in free energy due to entropy (-T delta S) on burying a side-chain range from 0 for Ala, Gly and Pro to +2.1 kcal/mol for Gln (T = 300 K). We explore the consistency of a simple model for protein folding that includes side-chain entropy, main-chain entropy, hydrophobicity and hydrogen bonding. The stability of site-directed mutations is discussed in terms of conformational entropy.

Models, Chemical↗
PubMed1993

Left-handed polyproline II helices commonly occur in globular proteins.

The main-chain conformations of 80 proteins were analysed to identify helical structures that commonly occur but do not fall into the known classes of alpha-helix, 3(10)-helix and beta-sheet. The analysis yielded 96 occurrences of four or more sequential residues forming the threefold left-handed poly-L-proline II (PPII) helix. This contradicts the previously held opinion that left-handed helices are rare in globular proteins. The main-chain dihedral angles of these helices form a cluster in phi,psi space that has a maximum at -75 degrees, 145 degrees, corresponding to conformations with the number of residues per turn (n) = -3.0. We show that 51% of PPII-helices lie within the range of n = -3.0(+/- 0.2). It is shown that the PPII segments are distinct from the conformation typical of beta-pleated sheets. Although proline residues commonly occurred in PPII-helices, this side-chain is not obligatory, as 28 of these helices did not contain proline. In addition, we found 120 segments with three C alpha atoms forming a PPII-helix. PPII-helices tend to occur on the surface of the protein and, having few main-chain hydrogen bonds with the rest of the protein, tend to be the more mobile segments of the molecule. The geometry of PPII helix allows the polypeptide chain to progress immediately from this conformation to right-handed alpha-helix and 3(10) helix, as well as to beta-sheet or reverse turn. We conclude that PPII-helices should be considered as a regular conformation and should be added to beta-sheets, alpha-helices and 3(10)-helices in databases of protein structures, in secondary structure prediction and in tertiary model-building.

Algorithms↗
PubMed1993

Prediction of the three-dimensional structures of the nerve growth factor and epidermal growth factor binding proteins (kallikreins) and an hypothetical structure of the high molecular weight complex of epidermal growth factor with its binding protein.

We have predicted the three-dimensional structures of the serine protease subunits (gamma-NGF, alpha-NGF, and EGF-BP) of the high molecular weight complexes of nerve growth factor (7S NGF) and epidermal growth factor (HMW-EGF) from the mouse submandibular gland (from the X-ray crystal structures of two related glandular kallikreins). The conformations of three of the six loops surrounding the active site are relatively well defined in the models of gamma-NGF and EGF-BP, but three other loops are likely to have flexible conformations. Although the amino acid sequence of alpha-NGF is closely related to those of gamma-NGF and EGF-BP, it is catalytically inactive. Model-building studies on alpha-NGF suggested that mutations (in alpha-NGF) just prior to the active site serine (195) and an unusual N-terminal sequence are consistent with alpha-NGF having a zymogen-like conformation (similar to that in chymotrypsinogen). An hypothetical model of the quaternary structure of HMW-EGF has been constructed using this model of EGF-BP and the NMR structure of murine EGF. The C-terminal arm of EGF was modeled into the active site of EGF-BP based on data indicating that the C-terminal arginine of EGF occupies the S1 subsite of EGF-BP. Data suggesting one of the surface loops of EGF-BP is buried in the HMW-EGF complex and symmetry constraints were important in deriving a schematic model. A molecular docking program was used to fit EGF to EGF-BP.

Amino Acid Sequence↗
PubMed1993

Autoimmune disease and molecular mimicry: an hypothesis.

Helper T lymphocytes are normally only stimulated to initiate an immune reaction through the recognition of peptides bound to class II major histocompatibility complex (MHC) molecules. Class II MHC molecules are constitutively expressed on antigen-presenting cells which play a critical role in the initiation of immune responses. In disease states, however, other cells often express class II MHC molecules inappropriately. This article suggests an hypothesis for the pathogenesis of autoimmune diseases based on molecular mimicry. The mimicry described is between microbial or viral peptides presented by antigen-presenting cells and self peptides presented inappropriately on a target tissue. This leads to helper T cells, stimulated by peptides derived from infectious organisms, initiating an autoimmune attack on the target tissue.

Amino Acid Sequence↗
PubMed1993

Specific short transmembrane sequences can inhibit transformation by the mutant neu growth factor receptor in vitro and in vivo.

The neu oncogene is activated by a point mutation within its transmembrane domain that results in the substitution of glutamic acid for valine at position 664, and is associated with constitutive activation of the tyrosine kinase. It has been proposed that the mutation allows for stabilization of homodimers of the receptor that are necessary for transduction of the mitogenic signal. To investigate the role of the alpha-helical transmembrane sequence in the function of neu, we constructed an expression vector to produce a variety of short transmembrane neu proteins, lacking ligand binding or intracellular kinase domains. Such sequences should interact with full-length receptors and prevent receptor dimerization and thus act as specific inhibitors of function. These small proteins all included a pentapeptide from position 661-665, which has been proposed to be necessary for packing. We show that the short transmembrane molecules are expressed at the cell surface and can retard the growth of neu-transformed cells in monolayers, as colonies in soft agar and as tumours in animals. As predicted by molecular modelling, the magnitude of inhibition depended on the nature of the packing surface, suggesting that the neu transmembrane domain is directly involved in neu protein dimerization.

3T3 Cells↗
PubMed1992

Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.

The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statistically more accurate on the training data and also better on the test data than a Hansch linear regression model. Importantly machine learning yields understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. These rules agree with the stereochemistry of the interaction observed crystallographically.

Artificial Intelligence↗
PubMed1992

Evaluation of the sequence template method for protein structure prediction. Discrimination of the (beta/alpha)8-barrel fold.

A multiple alignment of five (beta/alpha)8-barrel enzymes has been derived from their structure. The eight beta-strands and eight alpha-helices of the (beta/alpha)8-barrel are correctly aligned and the equivalenced residues in these regions fulfil similar structural roles. Each beta-strand has a central core of usually four residues, two residues contribute side-chains to the barrel core and the other two residues are involved in beta-strand/alpha-helix contacts. However, the fold imposes no constraints on the volumes of the residues at either a local or global level: the volume of the beta-barrel core varies between 1088 A3 in glycolate oxidase and 1571 A3 in taka-amylase. Sequence motifs derived from the multiple alignment were scanned against a database of 124 protein sequences, including 17 (beta/alpha)8-barrel enzymes. The results were evaluated in terms of the discrimination of (beta/alpha)8-barrel sequences and the quality of the alignments obtained. One motif was able to identify the top 12% of high scoring sequences as forming (beta/alpha)8-barrels with 50% accuracy and the bottom 50% of sequences as not being (beta/alpha)8-barrel proteins with 100% accuracy. However, in most instances the alignments were poor. The reasons for this are discussed with reference to the (beta/alpha)8-barrel proteins and the sequence motif method in general.

Alcohol Oxidoreductases↗
PubMed1992

New algorithm to model protein-protein recognition based on surface complementarity. Applications to antibody-antigen docking.

A novel algorithm is presented which models protein-protein interactions using surface complementarity. The method is applied to antibody-antigen docking. A steric scoring scheme, based upon a soft potential, is used to assess complementarity, and a simple electrostatic model is then used to remove infeasible interactions. The soft potential allows for structural changes that occur during docking. Biochemical knowledge is necessary to reduce the number of docking orientations produced by the method to a manageable size. The information used includes the known epitope residues and a single loose distance constraint. The method is applied to all three crystallographically determined antibody-lysozyme complexes, HyHEL-10, D1.3 and HyHEL-5. For the first time, a predicted antibody structure (that of D1.3) is used as a docking target. In the four systems modelled, the method identifies between 15 and 40 possible docking orientations. The root-mean-square (r.m.s.) deviation between these orientations and the relevant crystallographic complex is measured in the interface region. For all four complexes an orientation is found with r.m.s. deviation in the range 1.9 A and 4.8 A. The algorithm is implemented on a single instruction/multiple datastream (SI/MD) architecture computer. The use of a parallel architecture computer ensures detailed coverage of the search space, whilst still maintaining a search time of two days.

Algorithms↗
PubMed1992

Prediction of structural and functional features of protein and nucleic acid sequences by artificial neural networks.

The applications of artificial neural networks to the prediction of structural and functional features of protein and nucleic acid sequences are reviewed. A brief introduction to neural networks is given, including a discussion of learning algorithms and sequence encoding. The protein applications mostly involve the prediction of secondary and tertiary structure from sequence. The problems in nucleic acid analysis tackled by neural networks are the prediction of translation initiation sites in Escherichia coli, the recognition of splice junctions in human mRNA, and the prediction of promoter sites in E. coli. The performance of the approach is compared with other current statistical methods.

Algorithms↗
PubMed1992

A predicted three-dimensional structure for the carcinoembryonic antigen (CEA).

A three-dimensional model for the carcinoembryonic antigen (CEA) has been constructed by knowledge-based computer modelling. Each of the seven extracellular domains of CEA are expected to have immunoglobulin folds. The N-terminal domain of CEA was modelled using the first domain of the recently solved NMR structure of rat CD2, as well as the first domain of the X-ray crystal structure of human CD4 and an immunoglobulin variable domain REI as templates. The remaining domains were modelled from the first and second domains of CD4 and REI. Link conformations between the domains were taken from the elbow region of antibodies. A possible packing model between each of the seven domains is proposed. Each residue of the model is labelled as to its suitability for site-directed mutagenesis.

Amino Acid Sequence↗
PubMed1992

The binding site on ICAM-1 for Plasmodium falciparum-infected erythrocytes overlaps, but is distinct from, the LFA-1-binding site.

The intercellular adhesion molecule-1 (ICAM-1, CD54) is one of three putative endothelial receptors that mediate in vitro cytoadherence of P. falciparum-infected erythrocytes. Since cytoadherence to postcapillary venular endothelium is thought to be a major factor in the virulence of P. falciparum malaria, we have examined the interaction between ICAM-1 and the P. falciparum-infected cell, and have compared it with the interaction to the physiological counter receptor, the leukocyte integrin LFA-1. Our results demonstrate that the malaria-binding site resides in the first two domains of the ICAM-1 molecule and overlaps, but is distinct from, the LFA-1 site.

Amino Acid Sequence↗
PubMed1992

Three dimensional structure of the transmembrane region of the proto-oncogenic and oncogenic forms of the neu protein.

The neu proto-oncogene may be converted into a dominantly transforming oncogene by a single point mutation. Substitution of a valine residue at position 664 in the transmembrane region with glutamic acid activates the tyrosine kinase of the molecule and is associated with increased receptor dimerization. Previously we have proposed a model in which the glutamic acid side chain stabilizes receptor dimerization by hydrogen bonding. Other models have been proposed in which the mutation leads to a conformational change in the transmembrane region mimicking that assumed to occur following binding of a natural ligand. Synthetic peptides representing part of the transmembrane region were prepared. Some residues were replaced with serine in order to improve peptide solubility to allow purification and analysis. Both the peptides containing valine and glutamic acid dissolved in water and in an artificial lipid monolayer. The structures of the peptides were determined by NMR spectroscopy to be alpha-helical. No significant difference in conformation was observed between the two peptides. This result does not support the model proposing a conformational change. The receptor structures determined experimentally do allow alternative models involving receptor transmembrane region packing.

Amino Acid Sequence↗
PubMed1992

Modelling the structure and function of enzymes by machine learning.

A machine learning program, GOLEM, has been applied to two problems: (1) the prediction of protein secondary structure from sequence and (2) modelling a quantitative structure-activity relationship in drug design. GOLEM takes as input observations and combines them with background knowledge of chemistry to yield rules expressed as stereochemical principles for prediction. The secondary structure prediction was explored on the alpha/alpha class of proteins; on an unrelated test set it yielded 81% accuracy. The rules from GOLEM defined patterns of residues forming alpha-helices. The system studied for drug design was the activities of trimethoprim analogues binding to E. coli dihydrofolate reductase. The GOLEM rules were a better model than standard regression approaches. More importantly, these rules described the chemical properties of the enzyme-binding site that were in broad agreement with the crystallographic structure.

Amino Acid Sequence↗
PubMed1992

Towards an automatic method of predicting protein structure by homology: an evaluation of suboptimal sequence alignments.

A major problem in predicting protein structure by homology modelling is that the sequence alignment from which the model is built may not be the best one in terms of the correct equivalencing of residues assessed by structural or functional criteria. A useful strategy is to generate and examine a number of suboptimal alignments as better alignments can often be found away from the optimal. A procedure to filter rapidly suboptimal alignments based on measurement of core volumes and packing pair potentials is investigated. The approach is benchmarked on three pairs of sequences which are non-trivial to align correctly, namely two immunoglobulin domains, plastocyanin with azurin and two distant globin sequences. It is shown to be useful to reduce a large ensemble of possible alignments down to a few which correspond more closely to the correct (structure based) alignment.

Algorithms↗