Three‐dimensional, sequence order‐independent structural comparison of a serine protease against the crystallographic database reveals active site similarities: Potential implications to evolution and to protein folding

Abstract

We have recently developed a fast approach to comparisons of 3-dimensional structures. Our method is unique, treating protein structures as collections of unconnected points (atoms) in space. It is completely independent of the amino acid sequence order. It is unconstrained by insertions, deletions, and chain directionality. It matches single, isolated amino acids between 2 different structures strictly by their spatial positioning regardless of their relative sequential position in the amino acid chain. It automatically detects a recurring 3D motif in protein molecules. No predefinition of the motif is required. The motif can be either in the interior of the proteins or on their surfaces. In this work, we describe an enhancement over our previously developed technique, which considerably reduces the complexity of the algorithm. This results in an extremely fast technique. A typical pairwise comparison of 2 protein molecules requires less than 3 s on a workstation. We have scanned the structural database with dozens of probes, successfully detecting structures that are similar to the probe. To illustrate the power of this method, we compare the structure of a trypsin-like serine protease against the structural database. Besides detecting homologous trypsin-like proteases, we automatically obtain 3D, sequence order-independent, active-site similarities with subtilisin-like and sulfhydryl proteases. These similarities equivalence isolated residues, not conserving the linear order of the amino acids in the chains. The active-site similarities are well known and have been detected by manually inspecting the structures in a time-consuming, laborious procedure. This is the first time such equivalences are obtained automatically from the comparison of full structures. The far-reaching advantages and the implications of our novel algorithm to studies of protein folding, to evolution, and to searches for pharmacophoric patterns are discussed.Keywords: computer vision; protease active sites; protein database structural comparison; protein folding; 3D protein motifsWe have recently developed an extremely fast, template-free, sequence order-independent technique for the comparisons of the 3-dimensional structures of proteins. There are 3 novel aspects in our method. First, we compare the 3D structures of proteins (or any biological macromolecule) completely regardless of the order of the residues in the chain. This allows us to detect similarities between protein molecules, whether these are on their surfaces or in their interior. Our computer vision-based algorithm views atoms as collections of unconnected points in space.Reprint requests to: Ruth Nussinov, Building 469, Room 151, NCI-FCRF, Frederick, Maryland 21702; e-mail: ruthn@fcrfvl.ncifcrf.gov. This truly 3D approach overcomes a major limitation inherent in other structural comparison techniques which require that the linear order of the amino acid sequences be conserved (e.g., Matthews & Rossmann, 1985). Although some techniques overcome the insertion/del...

Derivation of 3D coordinate templates for searching structural databases: Application to ser‐His‐Asp catalytic triads in the serine proteinases and lipases

“…Our method differs substantially from previous methods (e.g., Barth et al, 1993;Artymiuk et al, 1994;Fischer et al, 1994) in that a simple template, specific to the SerHis-Asp catalytic triad, is derived. This template allows such triads to be identified uniquely in other structures and quantifies the differences in the triads from related proteins.…”

“…A different structural comparison of the serine proteases, using a less specific technique, has been performed by Fischer et al (1994). Their method, derived from geometric hashing methods used in computer vision research, treats all C a atoms in a protein as points in space and compares proteins purely on the geometric relationships between these points.…”

Functional site prediction by exploiting correlations between labels of interacting residues

“…It discovers functional sites by clustering the templates and selecting the ones that recur in proteins performing similar function. The template matching is performed via superposition transformations or geometric hashing [24,8]. DReSPat proposed an efficient scheme for template matching using a set of geometric invariant descriptors [33].…”

Tess: A geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites

“…The template also revealed the existence of a second Ser-His-Asp triad in trypsinogen and chymotrypsinogen. A different structural comparison of the serine proteinases, using a less specific technique, has been performed by Fischer et al (1994).…”