Virtual Screening: An Alternative or Complement to High Throughput Screening?

301 Pages·2000·7.33 MB·English

by  Gerhard Klebe

VIRTUAL SCREENING: AN ALTERNATIVE OR COMPLEMENT TO
HIGH THROUGHPUT SCREENING?
Virtual Screening:
An Alternative or Complement to High
Throughput Screening?
Proceedings of the Workshop
‘New Approaches in Drug Design and Discovery’,
special topic ‘Virtual Screening’,
SchloßRauischholzhausen, Germany,
March 15–18, 1999
Editedby
Gerhard Klebe
Instituteof PharmaceuticalChemistry,
Philipps University of Marburg,
Marbacher Weg 6,
D-35032Marburg,
Germany
Reprinted from Perspectives in Drug Discovery and Design, Volume 20,2000
Kluwer Academic Publishers
New York / Boston / Dordrecht / London / Moscow
eBookISBN: 0-306-46883-2
Print ISBN: 0-792-36633-6
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Table of Contents
Preface vii
Combination of molecular similarity measures using data fusion 1
C.M.R. Ginn, P. Willett and J. Bradshaw
Optimization of the drug-likeness of chemical libraries 17
J. Sadowski
Generating consistent sets of thermodynamic and structural data
for analysis of protein-ligand interactions 29
T.G. Davies, J.R.H. Tame and R.E. Hubbard
Multiple molecular superpositioning as an effective tool for virtual
database screening 43
C. Lemmen, M. Zimmermann and T. Lengauer
A recursive algorithm for efficient combinatorial library docking 63
M. Rarey and T. Lengauer
Modifications of the scoring function in FlexX for virtual screening
applications 83
M. Stahl
A knowledge-based scoring function for protein-ligand interactions:
Probing the reference state 99
I. Muegge
Predicting binding modes, binding affinities and ‘hot spots’ for
protein-ligand complexes using a knowledge-based scoring function 115
H. Gohlke, M. Hendlich and G. Klebe
Hydrophobicity maps and docking of molecular fragments with
solvation 145
N. Majeux, M. Scarsi, C. Tenette-Souaille and A. Caflisch
Virtual screening with solvation and ligand-induced complementarity 171
V. Schnecke and L.A. Kuhn
Similarityversusdocking in 3D virtual screening 191
J. Mestres and R.M.A. Knegtel
Discovering high-affinity ligands from the computationally pre-
dicted structures and affinities of small molecules bound to a tar-
get: A virtual screening approach 209
T.J. Marrone, B.A. Luty and P.W. Rose
In vitro andin silico affinity fingerprints: Finding similarities bey-
ond structural classes 231
H. Briem and U.F. Lessel
Computer-assisted synthesis and reaction planning in combinator-
ialchemistry 245
J. Gasteiger, M. Pförtner, M. Sitzmann, R. Höllering, O. Sacher, T.
Kostka and N. Karg
Evaluation of reactant-based and product-based approaches to the
design of combinatorial libraries 265
V.J. Gillet and O. Nicolotti
Author Index 289
Subject Index 29 1
Preface
Virtual Screening: An Alternative or Complement to High Throughput Screen-
ing?
GerhardKlebe
In the next couple of years the human genome will be fully sequenced [1].
This will provide us with the sequence and overall function of all human
genesaswellasthecompletegenomeformanymicroorganisms. Subsequent-
ly it is hoped, that by means of powerful bioinformatic tools, the gene variants
can be determined that contribute to various multifactorial diseases and genes
thatexistincertaininfectious agentsbutnothumans. Asaconsequence, this
will allow ustodefinethemostappropriatelevelsfordrugintervention.Itcan
be expected that the number of potential drug targets will increase, possibly
by a factor of 10 or more [2,3]. Nevertheless, sequencing the human genome
or, for that matter, the genome of other species will be only the starting point
for the understanding of their biological function. Structural genomics is a
likely follow-up, combined with new techniques to validate the therapeutic
relevance of such newly discovered targets [4]. Accordingly, it can be ex-
pected that in the near future we will witness a substantial increase in novel
putative targets for drugs. To address these new targets effectively, we require
new approaches and innovative tools [3].
At present two alternative, however complementary, techniques are em-
ployed: experimental high-throughput screening (HTS) of large compound
libraries, increasingly provided by combinatorial chemistry, and computa-
tional methods for virtual screening (VS) and de novo design [5]. Experi-
mental HTS involves highly sophisticated robotics and advanced engineering
know-how. Appropriate molecular test systems have to be automated and
adapted to the conditions of HTS. Advanced computer and informatics tech-
nology has to handle the logistics and the immense data flow. HTS typically
produces a tremendous amount of ligand binding data with typical hit rates
of about 1%.Perhaps, at first glance, this figure appears quite low. However,
considering one to several million compounds to be assayed per HTS run,
this hit rate still provides a fair number of active compounds.
Because HTS requires engagement in several cost- and labor-intensive
techniques, many attempts have been made to increase its efficacy. As a
consequence, in many companies, modelers have shifted their focus toward
viii
the design of libraries optimally suited for HTS. So-called ‘optimally diverse
libraries’ showing a minimum of redundancy have been created and compiled
on the basis of inventive, property-discriminating descriptors. However, the
enrichment with respect to discovered hits did not significantly depart from
a random selection taken from a large library holding various organic com-
pounds in the correct molecular weight range [6]. Perhaps these studies have
stimulated and improved our understanding of similarity and helped to design
targeted libraries for one particular binding site or as isosteres for a given
reference ligand. Similarity and likewise diversity are typical properties that
can only be defined relative to a reference and not globally over an entire
sample of compounds.
In the early stage of HTS quite optimistic and enthusiastic perspectives
have been predicted. Together with the emergence of combinatorial chem-
istry, that was expected to push the frontiers of compound synthesis ahead
by some orders of magnitude, the end of any rational and knowledge-based
approaches has been forecasted. Today, several years later, a more realistic
view has been accepted. First of all, automating biological testing is not
without problems. False positives or non-specific target binding of possible
test candidates are only some of the problems that puzzle scientists. Quite
depressing are the reported success rates to translate apparent actives from
HTS into leads that are suited for a subsequent optimization into a drug
candidate [7]. Nevertheless, although hits discovered by HTS provide medi-
cinal chemists with real chemical compounds that bind to a target [8], these
hits do not contribute to our understanding of why and how they act upon
the target. Any increase in knowledge is produced only once experimental
structural biology or molecular modeling come into play to detect structural
similarity or possible common binding modes among the obtained hits. Often
enough hits are quite diverse in their chemical structure, thus preventing any
reasonable intuitive comparison.
Virtual screening, VS, is an alternative where the selection of compounds
with predicted binding properties is attempted in the computer [9]. The ap-
proach appears quite tempting. Compounds to be studied do not necessarily
exist and their testing does not consume valuable substance material. Exper-
imental deficiencies, e.g. due to limited solubility or other effects that can
interfere with the assay conditions do not matter. In contrast to HTS, VS
requires as key prerequisite knowledge about the criteria responsible for bind-
ing to a particular target. Either the three-dimensional structure of the target is
given by crystal structure determination, by NMR and by homology model-
ing, or at least a rigid reference ligand with known bioactive conformation is
known that allows for sophisticated pharmacophore modelling. This provides
information about the binding-site geometry and helps to define and predict
ix
possible ligand-binding modes. Once the receptor-bound conformation of a
reference ligand is known or can be estimated, searches for molecules with
similar recognition properties, eventually experienced by quite different mo-
lecular skeletons, can be started. These comparative techniques either use fast
flexible docking algorithms or focus on sophisticated molecular superposition
techniques. However, if one sufficiently understands the features that make
topologically diverse ligands similar or that are responsible for achieving a
particular affinity toward a certain receptor, VS can be applied to screen either
compound libraries of existing substances or computer-generated molecules.
The latter examples could be detected as prospective leads and accordingly
potential candidates for subsequent synthesis. Speculations have been made
about the number of potential drug-like molecules (<500 Da) that could be
synthesized, principally. Impressive numbers of 1060up to 10200compounds
have been discussed [10]. Even the largest computer would not be sufficient
to handle, screen or select such a vast universe of molecules. However, the
computer provides the chance to generate molecules considering and reflect-
ing properties that meet the criteria required for drug bicding, simultaneously
satisfying the conditions for drug-likeness, e.g. defined by Lipinsky’s rule of
five [11]. Accordingly, virtual screening has to be seen in close connection
to de novo design of individual compounds, but even more of medium size
targeted libraries based on principles from combinatorial chemistry.
VS requires some key technologies. Docking and molecular superposi-
tioning are the most important techniques to select candidate molecules. Both
methods require a relevant and reliable scoring scheme to either predict the
expected binding affinity toward a particular protein reference or to estim-
ate the degree of similarity with a given reference ligand. Binding results
when individually solvated molecules combine, usually shedding some of
their association with solvent. Accordingly, solvation properties are of utmost
importance for ligand binding and their understanding is a clear prerequisite
to develop a successful scoring function.
As a kind of status report on the maturity of VS as a technique in drug
design, the first workshop on new approaches in drug design and discovery
was held in March 1999 at Schloß Rauischholzhausen close to Marburg in
Germany [ 12]. More than 80 scientists gathered and discussed their experi-
ence with the different techniques. The speakers were invited to summarize
their contributions together with their impression on the present applicability
of their approach. Several of the speakers followed this request, which is
summarized in this publication. The first two contributions, Combination of
molecularsimilarity measures using datafusion by C.R. Ginn, P. Willett and
J. Bradshaw, and Optimization of the drug-likeness of chemical libraries by J.
Sadowskifocus onthedefinition ofsimilarity, inparticular withrespecttothe
x
selection and optimization of compound libraries also satisfying the general
criteria for drug likeness. Since the prediction ofbinding affinity is of utmost
importance in VS, the analysis ofprotein-ligand interactions is discussed in a
special contribution of the York group by R.E. Hubbard, Th.G. Davies and
J.R.H. Tame, Generating consistent sets of thermodynamic and structural
data for analysis of protein-ligand interactions. Two contributions, one by
C. Lemmen, M. Zimmermann and Th. Lengauer, Molecular superpositioning
as an effective toolfor virtual database screening, and one by M. Rarey and
Th. Lengauer on A recursive algorithm for efficient combinatorial library
docking describe two of the key techniques used to screen molecules whether
they meet the similarity or binding-site criteria with a reference. Scoring
of the results obtained by docking is essential in VS. Accordingly, several
approaches have been summarized in this contribution.
M. Stahl, Modifications of the scoring function in FlexX for virtual screen-
ing applications, I. Muegge, A knowledge-based scoringfunctionforprotein-
ligand interactions: Probing the reference state and H. Gohlke, M. Hendlich
and G. Klebe, Predicting binding modes, binding affinities, and ‘hot spots’
for protein-ligand complexes using a knowledge-based scoring function de-
scribe new and improved scoring functions. The consideration in particular
of solvation properties is described in the articles of N. Majeux, M. Scarsi,
C. Tennette-Souaille and A. Caflisch, Hydrophobicity maps and docking of
fragments with solvation and ofV. Schnecke and L.A. Kuhn, Virtual screen-
ing with solvation and ligand-induced complementarity. A comparative study
facing similarity methods with docking techniques is described by J. Mestres
and R.M.A. Knegtel, Similarity versus docking in 3D virtual screening. An
application to compose new potential leads from small components by com-
puter screening is presented by T.J. Marrone, B.A. Luty and P.W. Rose, Dis-
covering high affinity ligands from computationally predicted structures and
affinities ofsmall molecules bound to a target: A virtual screening approach,
whereas H. Briem and U. Lessel try to find similarity criteria by docking
or superposition methods: In vitro and In silico affinity fingerprints: Find-
ing similarities beyond structural classes. A last section of the contributions
focuses on the design of new compound libraries exploiting principles of
combinational chemistry. In a contribution from J. Gasteiger, M. Pfortner, M.
Sitzmann, R. Hollering, O. Sacher, Th. Kostka and N. Karg, Computer as-
sisted synthesis and reaction planning in combinatorial chemistry, concepts
from synthesis planning are applied to the development of compound lib-
raries. V.J. Gillet and O. Nicolotti, Evaluation of reactant-based or product-
based approaches to the design of combinatorial libraries describe criteria to
design combinatorial libraries on the basis of properties of either the products
or the reactants.